[
  {
    "path": ".gitignore",
    "content": "*~\n*.pyc\n.idea/*\n*.egg-info/*\n*tmp*.py\n*tmp*\n*.*.backup\n**/__pycache__/\n**/results/\n**/results_*/\n**/template_icl/\n**/.ipynb_checkpoints/\n**/demo/\n.chainerui_commands\n*tmp_grigoris\n\n# personal testout files.\npersonal.py\n\n"
  },
  {
    "path": "LICENSE.txt",
    "content": "Attribution-NonCommercial 4.0 International License\n\nCopyright (c) 2020, Grigorios G Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng.\nAll rights reserved.\n\n\n=================================================================================================\n\n    CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE\n    LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN\n    ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS\n    INFORMATION ON AN \"AS-IS\" BASIS. 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  },
  {
    "path": "classification-NO-activation-function/README.md",
    "content": "# PiNet without Activation Function\r\n\r\n<!-- [ALGORITHM] -->\r\n\r\n## Abstract\r\n\r\nTypically, neural networks required activation functions to be able to approximate effectively complex distributions. ReLU-nets have been popular, e.g., ResNets. However, in the proposed polynomial nets, there is no strict requirement for activation functions as the Π-nets already include nonlinear interactions between the input elements. In fact, you could capture high-order correlations between the input elements without any activation functions, which is what we focus on in this experiment. In particular, we illustrate how Π-nets can learn classification even in the demanding ImageNet without activation functions. We hope that our code can inspire further experimentation with networks that do not require activation functions and can find alternative ways to express nonlinear relationships between the input elements. \r\n\r\n\r\n\r\n<div align=center>\r\n<img src=\"https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/Top1.png\" width=\"40%\"/>\r\n  <img src=\"https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/Top5.png\" width=\"40%\"/>\r\n</div>\r\n\r\n## Implemenation Details\r\n\r\nPlease follow [mmclassification](https://github.com/open-mmlab/mmclassification) to set up the training environment. Our models are trained by a single server with eight V100 GPUs.\r\n\r\nWe slightly modifiy [ResNet](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/models/backbones/resnet.py) for different [experiments](https://github.com/grigorisg9gr/polynomial_nets/tree/master/no_relu/backbones).\r\n\r\nAll other training details follow the standard [configuration](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py).\r\n\r\n## Results \r\n\r\n### ImageNet-1k\r\n\r\n|       Model        |  ReLu | Conv-1x1 |Top-1 (%) | Top-5 (%) |                                  Backbone                                  |                                  Logs                                   |\r\n| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: |\r\n|     ResNet-18      |   Yes  |   No   |   69.90   |   89.43   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) |\r\n|     ResNet-18      |   No   |   No    |   18.348   |   36.718   | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/resnet_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/resnet_norelu.log) |\r\n|     PiNet-18      |   No   |   No   |   63.666   |   84.340   | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_norelu.log) |\r\n|     PiNet-18      |   No   |   Yes  |   65.306   |   85.830   | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_1x1_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_1x1_norelu.log) |\r\n|     PiNet-18      |   Yes   |   No   |   70.350   |   89.434   | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_relu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_relu.log) |\r\n|     PiNet-18      |   Yes   |   Yes   |   71.644   |   90.232   | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_1x1_relu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_1x1_relu.log) |\r\n\r\n"
  },
  {
    "path": "classification-NO-activation-function/backbones/pinet_1x1_norelu.py",
    "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,\n                      build_norm_layer, constant_init)\nfrom mmcv.cnn.bricks import DropPath\nfrom mmcv.runner import BaseModule\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\n\nfrom ..builder import BACKBONES\nfrom .base_backbone import BaseBackbone\n\neps = 1.0e-5\n\n\nclass BasicBlock(BaseModule):\n    \"\"\"BasicBlock for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the output channels of conv1. This is a\n            reserved argument in BasicBlock and should always be 1. Default: 1.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): `pytorch` or `caffe`. It is unused and reserved for\n            unified API with Bottleneck.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=1,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 drop_path_rate=0.0,\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 init_cfg=None):\n        super(BasicBlock, self).__init__(init_cfg=init_cfg)\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert self.expansion == 1\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, out_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            3,\n            stride=stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            3,\n            padding=1,\n            bias=False)\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n#         self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n#         self.drop_path = DropPath(drop_prob=drop_path_rate\n#                                   ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n    \n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n#             out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n#             out = self.drop_path(out)\n               \n            second = self.conv3(out*identity)\n            out = out + identity + self.norm3(second)\n                \n\n\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n#         out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(BaseModule):\n    \"\"\"Bottleneck block for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the input/output channels of conv2. Default: 4.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): ``\"pytorch\"`` or ``\"caffe\"``. If set to \"pytorch\", the\n            stride-two layer is the 3x3 conv layer, otherwise the stride-two\n            layer is the first 1x1 conv layer. Default: \"pytorch\".\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=4,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 drop_path_rate=0.0,\n                 init_cfg=None):\n        super(Bottleneck, self).__init__(init_cfg=init_cfg)\n        assert style in ['pytorch', 'caffe']\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        if self.style == 'pytorch':\n            self.conv1_stride = 1\n            self.conv2_stride = stride\n        else:\n            self.conv1_stride = stride\n            self.conv2_stride = 1\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            kernel_size=1,\n            stride=self.conv1_stride,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            self.mid_channels,\n            kernel_size=3,\n            stride=self.conv2_stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n        self.drop_path = DropPath(drop_prob=drop_path_rate\n                                  ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n\n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n            out = self.relu(out)\n\n            out = self.conv3(out)\n            out = self.norm3(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n\n            out = self.drop_path(out)\n\n            out += identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\ndef get_expansion(block, expansion=None):\n    \"\"\"Get the expansion of a residual block.\n\n    The block expansion will be obtained by the following order:\n\n    1. If ``expansion`` is given, just return it.\n    2. If ``block`` has the attribute ``expansion``, then return\n       ``block.expansion``.\n    3. Return the default value according the the block type:\n       1 for ``BasicBlock`` and 4 for ``Bottleneck``.\n\n    Args:\n        block (class): The block class.\n        expansion (int | None): The given expansion ratio.\n\n    Returns:\n        int: The expansion of the block.\n    \"\"\"\n    if isinstance(expansion, int):\n        assert expansion > 0\n    elif expansion is None:\n        if hasattr(block, 'expansion'):\n            expansion = block.expansion\n        elif issubclass(block, BasicBlock):\n            expansion = 1\n        elif issubclass(block, Bottleneck):\n            expansion = 4\n        else:\n            raise TypeError(f'expansion is not specified for {block.__name__}')\n    else:\n        raise TypeError('expansion must be an integer or None')\n\n    return expansion\n\n\nclass ResLayer(nn.Sequential):\n    \"\"\"ResLayer to build ResNet style backbone.\n\n    Args:\n        block (nn.Module): Residual block used to build ResLayer.\n        num_blocks (int): Number of blocks.\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int, optional): The expansion for BasicBlock/Bottleneck.\n            If not specified, it will firstly be obtained via\n            ``block.expansion``. If the block has no attribute \"expansion\",\n            the following default values will be used: 1 for BasicBlock and\n            4 for Bottleneck. Default: None.\n        stride (int): stride of the first block. Default: 1.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 block,\n                 num_blocks,\n                 in_channels,\n                 out_channels,\n                 expansion=None,\n                 stride=1,\n                 avg_down=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 **kwargs):\n        self.block = block\n        self.expansion = get_expansion(block, expansion)\n\n        downsample = None\n        if stride != 1 or in_channels != out_channels:\n            downsample = []\n            conv_stride = stride\n            if avg_down and stride != 1:\n                conv_stride = 1\n                downsample.append(\n                    nn.AvgPool2d(\n                        kernel_size=stride,\n                        stride=stride,\n                        ceil_mode=True,\n                        count_include_pad=False))\n            downsample.extend([\n                build_conv_layer(\n                    conv_cfg,\n                    in_channels,\n                    out_channels,\n                    kernel_size=1,\n                    stride=conv_stride,\n                    bias=False),\n                build_norm_layer(norm_cfg, out_channels)[1]\n            ])\n            downsample = nn.Sequential(*downsample)\n\n        layers = []\n        layers.append(\n            block(\n                in_channels=in_channels,\n                out_channels=out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                downsample=downsample,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                **kwargs))\n        in_channels = out_channels\n        for i in range(1, num_blocks):\n            layers.append(\n                block(\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    expansion=self.expansion,\n                    stride=1,\n                    conv_cfg=conv_cfg,\n                    norm_cfg=norm_cfg,\n                    **kwargs))\n        super(ResLayer, self).__init__(*layers)\n\n\n@BACKBONES.register_module()\nclass ResNet(BaseBackbone):\n    \"\"\"ResNet backbone.\n\n    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for\n    details.\n\n    Args:\n        depth (int): Network depth, from {18, 34, 50, 101, 152}.\n        in_channels (int): Number of input image channels. Default: 3.\n        stem_channels (int): Output channels of the stem layer. Default: 64.\n        base_channels (int): Middle channels of the first stage. Default: 64.\n        num_stages (int): Stages of the network. Default: 4.\n        strides (Sequence[int]): Strides of the first block of each stage.\n            Default: ``(1, 2, 2, 2)``.\n        dilations (Sequence[int]): Dilation of each stage.\n            Default: ``(1, 1, 1, 1)``.\n        out_indices (Sequence[int]): Output from which stages.\n            Default: ``(3, )``.\n        style (str): `pytorch` or `caffe`. If set to \"pytorch\", the stride-two\n            layer is the 3x3 conv layer, otherwise the stride-two layer is\n            the first 1x1 conv layer.\n        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n            Default: False.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters. Default: -1.\n        conv_cfg (dict | None): The config dict for conv layers. Default: None.\n        norm_cfg (dict): The config dict for norm layers.\n        norm_eval (bool): Whether to set norm layers to eval mode, namely,\n            freeze running stats (mean and var). Note: Effect on Batch Norm\n            and its variants only. Default: False.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed. Default: False.\n        zero_init_residual (bool): Whether to use zero init for last norm layer\n            in resblocks to let them behave as identity. Default: True.\n\n    Example:\n        >>> from mmcls.models import ResNet\n        >>> import torch\n        >>> self = ResNet(depth=18)\n        >>> self.eval()\n        >>> inputs = torch.rand(1, 3, 32, 32)\n        >>> level_outputs = self.forward(inputs)\n        >>> for level_out in level_outputs:\n        ...     print(tuple(level_out.shape))\n        (1, 64, 8, 8)\n        (1, 128, 4, 4)\n        (1, 256, 2, 2)\n        (1, 512, 1, 1)\n    \"\"\"\n\n    arch_settings = {\n        18: (BasicBlock, (2, 2, 2, 2)),\n        34: (BasicBlock, (3, 4, 6, 3)),\n        50: (Bottleneck, (3, 4, 6, 3)),\n        101: (Bottleneck, (3, 4, 23, 3)),\n        152: (Bottleneck, (3, 8, 36, 3))\n    }\n\n    def __init__(self,\n                 depth,\n                 in_channels=3,\n                 stem_channels=64,\n                 base_channels=64,\n                 expansion=None,\n                 num_stages=4,\n                 strides=(1, 2, 2, 2),\n                 dilations=(1, 1, 1, 1),\n                 out_indices=(3, ),\n                 style='pytorch',\n                 deep_stem=False,\n                 avg_down=False,\n                 frozen_stages=-1,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN', requires_grad=True),\n                 norm_eval=False,\n                 with_cp=False,\n                 zero_init_residual=True,\n                 init_cfg=[\n                     dict(type='Kaiming', layer=['Conv2d']),\n                     dict(\n                         type='Constant',\n                         val=1,\n                         layer=['_BatchNorm', 'GroupNorm'])\n                 ],\n                 drop_path_rate=0.0):\n        super(ResNet, self).__init__(init_cfg)\n        if depth not in self.arch_settings:\n            raise KeyError(f'invalid depth {depth} for resnet')\n        self.depth = depth\n        self.stem_channels = stem_channels\n        self.base_channels = base_channels\n        self.num_stages = num_stages\n        assert num_stages >= 1 and num_stages <= 4\n        self.strides = strides\n        self.dilations = dilations\n        assert len(strides) == len(dilations) == num_stages\n        self.out_indices = out_indices\n        assert max(out_indices) < num_stages\n        self.style = style\n        self.deep_stem = deep_stem\n        self.avg_down = avg_down\n        self.frozen_stages = frozen_stages\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n        self.with_cp = with_cp\n        self.norm_eval = norm_eval\n        self.zero_init_residual = zero_init_residual\n        self.block, stage_blocks = self.arch_settings[depth]\n        self.stage_blocks = stage_blocks[:num_stages]\n        self.expansion = get_expansion(self.block, expansion)\n\n        self._make_stem_layer(in_channels, stem_channels)\n\n        self.res_layers = []\n        _in_channels = stem_channels\n        _out_channels = base_channels * self.expansion\n        for i, num_blocks in enumerate(self.stage_blocks):\n            stride = strides[i]\n            dilation = dilations[i]\n            res_layer = self.make_res_layer(\n                block=self.block,\n                num_blocks=num_blocks,\n                in_channels=_in_channels,\n                out_channels=_out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                dilation=dilation,\n                style=self.style,\n                avg_down=self.avg_down,\n                with_cp=with_cp,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                drop_path_rate=drop_path_rate)\n            _in_channels = _out_channels\n            _out_channels *= 2\n            layer_name = f'layer{i + 1}'\n            self.add_module(layer_name, res_layer)\n            self.res_layers.append(layer_name)\n\n        self._freeze_stages()\n\n        self.feat_dim = res_layer[-1].out_channels\n\n    def make_res_layer(self, **kwargs):\n        return ResLayer(**kwargs)\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    def _make_stem_layer(self, in_channels, stem_channels):\n        if self.deep_stem:\n            self.stem = nn.Sequential(\n                ConvModule(\n                    in_channels,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True))\n        else:\n            self.conv1 = build_conv_layer(\n                self.conv_cfg,\n                in_channels,\n                stem_channels,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False)\n            self.norm1_name, norm1 = build_norm_layer(\n                self.norm_cfg, stem_channels, postfix=1)\n            self.add_module(self.norm1_name, norm1)\n#             self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            if self.deep_stem:\n                self.stem.eval()\n                for param in self.stem.parameters():\n                    param.requires_grad = False\n            else:\n                self.norm1.eval()\n                for m in [self.conv1, self.norm1]:\n                    for param in m.parameters():\n                        param.requires_grad = False\n\n        for i in range(1, self.frozen_stages + 1):\n            m = getattr(self, f'layer{i}')\n            m.eval()\n            for param in m.parameters():\n                param.requires_grad = False\n\n    def init_weights(self):\n        super(ResNet, self).init_weights()\n\n        if (isinstance(self.init_cfg, dict)\n                and self.init_cfg['type'] == 'Pretrained'):\n            # Suppress zero_init_residual if use pretrained model.\n            return\n\n        if self.zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, Bottleneck):\n                    constant_init(m.norm3, 0)\n                elif isinstance(m, BasicBlock):\n                    constant_init(m.norm2, 0)\n\n    def forward(self, x):\n        if self.deep_stem:\n            x = self.stem(x)\n        else:\n            x = self.conv1(x)\n            x = self.norm1(x)\n#             x = self.relu(x)\n        x = self.maxpool(x)\n        outs = []\n        for i, layer_name in enumerate(self.res_layers):\n            res_layer = getattr(self, layer_name)\n            x = res_layer(x)\n            if i in self.out_indices:\n                outs.append(x)\n        return tuple(outs)\n\n    def train(self, mode=True):\n        super(ResNet, self).train(mode)\n        self._freeze_stages()\n        if mode and self.norm_eval:\n            for m in self.modules():\n                # trick: eval have effect on BatchNorm only\n                if isinstance(m, _BatchNorm):\n                    m.eval()\n\n\n@BACKBONES.register_module()\nclass ResNetV1c(ResNet):\n    \"\"\"ResNetV1c backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv\n    in the input stem with three 3x3 convs.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1c, self).__init__(\n            deep_stem=True, avg_down=False, **kwargs)\n\n\n@BACKBONES.register_module()\nclass ResNetV1d(ResNet):\n    \"\"\"ResNetV1d backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n    the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n    avg_pool with stride 2 is added before conv, whose stride is changed to 1.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1d, self).__init__(\n            deep_stem=True, avg_down=True, **kwargs)\n"
  },
  {
    "path": "classification-NO-activation-function/backbones/pinet_1x1_relu.py",
    "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,\n                      build_norm_layer, constant_init)\nfrom mmcv.cnn.bricks import DropPath\nfrom mmcv.runner import BaseModule\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\n\nfrom ..builder import BACKBONES\nfrom .base_backbone import BaseBackbone\n\neps = 1.0e-5\n\n\nclass BasicBlock(BaseModule):\n    \"\"\"BasicBlock for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the output channels of conv1. This is a\n            reserved argument in BasicBlock and should always be 1. Default: 1.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): `pytorch` or `caffe`. It is unused and reserved for\n            unified API with Bottleneck.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=1,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 drop_path_rate=0.0,\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 init_cfg=None):\n        super(BasicBlock, self).__init__(init_cfg=init_cfg)\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert self.expansion == 1\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, out_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            3,\n            stride=stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            3,\n            padding=1,\n            bias=False)\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n#         self.drop_path = DropPath(drop_prob=drop_path_rate\n#                                   ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n    \n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n#             out = self.drop_path(out)\n               \n            second = self.conv3(out*identity)\n            out = out + identity + self.norm3(second)\n                \n\n\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(BaseModule):\n    \"\"\"Bottleneck block for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the input/output channels of conv2. Default: 4.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): ``\"pytorch\"`` or ``\"caffe\"``. If set to \"pytorch\", the\n            stride-two layer is the 3x3 conv layer, otherwise the stride-two\n            layer is the first 1x1 conv layer. Default: \"pytorch\".\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=4,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 drop_path_rate=0.0,\n                 init_cfg=None):\n        super(Bottleneck, self).__init__(init_cfg=init_cfg)\n        assert style in ['pytorch', 'caffe']\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        if self.style == 'pytorch':\n            self.conv1_stride = 1\n            self.conv2_stride = stride\n        else:\n            self.conv1_stride = stride\n            self.conv2_stride = 1\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            kernel_size=1,\n            stride=self.conv1_stride,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            self.mid_channels,\n            kernel_size=3,\n            stride=self.conv2_stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n        self.drop_path = DropPath(drop_prob=drop_path_rate\n                                  ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n\n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n            out = self.relu(out)\n\n            out = self.conv3(out)\n            out = self.norm3(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n\n            out = self.drop_path(out)\n\n            out += identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\ndef get_expansion(block, expansion=None):\n    \"\"\"Get the expansion of a residual block.\n\n    The block expansion will be obtained by the following order:\n\n    1. If ``expansion`` is given, just return it.\n    2. If ``block`` has the attribute ``expansion``, then return\n       ``block.expansion``.\n    3. Return the default value according the the block type:\n       1 for ``BasicBlock`` and 4 for ``Bottleneck``.\n\n    Args:\n        block (class): The block class.\n        expansion (int | None): The given expansion ratio.\n\n    Returns:\n        int: The expansion of the block.\n    \"\"\"\n    if isinstance(expansion, int):\n        assert expansion > 0\n    elif expansion is None:\n        if hasattr(block, 'expansion'):\n            expansion = block.expansion\n        elif issubclass(block, BasicBlock):\n            expansion = 1\n        elif issubclass(block, Bottleneck):\n            expansion = 4\n        else:\n            raise TypeError(f'expansion is not specified for {block.__name__}')\n    else:\n        raise TypeError('expansion must be an integer or None')\n\n    return expansion\n\n\nclass ResLayer(nn.Sequential):\n    \"\"\"ResLayer to build ResNet style backbone.\n\n    Args:\n        block (nn.Module): Residual block used to build ResLayer.\n        num_blocks (int): Number of blocks.\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int, optional): The expansion for BasicBlock/Bottleneck.\n            If not specified, it will firstly be obtained via\n            ``block.expansion``. If the block has no attribute \"expansion\",\n            the following default values will be used: 1 for BasicBlock and\n            4 for Bottleneck. Default: None.\n        stride (int): stride of the first block. Default: 1.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 block,\n                 num_blocks,\n                 in_channels,\n                 out_channels,\n                 expansion=None,\n                 stride=1,\n                 avg_down=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 **kwargs):\n        self.block = block\n        self.expansion = get_expansion(block, expansion)\n\n        downsample = None\n        if stride != 1 or in_channels != out_channels:\n            downsample = []\n            conv_stride = stride\n            if avg_down and stride != 1:\n                conv_stride = 1\n                downsample.append(\n                    nn.AvgPool2d(\n                        kernel_size=stride,\n                        stride=stride,\n                        ceil_mode=True,\n                        count_include_pad=False))\n            downsample.extend([\n                build_conv_layer(\n                    conv_cfg,\n                    in_channels,\n                    out_channels,\n                    kernel_size=1,\n                    stride=conv_stride,\n                    bias=False),\n                build_norm_layer(norm_cfg, out_channels)[1]\n            ])\n            downsample = nn.Sequential(*downsample)\n\n        layers = []\n        layers.append(\n            block(\n                in_channels=in_channels,\n                out_channels=out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                downsample=downsample,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                **kwargs))\n        in_channels = out_channels\n        for i in range(1, num_blocks):\n            layers.append(\n                block(\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    expansion=self.expansion,\n                    stride=1,\n                    conv_cfg=conv_cfg,\n                    norm_cfg=norm_cfg,\n                    **kwargs))\n        super(ResLayer, self).__init__(*layers)\n\n\n@BACKBONES.register_module()\nclass ResNet(BaseBackbone):\n    \"\"\"ResNet backbone.\n\n    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for\n    details.\n\n    Args:\n        depth (int): Network depth, from {18, 34, 50, 101, 152}.\n        in_channels (int): Number of input image channels. Default: 3.\n        stem_channels (int): Output channels of the stem layer. Default: 64.\n        base_channels (int): Middle channels of the first stage. Default: 64.\n        num_stages (int): Stages of the network. Default: 4.\n        strides (Sequence[int]): Strides of the first block of each stage.\n            Default: ``(1, 2, 2, 2)``.\n        dilations (Sequence[int]): Dilation of each stage.\n            Default: ``(1, 1, 1, 1)``.\n        out_indices (Sequence[int]): Output from which stages.\n            Default: ``(3, )``.\n        style (str): `pytorch` or `caffe`. If set to \"pytorch\", the stride-two\n            layer is the 3x3 conv layer, otherwise the stride-two layer is\n            the first 1x1 conv layer.\n        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n            Default: False.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters. Default: -1.\n        conv_cfg (dict | None): The config dict for conv layers. Default: None.\n        norm_cfg (dict): The config dict for norm layers.\n        norm_eval (bool): Whether to set norm layers to eval mode, namely,\n            freeze running stats (mean and var). Note: Effect on Batch Norm\n            and its variants only. Default: False.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed. Default: False.\n        zero_init_residual (bool): Whether to use zero init for last norm layer\n            in resblocks to let them behave as identity. Default: True.\n\n    Example:\n        >>> from mmcls.models import ResNet\n        >>> import torch\n        >>> self = ResNet(depth=18)\n        >>> self.eval()\n        >>> inputs = torch.rand(1, 3, 32, 32)\n        >>> level_outputs = self.forward(inputs)\n        >>> for level_out in level_outputs:\n        ...     print(tuple(level_out.shape))\n        (1, 64, 8, 8)\n        (1, 128, 4, 4)\n        (1, 256, 2, 2)\n        (1, 512, 1, 1)\n    \"\"\"\n\n    arch_settings = {\n        18: (BasicBlock, (2, 2, 2, 2)),\n        34: (BasicBlock, (3, 4, 6, 3)),\n        50: (Bottleneck, (3, 4, 6, 3)),\n        101: (Bottleneck, (3, 4, 23, 3)),\n        152: (Bottleneck, (3, 8, 36, 3))\n    }\n\n    def __init__(self,\n                 depth,\n                 in_channels=3,\n                 stem_channels=64,\n                 base_channels=64,\n                 expansion=None,\n                 num_stages=4,\n                 strides=(1, 2, 2, 2),\n                 dilations=(1, 1, 1, 1),\n                 out_indices=(3, ),\n                 style='pytorch',\n                 deep_stem=False,\n                 avg_down=False,\n                 frozen_stages=-1,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN', requires_grad=True),\n                 norm_eval=False,\n                 with_cp=False,\n                 zero_init_residual=True,\n                 init_cfg=[\n                     dict(type='Kaiming', layer=['Conv2d']),\n                     dict(\n                         type='Constant',\n                         val=1,\n                         layer=['_BatchNorm', 'GroupNorm'])\n                 ],\n                 drop_path_rate=0.0):\n        super(ResNet, self).__init__(init_cfg)\n        if depth not in self.arch_settings:\n            raise KeyError(f'invalid depth {depth} for resnet')\n        self.depth = depth\n        self.stem_channels = stem_channels\n        self.base_channels = base_channels\n        self.num_stages = num_stages\n        assert num_stages >= 1 and num_stages <= 4\n        self.strides = strides\n        self.dilations = dilations\n        assert len(strides) == len(dilations) == num_stages\n        self.out_indices = out_indices\n        assert max(out_indices) < num_stages\n        self.style = style\n        self.deep_stem = deep_stem\n        self.avg_down = avg_down\n        self.frozen_stages = frozen_stages\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n        self.with_cp = with_cp\n        self.norm_eval = norm_eval\n        self.zero_init_residual = zero_init_residual\n        self.block, stage_blocks = self.arch_settings[depth]\n        self.stage_blocks = stage_blocks[:num_stages]\n        self.expansion = get_expansion(self.block, expansion)\n\n        self._make_stem_layer(in_channels, stem_channels)\n\n        self.res_layers = []\n        _in_channels = stem_channels\n        _out_channels = base_channels * self.expansion\n        for i, num_blocks in enumerate(self.stage_blocks):\n            stride = strides[i]\n            dilation = dilations[i]\n            res_layer = self.make_res_layer(\n                block=self.block,\n                num_blocks=num_blocks,\n                in_channels=_in_channels,\n                out_channels=_out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                dilation=dilation,\n                style=self.style,\n                avg_down=self.avg_down,\n                with_cp=with_cp,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                drop_path_rate=drop_path_rate)\n            _in_channels = _out_channels\n            _out_channels *= 2\n            layer_name = f'layer{i + 1}'\n            self.add_module(layer_name, res_layer)\n            self.res_layers.append(layer_name)\n\n        self._freeze_stages()\n\n        self.feat_dim = res_layer[-1].out_channels\n\n    def make_res_layer(self, **kwargs):\n        return ResLayer(**kwargs)\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    def _make_stem_layer(self, in_channels, stem_channels):\n        if self.deep_stem:\n            self.stem = nn.Sequential(\n                ConvModule(\n                    in_channels,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True))\n        else:\n            self.conv1 = build_conv_layer(\n                self.conv_cfg,\n                in_channels,\n                stem_channels,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False)\n            self.norm1_name, norm1 = build_norm_layer(\n                self.norm_cfg, stem_channels, postfix=1)\n            self.add_module(self.norm1_name, norm1)\n            self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            if self.deep_stem:\n                self.stem.eval()\n                for param in self.stem.parameters():\n                    param.requires_grad = False\n            else:\n                self.norm1.eval()\n                for m in [self.conv1, self.norm1]:\n                    for param in m.parameters():\n                        param.requires_grad = False\n\n        for i in range(1, self.frozen_stages + 1):\n            m = getattr(self, f'layer{i}')\n            m.eval()\n            for param in m.parameters():\n                param.requires_grad = False\n\n    def init_weights(self):\n        super(ResNet, self).init_weights()\n\n        if (isinstance(self.init_cfg, dict)\n                and self.init_cfg['type'] == 'Pretrained'):\n            # Suppress zero_init_residual if use pretrained model.\n            return\n\n        if self.zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, Bottleneck):\n                    constant_init(m.norm3, 0)\n                elif isinstance(m, BasicBlock):\n                    constant_init(m.norm2, 0)\n\n    def forward(self, x):\n        if self.deep_stem:\n            x = self.stem(x)\n        else:\n            x = self.conv1(x)\n            x = self.norm1(x)\n            x = self.relu(x)\n        x = self.maxpool(x)\n        outs = []\n        for i, layer_name in enumerate(self.res_layers):\n            res_layer = getattr(self, layer_name)\n            x = res_layer(x)\n            if i in self.out_indices:\n                outs.append(x)\n        return tuple(outs)\n\n    def train(self, mode=True):\n        super(ResNet, self).train(mode)\n        self._freeze_stages()\n        if mode and self.norm_eval:\n            for m in self.modules():\n                # trick: eval have effect on BatchNorm only\n                if isinstance(m, _BatchNorm):\n                    m.eval()\n\n\n@BACKBONES.register_module()\nclass ResNetV1c(ResNet):\n    \"\"\"ResNetV1c backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv\n    in the input stem with three 3x3 convs.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1c, self).__init__(\n            deep_stem=True, avg_down=False, **kwargs)\n\n\n@BACKBONES.register_module()\nclass ResNetV1d(ResNet):\n    \"\"\"ResNetV1d backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n    the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n    avg_pool with stride 2 is added before conv, whose stride is changed to 1.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1d, self).__init__(\n            deep_stem=True, avg_down=True, **kwargs)\n"
  },
  {
    "path": "classification-NO-activation-function/backbones/pinet_norelu.py",
    "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,\n                      build_norm_layer, constant_init)\nfrom mmcv.cnn.bricks import DropPath\nfrom mmcv.runner import BaseModule\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\n\nfrom ..builder import BACKBONES\nfrom .base_backbone import BaseBackbone\n\neps = 1.0e-5\n\n\nclass BasicBlock(BaseModule):\n    \"\"\"BasicBlock for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the output channels of conv1. This is a\n            reserved argument in BasicBlock and should always be 1. Default: 1.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): `pytorch` or `caffe`. It is unused and reserved for\n            unified API with Bottleneck.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=1,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 drop_path_rate=0.0,\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 init_cfg=None):\n        super(BasicBlock, self).__init__(init_cfg=init_cfg)\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert self.expansion == 1\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, out_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            3,\n            stride=stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            3,\n            padding=1,\n            bias=False)\n        self.add_module(self.norm2_name, norm2)\n#         self.conv3 = build_conv_layer(\n#             conv_cfg,\n#             self.mid_channels,\n#             out_channels,\n#             kernel_size=1,\n#             bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n#         self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n#         self.drop_path = DropPath(drop_prob=drop_path_rate\n#                                   ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n    \n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n#             out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n#             out = self.drop_path(out)\n               \n#             second = self.conv3(out*identity)\n            second = out*identity\n            out = out + identity + self.norm3(second)\n                \n\n\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n#         out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(BaseModule):\n    \"\"\"Bottleneck block for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the input/output channels of conv2. Default: 4.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): ``\"pytorch\"`` or ``\"caffe\"``. If set to \"pytorch\", the\n            stride-two layer is the 3x3 conv layer, otherwise the stride-two\n            layer is the first 1x1 conv layer. Default: \"pytorch\".\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=4,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 drop_path_rate=0.0,\n                 init_cfg=None):\n        super(Bottleneck, self).__init__(init_cfg=init_cfg)\n        assert style in ['pytorch', 'caffe']\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        if self.style == 'pytorch':\n            self.conv1_stride = 1\n            self.conv2_stride = stride\n        else:\n            self.conv1_stride = stride\n            self.conv2_stride = 1\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            kernel_size=1,\n            stride=self.conv1_stride,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            self.mid_channels,\n            kernel_size=3,\n            stride=self.conv2_stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n        self.drop_path = DropPath(drop_prob=drop_path_rate\n                                  ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n\n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n            out = self.relu(out)\n\n            out = self.conv3(out)\n            out = self.norm3(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n\n            out = self.drop_path(out)\n\n            out += identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\ndef get_expansion(block, expansion=None):\n    \"\"\"Get the expansion of a residual block.\n\n    The block expansion will be obtained by the following order:\n\n    1. If ``expansion`` is given, just return it.\n    2. If ``block`` has the attribute ``expansion``, then return\n       ``block.expansion``.\n    3. Return the default value according the the block type:\n       1 for ``BasicBlock`` and 4 for ``Bottleneck``.\n\n    Args:\n        block (class): The block class.\n        expansion (int | None): The given expansion ratio.\n\n    Returns:\n        int: The expansion of the block.\n    \"\"\"\n    if isinstance(expansion, int):\n        assert expansion > 0\n    elif expansion is None:\n        if hasattr(block, 'expansion'):\n            expansion = block.expansion\n        elif issubclass(block, BasicBlock):\n            expansion = 1\n        elif issubclass(block, Bottleneck):\n            expansion = 4\n        else:\n            raise TypeError(f'expansion is not specified for {block.__name__}')\n    else:\n        raise TypeError('expansion must be an integer or None')\n\n    return expansion\n\n\nclass ResLayer(nn.Sequential):\n    \"\"\"ResLayer to build ResNet style backbone.\n\n    Args:\n        block (nn.Module): Residual block used to build ResLayer.\n        num_blocks (int): Number of blocks.\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int, optional): The expansion for BasicBlock/Bottleneck.\n            If not specified, it will firstly be obtained via\n            ``block.expansion``. If the block has no attribute \"expansion\",\n            the following default values will be used: 1 for BasicBlock and\n            4 for Bottleneck. Default: None.\n        stride (int): stride of the first block. Default: 1.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 block,\n                 num_blocks,\n                 in_channels,\n                 out_channels,\n                 expansion=None,\n                 stride=1,\n                 avg_down=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 **kwargs):\n        self.block = block\n        self.expansion = get_expansion(block, expansion)\n\n        downsample = None\n        if stride != 1 or in_channels != out_channels:\n            downsample = []\n            conv_stride = stride\n            if avg_down and stride != 1:\n                conv_stride = 1\n                downsample.append(\n                    nn.AvgPool2d(\n                        kernel_size=stride,\n                        stride=stride,\n                        ceil_mode=True,\n                        count_include_pad=False))\n            downsample.extend([\n                build_conv_layer(\n                    conv_cfg,\n                    in_channels,\n                    out_channels,\n                    kernel_size=1,\n                    stride=conv_stride,\n                    bias=False),\n                build_norm_layer(norm_cfg, out_channels)[1]\n            ])\n            downsample = nn.Sequential(*downsample)\n\n        layers = []\n        layers.append(\n            block(\n                in_channels=in_channels,\n                out_channels=out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                downsample=downsample,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                **kwargs))\n        in_channels = out_channels\n        for i in range(1, num_blocks):\n            layers.append(\n                block(\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    expansion=self.expansion,\n                    stride=1,\n                    conv_cfg=conv_cfg,\n                    norm_cfg=norm_cfg,\n                    **kwargs))\n        super(ResLayer, self).__init__(*layers)\n\n\n@BACKBONES.register_module()\nclass ResNet(BaseBackbone):\n    \"\"\"ResNet backbone.\n\n    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for\n    details.\n\n    Args:\n        depth (int): Network depth, from {18, 34, 50, 101, 152}.\n        in_channels (int): Number of input image channels. Default: 3.\n        stem_channels (int): Output channels of the stem layer. Default: 64.\n        base_channels (int): Middle channels of the first stage. Default: 64.\n        num_stages (int): Stages of the network. Default: 4.\n        strides (Sequence[int]): Strides of the first block of each stage.\n            Default: ``(1, 2, 2, 2)``.\n        dilations (Sequence[int]): Dilation of each stage.\n            Default: ``(1, 1, 1, 1)``.\n        out_indices (Sequence[int]): Output from which stages.\n            Default: ``(3, )``.\n        style (str): `pytorch` or `caffe`. If set to \"pytorch\", the stride-two\n            layer is the 3x3 conv layer, otherwise the stride-two layer is\n            the first 1x1 conv layer.\n        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n            Default: False.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters. Default: -1.\n        conv_cfg (dict | None): The config dict for conv layers. Default: None.\n        norm_cfg (dict): The config dict for norm layers.\n        norm_eval (bool): Whether to set norm layers to eval mode, namely,\n            freeze running stats (mean and var). Note: Effect on Batch Norm\n            and its variants only. Default: False.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed. Default: False.\n        zero_init_residual (bool): Whether to use zero init for last norm layer\n            in resblocks to let them behave as identity. Default: True.\n\n    Example:\n        >>> from mmcls.models import ResNet\n        >>> import torch\n        >>> self = ResNet(depth=18)\n        >>> self.eval()\n        >>> inputs = torch.rand(1, 3, 32, 32)\n        >>> level_outputs = self.forward(inputs)\n        >>> for level_out in level_outputs:\n        ...     print(tuple(level_out.shape))\n        (1, 64, 8, 8)\n        (1, 128, 4, 4)\n        (1, 256, 2, 2)\n        (1, 512, 1, 1)\n    \"\"\"\n\n    arch_settings = {\n        18: (BasicBlock, (2, 2, 2, 2)),\n        34: (BasicBlock, (3, 4, 6, 3)),\n        50: (Bottleneck, (3, 4, 6, 3)),\n        101: (Bottleneck, (3, 4, 23, 3)),\n        152: (Bottleneck, (3, 8, 36, 3))\n    }\n\n    def __init__(self,\n                 depth,\n                 in_channels=3,\n                 stem_channels=64,\n                 base_channels=64,\n                 expansion=None,\n                 num_stages=4,\n                 strides=(1, 2, 2, 2),\n                 dilations=(1, 1, 1, 1),\n                 out_indices=(3, ),\n                 style='pytorch',\n                 deep_stem=False,\n                 avg_down=False,\n                 frozen_stages=-1,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN', requires_grad=True),\n                 norm_eval=False,\n                 with_cp=False,\n                 zero_init_residual=True,\n                 init_cfg=[\n                     dict(type='Kaiming', layer=['Conv2d']),\n                     dict(\n                         type='Constant',\n                         val=1,\n                         layer=['_BatchNorm', 'GroupNorm'])\n                 ],\n                 drop_path_rate=0.0):\n        super(ResNet, self).__init__(init_cfg)\n        if depth not in self.arch_settings:\n            raise KeyError(f'invalid depth {depth} for resnet')\n        self.depth = depth\n        self.stem_channels = stem_channels\n        self.base_channels = base_channels\n        self.num_stages = num_stages\n        assert num_stages >= 1 and num_stages <= 4\n        self.strides = strides\n        self.dilations = dilations\n        assert len(strides) == len(dilations) == num_stages\n        self.out_indices = out_indices\n        assert max(out_indices) < num_stages\n        self.style = style\n        self.deep_stem = deep_stem\n        self.avg_down = avg_down\n        self.frozen_stages = frozen_stages\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n        self.with_cp = with_cp\n        self.norm_eval = norm_eval\n        self.zero_init_residual = zero_init_residual\n        self.block, stage_blocks = self.arch_settings[depth]\n        self.stage_blocks = stage_blocks[:num_stages]\n        self.expansion = get_expansion(self.block, expansion)\n\n        self._make_stem_layer(in_channels, stem_channels)\n\n        self.res_layers = []\n        _in_channels = stem_channels\n        _out_channels = base_channels * self.expansion\n        for i, num_blocks in enumerate(self.stage_blocks):\n            stride = strides[i]\n            dilation = dilations[i]\n            res_layer = self.make_res_layer(\n                block=self.block,\n                num_blocks=num_blocks,\n                in_channels=_in_channels,\n                out_channels=_out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                dilation=dilation,\n                style=self.style,\n                avg_down=self.avg_down,\n                with_cp=with_cp,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                drop_path_rate=drop_path_rate)\n            _in_channels = _out_channels\n            _out_channels *= 2\n            layer_name = f'layer{i + 1}'\n            self.add_module(layer_name, res_layer)\n            self.res_layers.append(layer_name)\n\n        self._freeze_stages()\n\n        self.feat_dim = res_layer[-1].out_channels\n\n    def make_res_layer(self, **kwargs):\n        return ResLayer(**kwargs)\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    def _make_stem_layer(self, in_channels, stem_channels):\n        if self.deep_stem:\n            self.stem = nn.Sequential(\n                ConvModule(\n                    in_channels,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True))\n        else:\n            self.conv1 = build_conv_layer(\n                self.conv_cfg,\n                in_channels,\n                stem_channels,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False)\n            self.norm1_name, norm1 = build_norm_layer(\n                self.norm_cfg, stem_channels, postfix=1)\n            self.add_module(self.norm1_name, norm1)\n#             self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            if self.deep_stem:\n                self.stem.eval()\n                for param in self.stem.parameters():\n                    param.requires_grad = False\n            else:\n                self.norm1.eval()\n                for m in [self.conv1, self.norm1]:\n                    for param in m.parameters():\n                        param.requires_grad = False\n\n        for i in range(1, self.frozen_stages + 1):\n            m = getattr(self, f'layer{i}')\n            m.eval()\n            for param in m.parameters():\n                param.requires_grad = False\n\n    def init_weights(self):\n        super(ResNet, self).init_weights()\n\n        if (isinstance(self.init_cfg, dict)\n                and self.init_cfg['type'] == 'Pretrained'):\n            # Suppress zero_init_residual if use pretrained model.\n            return\n\n        if self.zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, Bottleneck):\n                    constant_init(m.norm3, 0)\n                elif isinstance(m, BasicBlock):\n                    constant_init(m.norm2, 0)\n\n    def forward(self, x):\n        if self.deep_stem:\n            x = self.stem(x)\n        else:\n            x = self.conv1(x)\n            x = self.norm1(x)\n#             x = self.relu(x)\n        x = self.maxpool(x)\n        outs = []\n        for i, layer_name in enumerate(self.res_layers):\n            res_layer = getattr(self, layer_name)\n            x = res_layer(x)\n            if i in self.out_indices:\n                outs.append(x)\n        return tuple(outs)\n\n    def train(self, mode=True):\n        super(ResNet, self).train(mode)\n        self._freeze_stages()\n        if mode and self.norm_eval:\n            for m in self.modules():\n                # trick: eval have effect on BatchNorm only\n                if isinstance(m, _BatchNorm):\n                    m.eval()\n\n\n@BACKBONES.register_module()\nclass ResNetV1c(ResNet):\n    \"\"\"ResNetV1c backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv\n    in the input stem with three 3x3 convs.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1c, self).__init__(\n            deep_stem=True, avg_down=False, **kwargs)\n\n\n@BACKBONES.register_module()\nclass ResNetV1d(ResNet):\n    \"\"\"ResNetV1d backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n    the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n    avg_pool with stride 2 is added before conv, whose stride is changed to 1.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1d, self).__init__(\n            deep_stem=True, avg_down=True, **kwargs)\n"
  },
  {
    "path": "classification-NO-activation-function/backbones/pinet_relu.py",
    "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,\n                      build_norm_layer, constant_init)\nfrom mmcv.cnn.bricks import DropPath\nfrom mmcv.runner import BaseModule\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\n\nfrom ..builder import BACKBONES\nfrom .base_backbone import BaseBackbone\n\neps = 1.0e-5\n\n\nclass BasicBlock(BaseModule):\n    \"\"\"BasicBlock for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the output channels of conv1. This is a\n            reserved argument in BasicBlock and should always be 1. Default: 1.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): `pytorch` or `caffe`. It is unused and reserved for\n            unified API with Bottleneck.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=1,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 drop_path_rate=0.0,\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 init_cfg=None):\n        super(BasicBlock, self).__init__(init_cfg=init_cfg)\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert self.expansion == 1\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, out_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            3,\n            stride=stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            3,\n            padding=1,\n            bias=False)\n        self.add_module(self.norm2_name, norm2)\n#         self.conv3 = build_conv_layer(\n#             conv_cfg,\n#             self.mid_channels,\n#             out_channels,\n#             kernel_size=1,\n#             bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n#         self.drop_path = DropPath(drop_prob=drop_path_rate\n#                                   ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n    \n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n#             out = self.drop_path(out)\n               \n#             second = self.conv3(out*identity)\n            second = out*identity\n            out = out + identity + self.norm3(second)\n                \n\n\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(BaseModule):\n    \"\"\"Bottleneck block for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the input/output channels of conv2. Default: 4.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): ``\"pytorch\"`` or ``\"caffe\"``. If set to \"pytorch\", the\n            stride-two layer is the 3x3 conv layer, otherwise the stride-two\n            layer is the first 1x1 conv layer. Default: \"pytorch\".\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=4,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 drop_path_rate=0.0,\n                 init_cfg=None):\n        super(Bottleneck, self).__init__(init_cfg=init_cfg)\n        assert style in ['pytorch', 'caffe']\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        if self.style == 'pytorch':\n            self.conv1_stride = 1\n            self.conv2_stride = stride\n        else:\n            self.conv1_stride = stride\n            self.conv2_stride = 1\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            kernel_size=1,\n            stride=self.conv1_stride,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            self.mid_channels,\n            kernel_size=3,\n            stride=self.conv2_stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n        self.drop_path = DropPath(drop_prob=drop_path_rate\n                                  ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n\n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n            out = self.relu(out)\n\n            out = self.conv3(out)\n            out = self.norm3(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n\n            out = self.drop_path(out)\n\n            out += identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\ndef get_expansion(block, expansion=None):\n    \"\"\"Get the expansion of a residual block.\n\n    The block expansion will be obtained by the following order:\n\n    1. If ``expansion`` is given, just return it.\n    2. If ``block`` has the attribute ``expansion``, then return\n       ``block.expansion``.\n    3. Return the default value according the the block type:\n       1 for ``BasicBlock`` and 4 for ``Bottleneck``.\n\n    Args:\n        block (class): The block class.\n        expansion (int | None): The given expansion ratio.\n\n    Returns:\n        int: The expansion of the block.\n    \"\"\"\n    if isinstance(expansion, int):\n        assert expansion > 0\n    elif expansion is None:\n        if hasattr(block, 'expansion'):\n            expansion = block.expansion\n        elif issubclass(block, BasicBlock):\n            expansion = 1\n        elif issubclass(block, Bottleneck):\n            expansion = 4\n        else:\n            raise TypeError(f'expansion is not specified for {block.__name__}')\n    else:\n        raise TypeError('expansion must be an integer or None')\n\n    return expansion\n\n\nclass ResLayer(nn.Sequential):\n    \"\"\"ResLayer to build ResNet style backbone.\n\n    Args:\n        block (nn.Module): Residual block used to build ResLayer.\n        num_blocks (int): Number of blocks.\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int, optional): The expansion for BasicBlock/Bottleneck.\n            If not specified, it will firstly be obtained via\n            ``block.expansion``. If the block has no attribute \"expansion\",\n            the following default values will be used: 1 for BasicBlock and\n            4 for Bottleneck. Default: None.\n        stride (int): stride of the first block. Default: 1.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 block,\n                 num_blocks,\n                 in_channels,\n                 out_channels,\n                 expansion=None,\n                 stride=1,\n                 avg_down=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 **kwargs):\n        self.block = block\n        self.expansion = get_expansion(block, expansion)\n\n        downsample = None\n        if stride != 1 or in_channels != out_channels:\n            downsample = []\n            conv_stride = stride\n            if avg_down and stride != 1:\n                conv_stride = 1\n                downsample.append(\n                    nn.AvgPool2d(\n                        kernel_size=stride,\n                        stride=stride,\n                        ceil_mode=True,\n                        count_include_pad=False))\n            downsample.extend([\n                build_conv_layer(\n                    conv_cfg,\n                    in_channels,\n                    out_channels,\n                    kernel_size=1,\n                    stride=conv_stride,\n                    bias=False),\n                build_norm_layer(norm_cfg, out_channels)[1]\n            ])\n            downsample = nn.Sequential(*downsample)\n\n        layers = []\n        layers.append(\n            block(\n                in_channels=in_channels,\n                out_channels=out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                downsample=downsample,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                **kwargs))\n        in_channels = out_channels\n        for i in range(1, num_blocks):\n            layers.append(\n                block(\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    expansion=self.expansion,\n                    stride=1,\n                    conv_cfg=conv_cfg,\n                    norm_cfg=norm_cfg,\n                    **kwargs))\n        super(ResLayer, self).__init__(*layers)\n\n\n@BACKBONES.register_module()\nclass ResNet(BaseBackbone):\n    \"\"\"ResNet backbone.\n\n    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for\n    details.\n\n    Args:\n        depth (int): Network depth, from {18, 34, 50, 101, 152}.\n        in_channels (int): Number of input image channels. Default: 3.\n        stem_channels (int): Output channels of the stem layer. Default: 64.\n        base_channels (int): Middle channels of the first stage. Default: 64.\n        num_stages (int): Stages of the network. Default: 4.\n        strides (Sequence[int]): Strides of the first block of each stage.\n            Default: ``(1, 2, 2, 2)``.\n        dilations (Sequence[int]): Dilation of each stage.\n            Default: ``(1, 1, 1, 1)``.\n        out_indices (Sequence[int]): Output from which stages.\n            Default: ``(3, )``.\n        style (str): `pytorch` or `caffe`. If set to \"pytorch\", the stride-two\n            layer is the 3x3 conv layer, otherwise the stride-two layer is\n            the first 1x1 conv layer.\n        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n            Default: False.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters. Default: -1.\n        conv_cfg (dict | None): The config dict for conv layers. Default: None.\n        norm_cfg (dict): The config dict for norm layers.\n        norm_eval (bool): Whether to set norm layers to eval mode, namely,\n            freeze running stats (mean and var). Note: Effect on Batch Norm\n            and its variants only. Default: False.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed. Default: False.\n        zero_init_residual (bool): Whether to use zero init for last norm layer\n            in resblocks to let them behave as identity. Default: True.\n\n    Example:\n        >>> from mmcls.models import ResNet\n        >>> import torch\n        >>> self = ResNet(depth=18)\n        >>> self.eval()\n        >>> inputs = torch.rand(1, 3, 32, 32)\n        >>> level_outputs = self.forward(inputs)\n        >>> for level_out in level_outputs:\n        ...     print(tuple(level_out.shape))\n        (1, 64, 8, 8)\n        (1, 128, 4, 4)\n        (1, 256, 2, 2)\n        (1, 512, 1, 1)\n    \"\"\"\n\n    arch_settings = {\n        18: (BasicBlock, (2, 2, 2, 2)),\n        34: (BasicBlock, (3, 4, 6, 3)),\n        50: (Bottleneck, (3, 4, 6, 3)),\n        101: (Bottleneck, (3, 4, 23, 3)),\n        152: (Bottleneck, (3, 8, 36, 3))\n    }\n\n    def __init__(self,\n                 depth,\n                 in_channels=3,\n                 stem_channels=64,\n                 base_channels=64,\n                 expansion=None,\n                 num_stages=4,\n                 strides=(1, 2, 2, 2),\n                 dilations=(1, 1, 1, 1),\n                 out_indices=(3, ),\n                 style='pytorch',\n                 deep_stem=False,\n                 avg_down=False,\n                 frozen_stages=-1,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN', requires_grad=True),\n                 norm_eval=False,\n                 with_cp=False,\n                 zero_init_residual=True,\n                 init_cfg=[\n                     dict(type='Kaiming', layer=['Conv2d']),\n                     dict(\n                         type='Constant',\n                         val=1,\n                         layer=['_BatchNorm', 'GroupNorm'])\n                 ],\n                 drop_path_rate=0.0):\n        super(ResNet, self).__init__(init_cfg)\n        if depth not in self.arch_settings:\n            raise KeyError(f'invalid depth {depth} for resnet')\n        self.depth = depth\n        self.stem_channels = stem_channels\n        self.base_channels = base_channels\n        self.num_stages = num_stages\n        assert num_stages >= 1 and num_stages <= 4\n        self.strides = strides\n        self.dilations = dilations\n        assert len(strides) == len(dilations) == num_stages\n        self.out_indices = out_indices\n        assert max(out_indices) < num_stages\n        self.style = style\n        self.deep_stem = deep_stem\n        self.avg_down = avg_down\n        self.frozen_stages = frozen_stages\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n        self.with_cp = with_cp\n        self.norm_eval = norm_eval\n        self.zero_init_residual = zero_init_residual\n        self.block, stage_blocks = self.arch_settings[depth]\n        self.stage_blocks = stage_blocks[:num_stages]\n        self.expansion = get_expansion(self.block, expansion)\n\n        self._make_stem_layer(in_channels, stem_channels)\n\n        self.res_layers = []\n        _in_channels = stem_channels\n        _out_channels = base_channels * self.expansion\n        for i, num_blocks in enumerate(self.stage_blocks):\n            stride = strides[i]\n            dilation = dilations[i]\n            res_layer = self.make_res_layer(\n                block=self.block,\n                num_blocks=num_blocks,\n                in_channels=_in_channels,\n                out_channels=_out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                dilation=dilation,\n                style=self.style,\n                avg_down=self.avg_down,\n                with_cp=with_cp,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                drop_path_rate=drop_path_rate)\n            _in_channels = _out_channels\n            _out_channels *= 2\n            layer_name = f'layer{i + 1}'\n            self.add_module(layer_name, res_layer)\n            self.res_layers.append(layer_name)\n\n        self._freeze_stages()\n\n        self.feat_dim = res_layer[-1].out_channels\n\n    def make_res_layer(self, **kwargs):\n        return ResLayer(**kwargs)\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    def _make_stem_layer(self, in_channels, stem_channels):\n        if self.deep_stem:\n            self.stem = nn.Sequential(\n                ConvModule(\n                    in_channels,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True))\n        else:\n            self.conv1 = build_conv_layer(\n                self.conv_cfg,\n                in_channels,\n                stem_channels,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False)\n            self.norm1_name, norm1 = build_norm_layer(\n                self.norm_cfg, stem_channels, postfix=1)\n            self.add_module(self.norm1_name, norm1)\n            self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            if self.deep_stem:\n                self.stem.eval()\n                for param in self.stem.parameters():\n                    param.requires_grad = False\n            else:\n                self.norm1.eval()\n                for m in [self.conv1, self.norm1]:\n                    for param in m.parameters():\n                        param.requires_grad = False\n\n        for i in range(1, self.frozen_stages + 1):\n            m = getattr(self, f'layer{i}')\n            m.eval()\n            for param in m.parameters():\n                param.requires_grad = False\n\n    def init_weights(self):\n        super(ResNet, self).init_weights()\n\n        if (isinstance(self.init_cfg, dict)\n                and self.init_cfg['type'] == 'Pretrained'):\n            # Suppress zero_init_residual if use pretrained model.\n            return\n\n        if self.zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, Bottleneck):\n                    constant_init(m.norm3, 0)\n                elif isinstance(m, BasicBlock):\n                    constant_init(m.norm2, 0)\n\n    def forward(self, x):\n        if self.deep_stem:\n            x = self.stem(x)\n        else:\n            x = self.conv1(x)\n            x = self.norm1(x)\n            x = self.relu(x)\n        x = self.maxpool(x)\n        outs = []\n        for i, layer_name in enumerate(self.res_layers):\n            res_layer = getattr(self, layer_name)\n            x = res_layer(x)\n            if i in self.out_indices:\n                outs.append(x)\n        return tuple(outs)\n\n    def train(self, mode=True):\n        super(ResNet, self).train(mode)\n        self._freeze_stages()\n        if mode and self.norm_eval:\n            for m in self.modules():\n                # trick: eval have effect on BatchNorm only\n                if isinstance(m, _BatchNorm):\n                    m.eval()\n\n\n@BACKBONES.register_module()\nclass ResNetV1c(ResNet):\n    \"\"\"ResNetV1c backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv\n    in the input stem with three 3x3 convs.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1c, self).__init__(\n            deep_stem=True, avg_down=False, **kwargs)\n\n\n@BACKBONES.register_module()\nclass ResNetV1d(ResNet):\n    \"\"\"ResNetV1d backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n    the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n    avg_pool with stride 2 is added before conv, whose stride is changed to 1.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1d, self).__init__(\n            deep_stem=True, avg_down=True, **kwargs)\n"
  },
  {
    "path": "classification-NO-activation-function/backbones/resnet_norelu.py",
    "content": "# Copyright (c) OpenMMLab. All rights reserved.\n\nimport torch.nn as nn\nimport torch.utils.checkpoint as cp\nfrom mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer,\n                      build_norm_layer, constant_init)\nfrom mmcv.cnn.bricks import DropPath\nfrom mmcv.runner import BaseModule\nfrom mmcv.utils.parrots_wrapper import _BatchNorm\n\nfrom ..builder import BACKBONES\nfrom .base_backbone import BaseBackbone\n\neps = 1.0e-5\n\n\nclass BasicBlock(BaseModule):\n    \"\"\"BasicBlock for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the output channels of conv1. This is a\n            reserved argument in BasicBlock and should always be 1. Default: 1.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): `pytorch` or `caffe`. It is unused and reserved for\n            unified API with Bottleneck.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=1,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 drop_path_rate=0.0,\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 init_cfg=None):\n        super(BasicBlock, self).__init__(init_cfg=init_cfg)\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert self.expansion == 1\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, out_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            3,\n            stride=stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            3,\n            padding=1,\n            bias=False)\n        self.add_module(self.norm2_name, norm2)\n#         self.conv3 = build_conv_layer(\n#             conv_cfg,\n#             self.mid_channels,\n#             out_channels,\n#             kernel_size=1,\n#             bias=False)\n#         self.add_module(self.norm3_name, norm3)\n\n#         self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n#         self.drop_path = DropPath(drop_prob=drop_path_rate\n#                                   ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n    \n#     @property\n#     def norm3(self):\n#         return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n#             out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n#             out = self.drop_path(out)\n               \n#             second = self.conv3(out*identity)\n#             second = out*identity\n#             out = out + identity + self.norm3(second)\n\n            out = out + identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n#         out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(BaseModule):\n    \"\"\"Bottleneck block for ResNet.\n\n    Args:\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int): The ratio of ``out_channels/mid_channels`` where\n            ``mid_channels`` is the input/output channels of conv2. Default: 4.\n        stride (int): stride of the block. Default: 1\n        dilation (int): dilation of convolution. Default: 1\n        downsample (nn.Module, optional): downsample operation on identity\n            branch. Default: None.\n        style (str): ``\"pytorch\"`` or ``\"caffe\"``. If set to \"pytorch\", the\n            stride-two layer is the 3x3 conv layer, otherwise the stride-two\n            layer is the first 1x1 conv layer. Default: \"pytorch\".\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed.\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 in_channels,\n                 out_channels,\n                 expansion=4,\n                 stride=1,\n                 dilation=1,\n                 downsample=None,\n                 style='pytorch',\n                 with_cp=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 act_cfg=dict(type='ReLU', inplace=True),\n                 drop_path_rate=0.0,\n                 init_cfg=None):\n        super(Bottleneck, self).__init__(init_cfg=init_cfg)\n        assert style in ['pytorch', 'caffe']\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.expansion = expansion\n        assert out_channels % expansion == 0\n        self.mid_channels = out_channels // expansion\n        self.stride = stride\n        self.dilation = dilation\n        self.style = style\n        self.with_cp = with_cp\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n\n        if self.style == 'pytorch':\n            self.conv1_stride = 1\n            self.conv2_stride = stride\n        else:\n            self.conv1_stride = stride\n            self.conv2_stride = 1\n\n        self.norm1_name, norm1 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=1)\n        self.norm2_name, norm2 = build_norm_layer(\n            norm_cfg, self.mid_channels, postfix=2)\n        self.norm3_name, norm3 = build_norm_layer(\n            norm_cfg, out_channels, postfix=3)\n\n        self.conv1 = build_conv_layer(\n            conv_cfg,\n            in_channels,\n            self.mid_channels,\n            kernel_size=1,\n            stride=self.conv1_stride,\n            bias=False)\n        self.add_module(self.norm1_name, norm1)\n        self.conv2 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            self.mid_channels,\n            kernel_size=3,\n            stride=self.conv2_stride,\n            padding=dilation,\n            dilation=dilation,\n            bias=False)\n\n        self.add_module(self.norm2_name, norm2)\n        self.conv3 = build_conv_layer(\n            conv_cfg,\n            self.mid_channels,\n            out_channels,\n            kernel_size=1,\n            bias=False)\n        self.add_module(self.norm3_name, norm3)\n\n        self.relu = build_activation_layer(act_cfg)\n        self.downsample = downsample\n        self.drop_path = DropPath(drop_prob=drop_path_rate\n                                  ) if drop_path_rate > eps else nn.Identity()\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    @property\n    def norm2(self):\n        return getattr(self, self.norm2_name)\n\n    @property\n    def norm3(self):\n        return getattr(self, self.norm3_name)\n\n    def forward(self, x):\n\n        def _inner_forward(x):\n            identity = x\n\n            out = self.conv1(x)\n            out = self.norm1(out)\n            out = self.relu(out)\n\n            out = self.conv2(out)\n            out = self.norm2(out)\n            out = self.relu(out)\n\n            out = self.conv3(out)\n            out = self.norm3(out)\n\n            if self.downsample is not None:\n                identity = self.downsample(x)\n\n            out = self.drop_path(out)\n\n            out += identity\n\n            return out\n\n        if self.with_cp and x.requires_grad:\n            out = cp.checkpoint(_inner_forward, x)\n        else:\n            out = _inner_forward(x)\n\n        out = self.relu(out)\n\n        return out\n\n\ndef get_expansion(block, expansion=None):\n    \"\"\"Get the expansion of a residual block.\n\n    The block expansion will be obtained by the following order:\n\n    1. If ``expansion`` is given, just return it.\n    2. If ``block`` has the attribute ``expansion``, then return\n       ``block.expansion``.\n    3. Return the default value according the the block type:\n       1 for ``BasicBlock`` and 4 for ``Bottleneck``.\n\n    Args:\n        block (class): The block class.\n        expansion (int | None): The given expansion ratio.\n\n    Returns:\n        int: The expansion of the block.\n    \"\"\"\n    if isinstance(expansion, int):\n        assert expansion > 0\n    elif expansion is None:\n        if hasattr(block, 'expansion'):\n            expansion = block.expansion\n        elif issubclass(block, BasicBlock):\n            expansion = 1\n        elif issubclass(block, Bottleneck):\n            expansion = 4\n        else:\n            raise TypeError(f'expansion is not specified for {block.__name__}')\n    else:\n        raise TypeError('expansion must be an integer or None')\n\n    return expansion\n\n\nclass ResLayer(nn.Sequential):\n    \"\"\"ResLayer to build ResNet style backbone.\n\n    Args:\n        block (nn.Module): Residual block used to build ResLayer.\n        num_blocks (int): Number of blocks.\n        in_channels (int): Input channels of this block.\n        out_channels (int): Output channels of this block.\n        expansion (int, optional): The expansion for BasicBlock/Bottleneck.\n            If not specified, it will firstly be obtained via\n            ``block.expansion``. If the block has no attribute \"expansion\",\n            the following default values will be used: 1 for BasicBlock and\n            4 for Bottleneck. Default: None.\n        stride (int): stride of the first block. Default: 1.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False\n        conv_cfg (dict, optional): dictionary to construct and config conv\n            layer. Default: None\n        norm_cfg (dict): dictionary to construct and config norm layer.\n            Default: dict(type='BN')\n    \"\"\"\n\n    def __init__(self,\n                 block,\n                 num_blocks,\n                 in_channels,\n                 out_channels,\n                 expansion=None,\n                 stride=1,\n                 avg_down=False,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN'),\n                 **kwargs):\n        self.block = block\n        self.expansion = get_expansion(block, expansion)\n\n        downsample = None\n        if stride != 1 or in_channels != out_channels:\n            downsample = []\n            conv_stride = stride\n            if avg_down and stride != 1:\n                conv_stride = 1\n                downsample.append(\n                    nn.AvgPool2d(\n                        kernel_size=stride,\n                        stride=stride,\n                        ceil_mode=True,\n                        count_include_pad=False))\n            downsample.extend([\n                build_conv_layer(\n                    conv_cfg,\n                    in_channels,\n                    out_channels,\n                    kernel_size=1,\n                    stride=conv_stride,\n                    bias=False),\n                build_norm_layer(norm_cfg, out_channels)[1]\n            ])\n            downsample = nn.Sequential(*downsample)\n\n        layers = []\n        layers.append(\n            block(\n                in_channels=in_channels,\n                out_channels=out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                downsample=downsample,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                **kwargs))\n        in_channels = out_channels\n        for i in range(1, num_blocks):\n            layers.append(\n                block(\n                    in_channels=in_channels,\n                    out_channels=out_channels,\n                    expansion=self.expansion,\n                    stride=1,\n                    conv_cfg=conv_cfg,\n                    norm_cfg=norm_cfg,\n                    **kwargs))\n        super(ResLayer, self).__init__(*layers)\n\n\n@BACKBONES.register_module()\nclass ResNet(BaseBackbone):\n    \"\"\"ResNet backbone.\n\n    Please refer to the `paper <https://arxiv.org/abs/1512.03385>`__ for\n    details.\n\n    Args:\n        depth (int): Network depth, from {18, 34, 50, 101, 152}.\n        in_channels (int): Number of input image channels. Default: 3.\n        stem_channels (int): Output channels of the stem layer. Default: 64.\n        base_channels (int): Middle channels of the first stage. Default: 64.\n        num_stages (int): Stages of the network. Default: 4.\n        strides (Sequence[int]): Strides of the first block of each stage.\n            Default: ``(1, 2, 2, 2)``.\n        dilations (Sequence[int]): Dilation of each stage.\n            Default: ``(1, 1, 1, 1)``.\n        out_indices (Sequence[int]): Output from which stages.\n            Default: ``(3, )``.\n        style (str): `pytorch` or `caffe`. If set to \"pytorch\", the stride-two\n            layer is the 3x3 conv layer, otherwise the stride-two layer is\n            the first 1x1 conv layer.\n        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv.\n            Default: False.\n        avg_down (bool): Use AvgPool instead of stride conv when\n            downsampling in the bottleneck. Default: False.\n        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).\n            -1 means not freezing any parameters. Default: -1.\n        conv_cfg (dict | None): The config dict for conv layers. Default: None.\n        norm_cfg (dict): The config dict for norm layers.\n        norm_eval (bool): Whether to set norm layers to eval mode, namely,\n            freeze running stats (mean and var). Note: Effect on Batch Norm\n            and its variants only. Default: False.\n        with_cp (bool): Use checkpoint or not. Using checkpoint will save some\n            memory while slowing down the training speed. Default: False.\n        zero_init_residual (bool): Whether to use zero init for last norm layer\n            in resblocks to let them behave as identity. Default: True.\n\n    Example:\n        >>> from mmcls.models import ResNet\n        >>> import torch\n        >>> self = ResNet(depth=18)\n        >>> self.eval()\n        >>> inputs = torch.rand(1, 3, 32, 32)\n        >>> level_outputs = self.forward(inputs)\n        >>> for level_out in level_outputs:\n        ...     print(tuple(level_out.shape))\n        (1, 64, 8, 8)\n        (1, 128, 4, 4)\n        (1, 256, 2, 2)\n        (1, 512, 1, 1)\n    \"\"\"\n\n    arch_settings = {\n        18: (BasicBlock, (2, 2, 2, 2)),\n        34: (BasicBlock, (3, 4, 6, 3)),\n        50: (Bottleneck, (3, 4, 6, 3)),\n        101: (Bottleneck, (3, 4, 23, 3)),\n        152: (Bottleneck, (3, 8, 36, 3))\n    }\n\n    def __init__(self,\n                 depth,\n                 in_channels=3,\n                 stem_channels=64,\n                 base_channels=64,\n                 expansion=None,\n                 num_stages=4,\n                 strides=(1, 2, 2, 2),\n                 dilations=(1, 1, 1, 1),\n                 out_indices=(3, ),\n                 style='pytorch',\n                 deep_stem=False,\n                 avg_down=False,\n                 frozen_stages=-1,\n                 conv_cfg=None,\n                 norm_cfg=dict(type='BN', requires_grad=True),\n                 norm_eval=False,\n                 with_cp=False,\n                 zero_init_residual=True,\n                 init_cfg=[\n                     dict(type='Kaiming', layer=['Conv2d']),\n                     dict(\n                         type='Constant',\n                         val=1,\n                         layer=['_BatchNorm', 'GroupNorm'])\n                 ],\n                 drop_path_rate=0.0):\n        super(ResNet, self).__init__(init_cfg)\n        if depth not in self.arch_settings:\n            raise KeyError(f'invalid depth {depth} for resnet')\n        self.depth = depth\n        self.stem_channels = stem_channels\n        self.base_channels = base_channels\n        self.num_stages = num_stages\n        assert num_stages >= 1 and num_stages <= 4\n        self.strides = strides\n        self.dilations = dilations\n        assert len(strides) == len(dilations) == num_stages\n        self.out_indices = out_indices\n        assert max(out_indices) < num_stages\n        self.style = style\n        self.deep_stem = deep_stem\n        self.avg_down = avg_down\n        self.frozen_stages = frozen_stages\n        self.conv_cfg = conv_cfg\n        self.norm_cfg = norm_cfg\n        self.with_cp = with_cp\n        self.norm_eval = norm_eval\n        self.zero_init_residual = zero_init_residual\n        self.block, stage_blocks = self.arch_settings[depth]\n        self.stage_blocks = stage_blocks[:num_stages]\n        self.expansion = get_expansion(self.block, expansion)\n\n        self._make_stem_layer(in_channels, stem_channels)\n\n        self.res_layers = []\n        _in_channels = stem_channels\n        _out_channels = base_channels * self.expansion\n        for i, num_blocks in enumerate(self.stage_blocks):\n            stride = strides[i]\n            dilation = dilations[i]\n            res_layer = self.make_res_layer(\n                block=self.block,\n                num_blocks=num_blocks,\n                in_channels=_in_channels,\n                out_channels=_out_channels,\n                expansion=self.expansion,\n                stride=stride,\n                dilation=dilation,\n                style=self.style,\n                avg_down=self.avg_down,\n                with_cp=with_cp,\n                conv_cfg=conv_cfg,\n                norm_cfg=norm_cfg,\n                drop_path_rate=drop_path_rate)\n            _in_channels = _out_channels\n            _out_channels *= 2\n            layer_name = f'layer{i + 1}'\n            self.add_module(layer_name, res_layer)\n            self.res_layers.append(layer_name)\n\n        self._freeze_stages()\n\n        self.feat_dim = res_layer[-1].out_channels\n\n    def make_res_layer(self, **kwargs):\n        return ResLayer(**kwargs)\n\n    @property\n    def norm1(self):\n        return getattr(self, self.norm1_name)\n\n    def _make_stem_layer(self, in_channels, stem_channels):\n        if self.deep_stem:\n            self.stem = nn.Sequential(\n                ConvModule(\n                    in_channels,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels // 2,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True),\n                ConvModule(\n                    stem_channels // 2,\n                    stem_channels,\n                    kernel_size=3,\n                    stride=1,\n                    padding=1,\n                    conv_cfg=self.conv_cfg,\n                    norm_cfg=self.norm_cfg,\n                    inplace=True))\n        else:\n            self.conv1 = build_conv_layer(\n                self.conv_cfg,\n                in_channels,\n                stem_channels,\n                kernel_size=7,\n                stride=2,\n                padding=3,\n                bias=False)\n            self.norm1_name, norm1 = build_norm_layer(\n                self.norm_cfg, stem_channels, postfix=1)\n            self.add_module(self.norm1_name, norm1)\n#             self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n    def _freeze_stages(self):\n        if self.frozen_stages >= 0:\n            if self.deep_stem:\n                self.stem.eval()\n                for param in self.stem.parameters():\n                    param.requires_grad = False\n            else:\n                self.norm1.eval()\n                for m in [self.conv1, self.norm1]:\n                    for param in m.parameters():\n                        param.requires_grad = False\n\n        for i in range(1, self.frozen_stages + 1):\n            m = getattr(self, f'layer{i}')\n            m.eval()\n            for param in m.parameters():\n                param.requires_grad = False\n\n    def init_weights(self):\n        super(ResNet, self).init_weights()\n\n        if (isinstance(self.init_cfg, dict)\n                and self.init_cfg['type'] == 'Pretrained'):\n            # Suppress zero_init_residual if use pretrained model.\n            return\n\n        if self.zero_init_residual:\n            for m in self.modules():\n                if isinstance(m, Bottleneck):\n                    constant_init(m.norm3, 0)\n                elif isinstance(m, BasicBlock):\n                    constant_init(m.norm2, 0)\n\n    def forward(self, x):\n        if self.deep_stem:\n            x = self.stem(x)\n        else:\n            x = self.conv1(x)\n            x = self.norm1(x)\n#             x = self.relu(x)\n        x = self.maxpool(x)\n        outs = []\n        for i, layer_name in enumerate(self.res_layers):\n            res_layer = getattr(self, layer_name)\n            x = res_layer(x)\n            if i in self.out_indices:\n                outs.append(x)\n        return tuple(outs)\n\n    def train(self, mode=True):\n        super(ResNet, self).train(mode)\n        self._freeze_stages()\n        if mode and self.norm_eval:\n            for m in self.modules():\n                # trick: eval have effect on BatchNorm only\n                if isinstance(m, _BatchNorm):\n                    m.eval()\n\n\n@BACKBONES.register_module()\nclass ResNetV1c(ResNet):\n    \"\"\"ResNetV1c backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv\n    in the input stem with three 3x3 convs.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1c, self).__init__(\n            deep_stem=True, avg_down=False, **kwargs)\n\n\n@BACKBONES.register_module()\nclass ResNetV1d(ResNet):\n    \"\"\"ResNetV1d backbone.\n\n    This variant is described in `Bag of Tricks.\n    <https://arxiv.org/pdf/1812.01187.pdf>`_.\n\n    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in\n    the input stem with three 3x3 convs. And in the downsampling block, a 2x2\n    avg_pool with stride 2 is added before conv, whose stride is changed to 1.\n    \"\"\"\n\n    def __init__(self, **kwargs):\n        super(ResNetV1d, self).__init__(\n            deep_stem=True, avg_down=True, **kwargs)\n"
  },
  {
    "path": "classification-NO-activation-function/logs/pinet_1x1_norelu.log",
    "content": "2023-01-01 16:31:14,068 - mmcls - INFO - Environment info:\n------------------------------------------------------------\nsys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 10.2, V10.2.8\nGCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 10.2\nMMClassification: 0.24.0+91b85bb\n------------------------------------------------------------\n\n2023-01-01 16:31:14,068 - mmcls - INFO - Distributed training: True\n2023-01-01 16:31:14,230 - mmcls - INFO - Config:\nmodel = dict(\n    type='ImageClassifier',\n    backbone=dict(\n        type='ResNet',\n        depth=18,\n        num_stages=4,\n        out_indices=(3, ),\n        style='pytorch'),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=1000,\n        in_channels=512,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1)),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=32,\n    workers_per_gpu=2,\n    train=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[30, 60, 90])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=50)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/resnet18_8xb32_in1k_pi'\ngpu_ids = range(0, 8)\n\n2023-01-01 16:31:19,484 - mmcls - INFO - Set random seed to 1232336006, deterministic: False\n2023-01-01 16:31:19,669 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n2023-01-01 16:31:19,760 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\nName of parameter - Initialization information\n\nbackbone.conv1.weight - torch.Size([64, 3, 7, 7]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.0.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv3.weight - torch.Size([64, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.1.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv3.weight - torch.Size([64, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.0.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv3.weight - torch.Size([128, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.downsample.1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.1.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv3.weight - torch.Size([128, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.0.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv3.weight - torch.Size([256, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.downsample.1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.1.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv3.weight - torch.Size([256, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.0.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv3.weight - torch.Size([512, 512, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.downsample.1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.1.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv3.weight - torch.Size([512, 512, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nhead.fc.weight - torch.Size([1000, 512]): \nNormalInit: mean=0, std=0.01, bias=0 \n\nhead.fc.bias - torch.Size([1000]): \nNormalInit: mean=0, std=0.01, bias=0 \n2023-01-01 16:31:48,444 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi\n2023-01-01 16:31:48,444 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) DistOptimizerHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2023-01-01 16:31:48,445 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs\n2023-01-01 16:31:48,445 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend.\n2023-01-01 16:32:01,103 - mmcls - INFO - Epoch [1][100/5005]\tlr: 1.000e-01, eta: 17:33:36, time: 0.126, data_time: 0.027, memory: 1224, loss: 6.7943\n2023-01-01 16:32:09,823 - mmcls - INFO - Epoch [1][200/5005]\tlr: 1.000e-01, eta: 14:50:16, time: 0.087, data_time: 0.007, memory: 1224, loss: 6.5618\n2023-01-01 16:32:18,667 - mmcls - INFO - Epoch [1][300/5005]\tlr: 1.000e-01, eta: 13:58:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 6.3981\n2023-01-01 16:32:28,033 - mmcls - INFO - Epoch [1][400/5005]\tlr: 1.000e-01, eta: 13:44:20, time: 0.094, data_time: 0.001, memory: 1224, loss: 6.2250\n2023-01-01 16:32:36,751 - mmcls - INFO - Epoch [1][500/5005]\tlr: 1.000e-01, eta: 13:24:30, time: 0.087, data_time: 0.005, memory: 1224, loss: 6.0684\n2023-01-01 16:32:45,889 - mmcls - INFO - Epoch [1][600/5005]\tlr: 1.000e-01, eta: 13:17:21, time: 0.092, data_time: 0.003, memory: 1224, loss: 5.9139\n2023-01-01 16:32:54,902 - mmcls - INFO - Epoch [1][700/5005]\tlr: 1.000e-01, eta: 13:10:29, time: 0.090, data_time: 0.022, memory: 1224, loss: 5.7723\n2023-01-01 16:33:04,093 - mmcls - INFO - Epoch [1][800/5005]\tlr: 1.000e-01, eta: 13:07:15, time: 0.092, data_time: 0.004, memory: 1224, loss: 5.6559\n2023-01-01 16:33:13,416 - mmcls - INFO - Epoch [1][900/5005]\tlr: 1.000e-01, eta: 13:05:53, time: 0.093, data_time: 0.001, memory: 1224, loss: 5.5643\n2023-01-01 16:33:22,401 - mmcls - INFO - Epoch [1][1000/5005]\tlr: 1.000e-01, eta: 13:01:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 5.4418\n2023-01-01 16:33:31,309 - mmcls - INFO - Epoch [1][1100/5005]\tlr: 1.000e-01, eta: 12:58:07, time: 0.089, data_time: 0.017, memory: 1224, loss: 5.3744\n2023-01-01 16:33:40,254 - mmcls - INFO - Epoch [1][1200/5005]\tlr: 1.000e-01, eta: 12:55:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 5.2766\n2023-01-01 16:33:49,209 - mmcls - INFO - Epoch [1][1300/5005]\tlr: 1.000e-01, eta: 12:52:39, time: 0.090, data_time: 0.003, memory: 1224, loss: 5.2192\n2023-01-01 16:33:58,435 - mmcls - INFO - Epoch [1][1400/5005]\tlr: 1.000e-01, eta: 12:52:08, time: 0.092, data_time: 0.021, memory: 1224, loss: 5.1091\n2023-01-01 16:34:07,654 - mmcls - INFO - Epoch [1][1500/5005]\tlr: 1.000e-01, eta: 12:51:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 5.0672\n2023-01-01 16:34:16,444 - mmcls - INFO - Epoch [1][1600/5005]\tlr: 1.000e-01, eta: 12:48:54, time: 0.088, data_time: 0.011, memory: 1224, loss: 4.9784\n2023-01-01 16:34:25,494 - mmcls - INFO - Epoch [1][1700/5005]\tlr: 1.000e-01, eta: 12:47:49, time: 0.091, data_time: 0.014, memory: 1224, loss: 4.9488\n2023-01-01 16:34:34,582 - mmcls - INFO - Epoch [1][1800/5005]\tlr: 1.000e-01, eta: 12:46:58, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.8974\n2023-01-01 16:34:43,683 - mmcls - INFO - Epoch [1][1900/5005]\tlr: 1.000e-01, eta: 12:46:14, time: 0.091, data_time: 0.011, memory: 1224, loss: 4.8291\n2023-01-01 16:34:52,944 - mmcls - INFO - Epoch [1][2000/5005]\tlr: 1.000e-01, eta: 12:46:14, time: 0.093, data_time: 0.010, memory: 1224, loss: 4.7950\n2023-01-01 16:35:01,799 - mmcls - INFO - Epoch [1][2100/5005]\tlr: 1.000e-01, eta: 12:44:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 4.7290\n2023-01-01 16:35:10,855 - mmcls - INFO - Epoch [1][2200/5005]\tlr: 1.000e-01, eta: 12:43:55, time: 0.091, data_time: 0.005, memory: 1224, loss: 4.6624\n2023-01-01 16:35:19,750 - mmcls - INFO - Epoch [1][2300/5005]\tlr: 1.000e-01, eta: 12:42:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 4.6366\n2023-01-01 16:35:29,019 - mmcls - INFO - Epoch [1][2400/5005]\tlr: 1.000e-01, eta: 12:42:49, time: 0.093, data_time: 0.001, memory: 1224, loss: 4.5740\n2023-01-01 16:35:37,864 - mmcls - INFO - Epoch [1][2500/5005]\tlr: 1.000e-01, eta: 12:41:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 4.5621\n2023-01-01 16:35:46,875 - mmcls - INFO - Epoch [1][2600/5005]\tlr: 1.000e-01, eta: 12:40:50, time: 0.090, data_time: 0.004, memory: 1224, loss: 4.5201\n2023-01-01 16:35:55,653 - mmcls - INFO - Epoch [1][2700/5005]\tlr: 1.000e-01, eta: 12:39:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.4704\n2023-01-01 16:36:04,364 - mmcls - INFO - Epoch [1][2800/5005]\tlr: 1.000e-01, eta: 12:38:00, time: 0.087, data_time: 0.007, memory: 1224, loss: 4.4646\n2023-01-01 16:36:13,390 - mmcls - INFO - Epoch [1][2900/5005]\tlr: 1.000e-01, eta: 12:37:32, time: 0.090, data_time: 0.027, memory: 1224, loss: 4.4182\n2023-01-01 16:36:22,304 - mmcls - INFO - Epoch [1][3000/5005]\tlr: 1.000e-01, eta: 12:36:45, time: 0.089, data_time: 0.007, memory: 1224, loss: 4.3918\n2023-01-01 16:36:31,063 - mmcls - INFO - Epoch [1][3100/5005]\tlr: 1.000e-01, eta: 12:35:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 4.3417\n2023-01-01 16:36:40,190 - mmcls - INFO - Epoch [1][3200/5005]\tlr: 1.000e-01, eta: 12:35:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.3231\n2023-01-01 16:36:49,028 - mmcls - INFO - Epoch [1][3300/5005]\tlr: 1.000e-01, eta: 12:34:39, time: 0.088, data_time: 0.005, memory: 1224, loss: 4.3135\n2023-01-01 16:36:58,094 - mmcls - INFO - Epoch [1][3400/5005]\tlr: 1.000e-01, eta: 12:34:23, time: 0.091, data_time: 0.004, memory: 1224, loss: 4.2897\n2023-01-01 16:37:07,001 - mmcls - INFO - Epoch [1][3500/5005]\tlr: 1.000e-01, eta: 12:33:48, time: 0.089, data_time: 0.005, memory: 1224, loss: 4.2420\n2023-01-01 16:37:15,801 - mmcls - INFO - Epoch [1][3600/5005]\tlr: 1.000e-01, eta: 12:32:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 4.2271\n2023-01-01 16:37:24,964 - mmcls - INFO - Epoch [1][3700/5005]\tlr: 1.000e-01, eta: 12:32:56, time: 0.092, data_time: 0.002, memory: 1224, loss: 4.2202\n2023-01-01 16:37:34,440 - mmcls - INFO - Epoch [1][3800/5005]\tlr: 1.000e-01, eta: 12:33:37, time: 0.095, data_time: 0.011, memory: 1224, loss: 4.1578\n2023-01-01 16:37:43,412 - mmcls - INFO - Epoch [1][3900/5005]\tlr: 1.000e-01, eta: 12:33:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 4.1460\n2023-01-01 16:37:51,984 - mmcls - INFO - Epoch [1][4000/5005]\tlr: 1.000e-01, eta: 12:31:56, time: 0.086, data_time: 0.018, memory: 1224, loss: 4.1256\n2023-01-01 16:38:01,139 - mmcls - INFO - Epoch [1][4100/5005]\tlr: 1.000e-01, eta: 12:31:55, time: 0.092, data_time: 0.002, memory: 1224, loss: 4.1535\n2023-01-01 16:38:10,030 - mmcls - INFO - Epoch [1][4200/5005]\tlr: 1.000e-01, eta: 12:31:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 4.1206\n2023-01-01 16:38:19,057 - mmcls - INFO - Epoch [1][4300/5005]\tlr: 1.000e-01, eta: 12:31:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 4.0641\n2023-01-01 16:38:28,015 - mmcls - INFO - Epoch [1][4400/5005]\tlr: 1.000e-01, eta: 12:30:43, time: 0.090, data_time: 0.018, memory: 1224, loss: 4.0750\n2023-01-01 16:38:37,135 - mmcls - INFO - Epoch [1][4500/5005]\tlr: 1.000e-01, eta: 12:30:39, time: 0.091, data_time: 0.003, memory: 1224, loss: 4.0476\n2023-01-01 16:38:46,191 - mmcls - INFO - Epoch [1][4600/5005]\tlr: 1.000e-01, eta: 12:30:28, time: 0.091, data_time: 0.006, memory: 1224, loss: 4.0405\n2023-01-01 16:38:55,786 - mmcls - INFO - Epoch [1][4700/5005]\tlr: 1.000e-01, eta: 12:31:13, time: 0.096, data_time: 0.002, memory: 1224, loss: 4.0158\n2023-01-01 16:39:04,790 - mmcls - INFO - Epoch [1][4800/5005]\tlr: 1.000e-01, eta: 12:30:55, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.9927\n2023-01-01 16:39:13,801 - mmcls - INFO - Epoch [1][4900/5005]\tlr: 1.000e-01, eta: 12:30:37, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.9823\n2023-01-01 16:39:22,580 - mmcls - INFO - Epoch [1][5000/5005]\tlr: 1.000e-01, eta: 12:29:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.9691\n2023-01-01 16:39:46,049 - mmcls - INFO - Epoch(val) [1][196]\taccuracy_top-1: 27.0620, accuracy_top-5: 51.3400\n2023-01-01 16:39:57,154 - mmcls - INFO - Epoch [2][100/5005]\tlr: 1.000e-01, eta: 12:32:19, time: 0.111, data_time: 0.050, memory: 1224, loss: 3.8937\n2023-01-01 16:40:05,913 - mmcls - INFO - Epoch [2][200/5005]\tlr: 1.000e-01, eta: 12:31:35, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.9343\n2023-01-01 16:40:14,549 - mmcls - INFO - Epoch [2][300/5005]\tlr: 1.000e-01, eta: 12:30:42, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.8934\n2023-01-01 16:40:23,809 - mmcls - INFO - Epoch [2][400/5005]\tlr: 1.000e-01, eta: 12:30:48, time: 0.093, data_time: 0.011, memory: 1224, loss: 3.8838\n2023-01-01 16:40:32,474 - mmcls - INFO - Epoch [2][500/5005]\tlr: 1.000e-01, eta: 12:30:00, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.8964\n2023-01-01 16:40:41,451 - mmcls - INFO - Epoch [2][600/5005]\tlr: 1.000e-01, eta: 12:29:40, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.8346\n2023-01-01 16:40:50,179 - mmcls - INFO - Epoch [2][700/5005]\tlr: 1.000e-01, eta: 12:29:00, time: 0.087, data_time: 0.017, memory: 1224, loss: 3.8494\n2023-01-01 16:40:59,384 - mmcls - INFO - Epoch [2][800/5005]\tlr: 1.000e-01, eta: 12:29:01, time: 0.092, data_time: 0.012, memory: 1224, loss: 3.8240\n2023-01-01 16:41:08,143 - mmcls - INFO - Epoch [2][900/5005]\tlr: 1.000e-01, eta: 12:28:25, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.8599\n2023-01-01 16:41:17,200 - mmcls - INFO - Epoch [2][1000/5005]\tlr: 1.000e-01, eta: 12:28:14, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.8085\n2023-01-01 16:41:26,152 - mmcls - INFO - Epoch [2][1100/5005]\tlr: 1.000e-01, eta: 12:27:54, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.8072\n2023-01-01 16:41:35,104 - mmcls - INFO - Epoch [2][1200/5005]\tlr: 1.000e-01, eta: 12:27:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.8290\n2023-01-01 16:41:43,992 - mmcls - INFO - Epoch [2][1300/5005]\tlr: 1.000e-01, eta: 12:27:11, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.8225\n2023-01-01 16:41:52,912 - mmcls - INFO - Epoch [2][1400/5005]\tlr: 1.000e-01, eta: 12:26:51, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.7633\n2023-01-01 16:42:01,992 - mmcls - INFO - Epoch [2][1500/5005]\tlr: 1.000e-01, eta: 12:26:42, time: 0.091, data_time: 0.012, memory: 1224, loss: 3.7492\n2023-01-01 16:42:10,788 - mmcls - INFO - Epoch [2][1600/5005]\tlr: 1.000e-01, eta: 12:26:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.7819\n2023-01-01 16:42:19,577 - mmcls - INFO - Epoch [2][1700/5005]\tlr: 1.000e-01, eta: 12:25:44, time: 0.088, data_time: 0.030, memory: 1224, loss: 3.7568\n2023-01-01 16:42:28,423 - mmcls - INFO - Epoch [2][1800/5005]\tlr: 1.000e-01, eta: 12:25:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.7011\n2023-01-01 16:42:37,539 - mmcls - INFO - Epoch [2][1900/5005]\tlr: 1.000e-01, eta: 12:25:14, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.6875\n2023-01-01 16:42:46,593 - mmcls - INFO - Epoch [2][2000/5005]\tlr: 1.000e-01, eta: 12:25:04, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.7433\n2023-01-01 16:42:55,891 - mmcls - INFO - Epoch [2][2100/5005]\tlr: 1.000e-01, eta: 12:25:11, time: 0.093, data_time: 0.013, memory: 1224, loss: 3.6791\n2023-01-01 16:43:04,825 - mmcls - INFO - Epoch [2][2200/5005]\tlr: 1.000e-01, eta: 12:24:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.7298\n2023-01-01 16:43:13,790 - mmcls - INFO - Epoch [2][2300/5005]\tlr: 1.000e-01, eta: 12:24:39, time: 0.090, data_time: 0.011, memory: 1224, loss: 3.7094\n2023-01-01 16:43:22,506 - mmcls - INFO - Epoch [2][2400/5005]\tlr: 1.000e-01, eta: 12:24:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.6950\n2023-01-01 16:43:31,663 - mmcls - INFO - Epoch [2][2500/5005]\tlr: 1.000e-01, eta: 12:24:04, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.6878\n2023-01-01 16:43:40,408 - mmcls - INFO - Epoch [2][2600/5005]\tlr: 1.000e-01, eta: 12:23:35, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.6642\n2023-01-01 16:43:49,467 - mmcls - INFO - Epoch [2][2700/5005]\tlr: 1.000e-01, eta: 12:23:27, time: 0.091, data_time: 0.018, memory: 1224, loss: 3.6835\n2023-01-01 16:43:58,312 - mmcls - INFO - Epoch [2][2800/5005]\tlr: 1.000e-01, eta: 12:23:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.6533\n2023-01-01 16:44:06,958 - mmcls - INFO - Epoch [2][2900/5005]\tlr: 1.000e-01, eta: 12:22:30, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.6510\n2023-01-01 16:44:15,826 - mmcls - INFO - Epoch [2][3000/5005]\tlr: 1.000e-01, eta: 12:22:10, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.6303\n2023-01-01 16:44:24,759 - mmcls - INFO - Epoch [2][3100/5005]\tlr: 1.000e-01, eta: 12:21:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.6254\n2023-01-01 16:44:33,787 - mmcls - INFO - Epoch [2][3200/5005]\tlr: 1.000e-01, eta: 12:21:44, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.6258\n2023-01-01 16:44:42,652 - mmcls - INFO - Epoch [2][3300/5005]\tlr: 1.000e-01, eta: 12:21:25, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.6113\n2023-01-01 16:44:51,325 - mmcls - INFO - Epoch [2][3400/5005]\tlr: 1.000e-01, eta: 12:20:54, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.5648\n2023-01-01 16:45:00,053 - mmcls - INFO - Epoch [2][3500/5005]\tlr: 1.000e-01, eta: 12:20:28, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.5955\n2023-01-01 16:45:08,746 - mmcls - INFO - Epoch [2][3600/5005]\tlr: 1.000e-01, eta: 12:19:59, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.5998\n2023-01-01 16:45:17,468 - mmcls - INFO - Epoch [2][3700/5005]\tlr: 1.000e-01, eta: 12:19:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.5449\n2023-01-01 16:45:26,400 - mmcls - INFO - Epoch [2][3800/5005]\tlr: 1.000e-01, eta: 12:19:19, time: 0.089, data_time: 0.020, memory: 1224, loss: 3.5836\n2023-01-01 16:45:35,128 - mmcls - INFO - Epoch [2][3900/5005]\tlr: 1.000e-01, eta: 12:18:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.5650\n2023-01-01 16:45:44,410 - mmcls - INFO - Epoch [2][4000/5005]\tlr: 1.000e-01, eta: 12:18:59, time: 0.093, data_time: 0.008, memory: 1224, loss: 3.5710\n2023-01-01 16:45:53,276 - mmcls - INFO - Epoch [2][4100/5005]\tlr: 1.000e-01, eta: 12:18:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.5962\n2023-01-01 16:46:02,000 - mmcls - INFO - Epoch [2][4200/5005]\tlr: 1.000e-01, eta: 12:18:16, time: 0.087, data_time: 0.013, memory: 1224, loss: 3.5322\n2023-01-01 16:46:11,414 - mmcls - INFO - Epoch [2][4300/5005]\tlr: 1.000e-01, eta: 12:18:28, time: 0.094, data_time: 0.005, memory: 1224, loss: 3.5285\n2023-01-01 16:46:20,631 - mmcls - INFO - Epoch [2][4400/5005]\tlr: 1.000e-01, eta: 12:18:30, time: 0.092, data_time: 0.001, memory: 1224, loss: 3.5371\n2023-01-01 16:46:29,368 - mmcls - INFO - Epoch [2][4500/5005]\tlr: 1.000e-01, eta: 12:18:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.5495\n2023-01-01 16:46:38,562 - mmcls - INFO - Epoch [2][4600/5005]\tlr: 1.000e-01, eta: 12:18:06, time: 0.092, data_time: 0.004, memory: 1224, loss: 3.5300\n2023-01-01 16:46:47,644 - mmcls - INFO - Epoch [2][4700/5005]\tlr: 1.000e-01, eta: 12:18:00, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.5532\n2023-01-01 16:46:56,597 - mmcls - INFO - Epoch [2][4800/5005]\tlr: 1.000e-01, eta: 12:17:47, time: 0.090, data_time: 0.000, memory: 1224, loss: 3.5306\n2023-01-01 16:47:05,637 - mmcls - INFO - Epoch [2][4900/5005]\tlr: 1.000e-01, eta: 12:17:39, time: 0.090, data_time: 0.009, memory: 1224, loss: 3.5050\n2023-01-01 16:47:14,053 - mmcls - INFO - Epoch [2][5000/5005]\tlr: 1.000e-01, eta: 12:17:00, time: 0.084, data_time: 0.011, memory: 1224, loss: 3.5432\n2023-01-01 16:47:37,395 - mmcls - INFO - Epoch(val) [2][196]\taccuracy_top-1: 31.7280, accuracy_top-5: 56.4860\n2023-01-01 16:47:48,377 - mmcls - INFO - Epoch [3][100/5005]\tlr: 1.000e-01, eta: 12:18:04, time: 0.110, data_time: 0.034, memory: 1224, loss: 3.4193\n2023-01-01 16:47:57,234 - mmcls - INFO - Epoch [3][200/5005]\tlr: 1.000e-01, eta: 12:17:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 3.4269\n2023-01-01 16:48:06,014 - mmcls - INFO - Epoch [3][300/5005]\tlr: 1.000e-01, eta: 12:17:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.4609\n2023-01-01 16:48:14,886 - mmcls - INFO - Epoch [3][400/5005]\tlr: 1.000e-01, eta: 12:17:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.4043\n2023-01-01 16:48:23,899 - mmcls - INFO - Epoch [3][500/5005]\tlr: 1.000e-01, eta: 12:16:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.4147\n2023-01-01 16:48:32,630 - mmcls - INFO - Epoch [3][600/5005]\tlr: 1.000e-01, eta: 12:16:36, time: 0.087, data_time: 0.019, memory: 1224, loss: 3.4490\n2023-01-01 16:48:41,614 - mmcls - INFO - Epoch [3][700/5005]\tlr: 1.000e-01, eta: 12:16:25, time: 0.090, data_time: 0.002, memory: 1224, loss: 3.4249\n2023-01-01 16:48:50,202 - mmcls - INFO - Epoch [3][800/5005]\tlr: 1.000e-01, eta: 12:15:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.4669\n2023-01-01 16:48:59,377 - mmcls - INFO - Epoch [3][900/5005]\tlr: 1.000e-01, eta: 12:15:55, time: 0.092, data_time: 0.004, memory: 1224, loss: 3.4597\n2023-01-01 16:49:08,176 - mmcls - INFO - Epoch [3][1000/5005]\tlr: 1.000e-01, eta: 12:15:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 3.4512\n2023-01-01 16:49:17,045 - mmcls - INFO - Epoch [3][1100/5005]\tlr: 1.000e-01, eta: 12:15:21, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.4199\n2023-01-01 16:49:26,353 - mmcls - INFO - Epoch [3][1200/5005]\tlr: 1.000e-01, eta: 12:15:24, time: 0.093, data_time: 0.002, memory: 1224, loss: 3.4413\n2023-01-01 16:49:35,401 - mmcls - INFO - Epoch [3][1300/5005]\tlr: 1.000e-01, eta: 12:15:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 3.4349\n2023-01-01 16:49:44,438 - mmcls - INFO - Epoch [3][1400/5005]\tlr: 1.000e-01, eta: 12:15:08, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.4003\n2023-01-01 16:49:53,498 - mmcls - INFO - Epoch [3][1500/5005]\tlr: 1.000e-01, eta: 12:15:01, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.3839\n2023-01-01 16:50:02,469 - mmcls - INFO - Epoch [3][1600/5005]\tlr: 1.000e-01, eta: 12:14:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.3908\n2023-01-01 16:50:11,371 - mmcls - INFO - Epoch [3][1700/5005]\tlr: 1.000e-01, eta: 12:14:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.3845\n2023-01-01 16:50:20,246 - mmcls - INFO - Epoch [3][1800/5005]\tlr: 1.000e-01, eta: 12:14:21, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.3920\n2023-01-01 16:50:29,299 - mmcls - INFO - Epoch [3][1900/5005]\tlr: 1.000e-01, eta: 12:14:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.4334\n2023-01-01 16:50:38,340 - mmcls - INFO - Epoch [3][2000/5005]\tlr: 1.000e-01, eta: 12:14:05, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.3754\n2023-01-01 16:50:47,308 - mmcls - INFO - Epoch [3][2100/5005]\tlr: 1.000e-01, eta: 12:13:54, time: 0.090, data_time: 0.007, memory: 1224, loss: 3.4104\n2023-01-01 16:50:56,408 - mmcls - INFO - Epoch [3][2200/5005]\tlr: 1.000e-01, eta: 12:13:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.3738\n2023-01-01 16:51:05,321 - mmcls - INFO - Epoch [3][2300/5005]\tlr: 1.000e-01, eta: 12:13:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.3685\n2023-01-01 16:51:14,103 - mmcls - INFO - Epoch [3][2400/5005]\tlr: 1.000e-01, eta: 12:13:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.4038\n2023-01-01 16:51:23,118 - mmcls - INFO - Epoch [3][2500/5005]\tlr: 1.000e-01, eta: 12:13:08, time: 0.090, data_time: 0.015, memory: 1224, loss: 3.3754\n2023-01-01 16:51:31,995 - mmcls - INFO - Epoch [3][2600/5005]\tlr: 1.000e-01, eta: 12:12:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.3689\n2023-01-01 16:51:40,741 - mmcls - INFO - Epoch [3][2700/5005]\tlr: 1.000e-01, eta: 12:12:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.4128\n2023-01-01 16:51:49,586 - mmcls - INFO - Epoch [3][2800/5005]\tlr: 1.000e-01, eta: 12:12:19, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.3487\n2023-01-01 16:51:58,464 - mmcls - INFO - Epoch [3][2900/5005]\tlr: 1.000e-01, eta: 12:12:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.3723\n2023-01-01 16:52:07,411 - mmcls - INFO - Epoch [3][3000/5005]\tlr: 1.000e-01, eta: 12:11:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3516\n2023-01-01 16:52:16,375 - mmcls - INFO - Epoch [3][3100/5005]\tlr: 1.000e-01, eta: 12:11:43, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.3452\n2023-01-01 16:52:25,536 - mmcls - INFO - Epoch [3][3200/5005]\tlr: 1.000e-01, eta: 12:11:40, time: 0.092, data_time: 0.001, memory: 1224, loss: 3.3139\n2023-01-01 16:52:34,444 - mmcls - INFO - Epoch [3][3300/5005]\tlr: 1.000e-01, eta: 12:11:27, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3368\n2023-01-01 16:52:43,118 - mmcls - INFO - Epoch [3][3400/5005]\tlr: 1.000e-01, eta: 12:11:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3583\n2023-01-01 16:52:52,318 - mmcls - INFO - Epoch [3][3500/5005]\tlr: 1.000e-01, eta: 12:11:04, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.3653\n2023-01-01 16:53:01,027 - mmcls - INFO - Epoch [3][3600/5005]\tlr: 1.000e-01, eta: 12:10:44, time: 0.087, data_time: 0.028, memory: 1224, loss: 3.3778\n2023-01-01 16:53:09,843 - mmcls - INFO - Epoch [3][3700/5005]\tlr: 1.000e-01, eta: 12:10:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.3397\n2023-01-01 16:53:18,790 - mmcls - INFO - Epoch [3][3800/5005]\tlr: 1.000e-01, eta: 12:10:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.3653\n2023-01-01 16:53:27,744 - mmcls - INFO - Epoch [3][3900/5005]\tlr: 1.000e-01, eta: 12:10:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3169\n2023-01-01 16:53:36,890 - mmcls - INFO - Epoch [3][4000/5005]\tlr: 1.000e-01, eta: 12:10:03, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.3401\n2023-01-01 16:53:45,638 - mmcls - INFO - Epoch [3][4100/5005]\tlr: 1.000e-01, eta: 12:09:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3496\n2023-01-01 16:53:54,609 - mmcls - INFO - Epoch [3][4200/5005]\tlr: 1.000e-01, eta: 12:09:34, time: 0.090, data_time: 0.020, memory: 1224, loss: 3.3620\n2023-01-01 16:54:03,668 - mmcls - INFO - Epoch [3][4300/5005]\tlr: 1.000e-01, eta: 12:09:27, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.3194\n2023-01-01 16:54:12,414 - mmcls - INFO - Epoch [3][4400/5005]\tlr: 1.000e-01, eta: 12:09:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3358\n2023-01-01 16:54:21,176 - mmcls - INFO - Epoch [3][4500/5005]\tlr: 1.000e-01, eta: 12:08:53, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.2975\n2023-01-01 16:54:30,110 - mmcls - INFO - Epoch [3][4600/5005]\tlr: 1.000e-01, eta: 12:08:42, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.2991\n2023-01-01 16:54:39,161 - mmcls - INFO - Epoch [3][4700/5005]\tlr: 1.000e-01, eta: 12:08:34, time: 0.091, data_time: 0.000, memory: 1224, loss: 3.2711\n2023-01-01 16:54:48,080 - mmcls - INFO - Epoch [3][4800/5005]\tlr: 1.000e-01, eta: 12:08:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3074\n2023-01-01 16:54:57,040 - mmcls - INFO - Epoch [3][4900/5005]\tlr: 1.000e-01, eta: 12:08:12, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.2890\n2023-01-01 16:55:06,312 - mmcls - INFO - Epoch [3][5000/5005]\tlr: 1.000e-01, eta: 12:08:12, time: 0.093, data_time: 0.004, memory: 1224, loss: 3.2838\n2023-01-01 16:55:29,302 - mmcls - INFO - Epoch(val) [3][196]\taccuracy_top-1: 38.3360, accuracy_top-5: 63.6980\n2023-01-01 16:55:40,471 - mmcls - INFO - Epoch [4][100/5005]\tlr: 1.000e-01, eta: 12:08:57, time: 0.111, data_time: 0.037, memory: 1224, loss: 3.2448\n2023-01-01 16:55:49,509 - mmcls - INFO - Epoch [4][200/5005]\tlr: 1.000e-01, eta: 12:08:49, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.2449\n2023-01-01 16:55:58,415 - mmcls - INFO - Epoch [4][300/5005]\tlr: 1.000e-01, eta: 12:08:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.2429\n2023-01-01 16:56:07,064 - mmcls - INFO - Epoch [4][400/5005]\tlr: 1.000e-01, eta: 12:08:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 3.2606\n2023-01-01 16:56:15,815 - mmcls - INFO - Epoch [4][500/5005]\tlr: 1.000e-01, eta: 12:07:59, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.2500\n2023-01-01 16:56:24,856 - mmcls - INFO - Epoch [4][600/5005]\tlr: 1.000e-01, eta: 12:07:51, time: 0.090, data_time: 0.002, memory: 1224, loss: 3.2272\n2023-01-01 16:56:33,422 - mmcls - INFO - Epoch [4][700/5005]\tlr: 1.000e-01, eta: 12:07:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.2703\n2023-01-01 16:56:42,108 - mmcls - INFO - Epoch [4][800/5005]\tlr: 1.000e-01, eta: 12:07:10, time: 0.087, data_time: 0.024, memory: 1224, loss: 3.2516\n2023-01-01 16:56:50,911 - mmcls - INFO - Epoch [4][900/5005]\tlr: 1.000e-01, eta: 12:06:54, time: 0.088, data_time: 0.015, memory: 1224, loss: 3.2497\n2023-01-01 16:56:59,572 - mmcls - INFO - Epoch [4][1000/5005]\tlr: 1.000e-01, eta: 12:06:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.2725\n2023-01-01 16:57:08,858 - mmcls - INFO - Epoch [4][1100/5005]\tlr: 1.000e-01, eta: 12:06:35, time: 0.093, data_time: 0.009, memory: 1224, loss: 3.2645\n2023-01-01 16:57:17,925 - mmcls - INFO - Epoch [4][1200/5005]\tlr: 1.000e-01, eta: 12:06:28, time: 0.091, data_time: 0.014, memory: 1224, loss: 3.2775\n2023-01-01 16:57:26,580 - mmcls - INFO - Epoch [4][1300/5005]\tlr: 1.000e-01, eta: 12:06:08, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.2889\n2023-01-01 16:57:35,312 - mmcls - INFO - Epoch [4][1400/5005]\tlr: 1.000e-01, eta: 12:05:52, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.2443\n2023-01-01 16:57:43,975 - mmcls - INFO - Epoch [4][1500/5005]\tlr: 1.000e-01, eta: 12:05:33, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.2706\n2023-01-01 16:57:52,704 - mmcls - INFO - Epoch [4][1600/5005]\tlr: 1.000e-01, eta: 12:05:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 3.2458\n2023-01-01 16:58:01,759 - mmcls - INFO - Epoch [4][1700/5005]\tlr: 1.000e-01, eta: 12:05:09, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.2260\n2023-01-01 16:58:10,565 - mmcls - INFO - Epoch [4][1800/5005]\tlr: 1.000e-01, eta: 12:04:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2172\n2023-01-01 16:58:19,623 - mmcls - INFO - Epoch [4][1900/5005]\tlr: 1.000e-01, eta: 12:04:47, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.2001\n2023-01-01 16:58:28,675 - mmcls - INFO - Epoch [4][2000/5005]\tlr: 1.000e-01, eta: 12:04:40, time: 0.090, data_time: 0.027, memory: 1224, loss: 3.2273\n2023-01-01 16:58:37,648 - mmcls - INFO - Epoch [4][2100/5005]\tlr: 1.000e-01, eta: 12:04:30, time: 0.090, data_time: 0.019, memory: 1224, loss: 3.2260\n2023-01-01 16:58:46,381 - mmcls - INFO - Epoch [4][2200/5005]\tlr: 1.000e-01, eta: 12:04:14, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.2671\n2023-01-01 16:58:55,019 - mmcls - INFO - Epoch [4][2300/5005]\tlr: 1.000e-01, eta: 12:03:55, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.2523\n2023-01-01 16:59:03,956 - mmcls - INFO - Epoch [4][2400/5005]\tlr: 1.000e-01, eta: 12:03:45, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.2252\n2023-01-01 16:59:12,779 - mmcls - INFO - Epoch [4][2500/5005]\tlr: 1.000e-01, eta: 12:03:31, time: 0.088, data_time: 0.021, memory: 1224, loss: 3.2529\n2023-01-01 16:59:21,630 - mmcls - INFO - Epoch [4][2600/5005]\tlr: 1.000e-01, eta: 12:03:18, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.2290\n2023-01-01 16:59:30,743 - mmcls - INFO - Epoch [4][2700/5005]\tlr: 1.000e-01, eta: 12:03:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.1988\n2023-01-01 16:59:39,411 - mmcls - INFO - Epoch [4][2800/5005]\tlr: 1.000e-01, eta: 12:02:55, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.2140\n2023-01-01 16:59:48,480 - mmcls - INFO - Epoch [4][2900/5005]\tlr: 1.000e-01, eta: 12:02:49, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.2289\n2023-01-01 16:59:57,289 - mmcls - INFO - Epoch [4][3000/5005]\tlr: 1.000e-01, eta: 12:02:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.2148\n2023-01-01 17:00:06,018 - mmcls - INFO - Epoch [4][3100/5005]\tlr: 1.000e-01, eta: 12:02:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.2195\n2023-01-01 17:00:14,946 - mmcls - INFO - Epoch [4][3200/5005]\tlr: 1.000e-01, eta: 12:02:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.2138\n2023-01-01 17:00:23,652 - mmcls - INFO - Epoch [4][3300/5005]\tlr: 1.000e-01, eta: 12:01:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.1915\n2023-01-01 17:00:32,409 - mmcls - INFO - Epoch [4][3400/5005]\tlr: 1.000e-01, eta: 12:01:37, time: 0.088, data_time: 0.031, memory: 1224, loss: 3.2242\n2023-01-01 17:00:41,202 - mmcls - INFO - Epoch [4][3500/5005]\tlr: 1.000e-01, eta: 12:01:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2080\n2023-01-01 17:00:50,144 - mmcls - INFO - Epoch [4][3600/5005]\tlr: 1.000e-01, eta: 12:01:13, time: 0.089, data_time: 0.016, memory: 1224, loss: 3.2262\n2023-01-01 17:00:58,647 - mmcls - INFO - Epoch [4][3700/5005]\tlr: 1.000e-01, eta: 12:00:52, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.2175\n2023-01-01 17:01:07,467 - mmcls - INFO - Epoch [4][3800/5005]\tlr: 1.000e-01, eta: 12:00:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2542\n2023-01-01 17:01:17,001 - mmcls - INFO - Epoch [4][3900/5005]\tlr: 1.000e-01, eta: 12:00:44, time: 0.095, data_time: 0.004, memory: 1224, loss: 3.2338\n2023-01-01 17:01:25,652 - mmcls - INFO - Epoch [4][4000/5005]\tlr: 1.000e-01, eta: 12:00:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 3.2197\n2023-01-01 17:01:34,642 - mmcls - INFO - Epoch [4][4100/5005]\tlr: 1.000e-01, eta: 12:00:18, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.1930\n2023-01-01 17:01:43,728 - mmcls - INFO - Epoch [4][4200/5005]\tlr: 1.000e-01, eta: 12:00:12, time: 0.091, data_time: 0.020, memory: 1224, loss: 3.1806\n2023-01-01 17:01:52,480 - mmcls - INFO - Epoch [4][4300/5005]\tlr: 1.000e-01, eta: 11:59:57, time: 0.087, data_time: 0.018, memory: 1224, loss: 3.1539\n2023-01-01 17:02:01,249 - mmcls - INFO - Epoch [4][4400/5005]\tlr: 1.000e-01, eta: 11:59:43, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.2174\n2023-01-01 17:02:10,267 - mmcls - INFO - Epoch [4][4500/5005]\tlr: 1.000e-01, eta: 11:59:35, time: 0.090, data_time: 0.019, memory: 1224, loss: 3.1706\n2023-01-01 17:02:18,883 - mmcls - INFO - Epoch [4][4600/5005]\tlr: 1.000e-01, eta: 11:59:17, time: 0.086, data_time: 0.012, memory: 1224, loss: 3.1525\n2023-01-01 17:02:27,768 - mmcls - INFO - Epoch [4][4700/5005]\tlr: 1.000e-01, eta: 11:59:06, time: 0.089, data_time: 0.016, memory: 1224, loss: 3.1732\n2023-01-01 17:02:36,603 - mmcls - INFO - Epoch [4][4800/5005]\tlr: 1.000e-01, eta: 11:58:54, time: 0.088, data_time: 0.024, memory: 1224, loss: 3.2078\n2023-01-01 17:02:45,610 - mmcls - INFO - Epoch [4][4900/5005]\tlr: 1.000e-01, eta: 11:58:45, time: 0.090, data_time: 0.041, memory: 1224, loss: 3.1928\n2023-01-01 17:02:54,404 - mmcls - INFO - Epoch [4][5000/5005]\tlr: 1.000e-01, eta: 11:58:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.2033\n2023-01-01 17:03:17,363 - mmcls - INFO - Epoch(val) [4][196]\taccuracy_top-1: 35.3300, accuracy_top-5: 60.1260\n2023-01-01 17:03:28,690 - mmcls - INFO - Epoch [5][100/5005]\tlr: 1.000e-01, eta: 11:59:07, time: 0.113, data_time: 0.047, memory: 1224, loss: 3.1171\n2023-01-01 17:03:37,473 - mmcls - INFO - Epoch [5][200/5005]\tlr: 1.000e-01, eta: 11:58:54, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.1460\n2023-01-01 17:03:46,147 - mmcls - INFO - Epoch [5][300/5005]\tlr: 1.000e-01, eta: 11:58:37, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.1248\n2023-01-01 17:03:54,884 - mmcls - INFO - Epoch [5][400/5005]\tlr: 1.000e-01, eta: 11:58:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.1608\n2023-01-01 17:04:03,514 - mmcls - INFO - Epoch [5][500/5005]\tlr: 1.000e-01, eta: 11:58:06, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.1328\n2023-01-01 17:04:12,491 - mmcls - INFO - Epoch [5][600/5005]\tlr: 1.000e-01, eta: 11:57:56, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.1315\n2023-01-01 17:04:21,387 - mmcls - INFO - Epoch [5][700/5005]\tlr: 1.000e-01, eta: 11:57:46, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.1464\n2023-01-01 17:04:30,263 - mmcls - INFO - Epoch [5][800/5005]\tlr: 1.000e-01, eta: 11:57:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.1444\n2023-01-01 17:04:39,087 - mmcls - INFO - Epoch [5][900/5005]\tlr: 1.000e-01, eta: 11:57:22, time: 0.088, data_time: 0.029, memory: 1224, loss: 3.1346\n2023-01-01 17:04:47,872 - mmcls - INFO - Epoch [5][1000/5005]\tlr: 1.000e-01, eta: 11:57:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1337\n2023-01-01 17:04:56,467 - mmcls - INFO - Epoch [5][1100/5005]\tlr: 1.000e-01, eta: 11:56:51, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1561\n2023-01-01 17:05:05,261 - mmcls - INFO - Epoch [5][1200/5005]\tlr: 1.000e-01, eta: 11:56:38, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.1225\n2023-01-01 17:05:14,148 - mmcls - INFO - Epoch [5][1300/5005]\tlr: 1.000e-01, eta: 11:56:27, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.1672\n2023-01-01 17:05:23,196 - mmcls - INFO - Epoch [5][1400/5005]\tlr: 1.000e-01, eta: 11:56:20, time: 0.091, data_time: 0.010, memory: 1224, loss: 3.1250\n2023-01-01 17:05:32,290 - mmcls - INFO - Epoch [5][1500/5005]\tlr: 1.000e-01, eta: 11:56:14, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.1445\n2023-01-01 17:05:41,368 - mmcls - INFO - Epoch [5][1600/5005]\tlr: 1.000e-01, eta: 11:56:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.1482\n2023-01-01 17:05:50,117 - mmcls - INFO - Epoch [5][1700/5005]\tlr: 1.000e-01, eta: 11:55:53, time: 0.088, data_time: 0.006, memory: 1224, loss: 3.1415\n2023-01-01 17:05:58,894 - mmcls - INFO - Epoch [5][1800/5005]\tlr: 1.000e-01, eta: 11:55:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.1379\n2023-01-01 17:06:07,634 - mmcls - INFO - Epoch [5][1900/5005]\tlr: 1.000e-01, eta: 11:55:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.1370\n2023-01-01 17:06:16,601 - mmcls - INFO - Epoch [5][2000/5005]\tlr: 1.000e-01, eta: 11:55:17, time: 0.090, data_time: 0.033, memory: 1224, loss: 3.1271\n2023-01-01 17:06:25,524 - mmcls - INFO - Epoch [5][2100/5005]\tlr: 1.000e-01, eta: 11:55:07, time: 0.089, data_time: 0.026, memory: 1224, loss: 3.1369\n2023-01-01 17:06:34,398 - mmcls - INFO - Epoch [5][2200/5005]\tlr: 1.000e-01, eta: 11:54:56, time: 0.089, data_time: 0.020, memory: 1224, loss: 3.1513\n2023-01-01 17:06:43,241 - mmcls - INFO - Epoch [5][2300/5005]\tlr: 1.000e-01, eta: 11:54:44, time: 0.088, data_time: 0.020, memory: 1224, loss: 3.1288\n2023-01-01 17:06:51,879 - mmcls - INFO - Epoch [5][2400/5005]\tlr: 1.000e-01, eta: 11:54:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1363\n2023-01-01 17:07:01,502 - mmcls - INFO - Epoch [5][2500/5005]\tlr: 1.000e-01, eta: 11:54:33, time: 0.096, data_time: 0.019, memory: 1224, loss: 3.1602\n2023-01-01 17:07:10,308 - mmcls - INFO - Epoch [5][2600/5005]\tlr: 1.000e-01, eta: 11:54:21, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.1627\n2023-01-01 17:07:19,102 - mmcls - INFO - Epoch [5][2700/5005]\tlr: 1.000e-01, eta: 11:54:08, time: 0.088, data_time: 0.017, memory: 1224, loss: 3.1463\n2023-01-01 17:07:27,648 - mmcls - INFO - Epoch [5][2800/5005]\tlr: 1.000e-01, eta: 11:53:50, time: 0.085, data_time: 0.004, memory: 1224, loss: 3.1158\n2023-01-01 17:07:36,461 - mmcls - INFO - Epoch [5][2900/5005]\tlr: 1.000e-01, eta: 11:53:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1317\n2023-01-01 17:07:45,190 - mmcls - INFO - Epoch [5][3000/5005]\tlr: 1.000e-01, eta: 11:53:24, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.1155\n2023-01-01 17:07:54,062 - mmcls - INFO - Epoch [5][3100/5005]\tlr: 1.000e-01, eta: 11:53:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1218\n2023-01-01 17:08:03,170 - mmcls - INFO - Epoch [5][3200/5005]\tlr: 1.000e-01, eta: 11:53:07, time: 0.091, data_time: 0.013, memory: 1224, loss: 3.1428\n2023-01-01 17:08:12,023 - mmcls - INFO - Epoch [5][3300/5005]\tlr: 1.000e-01, eta: 11:52:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1194\n2023-01-01 17:08:21,074 - mmcls - INFO - Epoch [5][3400/5005]\tlr: 1.000e-01, eta: 11:52:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0907\n2023-01-01 17:08:30,087 - mmcls - INFO - Epoch [5][3500/5005]\tlr: 1.000e-01, eta: 11:52:40, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.0890\n2023-01-01 17:08:39,004 - mmcls - INFO - Epoch [5][3600/5005]\tlr: 1.000e-01, eta: 11:52:31, time: 0.089, data_time: 0.022, memory: 1224, loss: 3.1272\n2023-01-01 17:08:47,559 - mmcls - INFO - Epoch [5][3700/5005]\tlr: 1.000e-01, eta: 11:52:13, time: 0.086, data_time: 0.018, memory: 1224, loss: 3.1515\n2023-01-01 17:08:56,407 - mmcls - INFO - Epoch [5][3800/5005]\tlr: 1.000e-01, eta: 11:52:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.1188\n2023-01-01 17:09:05,343 - mmcls - INFO - Epoch [5][3900/5005]\tlr: 1.000e-01, eta: 11:51:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.1060\n2023-01-01 17:09:13,931 - mmcls - INFO - Epoch [5][4000/5005]\tlr: 1.000e-01, eta: 11:51:36, time: 0.086, data_time: 0.009, memory: 1224, loss: 3.1605\n2023-01-01 17:09:22,848 - mmcls - INFO - Epoch [5][4100/5005]\tlr: 1.000e-01, eta: 11:51:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1304\n2023-01-01 17:09:31,658 - mmcls - INFO - Epoch [5][4200/5005]\tlr: 1.000e-01, eta: 11:51:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0961\n2023-01-01 17:09:40,307 - mmcls - INFO - Epoch [5][4300/5005]\tlr: 1.000e-01, eta: 11:50:59, time: 0.086, data_time: 0.005, memory: 1224, loss: 3.0905\n2023-01-01 17:09:49,266 - mmcls - INFO - Epoch [5][4400/5005]\tlr: 1.000e-01, eta: 11:50:50, time: 0.090, data_time: 0.023, memory: 1224, loss: 3.0922\n2023-01-01 17:09:58,136 - mmcls - INFO - Epoch [5][4500/5005]\tlr: 1.000e-01, eta: 11:50:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.1105\n2023-01-01 17:10:06,799 - mmcls - INFO - Epoch [5][4600/5005]\tlr: 1.000e-01, eta: 11:50:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.1382\n2023-01-01 17:10:15,397 - mmcls - INFO - Epoch [5][4700/5005]\tlr: 1.000e-01, eta: 11:50:09, time: 0.086, data_time: 0.021, memory: 1224, loss: 3.1155\n2023-01-01 17:10:24,271 - mmcls - INFO - Epoch [5][4800/5005]\tlr: 1.000e-01, eta: 11:49:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1194\n2023-01-01 17:10:32,812 - mmcls - INFO - Epoch [5][4900/5005]\tlr: 1.000e-01, eta: 11:49:42, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.0881\n2023-01-01 17:10:41,946 - mmcls - INFO - Epoch [5][5000/5005]\tlr: 1.000e-01, eta: 11:49:36, time: 0.091, data_time: 0.010, memory: 1224, loss: 3.1169\n2023-01-01 17:11:04,666 - mmcls - INFO - Epoch(val) [5][196]\taccuracy_top-1: 37.5040, accuracy_top-5: 62.3060\n2023-01-01 17:11:15,661 - mmcls - INFO - Epoch [6][100/5005]\tlr: 1.000e-01, eta: 11:49:56, time: 0.110, data_time: 0.031, memory: 1224, loss: 3.0327\n2023-01-01 17:11:24,223 - mmcls - INFO - Epoch [6][200/5005]\tlr: 1.000e-01, eta: 11:49:40, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.0600\n2023-01-01 17:11:33,009 - mmcls - INFO - Epoch [6][300/5005]\tlr: 1.000e-01, eta: 11:49:28, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0607\n2023-01-01 17:11:42,070 - mmcls - INFO - Epoch [6][400/5005]\tlr: 1.000e-01, eta: 11:49:21, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.0469\n2023-01-01 17:11:50,736 - mmcls - INFO - Epoch [6][500/5005]\tlr: 1.000e-01, eta: 11:49:06, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.0827\n2023-01-01 17:11:59,443 - mmcls - INFO - Epoch [6][600/5005]\tlr: 1.000e-01, eta: 11:48:53, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.0820\n2023-01-01 17:12:08,244 - mmcls - INFO - Epoch [6][700/5005]\tlr: 1.000e-01, eta: 11:48:41, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.0681\n2023-01-01 17:12:17,215 - mmcls - INFO - Epoch [6][800/5005]\tlr: 1.000e-01, eta: 11:48:32, time: 0.090, data_time: 0.000, memory: 1224, loss: 3.0837\n2023-01-01 17:12:26,152 - mmcls - INFO - Epoch [6][900/5005]\tlr: 1.000e-01, eta: 11:48:23, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0828\n2023-01-01 17:12:34,912 - mmcls - INFO - Epoch [6][1000/5005]\tlr: 1.000e-01, eta: 11:48:10, time: 0.088, data_time: 0.037, memory: 1224, loss: 3.0302\n2023-01-01 17:12:43,636 - mmcls - INFO - Epoch [6][1100/5005]\tlr: 1.000e-01, eta: 11:47:57, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.0992\n2023-01-01 17:12:52,188 - mmcls - INFO - Epoch [6][1200/5005]\tlr: 1.000e-01, eta: 11:47:41, time: 0.086, data_time: 0.013, memory: 1224, loss: 3.0468\n2023-01-01 17:13:00,929 - mmcls - INFO - Epoch [6][1300/5005]\tlr: 1.000e-01, eta: 11:47:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 3.0498\n2023-01-01 17:13:09,600 - mmcls - INFO - Epoch [6][1400/5005]\tlr: 1.000e-01, eta: 11:47:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.0651\n2023-01-01 17:13:18,185 - mmcls - INFO - Epoch [6][1500/5005]\tlr: 1.000e-01, eta: 11:46:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 3.0792\n2023-01-01 17:13:27,153 - mmcls - INFO - Epoch [6][1600/5005]\tlr: 1.000e-01, eta: 11:46:50, time: 0.090, data_time: 0.011, memory: 1224, loss: 3.0670\n2023-01-01 17:13:36,057 - mmcls - INFO - Epoch [6][1700/5005]\tlr: 1.000e-01, eta: 11:46:40, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.0871\n2023-01-01 17:13:44,989 - mmcls - INFO - Epoch [6][1800/5005]\tlr: 1.000e-01, eta: 11:46:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0289\n2023-01-01 17:13:53,691 - mmcls - INFO - Epoch [6][1900/5005]\tlr: 1.000e-01, eta: 11:46:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 3.0742\n2023-01-01 17:14:02,433 - mmcls - INFO - Epoch [6][2000/5005]\tlr: 1.000e-01, eta: 11:46:05, time: 0.087, data_time: 0.016, memory: 1224, loss: 3.0958\n2023-01-01 17:14:11,659 - mmcls - INFO - Epoch [6][2100/5005]\tlr: 1.000e-01, eta: 11:46:01, time: 0.092, data_time: 0.008, memory: 1224, loss: 3.0883\n2023-01-01 17:14:20,125 - mmcls - INFO - Epoch [6][2200/5005]\tlr: 1.000e-01, eta: 11:45:43, time: 0.085, data_time: 0.017, memory: 1224, loss: 3.0921\n2023-01-01 17:14:28,903 - mmcls - INFO - Epoch [6][2300/5005]\tlr: 1.000e-01, eta: 11:45:31, time: 0.088, data_time: 0.014, memory: 1224, loss: 3.0326\n2023-01-01 17:14:37,721 - mmcls - INFO - Epoch [6][2400/5005]\tlr: 1.000e-01, eta: 11:45:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0920\n2023-01-01 17:14:46,362 - mmcls - INFO - Epoch [6][2500/5005]\tlr: 1.000e-01, eta: 11:45:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 3.0888\n2023-01-01 17:14:55,289 - mmcls - INFO - Epoch [6][2600/5005]\tlr: 1.000e-01, eta: 11:44:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0545\n2023-01-01 17:15:04,062 - mmcls - INFO - Epoch [6][2700/5005]\tlr: 1.000e-01, eta: 11:44:45, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.0943\n2023-01-01 17:15:13,504 - mmcls - INFO - Epoch [6][2800/5005]\tlr: 1.000e-01, eta: 11:44:44, time: 0.094, data_time: 0.005, memory: 1224, loss: 3.0745\n2023-01-01 17:15:22,241 - mmcls - INFO - Epoch [6][2900/5005]\tlr: 1.000e-01, eta: 11:44:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.0324\n2023-01-01 17:15:31,027 - mmcls - INFO - Epoch [6][3000/5005]\tlr: 1.000e-01, eta: 11:44:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.0766\n2023-01-01 17:15:39,913 - mmcls - INFO - Epoch [6][3100/5005]\tlr: 1.000e-01, eta: 11:44:10, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.0506\n2023-01-01 17:15:48,822 - mmcls - INFO - Epoch [6][3200/5005]\tlr: 1.000e-01, eta: 11:44:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.0673\n2023-01-01 17:15:57,964 - mmcls - INFO - Epoch [6][3300/5005]\tlr: 1.000e-01, eta: 11:43:55, time: 0.091, data_time: 0.006, memory: 1224, loss: 3.0518\n2023-01-01 17:16:06,751 - mmcls - INFO - Epoch [6][3400/5005]\tlr: 1.000e-01, eta: 11:43:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.0311\n2023-01-01 17:16:15,667 - mmcls - INFO - Epoch [6][3500/5005]\tlr: 1.000e-01, eta: 11:43:34, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0718\n2023-01-01 17:16:24,656 - mmcls - INFO - Epoch [6][3600/5005]\tlr: 1.000e-01, eta: 11:43:26, time: 0.090, data_time: 0.020, memory: 1224, loss: 3.0704\n2023-01-01 17:16:33,212 - mmcls - INFO - Epoch [6][3700/5005]\tlr: 1.000e-01, eta: 11:43:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.0772\n2023-01-01 17:16:42,377 - mmcls - INFO - Epoch [6][3800/5005]\tlr: 1.000e-01, eta: 11:43:05, time: 0.092, data_time: 0.003, memory: 1224, loss: 3.0421\n2023-01-01 17:16:51,481 - mmcls - INFO - Epoch [6][3900/5005]\tlr: 1.000e-01, eta: 11:42:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.0320\n2023-01-01 17:17:00,653 - mmcls - INFO - Epoch [6][4000/5005]\tlr: 1.000e-01, eta: 11:42:54, time: 0.092, data_time: 0.028, memory: 1224, loss: 3.0905\n2023-01-01 17:17:09,221 - mmcls - INFO - Epoch [6][4100/5005]\tlr: 1.000e-01, eta: 11:42:39, time: 0.086, data_time: 0.020, memory: 1224, loss: 3.0758\n2023-01-01 17:17:18,096 - mmcls - INFO - Epoch [6][4200/5005]\tlr: 1.000e-01, eta: 11:42:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0431\n2023-01-01 17:17:26,982 - mmcls - INFO - Epoch [6][4300/5005]\tlr: 1.000e-01, eta: 11:42:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0363\n2023-01-01 17:17:35,905 - mmcls - INFO - Epoch [6][4400/5005]\tlr: 1.000e-01, eta: 11:42:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0449\n2023-01-01 17:17:44,938 - mmcls - INFO - Epoch [6][4500/5005]\tlr: 1.000e-01, eta: 11:42:02, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.0723\n2023-01-01 17:17:54,182 - mmcls - INFO - Epoch [6][4600/5005]\tlr: 1.000e-01, eta: 11:41:58, time: 0.092, data_time: 0.017, memory: 1224, loss: 3.0462\n2023-01-01 17:18:02,887 - mmcls - INFO - Epoch [6][4700/5005]\tlr: 1.000e-01, eta: 11:41:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.0306\n2023-01-01 17:18:11,563 - mmcls - INFO - Epoch [6][4800/5005]\tlr: 1.000e-01, eta: 11:41:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.0683\n2023-01-01 17:18:20,592 - mmcls - INFO - Epoch [6][4900/5005]\tlr: 1.000e-01, eta: 11:41:24, time: 0.090, data_time: 0.026, memory: 1224, loss: 3.0858\n2023-01-01 17:18:29,191 - mmcls - INFO - Epoch [6][5000/5005]\tlr: 1.000e-01, eta: 11:41:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.0535\n2023-01-01 17:18:52,021 - mmcls - INFO - Epoch(val) [6][196]\taccuracy_top-1: 40.1240, accuracy_top-5: 65.4240\n2023-01-01 17:19:03,298 - mmcls - INFO - Epoch [7][100/5005]\tlr: 1.000e-01, eta: 11:41:29, time: 0.112, data_time: 0.033, memory: 1224, loss: 3.0050\n2023-01-01 17:19:12,251 - mmcls - INFO - Epoch [7][200/5005]\tlr: 1.000e-01, eta: 11:41:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.0111\n2023-01-01 17:19:21,190 - mmcls - INFO - Epoch [7][300/5005]\tlr: 1.000e-01, eta: 11:41:11, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.9844\n2023-01-01 17:19:29,934 - mmcls - INFO - Epoch [7][400/5005]\tlr: 1.000e-01, eta: 11:40:59, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.0059\n2023-01-01 17:19:38,818 - mmcls - INFO - Epoch [7][500/5005]\tlr: 1.000e-01, eta: 11:40:50, time: 0.089, data_time: 0.005, memory: 1224, loss: 3.0522\n2023-01-01 17:19:47,427 - mmcls - INFO - Epoch [7][600/5005]\tlr: 1.000e-01, eta: 11:40:35, time: 0.086, data_time: 0.015, memory: 1224, loss: 3.0284\n2023-01-01 17:19:56,404 - mmcls - INFO - Epoch [7][700/5005]\tlr: 1.000e-01, eta: 11:40:27, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0172\n2023-01-01 17:20:04,890 - mmcls - INFO - Epoch [7][800/5005]\tlr: 1.000e-01, eta: 11:40:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.0147\n2023-01-01 17:20:13,758 - mmcls - INFO - Epoch [7][900/5005]\tlr: 1.000e-01, eta: 11:40:01, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.0045\n2023-01-01 17:20:22,735 - mmcls - INFO - Epoch [7][1000/5005]\tlr: 1.000e-01, eta: 11:39:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0035\n2023-01-01 17:20:31,745 - mmcls - INFO - Epoch [7][1100/5005]\tlr: 1.000e-01, eta: 11:39:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0162\n2023-01-01 17:20:40,344 - mmcls - INFO - Epoch [7][1200/5005]\tlr: 1.000e-01, eta: 11:39:30, time: 0.086, data_time: 0.017, memory: 1224, loss: 3.0379\n2023-01-01 17:20:49,081 - mmcls - INFO - Epoch [7][1300/5005]\tlr: 1.000e-01, eta: 11:39:18, time: 0.087, data_time: 0.025, memory: 1224, loss: 3.0156\n2023-01-01 17:20:57,956 - mmcls - INFO - Epoch [7][1400/5005]\tlr: 1.000e-01, eta: 11:39:08, time: 0.089, data_time: 0.005, memory: 1224, loss: 3.0224\n2023-01-01 17:21:06,858 - mmcls - INFO - Epoch [7][1500/5005]\tlr: 1.000e-01, eta: 11:38:58, time: 0.089, data_time: 0.026, memory: 1224, loss: 3.0242\n2023-01-01 17:21:15,606 - mmcls - INFO - Epoch [7][1600/5005]\tlr: 1.000e-01, eta: 11:38:47, time: 0.087, data_time: 0.015, memory: 1224, loss: 3.0291\n2023-01-01 17:21:24,518 - mmcls - INFO - Epoch [7][1700/5005]\tlr: 1.000e-01, eta: 11:38:37, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.0168\n2023-01-01 17:21:33,624 - mmcls - INFO - Epoch [7][1800/5005]\tlr: 1.000e-01, eta: 11:38:31, time: 0.091, data_time: 0.015, memory: 1224, loss: 3.0193\n2023-01-01 17:21:42,603 - mmcls - INFO - Epoch [7][1900/5005]\tlr: 1.000e-01, eta: 11:38:22, time: 0.090, data_time: 0.025, memory: 1224, loss: 3.0074\n2023-01-01 17:21:51,272 - mmcls - INFO - Epoch [7][2000/5005]\tlr: 1.000e-01, eta: 11:38:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.9837\n2023-01-01 17:22:00,348 - mmcls - INFO - Epoch [7][2100/5005]\tlr: 1.000e-01, eta: 11:38:02, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.0176\n2023-01-01 17:22:08,888 - mmcls - INFO - Epoch [7][2200/5005]\tlr: 1.000e-01, eta: 11:37:48, time: 0.085, data_time: 0.021, memory: 1224, loss: 3.0029\n2023-01-01 17:22:17,726 - mmcls - INFO - Epoch [7][2300/5005]\tlr: 1.000e-01, eta: 11:37:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.9938\n2023-01-01 17:22:26,595 - mmcls - INFO - Epoch [7][2400/5005]\tlr: 1.000e-01, eta: 11:37:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 3.0194\n2023-01-01 17:22:35,831 - mmcls - INFO - Epoch [7][2500/5005]\tlr: 1.000e-01, eta: 11:37:22, time: 0.092, data_time: 0.043, memory: 1224, loss: 3.0205\n2023-01-01 17:22:44,708 - mmcls - INFO - Epoch [7][2600/5005]\tlr: 1.000e-01, eta: 11:37:12, time: 0.089, data_time: 0.033, memory: 1224, loss: 3.0191\n2023-01-01 17:22:53,574 - mmcls - INFO - Epoch [7][2700/5005]\tlr: 1.000e-01, eta: 11:37:03, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.0532\n2023-01-01 17:23:02,200 - mmcls - INFO - Epoch [7][2800/5005]\tlr: 1.000e-01, eta: 11:36:49, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0222\n2023-01-01 17:23:11,079 - mmcls - INFO - Epoch [7][2900/5005]\tlr: 1.000e-01, eta: 11:36:39, time: 0.089, data_time: 0.024, memory: 1224, loss: 3.0170\n2023-01-01 17:23:20,129 - mmcls - INFO - Epoch [7][3000/5005]\tlr: 1.000e-01, eta: 11:36:32, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.0053\n2023-01-01 17:23:28,992 - mmcls - INFO - Epoch [7][3100/5005]\tlr: 1.000e-01, eta: 11:36:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0153\n2023-01-01 17:23:37,812 - mmcls - INFO - Epoch [7][3200/5005]\tlr: 1.000e-01, eta: 11:36:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0245\n2023-01-01 17:23:46,623 - mmcls - INFO - Epoch [7][3300/5005]\tlr: 1.000e-01, eta: 11:36:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.0046\n2023-01-01 17:23:55,369 - mmcls - INFO - Epoch [7][3400/5005]\tlr: 1.000e-01, eta: 11:35:49, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.9899\n2023-01-01 17:24:03,821 - mmcls - INFO - Epoch [7][3500/5005]\tlr: 1.000e-01, eta: 11:35:33, time: 0.084, data_time: 0.024, memory: 1224, loss: 2.9821\n2023-01-01 17:24:12,596 - mmcls - INFO - Epoch [7][3600/5005]\tlr: 1.000e-01, eta: 11:35:22, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.9858\n2023-01-01 17:24:21,403 - mmcls - INFO - Epoch [7][3700/5005]\tlr: 1.000e-01, eta: 11:35:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9862\n2023-01-01 17:24:30,371 - mmcls - INFO - Epoch [7][3800/5005]\tlr: 1.000e-01, eta: 11:35:02, time: 0.090, data_time: 0.025, memory: 1224, loss: 3.0326\n2023-01-01 17:24:39,293 - mmcls - INFO - Epoch [7][3900/5005]\tlr: 1.000e-01, eta: 11:34:53, time: 0.089, data_time: 0.009, memory: 1224, loss: 3.0219\n2023-01-01 17:24:48,035 - mmcls - INFO - Epoch [7][4000/5005]\tlr: 1.000e-01, eta: 11:34:42, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9970\n2023-01-01 17:24:56,688 - mmcls - INFO - Epoch [7][4100/5005]\tlr: 1.000e-01, eta: 11:34:29, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0158\n2023-01-01 17:25:05,694 - mmcls - INFO - Epoch [7][4200/5005]\tlr: 1.000e-01, eta: 11:34:21, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.0022\n2023-01-01 17:25:14,435 - mmcls - INFO - Epoch [7][4300/5005]\tlr: 1.000e-01, eta: 11:34:09, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9979\n2023-01-01 17:25:23,143 - mmcls - INFO - Epoch [7][4400/5005]\tlr: 1.000e-01, eta: 11:33:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9938\n2023-01-01 17:25:31,762 - mmcls - INFO - Epoch [7][4500/5005]\tlr: 1.000e-01, eta: 11:33:44, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.9839\n2023-01-01 17:25:40,634 - mmcls - INFO - Epoch [7][4600/5005]\tlr: 1.000e-01, eta: 11:33:34, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.9661\n2023-01-01 17:25:49,523 - mmcls - INFO - Epoch [7][4700/5005]\tlr: 1.000e-01, eta: 11:33:25, time: 0.089, data_time: 0.019, memory: 1224, loss: 3.0334\n2023-01-01 17:25:58,496 - mmcls - INFO - Epoch [7][4800/5005]\tlr: 1.000e-01, eta: 11:33:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0384\n2023-01-01 17:26:07,168 - mmcls - INFO - Epoch [7][4900/5005]\tlr: 1.000e-01, eta: 11:33:04, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.0438\n2023-01-01 17:26:16,234 - mmcls - INFO - Epoch [7][5000/5005]\tlr: 1.000e-01, eta: 11:32:57, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0386\n2023-01-01 17:26:39,414 - mmcls - INFO - Epoch(val) [7][196]\taccuracy_top-1: 43.1040, accuracy_top-5: 68.3360\n2023-01-01 17:26:50,544 - mmcls - INFO - Epoch [8][100/5005]\tlr: 1.000e-01, eta: 11:33:10, time: 0.111, data_time: 0.034, memory: 1224, loss: 2.9871\n2023-01-01 17:26:59,367 - mmcls - INFO - Epoch [8][200/5005]\tlr: 1.000e-01, eta: 11:33:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.9247\n2023-01-01 17:27:08,051 - mmcls - INFO - Epoch [8][300/5005]\tlr: 1.000e-01, eta: 11:32:47, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9394\n2023-01-01 17:27:16,883 - mmcls - INFO - Epoch [8][400/5005]\tlr: 1.000e-01, eta: 11:32:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9488\n2023-01-01 17:27:25,825 - mmcls - INFO - Epoch [8][500/5005]\tlr: 1.000e-01, eta: 11:32:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9593\n2023-01-01 17:27:34,765 - mmcls - INFO - Epoch [8][600/5005]\tlr: 1.000e-01, eta: 11:32:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9896\n2023-01-01 17:27:43,601 - mmcls - INFO - Epoch [8][700/5005]\tlr: 1.000e-01, eta: 11:32:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9796\n2023-01-01 17:27:52,384 - mmcls - INFO - Epoch [8][800/5005]\tlr: 1.000e-01, eta: 11:31:58, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.9740\n2023-01-01 17:28:01,507 - mmcls - INFO - Epoch [8][900/5005]\tlr: 1.000e-01, eta: 11:31:52, time: 0.091, data_time: 0.017, memory: 1224, loss: 2.9819\n2023-01-01 17:28:10,786 - mmcls - INFO - Epoch [8][1000/5005]\tlr: 1.000e-01, eta: 11:31:47, time: 0.093, data_time: 0.011, memory: 1224, loss: 2.9708\n2023-01-01 17:28:19,483 - mmcls - INFO - Epoch [8][1100/5005]\tlr: 1.000e-01, eta: 11:31:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9796\n2023-01-01 17:28:28,388 - mmcls - INFO - Epoch [8][1200/5005]\tlr: 1.000e-01, eta: 11:31:26, time: 0.089, data_time: 0.009, memory: 1224, loss: 3.0125\n2023-01-01 17:28:37,130 - mmcls - INFO - Epoch [8][1300/5005]\tlr: 1.000e-01, eta: 11:31:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9668\n2023-01-01 17:28:46,002 - mmcls - INFO - Epoch [8][1400/5005]\tlr: 1.000e-01, eta: 11:31:05, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.9714\n2023-01-01 17:28:54,824 - mmcls - INFO - Epoch [8][1500/5005]\tlr: 1.000e-01, eta: 11:30:54, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.9816\n2023-01-01 17:29:03,504 - mmcls - INFO - Epoch [8][1600/5005]\tlr: 1.000e-01, eta: 11:30:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.9691\n2023-01-01 17:29:12,575 - mmcls - INFO - Epoch [8][1700/5005]\tlr: 1.000e-01, eta: 11:30:35, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.9862\n2023-01-01 17:29:21,447 - mmcls - INFO - Epoch [8][1800/5005]\tlr: 1.000e-01, eta: 11:30:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.9870\n2023-01-01 17:29:30,430 - mmcls - INFO - Epoch [8][1900/5005]\tlr: 1.000e-01, eta: 11:30:17, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.9835\n2023-01-01 17:29:39,032 - mmcls - INFO - Epoch [8][2000/5005]\tlr: 1.000e-01, eta: 11:30:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.9438\n2023-01-01 17:29:47,999 - mmcls - INFO - Epoch [8][2100/5005]\tlr: 1.000e-01, eta: 11:29:55, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.9600\n2023-01-01 17:29:56,793 - mmcls - INFO - Epoch [8][2200/5005]\tlr: 1.000e-01, eta: 11:29:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9813\n2023-01-01 17:30:06,032 - mmcls - INFO - Epoch [8][2300/5005]\tlr: 1.000e-01, eta: 11:29:39, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.9689\n2023-01-01 17:30:15,107 - mmcls - INFO - Epoch [8][2400/5005]\tlr: 1.000e-01, eta: 11:29:32, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.0017\n2023-01-01 17:30:24,103 - mmcls - INFO - Epoch [8][2500/5005]\tlr: 1.000e-01, eta: 11:29:24, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.9997\n2023-01-01 17:30:32,911 - mmcls - INFO - Epoch [8][2600/5005]\tlr: 1.000e-01, eta: 11:29:13, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.9806\n2023-01-01 17:30:41,555 - mmcls - INFO - Epoch [8][2700/5005]\tlr: 1.000e-01, eta: 11:29:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9752\n2023-01-01 17:30:50,399 - mmcls - INFO - Epoch [8][2800/5005]\tlr: 1.000e-01, eta: 11:28:51, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9930\n2023-01-01 17:30:59,372 - mmcls - INFO - Epoch [8][2900/5005]\tlr: 1.000e-01, eta: 11:28:42, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.9966\n2023-01-01 17:31:08,811 - mmcls - INFO - Epoch [8][3000/5005]\tlr: 1.000e-01, eta: 11:28:40, time: 0.094, data_time: 0.006, memory: 1224, loss: 3.0226\n2023-01-01 17:31:17,530 - mmcls - INFO - Epoch [8][3100/5005]\tlr: 1.000e-01, eta: 11:28:28, time: 0.087, data_time: 0.025, memory: 1224, loss: 3.0087\n2023-01-01 17:31:26,139 - mmcls - INFO - Epoch [8][3200/5005]\tlr: 1.000e-01, eta: 11:28:15, time: 0.086, data_time: 0.007, memory: 1224, loss: 3.0116\n2023-01-01 17:31:34,616 - mmcls - INFO - Epoch [8][3300/5005]\tlr: 1.000e-01, eta: 11:28:01, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.9914\n2023-01-01 17:31:43,709 - mmcls - INFO - Epoch [8][3400/5005]\tlr: 1.000e-01, eta: 11:27:54, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.9976\n2023-01-01 17:31:52,613 - mmcls - INFO - Epoch [8][3500/5005]\tlr: 1.000e-01, eta: 11:27:44, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.9766\n2023-01-01 17:32:01,419 - mmcls - INFO - Epoch [8][3600/5005]\tlr: 1.000e-01, eta: 11:27:34, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9641\n2023-01-01 17:32:10,248 - mmcls - INFO - Epoch [8][3700/5005]\tlr: 1.000e-01, eta: 11:27:24, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.9954\n2023-01-01 17:32:18,906 - mmcls - INFO - Epoch [8][3800/5005]\tlr: 1.000e-01, eta: 11:27:12, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.9927\n2023-01-01 17:32:27,685 - mmcls - INFO - Epoch [8][3900/5005]\tlr: 1.000e-01, eta: 11:27:01, time: 0.088, data_time: 0.019, memory: 1224, loss: 3.0211\n2023-01-01 17:32:36,416 - mmcls - INFO - Epoch [8][4000/5005]\tlr: 1.000e-01, eta: 11:26:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.9797\n2023-01-01 17:32:45,283 - mmcls - INFO - Epoch [8][4100/5005]\tlr: 1.000e-01, eta: 11:26:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.9651\n2023-01-01 17:32:54,009 - mmcls - INFO - Epoch [8][4200/5005]\tlr: 1.000e-01, eta: 11:26:29, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.9402\n2023-01-01 17:33:02,657 - mmcls - INFO - Epoch [8][4300/5005]\tlr: 1.000e-01, eta: 11:26:16, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.9811\n2023-01-01 17:33:11,421 - mmcls - INFO - Epoch [8][4400/5005]\tlr: 1.000e-01, eta: 11:26:05, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0480\n2023-01-01 17:33:20,182 - mmcls - INFO - Epoch [8][4500/5005]\tlr: 1.000e-01, eta: 11:25:54, time: 0.088, data_time: 0.034, memory: 1224, loss: 3.0165\n2023-01-01 17:33:28,939 - mmcls - INFO - Epoch [8][4600/5005]\tlr: 1.000e-01, eta: 11:25:44, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0483\n2023-01-01 17:33:37,500 - mmcls - INFO - Epoch [8][4700/5005]\tlr: 1.000e-01, eta: 11:25:30, time: 0.086, data_time: 0.012, memory: 1224, loss: 3.0128\n2023-01-01 17:33:46,324 - mmcls - INFO - Epoch [8][4800/5005]\tlr: 1.000e-01, eta: 11:25:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 3.0261\n2023-01-01 17:33:55,115 - mmcls - INFO - Epoch [8][4900/5005]\tlr: 1.000e-01, eta: 11:25:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.0043\n2023-01-01 17:34:03,826 - mmcls - INFO - Epoch [8][5000/5005]\tlr: 1.000e-01, eta: 11:24:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.9815\n2023-01-01 17:34:26,620 - mmcls - INFO - Epoch(val) [8][196]\taccuracy_top-1: 43.4240, accuracy_top-5: 68.6540\n2023-01-01 17:34:37,723 - mmcls - INFO - Epoch [9][100/5005]\tlr: 1.000e-01, eta: 11:25:09, time: 0.111, data_time: 0.036, memory: 1224, loss: 2.9224\n2023-01-01 17:34:46,519 - mmcls - INFO - Epoch [9][200/5005]\tlr: 1.000e-01, eta: 11:24:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9173\n2023-01-01 17:34:55,704 - mmcls - INFO - Epoch [9][300/5005]\tlr: 1.000e-01, eta: 11:24:52, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.9005\n2023-01-01 17:35:04,732 - mmcls - INFO - Epoch [9][400/5005]\tlr: 1.000e-01, eta: 11:24:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9122\n2023-01-01 17:35:13,277 - mmcls - INFO - Epoch [9][500/5005]\tlr: 1.000e-01, eta: 11:24:31, time: 0.085, data_time: 0.000, memory: 1224, loss: 2.9260\n2023-01-01 17:35:22,081 - mmcls - INFO - Epoch [9][600/5005]\tlr: 1.000e-01, eta: 11:24:21, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9136\n2023-01-01 17:35:30,909 - mmcls - INFO - Epoch [9][700/5005]\tlr: 1.000e-01, eta: 11:24:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9156\n2023-01-01 17:35:39,738 - mmcls - INFO - Epoch [9][800/5005]\tlr: 1.000e-01, eta: 11:24:00, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9782\n2023-01-01 17:35:48,626 - mmcls - INFO - Epoch [9][900/5005]\tlr: 1.000e-01, eta: 11:23:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9208\n2023-01-01 17:35:57,351 - mmcls - INFO - Epoch [9][1000/5005]\tlr: 1.000e-01, eta: 11:23:40, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.9361\n2023-01-01 17:36:06,053 - mmcls - INFO - Epoch [9][1100/5005]\tlr: 1.000e-01, eta: 11:23:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.9480\n2023-01-01 17:36:15,342 - mmcls - INFO - Epoch [9][1200/5005]\tlr: 1.000e-01, eta: 11:23:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.9287\n2023-01-01 17:36:24,059 - mmcls - INFO - Epoch [9][1300/5005]\tlr: 1.000e-01, eta: 11:23:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9328\n2023-01-01 17:36:33,185 - mmcls - INFO - Epoch [9][1400/5005]\tlr: 1.000e-01, eta: 11:23:05, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.9830\n2023-01-01 17:36:41,989 - mmcls - INFO - Epoch [9][1500/5005]\tlr: 1.000e-01, eta: 11:22:55, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.9395\n2023-01-01 17:36:50,837 - mmcls - INFO - Epoch [9][1600/5005]\tlr: 1.000e-01, eta: 11:22:45, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.9345\n2023-01-01 17:36:59,337 - mmcls - INFO - Epoch [9][1700/5005]\tlr: 1.000e-01, eta: 11:22:32, time: 0.085, data_time: 0.021, memory: 1224, loss: 2.9607\n2023-01-01 17:37:08,358 - mmcls - INFO - Epoch [9][1800/5005]\tlr: 1.000e-01, eta: 11:22:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9294\n2023-01-01 17:37:17,106 - mmcls - INFO - Epoch [9][1900/5005]\tlr: 1.000e-01, eta: 11:22:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9440\n2023-01-01 17:37:26,116 - mmcls - INFO - Epoch [9][2000/5005]\tlr: 1.000e-01, eta: 11:22:05, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9361\n2023-01-01 17:37:34,627 - mmcls - INFO - Epoch [9][2100/5005]\tlr: 1.000e-01, eta: 11:21:51, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.9483\n2023-01-01 17:37:43,253 - mmcls - INFO - Epoch [9][2200/5005]\tlr: 1.000e-01, eta: 11:21:39, time: 0.086, data_time: 0.033, memory: 1224, loss: 2.9476\n2023-01-01 17:37:51,992 - mmcls - INFO - Epoch [9][2300/5005]\tlr: 1.000e-01, eta: 11:21:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9299\n2023-01-01 17:38:00,866 - mmcls - INFO - Epoch [9][2400/5005]\tlr: 1.000e-01, eta: 11:21:19, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.9952\n2023-01-01 17:38:09,623 - mmcls - INFO - Epoch [9][2500/5005]\tlr: 1.000e-01, eta: 11:21:08, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.9627\n2023-01-01 17:38:18,500 - mmcls - INFO - Epoch [9][2600/5005]\tlr: 1.000e-01, eta: 11:20:59, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.9388\n2023-01-01 17:38:27,149 - mmcls - INFO - Epoch [9][2700/5005]\tlr: 1.000e-01, eta: 11:20:47, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9549\n2023-01-01 17:38:35,981 - mmcls - INFO - Epoch [9][2800/5005]\tlr: 1.000e-01, eta: 11:20:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9707\n2023-01-01 17:38:44,780 - mmcls - INFO - Epoch [9][2900/5005]\tlr: 1.000e-01, eta: 11:20:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9686\n2023-01-01 17:38:53,564 - mmcls - INFO - Epoch [9][3000/5005]\tlr: 1.000e-01, eta: 11:20:16, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9485\n2023-01-01 17:39:02,284 - mmcls - INFO - Epoch [9][3100/5005]\tlr: 1.000e-01, eta: 11:20:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9829\n2023-01-01 17:39:10,741 - mmcls - INFO - Epoch [9][3200/5005]\tlr: 1.000e-01, eta: 11:19:51, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.9409\n2023-01-01 17:39:19,690 - mmcls - INFO - Epoch [9][3300/5005]\tlr: 1.000e-01, eta: 11:19:43, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.9579\n2023-01-01 17:39:28,496 - mmcls - INFO - Epoch [9][3400/5005]\tlr: 1.000e-01, eta: 11:19:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9546\n2023-01-01 17:39:37,394 - mmcls - INFO - Epoch [9][3500/5005]\tlr: 1.000e-01, eta: 11:19:23, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.9708\n2023-01-01 17:39:46,357 - mmcls - INFO - Epoch [9][3600/5005]\tlr: 1.000e-01, eta: 11:19:15, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.9522\n2023-01-01 17:39:55,207 - mmcls - INFO - Epoch [9][3700/5005]\tlr: 1.000e-01, eta: 11:19:05, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.9602\n2023-01-01 17:40:04,106 - mmcls - INFO - Epoch [9][3800/5005]\tlr: 1.000e-01, eta: 11:18:56, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.9585\n2023-01-01 17:40:12,923 - mmcls - INFO - Epoch [9][3900/5005]\tlr: 1.000e-01, eta: 11:18:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.9607\n2023-01-01 17:40:21,890 - mmcls - INFO - Epoch [9][4000/5005]\tlr: 1.000e-01, eta: 11:18:37, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.9681\n2023-01-01 17:40:30,706 - mmcls - INFO - Epoch [9][4100/5005]\tlr: 1.000e-01, eta: 11:18:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9580\n2023-01-01 17:40:39,428 - mmcls - INFO - Epoch [9][4200/5005]\tlr: 1.000e-01, eta: 11:18:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9615\n2023-01-01 17:40:48,444 - mmcls - INFO - Epoch [9][4300/5005]\tlr: 1.000e-01, eta: 11:18:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9719\n2023-01-01 17:40:57,457 - mmcls - INFO - Epoch [9][4400/5005]\tlr: 1.000e-01, eta: 11:18:00, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.9395\n2023-01-01 17:41:06,582 - mmcls - INFO - Epoch [9][4500/5005]\tlr: 1.000e-01, eta: 11:17:54, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.9709\n2023-01-01 17:41:15,458 - mmcls - INFO - Epoch [9][4600/5005]\tlr: 1.000e-01, eta: 11:17:44, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9493\n2023-01-01 17:41:24,139 - mmcls - INFO - Epoch [9][4700/5005]\tlr: 1.000e-01, eta: 11:17:33, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.9401\n2023-01-01 17:41:33,171 - mmcls - INFO - Epoch [9][4800/5005]\tlr: 1.000e-01, eta: 11:17:25, time: 0.090, data_time: 0.022, memory: 1224, loss: 2.9483\n2023-01-01 17:41:42,037 - mmcls - INFO - Epoch [9][4900/5005]\tlr: 1.000e-01, eta: 11:17:16, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.9504\n2023-01-01 17:41:50,838 - mmcls - INFO - Epoch [9][5000/5005]\tlr: 1.000e-01, eta: 11:17:06, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9758\n2023-01-01 17:42:13,632 - mmcls - INFO - Epoch(val) [9][196]\taccuracy_top-1: 42.0340, accuracy_top-5: 66.9720\n2023-01-01 17:42:24,943 - mmcls - INFO - Epoch [10][100/5005]\tlr: 1.000e-01, eta: 11:17:16, time: 0.113, data_time: 0.045, memory: 1224, loss: 2.8815\n2023-01-01 17:42:33,797 - mmcls - INFO - Epoch [10][200/5005]\tlr: 1.000e-01, eta: 11:17:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8680\n2023-01-01 17:42:42,664 - mmcls - INFO - Epoch [10][300/5005]\tlr: 1.000e-01, eta: 11:16:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8874\n2023-01-01 17:42:51,636 - mmcls - INFO - Epoch [10][400/5005]\tlr: 1.000e-01, eta: 11:16:48, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.9098\n2023-01-01 17:43:00,551 - mmcls - INFO - Epoch [10][500/5005]\tlr: 1.000e-01, eta: 11:16:39, time: 0.089, data_time: 0.037, memory: 1224, loss: 2.9122\n2023-01-01 17:43:09,621 - mmcls - INFO - Epoch [10][600/5005]\tlr: 1.000e-01, eta: 11:16:31, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.9020\n2023-01-01 17:43:18,517 - mmcls - INFO - Epoch [10][700/5005]\tlr: 1.000e-01, eta: 11:16:22, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.9223\n2023-01-01 17:43:27,467 - mmcls - INFO - Epoch [10][800/5005]\tlr: 1.000e-01, eta: 11:16:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9317\n2023-01-01 17:43:36,347 - mmcls - INFO - Epoch [10][900/5005]\tlr: 1.000e-01, eta: 11:16:04, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.9043\n2023-01-01 17:43:45,827 - mmcls - INFO - Epoch [10][1000/5005]\tlr: 1.000e-01, eta: 11:16:01, time: 0.095, data_time: 0.048, memory: 1224, loss: 2.9532\n2023-01-01 17:43:54,855 - mmcls - INFO - Epoch [10][1100/5005]\tlr: 1.000e-01, eta: 11:15:53, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.9246\n2023-01-01 17:44:03,661 - mmcls - INFO - Epoch [10][1200/5005]\tlr: 1.000e-01, eta: 11:15:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9281\n2023-01-01 17:44:12,160 - mmcls - INFO - Epoch [10][1300/5005]\tlr: 1.000e-01, eta: 11:15:30, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.9264\n2023-01-01 17:44:21,002 - mmcls - INFO - Epoch [10][1400/5005]\tlr: 1.000e-01, eta: 11:15:20, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9372\n2023-01-01 17:44:29,803 - mmcls - INFO - Epoch [10][1500/5005]\tlr: 1.000e-01, eta: 11:15:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9113\n2023-01-01 17:44:38,735 - mmcls - INFO - Epoch [10][1600/5005]\tlr: 1.000e-01, eta: 11:15:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9197\n2023-01-01 17:44:47,287 - mmcls - INFO - Epoch [10][1700/5005]\tlr: 1.000e-01, eta: 11:14:48, time: 0.085, data_time: 0.022, memory: 1224, loss: 2.9487\n2023-01-01 17:44:56,362 - mmcls - INFO - Epoch [10][1800/5005]\tlr: 1.000e-01, eta: 11:14:41, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.9276\n2023-01-01 17:45:05,157 - mmcls - INFO - Epoch [10][1900/5005]\tlr: 1.000e-01, eta: 11:14:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9231\n2023-01-01 17:45:13,782 - mmcls - INFO - Epoch [10][2000/5005]\tlr: 1.000e-01, eta: 11:14:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9052\n2023-01-01 17:45:22,804 - mmcls - INFO - Epoch [10][2100/5005]\tlr: 1.000e-01, eta: 11:14:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.9229\n2023-01-01 17:45:31,583 - mmcls - INFO - Epoch [10][2200/5005]\tlr: 1.000e-01, eta: 11:14:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9083\n2023-01-01 17:45:40,358 - mmcls - INFO - Epoch [10][2300/5005]\tlr: 1.000e-01, eta: 11:13:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9441\n2023-01-01 17:45:49,105 - mmcls - INFO - Epoch [10][2400/5005]\tlr: 1.000e-01, eta: 11:13:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.9028\n2023-01-01 17:45:57,996 - mmcls - INFO - Epoch [10][2500/5005]\tlr: 1.000e-01, eta: 11:13:31, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.9464\n2023-01-01 17:46:07,032 - mmcls - INFO - Epoch [10][2600/5005]\tlr: 1.000e-01, eta: 11:13:23, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.9520\n2023-01-01 17:46:15,740 - mmcls - INFO - Epoch [10][2700/5005]\tlr: 1.000e-01, eta: 11:13:12, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.9161\n2023-01-01 17:46:24,583 - mmcls - INFO - Epoch [10][2800/5005]\tlr: 1.000e-01, eta: 11:13:02, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.9055\n2023-01-01 17:46:33,527 - mmcls - INFO - Epoch [10][2900/5005]\tlr: 1.000e-01, eta: 11:12:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9309\n2023-01-01 17:46:42,056 - mmcls - INFO - Epoch [10][3000/5005]\tlr: 1.000e-01, eta: 11:12:41, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.9216\n2023-01-01 17:46:51,200 - mmcls - INFO - Epoch [10][3100/5005]\tlr: 1.000e-01, eta: 11:12:34, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.9311\n2023-01-01 17:46:59,826 - mmcls - INFO - Epoch [10][3200/5005]\tlr: 1.000e-01, eta: 11:12:22, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9680\n2023-01-01 17:47:08,668 - mmcls - INFO - Epoch [10][3300/5005]\tlr: 1.000e-01, eta: 11:12:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9656\n2023-01-01 17:47:17,352 - mmcls - INFO - Epoch [10][3400/5005]\tlr: 1.000e-01, eta: 11:12:01, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9638\n2023-01-01 17:47:26,269 - mmcls - INFO - Epoch [10][3500/5005]\tlr: 1.000e-01, eta: 11:11:52, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9439\n2023-01-01 17:47:34,834 - mmcls - INFO - Epoch [10][3600/5005]\tlr: 1.000e-01, eta: 11:11:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.9444\n2023-01-01 17:47:43,721 - mmcls - INFO - Epoch [10][3700/5005]\tlr: 1.000e-01, eta: 11:11:31, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.9297\n2023-01-01 17:47:52,359 - mmcls - INFO - Epoch [10][3800/5005]\tlr: 1.000e-01, eta: 11:11:19, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.9401\n2023-01-01 17:48:01,175 - mmcls - INFO - Epoch [10][3900/5005]\tlr: 1.000e-01, eta: 11:11:10, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9423\n2023-01-01 17:48:10,170 - mmcls - INFO - Epoch [10][4000/5005]\tlr: 1.000e-01, eta: 11:11:01, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9434\n2023-01-01 17:48:18,651 - mmcls - INFO - Epoch [10][4100/5005]\tlr: 1.000e-01, eta: 11:10:48, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.9034\n2023-01-01 17:48:27,710 - mmcls - INFO - Epoch [10][4200/5005]\tlr: 1.000e-01, eta: 11:10:41, time: 0.091, data_time: 0.023, memory: 1224, loss: 2.9344\n2023-01-01 17:48:36,367 - mmcls - INFO - Epoch [10][4300/5005]\tlr: 1.000e-01, eta: 11:10:30, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.9181\n2023-01-01 17:48:45,244 - mmcls - INFO - Epoch [10][4400/5005]\tlr: 1.000e-01, eta: 11:10:20, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.9413\n2023-01-01 17:48:53,964 - mmcls - INFO - Epoch [10][4500/5005]\tlr: 1.000e-01, eta: 11:10:09, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8755\n2023-01-01 17:49:03,159 - mmcls - INFO - Epoch [10][4600/5005]\tlr: 1.000e-01, eta: 11:10:03, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.9379\n2023-01-01 17:49:11,831 - mmcls - INFO - Epoch [10][4700/5005]\tlr: 1.000e-01, eta: 11:09:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9370\n2023-01-01 17:49:20,920 - mmcls - INFO - Epoch [10][4800/5005]\tlr: 1.000e-01, eta: 11:09:45, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.9478\n2023-01-01 17:49:29,490 - mmcls - INFO - Epoch [10][4900/5005]\tlr: 1.000e-01, eta: 11:09:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9568\n2023-01-01 17:49:38,550 - mmcls - INFO - Epoch [10][5000/5005]\tlr: 1.000e-01, eta: 11:09:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.9213\n2023-01-01 17:50:01,684 - mmcls - INFO - Epoch(val) [10][196]\taccuracy_top-1: 45.3460, accuracy_top-5: 70.5200\n2023-01-01 17:50:12,569 - mmcls - INFO - Epoch [11][100/5005]\tlr: 1.000e-01, eta: 11:09:29, time: 0.109, data_time: 0.039, memory: 1224, loss: 2.8707\n2023-01-01 17:50:21,246 - mmcls - INFO - Epoch [11][200/5005]\tlr: 1.000e-01, eta: 11:09:18, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8847\n2023-01-01 17:50:29,879 - mmcls - INFO - Epoch [11][300/5005]\tlr: 1.000e-01, eta: 11:09:06, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8744\n2023-01-01 17:50:38,819 - mmcls - INFO - Epoch [11][400/5005]\tlr: 1.000e-01, eta: 11:08:58, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8849\n2023-01-01 17:50:47,471 - mmcls - INFO - Epoch [11][500/5005]\tlr: 1.000e-01, eta: 11:08:46, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.9209\n2023-01-01 17:50:56,182 - mmcls - INFO - Epoch [11][600/5005]\tlr: 1.000e-01, eta: 11:08:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9191\n2023-01-01 17:51:05,147 - mmcls - INFO - Epoch [11][700/5005]\tlr: 1.000e-01, eta: 11:08:27, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8989\n2023-01-01 17:51:13,914 - mmcls - INFO - Epoch [11][800/5005]\tlr: 1.000e-01, eta: 11:08:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9083\n2023-01-01 17:51:22,587 - mmcls - INFO - Epoch [11][900/5005]\tlr: 1.000e-01, eta: 11:08:06, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8683\n2023-01-01 17:51:31,444 - mmcls - INFO - Epoch [11][1000/5005]\tlr: 1.000e-01, eta: 11:07:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9064\n2023-01-01 17:51:40,046 - mmcls - INFO - Epoch [11][1100/5005]\tlr: 1.000e-01, eta: 11:07:45, time: 0.086, data_time: 0.037, memory: 1224, loss: 2.8866\n2023-01-01 17:51:48,927 - mmcls - INFO - Epoch [11][1200/5005]\tlr: 1.000e-01, eta: 11:07:35, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.8735\n2023-01-01 17:51:57,515 - mmcls - INFO - Epoch [11][1300/5005]\tlr: 1.000e-01, eta: 11:07:24, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.8838\n2023-01-01 17:52:06,686 - mmcls - INFO - Epoch [11][1400/5005]\tlr: 1.000e-01, eta: 11:07:17, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8923\n2023-01-01 17:52:15,466 - mmcls - INFO - Epoch [11][1500/5005]\tlr: 1.000e-01, eta: 11:07:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8600\n2023-01-01 17:52:24,350 - mmcls - INFO - Epoch [11][1600/5005]\tlr: 1.000e-01, eta: 11:06:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9340\n2023-01-01 17:52:33,408 - mmcls - INFO - Epoch [11][1700/5005]\tlr: 1.000e-01, eta: 11:06:50, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.9463\n2023-01-01 17:52:42,151 - mmcls - INFO - Epoch [11][1800/5005]\tlr: 1.000e-01, eta: 11:06:40, time: 0.087, data_time: 0.030, memory: 1224, loss: 2.9059\n2023-01-01 17:52:50,974 - mmcls - INFO - Epoch [11][1900/5005]\tlr: 1.000e-01, eta: 11:06:30, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8768\n2023-01-01 17:52:59,921 - mmcls - INFO - Epoch [11][2000/5005]\tlr: 1.000e-01, eta: 11:06:21, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.9123\n2023-01-01 17:53:08,508 - mmcls - INFO - Epoch [11][2100/5005]\tlr: 1.000e-01, eta: 11:06:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9216\n2023-01-01 17:53:17,537 - mmcls - INFO - Epoch [11][2200/5005]\tlr: 1.000e-01, eta: 11:06:01, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.9117\n2023-01-01 17:53:26,346 - mmcls - INFO - Epoch [11][2300/5005]\tlr: 1.000e-01, eta: 11:05:52, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9033\n2023-01-01 17:53:35,578 - mmcls - INFO - Epoch [11][2400/5005]\tlr: 1.000e-01, eta: 11:05:45, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.8952\n2023-01-01 17:53:44,699 - mmcls - INFO - Epoch [11][2500/5005]\tlr: 1.000e-01, eta: 11:05:38, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.8917\n2023-01-01 17:53:53,755 - mmcls - INFO - Epoch [11][2600/5005]\tlr: 1.000e-01, eta: 11:05:30, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8979\n2023-01-01 17:54:02,375 - mmcls - INFO - Epoch [11][2700/5005]\tlr: 1.000e-01, eta: 11:05:19, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.9195\n2023-01-01 17:54:11,401 - mmcls - INFO - Epoch [11][2800/5005]\tlr: 1.000e-01, eta: 11:05:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9147\n2023-01-01 17:54:20,070 - mmcls - INFO - Epoch [11][2900/5005]\tlr: 1.000e-01, eta: 11:05:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9324\n2023-01-01 17:54:28,990 - mmcls - INFO - Epoch [11][3000/5005]\tlr: 1.000e-01, eta: 11:04:51, time: 0.089, data_time: 0.034, memory: 1224, loss: 2.9132\n2023-01-01 17:54:37,795 - mmcls - INFO - Epoch [11][3100/5005]\tlr: 1.000e-01, eta: 11:04:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9222\n2023-01-01 17:54:46,343 - mmcls - INFO - Epoch [11][3200/5005]\tlr: 1.000e-01, eta: 11:04:29, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.8876\n2023-01-01 17:54:55,159 - mmcls - INFO - Epoch [11][3300/5005]\tlr: 1.000e-01, eta: 11:04:20, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.9324\n2023-01-01 17:55:04,097 - mmcls - INFO - Epoch [11][3400/5005]\tlr: 1.000e-01, eta: 11:04:11, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8918\n2023-01-01 17:55:13,127 - mmcls - INFO - Epoch [11][3500/5005]\tlr: 1.000e-01, eta: 11:04:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8712\n2023-01-01 17:55:21,890 - mmcls - INFO - Epoch [11][3600/5005]\tlr: 1.000e-01, eta: 11:03:53, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.9122\n2023-01-01 17:55:30,681 - mmcls - INFO - Epoch [11][3700/5005]\tlr: 1.000e-01, eta: 11:03:43, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.9522\n2023-01-01 17:55:39,423 - mmcls - INFO - Epoch [11][3800/5005]\tlr: 1.000e-01, eta: 11:03:32, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8937\n2023-01-01 17:55:48,574 - mmcls - INFO - Epoch [11][3900/5005]\tlr: 1.000e-01, eta: 11:03:26, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.9054\n2023-01-01 17:55:57,303 - mmcls - INFO - Epoch [11][4000/5005]\tlr: 1.000e-01, eta: 11:03:15, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.9155\n2023-01-01 17:56:06,465 - mmcls - INFO - Epoch [11][4100/5005]\tlr: 1.000e-01, eta: 11:03:08, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.9249\n2023-01-01 17:56:15,337 - mmcls - INFO - Epoch [11][4200/5005]\tlr: 1.000e-01, eta: 11:02:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.9231\n2023-01-01 17:56:23,777 - mmcls - INFO - Epoch [11][4300/5005]\tlr: 1.000e-01, eta: 11:02:46, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.9136\n2023-01-01 17:56:32,569 - mmcls - INFO - Epoch [11][4400/5005]\tlr: 1.000e-01, eta: 11:02:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8811\n2023-01-01 17:56:41,404 - mmcls - INFO - Epoch [11][4500/5005]\tlr: 1.000e-01, eta: 11:02:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8921\n2023-01-01 17:56:50,284 - mmcls - INFO - Epoch [11][4600/5005]\tlr: 1.000e-01, eta: 11:02:17, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8787\n2023-01-01 17:56:59,174 - mmcls - INFO - Epoch [11][4700/5005]\tlr: 1.000e-01, eta: 11:02:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9124\n2023-01-01 17:57:08,458 - mmcls - INFO - Epoch [11][4800/5005]\tlr: 1.000e-01, eta: 11:02:02, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8871\n2023-01-01 17:57:17,246 - mmcls - INFO - Epoch [11][4900/5005]\tlr: 1.000e-01, eta: 11:01:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9125\n2023-01-01 17:57:26,064 - mmcls - INFO - Epoch [11][5000/5005]\tlr: 1.000e-01, eta: 11:01:43, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9279\n2023-01-01 17:57:48,939 - mmcls - INFO - Epoch(val) [11][196]\taccuracy_top-1: 42.1840, accuracy_top-5: 67.2420\n2023-01-01 17:57:59,877 - mmcls - INFO - Epoch [12][100/5005]\tlr: 1.000e-01, eta: 11:01:46, time: 0.109, data_time: 0.051, memory: 1224, loss: 2.8673\n2023-01-01 17:58:08,904 - mmcls - INFO - Epoch [12][200/5005]\tlr: 1.000e-01, eta: 11:01:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.8791\n2023-01-01 17:58:17,944 - mmcls - INFO - Epoch [12][300/5005]\tlr: 1.000e-01, eta: 11:01:30, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.8446\n2023-01-01 17:58:26,912 - mmcls - INFO - Epoch [12][400/5005]\tlr: 1.000e-01, eta: 11:01:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8587\n2023-01-01 17:58:35,919 - mmcls - INFO - Epoch [12][500/5005]\tlr: 1.000e-01, eta: 11:01:13, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8779\n2023-01-01 17:58:44,590 - mmcls - INFO - Epoch [12][600/5005]\tlr: 1.000e-01, eta: 11:01:02, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.9075\n2023-01-01 17:58:53,350 - mmcls - INFO - Epoch [12][700/5005]\tlr: 1.000e-01, eta: 11:00:52, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8611\n2023-01-01 17:59:02,310 - mmcls - INFO - Epoch [12][800/5005]\tlr: 1.000e-01, eta: 11:00:44, time: 0.090, data_time: 0.034, memory: 1224, loss: 2.8811\n2023-01-01 17:59:11,084 - mmcls - INFO - Epoch [12][900/5005]\tlr: 1.000e-01, eta: 11:00:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8743\n2023-01-01 17:59:20,022 - mmcls - INFO - Epoch [12][1000/5005]\tlr: 1.000e-01, eta: 11:00:25, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8555\n2023-01-01 17:59:29,077 - mmcls - INFO - Epoch [12][1100/5005]\tlr: 1.000e-01, eta: 11:00:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.8957\n2023-01-01 17:59:37,920 - mmcls - INFO - Epoch [12][1200/5005]\tlr: 1.000e-01, eta: 11:00:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9018\n2023-01-01 17:59:46,730 - mmcls - INFO - Epoch [12][1300/5005]\tlr: 1.000e-01, eta: 10:59:58, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.8756\n2023-01-01 17:59:55,834 - mmcls - INFO - Epoch [12][1400/5005]\tlr: 1.000e-01, eta: 10:59:50, time: 0.091, data_time: 0.016, memory: 1224, loss: 2.8914\n2023-01-01 18:00:04,628 - mmcls - INFO - Epoch [12][1500/5005]\tlr: 1.000e-01, eta: 10:59:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8917\n2023-01-01 18:00:13,860 - mmcls - INFO - Epoch [12][1600/5005]\tlr: 1.000e-01, eta: 10:59:34, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.8893\n2023-01-01 18:00:22,435 - mmcls - INFO - Epoch [12][1700/5005]\tlr: 1.000e-01, eta: 10:59:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9054\n2023-01-01 18:00:31,254 - mmcls - INFO - Epoch [12][1800/5005]\tlr: 1.000e-01, eta: 10:59:13, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.9176\n2023-01-01 18:00:39,830 - mmcls - INFO - Epoch [12][1900/5005]\tlr: 1.000e-01, eta: 10:59:01, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9184\n2023-01-01 18:00:48,772 - mmcls - INFO - Epoch [12][2000/5005]\tlr: 1.000e-01, eta: 10:58:52, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.9177\n2023-01-01 18:00:57,712 - mmcls - INFO - Epoch [12][2100/5005]\tlr: 1.000e-01, eta: 10:58:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9140\n2023-01-01 18:01:06,628 - mmcls - INFO - Epoch [12][2200/5005]\tlr: 1.000e-01, eta: 10:58:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8947\n2023-01-01 18:01:15,584 - mmcls - INFO - Epoch [12][2300/5005]\tlr: 1.000e-01, eta: 10:58:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9106\n2023-01-01 18:01:24,298 - mmcls - INFO - Epoch [12][2400/5005]\tlr: 1.000e-01, eta: 10:58:16, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.9081\n2023-01-01 18:01:33,144 - mmcls - INFO - Epoch [12][2500/5005]\tlr: 1.000e-01, eta: 10:58:06, time: 0.088, data_time: 0.039, memory: 1224, loss: 2.9039\n2023-01-01 18:01:41,902 - mmcls - INFO - Epoch [12][2600/5005]\tlr: 1.000e-01, eta: 10:57:56, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.8512\n2023-01-01 18:01:50,647 - mmcls - INFO - Epoch [12][2700/5005]\tlr: 1.000e-01, eta: 10:57:46, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8872\n2023-01-01 18:01:59,249 - mmcls - INFO - Epoch [12][2800/5005]\tlr: 1.000e-01, eta: 10:57:35, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9379\n2023-01-01 18:02:08,302 - mmcls - INFO - Epoch [12][2900/5005]\tlr: 1.000e-01, eta: 10:57:27, time: 0.091, data_time: 0.019, memory: 1224, loss: 2.9141\n2023-01-01 18:02:17,116 - mmcls - INFO - Epoch [12][3000/5005]\tlr: 1.000e-01, eta: 10:57:17, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.9002\n2023-01-01 18:02:25,954 - mmcls - INFO - Epoch [12][3100/5005]\tlr: 1.000e-01, eta: 10:57:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8741\n2023-01-01 18:02:34,644 - mmcls - INFO - Epoch [12][3200/5005]\tlr: 1.000e-01, eta: 10:56:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8687\n2023-01-01 18:02:43,541 - mmcls - INFO - Epoch [12][3300/5005]\tlr: 1.000e-01, eta: 10:56:48, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.8796\n2023-01-01 18:02:52,356 - mmcls - INFO - Epoch [12][3400/5005]\tlr: 1.000e-01, eta: 10:56:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8753\n2023-01-01 18:03:00,769 - mmcls - INFO - Epoch [12][3500/5005]\tlr: 1.000e-01, eta: 10:56:26, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.9126\n2023-01-01 18:03:10,077 - mmcls - INFO - Epoch [12][3600/5005]\tlr: 1.000e-01, eta: 10:56:20, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.8754\n2023-01-01 18:03:19,133 - mmcls - INFO - Epoch [12][3700/5005]\tlr: 1.000e-01, eta: 10:56:12, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.9340\n2023-01-01 18:03:27,841 - mmcls - INFO - Epoch [12][3800/5005]\tlr: 1.000e-01, eta: 10:56:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.9144\n2023-01-01 18:03:36,700 - mmcls - INFO - Epoch [12][3900/5005]\tlr: 1.000e-01, eta: 10:55:52, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.9014\n2023-01-01 18:03:45,363 - mmcls - INFO - Epoch [12][4000/5005]\tlr: 1.000e-01, eta: 10:55:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.9268\n2023-01-01 18:03:54,156 - mmcls - INFO - Epoch [12][4100/5005]\tlr: 1.000e-01, eta: 10:55:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8767\n2023-01-01 18:04:03,168 - mmcls - INFO - Epoch [12][4200/5005]\tlr: 1.000e-01, eta: 10:55:23, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8556\n2023-01-01 18:04:11,978 - mmcls - INFO - Epoch [12][4300/5005]\tlr: 1.000e-01, eta: 10:55:14, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.9001\n2023-01-01 18:04:20,510 - mmcls - INFO - Epoch [12][4400/5005]\tlr: 1.000e-01, eta: 10:55:02, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.8894\n2023-01-01 18:04:29,409 - mmcls - INFO - Epoch [12][4500/5005]\tlr: 1.000e-01, eta: 10:54:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8725\n2023-01-01 18:04:38,375 - mmcls - INFO - Epoch [12][4600/5005]\tlr: 1.000e-01, eta: 10:54:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8906\n2023-01-01 18:04:47,189 - mmcls - INFO - Epoch [12][4700/5005]\tlr: 1.000e-01, eta: 10:54:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8901\n2023-01-01 18:04:55,951 - mmcls - INFO - Epoch [12][4800/5005]\tlr: 1.000e-01, eta: 10:54:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8682\n2023-01-01 18:05:04,692 - mmcls - INFO - Epoch [12][4900/5005]\tlr: 1.000e-01, eta: 10:54:15, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.8634\n2023-01-01 18:05:13,941 - mmcls - INFO - Epoch [12][5000/5005]\tlr: 1.000e-01, eta: 10:54:08, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8675\n2023-01-01 18:05:36,797 - mmcls - INFO - Epoch(val) [12][196]\taccuracy_top-1: 42.3760, accuracy_top-5: 67.6460\n2023-01-01 18:05:47,933 - mmcls - INFO - Epoch [13][100/5005]\tlr: 1.000e-01, eta: 10:54:12, time: 0.111, data_time: 0.033, memory: 1224, loss: 2.7944\n2023-01-01 18:05:56,850 - mmcls - INFO - Epoch [13][200/5005]\tlr: 1.000e-01, eta: 10:54:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8828\n2023-01-01 18:06:05,523 - mmcls - INFO - Epoch [13][300/5005]\tlr: 1.000e-01, eta: 10:53:52, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8718\n2023-01-01 18:06:14,172 - mmcls - INFO - Epoch [13][400/5005]\tlr: 1.000e-01, eta: 10:53:41, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8808\n2023-01-01 18:06:22,963 - mmcls - INFO - Epoch [13][500/5005]\tlr: 1.000e-01, eta: 10:53:31, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8511\n2023-01-01 18:06:31,620 - mmcls - INFO - Epoch [13][600/5005]\tlr: 1.000e-01, eta: 10:53:21, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8709\n2023-01-01 18:06:40,488 - mmcls - INFO - Epoch [13][700/5005]\tlr: 1.000e-01, eta: 10:53:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8854\n2023-01-01 18:06:49,561 - mmcls - INFO - Epoch [13][800/5005]\tlr: 1.000e-01, eta: 10:53:04, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.8592\n2023-01-01 18:06:58,220 - mmcls - INFO - Epoch [13][900/5005]\tlr: 1.000e-01, eta: 10:52:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8835\n2023-01-01 18:07:07,670 - mmcls - INFO - Epoch [13][1000/5005]\tlr: 1.000e-01, eta: 10:52:48, time: 0.095, data_time: 0.003, memory: 1224, loss: 2.8671\n2023-01-01 18:07:16,243 - mmcls - INFO - Epoch [13][1100/5005]\tlr: 1.000e-01, eta: 10:52:36, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.8600\n2023-01-01 18:07:25,187 - mmcls - INFO - Epoch [13][1200/5005]\tlr: 1.000e-01, eta: 10:52:28, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8603\n2023-01-01 18:07:34,143 - mmcls - INFO - Epoch [13][1300/5005]\tlr: 1.000e-01, eta: 10:52:19, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8564\n2023-01-01 18:07:42,763 - mmcls - INFO - Epoch [13][1400/5005]\tlr: 1.000e-01, eta: 10:52:08, time: 0.086, data_time: 0.038, memory: 1224, loss: 2.9070\n2023-01-01 18:07:51,535 - mmcls - INFO - Epoch [13][1500/5005]\tlr: 1.000e-01, eta: 10:51:58, time: 0.088, data_time: 0.040, memory: 1224, loss: 2.8831\n2023-01-01 18:08:00,453 - mmcls - INFO - Epoch [13][1600/5005]\tlr: 1.000e-01, eta: 10:51:49, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8517\n2023-01-01 18:08:09,271 - mmcls - INFO - Epoch [13][1700/5005]\tlr: 1.000e-01, eta: 10:51:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8531\n2023-01-01 18:08:18,535 - mmcls - INFO - Epoch [13][1800/5005]\tlr: 1.000e-01, eta: 10:51:33, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8530\n2023-01-01 18:08:27,396 - mmcls - INFO - Epoch [13][1900/5005]\tlr: 1.000e-01, eta: 10:51:24, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.9069\n2023-01-01 18:08:36,331 - mmcls - INFO - Epoch [13][2000/5005]\tlr: 1.000e-01, eta: 10:51:15, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.8697\n2023-01-01 18:08:45,154 - mmcls - INFO - Epoch [13][2100/5005]\tlr: 1.000e-01, eta: 10:51:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8786\n2023-01-01 18:08:53,879 - mmcls - INFO - Epoch [13][2200/5005]\tlr: 1.000e-01, eta: 10:50:55, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.8938\n2023-01-01 18:09:02,754 - mmcls - INFO - Epoch [13][2300/5005]\tlr: 1.000e-01, eta: 10:50:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8565\n2023-01-01 18:09:11,528 - mmcls - INFO - Epoch [13][2400/5005]\tlr: 1.000e-01, eta: 10:50:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8962\n2023-01-01 18:09:20,164 - mmcls - INFO - Epoch [13][2500/5005]\tlr: 1.000e-01, eta: 10:50:26, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.8502\n2023-01-01 18:09:29,173 - mmcls - INFO - Epoch [13][2600/5005]\tlr: 1.000e-01, eta: 10:50:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8685\n2023-01-01 18:09:38,010 - mmcls - INFO - Epoch [13][2700/5005]\tlr: 1.000e-01, eta: 10:50:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9001\n2023-01-01 18:09:47,068 - mmcls - INFO - Epoch [13][2800/5005]\tlr: 1.000e-01, eta: 10:50:00, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8808\n2023-01-01 18:09:55,855 - mmcls - INFO - Epoch [13][2900/5005]\tlr: 1.000e-01, eta: 10:49:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8508\n2023-01-01 18:10:04,448 - mmcls - INFO - Epoch [13][3000/5005]\tlr: 1.000e-01, eta: 10:49:39, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.8626\n2023-01-01 18:10:13,349 - mmcls - INFO - Epoch [13][3100/5005]\tlr: 1.000e-01, eta: 10:49:30, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.8511\n2023-01-01 18:10:22,462 - mmcls - INFO - Epoch [13][3200/5005]\tlr: 1.000e-01, eta: 10:49:23, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.8690\n2023-01-01 18:10:31,222 - mmcls - INFO - Epoch [13][3300/5005]\tlr: 1.000e-01, eta: 10:49:13, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8606\n2023-01-01 18:10:39,964 - mmcls - INFO - Epoch [13][3400/5005]\tlr: 1.000e-01, eta: 10:49:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8881\n2023-01-01 18:10:48,979 - mmcls - INFO - Epoch [13][3500/5005]\tlr: 1.000e-01, eta: 10:48:54, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.8840\n2023-01-01 18:10:57,570 - mmcls - INFO - Epoch [13][3600/5005]\tlr: 1.000e-01, eta: 10:48:43, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.8745\n2023-01-01 18:11:06,455 - mmcls - INFO - Epoch [13][3700/5005]\tlr: 1.000e-01, eta: 10:48:34, time: 0.089, data_time: 0.035, memory: 1224, loss: 2.8910\n2023-01-01 18:11:15,481 - mmcls - INFO - Epoch [13][3800/5005]\tlr: 1.000e-01, eta: 10:48:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8787\n2023-01-01 18:11:24,208 - mmcls - INFO - Epoch [13][3900/5005]\tlr: 1.000e-01, eta: 10:48:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9135\n2023-01-01 18:11:33,042 - mmcls - INFO - Epoch [13][4000/5005]\tlr: 1.000e-01, eta: 10:48:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8755\n2023-01-01 18:11:41,579 - mmcls - INFO - Epoch [13][4100/5005]\tlr: 1.000e-01, eta: 10:47:55, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.8592\n2023-01-01 18:11:50,228 - mmcls - INFO - Epoch [13][4200/5005]\tlr: 1.000e-01, eta: 10:47:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.8966\n2023-01-01 18:11:58,942 - mmcls - INFO - Epoch [13][4300/5005]\tlr: 1.000e-01, eta: 10:47:34, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.9234\n2023-01-01 18:12:07,770 - mmcls - INFO - Epoch [13][4400/5005]\tlr: 1.000e-01, eta: 10:47:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8636\n2023-01-01 18:12:16,255 - mmcls - INFO - Epoch [13][4500/5005]\tlr: 1.000e-01, eta: 10:47:13, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8677\n2023-01-01 18:12:25,155 - mmcls - INFO - Epoch [13][4600/5005]\tlr: 1.000e-01, eta: 10:47:04, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.9348\n2023-01-01 18:12:34,144 - mmcls - INFO - Epoch [13][4700/5005]\tlr: 1.000e-01, eta: 10:46:55, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.8846\n2023-01-01 18:12:42,924 - mmcls - INFO - Epoch [13][4800/5005]\tlr: 1.000e-01, eta: 10:46:46, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8541\n2023-01-01 18:12:51,551 - mmcls - INFO - Epoch [13][4900/5005]\tlr: 1.000e-01, eta: 10:46:35, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.8595\n2023-01-01 18:13:00,382 - mmcls - INFO - Epoch [13][5000/5005]\tlr: 1.000e-01, eta: 10:46:26, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8337\n2023-01-01 18:13:23,299 - mmcls - INFO - Epoch(val) [13][196]\taccuracy_top-1: 44.2520, accuracy_top-5: 69.7160\n2023-01-01 18:13:34,388 - mmcls - INFO - Epoch [14][100/5005]\tlr: 1.000e-01, eta: 10:46:28, time: 0.111, data_time: 0.057, memory: 1224, loss: 2.7935\n2023-01-01 18:13:42,713 - mmcls - INFO - Epoch [14][200/5005]\tlr: 1.000e-01, eta: 10:46:15, time: 0.083, data_time: 0.011, memory: 1224, loss: 2.8015\n2023-01-01 18:13:51,632 - mmcls - INFO - Epoch [14][300/5005]\tlr: 1.000e-01, eta: 10:46:06, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.8187\n2023-01-01 18:14:00,366 - mmcls - INFO - Epoch [14][400/5005]\tlr: 1.000e-01, eta: 10:45:56, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.8258\n2023-01-01 18:14:09,510 - mmcls - INFO - Epoch [14][500/5005]\tlr: 1.000e-01, eta: 10:45:49, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8141\n2023-01-01 18:14:18,267 - mmcls - INFO - Epoch [14][600/5005]\tlr: 1.000e-01, eta: 10:45:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8197\n2023-01-01 18:14:26,945 - mmcls - INFO - Epoch [14][700/5005]\tlr: 1.000e-01, eta: 10:45:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8308\n2023-01-01 18:14:35,877 - mmcls - INFO - Epoch [14][800/5005]\tlr: 1.000e-01, eta: 10:45:19, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8570\n2023-01-01 18:14:44,651 - mmcls - INFO - Epoch [14][900/5005]\tlr: 1.000e-01, eta: 10:45:10, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8157\n2023-01-01 18:14:53,652 - mmcls - INFO - Epoch [14][1000/5005]\tlr: 1.000e-01, eta: 10:45:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8141\n2023-01-01 18:15:02,556 - mmcls - INFO - Epoch [14][1100/5005]\tlr: 1.000e-01, eta: 10:44:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8398\n2023-01-01 18:15:11,506 - mmcls - INFO - Epoch [14][1200/5005]\tlr: 1.000e-01, eta: 10:44:44, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8283\n2023-01-01 18:15:20,075 - mmcls - INFO - Epoch [14][1300/5005]\tlr: 1.000e-01, eta: 10:44:33, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8415\n2023-01-01 18:15:28,992 - mmcls - INFO - Epoch [14][1400/5005]\tlr: 1.000e-01, eta: 10:44:24, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8332\n2023-01-01 18:15:37,995 - mmcls - INFO - Epoch [14][1500/5005]\tlr: 1.000e-01, eta: 10:44:15, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8736\n2023-01-01 18:15:46,649 - mmcls - INFO - Epoch [14][1600/5005]\tlr: 1.000e-01, eta: 10:44:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9086\n2023-01-01 18:15:55,293 - mmcls - INFO - Epoch [14][1700/5005]\tlr: 1.000e-01, eta: 10:43:54, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8565\n2023-01-01 18:16:04,150 - mmcls - INFO - Epoch [14][1800/5005]\tlr: 1.000e-01, eta: 10:43:45, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8812\n2023-01-01 18:16:13,149 - mmcls - INFO - Epoch [14][1900/5005]\tlr: 1.000e-01, eta: 10:43:37, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8494\n2023-01-01 18:16:21,869 - mmcls - INFO - Epoch [14][2000/5005]\tlr: 1.000e-01, eta: 10:43:27, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8601\n2023-01-01 18:16:30,974 - mmcls - INFO - Epoch [14][2100/5005]\tlr: 1.000e-01, eta: 10:43:19, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.8450\n2023-01-01 18:16:39,580 - mmcls - INFO - Epoch [14][2200/5005]\tlr: 1.000e-01, eta: 10:43:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8632\n2023-01-01 18:16:48,450 - mmcls - INFO - Epoch [14][2300/5005]\tlr: 1.000e-01, eta: 10:42:59, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.8802\n2023-01-01 18:16:57,077 - mmcls - INFO - Epoch [14][2400/5005]\tlr: 1.000e-01, eta: 10:42:48, time: 0.086, data_time: 0.041, memory: 1224, loss: 2.8535\n2023-01-01 18:17:05,776 - mmcls - INFO - Epoch [14][2500/5005]\tlr: 1.000e-01, eta: 10:42:38, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.8662\n2023-01-01 18:17:14,710 - mmcls - INFO - Epoch [14][2600/5005]\tlr: 1.000e-01, eta: 10:42:29, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8997\n2023-01-01 18:17:23,267 - mmcls - INFO - Epoch [14][2700/5005]\tlr: 1.000e-01, eta: 10:42:18, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8571\n2023-01-01 18:17:32,079 - mmcls - INFO - Epoch [14][2800/5005]\tlr: 1.000e-01, eta: 10:42:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8401\n2023-01-01 18:17:41,105 - mmcls - INFO - Epoch [14][2900/5005]\tlr: 1.000e-01, eta: 10:42:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8582\n2023-01-01 18:17:49,938 - mmcls - INFO - Epoch [14][3000/5005]\tlr: 1.000e-01, eta: 10:41:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8416\n2023-01-01 18:17:59,158 - mmcls - INFO - Epoch [14][3100/5005]\tlr: 1.000e-01, eta: 10:41:44, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8693\n2023-01-01 18:18:07,769 - mmcls - INFO - Epoch [14][3200/5005]\tlr: 1.000e-01, eta: 10:41:33, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.8329\n2023-01-01 18:18:16,808 - mmcls - INFO - Epoch [14][3300/5005]\tlr: 1.000e-01, eta: 10:41:25, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.8599\n2023-01-01 18:18:26,008 - mmcls - INFO - Epoch [14][3400/5005]\tlr: 1.000e-01, eta: 10:41:18, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8525\n2023-01-01 18:18:34,896 - mmcls - INFO - Epoch [14][3500/5005]\tlr: 1.000e-01, eta: 10:41:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8555\n2023-01-01 18:18:43,933 - mmcls - INFO - Epoch [14][3600/5005]\tlr: 1.000e-01, eta: 10:41:01, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.8577\n2023-01-01 18:18:53,024 - mmcls - INFO - Epoch [14][3700/5005]\tlr: 1.000e-01, eta: 10:40:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8617\n2023-01-01 18:19:01,799 - mmcls - INFO - Epoch [14][3800/5005]\tlr: 1.000e-01, eta: 10:40:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8237\n2023-01-01 18:19:10,610 - mmcls - INFO - Epoch [14][3900/5005]\tlr: 1.000e-01, eta: 10:40:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8735\n2023-01-01 18:19:19,440 - mmcls - INFO - Epoch [14][4000/5005]\tlr: 1.000e-01, eta: 10:40:25, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8680\n2023-01-01 18:19:28,010 - mmcls - INFO - Epoch [14][4100/5005]\tlr: 1.000e-01, eta: 10:40:14, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8647\n2023-01-01 18:19:36,642 - mmcls - INFO - Epoch [14][4200/5005]\tlr: 1.000e-01, eta: 10:40:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.8996\n2023-01-01 18:19:45,606 - mmcls - INFO - Epoch [14][4300/5005]\tlr: 1.000e-01, eta: 10:39:54, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8546\n2023-01-01 18:19:54,606 - mmcls - INFO - Epoch [14][4400/5005]\tlr: 1.000e-01, eta: 10:39:46, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8890\n2023-01-01 18:20:03,710 - mmcls - INFO - Epoch [14][4500/5005]\tlr: 1.000e-01, eta: 10:39:38, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.8840\n2023-01-01 18:20:12,583 - mmcls - INFO - Epoch [14][4600/5005]\tlr: 1.000e-01, eta: 10:39:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8702\n2023-01-01 18:20:21,737 - mmcls - INFO - Epoch [14][4700/5005]\tlr: 1.000e-01, eta: 10:39:22, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.8727\n2023-01-01 18:20:30,468 - mmcls - INFO - Epoch [14][4800/5005]\tlr: 1.000e-01, eta: 10:39:12, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.8806\n2023-01-01 18:20:39,655 - mmcls - INFO - Epoch [14][4900/5005]\tlr: 1.000e-01, eta: 10:39:05, time: 0.092, data_time: 0.012, memory: 1224, loss: 2.8461\n2023-01-01 18:20:48,303 - mmcls - INFO - Epoch [14][5000/5005]\tlr: 1.000e-01, eta: 10:38:54, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8838\n2023-01-01 18:21:11,339 - mmcls - INFO - Epoch(val) [14][196]\taccuracy_top-1: 45.3740, accuracy_top-5: 70.6880\n2023-01-01 18:21:22,555 - mmcls - INFO - Epoch [15][100/5005]\tlr: 1.000e-01, eta: 10:38:56, time: 0.112, data_time: 0.056, memory: 1224, loss: 2.7827\n2023-01-01 18:21:31,378 - mmcls - INFO - Epoch [15][200/5005]\tlr: 1.000e-01, eta: 10:38:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7750\n2023-01-01 18:21:40,179 - mmcls - INFO - Epoch [15][300/5005]\tlr: 1.000e-01, eta: 10:38:37, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.7940\n2023-01-01 18:21:49,112 - mmcls - INFO - Epoch [15][400/5005]\tlr: 1.000e-01, eta: 10:38:28, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.8238\n2023-01-01 18:21:58,272 - mmcls - INFO - Epoch [15][500/5005]\tlr: 1.000e-01, eta: 10:38:21, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.8191\n2023-01-01 18:22:07,238 - mmcls - INFO - Epoch [15][600/5005]\tlr: 1.000e-01, eta: 10:38:13, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8276\n2023-01-01 18:22:15,995 - mmcls - INFO - Epoch [15][700/5005]\tlr: 1.000e-01, eta: 10:38:03, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8021\n2023-01-01 18:22:25,127 - mmcls - INFO - Epoch [15][800/5005]\tlr: 1.000e-01, eta: 10:37:55, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8141\n2023-01-01 18:22:34,012 - mmcls - INFO - Epoch [15][900/5005]\tlr: 1.000e-01, eta: 10:37:46, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.8106\n2023-01-01 18:22:42,633 - mmcls - INFO - Epoch [15][1000/5005]\tlr: 1.000e-01, eta: 10:37:35, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.8216\n2023-01-01 18:22:52,127 - mmcls - INFO - Epoch [15][1100/5005]\tlr: 1.000e-01, eta: 10:37:30, time: 0.095, data_time: 0.038, memory: 1224, loss: 2.8027\n2023-01-01 18:23:00,988 - mmcls - INFO - Epoch [15][1200/5005]\tlr: 1.000e-01, eta: 10:37:21, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.8530\n2023-01-01 18:23:09,868 - mmcls - INFO - Epoch [15][1300/5005]\tlr: 1.000e-01, eta: 10:37:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8490\n2023-01-01 18:23:18,557 - mmcls - INFO - Epoch [15][1400/5005]\tlr: 1.000e-01, eta: 10:37:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8358\n2023-01-01 18:23:27,229 - mmcls - INFO - Epoch [15][1500/5005]\tlr: 1.000e-01, eta: 10:36:51, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8385\n2023-01-01 18:23:35,992 - mmcls - INFO - Epoch [15][1600/5005]\tlr: 1.000e-01, eta: 10:36:41, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8280\n2023-01-01 18:23:44,710 - mmcls - INFO - Epoch [15][1700/5005]\tlr: 1.000e-01, eta: 10:36:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8302\n2023-01-01 18:23:53,416 - mmcls - INFO - Epoch [15][1800/5005]\tlr: 1.000e-01, eta: 10:36:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8568\n2023-01-01 18:24:02,300 - mmcls - INFO - Epoch [15][1900/5005]\tlr: 1.000e-01, eta: 10:36:12, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8121\n2023-01-01 18:24:11,093 - mmcls - INFO - Epoch [15][2000/5005]\tlr: 1.000e-01, eta: 10:36:03, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8800\n2023-01-01 18:24:20,081 - mmcls - INFO - Epoch [15][2100/5005]\tlr: 1.000e-01, eta: 10:35:54, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8038\n2023-01-01 18:24:28,978 - mmcls - INFO - Epoch [15][2200/5005]\tlr: 1.000e-01, eta: 10:35:45, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.8681\n2023-01-01 18:24:37,648 - mmcls - INFO - Epoch [15][2300/5005]\tlr: 1.000e-01, eta: 10:35:35, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.8630\n2023-01-01 18:24:46,387 - mmcls - INFO - Epoch [15][2400/5005]\tlr: 1.000e-01, eta: 10:35:25, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8245\n2023-01-01 18:24:54,972 - mmcls - INFO - Epoch [15][2500/5005]\tlr: 1.000e-01, eta: 10:35:14, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.8395\n2023-01-01 18:25:03,736 - mmcls - INFO - Epoch [15][2600/5005]\tlr: 1.000e-01, eta: 10:35:04, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8361\n2023-01-01 18:25:12,309 - mmcls - INFO - Epoch [15][2700/5005]\tlr: 1.000e-01, eta: 10:34:53, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.8797\n2023-01-01 18:25:20,935 - mmcls - INFO - Epoch [15][2800/5005]\tlr: 1.000e-01, eta: 10:34:43, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8237\n2023-01-01 18:25:29,622 - mmcls - INFO - Epoch [15][2900/5005]\tlr: 1.000e-01, eta: 10:34:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8554\n2023-01-01 18:25:38,430 - mmcls - INFO - Epoch [15][3000/5005]\tlr: 1.000e-01, eta: 10:34:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8307\n2023-01-01 18:25:47,256 - mmcls - INFO - Epoch [15][3100/5005]\tlr: 1.000e-01, eta: 10:34:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8273\n2023-01-01 18:25:56,290 - mmcls - INFO - Epoch [15][3200/5005]\tlr: 1.000e-01, eta: 10:34:06, time: 0.090, data_time: 0.027, memory: 1224, loss: 2.8749\n2023-01-01 18:26:05,095 - mmcls - INFO - Epoch [15][3300/5005]\tlr: 1.000e-01, eta: 10:33:56, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8572\n2023-01-01 18:26:13,844 - mmcls - INFO - Epoch [15][3400/5005]\tlr: 1.000e-01, eta: 10:33:46, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.8320\n2023-01-01 18:26:22,825 - mmcls - INFO - Epoch [15][3500/5005]\tlr: 1.000e-01, eta: 10:33:38, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.8556\n2023-01-01 18:26:31,872 - mmcls - INFO - Epoch [15][3600/5005]\tlr: 1.000e-01, eta: 10:33:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8330\n2023-01-01 18:26:40,707 - mmcls - INFO - Epoch [15][3700/5005]\tlr: 1.000e-01, eta: 10:33:20, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8733\n2023-01-01 18:26:49,461 - mmcls - INFO - Epoch [15][3800/5005]\tlr: 1.000e-01, eta: 10:33:11, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.8646\n2023-01-01 18:26:58,101 - mmcls - INFO - Epoch [15][3900/5005]\tlr: 1.000e-01, eta: 10:33:00, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.8307\n2023-01-01 18:27:07,076 - mmcls - INFO - Epoch [15][4000/5005]\tlr: 1.000e-01, eta: 10:32:52, time: 0.090, data_time: 0.034, memory: 1224, loss: 2.8511\n2023-01-01 18:27:15,736 - mmcls - INFO - Epoch [15][4100/5005]\tlr: 1.000e-01, eta: 10:32:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.8561\n2023-01-01 18:27:24,586 - mmcls - INFO - Epoch [15][4200/5005]\tlr: 1.000e-01, eta: 10:32:32, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.8543\n2023-01-01 18:27:33,353 - mmcls - INFO - Epoch [15][4300/5005]\tlr: 1.000e-01, eta: 10:32:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8844\n2023-01-01 18:27:42,159 - mmcls - INFO - Epoch [15][4400/5005]\tlr: 1.000e-01, eta: 10:32:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8123\n2023-01-01 18:27:51,126 - mmcls - INFO - Epoch [15][4500/5005]\tlr: 1.000e-01, eta: 10:32:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8486\n2023-01-01 18:28:00,006 - mmcls - INFO - Epoch [15][4600/5005]\tlr: 1.000e-01, eta: 10:31:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8573\n2023-01-01 18:28:08,864 - mmcls - INFO - Epoch [15][4700/5005]\tlr: 1.000e-01, eta: 10:31:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8559\n2023-01-01 18:28:17,676 - mmcls - INFO - Epoch [15][4800/5005]\tlr: 1.000e-01, eta: 10:31:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8512\n2023-01-01 18:28:26,498 - mmcls - INFO - Epoch [15][4900/5005]\tlr: 1.000e-01, eta: 10:31:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8550\n2023-01-01 18:28:35,832 - mmcls - INFO - Epoch [15][5000/5005]\tlr: 1.000e-01, eta: 10:31:21, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.8449\n2023-01-01 18:28:58,864 - mmcls - INFO - Epoch(val) [15][196]\taccuracy_top-1: 44.9880, accuracy_top-5: 70.7200\n2023-01-01 18:29:10,270 - mmcls - INFO - Epoch [16][100/5005]\tlr: 1.000e-01, eta: 10:31:23, time: 0.114, data_time: 0.030, memory: 1224, loss: 2.8095\n2023-01-01 18:29:19,001 - mmcls - INFO - Epoch [16][200/5005]\tlr: 1.000e-01, eta: 10:31:13, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7926\n2023-01-01 18:29:27,687 - mmcls - INFO - Epoch [16][300/5005]\tlr: 1.000e-01, eta: 10:31:03, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8496\n2023-01-01 18:29:36,419 - mmcls - INFO - Epoch [16][400/5005]\tlr: 1.000e-01, eta: 10:30:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7977\n2023-01-01 18:29:45,231 - mmcls - INFO - Epoch [16][500/5005]\tlr: 1.000e-01, eta: 10:30:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8101\n2023-01-01 18:29:53,882 - mmcls - INFO - Epoch [16][600/5005]\tlr: 1.000e-01, eta: 10:30:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.8143\n2023-01-01 18:30:02,783 - mmcls - INFO - Epoch [16][700/5005]\tlr: 1.000e-01, eta: 10:30:25, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.8410\n2023-01-01 18:30:11,678 - mmcls - INFO - Epoch [16][800/5005]\tlr: 1.000e-01, eta: 10:30:16, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.8496\n2023-01-01 18:30:20,643 - mmcls - INFO - Epoch [16][900/5005]\tlr: 1.000e-01, eta: 10:30:07, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8208\n2023-01-01 18:30:29,291 - mmcls - INFO - Epoch [16][1000/5005]\tlr: 1.000e-01, eta: 10:29:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8047\n2023-01-01 18:30:38,051 - mmcls - INFO - Epoch [16][1100/5005]\tlr: 1.000e-01, eta: 10:29:47, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8194\n2023-01-01 18:30:47,150 - mmcls - INFO - Epoch [16][1200/5005]\tlr: 1.000e-01, eta: 10:29:39, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8257\n2023-01-01 18:30:56,091 - mmcls - INFO - Epoch [16][1300/5005]\tlr: 1.000e-01, eta: 10:29:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8486\n2023-01-01 18:31:04,676 - mmcls - INFO - Epoch [16][1400/5005]\tlr: 1.000e-01, eta: 10:29:20, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.8375\n2023-01-01 18:31:13,499 - mmcls - INFO - Epoch [16][1500/5005]\tlr: 1.000e-01, eta: 10:29:10, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8230\n2023-01-01 18:31:22,280 - mmcls - INFO - Epoch [16][1600/5005]\tlr: 1.000e-01, eta: 10:29:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8115\n2023-01-01 18:31:30,996 - mmcls - INFO - Epoch [16][1700/5005]\tlr: 1.000e-01, eta: 10:28:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8087\n2023-01-01 18:31:40,346 - mmcls - INFO - Epoch [16][1800/5005]\tlr: 1.000e-01, eta: 10:28:44, time: 0.093, data_time: 0.029, memory: 1224, loss: 2.8086\n2023-01-01 18:31:49,320 - mmcls - INFO - Epoch [16][1900/5005]\tlr: 1.000e-01, eta: 10:28:36, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7935\n2023-01-01 18:31:58,031 - mmcls - INFO - Epoch [16][2000/5005]\tlr: 1.000e-01, eta: 10:28:26, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8225\n2023-01-01 18:32:06,890 - mmcls - INFO - Epoch [16][2100/5005]\tlr: 1.000e-01, eta: 10:28:17, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8718\n2023-01-01 18:32:15,762 - mmcls - INFO - Epoch [16][2200/5005]\tlr: 1.000e-01, eta: 10:28:08, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.8225\n2023-01-01 18:32:24,715 - mmcls - INFO - Epoch [16][2300/5005]\tlr: 1.000e-01, eta: 10:27:59, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8237\n2023-01-01 18:32:33,347 - mmcls - INFO - Epoch [16][2400/5005]\tlr: 1.000e-01, eta: 10:27:49, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8811\n2023-01-01 18:32:42,021 - mmcls - INFO - Epoch [16][2500/5005]\tlr: 1.000e-01, eta: 10:27:38, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8028\n2023-01-01 18:32:50,809 - mmcls - INFO - Epoch [16][2600/5005]\tlr: 1.000e-01, eta: 10:27:29, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8383\n2023-01-01 18:32:59,567 - mmcls - INFO - Epoch [16][2700/5005]\tlr: 1.000e-01, eta: 10:27:19, time: 0.088, data_time: 0.029, memory: 1224, loss: 2.8618\n2023-01-01 18:33:08,183 - mmcls - INFO - Epoch [16][2800/5005]\tlr: 1.000e-01, eta: 10:27:09, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8458\n2023-01-01 18:33:17,242 - mmcls - INFO - Epoch [16][2900/5005]\tlr: 1.000e-01, eta: 10:27:01, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.8384\n2023-01-01 18:33:26,094 - mmcls - INFO - Epoch [16][3000/5005]\tlr: 1.000e-01, eta: 10:26:51, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.8183\n2023-01-01 18:33:34,664 - mmcls - INFO - Epoch [16][3100/5005]\tlr: 1.000e-01, eta: 10:26:41, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.8433\n2023-01-01 18:33:43,886 - mmcls - INFO - Epoch [16][3200/5005]\tlr: 1.000e-01, eta: 10:26:34, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8285\n2023-01-01 18:33:52,998 - mmcls - INFO - Epoch [16][3300/5005]\tlr: 1.000e-01, eta: 10:26:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8167\n2023-01-01 18:34:01,682 - mmcls - INFO - Epoch [16][3400/5005]\tlr: 1.000e-01, eta: 10:26:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8381\n2023-01-01 18:34:10,534 - mmcls - INFO - Epoch [16][3500/5005]\tlr: 1.000e-01, eta: 10:26:06, time: 0.088, data_time: 0.046, memory: 1224, loss: 2.8302\n2023-01-01 18:34:19,423 - mmcls - INFO - Epoch [16][3600/5005]\tlr: 1.000e-01, eta: 10:25:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8409\n2023-01-01 18:34:28,142 - mmcls - INFO - Epoch [16][3700/5005]\tlr: 1.000e-01, eta: 10:25:48, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8514\n2023-01-01 18:34:37,003 - mmcls - INFO - Epoch [16][3800/5005]\tlr: 1.000e-01, eta: 10:25:38, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.8885\n2023-01-01 18:34:45,698 - mmcls - INFO - Epoch [16][3900/5005]\tlr: 1.000e-01, eta: 10:25:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8436\n2023-01-01 18:34:54,538 - mmcls - INFO - Epoch [16][4000/5005]\tlr: 1.000e-01, eta: 10:25:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8959\n2023-01-01 18:35:03,438 - mmcls - INFO - Epoch [16][4100/5005]\tlr: 1.000e-01, eta: 10:25:10, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.8188\n2023-01-01 18:35:12,293 - mmcls - INFO - Epoch [16][4200/5005]\tlr: 1.000e-01, eta: 10:25:01, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.8424\n2023-01-01 18:35:21,314 - mmcls - INFO - Epoch [16][4300/5005]\tlr: 1.000e-01, eta: 10:24:53, time: 0.090, data_time: 0.024, memory: 1224, loss: 2.8453\n2023-01-01 18:35:30,097 - mmcls - INFO - Epoch [16][4400/5005]\tlr: 1.000e-01, eta: 10:24:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8293\n2023-01-01 18:35:39,055 - mmcls - INFO - Epoch [16][4500/5005]\tlr: 1.000e-01, eta: 10:24:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8284\n2023-01-01 18:35:47,733 - mmcls - INFO - Epoch [16][4600/5005]\tlr: 1.000e-01, eta: 10:24:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8238\n2023-01-01 18:35:56,549 - mmcls - INFO - Epoch [16][4700/5005]\tlr: 1.000e-01, eta: 10:24:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8464\n2023-01-01 18:36:05,587 - mmcls - INFO - Epoch [16][4800/5005]\tlr: 1.000e-01, eta: 10:24:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8328\n2023-01-01 18:36:14,327 - mmcls - INFO - Epoch [16][4900/5005]\tlr: 1.000e-01, eta: 10:23:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8280\n2023-01-01 18:36:23,038 - mmcls - INFO - Epoch [16][5000/5005]\tlr: 1.000e-01, eta: 10:23:47, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.8785\n2023-01-01 18:36:46,223 - mmcls - INFO - Epoch(val) [16][196]\taccuracy_top-1: 44.7660, accuracy_top-5: 70.1300\n2023-01-01 18:36:57,219 - mmcls - INFO - Epoch [17][100/5005]\tlr: 1.000e-01, eta: 10:23:46, time: 0.110, data_time: 0.053, memory: 1224, loss: 2.7977\n2023-01-01 18:37:05,865 - mmcls - INFO - Epoch [17][200/5005]\tlr: 1.000e-01, eta: 10:23:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7951\n2023-01-01 18:37:14,492 - mmcls - INFO - Epoch [17][300/5005]\tlr: 1.000e-01, eta: 10:23:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7738\n2023-01-01 18:37:23,201 - mmcls - INFO - Epoch [17][400/5005]\tlr: 1.000e-01, eta: 10:23:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7861\n2023-01-01 18:37:32,045 - mmcls - INFO - Epoch [17][500/5005]\tlr: 1.000e-01, eta: 10:23:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7930\n2023-01-01 18:37:40,971 - mmcls - INFO - Epoch [17][600/5005]\tlr: 1.000e-01, eta: 10:22:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7740\n2023-01-01 18:37:50,097 - mmcls - INFO - Epoch [17][700/5005]\tlr: 1.000e-01, eta: 10:22:50, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7935\n2023-01-01 18:37:58,872 - mmcls - INFO - Epoch [17][800/5005]\tlr: 1.000e-01, eta: 10:22:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8047\n2023-01-01 18:38:07,508 - mmcls - INFO - Epoch [17][900/5005]\tlr: 1.000e-01, eta: 10:22:30, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.7949\n2023-01-01 18:38:16,308 - mmcls - INFO - Epoch [17][1000/5005]\tlr: 1.000e-01, eta: 10:22:21, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8195\n2023-01-01 18:38:25,448 - mmcls - INFO - Epoch [17][1100/5005]\tlr: 1.000e-01, eta: 10:22:13, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8391\n2023-01-01 18:38:33,866 - mmcls - INFO - Epoch [17][1200/5005]\tlr: 1.000e-01, eta: 10:22:02, time: 0.084, data_time: 0.001, memory: 1224, loss: 2.7852\n2023-01-01 18:38:43,149 - mmcls - INFO - Epoch [17][1300/5005]\tlr: 1.000e-01, eta: 10:21:55, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.7957\n2023-01-01 18:38:51,799 - mmcls - INFO - Epoch [17][1400/5005]\tlr: 1.000e-01, eta: 10:21:45, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8006\n2023-01-01 18:39:00,697 - mmcls - INFO - Epoch [17][1500/5005]\tlr: 1.000e-01, eta: 10:21:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8105\n2023-01-01 18:39:09,892 - mmcls - INFO - Epoch [17][1600/5005]\tlr: 1.000e-01, eta: 10:21:28, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7843\n2023-01-01 18:39:18,710 - mmcls - INFO - Epoch [17][1700/5005]\tlr: 1.000e-01, eta: 10:21:19, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8147\n2023-01-01 18:39:27,421 - mmcls - INFO - Epoch [17][1800/5005]\tlr: 1.000e-01, eta: 10:21:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8156\n2023-01-01 18:39:36,344 - mmcls - INFO - Epoch [17][1900/5005]\tlr: 1.000e-01, eta: 10:21:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8213\n2023-01-01 18:39:44,973 - mmcls - INFO - Epoch [17][2000/5005]\tlr: 1.000e-01, eta: 10:20:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8667\n2023-01-01 18:39:53,846 - mmcls - INFO - Epoch [17][2100/5005]\tlr: 1.000e-01, eta: 10:20:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8406\n2023-01-01 18:40:02,657 - mmcls - INFO - Epoch [17][2200/5005]\tlr: 1.000e-01, eta: 10:20:31, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8108\n2023-01-01 18:40:11,283 - mmcls - INFO - Epoch [17][2300/5005]\tlr: 1.000e-01, eta: 10:20:21, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7983\n2023-01-01 18:40:19,897 - mmcls - INFO - Epoch [17][2400/5005]\tlr: 1.000e-01, eta: 10:20:11, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.8337\n2023-01-01 18:40:28,656 - mmcls - INFO - Epoch [17][2500/5005]\tlr: 1.000e-01, eta: 10:20:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8307\n2023-01-01 18:40:37,335 - mmcls - INFO - Epoch [17][2600/5005]\tlr: 1.000e-01, eta: 10:19:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8125\n2023-01-01 18:40:46,215 - mmcls - INFO - Epoch [17][2700/5005]\tlr: 1.000e-01, eta: 10:19:42, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8037\n2023-01-01 18:40:54,979 - mmcls - INFO - Epoch [17][2800/5005]\tlr: 1.000e-01, eta: 10:19:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8419\n2023-01-01 18:41:03,890 - mmcls - INFO - Epoch [17][2900/5005]\tlr: 1.000e-01, eta: 10:19:24, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.8006\n2023-01-01 18:41:12,608 - mmcls - INFO - Epoch [17][3000/5005]\tlr: 1.000e-01, eta: 10:19:14, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.8589\n2023-01-01 18:41:21,308 - mmcls - INFO - Epoch [17][3100/5005]\tlr: 1.000e-01, eta: 10:19:04, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.8310\n2023-01-01 18:41:30,132 - mmcls - INFO - Epoch [17][3200/5005]\tlr: 1.000e-01, eta: 10:18:55, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8312\n2023-01-01 18:41:38,928 - mmcls - INFO - Epoch [17][3300/5005]\tlr: 1.000e-01, eta: 10:18:45, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8254\n2023-01-01 18:41:48,058 - mmcls - INFO - Epoch [17][3400/5005]\tlr: 1.000e-01, eta: 10:18:37, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.8047\n2023-01-01 18:41:57,111 - mmcls - INFO - Epoch [17][3500/5005]\tlr: 1.000e-01, eta: 10:18:29, time: 0.091, data_time: 0.036, memory: 1224, loss: 2.8272\n2023-01-01 18:42:06,276 - mmcls - INFO - Epoch [17][3600/5005]\tlr: 1.000e-01, eta: 10:18:22, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.8269\n2023-01-01 18:42:15,053 - mmcls - INFO - Epoch [17][3700/5005]\tlr: 1.000e-01, eta: 10:18:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8375\n2023-01-01 18:42:23,904 - mmcls - INFO - Epoch [17][3800/5005]\tlr: 1.000e-01, eta: 10:18:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8553\n2023-01-01 18:42:32,797 - mmcls - INFO - Epoch [17][3900/5005]\tlr: 1.000e-01, eta: 10:17:54, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8496\n2023-01-01 18:42:41,770 - mmcls - INFO - Epoch [17][4000/5005]\tlr: 1.000e-01, eta: 10:17:45, time: 0.090, data_time: 0.038, memory: 1224, loss: 2.7984\n2023-01-01 18:42:50,312 - mmcls - INFO - Epoch [17][4100/5005]\tlr: 1.000e-01, eta: 10:17:35, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.8005\n2023-01-01 18:42:59,047 - mmcls - INFO - Epoch [17][4200/5005]\tlr: 1.000e-01, eta: 10:17:25, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8140\n2023-01-01 18:43:07,743 - mmcls - INFO - Epoch [17][4300/5005]\tlr: 1.000e-01, eta: 10:17:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8645\n2023-01-01 18:43:16,578 - mmcls - INFO - Epoch [17][4400/5005]\tlr: 1.000e-01, eta: 10:17:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8705\n2023-01-01 18:43:25,228 - mmcls - INFO - Epoch [17][4500/5005]\tlr: 1.000e-01, eta: 10:16:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8311\n2023-01-01 18:43:34,006 - mmcls - INFO - Epoch [17][4600/5005]\tlr: 1.000e-01, eta: 10:16:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8238\n2023-01-01 18:43:42,880 - mmcls - INFO - Epoch [17][4700/5005]\tlr: 1.000e-01, eta: 10:16:37, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8192\n2023-01-01 18:43:52,014 - mmcls - INFO - Epoch [17][4800/5005]\tlr: 1.000e-01, eta: 10:16:30, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8418\n2023-01-01 18:44:00,890 - mmcls - INFO - Epoch [17][4900/5005]\tlr: 1.000e-01, eta: 10:16:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8147\n2023-01-01 18:44:09,974 - mmcls - INFO - Epoch [17][5000/5005]\tlr: 1.000e-01, eta: 10:16:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8275\n2023-01-01 18:44:32,758 - mmcls - INFO - Epoch(val) [17][196]\taccuracy_top-1: 45.6880, accuracy_top-5: 71.0200\n2023-01-01 18:44:43,769 - mmcls - INFO - Epoch [18][100/5005]\tlr: 1.000e-01, eta: 10:16:11, time: 0.110, data_time: 0.044, memory: 1224, loss: 2.7641\n2023-01-01 18:44:52,566 - mmcls - INFO - Epoch [18][200/5005]\tlr: 1.000e-01, eta: 10:16:02, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.7670\n2023-01-01 18:45:01,538 - mmcls - INFO - Epoch [18][300/5005]\tlr: 1.000e-01, eta: 10:15:53, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.7539\n2023-01-01 18:45:09,933 - mmcls - INFO - Epoch [18][400/5005]\tlr: 1.000e-01, eta: 10:15:42, time: 0.084, data_time: 0.006, memory: 1224, loss: 2.7645\n2023-01-01 18:45:18,877 - mmcls - INFO - Epoch [18][500/5005]\tlr: 1.000e-01, eta: 10:15:33, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7902\n2023-01-01 18:45:27,808 - mmcls - INFO - Epoch [18][600/5005]\tlr: 1.000e-01, eta: 10:15:24, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7845\n2023-01-01 18:45:36,488 - mmcls - INFO - Epoch [18][700/5005]\tlr: 1.000e-01, eta: 10:15:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8050\n2023-01-01 18:45:45,242 - mmcls - INFO - Epoch [18][800/5005]\tlr: 1.000e-01, eta: 10:15:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8243\n2023-01-01 18:45:53,940 - mmcls - INFO - Epoch [18][900/5005]\tlr: 1.000e-01, eta: 10:14:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8323\n2023-01-01 18:46:02,639 - mmcls - INFO - Epoch [18][1000/5005]\tlr: 1.000e-01, eta: 10:14:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8035\n2023-01-01 18:46:11,615 - mmcls - INFO - Epoch [18][1100/5005]\tlr: 1.000e-01, eta: 10:14:36, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8227\n2023-01-01 18:46:20,765 - mmcls - INFO - Epoch [18][1200/5005]\tlr: 1.000e-01, eta: 10:14:29, time: 0.092, data_time: 0.022, memory: 1224, loss: 2.8041\n2023-01-01 18:46:29,498 - mmcls - INFO - Epoch [18][1300/5005]\tlr: 1.000e-01, eta: 10:14:19, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.8561\n2023-01-01 18:46:38,437 - mmcls - INFO - Epoch [18][1400/5005]\tlr: 1.000e-01, eta: 10:14:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8598\n2023-01-01 18:46:47,076 - mmcls - INFO - Epoch [18][1500/5005]\tlr: 1.000e-01, eta: 10:14:00, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8182\n2023-01-01 18:46:56,428 - mmcls - INFO - Epoch [18][1600/5005]\tlr: 1.000e-01, eta: 10:13:53, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.8196\n2023-01-01 18:47:05,376 - mmcls - INFO - Epoch [18][1700/5005]\tlr: 1.000e-01, eta: 10:13:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8133\n2023-01-01 18:47:14,091 - mmcls - INFO - Epoch [18][1800/5005]\tlr: 1.000e-01, eta: 10:13:35, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.8085\n2023-01-01 18:47:22,758 - mmcls - INFO - Epoch [18][1900/5005]\tlr: 1.000e-01, eta: 10:13:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8420\n2023-01-01 18:47:31,511 - mmcls - INFO - Epoch [18][2000/5005]\tlr: 1.000e-01, eta: 10:13:15, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8074\n2023-01-01 18:47:40,208 - mmcls - INFO - Epoch [18][2100/5005]\tlr: 1.000e-01, eta: 10:13:05, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7817\n2023-01-01 18:47:49,158 - mmcls - INFO - Epoch [18][2200/5005]\tlr: 1.000e-01, eta: 10:12:57, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8264\n2023-01-01 18:47:57,580 - mmcls - INFO - Epoch [18][2300/5005]\tlr: 1.000e-01, eta: 10:12:46, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.8268\n2023-01-01 18:48:06,359 - mmcls - INFO - Epoch [18][2400/5005]\tlr: 1.000e-01, eta: 10:12:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8124\n2023-01-01 18:48:15,103 - mmcls - INFO - Epoch [18][2500/5005]\tlr: 1.000e-01, eta: 10:12:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7739\n2023-01-01 18:48:23,914 - mmcls - INFO - Epoch [18][2600/5005]\tlr: 1.000e-01, eta: 10:12:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8010\n2023-01-01 18:48:32,588 - mmcls - INFO - Epoch [18][2700/5005]\tlr: 1.000e-01, eta: 10:12:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8083\n2023-01-01 18:48:41,295 - mmcls - INFO - Epoch [18][2800/5005]\tlr: 1.000e-01, eta: 10:11:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8157\n2023-01-01 18:48:50,621 - mmcls - INFO - Epoch [18][2900/5005]\tlr: 1.000e-01, eta: 10:11:51, time: 0.093, data_time: 0.018, memory: 1224, loss: 2.8425\n2023-01-01 18:48:59,466 - mmcls - INFO - Epoch [18][3000/5005]\tlr: 1.000e-01, eta: 10:11:41, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8061\n2023-01-01 18:49:08,640 - mmcls - INFO - Epoch [18][3100/5005]\tlr: 1.000e-01, eta: 10:11:34, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8293\n2023-01-01 18:49:17,763 - mmcls - INFO - Epoch [18][3200/5005]\tlr: 1.000e-01, eta: 10:11:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8266\n2023-01-01 18:49:26,484 - mmcls - INFO - Epoch [18][3300/5005]\tlr: 1.000e-01, eta: 10:11:16, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8096\n2023-01-01 18:49:35,343 - mmcls - INFO - Epoch [18][3400/5005]\tlr: 1.000e-01, eta: 10:11:07, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.0121\n2023-01-01 18:49:44,134 - mmcls - INFO - Epoch [18][3500/5005]\tlr: 1.000e-01, eta: 10:10:58, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8624\n2023-01-01 18:49:53,033 - mmcls - INFO - Epoch [18][3600/5005]\tlr: 1.000e-01, eta: 10:10:49, time: 0.089, data_time: 0.037, memory: 1224, loss: 2.8598\n2023-01-01 18:50:01,728 - mmcls - INFO - Epoch [18][3700/5005]\tlr: 1.000e-01, eta: 10:10:39, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8362\n2023-01-01 18:50:10,607 - mmcls - INFO - Epoch [18][3800/5005]\tlr: 1.000e-01, eta: 10:10:30, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.8493\n2023-01-01 18:50:19,260 - mmcls - INFO - Epoch [18][3900/5005]\tlr: 1.000e-01, eta: 10:10:20, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8323\n2023-01-01 18:50:28,246 - mmcls - INFO - Epoch [18][4000/5005]\tlr: 1.000e-01, eta: 10:10:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8438\n2023-01-01 18:50:36,992 - mmcls - INFO - Epoch [18][4100/5005]\tlr: 1.000e-01, eta: 10:10:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8233\n2023-01-01 18:50:45,609 - mmcls - INFO - Epoch [18][4200/5005]\tlr: 1.000e-01, eta: 10:09:52, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8365\n2023-01-01 18:50:54,474 - mmcls - INFO - Epoch [18][4300/5005]\tlr: 1.000e-01, eta: 10:09:42, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7948\n2023-01-01 18:51:03,288 - mmcls - INFO - Epoch [18][4400/5005]\tlr: 1.000e-01, eta: 10:09:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8139\n2023-01-01 18:51:12,061 - mmcls - INFO - Epoch [18][4500/5005]\tlr: 1.000e-01, eta: 10:09:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8378\n2023-01-01 18:51:21,086 - mmcls - INFO - Epoch [18][4600/5005]\tlr: 1.000e-01, eta: 10:09:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8230\n2023-01-01 18:51:29,938 - mmcls - INFO - Epoch [18][4700/5005]\tlr: 1.000e-01, eta: 10:09:06, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8449\n2023-01-01 18:51:39,217 - mmcls - INFO - Epoch [18][4800/5005]\tlr: 1.000e-01, eta: 10:08:59, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.8339\n2023-01-01 18:51:48,185 - mmcls - INFO - Epoch [18][4900/5005]\tlr: 1.000e-01, eta: 10:08:51, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.8216\n2023-01-01 18:51:56,894 - mmcls - INFO - Epoch [18][5000/5005]\tlr: 1.000e-01, eta: 10:08:41, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8588\n2023-01-01 18:52:19,940 - mmcls - INFO - Epoch(val) [18][196]\taccuracy_top-1: 45.6100, accuracy_top-5: 70.6080\n2023-01-01 18:52:31,124 - mmcls - INFO - Epoch [19][100/5005]\tlr: 1.000e-01, eta: 10:08:40, time: 0.112, data_time: 0.050, memory: 1224, loss: 2.7749\n2023-01-01 18:52:39,642 - mmcls - INFO - Epoch [19][200/5005]\tlr: 1.000e-01, eta: 10:08:29, time: 0.085, data_time: 0.028, memory: 1224, loss: 2.7703\n2023-01-01 18:52:48,688 - mmcls - INFO - Epoch [19][300/5005]\tlr: 1.000e-01, eta: 10:08:21, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7471\n2023-01-01 18:52:57,359 - mmcls - INFO - Epoch [19][400/5005]\tlr: 1.000e-01, eta: 10:08:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7805\n2023-01-01 18:53:06,218 - mmcls - INFO - Epoch [19][500/5005]\tlr: 1.000e-01, eta: 10:08:02, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7727\n2023-01-01 18:53:14,762 - mmcls - INFO - Epoch [19][600/5005]\tlr: 1.000e-01, eta: 10:07:51, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.7893\n2023-01-01 18:53:23,395 - mmcls - INFO - Epoch [19][700/5005]\tlr: 1.000e-01, eta: 10:07:41, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8259\n2023-01-01 18:53:32,236 - mmcls - INFO - Epoch [19][800/5005]\tlr: 1.000e-01, eta: 10:07:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7705\n2023-01-01 18:53:41,338 - mmcls - INFO - Epoch [19][900/5005]\tlr: 1.000e-01, eta: 10:07:24, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.8097\n2023-01-01 18:53:50,233 - mmcls - INFO - Epoch [19][1000/5005]\tlr: 1.000e-01, eta: 10:07:15, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7955\n2023-01-01 18:53:59,089 - mmcls - INFO - Epoch [19][1100/5005]\tlr: 1.000e-01, eta: 10:07:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8413\n2023-01-01 18:54:07,937 - mmcls - INFO - Epoch [19][1200/5005]\tlr: 1.000e-01, eta: 10:06:57, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8032\n2023-01-01 18:54:16,778 - mmcls - INFO - Epoch [19][1300/5005]\tlr: 1.000e-01, eta: 10:06:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7775\n2023-01-01 18:54:25,377 - mmcls - INFO - Epoch [19][1400/5005]\tlr: 1.000e-01, eta: 10:06:37, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.7895\n2023-01-01 18:54:34,913 - mmcls - INFO - Epoch [19][1500/5005]\tlr: 1.000e-01, eta: 10:06:31, time: 0.095, data_time: 0.005, memory: 1224, loss: 2.7762\n2023-01-01 18:54:43,975 - mmcls - INFO - Epoch [19][1600/5005]\tlr: 1.000e-01, eta: 10:06:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8146\n2023-01-01 18:54:52,570 - mmcls - INFO - Epoch [19][1700/5005]\tlr: 1.000e-01, eta: 10:06:13, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8036\n2023-01-01 18:55:01,494 - mmcls - INFO - Epoch [19][1800/5005]\tlr: 1.000e-01, eta: 10:06:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7734\n2023-01-01 18:55:10,275 - mmcls - INFO - Epoch [19][1900/5005]\tlr: 1.000e-01, eta: 10:05:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8093\n2023-01-01 18:55:19,194 - mmcls - INFO - Epoch [19][2000/5005]\tlr: 1.000e-01, eta: 10:05:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7880\n2023-01-01 18:55:27,931 - mmcls - INFO - Epoch [19][2100/5005]\tlr: 1.000e-01, eta: 10:05:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8012\n2023-01-01 18:55:36,600 - mmcls - INFO - Epoch [19][2200/5005]\tlr: 1.000e-01, eta: 10:05:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7891\n2023-01-01 18:55:45,301 - mmcls - INFO - Epoch [19][2300/5005]\tlr: 1.000e-01, eta: 10:05:17, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.8284\n2023-01-01 18:55:54,167 - mmcls - INFO - Epoch [19][2400/5005]\tlr: 1.000e-01, eta: 10:05:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.8413\n2023-01-01 18:56:02,919 - mmcls - INFO - Epoch [19][2500/5005]\tlr: 1.000e-01, eta: 10:04:58, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.8005\n2023-01-01 18:56:11,719 - mmcls - INFO - Epoch [19][2600/5005]\tlr: 1.000e-01, eta: 10:04:49, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8138\n2023-01-01 18:56:20,541 - mmcls - INFO - Epoch [19][2700/5005]\tlr: 1.000e-01, eta: 10:04:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7943\n2023-01-01 18:56:29,244 - mmcls - INFO - Epoch [19][2800/5005]\tlr: 1.000e-01, eta: 10:04:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7979\n2023-01-01 18:56:38,019 - mmcls - INFO - Epoch [19][2900/5005]\tlr: 1.000e-01, eta: 10:04:20, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8026\n2023-01-01 18:56:47,031 - mmcls - INFO - Epoch [19][3000/5005]\tlr: 1.000e-01, eta: 10:04:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8449\n2023-01-01 18:56:55,812 - mmcls - INFO - Epoch [19][3100/5005]\tlr: 1.000e-01, eta: 10:04:02, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8306\n2023-01-01 18:57:04,672 - mmcls - INFO - Epoch [19][3200/5005]\tlr: 1.000e-01, eta: 10:03:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8141\n2023-01-01 18:57:13,602 - mmcls - INFO - Epoch [19][3300/5005]\tlr: 1.000e-01, eta: 10:03:45, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8094\n2023-01-01 18:57:22,336 - mmcls - INFO - Epoch [19][3400/5005]\tlr: 1.000e-01, eta: 10:03:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8063\n2023-01-01 18:57:31,374 - mmcls - INFO - Epoch [19][3500/5005]\tlr: 1.000e-01, eta: 10:03:27, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.7800\n2023-01-01 18:57:39,926 - mmcls - INFO - Epoch [19][3600/5005]\tlr: 1.000e-01, eta: 10:03:16, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.8063\n2023-01-01 18:57:48,838 - mmcls - INFO - Epoch [19][3700/5005]\tlr: 1.000e-01, eta: 10:03:07, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7726\n2023-01-01 18:57:57,957 - mmcls - INFO - Epoch [19][3800/5005]\tlr: 1.000e-01, eta: 10:02:59, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.8006\n2023-01-01 18:58:06,724 - mmcls - INFO - Epoch [19][3900/5005]\tlr: 1.000e-01, eta: 10:02:50, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.7841\n2023-01-01 18:58:15,574 - mmcls - INFO - Epoch [19][4000/5005]\tlr: 1.000e-01, eta: 10:02:41, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.7753\n2023-01-01 18:58:24,481 - mmcls - INFO - Epoch [19][4100/5005]\tlr: 1.000e-01, eta: 10:02:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7995\n2023-01-01 18:58:33,568 - mmcls - INFO - Epoch [19][4200/5005]\tlr: 1.000e-01, eta: 10:02:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8641\n2023-01-01 18:58:42,465 - mmcls - INFO - Epoch [19][4300/5005]\tlr: 1.000e-01, eta: 10:02:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.8459\n2023-01-01 18:58:51,214 - mmcls - INFO - Epoch [19][4400/5005]\tlr: 1.000e-01, eta: 10:02:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8239\n2023-01-01 18:58:59,995 - mmcls - INFO - Epoch [19][4500/5005]\tlr: 1.000e-01, eta: 10:01:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8008\n2023-01-01 18:59:09,161 - mmcls - INFO - Epoch [19][4600/5005]\tlr: 1.000e-01, eta: 10:01:48, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8140\n2023-01-01 18:59:17,727 - mmcls - INFO - Epoch [19][4700/5005]\tlr: 1.000e-01, eta: 10:01:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8113\n2023-01-01 18:59:26,499 - mmcls - INFO - Epoch [19][4800/5005]\tlr: 1.000e-01, eta: 10:01:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7750\n2023-01-01 18:59:35,551 - mmcls - INFO - Epoch [19][4900/5005]\tlr: 1.000e-01, eta: 10:01:20, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.8337\n2023-01-01 18:59:44,109 - mmcls - INFO - Epoch [19][5000/5005]\tlr: 1.000e-01, eta: 10:01:10, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.8160\n2023-01-01 19:00:07,011 - mmcls - INFO - Epoch(val) [19][196]\taccuracy_top-1: 45.4540, accuracy_top-5: 70.8540\n2023-01-01 19:00:18,086 - mmcls - INFO - Epoch [20][100/5005]\tlr: 1.000e-01, eta: 10:01:08, time: 0.111, data_time: 0.060, memory: 1224, loss: 2.7599\n2023-01-01 19:00:26,845 - mmcls - INFO - Epoch [20][200/5005]\tlr: 1.000e-01, eta: 10:00:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7864\n2023-01-01 19:00:35,373 - mmcls - INFO - Epoch [20][300/5005]\tlr: 1.000e-01, eta: 10:00:48, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.7772\n2023-01-01 19:00:44,184 - mmcls - INFO - Epoch [20][400/5005]\tlr: 1.000e-01, eta: 10:00:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7764\n2023-01-01 19:00:53,307 - mmcls - INFO - Epoch [20][500/5005]\tlr: 1.000e-01, eta: 10:00:31, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.7466\n2023-01-01 19:01:02,342 - mmcls - INFO - Epoch [20][600/5005]\tlr: 1.000e-01, eta: 10:00:22, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.7797\n2023-01-01 19:01:11,033 - mmcls - INFO - Epoch [20][700/5005]\tlr: 1.000e-01, eta: 10:00:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7599\n2023-01-01 19:01:20,044 - mmcls - INFO - Epoch [20][800/5005]\tlr: 1.000e-01, eta: 10:00:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7714\n2023-01-01 19:01:28,735 - mmcls - INFO - Epoch [20][900/5005]\tlr: 1.000e-01, eta: 9:59:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8392\n2023-01-01 19:01:37,389 - mmcls - INFO - Epoch [20][1000/5005]\tlr: 1.000e-01, eta: 9:59:44, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7852\n2023-01-01 19:01:46,018 - mmcls - INFO - Epoch [20][1100/5005]\tlr: 1.000e-01, eta: 9:59:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8574\n2023-01-01 19:01:55,473 - mmcls - INFO - Epoch [20][1200/5005]\tlr: 1.000e-01, eta: 9:59:28, time: 0.094, data_time: 0.002, memory: 1224, loss: 2.7973\n2023-01-01 19:02:03,920 - mmcls - INFO - Epoch [20][1300/5005]\tlr: 1.000e-01, eta: 9:59:17, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.7977\n2023-01-01 19:02:12,439 - mmcls - INFO - Epoch [20][1400/5005]\tlr: 1.000e-01, eta: 9:59:07, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.8222\n2023-01-01 19:02:21,561 - mmcls - INFO - Epoch [20][1500/5005]\tlr: 1.000e-01, eta: 9:58:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8064\n2023-01-01 19:02:30,543 - mmcls - INFO - Epoch [20][1600/5005]\tlr: 1.000e-01, eta: 9:58:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8148\n2023-01-01 19:02:39,574 - mmcls - INFO - Epoch [20][1700/5005]\tlr: 1.000e-01, eta: 9:58:42, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.8034\n2023-01-01 19:02:48,347 - mmcls - INFO - Epoch [20][1800/5005]\tlr: 1.000e-01, eta: 9:58:32, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8134\n2023-01-01 19:02:57,028 - mmcls - INFO - Epoch [20][1900/5005]\tlr: 1.000e-01, eta: 9:58:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8395\n2023-01-01 19:03:05,574 - mmcls - INFO - Epoch [20][2000/5005]\tlr: 1.000e-01, eta: 9:58:12, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.8156\n2023-01-01 19:03:15,375 - mmcls - INFO - Epoch [20][2100/5005]\tlr: 1.000e-01, eta: 9:58:07, time: 0.098, data_time: 0.046, memory: 1224, loss: 2.8219\n2023-01-01 19:03:24,278 - mmcls - INFO - Epoch [20][2200/5005]\tlr: 1.000e-01, eta: 9:57:58, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.7853\n2023-01-01 19:03:33,017 - mmcls - INFO - Epoch [20][2300/5005]\tlr: 1.000e-01, eta: 9:57:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8123\n2023-01-01 19:03:41,729 - mmcls - INFO - Epoch [20][2400/5005]\tlr: 1.000e-01, eta: 9:57:39, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.8268\n2023-01-01 19:03:50,512 - mmcls - INFO - Epoch [20][2500/5005]\tlr: 1.000e-01, eta: 9:57:29, time: 0.088, data_time: 0.035, memory: 1224, loss: 2.8017\n2023-01-01 19:03:59,116 - mmcls - INFO - Epoch [20][2600/5005]\tlr: 1.000e-01, eta: 9:57:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8310\n2023-01-01 19:04:07,806 - mmcls - INFO - Epoch [20][2700/5005]\tlr: 1.000e-01, eta: 9:57:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8070\n2023-01-01 19:04:16,858 - mmcls - INFO - Epoch [20][2800/5005]\tlr: 1.000e-01, eta: 9:57:01, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.8061\n2023-01-01 19:04:25,769 - mmcls - INFO - Epoch [20][2900/5005]\tlr: 1.000e-01, eta: 9:56:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7847\n2023-01-01 19:04:34,585 - mmcls - INFO - Epoch [20][3000/5005]\tlr: 1.000e-01, eta: 9:56:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8043\n2023-01-01 19:04:43,210 - mmcls - INFO - Epoch [20][3100/5005]\tlr: 1.000e-01, eta: 9:56:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7938\n2023-01-01 19:04:52,156 - mmcls - INFO - Epoch [20][3200/5005]\tlr: 1.000e-01, eta: 9:56:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8047\n2023-01-01 19:05:01,392 - mmcls - INFO - Epoch [20][3300/5005]\tlr: 1.000e-01, eta: 9:56:17, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.8220\n2023-01-01 19:05:10,138 - mmcls - INFO - Epoch [20][3400/5005]\tlr: 1.000e-01, eta: 9:56:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7974\n2023-01-01 19:05:18,742 - mmcls - INFO - Epoch [20][3500/5005]\tlr: 1.000e-01, eta: 9:55:57, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.7968\n2023-01-01 19:05:27,560 - mmcls - INFO - Epoch [20][3600/5005]\tlr: 1.000e-01, eta: 9:55:48, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.8283\n2023-01-01 19:05:36,777 - mmcls - INFO - Epoch [20][3700/5005]\tlr: 1.000e-01, eta: 9:55:41, time: 0.092, data_time: 0.012, memory: 1224, loss: 2.8379\n2023-01-01 19:05:45,520 - mmcls - INFO - Epoch [20][3800/5005]\tlr: 1.000e-01, eta: 9:55:31, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7703\n2023-01-01 19:05:55,399 - mmcls - INFO - Epoch [20][3900/5005]\tlr: 1.000e-01, eta: 9:55:26, time: 0.099, data_time: 0.003, memory: 1224, loss: 2.8143\n2023-01-01 19:06:03,969 - mmcls - INFO - Epoch [20][4000/5005]\tlr: 1.000e-01, eta: 9:55:16, time: 0.086, data_time: 0.032, memory: 1224, loss: 2.8365\n2023-01-01 19:06:12,782 - mmcls - INFO - Epoch [20][4100/5005]\tlr: 1.000e-01, eta: 9:55:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7920\n2023-01-01 19:06:21,603 - mmcls - INFO - Epoch [20][4200/5005]\tlr: 1.000e-01, eta: 9:54:57, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8165\n2023-01-01 19:06:30,268 - mmcls - INFO - Epoch [20][4300/5005]\tlr: 1.000e-01, eta: 9:54:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8343\n2023-01-01 19:06:39,120 - mmcls - INFO - Epoch [20][4400/5005]\tlr: 1.000e-01, eta: 9:54:39, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8025\n2023-01-01 19:06:47,756 - mmcls - INFO - Epoch [20][4500/5005]\tlr: 1.000e-01, eta: 9:54:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8174\n2023-01-01 19:06:56,911 - mmcls - INFO - Epoch [20][4600/5005]\tlr: 1.000e-01, eta: 9:54:21, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.7819\n2023-01-01 19:07:05,631 - mmcls - INFO - Epoch [20][4700/5005]\tlr: 1.000e-01, eta: 9:54:11, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.7936\n2023-01-01 19:07:15,036 - mmcls - INFO - Epoch [20][4800/5005]\tlr: 1.000e-01, eta: 9:54:04, time: 0.094, data_time: 0.007, memory: 1224, loss: 2.7941\n2023-01-01 19:07:23,931 - mmcls - INFO - Epoch [20][4900/5005]\tlr: 1.000e-01, eta: 9:53:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7705\n2023-01-01 19:07:32,603 - mmcls - INFO - Epoch [20][5000/5005]\tlr: 1.000e-01, eta: 9:53:46, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7856\n2023-01-01 19:07:55,584 - mmcls - INFO - Epoch(val) [20][196]\taccuracy_top-1: 45.4140, accuracy_top-5: 71.0980\n2023-01-01 19:08:06,470 - mmcls - INFO - Epoch [21][100/5005]\tlr: 1.000e-01, eta: 9:53:42, time: 0.109, data_time: 0.048, memory: 1224, loss: 2.7447\n2023-01-01 19:08:15,769 - mmcls - INFO - Epoch [21][200/5005]\tlr: 1.000e-01, eta: 9:53:35, time: 0.093, data_time: 0.020, memory: 1224, loss: 2.7467\n2023-01-01 19:08:24,303 - mmcls - INFO - Epoch [21][300/5005]\tlr: 1.000e-01, eta: 9:53:25, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.7388\n2023-01-01 19:08:33,220 - mmcls - INFO - Epoch [21][400/5005]\tlr: 1.000e-01, eta: 9:53:16, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.7951\n2023-01-01 19:08:42,146 - mmcls - INFO - Epoch [21][500/5005]\tlr: 1.000e-01, eta: 9:53:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7818\n2023-01-01 19:08:51,263 - mmcls - INFO - Epoch [21][600/5005]\tlr: 1.000e-01, eta: 9:52:59, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.7836\n2023-01-01 19:09:00,092 - mmcls - INFO - Epoch [21][700/5005]\tlr: 1.000e-01, eta: 9:52:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7745\n2023-01-01 19:09:08,785 - mmcls - INFO - Epoch [21][800/5005]\tlr: 1.000e-01, eta: 9:52:40, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.7639\n2023-01-01 19:09:17,655 - mmcls - INFO - Epoch [21][900/5005]\tlr: 1.000e-01, eta: 9:52:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7570\n2023-01-01 19:09:26,419 - mmcls - INFO - Epoch [21][1000/5005]\tlr: 1.000e-01, eta: 9:52:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7890\n2023-01-01 19:09:35,235 - mmcls - INFO - Epoch [21][1100/5005]\tlr: 1.000e-01, eta: 9:52:12, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7912\n2023-01-01 19:09:43,981 - mmcls - INFO - Epoch [21][1200/5005]\tlr: 1.000e-01, eta: 9:52:03, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7578\n2023-01-01 19:09:52,792 - mmcls - INFO - Epoch [21][1300/5005]\tlr: 1.000e-01, eta: 9:51:54, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7724\n2023-01-01 19:10:01,597 - mmcls - INFO - Epoch [21][1400/5005]\tlr: 1.000e-01, eta: 9:51:44, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7914\n2023-01-01 19:10:10,417 - mmcls - INFO - Epoch [21][1500/5005]\tlr: 1.000e-01, eta: 9:51:35, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7852\n2023-01-01 19:10:19,413 - mmcls - INFO - Epoch [21][1600/5005]\tlr: 1.000e-01, eta: 9:51:27, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.7990\n2023-01-01 19:10:27,999 - mmcls - INFO - Epoch [21][1700/5005]\tlr: 1.000e-01, eta: 9:51:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8021\n2023-01-01 19:10:37,218 - mmcls - INFO - Epoch [21][1800/5005]\tlr: 1.000e-01, eta: 9:51:09, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7961\n2023-01-01 19:10:46,821 - mmcls - INFO - Epoch [21][1900/5005]\tlr: 1.000e-01, eta: 9:51:03, time: 0.096, data_time: 0.002, memory: 1224, loss: 2.8040\n2023-01-01 19:10:55,504 - mmcls - INFO - Epoch [21][2000/5005]\tlr: 1.000e-01, eta: 9:50:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8158\n2023-01-01 19:11:04,207 - mmcls - INFO - Epoch [21][2100/5005]\tlr: 1.000e-01, eta: 9:50:43, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.8143\n2023-01-01 19:11:13,015 - mmcls - INFO - Epoch [21][2200/5005]\tlr: 1.000e-01, eta: 9:50:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8256\n2023-01-01 19:11:22,024 - mmcls - INFO - Epoch [21][2300/5005]\tlr: 1.000e-01, eta: 9:50:26, time: 0.090, data_time: 0.042, memory: 1224, loss: 2.7909\n2023-01-01 19:11:31,456 - mmcls - INFO - Epoch [21][2400/5005]\tlr: 1.000e-01, eta: 9:50:19, time: 0.094, data_time: 0.022, memory: 1224, loss: 2.7762\n2023-01-01 19:11:40,086 - mmcls - INFO - Epoch [21][2500/5005]\tlr: 1.000e-01, eta: 9:50:09, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7941\n2023-01-01 19:11:49,634 - mmcls - INFO - Epoch [21][2600/5005]\tlr: 1.000e-01, eta: 9:50:02, time: 0.096, data_time: 0.007, memory: 1224, loss: 2.8097\n2023-01-01 19:11:58,347 - mmcls - INFO - Epoch [21][2700/5005]\tlr: 1.000e-01, eta: 9:49:53, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8030\n2023-01-01 19:12:07,261 - mmcls - INFO - Epoch [21][2800/5005]\tlr: 1.000e-01, eta: 9:49:44, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7940\n2023-01-01 19:12:15,765 - mmcls - INFO - Epoch [21][2900/5005]\tlr: 1.000e-01, eta: 9:49:33, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.7860\n2023-01-01 19:12:24,486 - mmcls - INFO - Epoch [21][3000/5005]\tlr: 1.000e-01, eta: 9:49:24, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8145\n2023-01-01 19:12:33,028 - mmcls - INFO - Epoch [21][3100/5005]\tlr: 1.000e-01, eta: 9:49:14, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.7976\n2023-01-01 19:12:42,166 - mmcls - INFO - Epoch [21][3200/5005]\tlr: 1.000e-01, eta: 9:49:06, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.7971\n2023-01-01 19:12:51,019 - mmcls - INFO - Epoch [21][3300/5005]\tlr: 1.000e-01, eta: 9:48:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7816\n2023-01-01 19:12:59,767 - mmcls - INFO - Epoch [21][3400/5005]\tlr: 1.000e-01, eta: 9:48:47, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.7837\n2023-01-01 19:13:08,665 - mmcls - INFO - Epoch [21][3500/5005]\tlr: 1.000e-01, eta: 9:48:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7720\n2023-01-01 19:13:17,965 - mmcls - INFO - Epoch [21][3600/5005]\tlr: 1.000e-01, eta: 9:48:31, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8038\n2023-01-01 19:13:26,639 - mmcls - INFO - Epoch [21][3700/5005]\tlr: 1.000e-01, eta: 9:48:21, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.7998\n2023-01-01 19:13:35,399 - mmcls - INFO - Epoch [21][3800/5005]\tlr: 1.000e-01, eta: 9:48:12, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8485\n2023-01-01 19:13:44,103 - mmcls - INFO - Epoch [21][3900/5005]\tlr: 1.000e-01, eta: 9:48:02, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8130\n2023-01-01 19:13:52,858 - mmcls - INFO - Epoch [21][4000/5005]\tlr: 1.000e-01, eta: 9:47:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7906\n2023-01-01 19:14:01,832 - mmcls - INFO - Epoch [21][4100/5005]\tlr: 1.000e-01, eta: 9:47:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8171\n2023-01-01 19:14:10,678 - mmcls - INFO - Epoch [21][4200/5005]\tlr: 1.000e-01, eta: 9:47:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8001\n2023-01-01 19:14:19,342 - mmcls - INFO - Epoch [21][4300/5005]\tlr: 1.000e-01, eta: 9:47:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8178\n2023-01-01 19:14:27,926 - mmcls - INFO - Epoch [21][4400/5005]\tlr: 1.000e-01, eta: 9:47:15, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8051\n2023-01-01 19:14:37,299 - mmcls - INFO - Epoch [21][4500/5005]\tlr: 1.000e-01, eta: 9:47:08, time: 0.094, data_time: 0.010, memory: 1224, loss: 2.7969\n2023-01-01 19:14:46,266 - mmcls - INFO - Epoch [21][4600/5005]\tlr: 1.000e-01, eta: 9:46:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7744\n2023-01-01 19:14:55,134 - mmcls - INFO - Epoch [21][4700/5005]\tlr: 1.000e-01, eta: 9:46:50, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7789\n2023-01-01 19:15:03,673 - mmcls - INFO - Epoch [21][4800/5005]\tlr: 1.000e-01, eta: 9:46:40, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8042\n2023-01-01 19:15:12,253 - mmcls - INFO - Epoch [21][4900/5005]\tlr: 1.000e-01, eta: 9:46:30, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7768\n2023-01-01 19:15:21,144 - mmcls - INFO - Epoch [21][5000/5005]\tlr: 1.000e-01, eta: 9:46:21, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7915\n2023-01-01 19:15:43,986 - mmcls - INFO - Epoch(val) [21][196]\taccuracy_top-1: 46.8700, accuracy_top-5: 72.3420\n2023-01-01 19:15:55,256 - mmcls - INFO - Epoch [22][100/5005]\tlr: 1.000e-01, eta: 9:46:19, time: 0.112, data_time: 0.034, memory: 1224, loss: 2.7227\n2023-01-01 19:16:04,162 - mmcls - INFO - Epoch [22][200/5005]\tlr: 1.000e-01, eta: 9:46:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7299\n2023-01-01 19:16:13,024 - mmcls - INFO - Epoch [22][300/5005]\tlr: 1.000e-01, eta: 9:46:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7159\n2023-01-01 19:16:21,695 - mmcls - INFO - Epoch [22][400/5005]\tlr: 1.000e-01, eta: 9:45:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7470\n2023-01-01 19:16:30,567 - mmcls - INFO - Epoch [22][500/5005]\tlr: 1.000e-01, eta: 9:45:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7344\n2023-01-01 19:16:39,223 - mmcls - INFO - Epoch [22][600/5005]\tlr: 1.000e-01, eta: 9:45:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7441\n2023-01-01 19:16:48,083 - mmcls - INFO - Epoch [22][700/5005]\tlr: 1.000e-01, eta: 9:45:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7657\n2023-01-01 19:16:56,824 - mmcls - INFO - Epoch [22][800/5005]\tlr: 1.000e-01, eta: 9:45:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7449\n2023-01-01 19:17:05,984 - mmcls - INFO - Epoch [22][900/5005]\tlr: 1.000e-01, eta: 9:45:06, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.7592\n2023-01-01 19:17:14,807 - mmcls - INFO - Epoch [22][1000/5005]\tlr: 1.000e-01, eta: 9:44:56, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7583\n2023-01-01 19:17:23,528 - mmcls - INFO - Epoch [22][1100/5005]\tlr: 1.000e-01, eta: 9:44:47, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7653\n2023-01-01 19:17:32,403 - mmcls - INFO - Epoch [22][1200/5005]\tlr: 1.000e-01, eta: 9:44:38, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7614\n2023-01-01 19:17:40,890 - mmcls - INFO - Epoch [22][1300/5005]\tlr: 1.000e-01, eta: 9:44:27, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.8044\n2023-01-01 19:17:49,729 - mmcls - INFO - Epoch [22][1400/5005]\tlr: 1.000e-01, eta: 9:44:18, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8192\n2023-01-01 19:17:58,564 - mmcls - INFO - Epoch [22][1500/5005]\tlr: 1.000e-01, eta: 9:44:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7751\n2023-01-01 19:18:07,499 - mmcls - INFO - Epoch [22][1600/5005]\tlr: 1.000e-01, eta: 9:44:00, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7718\n2023-01-01 19:18:16,448 - mmcls - INFO - Epoch [22][1700/5005]\tlr: 1.000e-01, eta: 9:43:52, time: 0.090, data_time: 0.022, memory: 1224, loss: 2.7638\n2023-01-01 19:18:25,131 - mmcls - INFO - Epoch [22][1800/5005]\tlr: 1.000e-01, eta: 9:43:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7776\n2023-01-01 19:18:33,936 - mmcls - INFO - Epoch [22][1900/5005]\tlr: 1.000e-01, eta: 9:43:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7820\n2023-01-01 19:18:43,014 - mmcls - INFO - Epoch [22][2000/5005]\tlr: 1.000e-01, eta: 9:43:25, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.7812\n2023-01-01 19:18:51,638 - mmcls - INFO - Epoch [22][2100/5005]\tlr: 1.000e-01, eta: 9:43:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.7739\n2023-01-01 19:19:00,674 - mmcls - INFO - Epoch [22][2200/5005]\tlr: 1.000e-01, eta: 9:43:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7759\n2023-01-01 19:19:09,368 - mmcls - INFO - Epoch [22][2300/5005]\tlr: 1.000e-01, eta: 9:42:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7634\n2023-01-01 19:19:18,146 - mmcls - INFO - Epoch [22][2400/5005]\tlr: 1.000e-01, eta: 9:42:47, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8217\n2023-01-01 19:19:27,130 - mmcls - INFO - Epoch [22][2500/5005]\tlr: 1.000e-01, eta: 9:42:39, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7715\n2023-01-01 19:19:35,894 - mmcls - INFO - Epoch [22][2600/5005]\tlr: 1.000e-01, eta: 9:42:29, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7814\n2023-01-01 19:19:44,696 - mmcls - INFO - Epoch [22][2700/5005]\tlr: 1.000e-01, eta: 9:42:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8253\n2023-01-01 19:19:53,672 - mmcls - INFO - Epoch [22][2800/5005]\tlr: 1.000e-01, eta: 9:42:11, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.7687\n2023-01-01 19:20:02,122 - mmcls - INFO - Epoch [22][2900/5005]\tlr: 1.000e-01, eta: 9:42:01, time: 0.085, data_time: 0.021, memory: 1224, loss: 2.7922\n2023-01-01 19:20:10,858 - mmcls - INFO - Epoch [22][3000/5005]\tlr: 1.000e-01, eta: 9:41:51, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.8140\n2023-01-01 19:20:19,819 - mmcls - INFO - Epoch [22][3100/5005]\tlr: 1.000e-01, eta: 9:41:43, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8460\n2023-01-01 19:20:28,900 - mmcls - INFO - Epoch [22][3200/5005]\tlr: 1.000e-01, eta: 9:41:35, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.8070\n2023-01-01 19:20:37,477 - mmcls - INFO - Epoch [22][3300/5005]\tlr: 1.000e-01, eta: 9:41:24, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.7976\n2023-01-01 19:20:46,207 - mmcls - INFO - Epoch [22][3400/5005]\tlr: 1.000e-01, eta: 9:41:15, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.7740\n2023-01-01 19:20:55,053 - mmcls - INFO - Epoch [22][3500/5005]\tlr: 1.000e-01, eta: 9:41:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7780\n2023-01-01 19:21:04,105 - mmcls - INFO - Epoch [22][3600/5005]\tlr: 1.000e-01, eta: 9:40:58, time: 0.091, data_time: 0.008, memory: 1224, loss: 2.7718\n2023-01-01 19:21:12,752 - mmcls - INFO - Epoch [22][3700/5005]\tlr: 1.000e-01, eta: 9:40:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7656\n2023-01-01 19:21:21,492 - mmcls - INFO - Epoch [22][3800/5005]\tlr: 1.000e-01, eta: 9:40:38, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.7575\n2023-01-01 19:21:30,543 - mmcls - INFO - Epoch [22][3900/5005]\tlr: 1.000e-01, eta: 9:40:30, time: 0.091, data_time: 0.031, memory: 1224, loss: 2.7498\n2023-01-01 19:21:39,404 - mmcls - INFO - Epoch [22][4000/5005]\tlr: 1.000e-01, eta: 9:40:21, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.7613\n2023-01-01 19:21:48,220 - mmcls - INFO - Epoch [22][4100/5005]\tlr: 1.000e-01, eta: 9:40:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7597\n2023-01-01 19:21:57,237 - mmcls - INFO - Epoch [22][4200/5005]\tlr: 1.000e-01, eta: 9:40:03, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.7599\n2023-01-01 19:22:06,161 - mmcls - INFO - Epoch [22][4300/5005]\tlr: 1.000e-01, eta: 9:39:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8074\n2023-01-01 19:22:14,978 - mmcls - INFO - Epoch [22][4400/5005]\tlr: 1.000e-01, eta: 9:39:45, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7439\n2023-01-01 19:22:23,794 - mmcls - INFO - Epoch [22][4500/5005]\tlr: 1.000e-01, eta: 9:39:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8450\n2023-01-01 19:22:32,406 - mmcls - INFO - Epoch [22][4600/5005]\tlr: 1.000e-01, eta: 9:39:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8172\n2023-01-01 19:22:41,218 - mmcls - INFO - Epoch [22][4700/5005]\tlr: 1.000e-01, eta: 9:39:17, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7959\n2023-01-01 19:22:49,766 - mmcls - INFO - Epoch [22][4800/5005]\tlr: 1.000e-01, eta: 9:39:07, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.7802\n2023-01-01 19:22:58,569 - mmcls - INFO - Epoch [22][4900/5005]\tlr: 1.000e-01, eta: 9:38:58, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8067\n2023-01-01 19:23:07,683 - mmcls - INFO - Epoch [22][5000/5005]\tlr: 1.000e-01, eta: 9:38:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.7936\n2023-01-01 19:23:30,419 - mmcls - INFO - Epoch(val) [22][196]\taccuracy_top-1: 44.6380, accuracy_top-5: 69.8540\n2023-01-01 19:23:41,557 - mmcls - INFO - Epoch [23][100/5005]\tlr: 1.000e-01, eta: 9:38:46, time: 0.111, data_time: 0.048, memory: 1224, loss: 2.7187\n2023-01-01 19:23:50,295 - mmcls - INFO - Epoch [23][200/5005]\tlr: 1.000e-01, eta: 9:38:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7041\n2023-01-01 19:23:59,173 - mmcls - INFO - Epoch [23][300/5005]\tlr: 1.000e-01, eta: 9:38:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7659\n2023-01-01 19:24:07,755 - mmcls - INFO - Epoch [23][400/5005]\tlr: 1.000e-01, eta: 9:38:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7833\n2023-01-01 19:24:16,612 - mmcls - INFO - Epoch [23][500/5005]\tlr: 1.000e-01, eta: 9:38:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7519\n2023-01-01 19:24:25,355 - mmcls - INFO - Epoch [23][600/5005]\tlr: 1.000e-01, eta: 9:37:59, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7706\n2023-01-01 19:24:34,076 - mmcls - INFO - Epoch [23][700/5005]\tlr: 1.000e-01, eta: 9:37:50, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7611\n2023-01-01 19:24:43,106 - mmcls - INFO - Epoch [23][800/5005]\tlr: 1.000e-01, eta: 9:37:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7655\n2023-01-01 19:24:51,828 - mmcls - INFO - Epoch [23][900/5005]\tlr: 1.000e-01, eta: 9:37:32, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7517\n2023-01-01 19:25:00,477 - mmcls - INFO - Epoch [23][1000/5005]\tlr: 1.000e-01, eta: 9:37:22, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7497\n2023-01-01 19:25:09,452 - mmcls - INFO - Epoch [23][1100/5005]\tlr: 1.000e-01, eta: 9:37:14, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.7975\n2023-01-01 19:25:18,252 - mmcls - INFO - Epoch [23][1200/5005]\tlr: 1.000e-01, eta: 9:37:04, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7742\n2023-01-01 19:25:27,179 - mmcls - INFO - Epoch [23][1300/5005]\tlr: 1.000e-01, eta: 9:36:56, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7442\n2023-01-01 19:25:36,354 - mmcls - INFO - Epoch [23][1400/5005]\tlr: 1.000e-01, eta: 9:36:48, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7587\n2023-01-01 19:25:44,996 - mmcls - INFO - Epoch [23][1500/5005]\tlr: 1.000e-01, eta: 9:36:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7657\n2023-01-01 19:25:53,964 - mmcls - INFO - Epoch [23][1600/5005]\tlr: 1.000e-01, eta: 9:36:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7924\n2023-01-01 19:26:02,926 - mmcls - INFO - Epoch [23][1700/5005]\tlr: 1.000e-01, eta: 9:36:21, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.8093\n2023-01-01 19:26:11,611 - mmcls - INFO - Epoch [23][1800/5005]\tlr: 1.000e-01, eta: 9:36:11, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7722\n2023-01-01 19:26:20,415 - mmcls - INFO - Epoch [23][1900/5005]\tlr: 1.000e-01, eta: 9:36:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7938\n2023-01-01 19:26:29,329 - mmcls - INFO - Epoch [23][2000/5005]\tlr: 1.000e-01, eta: 9:35:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7847\n2023-01-01 19:26:37,923 - mmcls - INFO - Epoch [23][2100/5005]\tlr: 1.000e-01, eta: 9:35:43, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7884\n2023-01-01 19:26:46,572 - mmcls - INFO - Epoch [23][2200/5005]\tlr: 1.000e-01, eta: 9:35:33, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7556\n2023-01-01 19:26:55,441 - mmcls - INFO - Epoch [23][2300/5005]\tlr: 1.000e-01, eta: 9:35:24, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.7973\n2023-01-01 19:27:04,247 - mmcls - INFO - Epoch [23][2400/5005]\tlr: 1.000e-01, eta: 9:35:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8010\n2023-01-01 19:27:13,015 - mmcls - INFO - Epoch [23][2500/5005]\tlr: 1.000e-01, eta: 9:35:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7827\n2023-01-01 19:27:21,886 - mmcls - INFO - Epoch [23][2600/5005]\tlr: 1.000e-01, eta: 9:34:57, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8035\n2023-01-01 19:27:30,632 - mmcls - INFO - Epoch [23][2700/5005]\tlr: 1.000e-01, eta: 9:34:47, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7645\n2023-01-01 19:27:39,492 - mmcls - INFO - Epoch [23][2800/5005]\tlr: 1.000e-01, eta: 9:34:38, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.7789\n2023-01-01 19:27:48,382 - mmcls - INFO - Epoch [23][2900/5005]\tlr: 1.000e-01, eta: 9:34:29, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.7809\n2023-01-01 19:27:56,908 - mmcls - INFO - Epoch [23][3000/5005]\tlr: 1.000e-01, eta: 9:34:19, time: 0.085, data_time: 0.023, memory: 1224, loss: 2.7729\n2023-01-01 19:28:05,749 - mmcls - INFO - Epoch [23][3100/5005]\tlr: 1.000e-01, eta: 9:34:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7980\n2023-01-01 19:28:14,661 - mmcls - INFO - Epoch [23][3200/5005]\tlr: 1.000e-01, eta: 9:34:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7804\n2023-01-01 19:28:23,256 - mmcls - INFO - Epoch [23][3300/5005]\tlr: 1.000e-01, eta: 9:33:51, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.7697\n2023-01-01 19:28:31,840 - mmcls - INFO - Epoch [23][3400/5005]\tlr: 1.000e-01, eta: 9:33:41, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7951\n2023-01-01 19:28:40,579 - mmcls - INFO - Epoch [23][3500/5005]\tlr: 1.000e-01, eta: 9:33:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8011\n2023-01-01 19:28:49,220 - mmcls - INFO - Epoch [23][3600/5005]\tlr: 1.000e-01, eta: 9:33:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7970\n2023-01-01 19:28:57,883 - mmcls - INFO - Epoch [23][3700/5005]\tlr: 1.000e-01, eta: 9:33:12, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.7685\n2023-01-01 19:29:06,908 - mmcls - INFO - Epoch [23][3800/5005]\tlr: 1.000e-01, eta: 9:33:04, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8043\n2023-01-01 19:29:15,465 - mmcls - INFO - Epoch [23][3900/5005]\tlr: 1.000e-01, eta: 9:32:54, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7875\n2023-01-01 19:29:24,606 - mmcls - INFO - Epoch [23][4000/5005]\tlr: 1.000e-01, eta: 9:32:46, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7943\n2023-01-01 19:29:33,570 - mmcls - INFO - Epoch [23][4100/5005]\tlr: 1.000e-01, eta: 9:32:37, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.7995\n2023-01-01 19:29:42,362 - mmcls - INFO - Epoch [23][4200/5005]\tlr: 1.000e-01, eta: 9:32:28, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7652\n2023-01-01 19:29:51,167 - mmcls - INFO - Epoch [23][4300/5005]\tlr: 1.000e-01, eta: 9:32:19, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7956\n2023-01-01 19:30:00,137 - mmcls - INFO - Epoch [23][4400/5005]\tlr: 1.000e-01, eta: 9:32:10, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8169\n2023-01-01 19:30:09,359 - mmcls - INFO - Epoch [23][4500/5005]\tlr: 1.000e-01, eta: 9:32:02, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.7802\n2023-01-01 19:30:18,432 - mmcls - INFO - Epoch [23][4600/5005]\tlr: 1.000e-01, eta: 9:31:54, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.7723\n2023-01-01 19:30:27,008 - mmcls - INFO - Epoch [23][4700/5005]\tlr: 1.000e-01, eta: 9:31:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8180\n2023-01-01 19:30:35,596 - mmcls - INFO - Epoch [23][4800/5005]\tlr: 1.000e-01, eta: 9:31:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8209\n2023-01-01 19:30:44,382 - mmcls - INFO - Epoch [23][4900/5005]\tlr: 1.000e-01, eta: 9:31:25, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7853\n2023-01-01 19:30:53,134 - mmcls - INFO - Epoch [23][5000/5005]\tlr: 1.000e-01, eta: 9:31:16, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7570\n2023-01-01 19:31:15,910 - mmcls - INFO - Epoch(val) [23][196]\taccuracy_top-1: 44.3780, accuracy_top-5: 69.8640\n2023-01-01 19:31:27,019 - mmcls - INFO - Epoch [24][100/5005]\tlr: 1.000e-01, eta: 9:31:12, time: 0.111, data_time: 0.047, memory: 1224, loss: 2.7304\n2023-01-01 19:31:35,725 - mmcls - INFO - Epoch [24][200/5005]\tlr: 1.000e-01, eta: 9:31:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7188\n2023-01-01 19:31:44,347 - mmcls - INFO - Epoch [24][300/5005]\tlr: 1.000e-01, eta: 9:30:53, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7522\n2023-01-01 19:31:53,106 - mmcls - INFO - Epoch [24][400/5005]\tlr: 1.000e-01, eta: 9:30:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7502\n2023-01-01 19:32:01,928 - mmcls - INFO - Epoch [24][500/5005]\tlr: 1.000e-01, eta: 9:30:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7164\n2023-01-01 19:32:10,778 - mmcls - INFO - Epoch [24][600/5005]\tlr: 1.000e-01, eta: 9:30:25, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.7338\n2023-01-01 19:32:19,607 - mmcls - INFO - Epoch [24][700/5005]\tlr: 1.000e-01, eta: 9:30:16, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7841\n2023-01-01 19:32:28,093 - mmcls - INFO - Epoch [24][800/5005]\tlr: 1.000e-01, eta: 9:30:06, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.8070\n2023-01-01 19:32:36,867 - mmcls - INFO - Epoch [24][900/5005]\tlr: 1.000e-01, eta: 9:29:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7978\n2023-01-01 19:32:45,470 - mmcls - INFO - Epoch [24][1000/5005]\tlr: 1.000e-01, eta: 9:29:47, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8224\n2023-01-01 19:32:54,272 - mmcls - INFO - Epoch [24][1100/5005]\tlr: 1.000e-01, eta: 9:29:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7793\n2023-01-01 19:33:02,931 - mmcls - INFO - Epoch [24][1200/5005]\tlr: 1.000e-01, eta: 9:29:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7881\n2023-01-01 19:33:11,653 - mmcls - INFO - Epoch [24][1300/5005]\tlr: 1.000e-01, eta: 9:29:18, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7435\n2023-01-01 19:33:20,637 - mmcls - INFO - Epoch [24][1400/5005]\tlr: 1.000e-01, eta: 9:29:10, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.7754\n2023-01-01 19:33:29,507 - mmcls - INFO - Epoch [24][1500/5005]\tlr: 1.000e-01, eta: 9:29:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7443\n2023-01-01 19:33:38,320 - mmcls - INFO - Epoch [24][1600/5005]\tlr: 1.000e-01, eta: 9:28:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7663\n2023-01-01 19:33:47,055 - mmcls - INFO - Epoch [24][1700/5005]\tlr: 1.000e-01, eta: 9:28:42, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7721\n2023-01-01 19:33:55,657 - mmcls - INFO - Epoch [24][1800/5005]\tlr: 1.000e-01, eta: 9:28:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7669\n2023-01-01 19:34:04,519 - mmcls - INFO - Epoch [24][1900/5005]\tlr: 1.000e-01, eta: 9:28:23, time: 0.089, data_time: 0.028, memory: 1224, loss: 2.7907\n2023-01-01 19:34:13,160 - mmcls - INFO - Epoch [24][2000/5005]\tlr: 1.000e-01, eta: 9:28:14, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.7793\n2023-01-01 19:34:21,766 - mmcls - INFO - Epoch [24][2100/5005]\tlr: 1.000e-01, eta: 9:28:04, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.7763\n2023-01-01 19:34:30,751 - mmcls - INFO - Epoch [24][2200/5005]\tlr: 1.000e-01, eta: 9:27:55, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7788\n2023-01-01 19:34:39,596 - mmcls - INFO - Epoch [24][2300/5005]\tlr: 1.000e-01, eta: 9:27:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7931\n2023-01-01 19:34:48,416 - mmcls - INFO - Epoch [24][2400/5005]\tlr: 1.000e-01, eta: 9:27:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7902\n2023-01-01 19:34:57,218 - mmcls - INFO - Epoch [24][2500/5005]\tlr: 1.000e-01, eta: 9:27:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7892\n2023-01-01 19:35:06,135 - mmcls - INFO - Epoch [24][2600/5005]\tlr: 1.000e-01, eta: 9:27:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7552\n2023-01-01 19:35:15,129 - mmcls - INFO - Epoch [24][2700/5005]\tlr: 1.000e-01, eta: 9:27:10, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7964\n2023-01-01 19:35:23,820 - mmcls - INFO - Epoch [24][2800/5005]\tlr: 1.000e-01, eta: 9:27:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7721\n2023-01-01 19:35:32,506 - mmcls - INFO - Epoch [24][2900/5005]\tlr: 1.000e-01, eta: 9:26:51, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.8024\n2023-01-01 19:35:41,204 - mmcls - INFO - Epoch [24][3000/5005]\tlr: 1.000e-01, eta: 9:26:42, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7913\n2023-01-01 19:35:49,846 - mmcls - INFO - Epoch [24][3100/5005]\tlr: 1.000e-01, eta: 9:26:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7976\n2023-01-01 19:35:58,814 - mmcls - INFO - Epoch [24][3200/5005]\tlr: 1.000e-01, eta: 9:26:23, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7776\n2023-01-01 19:36:07,771 - mmcls - INFO - Epoch [24][3300/5005]\tlr: 1.000e-01, eta: 9:26:15, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7834\n2023-01-01 19:36:17,000 - mmcls - INFO - Epoch [24][3400/5005]\tlr: 1.000e-01, eta: 9:26:07, time: 0.092, data_time: 0.025, memory: 1224, loss: 2.7832\n2023-01-01 19:36:26,051 - mmcls - INFO - Epoch [24][3500/5005]\tlr: 1.000e-01, eta: 9:25:58, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7628\n2023-01-01 19:36:34,846 - mmcls - INFO - Epoch [24][3600/5005]\tlr: 1.000e-01, eta: 9:25:49, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7629\n2023-01-01 19:36:43,775 - mmcls - INFO - Epoch [24][3700/5005]\tlr: 1.000e-01, eta: 9:25:40, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.7895\n2023-01-01 19:36:53,037 - mmcls - INFO - Epoch [24][3800/5005]\tlr: 1.000e-01, eta: 9:25:33, time: 0.093, data_time: 0.027, memory: 1224, loss: 2.7788\n2023-01-01 19:37:01,909 - mmcls - INFO - Epoch [24][3900/5005]\tlr: 1.000e-01, eta: 9:25:24, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.7800\n2023-01-01 19:37:10,627 - mmcls - INFO - Epoch [24][4000/5005]\tlr: 1.000e-01, eta: 9:25:14, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7709\n2023-01-01 19:37:19,437 - mmcls - INFO - Epoch [24][4100/5005]\tlr: 1.000e-01, eta: 9:25:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8120\n2023-01-01 19:37:28,129 - mmcls - INFO - Epoch [24][4200/5005]\tlr: 1.000e-01, eta: 9:24:56, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.7848\n2023-01-01 19:37:37,120 - mmcls - INFO - Epoch [24][4300/5005]\tlr: 1.000e-01, eta: 9:24:47, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7732\n2023-01-01 19:37:45,913 - mmcls - INFO - Epoch [24][4400/5005]\tlr: 1.000e-01, eta: 9:24:38, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7751\n2023-01-01 19:37:54,606 - mmcls - INFO - Epoch [24][4500/5005]\tlr: 1.000e-01, eta: 9:24:28, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7556\n2023-01-01 19:38:03,423 - mmcls - INFO - Epoch [24][4600/5005]\tlr: 1.000e-01, eta: 9:24:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7778\n2023-01-01 19:38:12,142 - mmcls - INFO - Epoch [24][4700/5005]\tlr: 1.000e-01, eta: 9:24:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7780\n2023-01-01 19:38:20,667 - mmcls - INFO - Epoch [24][4800/5005]\tlr: 1.000e-01, eta: 9:24:00, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.7424\n2023-01-01 19:38:29,721 - mmcls - INFO - Epoch [24][4900/5005]\tlr: 1.000e-01, eta: 9:23:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7539\n2023-01-01 19:38:38,451 - mmcls - INFO - Epoch [24][5000/5005]\tlr: 1.000e-01, eta: 9:23:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7717\n2023-01-01 19:39:01,473 - mmcls - INFO - Epoch(val) [24][196]\taccuracy_top-1: 47.1760, accuracy_top-5: 72.0040\n2023-01-01 19:39:12,312 - mmcls - INFO - Epoch [25][100/5005]\tlr: 1.000e-01, eta: 9:23:37, time: 0.108, data_time: 0.032, memory: 1224, loss: 2.7133\n2023-01-01 19:39:21,333 - mmcls - INFO - Epoch [25][200/5005]\tlr: 1.000e-01, eta: 9:23:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7445\n2023-01-01 19:39:30,170 - mmcls - INFO - Epoch [25][300/5005]\tlr: 1.000e-01, eta: 9:23:20, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7326\n2023-01-01 19:39:38,893 - mmcls - INFO - Epoch [25][400/5005]\tlr: 1.000e-01, eta: 9:23:10, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7569\n2023-01-01 19:39:47,494 - mmcls - INFO - Epoch [25][500/5005]\tlr: 1.000e-01, eta: 9:23:00, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7322\n2023-01-01 19:39:56,408 - mmcls - INFO - Epoch [25][600/5005]\tlr: 1.000e-01, eta: 9:22:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7255\n2023-01-01 19:40:05,191 - mmcls - INFO - Epoch [25][700/5005]\tlr: 1.000e-01, eta: 9:22:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7286\n2023-01-01 19:40:14,078 - mmcls - INFO - Epoch [25][800/5005]\tlr: 1.000e-01, eta: 9:22:33, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7564\n2023-01-01 19:40:22,861 - mmcls - INFO - Epoch [25][900/5005]\tlr: 1.000e-01, eta: 9:22:24, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7680\n2023-01-01 19:40:31,803 - mmcls - INFO - Epoch [25][1000/5005]\tlr: 1.000e-01, eta: 9:22:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7381\n2023-01-01 19:40:40,455 - mmcls - INFO - Epoch [25][1100/5005]\tlr: 1.000e-01, eta: 9:22:06, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.7329\n2023-01-01 19:40:49,036 - mmcls - INFO - Epoch [25][1200/5005]\tlr: 1.000e-01, eta: 9:21:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.7580\n2023-01-01 19:40:57,946 - mmcls - INFO - Epoch [25][1300/5005]\tlr: 1.000e-01, eta: 9:21:47, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.7665\n2023-01-01 19:41:06,531 - mmcls - INFO - Epoch [25][1400/5005]\tlr: 1.000e-01, eta: 9:21:37, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7535\n2023-01-01 19:41:15,500 - mmcls - INFO - Epoch [25][1500/5005]\tlr: 1.000e-01, eta: 9:21:29, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.7599\n2023-01-01 19:41:24,189 - mmcls - INFO - Epoch [25][1600/5005]\tlr: 1.000e-01, eta: 9:21:19, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.7802\n2023-01-01 19:41:32,930 - mmcls - INFO - Epoch [25][1700/5005]\tlr: 1.000e-01, eta: 9:21:10, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7620\n2023-01-01 19:41:41,562 - mmcls - INFO - Epoch [25][1800/5005]\tlr: 1.000e-01, eta: 9:21:00, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.7637\n2023-01-01 19:41:50,538 - mmcls - INFO - Epoch [25][1900/5005]\tlr: 1.000e-01, eta: 9:20:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7903\n2023-01-01 19:41:59,281 - mmcls - INFO - Epoch [25][2000/5005]\tlr: 1.000e-01, eta: 9:20:42, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7938\n2023-01-01 19:42:08,139 - mmcls - INFO - Epoch [25][2100/5005]\tlr: 1.000e-01, eta: 9:20:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7797\n2023-01-01 19:42:16,879 - mmcls - INFO - Epoch [25][2200/5005]\tlr: 1.000e-01, eta: 9:20:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7760\n2023-01-01 19:42:25,693 - mmcls - INFO - Epoch [25][2300/5005]\tlr: 1.000e-01, eta: 9:20:15, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7595\n2023-01-01 19:42:34,669 - mmcls - INFO - Epoch [25][2400/5005]\tlr: 1.000e-01, eta: 9:20:06, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.7672\n2023-01-01 19:42:43,458 - mmcls - INFO - Epoch [25][2500/5005]\tlr: 1.000e-01, eta: 9:19:57, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.7564\n2023-01-01 19:42:52,533 - mmcls - INFO - Epoch [25][2600/5005]\tlr: 1.000e-01, eta: 9:19:48, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.7714\n2023-01-01 19:43:01,039 - mmcls - INFO - Epoch [25][2700/5005]\tlr: 1.000e-01, eta: 9:19:38, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.7853\n2023-01-01 19:43:09,646 - mmcls - INFO - Epoch [25][2800/5005]\tlr: 1.000e-01, eta: 9:19:29, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7990\n2023-01-01 19:43:18,460 - mmcls - INFO - Epoch [25][2900/5005]\tlr: 1.000e-01, eta: 9:19:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7810\n2023-01-01 19:43:27,218 - mmcls - INFO - Epoch [25][3000/5005]\tlr: 1.000e-01, eta: 9:19:10, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7777\n2023-01-01 19:43:36,341 - mmcls - INFO - Epoch [25][3100/5005]\tlr: 1.000e-01, eta: 9:19:02, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.7931\n2023-01-01 19:43:44,761 - mmcls - INFO - Epoch [25][3200/5005]\tlr: 1.000e-01, eta: 9:18:52, time: 0.084, data_time: 0.015, memory: 1224, loss: 2.7844\n2023-01-01 19:43:53,750 - mmcls - INFO - Epoch [25][3300/5005]\tlr: 1.000e-01, eta: 9:18:43, time: 0.090, data_time: 0.040, memory: 1224, loss: 2.7501\n2023-01-01 19:44:02,613 - mmcls - INFO - Epoch [25][3400/5005]\tlr: 1.000e-01, eta: 9:18:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7945\n2023-01-01 19:44:11,566 - mmcls - INFO - Epoch [25][3500/5005]\tlr: 1.000e-01, eta: 9:18:25, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8023\n2023-01-01 19:44:20,525 - mmcls - INFO - Epoch [25][3600/5005]\tlr: 1.000e-01, eta: 9:18:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7731\n2023-01-01 19:44:29,278 - mmcls - INFO - Epoch [25][3700/5005]\tlr: 1.000e-01, eta: 9:18:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7503\n2023-01-01 19:44:38,066 - mmcls - INFO - Epoch [25][3800/5005]\tlr: 1.000e-01, eta: 9:17:58, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8349\n2023-01-01 19:44:46,698 - mmcls - INFO - Epoch [25][3900/5005]\tlr: 1.000e-01, eta: 9:17:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7838\n2023-01-01 19:44:55,525 - mmcls - INFO - Epoch [25][4000/5005]\tlr: 1.000e-01, eta: 9:17:39, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7829\n2023-01-01 19:45:04,209 - mmcls - INFO - Epoch [25][4100/5005]\tlr: 1.000e-01, eta: 9:17:30, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.7779\n2023-01-01 19:45:13,084 - mmcls - INFO - Epoch [25][4200/5005]\tlr: 1.000e-01, eta: 9:17:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7788\n2023-01-01 19:45:22,244 - mmcls - INFO - Epoch [25][4300/5005]\tlr: 1.000e-01, eta: 9:17:13, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7808\n2023-01-01 19:45:31,579 - mmcls - INFO - Epoch [25][4400/5005]\tlr: 1.000e-01, eta: 9:17:05, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.7370\n2023-01-01 19:45:40,249 - mmcls - INFO - Epoch [25][4500/5005]\tlr: 1.000e-01, eta: 9:16:56, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7778\n2023-01-01 19:45:49,282 - mmcls - INFO - Epoch [25][4600/5005]\tlr: 1.000e-01, eta: 9:16:47, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7600\n2023-01-01 19:45:57,823 - mmcls - INFO - Epoch [25][4700/5005]\tlr: 1.000e-01, eta: 9:16:37, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.7661\n2023-01-01 19:46:06,771 - mmcls - INFO - Epoch [25][4800/5005]\tlr: 1.000e-01, eta: 9:16:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8104\n2023-01-01 19:46:15,706 - mmcls - INFO - Epoch [25][4900/5005]\tlr: 1.000e-01, eta: 9:16:20, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8708\n2023-01-01 19:46:24,742 - mmcls - INFO - Epoch [25][5000/5005]\tlr: 1.000e-01, eta: 9:16:11, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8146\n2023-01-01 19:46:47,615 - mmcls - INFO - Epoch(val) [25][196]\taccuracy_top-1: 44.8280, accuracy_top-5: 69.8080\n2023-01-01 19:46:58,445 - mmcls - INFO - Epoch [26][100/5005]\tlr: 1.000e-01, eta: 9:16:06, time: 0.108, data_time: 0.046, memory: 1224, loss: 2.7426\n2023-01-01 19:47:07,302 - mmcls - INFO - Epoch [26][200/5005]\tlr: 1.000e-01, eta: 9:15:57, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7795\n2023-01-01 19:47:15,917 - mmcls - INFO - Epoch [26][300/5005]\tlr: 1.000e-01, eta: 9:15:48, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.7257\n2023-01-01 19:47:24,952 - mmcls - INFO - Epoch [26][400/5005]\tlr: 1.000e-01, eta: 9:15:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7517\n2023-01-01 19:47:33,655 - mmcls - INFO - Epoch [26][500/5005]\tlr: 1.000e-01, eta: 9:15:30, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7436\n2023-01-01 19:47:42,858 - mmcls - INFO - Epoch [26][600/5005]\tlr: 1.000e-01, eta: 9:15:22, time: 0.092, data_time: 0.022, memory: 1224, loss: 2.7909\n2023-01-01 19:47:51,524 - mmcls - INFO - Epoch [26][700/5005]\tlr: 1.000e-01, eta: 9:15:12, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.7748\n2023-01-01 19:48:00,239 - mmcls - INFO - Epoch [26][800/5005]\tlr: 1.000e-01, eta: 9:15:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7508\n2023-01-01 19:48:09,148 - mmcls - INFO - Epoch [26][900/5005]\tlr: 1.000e-01, eta: 9:14:54, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.7756\n2023-01-01 19:48:17,714 - mmcls - INFO - Epoch [26][1000/5005]\tlr: 1.000e-01, eta: 9:14:44, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.7737\n2023-01-01 19:48:26,531 - mmcls - INFO - Epoch [26][1100/5005]\tlr: 1.000e-01, eta: 9:14:35, time: 0.088, data_time: 0.043, memory: 1224, loss: 2.7731\n2023-01-01 19:48:35,410 - mmcls - INFO - Epoch [26][1200/5005]\tlr: 1.000e-01, eta: 9:14:26, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.7841\n2023-01-01 19:48:44,053 - mmcls - INFO - Epoch [26][1300/5005]\tlr: 1.000e-01, eta: 9:14:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7683\n2023-01-01 19:48:53,092 - mmcls - INFO - Epoch [26][1400/5005]\tlr: 1.000e-01, eta: 9:14:08, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.7608\n2023-01-01 19:49:02,297 - mmcls - INFO - Epoch [26][1500/5005]\tlr: 1.000e-01, eta: 9:14:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7686\n2023-01-01 19:49:11,382 - mmcls - INFO - Epoch [26][1600/5005]\tlr: 1.000e-01, eta: 9:13:52, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7538\n2023-01-01 19:49:19,843 - mmcls - INFO - Epoch [26][1700/5005]\tlr: 1.000e-01, eta: 9:13:42, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.7633\n2023-01-01 19:49:28,657 - mmcls - INFO - Epoch [26][1800/5005]\tlr: 1.000e-01, eta: 9:13:33, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7768\n2023-01-01 19:49:37,902 - mmcls - INFO - Epoch [26][1900/5005]\tlr: 1.000e-01, eta: 9:13:25, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7470\n2023-01-01 19:49:46,366 - mmcls - INFO - Epoch [26][2000/5005]\tlr: 1.000e-01, eta: 9:13:15, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.7425\n2023-01-01 19:49:54,907 - mmcls - INFO - Epoch [26][2100/5005]\tlr: 1.000e-01, eta: 9:13:05, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7686\n2023-01-01 19:50:03,707 - mmcls - INFO - Epoch [26][2200/5005]\tlr: 1.000e-01, eta: 9:12:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7752\n2023-01-01 19:50:12,541 - mmcls - INFO - Epoch [26][2300/5005]\tlr: 1.000e-01, eta: 9:12:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7799\n2023-01-01 19:50:21,483 - mmcls - INFO - Epoch [26][2400/5005]\tlr: 1.000e-01, eta: 9:12:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7852\n2023-01-01 19:50:30,109 - mmcls - INFO - Epoch [26][2500/5005]\tlr: 1.000e-01, eta: 9:12:28, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.7790\n2023-01-01 19:50:38,838 - mmcls - INFO - Epoch [26][2600/5005]\tlr: 1.000e-01, eta: 9:12:19, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.7522\n2023-01-01 19:50:47,919 - mmcls - INFO - Epoch [26][2700/5005]\tlr: 1.000e-01, eta: 9:12:10, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.7829\n2023-01-01 19:50:56,825 - mmcls - INFO - Epoch [26][2800/5005]\tlr: 1.000e-01, eta: 9:12:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8025\n2023-01-01 19:51:05,522 - mmcls - INFO - Epoch [26][2900/5005]\tlr: 1.000e-01, eta: 9:11:52, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8085\n2023-01-01 19:51:14,360 - mmcls - INFO - Epoch [26][3000/5005]\tlr: 1.000e-01, eta: 9:11:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.7846\n2023-01-01 19:51:23,238 - mmcls - INFO - Epoch [26][3100/5005]\tlr: 1.000e-01, eta: 9:11:34, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.7956\n2023-01-01 19:51:32,179 - mmcls - INFO - Epoch [26][3200/5005]\tlr: 1.000e-01, eta: 9:11:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7814\n2023-01-01 19:51:40,700 - mmcls - INFO - Epoch [26][3300/5005]\tlr: 1.000e-01, eta: 9:11:15, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.7840\n2023-01-01 19:51:49,491 - mmcls - INFO - Epoch [26][3400/5005]\tlr: 1.000e-01, eta: 9:11:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7754\n2023-01-01 19:51:58,447 - mmcls - INFO - Epoch [26][3500/5005]\tlr: 1.000e-01, eta: 9:10:57, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7374\n2023-01-01 19:52:07,168 - mmcls - INFO - Epoch [26][3600/5005]\tlr: 1.000e-01, eta: 9:10:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7622\n2023-01-01 19:52:15,726 - mmcls - INFO - Epoch [26][3700/5005]\tlr: 1.000e-01, eta: 9:10:38, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7688\n2023-01-01 19:52:24,680 - mmcls - INFO - Epoch [26][3800/5005]\tlr: 1.000e-01, eta: 9:10:30, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7714\n2023-01-01 19:52:33,529 - mmcls - INFO - Epoch [26][3900/5005]\tlr: 1.000e-01, eta: 9:10:21, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.7733\n2023-01-01 19:52:42,235 - mmcls - INFO - Epoch [26][4000/5005]\tlr: 1.000e-01, eta: 9:10:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7743\n2023-01-01 19:52:51,044 - mmcls - INFO - Epoch [26][4100/5005]\tlr: 1.000e-01, eta: 9:10:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8079\n2023-01-01 19:52:59,728 - mmcls - INFO - Epoch [26][4200/5005]\tlr: 1.000e-01, eta: 9:09:52, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7664\n2023-01-01 19:53:08,602 - mmcls - INFO - Epoch [26][4300/5005]\tlr: 1.000e-01, eta: 9:09:44, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7967\n2023-01-01 19:53:17,435 - mmcls - INFO - Epoch [26][4400/5005]\tlr: 1.000e-01, eta: 9:09:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7679\n2023-01-01 19:53:26,327 - mmcls - INFO - Epoch [26][4500/5005]\tlr: 1.000e-01, eta: 9:09:26, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7893\n2023-01-01 19:53:35,167 - mmcls - INFO - Epoch [26][4600/5005]\tlr: 1.000e-01, eta: 9:09:17, time: 0.088, data_time: 0.030, memory: 1224, loss: 2.7883\n2023-01-01 19:53:43,816 - mmcls - INFO - Epoch [26][4700/5005]\tlr: 1.000e-01, eta: 9:09:07, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.8162\n2023-01-01 19:53:52,615 - mmcls - INFO - Epoch [26][4800/5005]\tlr: 1.000e-01, eta: 9:08:58, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7702\n2023-01-01 19:54:01,135 - mmcls - INFO - Epoch [26][4900/5005]\tlr: 1.000e-01, eta: 9:08:48, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7672\n2023-01-01 19:54:09,766 - mmcls - INFO - Epoch [26][5000/5005]\tlr: 1.000e-01, eta: 9:08:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.7781\n2023-01-01 19:54:32,762 - mmcls - INFO - Epoch(val) [26][196]\taccuracy_top-1: 41.7900, accuracy_top-5: 65.7740\n2023-01-01 19:54:43,944 - mmcls - INFO - Epoch [27][100/5005]\tlr: 1.000e-01, eta: 9:08:34, time: 0.112, data_time: 0.051, memory: 1224, loss: 2.7176\n2023-01-01 19:54:52,773 - mmcls - INFO - Epoch [27][200/5005]\tlr: 1.000e-01, eta: 9:08:25, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.6871\n2023-01-01 19:55:01,697 - mmcls - INFO - Epoch [27][300/5005]\tlr: 1.000e-01, eta: 9:08:16, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.7214\n2023-01-01 19:55:10,914 - mmcls - INFO - Epoch [27][400/5005]\tlr: 1.000e-01, eta: 9:08:08, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.7323\n2023-01-01 19:55:19,596 - mmcls - INFO - Epoch [27][500/5005]\tlr: 1.000e-01, eta: 9:07:59, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7587\n2023-01-01 19:55:28,221 - mmcls - INFO - Epoch [27][600/5005]\tlr: 1.000e-01, eta: 9:07:49, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.7364\n2023-01-01 19:55:37,046 - mmcls - INFO - Epoch [27][700/5005]\tlr: 1.000e-01, eta: 9:07:40, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.7271\n2023-01-01 19:55:45,913 - mmcls - INFO - Epoch [27][800/5005]\tlr: 1.000e-01, eta: 9:07:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7174\n2023-01-01 19:55:54,914 - mmcls - INFO - Epoch [27][900/5005]\tlr: 1.000e-01, eta: 9:07:23, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.7439\n2023-01-01 19:56:03,685 - mmcls - INFO - Epoch [27][1000/5005]\tlr: 1.000e-01, eta: 9:07:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7257\n2023-01-01 19:56:12,727 - mmcls - INFO - Epoch [27][1100/5005]\tlr: 1.000e-01, eta: 9:07:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7725\n2023-01-01 19:56:22,231 - mmcls - INFO - Epoch [27][1200/5005]\tlr: 1.000e-01, eta: 9:06:58, time: 0.095, data_time: 0.008, memory: 1224, loss: 2.7633\n2023-01-01 19:56:31,068 - mmcls - INFO - Epoch [27][1300/5005]\tlr: 1.000e-01, eta: 9:06:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7606\n2023-01-01 19:56:39,575 - mmcls - INFO - Epoch [27][1400/5005]\tlr: 1.000e-01, eta: 9:06:39, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.7945\n2023-01-01 19:56:48,249 - mmcls - INFO - Epoch [27][1500/5005]\tlr: 1.000e-01, eta: 9:06:29, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7665\n2023-01-01 19:56:56,924 - mmcls - INFO - Epoch [27][1600/5005]\tlr: 1.000e-01, eta: 9:06:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7645\n2023-01-01 19:57:05,652 - mmcls - INFO - Epoch [27][1700/5005]\tlr: 1.000e-01, eta: 9:06:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7569\n2023-01-01 19:57:14,501 - mmcls - INFO - Epoch [27][1800/5005]\tlr: 1.000e-01, eta: 9:06:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7325\n2023-01-01 19:57:23,249 - mmcls - INFO - Epoch [27][1900/5005]\tlr: 1.000e-01, eta: 9:05:52, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7507\n2023-01-01 19:57:32,102 - mmcls - INFO - Epoch [27][2000/5005]\tlr: 1.000e-01, eta: 9:05:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.7675\n2023-01-01 19:57:40,620 - mmcls - INFO - Epoch [27][2100/5005]\tlr: 1.000e-01, eta: 9:05:33, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.7827\n2023-01-01 19:57:49,372 - mmcls - INFO - Epoch [27][2200/5005]\tlr: 1.000e-01, eta: 9:05:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7723\n2023-01-01 19:57:58,098 - mmcls - INFO - Epoch [27][2300/5005]\tlr: 1.000e-01, eta: 9:05:15, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7588\n2023-01-01 19:58:07,127 - mmcls - INFO - Epoch [27][2400/5005]\tlr: 1.000e-01, eta: 9:05:06, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.7637\n2023-01-01 19:58:15,815 - mmcls - INFO - Epoch [27][2500/5005]\tlr: 1.000e-01, eta: 9:04:57, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.7904\n2023-01-01 19:58:24,660 - mmcls - INFO - Epoch [27][2600/5005]\tlr: 1.000e-01, eta: 9:04:48, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7860\n2023-01-01 19:58:33,140 - mmcls - INFO - Epoch [27][2700/5005]\tlr: 1.000e-01, eta: 9:04:38, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7680\n2023-01-01 19:58:42,038 - mmcls - INFO - Epoch [27][2800/5005]\tlr: 1.000e-01, eta: 9:04:29, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8031\n2023-01-01 19:58:50,859 - mmcls - INFO - Epoch [27][2900/5005]\tlr: 1.000e-01, eta: 9:04:20, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7935\n2023-01-01 19:58:59,696 - mmcls - INFO - Epoch [27][3000/5005]\tlr: 1.000e-01, eta: 9:04:11, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.7772\n2023-01-01 19:59:08,226 - mmcls - INFO - Epoch [27][3100/5005]\tlr: 1.000e-01, eta: 9:04:01, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.7737\n2023-01-01 19:59:17,010 - mmcls - INFO - Epoch [27][3200/5005]\tlr: 1.000e-01, eta: 9:03:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7491\n2023-01-01 19:59:25,599 - mmcls - INFO - Epoch [27][3300/5005]\tlr: 1.000e-01, eta: 9:03:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7319\n2023-01-01 19:59:34,532 - mmcls - INFO - Epoch [27][3400/5005]\tlr: 1.000e-01, eta: 9:03:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7490\n2023-01-01 19:59:43,595 - mmcls - INFO - Epoch [27][3500/5005]\tlr: 1.000e-01, eta: 9:03:25, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7564\n2023-01-01 19:59:52,510 - mmcls - INFO - Epoch [27][3600/5005]\tlr: 1.000e-01, eta: 9:03:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7686\n2023-01-01 20:00:01,236 - mmcls - INFO - Epoch [27][3700/5005]\tlr: 1.000e-01, eta: 9:03:07, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.8015\n2023-01-01 20:00:09,947 - mmcls - INFO - Epoch [27][3800/5005]\tlr: 1.000e-01, eta: 9:02:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7945\n2023-01-01 20:00:18,594 - mmcls - INFO - Epoch [27][3900/5005]\tlr: 1.000e-01, eta: 9:02:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7905\n2023-01-01 20:00:27,129 - mmcls - INFO - Epoch [27][4000/5005]\tlr: 1.000e-01, eta: 9:02:38, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7798\n2023-01-01 20:00:36,001 - mmcls - INFO - Epoch [27][4100/5005]\tlr: 1.000e-01, eta: 9:02:29, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.7653\n2023-01-01 20:00:45,293 - mmcls - INFO - Epoch [27][4200/5005]\tlr: 1.000e-01, eta: 9:02:21, time: 0.093, data_time: 0.019, memory: 1224, loss: 2.7978\n2023-01-01 20:00:54,024 - mmcls - INFO - Epoch [27][4300/5005]\tlr: 1.000e-01, eta: 9:02:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7722\n2023-01-01 20:01:02,820 - mmcls - INFO - Epoch [27][4400/5005]\tlr: 1.000e-01, eta: 9:02:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7811\n2023-01-01 20:01:11,772 - mmcls - INFO - Epoch [27][4500/5005]\tlr: 1.000e-01, eta: 9:01:54, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7696\n2023-01-01 20:01:20,764 - mmcls - INFO - Epoch [27][4600/5005]\tlr: 1.000e-01, eta: 9:01:45, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.7523\n2023-01-01 20:01:29,683 - mmcls - INFO - Epoch [27][4700/5005]\tlr: 1.000e-01, eta: 9:01:37, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.7847\n2023-01-01 20:01:38,332 - mmcls - INFO - Epoch [27][4800/5005]\tlr: 1.000e-01, eta: 9:01:27, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.7706\n2023-01-01 20:01:47,006 - mmcls - INFO - Epoch [27][4900/5005]\tlr: 1.000e-01, eta: 9:01:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7771\n2023-01-01 20:01:55,835 - mmcls - INFO - Epoch [27][5000/5005]\tlr: 1.000e-01, eta: 9:01:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7912\n2023-01-01 20:02:18,590 - mmcls - INFO - Epoch(val) [27][196]\taccuracy_top-1: 46.6480, accuracy_top-5: 72.1560\n2023-01-01 20:02:29,471 - mmcls - INFO - Epoch [28][100/5005]\tlr: 1.000e-01, eta: 9:01:03, time: 0.109, data_time: 0.040, memory: 1224, loss: 2.7172\n2023-01-01 20:02:38,397 - mmcls - INFO - Epoch [28][200/5005]\tlr: 1.000e-01, eta: 9:00:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7352\n2023-01-01 20:02:47,104 - mmcls - INFO - Epoch [28][300/5005]\tlr: 1.000e-01, eta: 9:00:45, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7388\n2023-01-01 20:02:55,641 - mmcls - INFO - Epoch [28][400/5005]\tlr: 1.000e-01, eta: 9:00:35, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.7009\n2023-01-01 20:03:04,283 - mmcls - INFO - Epoch [28][500/5005]\tlr: 1.000e-01, eta: 9:00:26, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.7223\n2023-01-01 20:03:13,120 - mmcls - INFO - Epoch [28][600/5005]\tlr: 1.000e-01, eta: 9:00:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7071\n2023-01-01 20:03:21,788 - mmcls - INFO - Epoch [28][700/5005]\tlr: 1.000e-01, eta: 9:00:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7277\n2023-01-01 20:03:30,669 - mmcls - INFO - Epoch [28][800/5005]\tlr: 1.000e-01, eta: 8:59:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7619\n2023-01-01 20:03:39,566 - mmcls - INFO - Epoch [28][900/5005]\tlr: 1.000e-01, eta: 8:59:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7439\n2023-01-01 20:03:48,597 - mmcls - INFO - Epoch [28][1000/5005]\tlr: 1.000e-01, eta: 8:59:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7743\n2023-01-01 20:03:57,306 - mmcls - INFO - Epoch [28][1100/5005]\tlr: 1.000e-01, eta: 8:59:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7485\n2023-01-01 20:04:06,190 - mmcls - INFO - Epoch [28][1200/5005]\tlr: 1.000e-01, eta: 8:59:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7363\n2023-01-01 20:04:14,998 - mmcls - INFO - Epoch [28][1300/5005]\tlr: 1.000e-01, eta: 8:59:14, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7374\n2023-01-01 20:04:23,875 - mmcls - INFO - Epoch [28][1400/5005]\tlr: 1.000e-01, eta: 8:59:05, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7404\n2023-01-01 20:04:32,763 - mmcls - INFO - Epoch [28][1500/5005]\tlr: 1.000e-01, eta: 8:58:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.7693\n2023-01-01 20:04:41,149 - mmcls - INFO - Epoch [28][1600/5005]\tlr: 1.000e-01, eta: 8:58:46, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.7168\n2023-01-01 20:04:50,141 - mmcls - INFO - Epoch [28][1700/5005]\tlr: 1.000e-01, eta: 8:58:37, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7724\n2023-01-01 20:04:59,054 - mmcls - INFO - Epoch [28][1800/5005]\tlr: 1.000e-01, eta: 8:58:28, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.7378\n2023-01-01 20:05:07,600 - mmcls - INFO - Epoch [28][1900/5005]\tlr: 1.000e-01, eta: 8:58:18, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7347\n2023-01-01 20:05:16,366 - mmcls - INFO - Epoch [28][2000/5005]\tlr: 1.000e-01, eta: 8:58:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7503\n2023-01-01 20:05:25,335 - mmcls - INFO - Epoch [28][2100/5005]\tlr: 1.000e-01, eta: 8:58:00, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7751\n2023-01-01 20:05:34,043 - mmcls - INFO - Epoch [28][2200/5005]\tlr: 1.000e-01, eta: 8:57:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7731\n2023-01-01 20:05:42,878 - mmcls - INFO - Epoch [28][2300/5005]\tlr: 1.000e-01, eta: 8:57:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7701\n2023-01-01 20:05:51,730 - mmcls - INFO - Epoch [28][2400/5005]\tlr: 1.000e-01, eta: 8:57:33, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7400\n2023-01-01 20:06:00,396 - mmcls - INFO - Epoch [28][2500/5005]\tlr: 1.000e-01, eta: 8:57:24, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7372\n2023-01-01 20:06:09,190 - mmcls - INFO - Epoch [28][2600/5005]\tlr: 1.000e-01, eta: 8:57:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7703\n2023-01-01 20:06:18,309 - mmcls - INFO - Epoch [28][2700/5005]\tlr: 1.000e-01, eta: 8:57:06, time: 0.091, data_time: 0.025, memory: 1224, loss: 2.7663\n2023-01-01 20:06:27,208 - mmcls - INFO - Epoch [28][2800/5005]\tlr: 1.000e-01, eta: 8:56:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7655\n2023-01-01 20:06:35,800 - mmcls - INFO - Epoch [28][2900/5005]\tlr: 1.000e-01, eta: 8:56:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7733\n2023-01-01 20:06:45,012 - mmcls - INFO - Epoch [28][3000/5005]\tlr: 1.000e-01, eta: 8:56:40, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.7598\n2023-01-01 20:06:53,743 - mmcls - INFO - Epoch [28][3100/5005]\tlr: 1.000e-01, eta: 8:56:30, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7615\n2023-01-01 20:07:02,912 - mmcls - INFO - Epoch [28][3200/5005]\tlr: 1.000e-01, eta: 8:56:22, time: 0.092, data_time: 0.037, memory: 1224, loss: 2.8076\n2023-01-01 20:07:11,461 - mmcls - INFO - Epoch [28][3300/5005]\tlr: 1.000e-01, eta: 8:56:12, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7913\n2023-01-01 20:07:20,306 - mmcls - INFO - Epoch [28][3400/5005]\tlr: 1.000e-01, eta: 8:56:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7638\n2023-01-01 20:07:28,722 - mmcls - INFO - Epoch [28][3500/5005]\tlr: 1.000e-01, eta: 8:55:53, time: 0.084, data_time: 0.029, memory: 1224, loss: 2.7822\n2023-01-01 20:07:37,952 - mmcls - INFO - Epoch [28][3600/5005]\tlr: 1.000e-01, eta: 8:55:45, time: 0.092, data_time: 0.015, memory: 1224, loss: 2.7877\n2023-01-01 20:07:47,475 - mmcls - INFO - Epoch [28][3700/5005]\tlr: 1.000e-01, eta: 8:55:38, time: 0.095, data_time: 0.014, memory: 1224, loss: 2.7793\n2023-01-01 20:07:56,018 - mmcls - INFO - Epoch [28][3800/5005]\tlr: 1.000e-01, eta: 8:55:28, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.7544\n2023-01-01 20:08:05,199 - mmcls - INFO - Epoch [28][3900/5005]\tlr: 1.000e-01, eta: 8:55:20, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.7632\n2023-01-01 20:08:14,179 - mmcls - INFO - Epoch [28][4000/5005]\tlr: 1.000e-01, eta: 8:55:12, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7644\n2023-01-01 20:08:22,875 - mmcls - INFO - Epoch [28][4100/5005]\tlr: 1.000e-01, eta: 8:55:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7613\n2023-01-01 20:08:31,619 - mmcls - INFO - Epoch [28][4200/5005]\tlr: 1.000e-01, eta: 8:54:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7672\n2023-01-01 20:08:40,189 - mmcls - INFO - Epoch [28][4300/5005]\tlr: 1.000e-01, eta: 8:54:43, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.7507\n2023-01-01 20:08:48,697 - mmcls - INFO - Epoch [28][4400/5005]\tlr: 1.000e-01, eta: 8:54:33, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.7728\n2023-01-01 20:08:57,411 - mmcls - INFO - Epoch [28][4500/5005]\tlr: 1.000e-01, eta: 8:54:24, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7857\n2023-01-01 20:09:06,132 - mmcls - INFO - Epoch [28][4600/5005]\tlr: 1.000e-01, eta: 8:54:15, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7912\n2023-01-01 20:09:15,199 - mmcls - INFO - Epoch [28][4700/5005]\tlr: 1.000e-01, eta: 8:54:06, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.7575\n2023-01-01 20:09:23,979 - mmcls - INFO - Epoch [28][4800/5005]\tlr: 1.000e-01, eta: 8:53:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7620\n2023-01-01 20:09:33,009 - mmcls - INFO - Epoch [28][4900/5005]\tlr: 1.000e-01, eta: 8:53:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7796\n2023-01-01 20:09:41,939 - mmcls - INFO - Epoch [28][5000/5005]\tlr: 1.000e-01, eta: 8:53:40, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7677\n2023-01-01 20:10:04,552 - mmcls - INFO - Epoch(val) [28][196]\taccuracy_top-1: 46.0460, accuracy_top-5: 71.5160\n2023-01-01 20:10:15,453 - mmcls - INFO - Epoch [29][100/5005]\tlr: 1.000e-01, eta: 8:53:35, time: 0.109, data_time: 0.045, memory: 1224, loss: 2.7153\n2023-01-01 20:10:24,538 - mmcls - INFO - Epoch [29][200/5005]\tlr: 1.000e-01, eta: 8:53:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.6954\n2023-01-01 20:10:33,496 - mmcls - INFO - Epoch [29][300/5005]\tlr: 1.000e-01, eta: 8:53:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7148\n2023-01-01 20:10:42,272 - mmcls - INFO - Epoch [29][400/5005]\tlr: 1.000e-01, eta: 8:53:08, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.7166\n2023-01-01 20:10:51,015 - mmcls - INFO - Epoch [29][500/5005]\tlr: 1.000e-01, eta: 8:52:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7481\n2023-01-01 20:11:00,028 - mmcls - INFO - Epoch [29][600/5005]\tlr: 1.000e-01, eta: 8:52:50, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7401\n2023-01-01 20:11:08,826 - mmcls - INFO - Epoch [29][700/5005]\tlr: 1.000e-01, eta: 8:52:41, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7554\n2023-01-01 20:11:17,388 - mmcls - INFO - Epoch [29][800/5005]\tlr: 1.000e-01, eta: 8:52:32, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7556\n2023-01-01 20:11:26,109 - mmcls - INFO - Epoch [29][900/5005]\tlr: 1.000e-01, eta: 8:52:22, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.7336\n2023-01-01 20:11:34,943 - mmcls - INFO - Epoch [29][1000/5005]\tlr: 1.000e-01, eta: 8:52:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7336\n2023-01-01 20:11:43,770 - mmcls - INFO - Epoch [29][1100/5005]\tlr: 1.000e-01, eta: 8:52:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7126\n2023-01-01 20:11:52,561 - mmcls - INFO - Epoch [29][1200/5005]\tlr: 1.000e-01, eta: 8:51:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7351\n2023-01-01 20:12:01,507 - mmcls - INFO - Epoch [29][1300/5005]\tlr: 1.000e-01, eta: 8:51:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7185\n2023-01-01 20:12:10,597 - mmcls - INFO - Epoch [29][1400/5005]\tlr: 1.000e-01, eta: 8:51:38, time: 0.091, data_time: 0.018, memory: 1224, loss: 2.7401\n2023-01-01 20:12:19,623 - mmcls - INFO - Epoch [29][1500/5005]\tlr: 1.000e-01, eta: 8:51:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7606\n2023-01-01 20:12:28,260 - mmcls - INFO - Epoch [29][1600/5005]\tlr: 1.000e-01, eta: 8:51:20, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7631\n2023-01-01 20:12:36,988 - mmcls - INFO - Epoch [29][1700/5005]\tlr: 1.000e-01, eta: 8:51:11, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7478\n2023-01-01 20:12:45,832 - mmcls - INFO - Epoch [29][1800/5005]\tlr: 1.000e-01, eta: 8:51:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7355\n2023-01-01 20:12:54,804 - mmcls - INFO - Epoch [29][1900/5005]\tlr: 1.000e-01, eta: 8:50:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7555\n2023-01-01 20:13:03,878 - mmcls - INFO - Epoch [29][2000/5005]\tlr: 1.000e-01, eta: 8:50:45, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.7437\n2023-01-01 20:13:12,672 - mmcls - INFO - Epoch [29][2100/5005]\tlr: 1.000e-01, eta: 8:50:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7221\n2023-01-01 20:13:21,666 - mmcls - INFO - Epoch [29][2200/5005]\tlr: 1.000e-01, eta: 8:50:27, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.7684\n2023-01-01 20:13:30,524 - mmcls - INFO - Epoch [29][2300/5005]\tlr: 1.000e-01, eta: 8:50:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7748\n2023-01-01 20:13:39,169 - mmcls - INFO - Epoch [29][2400/5005]\tlr: 1.000e-01, eta: 8:50:08, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.7237\n2023-01-01 20:13:48,152 - mmcls - INFO - Epoch [29][2500/5005]\tlr: 1.000e-01, eta: 8:50:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7589\n2023-01-01 20:13:56,809 - mmcls - INFO - Epoch [29][2600/5005]\tlr: 1.000e-01, eta: 8:49:50, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7717\n2023-01-01 20:14:06,180 - mmcls - INFO - Epoch [29][2700/5005]\tlr: 1.000e-01, eta: 8:49:43, time: 0.094, data_time: 0.004, memory: 1224, loss: 2.8122\n2023-01-01 20:14:14,903 - mmcls - INFO - Epoch [29][2800/5005]\tlr: 1.000e-01, eta: 8:49:33, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7647\n2023-01-01 20:14:23,632 - mmcls - INFO - Epoch [29][2900/5005]\tlr: 1.000e-01, eta: 8:49:24, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7618\n2023-01-01 20:14:32,148 - mmcls - INFO - Epoch [29][3000/5005]\tlr: 1.000e-01, eta: 8:49:14, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.7527\n2023-01-01 20:14:40,825 - mmcls - INFO - Epoch [29][3100/5005]\tlr: 1.000e-01, eta: 8:49:05, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7685\n2023-01-01 20:14:49,518 - mmcls - INFO - Epoch [29][3200/5005]\tlr: 1.000e-01, eta: 8:48:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7895\n2023-01-01 20:14:58,385 - mmcls - INFO - Epoch [29][3300/5005]\tlr: 1.000e-01, eta: 8:48:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7705\n2023-01-01 20:15:07,003 - mmcls - INFO - Epoch [29][3400/5005]\tlr: 1.000e-01, eta: 8:48:37, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.7782\n2023-01-01 20:15:15,610 - mmcls - INFO - Epoch [29][3500/5005]\tlr: 1.000e-01, eta: 8:48:27, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7496\n2023-01-01 20:15:24,432 - mmcls - INFO - Epoch [29][3600/5005]\tlr: 1.000e-01, eta: 8:48:18, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7691\n2023-01-01 20:15:33,359 - mmcls - INFO - Epoch [29][3700/5005]\tlr: 1.000e-01, eta: 8:48:10, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7721\n2023-01-01 20:15:41,920 - mmcls - INFO - Epoch [29][3800/5005]\tlr: 1.000e-01, eta: 8:48:00, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.7441\n2023-01-01 20:15:50,633 - mmcls - INFO - Epoch [29][3900/5005]\tlr: 1.000e-01, eta: 8:47:51, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7536\n2023-01-01 20:15:59,384 - mmcls - INFO - Epoch [29][4000/5005]\tlr: 1.000e-01, eta: 8:47:41, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7585\n2023-01-01 20:16:08,105 - mmcls - INFO - Epoch [29][4100/5005]\tlr: 1.000e-01, eta: 8:47:32, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7612\n2023-01-01 20:16:16,901 - mmcls - INFO - Epoch [29][4200/5005]\tlr: 1.000e-01, eta: 8:47:23, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7578\n2023-01-01 20:16:25,730 - mmcls - INFO - Epoch [29][4300/5005]\tlr: 1.000e-01, eta: 8:47:14, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7727\n2023-01-01 20:16:34,473 - mmcls - INFO - Epoch [29][4400/5005]\tlr: 1.000e-01, eta: 8:47:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7902\n2023-01-01 20:16:43,189 - mmcls - INFO - Epoch [29][4500/5005]\tlr: 1.000e-01, eta: 8:46:55, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7702\n2023-01-01 20:16:52,106 - mmcls - INFO - Epoch [29][4600/5005]\tlr: 1.000e-01, eta: 8:46:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.7924\n2023-01-01 20:17:01,253 - mmcls - INFO - Epoch [29][4700/5005]\tlr: 1.000e-01, eta: 8:46:38, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7346\n2023-01-01 20:17:10,187 - mmcls - INFO - Epoch [29][4800/5005]\tlr: 1.000e-01, eta: 8:46:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7706\n2023-01-01 20:17:19,209 - mmcls - INFO - Epoch [29][4900/5005]\tlr: 1.000e-01, eta: 8:46:21, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.8165\n2023-01-01 20:17:28,093 - mmcls - INFO - Epoch [29][5000/5005]\tlr: 1.000e-01, eta: 8:46:12, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7818\n2023-01-01 20:17:50,722 - mmcls - INFO - Epoch(val) [29][196]\taccuracy_top-1: 47.2720, accuracy_top-5: 72.7520\n2023-01-01 20:18:01,820 - mmcls - INFO - Epoch [30][100/5005]\tlr: 1.000e-01, eta: 8:46:07, time: 0.111, data_time: 0.050, memory: 1224, loss: 2.7071\n2023-01-01 20:18:10,605 - mmcls - INFO - Epoch [30][200/5005]\tlr: 1.000e-01, eta: 8:45:58, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7325\n2023-01-01 20:18:19,215 - mmcls - INFO - Epoch [30][300/5005]\tlr: 1.000e-01, eta: 8:45:48, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.7330\n2023-01-01 20:18:28,339 - mmcls - INFO - Epoch [30][400/5005]\tlr: 1.000e-01, eta: 8:45:40, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7380\n2023-01-01 20:18:37,208 - mmcls - INFO - Epoch [30][500/5005]\tlr: 1.000e-01, eta: 8:45:31, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7402\n2023-01-01 20:18:45,756 - mmcls - INFO - Epoch [30][600/5005]\tlr: 1.000e-01, eta: 8:45:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7143\n2023-01-01 20:18:54,490 - mmcls - INFO - Epoch [30][700/5005]\tlr: 1.000e-01, eta: 8:45:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7246\n2023-01-01 20:19:03,090 - mmcls - INFO - Epoch [30][800/5005]\tlr: 1.000e-01, eta: 8:45:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7671\n2023-01-01 20:19:12,188 - mmcls - INFO - Epoch [30][900/5005]\tlr: 1.000e-01, eta: 8:44:54, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7125\n2023-01-01 20:19:20,864 - mmcls - INFO - Epoch [30][1000/5005]\tlr: 1.000e-01, eta: 8:44:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7587\n2023-01-01 20:19:29,598 - mmcls - INFO - Epoch [30][1100/5005]\tlr: 1.000e-01, eta: 8:44:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7362\n2023-01-01 20:19:38,217 - mmcls - INFO - Epoch [30][1200/5005]\tlr: 1.000e-01, eta: 8:44:26, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.7383\n2023-01-01 20:19:47,300 - mmcls - INFO - Epoch [30][1300/5005]\tlr: 1.000e-01, eta: 8:44:18, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.7227\n2023-01-01 20:19:56,019 - mmcls - INFO - Epoch [30][1400/5005]\tlr: 1.000e-01, eta: 8:44:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7600\n2023-01-01 20:20:04,756 - mmcls - INFO - Epoch [30][1500/5005]\tlr: 1.000e-01, eta: 8:43:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7282\n2023-01-01 20:20:13,724 - mmcls - INFO - Epoch [30][1600/5005]\tlr: 1.000e-01, eta: 8:43:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7388\n2023-01-01 20:20:22,700 - mmcls - INFO - Epoch [30][1700/5005]\tlr: 1.000e-01, eta: 8:43:42, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7558\n2023-01-01 20:20:31,726 - mmcls - INFO - Epoch [30][1800/5005]\tlr: 1.000e-01, eta: 8:43:33, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.7489\n2023-01-01 20:20:41,094 - mmcls - INFO - Epoch [30][1900/5005]\tlr: 1.000e-01, eta: 8:43:25, time: 0.094, data_time: 0.034, memory: 1224, loss: 2.7420\n2023-01-01 20:20:50,176 - mmcls - INFO - Epoch [30][2000/5005]\tlr: 1.000e-01, eta: 8:43:17, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7453\n2023-01-01 20:20:59,021 - mmcls - INFO - Epoch [30][2100/5005]\tlr: 1.000e-01, eta: 8:43:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7445\n2023-01-01 20:21:07,576 - mmcls - INFO - Epoch [30][2200/5005]\tlr: 1.000e-01, eta: 8:42:58, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7967\n2023-01-01 20:21:16,688 - mmcls - INFO - Epoch [30][2300/5005]\tlr: 1.000e-01, eta: 8:42:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7671\n2023-01-01 20:21:25,292 - mmcls - INFO - Epoch [30][2400/5005]\tlr: 1.000e-01, eta: 8:42:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7645\n2023-01-01 20:21:34,010 - mmcls - INFO - Epoch [30][2500/5005]\tlr: 1.000e-01, eta: 8:42:31, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7657\n2023-01-01 20:21:42,718 - mmcls - INFO - Epoch [30][2600/5005]\tlr: 1.000e-01, eta: 8:42:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7422\n2023-01-01 20:21:51,327 - mmcls - INFO - Epoch [30][2700/5005]\tlr: 1.000e-01, eta: 8:42:12, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7602\n2023-01-01 20:22:00,066 - mmcls - INFO - Epoch [30][2800/5005]\tlr: 1.000e-01, eta: 8:42:03, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7501\n2023-01-01 20:22:08,916 - mmcls - INFO - Epoch [30][2900/5005]\tlr: 1.000e-01, eta: 8:41:54, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7615\n2023-01-01 20:22:17,347 - mmcls - INFO - Epoch [30][3000/5005]\tlr: 1.000e-01, eta: 8:41:44, time: 0.084, data_time: 0.012, memory: 1224, loss: 2.7255\n2023-01-01 20:22:26,330 - mmcls - INFO - Epoch [30][3100/5005]\tlr: 1.000e-01, eta: 8:41:35, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7568\n2023-01-01 20:22:35,577 - mmcls - INFO - Epoch [30][3200/5005]\tlr: 1.000e-01, eta: 8:41:27, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.7499\n2023-01-01 20:22:44,594 - mmcls - INFO - Epoch [30][3300/5005]\tlr: 1.000e-01, eta: 8:41:19, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7649\n2023-01-01 20:22:53,453 - mmcls - INFO - Epoch [30][3400/5005]\tlr: 1.000e-01, eta: 8:41:10, time: 0.088, data_time: 0.038, memory: 1224, loss: 2.7258\n2023-01-01 20:23:02,300 - mmcls - INFO - Epoch [30][3500/5005]\tlr: 1.000e-01, eta: 8:41:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7685\n2023-01-01 20:23:10,837 - mmcls - INFO - Epoch [30][3600/5005]\tlr: 1.000e-01, eta: 8:40:51, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8038\n2023-01-01 20:23:19,708 - mmcls - INFO - Epoch [30][3700/5005]\tlr: 1.000e-01, eta: 8:40:42, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7720\n2023-01-01 20:23:28,298 - mmcls - INFO - Epoch [30][3800/5005]\tlr: 1.000e-01, eta: 8:40:33, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.7529\n2023-01-01 20:23:37,471 - mmcls - INFO - Epoch [30][3900/5005]\tlr: 1.000e-01, eta: 8:40:25, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.7794\n2023-01-01 20:23:46,132 - mmcls - INFO - Epoch [30][4000/5005]\tlr: 1.000e-01, eta: 8:40:15, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7609\n2023-01-01 20:23:55,140 - mmcls - INFO - Epoch [30][4100/5005]\tlr: 1.000e-01, eta: 8:40:07, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.7684\n2023-01-01 20:24:03,797 - mmcls - INFO - Epoch [30][4200/5005]\tlr: 1.000e-01, eta: 8:39:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7519\n2023-01-01 20:24:12,673 - mmcls - INFO - Epoch [30][4300/5005]\tlr: 1.000e-01, eta: 8:39:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7702\n2023-01-01 20:24:21,970 - mmcls - INFO - Epoch [30][4400/5005]\tlr: 1.000e-01, eta: 8:39:40, time: 0.093, data_time: 0.039, memory: 1224, loss: 2.7612\n2023-01-01 20:24:30,724 - mmcls - INFO - Epoch [30][4500/5005]\tlr: 1.000e-01, eta: 8:39:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7538\n2023-01-01 20:24:39,602 - mmcls - INFO - Epoch [30][4600/5005]\tlr: 1.000e-01, eta: 8:39:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7690\n2023-01-01 20:24:48,131 - mmcls - INFO - Epoch [30][4700/5005]\tlr: 1.000e-01, eta: 8:39:12, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8017\n2023-01-01 20:24:56,759 - mmcls - INFO - Epoch [30][4800/5005]\tlr: 1.000e-01, eta: 8:39:03, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.7774\n2023-01-01 20:25:05,817 - mmcls - INFO - Epoch [30][4900/5005]\tlr: 1.000e-01, eta: 8:38:55, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.7521\n2023-01-01 20:25:14,706 - mmcls - INFO - Epoch [30][5000/5005]\tlr: 1.000e-01, eta: 8:38:46, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7527\n2023-01-01 20:25:37,633 - mmcls - INFO - Epoch(val) [30][196]\taccuracy_top-1: 46.1860, accuracy_top-5: 71.7180\n2023-01-01 20:25:48,535 - mmcls - INFO - Epoch [31][100/5005]\tlr: 1.000e-02, eta: 8:38:40, time: 0.109, data_time: 0.050, memory: 1224, loss: 2.5633\n2023-01-01 20:25:57,317 - mmcls - INFO - Epoch [31][200/5005]\tlr: 1.000e-02, eta: 8:38:31, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.4726\n2023-01-01 20:26:06,199 - mmcls - INFO - Epoch [31][300/5005]\tlr: 1.000e-02, eta: 8:38:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4180\n2023-01-01 20:26:15,137 - mmcls - INFO - Epoch [31][400/5005]\tlr: 1.000e-02, eta: 8:38:13, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3921\n2023-01-01 20:26:23,703 - mmcls - INFO - Epoch [31][500/5005]\tlr: 1.000e-02, eta: 8:38:04, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3569\n2023-01-01 20:26:32,974 - mmcls - INFO - Epoch [31][600/5005]\tlr: 1.000e-02, eta: 8:37:56, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3560\n2023-01-01 20:26:41,739 - mmcls - INFO - Epoch [31][700/5005]\tlr: 1.000e-02, eta: 8:37:46, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3546\n2023-01-01 20:26:50,449 - mmcls - INFO - Epoch [31][800/5005]\tlr: 1.000e-02, eta: 8:37:37, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.3425\n2023-01-01 20:26:59,714 - mmcls - INFO - Epoch [31][900/5005]\tlr: 1.000e-02, eta: 8:37:29, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.3273\n2023-01-01 20:27:08,247 - mmcls - INFO - Epoch [31][1000/5005]\tlr: 1.000e-02, eta: 8:37:19, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.3128\n2023-01-01 20:27:17,523 - mmcls - INFO - Epoch [31][1100/5005]\tlr: 1.000e-02, eta: 8:37:11, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.2898\n2023-01-01 20:27:26,430 - mmcls - INFO - Epoch [31][1200/5005]\tlr: 1.000e-02, eta: 8:37:03, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3238\n2023-01-01 20:27:35,443 - mmcls - INFO - Epoch [31][1300/5005]\tlr: 1.000e-02, eta: 8:36:54, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.2919\n2023-01-01 20:27:44,318 - mmcls - INFO - Epoch [31][1400/5005]\tlr: 1.000e-02, eta: 8:36:45, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.2855\n2023-01-01 20:27:52,824 - mmcls - INFO - Epoch [31][1500/5005]\tlr: 1.000e-02, eta: 8:36:35, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.2829\n2023-01-01 20:28:01,342 - mmcls - INFO - Epoch [31][1600/5005]\tlr: 1.000e-02, eta: 8:36:26, time: 0.085, data_time: 0.019, memory: 1224, loss: 2.2696\n2023-01-01 20:28:10,610 - mmcls - INFO - Epoch [31][1700/5005]\tlr: 1.000e-02, eta: 8:36:18, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.2579\n2023-01-01 20:28:19,418 - mmcls - INFO - Epoch [31][1800/5005]\tlr: 1.000e-02, eta: 8:36:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.2680\n2023-01-01 20:28:28,116 - mmcls - INFO - Epoch [31][1900/5005]\tlr: 1.000e-02, eta: 8:35:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.2580\n2023-01-01 20:28:37,218 - mmcls - INFO - Epoch [31][2000/5005]\tlr: 1.000e-02, eta: 8:35:51, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.2433\n2023-01-01 20:28:46,121 - mmcls - INFO - Epoch [31][2100/5005]\tlr: 1.000e-02, eta: 8:35:42, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.2593\n2023-01-01 20:28:55,100 - mmcls - INFO - Epoch [31][2200/5005]\tlr: 1.000e-02, eta: 8:35:33, time: 0.090, data_time: 0.000, memory: 1224, loss: 2.2512\n2023-01-01 20:29:04,194 - mmcls - INFO - Epoch [31][2300/5005]\tlr: 1.000e-02, eta: 8:35:25, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.2417\n2023-01-01 20:29:13,027 - mmcls - INFO - Epoch [31][2400/5005]\tlr: 1.000e-02, eta: 8:35:16, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.2109\n2023-01-01 20:29:21,994 - mmcls - INFO - Epoch [31][2500/5005]\tlr: 1.000e-02, eta: 8:35:07, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.2313\n2023-01-01 20:29:30,906 - mmcls - INFO - Epoch [31][2600/5005]\tlr: 1.000e-02, eta: 8:34:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.2176\n2023-01-01 20:29:39,623 - mmcls - INFO - Epoch [31][2700/5005]\tlr: 1.000e-02, eta: 8:34:49, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.2505\n2023-01-01 20:29:48,562 - mmcls - INFO - Epoch [31][2800/5005]\tlr: 1.000e-02, eta: 8:34:40, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.2186\n2023-01-01 20:29:57,364 - mmcls - INFO - Epoch [31][2900/5005]\tlr: 1.000e-02, eta: 8:34:31, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.2366\n2023-01-01 20:30:06,104 - mmcls - INFO - Epoch [31][3000/5005]\tlr: 1.000e-02, eta: 8:34:22, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.2078\n2023-01-01 20:30:14,540 - mmcls - INFO - Epoch [31][3100/5005]\tlr: 1.000e-02, eta: 8:34:12, time: 0.084, data_time: 0.010, memory: 1224, loss: 2.2215\n2023-01-01 20:30:23,298 - mmcls - INFO - Epoch [31][3200/5005]\tlr: 1.000e-02, eta: 8:34:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.2162\n2023-01-01 20:30:32,155 - mmcls - INFO - Epoch [31][3300/5005]\tlr: 1.000e-02, eta: 8:33:54, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.2413\n2023-01-01 20:30:41,156 - mmcls - INFO - Epoch [31][3400/5005]\tlr: 1.000e-02, eta: 8:33:45, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.2208\n2023-01-01 20:30:49,891 - mmcls - INFO - Epoch [31][3500/5005]\tlr: 1.000e-02, eta: 8:33:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.2211\n2023-01-01 20:30:58,913 - mmcls - INFO - Epoch [31][3600/5005]\tlr: 1.000e-02, eta: 8:33:27, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.1944\n2023-01-01 20:31:08,091 - mmcls - INFO - Epoch [31][3700/5005]\tlr: 1.000e-02, eta: 8:33:19, time: 0.092, data_time: 0.016, memory: 1224, loss: 2.2135\n2023-01-01 20:31:17,057 - mmcls - INFO - Epoch [31][3800/5005]\tlr: 1.000e-02, eta: 8:33:11, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.2083\n2023-01-01 20:31:25,896 - mmcls - INFO - Epoch [31][3900/5005]\tlr: 1.000e-02, eta: 8:33:02, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.2001\n2023-01-01 20:31:34,838 - mmcls - INFO - Epoch [31][4000/5005]\tlr: 1.000e-02, eta: 8:32:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.2040\n2023-01-01 20:31:43,475 - mmcls - INFO - Epoch [31][4100/5005]\tlr: 1.000e-02, eta: 8:32:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.2026\n2023-01-01 20:31:52,257 - mmcls - INFO - Epoch [31][4200/5005]\tlr: 1.000e-02, eta: 8:32:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.2226\n2023-01-01 20:32:01,111 - mmcls - INFO - Epoch [31][4300/5005]\tlr: 1.000e-02, eta: 8:32:25, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.1889\n2023-01-01 20:32:09,916 - mmcls - INFO - Epoch [31][4400/5005]\tlr: 1.000e-02, eta: 8:32:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1868\n2023-01-01 20:32:18,690 - mmcls - INFO - Epoch [31][4500/5005]\tlr: 1.000e-02, eta: 8:32:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.1913\n2023-01-01 20:32:27,465 - mmcls - INFO - Epoch [31][4600/5005]\tlr: 1.000e-02, eta: 8:31:58, time: 0.088, data_time: 0.041, memory: 1224, loss: 2.1809\n2023-01-01 20:32:36,043 - mmcls - INFO - Epoch [31][4700/5005]\tlr: 1.000e-02, eta: 8:31:48, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.1870\n2023-01-01 20:32:44,667 - mmcls - INFO - Epoch [31][4800/5005]\tlr: 1.000e-02, eta: 8:31:39, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.1807\n2023-01-01 20:32:53,387 - mmcls - INFO - Epoch [31][4900/5005]\tlr: 1.000e-02, eta: 8:31:30, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.1588\n2023-01-01 20:33:02,211 - mmcls - INFO - Epoch [31][5000/5005]\tlr: 1.000e-02, eta: 8:31:21, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.1699\n2023-01-01 20:33:25,066 - mmcls - INFO - Epoch(val) [31][196]\taccuracy_top-1: 58.0100, accuracy_top-5: 80.6720\n2023-01-01 20:33:35,943 - mmcls - INFO - Epoch [32][100/5005]\tlr: 1.000e-02, eta: 8:31:15, time: 0.109, data_time: 0.068, memory: 1224, loss: 2.1557\n2023-01-01 20:33:44,714 - mmcls - INFO - Epoch [32][200/5005]\tlr: 1.000e-02, eta: 8:31:06, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.1271\n2023-01-01 20:33:53,622 - mmcls - INFO - Epoch [32][300/5005]\tlr: 1.000e-02, eta: 8:30:57, time: 0.089, data_time: 0.031, memory: 1224, loss: 2.1291\n2023-01-01 20:34:02,367 - mmcls - INFO - Epoch [32][400/5005]\tlr: 1.000e-02, eta: 8:30:48, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.1274\n2023-01-01 20:34:11,121 - mmcls - INFO - Epoch [32][500/5005]\tlr: 1.000e-02, eta: 8:30:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.1331\n2023-01-01 20:34:19,843 - mmcls - INFO - Epoch [32][600/5005]\tlr: 1.000e-02, eta: 8:30:29, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.1304\n2023-01-01 20:34:28,567 - mmcls - INFO - Epoch [32][700/5005]\tlr: 1.000e-02, eta: 8:30:20, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.1168\n2023-01-01 20:34:37,530 - mmcls - INFO - Epoch [32][800/5005]\tlr: 1.000e-02, eta: 8:30:11, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.1541\n2023-01-01 20:34:46,220 - mmcls - INFO - Epoch [32][900/5005]\tlr: 1.000e-02, eta: 8:30:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.1220\n2023-01-01 20:34:55,184 - mmcls - INFO - Epoch [32][1000/5005]\tlr: 1.000e-02, eta: 8:29:53, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.1375\n2023-01-01 20:35:04,107 - mmcls - INFO - Epoch [32][1100/5005]\tlr: 1.000e-02, eta: 8:29:44, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.1422\n2023-01-01 20:35:12,981 - mmcls - INFO - Epoch [32][1200/5005]\tlr: 1.000e-02, eta: 8:29:35, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.1191\n2023-01-01 20:35:21,399 - mmcls - INFO - Epoch [32][1300/5005]\tlr: 1.000e-02, eta: 8:29:26, time: 0.084, data_time: 0.018, memory: 1224, loss: 2.1336\n2023-01-01 20:35:30,273 - mmcls - INFO - Epoch [32][1400/5005]\tlr: 1.000e-02, eta: 8:29:17, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.1359\n2023-01-01 20:35:38,970 - mmcls - INFO - Epoch [32][1500/5005]\tlr: 1.000e-02, eta: 8:29:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.1335\n2023-01-01 20:35:47,653 - mmcls - INFO - Epoch [32][1600/5005]\tlr: 1.000e-02, eta: 8:28:58, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.1389\n2023-01-01 20:35:56,338 - mmcls - INFO - Epoch [32][1700/5005]\tlr: 1.000e-02, eta: 8:28:49, time: 0.087, data_time: 0.036, memory: 1224, loss: 2.1490\n2023-01-01 20:36:05,107 - mmcls - INFO - Epoch [32][1800/5005]\tlr: 1.000e-02, eta: 8:28:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.1379\n2023-01-01 20:36:14,519 - mmcls - INFO - Epoch [32][1900/5005]\tlr: 1.000e-02, eta: 8:28:32, time: 0.094, data_time: 0.020, memory: 1224, loss: 2.1357\n2023-01-01 20:36:23,198 - mmcls - INFO - Epoch [32][2000/5005]\tlr: 1.000e-02, eta: 8:28:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.1300\n2023-01-01 20:36:32,046 - mmcls - INFO - Epoch [32][2100/5005]\tlr: 1.000e-02, eta: 8:28:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.1237\n2023-01-01 20:36:40,652 - mmcls - INFO - Epoch [32][2200/5005]\tlr: 1.000e-02, eta: 8:28:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.1317\n2023-01-01 20:36:49,685 - mmcls - INFO - Epoch [32][2300/5005]\tlr: 1.000e-02, eta: 8:27:55, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.1148\n2023-01-01 20:36:58,496 - mmcls - INFO - Epoch [32][2400/5005]\tlr: 1.000e-02, eta: 8:27:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.1260\n2023-01-01 20:37:07,373 - mmcls - INFO - Epoch [32][2500/5005]\tlr: 1.000e-02, eta: 8:27:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.1136\n2023-01-01 20:37:16,060 - mmcls - INFO - Epoch [32][2600/5005]\tlr: 1.000e-02, eta: 8:27:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.1033\n2023-01-01 20:37:24,920 - mmcls - INFO - Epoch [32][2700/5005]\tlr: 1.000e-02, eta: 8:27:19, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.1109\n2023-01-01 20:37:33,700 - mmcls - INFO - Epoch [32][2800/5005]\tlr: 1.000e-02, eta: 8:27:10, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.1300\n2023-01-01 20:37:42,561 - mmcls - INFO - Epoch [32][2900/5005]\tlr: 1.000e-02, eta: 8:27:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.1245\n2023-01-01 20:37:51,074 - mmcls - INFO - Epoch [32][3000/5005]\tlr: 1.000e-02, eta: 8:26:52, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.1290\n2023-01-01 20:38:00,130 - mmcls - INFO - Epoch [32][3100/5005]\tlr: 1.000e-02, eta: 8:26:43, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.1403\n2023-01-01 20:38:08,883 - mmcls - INFO - Epoch [32][3200/5005]\tlr: 1.000e-02, eta: 8:26:34, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.1370\n2023-01-01 20:38:17,927 - mmcls - INFO - Epoch [32][3300/5005]\tlr: 1.000e-02, eta: 8:26:25, time: 0.090, data_time: 0.025, memory: 1224, loss: 2.1081\n2023-01-01 20:38:26,720 - mmcls - INFO - Epoch [32][3400/5005]\tlr: 1.000e-02, eta: 8:26:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1218\n2023-01-01 20:38:35,570 - mmcls - INFO - Epoch [32][3500/5005]\tlr: 1.000e-02, eta: 8:26:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1176\n2023-01-01 20:38:44,136 - mmcls - INFO - Epoch [32][3600/5005]\tlr: 1.000e-02, eta: 8:25:58, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.1267\n2023-01-01 20:38:52,799 - mmcls - INFO - Epoch [32][3700/5005]\tlr: 1.000e-02, eta: 8:25:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.1455\n2023-01-01 20:39:01,553 - mmcls - INFO - Epoch [32][3800/5005]\tlr: 1.000e-02, eta: 8:25:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.1200\n2023-01-01 20:39:10,243 - mmcls - INFO - Epoch [32][3900/5005]\tlr: 1.000e-02, eta: 8:25:30, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.1015\n2023-01-01 20:39:19,482 - mmcls - INFO - Epoch [32][4000/5005]\tlr: 1.000e-02, eta: 8:25:22, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.1197\n2023-01-01 20:39:28,261 - mmcls - INFO - Epoch [32][4100/5005]\tlr: 1.000e-02, eta: 8:25:13, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.0802\n2023-01-01 20:39:36,769 - mmcls - INFO - Epoch [32][4200/5005]\tlr: 1.000e-02, eta: 8:25:03, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.1146\n2023-01-01 20:39:45,691 - mmcls - INFO - Epoch [32][4300/5005]\tlr: 1.000e-02, eta: 8:24:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.1044\n2023-01-01 20:39:54,604 - mmcls - INFO - Epoch [32][4400/5005]\tlr: 1.000e-02, eta: 8:24:45, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.1220\n2023-01-01 20:40:03,493 - mmcls - INFO - Epoch [32][4500/5005]\tlr: 1.000e-02, eta: 8:24:36, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.1024\n2023-01-01 20:40:12,249 - mmcls - INFO - Epoch [32][4600/5005]\tlr: 1.000e-02, eta: 8:24:27, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0998\n2023-01-01 20:40:21,104 - mmcls - INFO - Epoch [32][4700/5005]\tlr: 1.000e-02, eta: 8:24:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.1278\n2023-01-01 20:40:29,735 - mmcls - INFO - Epoch [32][4800/5005]\tlr: 1.000e-02, eta: 8:24:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.1059\n2023-01-01 20:40:38,717 - mmcls - INFO - Epoch [32][4900/5005]\tlr: 1.000e-02, eta: 8:24:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.1058\n2023-01-01 20:40:47,524 - mmcls - INFO - Epoch [32][5000/5005]\tlr: 1.000e-02, eta: 8:23:51, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.1022\n2023-01-01 20:41:10,338 - mmcls - INFO - Epoch(val) [32][196]\taccuracy_top-1: 58.3000, accuracy_top-5: 80.9400\n2023-01-01 20:41:21,419 - mmcls - INFO - Epoch [33][100/5005]\tlr: 1.000e-02, eta: 8:23:46, time: 0.111, data_time: 0.032, memory: 1224, loss: 2.0597\n2023-01-01 20:41:29,868 - mmcls - INFO - Epoch [33][200/5005]\tlr: 1.000e-02, eta: 8:23:36, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.0608\n2023-01-01 20:41:38,456 - mmcls - INFO - Epoch [33][300/5005]\tlr: 1.000e-02, eta: 8:23:26, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.0562\n2023-01-01 20:41:47,298 - mmcls - INFO - Epoch [33][400/5005]\tlr: 1.000e-02, eta: 8:23:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0791\n2023-01-01 20:41:56,067 - mmcls - INFO - Epoch [33][500/5005]\tlr: 1.000e-02, eta: 8:23:08, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.0586\n2023-01-01 20:42:04,790 - mmcls - INFO - Epoch [33][600/5005]\tlr: 1.000e-02, eta: 8:22:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0842\n2023-01-01 20:42:13,665 - mmcls - INFO - Epoch [33][700/5005]\tlr: 1.000e-02, eta: 8:22:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0767\n2023-01-01 20:42:22,360 - mmcls - INFO - Epoch [33][800/5005]\tlr: 1.000e-02, eta: 8:22:41, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.1036\n2023-01-01 20:42:31,059 - mmcls - INFO - Epoch [33][900/5005]\tlr: 1.000e-02, eta: 8:22:31, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.0852\n2023-01-01 20:42:39,851 - mmcls - INFO - Epoch [33][1000/5005]\tlr: 1.000e-02, eta: 8:22:22, time: 0.088, data_time: 0.035, memory: 1224, loss: 2.0861\n2023-01-01 20:42:48,519 - mmcls - INFO - Epoch [33][1100/5005]\tlr: 1.000e-02, eta: 8:22:13, time: 0.087, data_time: 0.039, memory: 1224, loss: 2.0638\n2023-01-01 20:42:57,342 - mmcls - INFO - Epoch [33][1200/5005]\tlr: 1.000e-02, eta: 8:22:04, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0594\n2023-01-01 20:43:05,967 - mmcls - INFO - Epoch [33][1300/5005]\tlr: 1.000e-02, eta: 8:21:55, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.1028\n2023-01-01 20:43:14,518 - mmcls - INFO - Epoch [33][1400/5005]\tlr: 1.000e-02, eta: 8:21:45, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0617\n2023-01-01 20:43:23,413 - mmcls - INFO - Epoch [33][1500/5005]\tlr: 1.000e-02, eta: 8:21:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0880\n2023-01-01 20:43:32,552 - mmcls - INFO - Epoch [33][1600/5005]\tlr: 1.000e-02, eta: 8:21:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0487\n2023-01-01 20:43:41,046 - mmcls - INFO - Epoch [33][1700/5005]\tlr: 1.000e-02, eta: 8:21:18, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.0882\n2023-01-01 20:43:49,561 - mmcls - INFO - Epoch [33][1800/5005]\tlr: 1.000e-02, eta: 8:21:08, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.0911\n2023-01-01 20:43:58,336 - mmcls - INFO - Epoch [33][1900/5005]\tlr: 1.000e-02, eta: 8:20:59, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0653\n2023-01-01 20:44:07,383 - mmcls - INFO - Epoch [33][2000/5005]\tlr: 1.000e-02, eta: 8:20:51, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0707\n2023-01-01 20:44:16,154 - mmcls - INFO - Epoch [33][2100/5005]\tlr: 1.000e-02, eta: 8:20:42, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0702\n2023-01-01 20:44:25,002 - mmcls - INFO - Epoch [33][2200/5005]\tlr: 1.000e-02, eta: 8:20:33, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0646\n2023-01-01 20:44:33,667 - mmcls - INFO - Epoch [33][2300/5005]\tlr: 1.000e-02, eta: 8:20:23, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.0949\n2023-01-01 20:44:42,403 - mmcls - INFO - Epoch [33][2400/5005]\tlr: 1.000e-02, eta: 8:20:14, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0548\n2023-01-01 20:44:51,354 - mmcls - INFO - Epoch [33][2500/5005]\tlr: 1.000e-02, eta: 8:20:06, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.0768\n2023-01-01 20:45:00,200 - mmcls - INFO - Epoch [33][2600/5005]\tlr: 1.000e-02, eta: 8:19:57, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.0827\n2023-01-01 20:45:09,328 - mmcls - INFO - Epoch [33][2700/5005]\tlr: 1.000e-02, eta: 8:19:48, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.0599\n2023-01-01 20:45:18,212 - mmcls - INFO - Epoch [33][2800/5005]\tlr: 1.000e-02, eta: 8:19:39, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.0882\n2023-01-01 20:45:27,301 - mmcls - INFO - Epoch [33][2900/5005]\tlr: 1.000e-02, eta: 8:19:31, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.0539\n2023-01-01 20:45:36,292 - mmcls - INFO - Epoch [33][3000/5005]\tlr: 1.000e-02, eta: 8:19:22, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.0917\n2023-01-01 20:45:44,859 - mmcls - INFO - Epoch [33][3100/5005]\tlr: 1.000e-02, eta: 8:19:13, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.0650\n2023-01-01 20:45:53,560 - mmcls - INFO - Epoch [33][3200/5005]\tlr: 1.000e-02, eta: 8:19:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0897\n2023-01-01 20:46:02,470 - mmcls - INFO - Epoch [33][3300/5005]\tlr: 1.000e-02, eta: 8:18:55, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0867\n2023-01-01 20:46:11,226 - mmcls - INFO - Epoch [33][3400/5005]\tlr: 1.000e-02, eta: 8:18:45, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0792\n2023-01-01 20:46:20,070 - mmcls - INFO - Epoch [33][3500/5005]\tlr: 1.000e-02, eta: 8:18:36, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0691\n2023-01-01 20:46:28,815 - mmcls - INFO - Epoch [33][3600/5005]\tlr: 1.000e-02, eta: 8:18:27, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0737\n2023-01-01 20:46:37,662 - mmcls - INFO - Epoch [33][3700/5005]\tlr: 1.000e-02, eta: 8:18:18, time: 0.088, data_time: 0.040, memory: 1224, loss: 2.0461\n2023-01-01 20:46:46,437 - mmcls - INFO - Epoch [33][3800/5005]\tlr: 1.000e-02, eta: 8:18:09, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.0868\n2023-01-01 20:46:55,491 - mmcls - INFO - Epoch [33][3900/5005]\tlr: 1.000e-02, eta: 8:18:01, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0776\n2023-01-01 20:47:04,685 - mmcls - INFO - Epoch [33][4000/5005]\tlr: 1.000e-02, eta: 8:17:52, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.0717\n2023-01-01 20:47:13,523 - mmcls - INFO - Epoch [33][4100/5005]\tlr: 1.000e-02, eta: 8:17:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0571\n2023-01-01 20:47:22,268 - mmcls - INFO - Epoch [33][4200/5005]\tlr: 1.000e-02, eta: 8:17:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0711\n2023-01-01 20:47:31,304 - mmcls - INFO - Epoch [33][4300/5005]\tlr: 1.000e-02, eta: 8:17:26, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0816\n2023-01-01 20:47:40,121 - mmcls - INFO - Epoch [33][4400/5005]\tlr: 1.000e-02, eta: 8:17:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0923\n2023-01-01 20:47:48,863 - mmcls - INFO - Epoch [33][4500/5005]\tlr: 1.000e-02, eta: 8:17:08, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0737\n2023-01-01 20:47:57,699 - mmcls - INFO - Epoch [33][4600/5005]\tlr: 1.000e-02, eta: 8:16:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0527\n2023-01-01 20:48:06,721 - mmcls - INFO - Epoch [33][4700/5005]\tlr: 1.000e-02, eta: 8:16:50, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0830\n2023-01-01 20:48:15,663 - mmcls - INFO - Epoch [33][4800/5005]\tlr: 1.000e-02, eta: 8:16:41, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0795\n2023-01-01 20:48:24,834 - mmcls - INFO - Epoch [33][4900/5005]\tlr: 1.000e-02, eta: 8:16:33, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0710\n2023-01-01 20:48:33,352 - mmcls - INFO - Epoch [33][5000/5005]\tlr: 1.000e-02, eta: 8:16:23, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.0783\n2023-01-01 20:48:56,154 - mmcls - INFO - Epoch(val) [33][196]\taccuracy_top-1: 59.6980, accuracy_top-5: 81.7960\n2023-01-01 20:49:06,856 - mmcls - INFO - Epoch [34][100/5005]\tlr: 1.000e-02, eta: 8:16:17, time: 0.107, data_time: 0.035, memory: 1224, loss: 2.0138\n2023-01-01 20:49:15,712 - mmcls - INFO - Epoch [34][200/5005]\tlr: 1.000e-02, eta: 8:16:08, time: 0.089, data_time: 0.032, memory: 1224, loss: 2.0326\n2023-01-01 20:49:24,944 - mmcls - INFO - Epoch [34][300/5005]\tlr: 1.000e-02, eta: 8:16:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.0201\n2023-01-01 20:49:33,541 - mmcls - INFO - Epoch [34][400/5005]\tlr: 1.000e-02, eta: 8:15:50, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0534\n2023-01-01 20:49:42,132 - mmcls - INFO - Epoch [34][500/5005]\tlr: 1.000e-02, eta: 8:15:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0253\n2023-01-01 20:49:50,964 - mmcls - INFO - Epoch [34][600/5005]\tlr: 1.000e-02, eta: 8:15:32, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0148\n2023-01-01 20:49:59,695 - mmcls - INFO - Epoch [34][700/5005]\tlr: 1.000e-02, eta: 8:15:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0518\n2023-01-01 20:50:08,970 - mmcls - INFO - Epoch [34][800/5005]\tlr: 1.000e-02, eta: 8:15:14, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.0145\n2023-01-01 20:50:17,413 - mmcls - INFO - Epoch [34][900/5005]\tlr: 1.000e-02, eta: 8:15:05, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.0398\n2023-01-01 20:50:26,109 - mmcls - INFO - Epoch [34][1000/5005]\tlr: 1.000e-02, eta: 8:14:55, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0416\n2023-01-01 20:50:34,852 - mmcls - INFO - Epoch [34][1100/5005]\tlr: 1.000e-02, eta: 8:14:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0263\n2023-01-01 20:50:44,031 - mmcls - INFO - Epoch [34][1200/5005]\tlr: 1.000e-02, eta: 8:14:38, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.0521\n2023-01-01 20:50:53,048 - mmcls - INFO - Epoch [34][1300/5005]\tlr: 1.000e-02, eta: 8:14:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0341\n2023-01-01 20:51:01,681 - mmcls - INFO - Epoch [34][1400/5005]\tlr: 1.000e-02, eta: 8:14:20, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.0291\n2023-01-01 20:51:10,693 - mmcls - INFO - Epoch [34][1500/5005]\tlr: 1.000e-02, eta: 8:14:11, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0316\n2023-01-01 20:51:19,413 - mmcls - INFO - Epoch [34][1600/5005]\tlr: 1.000e-02, eta: 8:14:02, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.0237\n2023-01-01 20:51:27,865 - mmcls - INFO - Epoch [34][1700/5005]\tlr: 1.000e-02, eta: 8:13:52, time: 0.084, data_time: 0.019, memory: 1224, loss: 2.0263\n2023-01-01 20:51:36,577 - mmcls - INFO - Epoch [34][1800/5005]\tlr: 1.000e-02, eta: 8:13:43, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0308\n2023-01-01 20:51:45,341 - mmcls - INFO - Epoch [34][1900/5005]\tlr: 1.000e-02, eta: 8:13:34, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.0426\n2023-01-01 20:51:54,084 - mmcls - INFO - Epoch [34][2000/5005]\tlr: 1.000e-02, eta: 8:13:25, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0504\n2023-01-01 20:52:02,823 - mmcls - INFO - Epoch [34][2100/5005]\tlr: 1.000e-02, eta: 8:13:16, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.0591\n2023-01-01 20:52:11,811 - mmcls - INFO - Epoch [34][2200/5005]\tlr: 1.000e-02, eta: 8:13:07, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.0478\n2023-01-01 20:52:20,416 - mmcls - INFO - Epoch [34][2300/5005]\tlr: 1.000e-02, eta: 8:12:58, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.0524\n2023-01-01 20:52:29,160 - mmcls - INFO - Epoch [34][2400/5005]\tlr: 1.000e-02, eta: 8:12:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0216\n2023-01-01 20:52:37,847 - mmcls - INFO - Epoch [34][2500/5005]\tlr: 1.000e-02, eta: 8:12:39, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0481\n2023-01-01 20:52:46,704 - mmcls - INFO - Epoch [34][2600/5005]\tlr: 1.000e-02, eta: 8:12:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.0480\n2023-01-01 20:52:55,696 - mmcls - INFO - Epoch [34][2700/5005]\tlr: 1.000e-02, eta: 8:12:22, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0610\n2023-01-01 20:53:04,660 - mmcls - INFO - Epoch [34][2800/5005]\tlr: 1.000e-02, eta: 8:12:13, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.0521\n2023-01-01 20:53:13,397 - mmcls - INFO - Epoch [34][2900/5005]\tlr: 1.000e-02, eta: 8:12:04, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0638\n2023-01-01 20:53:21,903 - mmcls - INFO - Epoch [34][3000/5005]\tlr: 1.000e-02, eta: 8:11:54, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.0555\n2023-01-01 20:53:30,789 - mmcls - INFO - Epoch [34][3100/5005]\tlr: 1.000e-02, eta: 8:11:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0382\n2023-01-01 20:53:39,456 - mmcls - INFO - Epoch [34][3200/5005]\tlr: 1.000e-02, eta: 8:11:36, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0516\n2023-01-01 20:53:48,647 - mmcls - INFO - Epoch [34][3300/5005]\tlr: 1.000e-02, eta: 8:11:28, time: 0.092, data_time: 0.018, memory: 1224, loss: 2.0210\n2023-01-01 20:53:57,678 - mmcls - INFO - Epoch [34][3400/5005]\tlr: 1.000e-02, eta: 8:11:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0499\n2023-01-01 20:54:06,439 - mmcls - INFO - Epoch [34][3500/5005]\tlr: 1.000e-02, eta: 8:11:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0469\n2023-01-01 20:54:15,122 - mmcls - INFO - Epoch [34][3600/5005]\tlr: 1.000e-02, eta: 8:11:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0547\n2023-01-01 20:54:23,988 - mmcls - INFO - Epoch [34][3700/5005]\tlr: 1.000e-02, eta: 8:10:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0412\n2023-01-01 20:54:32,708 - mmcls - INFO - Epoch [34][3800/5005]\tlr: 1.000e-02, eta: 8:10:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0477\n2023-01-01 20:54:41,705 - mmcls - INFO - Epoch [34][3900/5005]\tlr: 1.000e-02, eta: 8:10:34, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0402\n2023-01-01 20:54:50,750 - mmcls - INFO - Epoch [34][4000/5005]\tlr: 1.000e-02, eta: 8:10:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0336\n2023-01-01 20:54:59,643 - mmcls - INFO - Epoch [34][4100/5005]\tlr: 1.000e-02, eta: 8:10:16, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.0651\n2023-01-01 20:55:08,636 - mmcls - INFO - Epoch [34][4200/5005]\tlr: 1.000e-02, eta: 8:10:08, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0448\n2023-01-01 20:55:17,301 - mmcls - INFO - Epoch [34][4300/5005]\tlr: 1.000e-02, eta: 8:09:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0612\n2023-01-01 20:55:26,087 - mmcls - INFO - Epoch [34][4400/5005]\tlr: 1.000e-02, eta: 8:09:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0272\n2023-01-01 20:55:34,897 - mmcls - INFO - Epoch [34][4500/5005]\tlr: 1.000e-02, eta: 8:09:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0322\n2023-01-01 20:55:43,354 - mmcls - INFO - Epoch [34][4600/5005]\tlr: 1.000e-02, eta: 8:09:31, time: 0.085, data_time: 0.027, memory: 1224, loss: 2.0507\n2023-01-01 20:55:52,271 - mmcls - INFO - Epoch [34][4700/5005]\tlr: 1.000e-02, eta: 8:09:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0432\n2023-01-01 20:56:01,119 - mmcls - INFO - Epoch [34][4800/5005]\tlr: 1.000e-02, eta: 8:09:13, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0647\n2023-01-01 20:56:10,109 - mmcls - INFO - Epoch [34][4900/5005]\tlr: 1.000e-02, eta: 8:09:04, time: 0.090, data_time: 0.033, memory: 1224, loss: 2.0481\n2023-01-01 20:56:18,944 - mmcls - INFO - Epoch [34][5000/5005]\tlr: 1.000e-02, eta: 8:08:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0590\n2023-01-01 20:56:41,792 - mmcls - INFO - Epoch(val) [34][196]\taccuracy_top-1: 59.9340, accuracy_top-5: 81.9720\n2023-01-01 20:56:53,119 - mmcls - INFO - Epoch [35][100/5005]\tlr: 1.000e-02, eta: 8:08:50, time: 0.113, data_time: 0.037, memory: 1224, loss: 1.9842\n2023-01-01 20:57:01,808 - mmcls - INFO - Epoch [35][200/5005]\tlr: 1.000e-02, eta: 8:08:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0082\n2023-01-01 20:57:10,538 - mmcls - INFO - Epoch [35][300/5005]\tlr: 1.000e-02, eta: 8:08:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0205\n2023-01-01 20:57:19,451 - mmcls - INFO - Epoch [35][400/5005]\tlr: 1.000e-02, eta: 8:08:23, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.0001\n2023-01-01 20:57:28,046 - mmcls - INFO - Epoch [35][500/5005]\tlr: 1.000e-02, eta: 8:08:13, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.9796\n2023-01-01 20:57:36,761 - mmcls - INFO - Epoch [35][600/5005]\tlr: 1.000e-02, eta: 8:08:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0037\n2023-01-01 20:57:45,609 - mmcls - INFO - Epoch [35][700/5005]\tlr: 1.000e-02, eta: 8:07:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0019\n2023-01-01 20:57:54,375 - mmcls - INFO - Epoch [35][800/5005]\tlr: 1.000e-02, eta: 8:07:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0166\n2023-01-01 20:58:03,067 - mmcls - INFO - Epoch [35][900/5005]\tlr: 1.000e-02, eta: 8:07:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0181\n2023-01-01 20:58:12,234 - mmcls - INFO - Epoch [35][1000/5005]\tlr: 1.000e-02, eta: 8:07:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9936\n2023-01-01 20:58:20,895 - mmcls - INFO - Epoch [35][1100/5005]\tlr: 1.000e-02, eta: 8:07:19, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.0135\n2023-01-01 20:58:29,891 - mmcls - INFO - Epoch [35][1200/5005]\tlr: 1.000e-02, eta: 8:07:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0151\n2023-01-01 20:58:38,548 - mmcls - INFO - Epoch [35][1300/5005]\tlr: 1.000e-02, eta: 8:07:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0202\n2023-01-01 20:58:47,242 - mmcls - INFO - Epoch [35][1400/5005]\tlr: 1.000e-02, eta: 8:06:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0158\n2023-01-01 20:58:56,042 - mmcls - INFO - Epoch [35][1500/5005]\tlr: 1.000e-02, eta: 8:06:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0557\n2023-01-01 20:59:05,022 - mmcls - INFO - Epoch [35][1600/5005]\tlr: 1.000e-02, eta: 8:06:34, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.0388\n2023-01-01 20:59:13,748 - mmcls - INFO - Epoch [35][1700/5005]\tlr: 1.000e-02, eta: 8:06:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0465\n2023-01-01 20:59:22,338 - mmcls - INFO - Epoch [35][1800/5005]\tlr: 1.000e-02, eta: 8:06:15, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0442\n2023-01-01 20:59:31,472 - mmcls - INFO - Epoch [35][1900/5005]\tlr: 1.000e-02, eta: 8:06:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9881\n2023-01-01 20:59:40,202 - mmcls - INFO - Epoch [35][2000/5005]\tlr: 1.000e-02, eta: 8:05:58, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.0312\n2023-01-01 20:59:48,971 - mmcls - INFO - Epoch [35][2100/5005]\tlr: 1.000e-02, eta: 8:05:49, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.0247\n2023-01-01 20:59:57,908 - mmcls - INFO - Epoch [35][2200/5005]\tlr: 1.000e-02, eta: 8:05:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0195\n2023-01-01 21:00:06,661 - mmcls - INFO - Epoch [35][2300/5005]\tlr: 1.000e-02, eta: 8:05:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0497\n2023-01-01 21:00:15,551 - mmcls - INFO - Epoch [35][2400/5005]\tlr: 1.000e-02, eta: 8:05:22, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0302\n2023-01-01 21:00:24,044 - mmcls - INFO - Epoch [35][2500/5005]\tlr: 1.000e-02, eta: 8:05:12, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.0316\n2023-01-01 21:00:32,710 - mmcls - INFO - Epoch [35][2600/5005]\tlr: 1.000e-02, eta: 8:05:03, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0612\n2023-01-01 21:00:41,321 - mmcls - INFO - Epoch [35][2700/5005]\tlr: 1.000e-02, eta: 8:04:54, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.0289\n2023-01-01 21:00:50,484 - mmcls - INFO - Epoch [35][2800/5005]\tlr: 1.000e-02, eta: 8:04:45, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.0192\n2023-01-01 21:00:59,121 - mmcls - INFO - Epoch [35][2900/5005]\tlr: 1.000e-02, eta: 8:04:36, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0315\n2023-01-01 21:01:07,776 - mmcls - INFO - Epoch [35][3000/5005]\tlr: 1.000e-02, eta: 8:04:27, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0071\n2023-01-01 21:01:16,666 - mmcls - INFO - Epoch [35][3100/5005]\tlr: 1.000e-02, eta: 8:04:18, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0364\n2023-01-01 21:01:25,529 - mmcls - INFO - Epoch [35][3200/5005]\tlr: 1.000e-02, eta: 8:04:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.0163\n2023-01-01 21:01:34,426 - mmcls - INFO - Epoch [35][3300/5005]\tlr: 1.000e-02, eta: 8:04:00, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.0440\n2023-01-01 21:01:43,201 - mmcls - INFO - Epoch [35][3400/5005]\tlr: 1.000e-02, eta: 8:03:51, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0327\n2023-01-01 21:01:51,986 - mmcls - INFO - Epoch [35][3500/5005]\tlr: 1.000e-02, eta: 8:03:42, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0724\n2023-01-01 21:02:00,702 - mmcls - INFO - Epoch [35][3600/5005]\tlr: 1.000e-02, eta: 8:03:33, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.0401\n2023-01-01 21:02:09,350 - mmcls - INFO - Epoch [35][3700/5005]\tlr: 1.000e-02, eta: 8:03:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0098\n2023-01-01 21:02:18,368 - mmcls - INFO - Epoch [35][3800/5005]\tlr: 1.000e-02, eta: 8:03:15, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.0357\n2023-01-01 21:02:27,262 - mmcls - INFO - Epoch [35][3900/5005]\tlr: 1.000e-02, eta: 8:03:06, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0170\n2023-01-01 21:02:36,153 - mmcls - INFO - Epoch [35][4000/5005]\tlr: 1.000e-02, eta: 8:02:57, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.0174\n2023-01-01 21:02:45,180 - mmcls - INFO - Epoch [35][4100/5005]\tlr: 1.000e-02, eta: 8:02:48, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0220\n2023-01-01 21:02:54,183 - mmcls - INFO - Epoch [35][4200/5005]\tlr: 1.000e-02, eta: 8:02:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0402\n2023-01-01 21:03:03,110 - mmcls - INFO - Epoch [35][4300/5005]\tlr: 1.000e-02, eta: 8:02:31, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.0275\n2023-01-01 21:03:12,119 - mmcls - INFO - Epoch [35][4400/5005]\tlr: 1.000e-02, eta: 8:02:22, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.0406\n2023-01-01 21:03:20,950 - mmcls - INFO - Epoch [35][4500/5005]\tlr: 1.000e-02, eta: 8:02:13, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.0444\n2023-01-01 21:03:29,699 - mmcls - INFO - Epoch [35][4600/5005]\tlr: 1.000e-02, eta: 8:02:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0392\n2023-01-01 21:03:38,541 - mmcls - INFO - Epoch [35][4700/5005]\tlr: 1.000e-02, eta: 8:01:55, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0516\n2023-01-01 21:03:47,175 - mmcls - INFO - Epoch [35][4800/5005]\tlr: 1.000e-02, eta: 8:01:46, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0668\n2023-01-01 21:03:55,980 - mmcls - INFO - Epoch [35][4900/5005]\tlr: 1.000e-02, eta: 8:01:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0396\n2023-01-01 21:04:05,115 - mmcls - INFO - Epoch [35][5000/5005]\tlr: 1.000e-02, eta: 8:01:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0373\n2023-01-01 21:04:27,674 - mmcls - INFO - Epoch(val) [35][196]\taccuracy_top-1: 59.6540, accuracy_top-5: 82.0160\n2023-01-01 21:04:38,726 - mmcls - INFO - Epoch [36][100/5005]\tlr: 1.000e-02, eta: 8:01:22, time: 0.110, data_time: 0.035, memory: 1224, loss: 2.0153\n2023-01-01 21:04:47,272 - mmcls - INFO - Epoch [36][200/5005]\tlr: 1.000e-02, eta: 8:01:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9783\n2023-01-01 21:04:56,199 - mmcls - INFO - Epoch [36][300/5005]\tlr: 1.000e-02, eta: 8:01:04, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9755\n2023-01-01 21:05:05,114 - mmcls - INFO - Epoch [36][400/5005]\tlr: 1.000e-02, eta: 8:00:55, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.9788\n2023-01-01 21:05:14,217 - mmcls - INFO - Epoch [36][500/5005]\tlr: 1.000e-02, eta: 8:00:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9882\n2023-01-01 21:05:23,150 - mmcls - INFO - Epoch [36][600/5005]\tlr: 1.000e-02, eta: 8:00:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.0071\n2023-01-01 21:05:32,166 - mmcls - INFO - Epoch [36][700/5005]\tlr: 1.000e-02, eta: 8:00:29, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0043\n2023-01-01 21:05:40,693 - mmcls - INFO - Epoch [36][800/5005]\tlr: 1.000e-02, eta: 8:00:20, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.9942\n2023-01-01 21:05:49,555 - mmcls - INFO - Epoch [36][900/5005]\tlr: 1.000e-02, eta: 8:00:11, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9971\n2023-01-01 21:05:58,001 - mmcls - INFO - Epoch [36][1000/5005]\tlr: 1.000e-02, eta: 8:00:01, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.0144\n2023-01-01 21:06:06,915 - mmcls - INFO - Epoch [36][1100/5005]\tlr: 1.000e-02, eta: 7:59:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0003\n2023-01-01 21:06:15,336 - mmcls - INFO - Epoch [36][1200/5005]\tlr: 1.000e-02, eta: 7:59:43, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.9985\n2023-01-01 21:06:24,261 - mmcls - INFO - Epoch [36][1300/5005]\tlr: 1.000e-02, eta: 7:59:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9945\n2023-01-01 21:06:33,378 - mmcls - INFO - Epoch [36][1400/5005]\tlr: 1.000e-02, eta: 7:59:25, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.0160\n2023-01-01 21:06:42,187 - mmcls - INFO - Epoch [36][1500/5005]\tlr: 1.000e-02, eta: 7:59:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0066\n2023-01-01 21:06:50,946 - mmcls - INFO - Epoch [36][1600/5005]\tlr: 1.000e-02, eta: 7:59:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0199\n2023-01-01 21:06:59,854 - mmcls - INFO - Epoch [36][1700/5005]\tlr: 1.000e-02, eta: 7:58:59, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0262\n2023-01-01 21:07:08,677 - mmcls - INFO - Epoch [36][1800/5005]\tlr: 1.000e-02, eta: 7:58:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0105\n2023-01-01 21:07:17,435 - mmcls - INFO - Epoch [36][1900/5005]\tlr: 1.000e-02, eta: 7:58:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0168\n2023-01-01 21:07:26,118 - mmcls - INFO - Epoch [36][2000/5005]\tlr: 1.000e-02, eta: 7:58:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0247\n2023-01-01 21:07:34,809 - mmcls - INFO - Epoch [36][2100/5005]\tlr: 1.000e-02, eta: 7:58:22, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0099\n2023-01-01 21:07:43,685 - mmcls - INFO - Epoch [36][2200/5005]\tlr: 1.000e-02, eta: 7:58:13, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9837\n2023-01-01 21:07:52,431 - mmcls - INFO - Epoch [36][2300/5005]\tlr: 1.000e-02, eta: 7:58:04, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.0155\n2023-01-01 21:08:01,099 - mmcls - INFO - Epoch [36][2400/5005]\tlr: 1.000e-02, eta: 7:57:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0159\n2023-01-01 21:08:10,325 - mmcls - INFO - Epoch [36][2500/5005]\tlr: 1.000e-02, eta: 7:57:46, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9955\n2023-01-01 21:08:19,041 - mmcls - INFO - Epoch [36][2600/5005]\tlr: 1.000e-02, eta: 7:57:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0166\n2023-01-01 21:08:28,007 - mmcls - INFO - Epoch [36][2700/5005]\tlr: 1.000e-02, eta: 7:57:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0133\n2023-01-01 21:08:36,616 - mmcls - INFO - Epoch [36][2800/5005]\tlr: 1.000e-02, eta: 7:57:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0216\n2023-01-01 21:08:45,305 - mmcls - INFO - Epoch [36][2900/5005]\tlr: 1.000e-02, eta: 7:57:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9979\n2023-01-01 21:08:54,004 - mmcls - INFO - Epoch [36][3000/5005]\tlr: 1.000e-02, eta: 7:57:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0172\n2023-01-01 21:09:02,591 - mmcls - INFO - Epoch [36][3100/5005]\tlr: 1.000e-02, eta: 7:56:51, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.0167\n2023-01-01 21:09:11,236 - mmcls - INFO - Epoch [36][3200/5005]\tlr: 1.000e-02, eta: 7:56:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9899\n2023-01-01 21:09:19,984 - mmcls - INFO - Epoch [36][3300/5005]\tlr: 1.000e-02, eta: 7:56:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0128\n2023-01-01 21:09:29,024 - mmcls - INFO - Epoch [36][3400/5005]\tlr: 1.000e-02, eta: 7:56:24, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0196\n2023-01-01 21:09:38,146 - mmcls - INFO - Epoch [36][3500/5005]\tlr: 1.000e-02, eta: 7:56:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9803\n2023-01-01 21:09:47,014 - mmcls - INFO - Epoch [36][3600/5005]\tlr: 1.000e-02, eta: 7:56:07, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0396\n2023-01-01 21:09:55,851 - mmcls - INFO - Epoch [36][3700/5005]\tlr: 1.000e-02, eta: 7:55:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0044\n2023-01-01 21:10:04,608 - mmcls - INFO - Epoch [36][3800/5005]\tlr: 1.000e-02, eta: 7:55:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0199\n2023-01-01 21:10:13,247 - mmcls - INFO - Epoch [36][3900/5005]\tlr: 1.000e-02, eta: 7:55:40, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.0069\n2023-01-01 21:10:21,908 - mmcls - INFO - Epoch [36][4000/5005]\tlr: 1.000e-02, eta: 7:55:30, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0287\n2023-01-01 21:10:30,988 - mmcls - INFO - Epoch [36][4100/5005]\tlr: 1.000e-02, eta: 7:55:22, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.0189\n2023-01-01 21:10:39,779 - mmcls - INFO - Epoch [36][4200/5005]\tlr: 1.000e-02, eta: 7:55:13, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.0374\n2023-01-01 21:10:48,432 - mmcls - INFO - Epoch [36][4300/5005]\tlr: 1.000e-02, eta: 7:55:03, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9757\n2023-01-01 21:10:57,103 - mmcls - INFO - Epoch [36][4400/5005]\tlr: 1.000e-02, eta: 7:54:54, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0044\n2023-01-01 21:11:05,907 - mmcls - INFO - Epoch [36][4500/5005]\tlr: 1.000e-02, eta: 7:54:45, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0206\n2023-01-01 21:11:14,851 - mmcls - INFO - Epoch [36][4600/5005]\tlr: 1.000e-02, eta: 7:54:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0302\n2023-01-01 21:11:23,769 - mmcls - INFO - Epoch [36][4700/5005]\tlr: 1.000e-02, eta: 7:54:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9940\n2023-01-01 21:11:32,726 - mmcls - INFO - Epoch [36][4800/5005]\tlr: 1.000e-02, eta: 7:54:19, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0242\n2023-01-01 21:11:41,478 - mmcls - INFO - Epoch [36][4900/5005]\tlr: 1.000e-02, eta: 7:54:10, time: 0.087, data_time: 0.037, memory: 1224, loss: 2.0044\n2023-01-01 21:11:50,395 - mmcls - INFO - Epoch [36][5000/5005]\tlr: 1.000e-02, eta: 7:54:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0051\n2023-01-01 21:12:13,154 - mmcls - INFO - Epoch(val) [36][196]\taccuracy_top-1: 59.8420, accuracy_top-5: 82.1840\n2023-01-01 21:12:24,198 - mmcls - INFO - Epoch [37][100/5005]\tlr: 1.000e-02, eta: 7:53:55, time: 0.110, data_time: 0.046, memory: 1224, loss: 1.9891\n2023-01-01 21:12:33,053 - mmcls - INFO - Epoch [37][200/5005]\tlr: 1.000e-02, eta: 7:53:46, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9620\n2023-01-01 21:12:42,346 - mmcls - INFO - Epoch [37][300/5005]\tlr: 1.000e-02, eta: 7:53:38, time: 0.093, data_time: 0.011, memory: 1224, loss: 1.9992\n2023-01-01 21:12:50,953 - mmcls - INFO - Epoch [37][400/5005]\tlr: 1.000e-02, eta: 7:53:28, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9836\n2023-01-01 21:12:59,928 - mmcls - INFO - Epoch [37][500/5005]\tlr: 1.000e-02, eta: 7:53:19, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.9830\n2023-01-01 21:13:08,556 - mmcls - INFO - Epoch [37][600/5005]\tlr: 1.000e-02, eta: 7:53:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9715\n2023-01-01 21:13:17,224 - mmcls - INFO - Epoch [37][700/5005]\tlr: 1.000e-02, eta: 7:53:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9938\n2023-01-01 21:13:26,148 - mmcls - INFO - Epoch [37][800/5005]\tlr: 1.000e-02, eta: 7:52:52, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9829\n2023-01-01 21:13:34,579 - mmcls - INFO - Epoch [37][900/5005]\tlr: 1.000e-02, eta: 7:52:42, time: 0.084, data_time: 0.003, memory: 1224, loss: 1.9892\n2023-01-01 21:13:43,467 - mmcls - INFO - Epoch [37][1000/5005]\tlr: 1.000e-02, eta: 7:52:34, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0020\n2023-01-01 21:13:52,562 - mmcls - INFO - Epoch [37][1100/5005]\tlr: 1.000e-02, eta: 7:52:25, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.9837\n2023-01-01 21:14:01,789 - mmcls - INFO - Epoch [37][1200/5005]\tlr: 1.000e-02, eta: 7:52:17, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0050\n2023-01-01 21:14:10,659 - mmcls - INFO - Epoch [37][1300/5005]\tlr: 1.000e-02, eta: 7:52:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0061\n2023-01-01 21:14:19,494 - mmcls - INFO - Epoch [37][1400/5005]\tlr: 1.000e-02, eta: 7:51:59, time: 0.088, data_time: 0.027, memory: 1224, loss: 2.0022\n2023-01-01 21:14:28,240 - mmcls - INFO - Epoch [37][1500/5005]\tlr: 1.000e-02, eta: 7:51:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9798\n2023-01-01 21:14:37,229 - mmcls - INFO - Epoch [37][1600/5005]\tlr: 1.000e-02, eta: 7:51:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9909\n2023-01-01 21:14:46,089 - mmcls - INFO - Epoch [37][1700/5005]\tlr: 1.000e-02, eta: 7:51:32, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9925\n2023-01-01 21:14:54,898 - mmcls - INFO - Epoch [37][1800/5005]\tlr: 1.000e-02, eta: 7:51:23, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0007\n2023-01-01 21:15:03,642 - mmcls - INFO - Epoch [37][1900/5005]\tlr: 1.000e-02, eta: 7:51:14, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9825\n2023-01-01 21:15:12,448 - mmcls - INFO - Epoch [37][2000/5005]\tlr: 1.000e-02, eta: 7:51:05, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9852\n2023-01-01 21:15:21,221 - mmcls - INFO - Epoch [37][2100/5005]\tlr: 1.000e-02, eta: 7:50:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0168\n2023-01-01 21:15:30,072 - mmcls - INFO - Epoch [37][2200/5005]\tlr: 1.000e-02, eta: 7:50:47, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0190\n2023-01-01 21:15:38,571 - mmcls - INFO - Epoch [37][2300/5005]\tlr: 1.000e-02, eta: 7:50:38, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9916\n2023-01-01 21:15:47,333 - mmcls - INFO - Epoch [37][2400/5005]\tlr: 1.000e-02, eta: 7:50:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0078\n2023-01-01 21:15:56,318 - mmcls - INFO - Epoch [37][2500/5005]\tlr: 1.000e-02, eta: 7:50:20, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9654\n2023-01-01 21:16:04,639 - mmcls - INFO - Epoch [37][2600/5005]\tlr: 1.000e-02, eta: 7:50:10, time: 0.083, data_time: 0.003, memory: 1224, loss: 1.9990\n2023-01-01 21:16:13,427 - mmcls - INFO - Epoch [37][2700/5005]\tlr: 1.000e-02, eta: 7:50:01, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.0155\n2023-01-01 21:16:22,342 - mmcls - INFO - Epoch [37][2800/5005]\tlr: 1.000e-02, eta: 7:49:52, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0243\n2023-01-01 21:16:31,597 - mmcls - INFO - Epoch [37][2900/5005]\tlr: 1.000e-02, eta: 7:49:44, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.0250\n2023-01-01 21:16:40,568 - mmcls - INFO - Epoch [37][3000/5005]\tlr: 1.000e-02, eta: 7:49:35, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0248\n2023-01-01 21:16:49,549 - mmcls - INFO - Epoch [37][3100/5005]\tlr: 1.000e-02, eta: 7:49:26, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0066\n2023-01-01 21:16:57,996 - mmcls - INFO - Epoch [37][3200/5005]\tlr: 1.000e-02, eta: 7:49:17, time: 0.085, data_time: 0.031, memory: 1224, loss: 2.0176\n2023-01-01 21:17:06,871 - mmcls - INFO - Epoch [37][3300/5005]\tlr: 1.000e-02, eta: 7:49:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9998\n2023-01-01 21:17:15,980 - mmcls - INFO - Epoch [37][3400/5005]\tlr: 1.000e-02, eta: 7:48:59, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.0160\n2023-01-01 21:17:24,720 - mmcls - INFO - Epoch [37][3500/5005]\tlr: 1.000e-02, eta: 7:48:50, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.0241\n2023-01-01 21:17:33,572 - mmcls - INFO - Epoch [37][3600/5005]\tlr: 1.000e-02, eta: 7:48:41, time: 0.088, data_time: 0.046, memory: 1224, loss: 2.0108\n2023-01-01 21:17:42,886 - mmcls - INFO - Epoch [37][3700/5005]\tlr: 1.000e-02, eta: 7:48:33, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.0424\n2023-01-01 21:17:51,411 - mmcls - INFO - Epoch [37][3800/5005]\tlr: 1.000e-02, eta: 7:48:24, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.9907\n2023-01-01 21:18:00,522 - mmcls - INFO - Epoch [37][3900/5005]\tlr: 1.000e-02, eta: 7:48:15, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9912\n2023-01-01 21:18:09,337 - mmcls - INFO - Epoch [37][4000/5005]\tlr: 1.000e-02, eta: 7:48:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9959\n2023-01-01 21:18:18,300 - mmcls - INFO - Epoch [37][4100/5005]\tlr: 1.000e-02, eta: 7:47:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0197\n2023-01-01 21:18:26,807 - mmcls - INFO - Epoch [37][4200/5005]\tlr: 1.000e-02, eta: 7:47:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.0110\n2023-01-01 21:18:35,639 - mmcls - INFO - Epoch [37][4300/5005]\tlr: 1.000e-02, eta: 7:47:39, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0000\n2023-01-01 21:18:44,353 - mmcls - INFO - Epoch [37][4400/5005]\tlr: 1.000e-02, eta: 7:47:30, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9875\n2023-01-01 21:18:53,186 - mmcls - INFO - Epoch [37][4500/5005]\tlr: 1.000e-02, eta: 7:47:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0020\n2023-01-01 21:19:01,861 - mmcls - INFO - Epoch [37][4600/5005]\tlr: 1.000e-02, eta: 7:47:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9862\n2023-01-01 21:19:10,672 - mmcls - INFO - Epoch [37][4700/5005]\tlr: 1.000e-02, eta: 7:47:03, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.0173\n2023-01-01 21:19:19,535 - mmcls - INFO - Epoch [37][4800/5005]\tlr: 1.000e-02, eta: 7:46:54, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.0081\n2023-01-01 21:19:28,227 - mmcls - INFO - Epoch [37][4900/5005]\tlr: 1.000e-02, eta: 7:46:45, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9991\n2023-01-01 21:19:37,007 - mmcls - INFO - Epoch [37][5000/5005]\tlr: 1.000e-02, eta: 7:46:36, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0292\n2023-01-01 21:19:59,637 - mmcls - INFO - Epoch(val) [37][196]\taccuracy_top-1: 59.9580, accuracy_top-5: 82.0760\n2023-01-01 21:20:10,932 - mmcls - INFO - Epoch [38][100/5005]\tlr: 1.000e-02, eta: 7:46:30, time: 0.113, data_time: 0.056, memory: 1224, loss: 1.9783\n2023-01-01 21:20:19,534 - mmcls - INFO - Epoch [38][200/5005]\tlr: 1.000e-02, eta: 7:46:20, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9710\n2023-01-01 21:20:28,187 - mmcls - INFO - Epoch [38][300/5005]\tlr: 1.000e-02, eta: 7:46:11, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9801\n2023-01-01 21:20:36,878 - mmcls - INFO - Epoch [38][400/5005]\tlr: 1.000e-02, eta: 7:46:02, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9699\n2023-01-01 21:20:45,597 - mmcls - INFO - Epoch [38][500/5005]\tlr: 1.000e-02, eta: 7:45:53, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9811\n2023-01-01 21:20:54,238 - mmcls - INFO - Epoch [38][600/5005]\tlr: 1.000e-02, eta: 7:45:43, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9629\n2023-01-01 21:21:03,328 - mmcls - INFO - Epoch [38][700/5005]\tlr: 1.000e-02, eta: 7:45:35, time: 0.091, data_time: 0.040, memory: 1224, loss: 1.9715\n2023-01-01 21:21:12,195 - mmcls - INFO - Epoch [38][800/5005]\tlr: 1.000e-02, eta: 7:45:26, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9521\n2023-01-01 21:21:20,853 - mmcls - INFO - Epoch [38][900/5005]\tlr: 1.000e-02, eta: 7:45:17, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0034\n2023-01-01 21:21:29,635 - mmcls - INFO - Epoch [38][1000/5005]\tlr: 1.000e-02, eta: 7:45:08, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9988\n2023-01-01 21:21:38,334 - mmcls - INFO - Epoch [38][1100/5005]\tlr: 1.000e-02, eta: 7:44:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9791\n2023-01-01 21:21:47,381 - mmcls - INFO - Epoch [38][1200/5005]\tlr: 1.000e-02, eta: 7:44:50, time: 0.090, data_time: 0.050, memory: 1224, loss: 1.9687\n2023-01-01 21:21:56,469 - mmcls - INFO - Epoch [38][1300/5005]\tlr: 1.000e-02, eta: 7:44:41, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.9900\n2023-01-01 21:22:05,060 - mmcls - INFO - Epoch [38][1400/5005]\tlr: 1.000e-02, eta: 7:44:32, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9753\n2023-01-01 21:22:14,090 - mmcls - INFO - Epoch [38][1500/5005]\tlr: 1.000e-02, eta: 7:44:23, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0061\n2023-01-01 21:22:22,830 - mmcls - INFO - Epoch [38][1600/5005]\tlr: 1.000e-02, eta: 7:44:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9766\n2023-01-01 21:22:31,551 - mmcls - INFO - Epoch [38][1700/5005]\tlr: 1.000e-02, eta: 7:44:05, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9793\n2023-01-01 21:22:40,341 - mmcls - INFO - Epoch [38][1800/5005]\tlr: 1.000e-02, eta: 7:43:56, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9629\n2023-01-01 21:22:49,001 - mmcls - INFO - Epoch [38][1900/5005]\tlr: 1.000e-02, eta: 7:43:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0169\n2023-01-01 21:22:57,763 - mmcls - INFO - Epoch [38][2000/5005]\tlr: 1.000e-02, eta: 7:43:38, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0075\n2023-01-01 21:23:06,191 - mmcls - INFO - Epoch [38][2100/5005]\tlr: 1.000e-02, eta: 7:43:28, time: 0.084, data_time: 0.020, memory: 1224, loss: 1.9871\n2023-01-01 21:23:14,878 - mmcls - INFO - Epoch [38][2200/5005]\tlr: 1.000e-02, eta: 7:43:19, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9904\n2023-01-01 21:23:23,505 - mmcls - INFO - Epoch [38][2300/5005]\tlr: 1.000e-02, eta: 7:43:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9887\n2023-01-01 21:23:32,449 - mmcls - INFO - Epoch [38][2400/5005]\tlr: 1.000e-02, eta: 7:43:01, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9911\n2023-01-01 21:23:41,356 - mmcls - INFO - Epoch [38][2500/5005]\tlr: 1.000e-02, eta: 7:42:52, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.0088\n2023-01-01 21:23:50,279 - mmcls - INFO - Epoch [38][2600/5005]\tlr: 1.000e-02, eta: 7:42:43, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0140\n2023-01-01 21:23:58,852 - mmcls - INFO - Epoch [38][2700/5005]\tlr: 1.000e-02, eta: 7:42:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9738\n2023-01-01 21:24:07,834 - mmcls - INFO - Epoch [38][2800/5005]\tlr: 1.000e-02, eta: 7:42:25, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0138\n2023-01-01 21:24:16,732 - mmcls - INFO - Epoch [38][2900/5005]\tlr: 1.000e-02, eta: 7:42:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.0144\n2023-01-01 21:24:25,730 - mmcls - INFO - Epoch [38][3000/5005]\tlr: 1.000e-02, eta: 7:42:07, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0150\n2023-01-01 21:24:34,447 - mmcls - INFO - Epoch [38][3100/5005]\tlr: 1.000e-02, eta: 7:41:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9928\n2023-01-01 21:24:43,384 - mmcls - INFO - Epoch [38][3200/5005]\tlr: 1.000e-02, eta: 7:41:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0181\n2023-01-01 21:24:52,162 - mmcls - INFO - Epoch [38][3300/5005]\tlr: 1.000e-02, eta: 7:41:40, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0021\n2023-01-01 21:25:00,847 - mmcls - INFO - Epoch [38][3400/5005]\tlr: 1.000e-02, eta: 7:41:31, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.9890\n2023-01-01 21:25:09,862 - mmcls - INFO - Epoch [38][3500/5005]\tlr: 1.000e-02, eta: 7:41:23, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9884\n2023-01-01 21:25:18,667 - mmcls - INFO - Epoch [38][3600/5005]\tlr: 1.000e-02, eta: 7:41:14, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9896\n2023-01-01 21:25:27,730 - mmcls - INFO - Epoch [38][3700/5005]\tlr: 1.000e-02, eta: 7:41:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0287\n2023-01-01 21:25:36,522 - mmcls - INFO - Epoch [38][3800/5005]\tlr: 1.000e-02, eta: 7:40:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0057\n2023-01-01 21:25:45,056 - mmcls - INFO - Epoch [38][3900/5005]\tlr: 1.000e-02, eta: 7:40:47, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.0240\n2023-01-01 21:25:53,892 - mmcls - INFO - Epoch [38][4000/5005]\tlr: 1.000e-02, eta: 7:40:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9931\n2023-01-01 21:26:02,455 - mmcls - INFO - Epoch [38][4100/5005]\tlr: 1.000e-02, eta: 7:40:28, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.9881\n2023-01-01 21:26:11,485 - mmcls - INFO - Epoch [38][4200/5005]\tlr: 1.000e-02, eta: 7:40:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0198\n2023-01-01 21:26:20,754 - mmcls - INFO - Epoch [38][4300/5005]\tlr: 1.000e-02, eta: 7:40:11, time: 0.093, data_time: 0.019, memory: 1224, loss: 2.0022\n2023-01-01 21:26:29,670 - mmcls - INFO - Epoch [38][4400/5005]\tlr: 1.000e-02, eta: 7:40:03, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.0011\n2023-01-01 21:26:38,150 - mmcls - INFO - Epoch [38][4500/5005]\tlr: 1.000e-02, eta: 7:39:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9922\n2023-01-01 21:26:46,979 - mmcls - INFO - Epoch [38][4600/5005]\tlr: 1.000e-02, eta: 7:39:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9908\n2023-01-01 21:26:55,951 - mmcls - INFO - Epoch [38][4700/5005]\tlr: 1.000e-02, eta: 7:39:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0053\n2023-01-01 21:27:04,804 - mmcls - INFO - Epoch [38][4800/5005]\tlr: 1.000e-02, eta: 7:39:26, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9979\n2023-01-01 21:27:13,558 - mmcls - INFO - Epoch [38][4900/5005]\tlr: 1.000e-02, eta: 7:39:17, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0238\n2023-01-01 21:27:22,159 - mmcls - INFO - Epoch [38][5000/5005]\tlr: 1.000e-02, eta: 7:39:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0032\n2023-01-01 21:27:44,974 - mmcls - INFO - Epoch(val) [38][196]\taccuracy_top-1: 60.1100, accuracy_top-5: 82.4720\n2023-01-01 21:27:56,253 - mmcls - INFO - Epoch [39][100/5005]\tlr: 1.000e-02, eta: 7:39:02, time: 0.112, data_time: 0.056, memory: 1224, loss: 1.9716\n2023-01-01 21:28:04,993 - mmcls - INFO - Epoch [39][200/5005]\tlr: 1.000e-02, eta: 7:38:53, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9561\n2023-01-01 21:28:13,591 - mmcls - INFO - Epoch [39][300/5005]\tlr: 1.000e-02, eta: 7:38:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9695\n2023-01-01 21:28:22,412 - mmcls - INFO - Epoch [39][400/5005]\tlr: 1.000e-02, eta: 7:38:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9679\n2023-01-01 21:28:31,015 - mmcls - INFO - Epoch [39][500/5005]\tlr: 1.000e-02, eta: 7:38:25, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9903\n2023-01-01 21:28:39,731 - mmcls - INFO - Epoch [39][600/5005]\tlr: 1.000e-02, eta: 7:38:16, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.9873\n2023-01-01 21:28:48,484 - mmcls - INFO - Epoch [39][700/5005]\tlr: 1.000e-02, eta: 7:38:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9448\n2023-01-01 21:28:57,191 - mmcls - INFO - Epoch [39][800/5005]\tlr: 1.000e-02, eta: 7:37:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9809\n2023-01-01 21:29:05,968 - mmcls - INFO - Epoch [39][900/5005]\tlr: 1.000e-02, eta: 7:37:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9661\n2023-01-01 21:29:14,824 - mmcls - INFO - Epoch [39][1000/5005]\tlr: 1.000e-02, eta: 7:37:40, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9584\n2023-01-01 21:29:23,282 - mmcls - INFO - Epoch [39][1100/5005]\tlr: 1.000e-02, eta: 7:37:30, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9662\n2023-01-01 21:29:32,350 - mmcls - INFO - Epoch [39][1200/5005]\tlr: 1.000e-02, eta: 7:37:22, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.9704\n2023-01-01 21:29:41,035 - mmcls - INFO - Epoch [39][1300/5005]\tlr: 1.000e-02, eta: 7:37:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9984\n2023-01-01 21:29:49,789 - mmcls - INFO - Epoch [39][1400/5005]\tlr: 1.000e-02, eta: 7:37:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.0038\n2023-01-01 21:29:58,532 - mmcls - INFO - Epoch [39][1500/5005]\tlr: 1.000e-02, eta: 7:36:54, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9973\n2023-01-01 21:30:07,150 - mmcls - INFO - Epoch [39][1600/5005]\tlr: 1.000e-02, eta: 7:36:45, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9774\n2023-01-01 21:30:16,102 - mmcls - INFO - Epoch [39][1700/5005]\tlr: 1.000e-02, eta: 7:36:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9993\n2023-01-01 21:30:25,065 - mmcls - INFO - Epoch [39][1800/5005]\tlr: 1.000e-02, eta: 7:36:27, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0034\n2023-01-01 21:30:34,067 - mmcls - INFO - Epoch [39][1900/5005]\tlr: 1.000e-02, eta: 7:36:19, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0081\n2023-01-01 21:30:42,927 - mmcls - INFO - Epoch [39][2000/5005]\tlr: 1.000e-02, eta: 7:36:10, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9662\n2023-01-01 21:30:51,734 - mmcls - INFO - Epoch [39][2100/5005]\tlr: 1.000e-02, eta: 7:36:01, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9818\n2023-01-01 21:31:00,206 - mmcls - INFO - Epoch [39][2200/5005]\tlr: 1.000e-02, eta: 7:35:51, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.9855\n2023-01-01 21:31:09,051 - mmcls - INFO - Epoch [39][2300/5005]\tlr: 1.000e-02, eta: 7:35:42, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9811\n2023-01-01 21:31:17,764 - mmcls - INFO - Epoch [39][2400/5005]\tlr: 1.000e-02, eta: 7:35:33, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9913\n2023-01-01 21:31:26,545 - mmcls - INFO - Epoch [39][2500/5005]\tlr: 1.000e-02, eta: 7:35:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9788\n2023-01-01 21:31:35,754 - mmcls - INFO - Epoch [39][2600/5005]\tlr: 1.000e-02, eta: 7:35:16, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.0000\n2023-01-01 21:31:44,640 - mmcls - INFO - Epoch [39][2700/5005]\tlr: 1.000e-02, eta: 7:35:07, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9918\n2023-01-01 21:31:53,744 - mmcls - INFO - Epoch [39][2800/5005]\tlr: 1.000e-02, eta: 7:34:59, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9661\n2023-01-01 21:32:02,281 - mmcls - INFO - Epoch [39][2900/5005]\tlr: 1.000e-02, eta: 7:34:49, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.9849\n2023-01-01 21:32:10,860 - mmcls - INFO - Epoch [39][3000/5005]\tlr: 1.000e-02, eta: 7:34:40, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.0024\n2023-01-01 21:32:19,854 - mmcls - INFO - Epoch [39][3100/5005]\tlr: 1.000e-02, eta: 7:34:31, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0073\n2023-01-01 21:32:28,463 - mmcls - INFO - Epoch [39][3200/5005]\tlr: 1.000e-02, eta: 7:34:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9900\n2023-01-01 21:32:36,899 - mmcls - INFO - Epoch [39][3300/5005]\tlr: 1.000e-02, eta: 7:34:12, time: 0.084, data_time: 0.011, memory: 1224, loss: 1.9958\n2023-01-01 21:32:45,970 - mmcls - INFO - Epoch [39][3400/5005]\tlr: 1.000e-02, eta: 7:34:04, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9831\n2023-01-01 21:32:54,803 - mmcls - INFO - Epoch [39][3500/5005]\tlr: 1.000e-02, eta: 7:33:55, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0107\n2023-01-01 21:33:03,507 - mmcls - INFO - Epoch [39][3600/5005]\tlr: 1.000e-02, eta: 7:33:46, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0079\n2023-01-01 21:33:12,212 - mmcls - INFO - Epoch [39][3700/5005]\tlr: 1.000e-02, eta: 7:33:36, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0094\n2023-01-01 21:33:21,041 - mmcls - INFO - Epoch [39][3800/5005]\tlr: 1.000e-02, eta: 7:33:27, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9772\n2023-01-01 21:33:29,806 - mmcls - INFO - Epoch [39][3900/5005]\tlr: 1.000e-02, eta: 7:33:18, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0024\n2023-01-01 21:33:38,815 - mmcls - INFO - Epoch [39][4000/5005]\tlr: 1.000e-02, eta: 7:33:10, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0008\n2023-01-01 21:33:47,621 - mmcls - INFO - Epoch [39][4100/5005]\tlr: 1.000e-02, eta: 7:33:01, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0101\n2023-01-01 21:33:56,280 - mmcls - INFO - Epoch [39][4200/5005]\tlr: 1.000e-02, eta: 7:32:51, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9968\n2023-01-01 21:34:04,956 - mmcls - INFO - Epoch [39][4300/5005]\tlr: 1.000e-02, eta: 7:32:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9680\n2023-01-01 21:34:13,806 - mmcls - INFO - Epoch [39][4400/5005]\tlr: 1.000e-02, eta: 7:32:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9960\n2023-01-01 21:34:22,492 - mmcls - INFO - Epoch [39][4500/5005]\tlr: 1.000e-02, eta: 7:32:24, time: 0.087, data_time: 0.031, memory: 1224, loss: 2.0099\n2023-01-01 21:34:31,460 - mmcls - INFO - Epoch [39][4600/5005]\tlr: 1.000e-02, eta: 7:32:15, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0028\n2023-01-01 21:34:40,479 - mmcls - INFO - Epoch [39][4700/5005]\tlr: 1.000e-02, eta: 7:32:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0218\n2023-01-01 21:34:49,075 - mmcls - INFO - Epoch [39][4800/5005]\tlr: 1.000e-02, eta: 7:31:57, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.0157\n2023-01-01 21:34:58,064 - mmcls - INFO - Epoch [39][4900/5005]\tlr: 1.000e-02, eta: 7:31:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9985\n2023-01-01 21:35:06,931 - mmcls - INFO - Epoch [39][5000/5005]\tlr: 1.000e-02, eta: 7:31:40, time: 0.089, data_time: 0.029, memory: 1224, loss: 2.0098\n2023-01-01 21:35:29,959 - mmcls - INFO - Epoch(val) [39][196]\taccuracy_top-1: 60.1380, accuracy_top-5: 82.2380\n2023-01-01 21:35:41,327 - mmcls - INFO - Epoch [40][100/5005]\tlr: 1.000e-02, eta: 7:31:34, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.9633\n2023-01-01 21:35:49,822 - mmcls - INFO - Epoch [40][200/5005]\tlr: 1.000e-02, eta: 7:31:24, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.9596\n2023-01-01 21:35:58,464 - mmcls - INFO - Epoch [40][300/5005]\tlr: 1.000e-02, eta: 7:31:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9569\n2023-01-01 21:36:07,177 - mmcls - INFO - Epoch [40][400/5005]\tlr: 1.000e-02, eta: 7:31:06, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.9534\n2023-01-01 21:36:15,925 - mmcls - INFO - Epoch [40][500/5005]\tlr: 1.000e-02, eta: 7:30:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9483\n2023-01-01 21:36:24,880 - mmcls - INFO - Epoch [40][600/5005]\tlr: 1.000e-02, eta: 7:30:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9422\n2023-01-01 21:36:33,594 - mmcls - INFO - Epoch [40][700/5005]\tlr: 1.000e-02, eta: 7:30:39, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9570\n2023-01-01 21:36:42,781 - mmcls - INFO - Epoch [40][800/5005]\tlr: 1.000e-02, eta: 7:30:31, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.9759\n2023-01-01 21:36:51,920 - mmcls - INFO - Epoch [40][900/5005]\tlr: 1.000e-02, eta: 7:30:22, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9794\n2023-01-01 21:37:01,018 - mmcls - INFO - Epoch [40][1000/5005]\tlr: 1.000e-02, eta: 7:30:14, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.9866\n2023-01-01 21:37:09,690 - mmcls - INFO - Epoch [40][1100/5005]\tlr: 1.000e-02, eta: 7:30:04, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9477\n2023-01-01 21:37:18,237 - mmcls - INFO - Epoch [40][1200/5005]\tlr: 1.000e-02, eta: 7:29:55, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.9452\n2023-01-01 21:37:27,049 - mmcls - INFO - Epoch [40][1300/5005]\tlr: 1.000e-02, eta: 7:29:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9701\n2023-01-01 21:37:35,846 - mmcls - INFO - Epoch [40][1400/5005]\tlr: 1.000e-02, eta: 7:29:37, time: 0.088, data_time: 0.032, memory: 1224, loss: 1.9594\n2023-01-01 21:37:44,725 - mmcls - INFO - Epoch [40][1500/5005]\tlr: 1.000e-02, eta: 7:29:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9813\n2023-01-01 21:37:53,706 - mmcls - INFO - Epoch [40][1600/5005]\tlr: 1.000e-02, eta: 7:29:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9888\n2023-01-01 21:38:02,285 - mmcls - INFO - Epoch [40][1700/5005]\tlr: 1.000e-02, eta: 7:29:10, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9900\n2023-01-01 21:38:11,002 - mmcls - INFO - Epoch [40][1800/5005]\tlr: 1.000e-02, eta: 7:29:01, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9814\n2023-01-01 21:38:19,827 - mmcls - INFO - Epoch [40][1900/5005]\tlr: 1.000e-02, eta: 7:28:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9811\n2023-01-01 21:38:28,655 - mmcls - INFO - Epoch [40][2000/5005]\tlr: 1.000e-02, eta: 7:28:43, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9609\n2023-01-01 21:38:37,239 - mmcls - INFO - Epoch [40][2100/5005]\tlr: 1.000e-02, eta: 7:28:34, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9737\n2023-01-01 21:38:46,198 - mmcls - INFO - Epoch [40][2200/5005]\tlr: 1.000e-02, eta: 7:28:25, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9839\n2023-01-01 21:38:55,066 - mmcls - INFO - Epoch [40][2300/5005]\tlr: 1.000e-02, eta: 7:28:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9734\n2023-01-01 21:39:03,770 - mmcls - INFO - Epoch [40][2400/5005]\tlr: 1.000e-02, eta: 7:28:07, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9775\n2023-01-01 21:39:12,485 - mmcls - INFO - Epoch [40][2500/5005]\tlr: 1.000e-02, eta: 7:27:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9600\n2023-01-01 21:39:21,299 - mmcls - INFO - Epoch [40][2600/5005]\tlr: 1.000e-02, eta: 7:27:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0121\n2023-01-01 21:39:29,854 - mmcls - INFO - Epoch [40][2700/5005]\tlr: 1.000e-02, eta: 7:27:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9987\n2023-01-01 21:39:38,564 - mmcls - INFO - Epoch [40][2800/5005]\tlr: 1.000e-02, eta: 7:27:30, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9744\n2023-01-01 21:39:47,674 - mmcls - INFO - Epoch [40][2900/5005]\tlr: 1.000e-02, eta: 7:27:22, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.9926\n2023-01-01 21:39:56,519 - mmcls - INFO - Epoch [40][3000/5005]\tlr: 1.000e-02, eta: 7:27:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0007\n2023-01-01 21:40:05,452 - mmcls - INFO - Epoch [40][3100/5005]\tlr: 1.000e-02, eta: 7:27:04, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.0027\n2023-01-01 21:40:14,517 - mmcls - INFO - Epoch [40][3200/5005]\tlr: 1.000e-02, eta: 7:26:55, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9963\n2023-01-01 21:40:23,173 - mmcls - INFO - Epoch [40][3300/5005]\tlr: 1.000e-02, eta: 7:26:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0160\n2023-01-01 21:40:31,790 - mmcls - INFO - Epoch [40][3400/5005]\tlr: 1.000e-02, eta: 7:26:37, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.9950\n2023-01-01 21:40:40,298 - mmcls - INFO - Epoch [40][3500/5005]\tlr: 1.000e-02, eta: 7:26:28, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.9940\n2023-01-01 21:40:49,437 - mmcls - INFO - Epoch [40][3600/5005]\tlr: 1.000e-02, eta: 7:26:19, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.9804\n2023-01-01 21:40:58,494 - mmcls - INFO - Epoch [40][3700/5005]\tlr: 1.000e-02, eta: 7:26:10, time: 0.091, data_time: 0.015, memory: 1224, loss: 2.0457\n2023-01-01 21:41:07,236 - mmcls - INFO - Epoch [40][3800/5005]\tlr: 1.000e-02, eta: 7:26:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9911\n2023-01-01 21:41:15,796 - mmcls - INFO - Epoch [40][3900/5005]\tlr: 1.000e-02, eta: 7:25:52, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9765\n2023-01-01 21:41:24,581 - mmcls - INFO - Epoch [40][4000/5005]\tlr: 1.000e-02, eta: 7:25:43, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0215\n2023-01-01 21:41:33,225 - mmcls - INFO - Epoch [40][4100/5005]\tlr: 1.000e-02, eta: 7:25:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9983\n2023-01-01 21:41:42,458 - mmcls - INFO - Epoch [40][4200/5005]\tlr: 1.000e-02, eta: 7:25:25, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0088\n2023-01-01 21:41:50,987 - mmcls - INFO - Epoch [40][4300/5005]\tlr: 1.000e-02, eta: 7:25:16, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.0228\n2023-01-01 21:42:00,021 - mmcls - INFO - Epoch [40][4400/5005]\tlr: 1.000e-02, eta: 7:25:07, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.0208\n2023-01-01 21:42:09,018 - mmcls - INFO - Epoch [40][4500/5005]\tlr: 1.000e-02, eta: 7:24:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.0376\n2023-01-01 21:42:18,047 - mmcls - INFO - Epoch [40][4600/5005]\tlr: 1.000e-02, eta: 7:24:50, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0042\n2023-01-01 21:42:26,963 - mmcls - INFO - Epoch [40][4700/5005]\tlr: 1.000e-02, eta: 7:24:41, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.0077\n2023-01-01 21:42:35,801 - mmcls - INFO - Epoch [40][4800/5005]\tlr: 1.000e-02, eta: 7:24:32, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.9915\n2023-01-01 21:42:44,446 - mmcls - INFO - Epoch [40][4900/5005]\tlr: 1.000e-02, eta: 7:24:23, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0001\n2023-01-01 21:42:53,256 - mmcls - INFO - Epoch [40][5000/5005]\tlr: 1.000e-02, eta: 7:24:14, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9710\n2023-01-01 21:43:16,056 - mmcls - INFO - Epoch(val) [40][196]\taccuracy_top-1: 59.7400, accuracy_top-5: 82.0020\n2023-01-01 21:43:26,838 - mmcls - INFO - Epoch [41][100/5005]\tlr: 1.000e-02, eta: 7:24:07, time: 0.108, data_time: 0.042, memory: 1224, loss: 1.9626\n2023-01-01 21:43:35,684 - mmcls - INFO - Epoch [41][200/5005]\tlr: 1.000e-02, eta: 7:23:58, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.9440\n2023-01-01 21:43:44,539 - mmcls - INFO - Epoch [41][300/5005]\tlr: 1.000e-02, eta: 7:23:49, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.9651\n2023-01-01 21:43:53,323 - mmcls - INFO - Epoch [41][400/5005]\tlr: 1.000e-02, eta: 7:23:40, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.9888\n2023-01-01 21:44:01,952 - mmcls - INFO - Epoch [41][500/5005]\tlr: 1.000e-02, eta: 7:23:31, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9643\n2023-01-01 21:44:10,608 - mmcls - INFO - Epoch [41][600/5005]\tlr: 1.000e-02, eta: 7:23:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9396\n2023-01-01 21:44:19,684 - mmcls - INFO - Epoch [41][700/5005]\tlr: 1.000e-02, eta: 7:23:13, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.9401\n2023-01-01 21:44:28,967 - mmcls - INFO - Epoch [41][800/5005]\tlr: 1.000e-02, eta: 7:23:05, time: 0.093, data_time: 0.000, memory: 1224, loss: 1.9789\n2023-01-01 21:44:37,621 - mmcls - INFO - Epoch [41][900/5005]\tlr: 1.000e-02, eta: 7:22:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9753\n2023-01-01 21:44:46,588 - mmcls - INFO - Epoch [41][1000/5005]\tlr: 1.000e-02, eta: 7:22:47, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9788\n2023-01-01 21:44:55,298 - mmcls - INFO - Epoch [41][1100/5005]\tlr: 1.000e-02, eta: 7:22:38, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9762\n2023-01-01 21:45:04,154 - mmcls - INFO - Epoch [41][1200/5005]\tlr: 1.000e-02, eta: 7:22:29, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9901\n2023-01-01 21:45:13,115 - mmcls - INFO - Epoch [41][1300/5005]\tlr: 1.000e-02, eta: 7:22:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9788\n2023-01-01 21:45:21,863 - mmcls - INFO - Epoch [41][1400/5005]\tlr: 1.000e-02, eta: 7:22:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9971\n2023-01-01 21:45:30,560 - mmcls - INFO - Epoch [41][1500/5005]\tlr: 1.000e-02, eta: 7:22:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9915\n2023-01-01 21:45:39,577 - mmcls - INFO - Epoch [41][1600/5005]\tlr: 1.000e-02, eta: 7:21:53, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9784\n2023-01-01 21:45:48,258 - mmcls - INFO - Epoch [41][1700/5005]\tlr: 1.000e-02, eta: 7:21:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9770\n2023-01-01 21:45:57,030 - mmcls - INFO - Epoch [41][1800/5005]\tlr: 1.000e-02, eta: 7:21:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9475\n2023-01-01 21:46:05,830 - mmcls - INFO - Epoch [41][1900/5005]\tlr: 1.000e-02, eta: 7:21:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9576\n2023-01-01 21:46:14,566 - mmcls - INFO - Epoch [41][2000/5005]\tlr: 1.000e-02, eta: 7:21:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9857\n2023-01-01 21:46:23,338 - mmcls - INFO - Epoch [41][2100/5005]\tlr: 1.000e-02, eta: 7:21:08, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9810\n2023-01-01 21:46:32,250 - mmcls - INFO - Epoch [41][2200/5005]\tlr: 1.000e-02, eta: 7:20:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9614\n2023-01-01 21:46:41,108 - mmcls - INFO - Epoch [41][2300/5005]\tlr: 1.000e-02, eta: 7:20:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9861\n2023-01-01 21:46:49,829 - mmcls - INFO - Epoch [41][2400/5005]\tlr: 1.000e-02, eta: 7:20:41, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9821\n2023-01-01 21:46:58,534 - mmcls - INFO - Epoch [41][2500/5005]\tlr: 1.000e-02, eta: 7:20:32, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9829\n2023-01-01 21:47:07,576 - mmcls - INFO - Epoch [41][2600/5005]\tlr: 1.000e-02, eta: 7:20:23, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0155\n2023-01-01 21:47:16,477 - mmcls - INFO - Epoch [41][2700/5005]\tlr: 1.000e-02, eta: 7:20:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0011\n2023-01-01 21:47:25,234 - mmcls - INFO - Epoch [41][2800/5005]\tlr: 1.000e-02, eta: 7:20:05, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9756\n2023-01-01 21:47:34,062 - mmcls - INFO - Epoch [41][2900/5005]\tlr: 1.000e-02, eta: 7:19:56, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9744\n2023-01-01 21:47:42,694 - mmcls - INFO - Epoch [41][3000/5005]\tlr: 1.000e-02, eta: 7:19:47, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9974\n2023-01-01 21:47:51,619 - mmcls - INFO - Epoch [41][3100/5005]\tlr: 1.000e-02, eta: 7:19:38, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9466\n2023-01-01 21:48:00,495 - mmcls - INFO - Epoch [41][3200/5005]\tlr: 1.000e-02, eta: 7:19:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9884\n2023-01-01 21:48:09,808 - mmcls - INFO - Epoch [41][3300/5005]\tlr: 1.000e-02, eta: 7:19:21, time: 0.093, data_time: 0.007, memory: 1224, loss: 2.0207\n2023-01-01 21:48:18,540 - mmcls - INFO - Epoch [41][3400/5005]\tlr: 1.000e-02, eta: 7:19:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9994\n2023-01-01 21:48:27,502 - mmcls - INFO - Epoch [41][3500/5005]\tlr: 1.000e-02, eta: 7:19:03, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9754\n2023-01-01 21:48:36,178 - mmcls - INFO - Epoch [41][3600/5005]\tlr: 1.000e-02, eta: 7:18:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9866\n2023-01-01 21:48:44,683 - mmcls - INFO - Epoch [41][3700/5005]\tlr: 1.000e-02, eta: 7:18:45, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9967\n2023-01-01 21:48:53,569 - mmcls - INFO - Epoch [41][3800/5005]\tlr: 1.000e-02, eta: 7:18:36, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9914\n2023-01-01 21:49:02,229 - mmcls - INFO - Epoch [41][3900/5005]\tlr: 1.000e-02, eta: 7:18:27, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0341\n2023-01-01 21:49:11,050 - mmcls - INFO - Epoch [41][4000/5005]\tlr: 1.000e-02, eta: 7:18:18, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0013\n2023-01-01 21:49:20,384 - mmcls - INFO - Epoch [41][4100/5005]\tlr: 1.000e-02, eta: 7:18:10, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.9797\n2023-01-01 21:49:29,305 - mmcls - INFO - Epoch [41][4200/5005]\tlr: 1.000e-02, eta: 7:18:01, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.0109\n2023-01-01 21:49:38,061 - mmcls - INFO - Epoch [41][4300/5005]\tlr: 1.000e-02, eta: 7:17:52, time: 0.088, data_time: 0.032, memory: 1224, loss: 2.0087\n2023-01-01 21:49:46,619 - mmcls - INFO - Epoch [41][4400/5005]\tlr: 1.000e-02, eta: 7:17:42, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.0201\n2023-01-01 21:49:55,248 - mmcls - INFO - Epoch [41][4500/5005]\tlr: 1.000e-02, eta: 7:17:33, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9949\n2023-01-01 21:50:04,425 - mmcls - INFO - Epoch [41][4600/5005]\tlr: 1.000e-02, eta: 7:17:25, time: 0.092, data_time: 0.019, memory: 1224, loss: 2.0125\n2023-01-01 21:50:13,381 - mmcls - INFO - Epoch [41][4700/5005]\tlr: 1.000e-02, eta: 7:17:16, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.0039\n2023-01-01 21:50:22,422 - mmcls - INFO - Epoch [41][4800/5005]\tlr: 1.000e-02, eta: 7:17:07, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0415\n2023-01-01 21:50:31,229 - mmcls - INFO - Epoch [41][4900/5005]\tlr: 1.000e-02, eta: 7:16:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0149\n2023-01-01 21:50:40,092 - mmcls - INFO - Epoch [41][5000/5005]\tlr: 1.000e-02, eta: 7:16:49, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.0131\n2023-01-01 21:51:02,682 - mmcls - INFO - Epoch(val) [41][196]\taccuracy_top-1: 60.1460, accuracy_top-5: 82.4200\n2023-01-01 21:51:13,537 - mmcls - INFO - Epoch [42][100/5005]\tlr: 1.000e-02, eta: 7:16:42, time: 0.108, data_time: 0.041, memory: 1224, loss: 1.9721\n2023-01-01 21:51:22,418 - mmcls - INFO - Epoch [42][200/5005]\tlr: 1.000e-02, eta: 7:16:33, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9434\n2023-01-01 21:51:31,191 - mmcls - INFO - Epoch [42][300/5005]\tlr: 1.000e-02, eta: 7:16:24, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9429\n2023-01-01 21:51:40,038 - mmcls - INFO - Epoch [42][400/5005]\tlr: 1.000e-02, eta: 7:16:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9518\n2023-01-01 21:51:48,913 - mmcls - INFO - Epoch [42][500/5005]\tlr: 1.000e-02, eta: 7:16:07, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9602\n2023-01-01 21:51:57,525 - mmcls - INFO - Epoch [42][600/5005]\tlr: 1.000e-02, eta: 7:15:57, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9585\n2023-01-01 21:52:06,257 - mmcls - INFO - Epoch [42][700/5005]\tlr: 1.000e-02, eta: 7:15:48, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.9848\n2023-01-01 21:52:15,105 - mmcls - INFO - Epoch [42][800/5005]\tlr: 1.000e-02, eta: 7:15:39, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9562\n2023-01-01 21:52:23,930 - mmcls - INFO - Epoch [42][900/5005]\tlr: 1.000e-02, eta: 7:15:30, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9558\n2023-01-01 21:52:32,981 - mmcls - INFO - Epoch [42][1000/5005]\tlr: 1.000e-02, eta: 7:15:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9609\n2023-01-01 21:52:41,688 - mmcls - INFO - Epoch [42][1100/5005]\tlr: 1.000e-02, eta: 7:15:13, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.9659\n2023-01-01 21:52:50,378 - mmcls - INFO - Epoch [42][1200/5005]\tlr: 1.000e-02, eta: 7:15:04, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9903\n2023-01-01 21:52:59,189 - mmcls - INFO - Epoch [42][1300/5005]\tlr: 1.000e-02, eta: 7:14:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9701\n2023-01-01 21:53:08,172 - mmcls - INFO - Epoch [42][1400/5005]\tlr: 1.000e-02, eta: 7:14:46, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0111\n2023-01-01 21:53:16,946 - mmcls - INFO - Epoch [42][1500/5005]\tlr: 1.000e-02, eta: 7:14:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9948\n2023-01-01 21:53:26,049 - mmcls - INFO - Epoch [42][1600/5005]\tlr: 1.000e-02, eta: 7:14:28, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9565\n2023-01-01 21:53:35,031 - mmcls - INFO - Epoch [42][1700/5005]\tlr: 1.000e-02, eta: 7:14:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9859\n2023-01-01 21:53:44,213 - mmcls - INFO - Epoch [42][1800/5005]\tlr: 1.000e-02, eta: 7:14:11, time: 0.092, data_time: 0.044, memory: 1224, loss: 1.9721\n2023-01-01 21:53:53,465 - mmcls - INFO - Epoch [42][1900/5005]\tlr: 1.000e-02, eta: 7:14:03, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.9934\n2023-01-01 21:54:02,165 - mmcls - INFO - Epoch [42][2000/5005]\tlr: 1.000e-02, eta: 7:13:54, time: 0.087, data_time: 0.038, memory: 1224, loss: 2.0165\n2023-01-01 21:54:10,901 - mmcls - INFO - Epoch [42][2100/5005]\tlr: 1.000e-02, eta: 7:13:45, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9883\n2023-01-01 21:54:19,671 - mmcls - INFO - Epoch [42][2200/5005]\tlr: 1.000e-02, eta: 7:13:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9753\n2023-01-01 21:54:28,246 - mmcls - INFO - Epoch [42][2300/5005]\tlr: 1.000e-02, eta: 7:13:26, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9873\n2023-01-01 21:54:37,073 - mmcls - INFO - Epoch [42][2400/5005]\tlr: 1.000e-02, eta: 7:13:17, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9565\n2023-01-01 21:54:45,785 - mmcls - INFO - Epoch [42][2500/5005]\tlr: 1.000e-02, eta: 7:13:08, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9878\n2023-01-01 21:54:54,396 - mmcls - INFO - Epoch [42][2600/5005]\tlr: 1.000e-02, eta: 7:12:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9880\n2023-01-01 21:55:03,404 - mmcls - INFO - Epoch [42][2700/5005]\tlr: 1.000e-02, eta: 7:12:50, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.9737\n2023-01-01 21:55:12,158 - mmcls - INFO - Epoch [42][2800/5005]\tlr: 1.000e-02, eta: 7:12:41, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9949\n2023-01-01 21:55:21,238 - mmcls - INFO - Epoch [42][2900/5005]\tlr: 1.000e-02, eta: 7:12:33, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.9937\n2023-01-01 21:55:29,929 - mmcls - INFO - Epoch [42][3000/5005]\tlr: 1.000e-02, eta: 7:12:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9938\n2023-01-01 21:55:38,691 - mmcls - INFO - Epoch [42][3100/5005]\tlr: 1.000e-02, eta: 7:12:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9962\n2023-01-01 21:55:47,529 - mmcls - INFO - Epoch [42][3200/5005]\tlr: 1.000e-02, eta: 7:12:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9725\n2023-01-01 21:55:56,280 - mmcls - INFO - Epoch [42][3300/5005]\tlr: 1.000e-02, eta: 7:11:56, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.9933\n2023-01-01 21:56:04,922 - mmcls - INFO - Epoch [42][3400/5005]\tlr: 1.000e-02, eta: 7:11:47, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.0084\n2023-01-01 21:56:14,161 - mmcls - INFO - Epoch [42][3500/5005]\tlr: 1.000e-02, eta: 7:11:39, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.9746\n2023-01-01 21:56:23,014 - mmcls - INFO - Epoch [42][3600/5005]\tlr: 1.000e-02, eta: 7:11:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0211\n2023-01-01 21:56:31,621 - mmcls - INFO - Epoch [42][3700/5005]\tlr: 1.000e-02, eta: 7:11:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9802\n2023-01-01 21:56:40,447 - mmcls - INFO - Epoch [42][3800/5005]\tlr: 1.000e-02, eta: 7:11:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0079\n2023-01-01 21:56:49,089 - mmcls - INFO - Epoch [42][3900/5005]\tlr: 1.000e-02, eta: 7:11:03, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0166\n2023-01-01 21:56:57,885 - mmcls - INFO - Epoch [42][4000/5005]\tlr: 1.000e-02, eta: 7:10:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0262\n2023-01-01 21:57:06,621 - mmcls - INFO - Epoch [42][4100/5005]\tlr: 1.000e-02, eta: 7:10:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0088\n2023-01-01 21:57:15,455 - mmcls - INFO - Epoch [42][4200/5005]\tlr: 1.000e-02, eta: 7:10:36, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9827\n2023-01-01 21:57:24,215 - mmcls - INFO - Epoch [42][4300/5005]\tlr: 1.000e-02, eta: 7:10:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0102\n2023-01-01 21:57:32,863 - mmcls - INFO - Epoch [42][4400/5005]\tlr: 1.000e-02, eta: 7:10:17, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9975\n2023-01-01 21:57:41,689 - mmcls - INFO - Epoch [42][4500/5005]\tlr: 1.000e-02, eta: 7:10:08, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0102\n2023-01-01 21:57:50,365 - mmcls - INFO - Epoch [42][4600/5005]\tlr: 1.000e-02, eta: 7:09:59, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0131\n2023-01-01 21:57:59,328 - mmcls - INFO - Epoch [42][4700/5005]\tlr: 1.000e-02, eta: 7:09:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0032\n2023-01-01 21:58:08,289 - mmcls - INFO - Epoch [42][4800/5005]\tlr: 1.000e-02, eta: 7:09:42, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0020\n2023-01-01 21:58:17,194 - mmcls - INFO - Epoch [42][4900/5005]\tlr: 1.000e-02, eta: 7:09:33, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0042\n2023-01-01 21:58:26,532 - mmcls - INFO - Epoch [42][5000/5005]\tlr: 1.000e-02, eta: 7:09:25, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.0050\n2023-01-01 21:58:49,292 - mmcls - INFO - Epoch(val) [42][196]\taccuracy_top-1: 60.2500, accuracy_top-5: 82.4340\n2023-01-01 21:59:00,376 - mmcls - INFO - Epoch [43][100/5005]\tlr: 1.000e-02, eta: 7:09:18, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.9543\n2023-01-01 21:59:09,445 - mmcls - INFO - Epoch [43][200/5005]\tlr: 1.000e-02, eta: 7:09:09, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.9598\n2023-01-01 21:59:18,105 - mmcls - INFO - Epoch [43][300/5005]\tlr: 1.000e-02, eta: 7:09:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9631\n2023-01-01 21:59:26,908 - mmcls - INFO - Epoch [43][400/5005]\tlr: 1.000e-02, eta: 7:08:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9834\n2023-01-01 21:59:35,655 - mmcls - INFO - Epoch [43][500/5005]\tlr: 1.000e-02, eta: 7:08:42, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9775\n2023-01-01 21:59:44,358 - mmcls - INFO - Epoch [43][600/5005]\tlr: 1.000e-02, eta: 7:08:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9622\n2023-01-01 21:59:53,459 - mmcls - INFO - Epoch [43][700/5005]\tlr: 1.000e-02, eta: 7:08:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9347\n2023-01-01 22:00:01,988 - mmcls - INFO - Epoch [43][800/5005]\tlr: 1.000e-02, eta: 7:08:15, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9557\n2023-01-01 22:00:10,941 - mmcls - INFO - Epoch [43][900/5005]\tlr: 1.000e-02, eta: 7:08:06, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9650\n2023-01-01 22:00:19,828 - mmcls - INFO - Epoch [43][1000/5005]\tlr: 1.000e-02, eta: 7:07:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9689\n2023-01-01 22:00:28,480 - mmcls - INFO - Epoch [43][1100/5005]\tlr: 1.000e-02, eta: 7:07:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9662\n2023-01-01 22:00:37,270 - mmcls - INFO - Epoch [43][1200/5005]\tlr: 1.000e-02, eta: 7:07:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0032\n2023-01-01 22:00:46,204 - mmcls - INFO - Epoch [43][1300/5005]\tlr: 1.000e-02, eta: 7:07:30, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9961\n2023-01-01 22:00:54,988 - mmcls - INFO - Epoch [43][1400/5005]\tlr: 1.000e-02, eta: 7:07:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9822\n2023-01-01 22:01:04,049 - mmcls - INFO - Epoch [43][1500/5005]\tlr: 1.000e-02, eta: 7:07:13, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.9730\n2023-01-01 22:01:12,968 - mmcls - INFO - Epoch [43][1600/5005]\tlr: 1.000e-02, eta: 7:07:04, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.0200\n2023-01-01 22:01:22,048 - mmcls - INFO - Epoch [43][1700/5005]\tlr: 1.000e-02, eta: 7:06:55, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9647\n2023-01-01 22:01:30,597 - mmcls - INFO - Epoch [43][1800/5005]\tlr: 1.000e-02, eta: 7:06:46, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0039\n2023-01-01 22:01:39,519 - mmcls - INFO - Epoch [43][1900/5005]\tlr: 1.000e-02, eta: 7:06:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9793\n2023-01-01 22:01:48,262 - mmcls - INFO - Epoch [43][2000/5005]\tlr: 1.000e-02, eta: 7:06:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9827\n2023-01-01 22:01:57,062 - mmcls - INFO - Epoch [43][2100/5005]\tlr: 1.000e-02, eta: 7:06:19, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9911\n2023-01-01 22:02:05,982 - mmcls - INFO - Epoch [43][2200/5005]\tlr: 1.000e-02, eta: 7:06:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9853\n2023-01-01 22:02:14,674 - mmcls - INFO - Epoch [43][2300/5005]\tlr: 1.000e-02, eta: 7:06:01, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.9887\n2023-01-01 22:02:23,296 - mmcls - INFO - Epoch [43][2400/5005]\tlr: 1.000e-02, eta: 7:05:52, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9852\n2023-01-01 22:02:32,069 - mmcls - INFO - Epoch [43][2500/5005]\tlr: 1.000e-02, eta: 7:05:43, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0018\n2023-01-01 22:02:40,856 - mmcls - INFO - Epoch [43][2600/5005]\tlr: 1.000e-02, eta: 7:05:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9805\n2023-01-01 22:02:49,684 - mmcls - INFO - Epoch [43][2700/5005]\tlr: 1.000e-02, eta: 7:05:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0048\n2023-01-01 22:02:58,740 - mmcls - INFO - Epoch [43][2800/5005]\tlr: 1.000e-02, eta: 7:05:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9770\n2023-01-01 22:03:07,285 - mmcls - INFO - Epoch [43][2900/5005]\tlr: 1.000e-02, eta: 7:05:07, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.9701\n2023-01-01 22:03:15,906 - mmcls - INFO - Epoch [43][3000/5005]\tlr: 1.000e-02, eta: 7:04:58, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9857\n2023-01-01 22:03:24,415 - mmcls - INFO - Epoch [43][3100/5005]\tlr: 1.000e-02, eta: 7:04:48, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9919\n2023-01-01 22:03:33,185 - mmcls - INFO - Epoch [43][3200/5005]\tlr: 1.000e-02, eta: 7:04:39, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9995\n2023-01-01 22:03:42,020 - mmcls - INFO - Epoch [43][3300/5005]\tlr: 1.000e-02, eta: 7:04:30, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0059\n2023-01-01 22:03:50,891 - mmcls - INFO - Epoch [43][3400/5005]\tlr: 1.000e-02, eta: 7:04:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0069\n2023-01-01 22:03:59,637 - mmcls - INFO - Epoch [43][3500/5005]\tlr: 1.000e-02, eta: 7:04:12, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9900\n2023-01-01 22:04:08,224 - mmcls - INFO - Epoch [43][3600/5005]\tlr: 1.000e-02, eta: 7:04:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9981\n2023-01-01 22:04:17,113 - mmcls - INFO - Epoch [43][3700/5005]\tlr: 1.000e-02, eta: 7:03:54, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0301\n2023-01-01 22:04:25,863 - mmcls - INFO - Epoch [43][3800/5005]\tlr: 1.000e-02, eta: 7:03:45, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9814\n2023-01-01 22:04:34,610 - mmcls - INFO - Epoch [43][3900/5005]\tlr: 1.000e-02, eta: 7:03:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9925\n2023-01-01 22:04:43,201 - mmcls - INFO - Epoch [43][4000/5005]\tlr: 1.000e-02, eta: 7:03:27, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9893\n2023-01-01 22:04:51,951 - mmcls - INFO - Epoch [43][4100/5005]\tlr: 1.000e-02, eta: 7:03:18, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9826\n2023-01-01 22:05:00,941 - mmcls - INFO - Epoch [43][4200/5005]\tlr: 1.000e-02, eta: 7:03:09, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.9609\n2023-01-01 22:05:09,498 - mmcls - INFO - Epoch [43][4300/5005]\tlr: 1.000e-02, eta: 7:03:00, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9838\n2023-01-01 22:05:18,596 - mmcls - INFO - Epoch [43][4400/5005]\tlr: 1.000e-02, eta: 7:02:51, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0039\n2023-01-01 22:05:27,411 - mmcls - INFO - Epoch [43][4500/5005]\tlr: 1.000e-02, eta: 7:02:42, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9942\n2023-01-01 22:05:36,519 - mmcls - INFO - Epoch [43][4600/5005]\tlr: 1.000e-02, eta: 7:02:34, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9952\n2023-01-01 22:05:45,154 - mmcls - INFO - Epoch [43][4700/5005]\tlr: 1.000e-02, eta: 7:02:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9911\n2023-01-01 22:05:54,394 - mmcls - INFO - Epoch [43][4800/5005]\tlr: 1.000e-02, eta: 7:02:16, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.0122\n2023-01-01 22:06:03,317 - mmcls - INFO - Epoch [43][4900/5005]\tlr: 1.000e-02, eta: 7:02:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9743\n2023-01-01 22:06:12,271 - mmcls - INFO - Epoch [43][5000/5005]\tlr: 1.000e-02, eta: 7:01:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9938\n2023-01-01 22:06:35,255 - mmcls - INFO - Epoch(val) [43][196]\taccuracy_top-1: 58.9880, accuracy_top-5: 81.5180\n2023-01-01 22:06:46,397 - mmcls - INFO - Epoch [44][100/5005]\tlr: 1.000e-02, eta: 7:01:52, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.9538\n2023-01-01 22:06:55,369 - mmcls - INFO - Epoch [44][200/5005]\tlr: 1.000e-02, eta: 7:01:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9397\n2023-01-01 22:07:04,413 - mmcls - INFO - Epoch [44][300/5005]\tlr: 1.000e-02, eta: 7:01:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9440\n2023-01-01 22:07:12,896 - mmcls - INFO - Epoch [44][400/5005]\tlr: 1.000e-02, eta: 7:01:25, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9648\n2023-01-01 22:07:21,913 - mmcls - INFO - Epoch [44][500/5005]\tlr: 1.000e-02, eta: 7:01:16, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9362\n2023-01-01 22:07:30,738 - mmcls - INFO - Epoch [44][600/5005]\tlr: 1.000e-02, eta: 7:01:07, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.9585\n2023-01-01 22:07:39,842 - mmcls - INFO - Epoch [44][700/5005]\tlr: 1.000e-02, eta: 7:00:59, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9414\n2023-01-01 22:07:48,411 - mmcls - INFO - Epoch [44][800/5005]\tlr: 1.000e-02, eta: 7:00:49, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9659\n2023-01-01 22:07:57,271 - mmcls - INFO - Epoch [44][900/5005]\tlr: 1.000e-02, eta: 7:00:41, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9740\n2023-01-01 22:08:05,824 - mmcls - INFO - Epoch [44][1000/5005]\tlr: 1.000e-02, eta: 7:00:31, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9624\n2023-01-01 22:08:14,364 - mmcls - INFO - Epoch [44][1100/5005]\tlr: 1.000e-02, eta: 7:00:22, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9713\n2023-01-01 22:08:23,332 - mmcls - INFO - Epoch [44][1200/5005]\tlr: 1.000e-02, eta: 7:00:13, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9555\n2023-01-01 22:08:32,144 - mmcls - INFO - Epoch [44][1300/5005]\tlr: 1.000e-02, eta: 7:00:04, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9780\n2023-01-01 22:08:40,928 - mmcls - INFO - Epoch [44][1400/5005]\tlr: 1.000e-02, eta: 6:59:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9576\n2023-01-01 22:08:49,669 - mmcls - INFO - Epoch [44][1500/5005]\tlr: 1.000e-02, eta: 6:59:46, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9873\n2023-01-01 22:08:58,459 - mmcls - INFO - Epoch [44][1600/5005]\tlr: 1.000e-02, eta: 6:59:37, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.9639\n2023-01-01 22:09:07,208 - mmcls - INFO - Epoch [44][1700/5005]\tlr: 1.000e-02, eta: 6:59:28, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9800\n2023-01-01 22:09:15,927 - mmcls - INFO - Epoch [44][1800/5005]\tlr: 1.000e-02, eta: 6:59:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0027\n2023-01-01 22:09:24,716 - mmcls - INFO - Epoch [44][1900/5005]\tlr: 1.000e-02, eta: 6:59:10, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9543\n2023-01-01 22:09:33,627 - mmcls - INFO - Epoch [44][2000/5005]\tlr: 1.000e-02, eta: 6:59:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9872\n2023-01-01 22:09:42,239 - mmcls - INFO - Epoch [44][2100/5005]\tlr: 1.000e-02, eta: 6:58:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9748\n2023-01-01 22:09:50,948 - mmcls - INFO - Epoch [44][2200/5005]\tlr: 1.000e-02, eta: 6:58:43, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9699\n2023-01-01 22:09:59,780 - mmcls - INFO - Epoch [44][2300/5005]\tlr: 1.000e-02, eta: 6:58:34, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9956\n2023-01-01 22:10:08,407 - mmcls - INFO - Epoch [44][2400/5005]\tlr: 1.000e-02, eta: 6:58:25, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.0078\n2023-01-01 22:10:17,315 - mmcls - INFO - Epoch [44][2500/5005]\tlr: 1.000e-02, eta: 6:58:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9806\n2023-01-01 22:10:26,330 - mmcls - INFO - Epoch [44][2600/5005]\tlr: 1.000e-02, eta: 6:58:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9993\n2023-01-01 22:10:34,871 - mmcls - INFO - Epoch [44][2700/5005]\tlr: 1.000e-02, eta: 6:57:58, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.0132\n2023-01-01 22:10:43,778 - mmcls - INFO - Epoch [44][2800/5005]\tlr: 1.000e-02, eta: 6:57:49, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9746\n2023-01-01 22:10:52,659 - mmcls - INFO - Epoch [44][2900/5005]\tlr: 1.000e-02, eta: 6:57:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9905\n2023-01-01 22:11:01,396 - mmcls - INFO - Epoch [44][3000/5005]\tlr: 1.000e-02, eta: 6:57:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9877\n2023-01-01 22:11:10,147 - mmcls - INFO - Epoch [44][3100/5005]\tlr: 1.000e-02, eta: 6:57:22, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.9986\n2023-01-01 22:11:18,819 - mmcls - INFO - Epoch [44][3200/5005]\tlr: 1.000e-02, eta: 6:57:13, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0021\n2023-01-01 22:11:27,435 - mmcls - INFO - Epoch [44][3300/5005]\tlr: 1.000e-02, eta: 6:57:04, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.0049\n2023-01-01 22:11:36,112 - mmcls - INFO - Epoch [44][3400/5005]\tlr: 1.000e-02, eta: 6:56:55, time: 0.087, data_time: 0.039, memory: 1224, loss: 2.0108\n2023-01-01 22:11:44,925 - mmcls - INFO - Epoch [44][3500/5005]\tlr: 1.000e-02, eta: 6:56:46, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0099\n2023-01-01 22:11:53,644 - mmcls - INFO - Epoch [44][3600/5005]\tlr: 1.000e-02, eta: 6:56:37, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9849\n2023-01-01 22:12:02,388 - mmcls - INFO - Epoch [44][3700/5005]\tlr: 1.000e-02, eta: 6:56:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9945\n2023-01-01 22:12:11,259 - mmcls - INFO - Epoch [44][3800/5005]\tlr: 1.000e-02, eta: 6:56:19, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.0150\n2023-01-01 22:12:20,381 - mmcls - INFO - Epoch [44][3900/5005]\tlr: 1.000e-02, eta: 6:56:10, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9821\n2023-01-01 22:12:29,052 - mmcls - INFO - Epoch [44][4000/5005]\tlr: 1.000e-02, eta: 6:56:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0252\n2023-01-01 22:12:37,973 - mmcls - INFO - Epoch [44][4100/5005]\tlr: 1.000e-02, eta: 6:55:52, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.0007\n2023-01-01 22:12:46,734 - mmcls - INFO - Epoch [44][4200/5005]\tlr: 1.000e-02, eta: 6:55:43, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9891\n2023-01-01 22:12:55,593 - mmcls - INFO - Epoch [44][4300/5005]\tlr: 1.000e-02, eta: 6:55:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9783\n2023-01-01 22:13:04,220 - mmcls - INFO - Epoch [44][4400/5005]\tlr: 1.000e-02, eta: 6:55:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9780\n2023-01-01 22:13:13,241 - mmcls - INFO - Epoch [44][4500/5005]\tlr: 1.000e-02, eta: 6:55:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9969\n2023-01-01 22:13:21,906 - mmcls - INFO - Epoch [44][4600/5005]\tlr: 1.000e-02, eta: 6:55:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0039\n2023-01-01 22:13:30,481 - mmcls - INFO - Epoch [44][4700/5005]\tlr: 1.000e-02, eta: 6:54:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0019\n2023-01-01 22:13:39,228 - mmcls - INFO - Epoch [44][4800/5005]\tlr: 1.000e-02, eta: 6:54:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0268\n2023-01-01 22:13:48,357 - mmcls - INFO - Epoch [44][4900/5005]\tlr: 1.000e-02, eta: 6:54:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0143\n2023-01-01 22:13:57,396 - mmcls - INFO - Epoch [44][5000/5005]\tlr: 1.000e-02, eta: 6:54:32, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0219\n2023-01-01 22:14:19,926 - mmcls - INFO - Epoch(val) [44][196]\taccuracy_top-1: 60.0740, accuracy_top-5: 82.3200\n2023-01-01 22:14:30,936 - mmcls - INFO - Epoch [45][100/5005]\tlr: 1.000e-02, eta: 6:54:25, time: 0.110, data_time: 0.048, memory: 1224, loss: 1.9773\n2023-01-01 22:14:39,689 - mmcls - INFO - Epoch [45][200/5005]\tlr: 1.000e-02, eta: 6:54:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9544\n2023-01-01 22:14:48,759 - mmcls - INFO - Epoch [45][300/5005]\tlr: 1.000e-02, eta: 6:54:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9753\n2023-01-01 22:14:57,601 - mmcls - INFO - Epoch [45][400/5005]\tlr: 1.000e-02, eta: 6:53:58, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9565\n2023-01-01 22:15:06,152 - mmcls - INFO - Epoch [45][500/5005]\tlr: 1.000e-02, eta: 6:53:49, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.9583\n2023-01-01 22:15:15,083 - mmcls - INFO - Epoch [45][600/5005]\tlr: 1.000e-02, eta: 6:53:40, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.9509\n2023-01-01 22:15:23,607 - mmcls - INFO - Epoch [45][700/5005]\tlr: 1.000e-02, eta: 6:53:31, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9662\n2023-01-01 22:15:32,611 - mmcls - INFO - Epoch [45][800/5005]\tlr: 1.000e-02, eta: 6:53:22, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9630\n2023-01-01 22:15:41,735 - mmcls - INFO - Epoch [45][900/5005]\tlr: 1.000e-02, eta: 6:53:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9597\n2023-01-01 22:15:50,259 - mmcls - INFO - Epoch [45][1000/5005]\tlr: 1.000e-02, eta: 6:53:04, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9673\n2023-01-01 22:15:59,112 - mmcls - INFO - Epoch [45][1100/5005]\tlr: 1.000e-02, eta: 6:52:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9633\n2023-01-01 22:16:07,848 - mmcls - INFO - Epoch [45][1200/5005]\tlr: 1.000e-02, eta: 6:52:46, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9475\n2023-01-01 22:16:16,485 - mmcls - INFO - Epoch [45][1300/5005]\tlr: 1.000e-02, eta: 6:52:37, time: 0.086, data_time: 0.030, memory: 1224, loss: 1.9815\n2023-01-01 22:16:25,093 - mmcls - INFO - Epoch [45][1400/5005]\tlr: 1.000e-02, eta: 6:52:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9580\n2023-01-01 22:16:33,631 - mmcls - INFO - Epoch [45][1500/5005]\tlr: 1.000e-02, eta: 6:52:18, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.9495\n2023-01-01 22:16:42,416 - mmcls - INFO - Epoch [45][1600/5005]\tlr: 1.000e-02, eta: 6:52:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.9599\n2023-01-01 22:16:51,142 - mmcls - INFO - Epoch [45][1700/5005]\tlr: 1.000e-02, eta: 6:52:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0034\n2023-01-01 22:17:00,359 - mmcls - INFO - Epoch [45][1800/5005]\tlr: 1.000e-02, eta: 6:51:52, time: 0.092, data_time: 0.000, memory: 1224, loss: 1.9618\n2023-01-01 22:17:09,382 - mmcls - INFO - Epoch [45][1900/5005]\tlr: 1.000e-02, eta: 6:51:43, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.9545\n2023-01-01 22:17:18,262 - mmcls - INFO - Epoch [45][2000/5005]\tlr: 1.000e-02, eta: 6:51:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9778\n2023-01-01 22:17:27,186 - mmcls - INFO - Epoch [45][2100/5005]\tlr: 1.000e-02, eta: 6:51:26, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9717\n2023-01-01 22:17:35,783 - mmcls - INFO - Epoch [45][2200/5005]\tlr: 1.000e-02, eta: 6:51:16, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.0058\n2023-01-01 22:17:44,470 - mmcls - INFO - Epoch [45][2300/5005]\tlr: 1.000e-02, eta: 6:51:07, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.9616\n2023-01-01 22:17:53,113 - mmcls - INFO - Epoch [45][2400/5005]\tlr: 1.000e-02, eta: 6:50:58, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9691\n2023-01-01 22:18:02,102 - mmcls - INFO - Epoch [45][2500/5005]\tlr: 1.000e-02, eta: 6:50:49, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9982\n2023-01-01 22:18:10,947 - mmcls - INFO - Epoch [45][2600/5005]\tlr: 1.000e-02, eta: 6:50:40, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0059\n2023-01-01 22:18:19,628 - mmcls - INFO - Epoch [45][2700/5005]\tlr: 1.000e-02, eta: 6:50:31, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9944\n2023-01-01 22:18:28,346 - mmcls - INFO - Epoch [45][2800/5005]\tlr: 1.000e-02, eta: 6:50:22, time: 0.087, data_time: 0.042, memory: 1224, loss: 1.9821\n2023-01-01 22:18:37,190 - mmcls - INFO - Epoch [45][2900/5005]\tlr: 1.000e-02, eta: 6:50:13, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9866\n2023-01-01 22:18:45,830 - mmcls - INFO - Epoch [45][3000/5005]\tlr: 1.000e-02, eta: 6:50:04, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9930\n2023-01-01 22:18:54,768 - mmcls - INFO - Epoch [45][3100/5005]\tlr: 1.000e-02, eta: 6:49:55, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9896\n2023-01-01 22:19:03,460 - mmcls - INFO - Epoch [45][3200/5005]\tlr: 1.000e-02, eta: 6:49:46, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.9784\n2023-01-01 22:19:12,093 - mmcls - INFO - Epoch [45][3300/5005]\tlr: 1.000e-02, eta: 6:49:37, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9775\n2023-01-01 22:19:20,898 - mmcls - INFO - Epoch [45][3400/5005]\tlr: 1.000e-02, eta: 6:49:28, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.0002\n2023-01-01 22:19:29,827 - mmcls - INFO - Epoch [45][3500/5005]\tlr: 1.000e-02, eta: 6:49:19, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9742\n2023-01-01 22:19:38,228 - mmcls - INFO - Epoch [45][3600/5005]\tlr: 1.000e-02, eta: 6:49:10, time: 0.084, data_time: 0.011, memory: 1224, loss: 1.9808\n2023-01-01 22:19:46,942 - mmcls - INFO - Epoch [45][3700/5005]\tlr: 1.000e-02, eta: 6:49:01, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.9719\n2023-01-01 22:19:55,806 - mmcls - INFO - Epoch [45][3800/5005]\tlr: 1.000e-02, eta: 6:48:52, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.9952\n2023-01-01 22:20:04,734 - mmcls - INFO - Epoch [45][3900/5005]\tlr: 1.000e-02, eta: 6:48:43, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9781\n2023-01-01 22:20:13,721 - mmcls - INFO - Epoch [45][4000/5005]\tlr: 1.000e-02, eta: 6:48:34, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9893\n2023-01-01 22:20:22,868 - mmcls - INFO - Epoch [45][4100/5005]\tlr: 1.000e-02, eta: 6:48:26, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.9751\n2023-01-01 22:20:31,743 - mmcls - INFO - Epoch [45][4200/5005]\tlr: 1.000e-02, eta: 6:48:17, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9991\n2023-01-01 22:20:40,303 - mmcls - INFO - Epoch [45][4300/5005]\tlr: 1.000e-02, eta: 6:48:08, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9969\n2023-01-01 22:20:49,010 - mmcls - INFO - Epoch [45][4400/5005]\tlr: 1.000e-02, eta: 6:47:59, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9999\n2023-01-01 22:20:57,800 - mmcls - INFO - Epoch [45][4500/5005]\tlr: 1.000e-02, eta: 6:47:50, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9923\n2023-01-01 22:21:06,485 - mmcls - INFO - Epoch [45][4600/5005]\tlr: 1.000e-02, eta: 6:47:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9889\n2023-01-01 22:21:15,445 - mmcls - INFO - Epoch [45][4700/5005]\tlr: 1.000e-02, eta: 6:47:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9776\n2023-01-01 22:21:24,328 - mmcls - INFO - Epoch [45][4800/5005]\tlr: 1.000e-02, eta: 6:47:23, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9914\n2023-01-01 22:21:33,035 - mmcls - INFO - Epoch [45][4900/5005]\tlr: 1.000e-02, eta: 6:47:14, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0196\n2023-01-01 22:21:41,984 - mmcls - INFO - Epoch [45][5000/5005]\tlr: 1.000e-02, eta: 6:47:05, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.0049\n2023-01-01 22:22:04,915 - mmcls - INFO - Epoch(val) [45][196]\taccuracy_top-1: 59.9800, accuracy_top-5: 82.2840\n2023-01-01 22:22:15,716 - mmcls - INFO - Epoch [46][100/5005]\tlr: 1.000e-02, eta: 6:46:57, time: 0.108, data_time: 0.040, memory: 1224, loss: 1.9558\n2023-01-01 22:22:24,968 - mmcls - INFO - Epoch [46][200/5005]\tlr: 1.000e-02, eta: 6:46:49, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.9559\n2023-01-01 22:22:33,660 - mmcls - INFO - Epoch [46][300/5005]\tlr: 1.000e-02, eta: 6:46:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9331\n2023-01-01 22:22:42,483 - mmcls - INFO - Epoch [46][400/5005]\tlr: 1.000e-02, eta: 6:46:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9462\n2023-01-01 22:22:51,296 - mmcls - INFO - Epoch [46][500/5005]\tlr: 1.000e-02, eta: 6:46:22, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9699\n2023-01-01 22:23:00,372 - mmcls - INFO - Epoch [46][600/5005]\tlr: 1.000e-02, eta: 6:46:13, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.9612\n2023-01-01 22:23:09,365 - mmcls - INFO - Epoch [46][700/5005]\tlr: 1.000e-02, eta: 6:46:05, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9596\n2023-01-01 22:23:18,459 - mmcls - INFO - Epoch [46][800/5005]\tlr: 1.000e-02, eta: 6:45:56, time: 0.091, data_time: 0.027, memory: 1224, loss: 1.9675\n2023-01-01 22:23:27,584 - mmcls - INFO - Epoch [46][900/5005]\tlr: 1.000e-02, eta: 6:45:48, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.9823\n2023-01-01 22:23:36,089 - mmcls - INFO - Epoch [46][1000/5005]\tlr: 1.000e-02, eta: 6:45:38, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.9681\n2023-01-01 22:23:45,000 - mmcls - INFO - Epoch [46][1100/5005]\tlr: 1.000e-02, eta: 6:45:29, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9721\n2023-01-01 22:23:53,716 - mmcls - INFO - Epoch [46][1200/5005]\tlr: 1.000e-02, eta: 6:45:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9756\n2023-01-01 22:24:02,503 - mmcls - INFO - Epoch [46][1300/5005]\tlr: 1.000e-02, eta: 6:45:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9684\n2023-01-01 22:24:11,310 - mmcls - INFO - Epoch [46][1400/5005]\tlr: 1.000e-02, eta: 6:45:02, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.9644\n2023-01-01 22:24:20,158 - mmcls - INFO - Epoch [46][1500/5005]\tlr: 1.000e-02, eta: 6:44:53, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9677\n2023-01-01 22:24:28,871 - mmcls - INFO - Epoch [46][1600/5005]\tlr: 1.000e-02, eta: 6:44:44, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9747\n2023-01-01 22:24:37,638 - mmcls - INFO - Epoch [46][1700/5005]\tlr: 1.000e-02, eta: 6:44:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9806\n2023-01-01 22:24:46,269 - mmcls - INFO - Epoch [46][1800/5005]\tlr: 1.000e-02, eta: 6:44:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9736\n2023-01-01 22:24:54,912 - mmcls - INFO - Epoch [46][1900/5005]\tlr: 1.000e-02, eta: 6:44:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0075\n2023-01-01 22:25:03,823 - mmcls - INFO - Epoch [46][2000/5005]\tlr: 1.000e-02, eta: 6:44:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9765\n2023-01-01 22:25:12,449 - mmcls - INFO - Epoch [46][2100/5005]\tlr: 1.000e-02, eta: 6:43:59, time: 0.086, data_time: 0.031, memory: 1224, loss: 1.9795\n2023-01-01 22:25:21,103 - mmcls - INFO - Epoch [46][2200/5005]\tlr: 1.000e-02, eta: 6:43:50, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9791\n2023-01-01 22:25:30,285 - mmcls - INFO - Epoch [46][2300/5005]\tlr: 1.000e-02, eta: 6:43:41, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9955\n2023-01-01 22:25:39,391 - mmcls - INFO - Epoch [46][2400/5005]\tlr: 1.000e-02, eta: 6:43:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9640\n2023-01-01 22:25:48,355 - mmcls - INFO - Epoch [46][2500/5005]\tlr: 1.000e-02, eta: 6:43:24, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9758\n2023-01-01 22:25:57,376 - mmcls - INFO - Epoch [46][2600/5005]\tlr: 1.000e-02, eta: 6:43:15, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9810\n2023-01-01 22:26:06,268 - mmcls - INFO - Epoch [46][2700/5005]\tlr: 1.000e-02, eta: 6:43:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9806\n2023-01-01 22:26:14,886 - mmcls - INFO - Epoch [46][2800/5005]\tlr: 1.000e-02, eta: 6:42:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0001\n2023-01-01 22:26:23,916 - mmcls - INFO - Epoch [46][2900/5005]\tlr: 1.000e-02, eta: 6:42:49, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0063\n2023-01-01 22:26:32,695 - mmcls - INFO - Epoch [46][3000/5005]\tlr: 1.000e-02, eta: 6:42:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0072\n2023-01-01 22:26:41,444 - mmcls - INFO - Epoch [46][3100/5005]\tlr: 1.000e-02, eta: 6:42:31, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9813\n2023-01-01 22:26:50,172 - mmcls - INFO - Epoch [46][3200/5005]\tlr: 1.000e-02, eta: 6:42:22, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9799\n2023-01-01 22:26:58,955 - mmcls - INFO - Epoch [46][3300/5005]\tlr: 1.000e-02, eta: 6:42:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9887\n2023-01-01 22:27:07,566 - mmcls - INFO - Epoch [46][3400/5005]\tlr: 1.000e-02, eta: 6:42:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.0079\n2023-01-01 22:27:16,229 - mmcls - INFO - Epoch [46][3500/5005]\tlr: 1.000e-02, eta: 6:41:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9707\n2023-01-01 22:27:25,204 - mmcls - INFO - Epoch [46][3600/5005]\tlr: 1.000e-02, eta: 6:41:46, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9809\n2023-01-01 22:27:34,018 - mmcls - INFO - Epoch [46][3700/5005]\tlr: 1.000e-02, eta: 6:41:37, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0062\n2023-01-01 22:27:42,673 - mmcls - INFO - Epoch [46][3800/5005]\tlr: 1.000e-02, eta: 6:41:27, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0000\n2023-01-01 22:27:51,321 - mmcls - INFO - Epoch [46][3900/5005]\tlr: 1.000e-02, eta: 6:41:18, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9771\n2023-01-01 22:28:00,025 - mmcls - INFO - Epoch [46][4000/5005]\tlr: 1.000e-02, eta: 6:41:09, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9837\n2023-01-01 22:28:08,890 - mmcls - INFO - Epoch [46][4100/5005]\tlr: 1.000e-02, eta: 6:41:00, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9821\n2023-01-01 22:28:17,588 - mmcls - INFO - Epoch [46][4200/5005]\tlr: 1.000e-02, eta: 6:40:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0003\n2023-01-01 22:28:26,586 - mmcls - INFO - Epoch [46][4300/5005]\tlr: 1.000e-02, eta: 6:40:43, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.9898\n2023-01-01 22:28:35,586 - mmcls - INFO - Epoch [46][4400/5005]\tlr: 1.000e-02, eta: 6:40:34, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9894\n2023-01-01 22:28:44,321 - mmcls - INFO - Epoch [46][4500/5005]\tlr: 1.000e-02, eta: 6:40:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0103\n2023-01-01 22:28:52,888 - mmcls - INFO - Epoch [46][4600/5005]\tlr: 1.000e-02, eta: 6:40:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0016\n2023-01-01 22:29:01,533 - mmcls - INFO - Epoch [46][4700/5005]\tlr: 1.000e-02, eta: 6:40:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9945\n2023-01-01 22:29:10,657 - mmcls - INFO - Epoch [46][4800/5005]\tlr: 1.000e-02, eta: 6:39:58, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.0209\n2023-01-01 22:29:19,866 - mmcls - INFO - Epoch [46][4900/5005]\tlr: 1.000e-02, eta: 6:39:49, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.9855\n2023-01-01 22:29:28,820 - mmcls - INFO - Epoch [46][5000/5005]\tlr: 1.000e-02, eta: 6:39:41, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0082\n2023-01-01 22:29:51,562 - mmcls - INFO - Epoch(val) [46][196]\taccuracy_top-1: 60.1540, accuracy_top-5: 82.4240\n2023-01-01 22:30:02,745 - mmcls - INFO - Epoch [47][100/5005]\tlr: 1.000e-02, eta: 6:39:33, time: 0.112, data_time: 0.064, memory: 1224, loss: 1.9347\n2023-01-01 22:30:11,476 - mmcls - INFO - Epoch [47][200/5005]\tlr: 1.000e-02, eta: 6:39:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9294\n2023-01-01 22:30:20,231 - mmcls - INFO - Epoch [47][300/5005]\tlr: 1.000e-02, eta: 6:39:15, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.9479\n2023-01-01 22:30:28,865 - mmcls - INFO - Epoch [47][400/5005]\tlr: 1.000e-02, eta: 6:39:06, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9499\n2023-01-01 22:30:37,719 - mmcls - INFO - Epoch [47][500/5005]\tlr: 1.000e-02, eta: 6:38:57, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.9551\n2023-01-01 22:30:46,574 - mmcls - INFO - Epoch [47][600/5005]\tlr: 1.000e-02, eta: 6:38:48, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9452\n2023-01-01 22:30:55,146 - mmcls - INFO - Epoch [47][700/5005]\tlr: 1.000e-02, eta: 6:38:39, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9622\n2023-01-01 22:31:04,239 - mmcls - INFO - Epoch [47][800/5005]\tlr: 1.000e-02, eta: 6:38:31, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.9897\n2023-01-01 22:31:12,745 - mmcls - INFO - Epoch [47][900/5005]\tlr: 1.000e-02, eta: 6:38:21, time: 0.085, data_time: 0.036, memory: 1224, loss: 1.9671\n2023-01-01 22:31:21,475 - mmcls - INFO - Epoch [47][1000/5005]\tlr: 1.000e-02, eta: 6:38:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9741\n2023-01-01 22:31:30,275 - mmcls - INFO - Epoch [47][1100/5005]\tlr: 1.000e-02, eta: 6:38:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9603\n2023-01-01 22:31:38,900 - mmcls - INFO - Epoch [47][1200/5005]\tlr: 1.000e-02, eta: 6:37:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9545\n2023-01-01 22:31:47,666 - mmcls - INFO - Epoch [47][1300/5005]\tlr: 1.000e-02, eta: 6:37:45, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9501\n2023-01-01 22:31:56,364 - mmcls - INFO - Epoch [47][1400/5005]\tlr: 1.000e-02, eta: 6:37:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9916\n2023-01-01 22:32:05,722 - mmcls - INFO - Epoch [47][1500/5005]\tlr: 1.000e-02, eta: 6:37:28, time: 0.094, data_time: 0.018, memory: 1224, loss: 1.9951\n2023-01-01 22:32:14,476 - mmcls - INFO - Epoch [47][1600/5005]\tlr: 1.000e-02, eta: 6:37:19, time: 0.088, data_time: 0.035, memory: 1224, loss: 1.9696\n2023-01-01 22:32:23,441 - mmcls - INFO - Epoch [47][1700/5005]\tlr: 1.000e-02, eta: 6:37:10, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9574\n2023-01-01 22:32:32,662 - mmcls - INFO - Epoch [47][1800/5005]\tlr: 1.000e-02, eta: 6:37:02, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9826\n2023-01-01 22:32:41,670 - mmcls - INFO - Epoch [47][1900/5005]\tlr: 1.000e-02, eta: 6:36:53, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9744\n2023-01-01 22:32:50,751 - mmcls - INFO - Epoch [47][2000/5005]\tlr: 1.000e-02, eta: 6:36:44, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9650\n2023-01-01 22:32:59,479 - mmcls - INFO - Epoch [47][2100/5005]\tlr: 1.000e-02, eta: 6:36:35, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9618\n2023-01-01 22:33:08,155 - mmcls - INFO - Epoch [47][2200/5005]\tlr: 1.000e-02, eta: 6:36:26, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9835\n2023-01-01 22:33:17,196 - mmcls - INFO - Epoch [47][2300/5005]\tlr: 1.000e-02, eta: 6:36:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9663\n2023-01-01 22:33:26,076 - mmcls - INFO - Epoch [47][2400/5005]\tlr: 1.000e-02, eta: 6:36:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9600\n2023-01-01 22:33:35,027 - mmcls - INFO - Epoch [47][2500/5005]\tlr: 1.000e-02, eta: 6:36:00, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0168\n2023-01-01 22:33:43,667 - mmcls - INFO - Epoch [47][2600/5005]\tlr: 1.000e-02, eta: 6:35:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9639\n2023-01-01 22:33:53,218 - mmcls - INFO - Epoch [47][2700/5005]\tlr: 1.000e-02, eta: 6:35:42, time: 0.095, data_time: 0.018, memory: 1224, loss: 1.9691\n2023-01-01 22:34:02,022 - mmcls - INFO - Epoch [47][2800/5005]\tlr: 1.000e-02, eta: 6:35:33, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9954\n2023-01-01 22:34:11,015 - mmcls - INFO - Epoch [47][2900/5005]\tlr: 1.000e-02, eta: 6:35:25, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.9947\n2023-01-01 22:34:19,499 - mmcls - INFO - Epoch [47][3000/5005]\tlr: 1.000e-02, eta: 6:35:15, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9637\n2023-01-01 22:34:28,190 - mmcls - INFO - Epoch [47][3100/5005]\tlr: 1.000e-02, eta: 6:35:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0162\n2023-01-01 22:34:36,894 - mmcls - INFO - Epoch [47][3200/5005]\tlr: 1.000e-02, eta: 6:34:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.0215\n2023-01-01 22:34:45,847 - mmcls - INFO - Epoch [47][3300/5005]\tlr: 1.000e-02, eta: 6:34:48, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9878\n2023-01-01 22:34:54,312 - mmcls - INFO - Epoch [47][3400/5005]\tlr: 1.000e-02, eta: 6:34:39, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9978\n2023-01-01 22:35:02,908 - mmcls - INFO - Epoch [47][3500/5005]\tlr: 1.000e-02, eta: 6:34:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0133\n2023-01-01 22:35:11,645 - mmcls - INFO - Epoch [47][3600/5005]\tlr: 1.000e-02, eta: 6:34:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9963\n2023-01-01 22:35:20,436 - mmcls - INFO - Epoch [47][3700/5005]\tlr: 1.000e-02, eta: 6:34:12, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0108\n2023-01-01 22:35:29,215 - mmcls - INFO - Epoch [47][3800/5005]\tlr: 1.000e-02, eta: 6:34:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9803\n2023-01-01 22:35:37,763 - mmcls - INFO - Epoch [47][3900/5005]\tlr: 1.000e-02, eta: 6:33:54, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9702\n2023-01-01 22:35:46,855 - mmcls - INFO - Epoch [47][4000/5005]\tlr: 1.000e-02, eta: 6:33:45, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.9678\n2023-01-01 22:35:55,717 - mmcls - INFO - Epoch [47][4100/5005]\tlr: 1.000e-02, eta: 6:33:36, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.0200\n2023-01-01 22:36:04,413 - mmcls - INFO - Epoch [47][4200/5005]\tlr: 1.000e-02, eta: 6:33:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0120\n2023-01-01 22:36:13,165 - mmcls - INFO - Epoch [47][4300/5005]\tlr: 1.000e-02, eta: 6:33:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9894\n2023-01-01 22:36:21,919 - mmcls - INFO - Epoch [47][4400/5005]\tlr: 1.000e-02, eta: 6:33:09, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.9911\n2023-01-01 22:36:30,913 - mmcls - INFO - Epoch [47][4500/5005]\tlr: 1.000e-02, eta: 6:33:00, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9695\n2023-01-01 22:36:40,011 - mmcls - INFO - Epoch [47][4600/5005]\tlr: 1.000e-02, eta: 6:32:52, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.0177\n2023-01-01 22:36:49,161 - mmcls - INFO - Epoch [47][4700/5005]\tlr: 1.000e-02, eta: 6:32:43, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.9954\n2023-01-01 22:36:57,799 - mmcls - INFO - Epoch [47][4800/5005]\tlr: 1.000e-02, eta: 6:32:34, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.0167\n2023-01-01 22:37:06,483 - mmcls - INFO - Epoch [47][4900/5005]\tlr: 1.000e-02, eta: 6:32:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0093\n2023-01-01 22:37:15,515 - mmcls - INFO - Epoch [47][5000/5005]\tlr: 1.000e-02, eta: 6:32:16, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.0198\n2023-01-01 22:37:38,230 - mmcls - INFO - Epoch(val) [47][196]\taccuracy_top-1: 60.1000, accuracy_top-5: 82.1960\n2023-01-01 22:37:49,028 - mmcls - INFO - Epoch [48][100/5005]\tlr: 1.000e-02, eta: 6:32:09, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.9578\n2023-01-01 22:37:57,843 - mmcls - INFO - Epoch [48][200/5005]\tlr: 1.000e-02, eta: 6:32:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9525\n2023-01-01 22:38:06,609 - mmcls - INFO - Epoch [48][300/5005]\tlr: 1.000e-02, eta: 6:31:51, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9597\n2023-01-01 22:38:15,370 - mmcls - INFO - Epoch [48][400/5005]\tlr: 1.000e-02, eta: 6:31:42, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9493\n2023-01-01 22:38:24,140 - mmcls - INFO - Epoch [48][500/5005]\tlr: 1.000e-02, eta: 6:31:33, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9762\n2023-01-01 22:38:32,941 - mmcls - INFO - Epoch [48][600/5005]\tlr: 1.000e-02, eta: 6:31:24, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9700\n2023-01-01 22:38:41,851 - mmcls - INFO - Epoch [48][700/5005]\tlr: 1.000e-02, eta: 6:31:15, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9494\n2023-01-01 22:38:50,921 - mmcls - INFO - Epoch [48][800/5005]\tlr: 1.000e-02, eta: 6:31:06, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9534\n2023-01-01 22:38:59,734 - mmcls - INFO - Epoch [48][900/5005]\tlr: 1.000e-02, eta: 6:30:57, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9586\n2023-01-01 22:39:08,724 - mmcls - INFO - Epoch [48][1000/5005]\tlr: 1.000e-02, eta: 6:30:49, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9642\n2023-01-01 22:39:17,327 - mmcls - INFO - Epoch [48][1100/5005]\tlr: 1.000e-02, eta: 6:30:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9529\n2023-01-01 22:39:26,349 - mmcls - INFO - Epoch [48][1200/5005]\tlr: 1.000e-02, eta: 6:30:31, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9697\n2023-01-01 22:39:34,950 - mmcls - INFO - Epoch [48][1300/5005]\tlr: 1.000e-02, eta: 6:30:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9840\n2023-01-01 22:39:43,660 - mmcls - INFO - Epoch [48][1400/5005]\tlr: 1.000e-02, eta: 6:30:12, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9798\n2023-01-01 22:39:52,324 - mmcls - INFO - Epoch [48][1500/5005]\tlr: 1.000e-02, eta: 6:30:03, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9449\n2023-01-01 22:40:01,155 - mmcls - INFO - Epoch [48][1600/5005]\tlr: 1.000e-02, eta: 6:29:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9586\n2023-01-01 22:40:09,788 - mmcls - INFO - Epoch [48][1700/5005]\tlr: 1.000e-02, eta: 6:29:45, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9709\n2023-01-01 22:40:18,731 - mmcls - INFO - Epoch [48][1800/5005]\tlr: 1.000e-02, eta: 6:29:36, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9833\n2023-01-01 22:40:27,409 - mmcls - INFO - Epoch [48][1900/5005]\tlr: 1.000e-02, eta: 6:29:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9762\n2023-01-01 22:40:36,369 - mmcls - INFO - Epoch [48][2000/5005]\tlr: 1.000e-02, eta: 6:29:19, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9493\n2023-01-01 22:40:44,735 - mmcls - INFO - Epoch [48][2100/5005]\tlr: 1.000e-02, eta: 6:29:09, time: 0.084, data_time: 0.023, memory: 1224, loss: 2.0041\n2023-01-01 22:40:53,742 - mmcls - INFO - Epoch [48][2200/5005]\tlr: 1.000e-02, eta: 6:29:00, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9569\n2023-01-01 22:41:02,433 - mmcls - INFO - Epoch [48][2300/5005]\tlr: 1.000e-02, eta: 6:28:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9905\n2023-01-01 22:41:11,322 - mmcls - INFO - Epoch [48][2400/5005]\tlr: 1.000e-02, eta: 6:28:43, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9834\n2023-01-01 22:41:19,975 - mmcls - INFO - Epoch [48][2500/5005]\tlr: 1.000e-02, eta: 6:28:33, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9676\n2023-01-01 22:41:29,025 - mmcls - INFO - Epoch [48][2600/5005]\tlr: 1.000e-02, eta: 6:28:25, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9898\n2023-01-01 22:41:37,900 - mmcls - INFO - Epoch [48][2700/5005]\tlr: 1.000e-02, eta: 6:28:16, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9709\n2023-01-01 22:41:46,534 - mmcls - INFO - Epoch [48][2800/5005]\tlr: 1.000e-02, eta: 6:28:07, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.9800\n2023-01-01 22:41:55,047 - mmcls - INFO - Epoch [48][2900/5005]\tlr: 1.000e-02, eta: 6:27:57, time: 0.085, data_time: 0.021, memory: 1224, loss: 1.9893\n2023-01-01 22:42:04,130 - mmcls - INFO - Epoch [48][3000/5005]\tlr: 1.000e-02, eta: 6:27:49, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9856\n2023-01-01 22:42:12,770 - mmcls - INFO - Epoch [48][3100/5005]\tlr: 1.000e-02, eta: 6:27:40, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.9869\n2023-01-01 22:42:21,538 - mmcls - INFO - Epoch [48][3200/5005]\tlr: 1.000e-02, eta: 6:27:31, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9758\n2023-01-01 22:42:30,424 - mmcls - INFO - Epoch [48][3300/5005]\tlr: 1.000e-02, eta: 6:27:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9768\n2023-01-01 22:42:39,180 - mmcls - INFO - Epoch [48][3400/5005]\tlr: 1.000e-02, eta: 6:27:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0173\n2023-01-01 22:42:47,811 - mmcls - INFO - Epoch [48][3500/5005]\tlr: 1.000e-02, eta: 6:27:04, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.0173\n2023-01-01 22:42:56,494 - mmcls - INFO - Epoch [48][3600/5005]\tlr: 1.000e-02, eta: 6:26:55, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0000\n2023-01-01 22:43:05,254 - mmcls - INFO - Epoch [48][3700/5005]\tlr: 1.000e-02, eta: 6:26:46, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9823\n2023-01-01 22:43:13,911 - mmcls - INFO - Epoch [48][3800/5005]\tlr: 1.000e-02, eta: 6:26:37, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9777\n2023-01-01 22:43:22,616 - mmcls - INFO - Epoch [48][3900/5005]\tlr: 1.000e-02, eta: 6:26:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9976\n2023-01-01 22:43:31,425 - mmcls - INFO - Epoch [48][4000/5005]\tlr: 1.000e-02, eta: 6:26:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9884\n2023-01-01 22:43:40,198 - mmcls - INFO - Epoch [48][4100/5005]\tlr: 1.000e-02, eta: 6:26:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9745\n2023-01-01 22:43:49,340 - mmcls - INFO - Epoch [48][4200/5005]\tlr: 1.000e-02, eta: 6:26:01, time: 0.091, data_time: 0.034, memory: 1224, loss: 1.9883\n2023-01-01 22:43:58,121 - mmcls - INFO - Epoch [48][4300/5005]\tlr: 1.000e-02, eta: 6:25:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0062\n2023-01-01 22:44:07,078 - mmcls - INFO - Epoch [48][4400/5005]\tlr: 1.000e-02, eta: 6:25:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9907\n2023-01-01 22:44:15,980 - mmcls - INFO - Epoch [48][4500/5005]\tlr: 1.000e-02, eta: 6:25:34, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.0024\n2023-01-01 22:44:24,706 - mmcls - INFO - Epoch [48][4600/5005]\tlr: 1.000e-02, eta: 6:25:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9819\n2023-01-01 22:44:33,727 - mmcls - INFO - Epoch [48][4700/5005]\tlr: 1.000e-02, eta: 6:25:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9606\n2023-01-01 22:44:42,441 - mmcls - INFO - Epoch [48][4800/5005]\tlr: 1.000e-02, eta: 6:25:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9792\n2023-01-01 22:44:51,469 - mmcls - INFO - Epoch [48][4900/5005]\tlr: 1.000e-02, eta: 6:24:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9479\n2023-01-01 22:45:00,303 - mmcls - INFO - Epoch [48][5000/5005]\tlr: 1.000e-02, eta: 6:24:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0090\n2023-01-01 22:45:22,927 - mmcls - INFO - Epoch(val) [48][196]\taccuracy_top-1: 60.3420, accuracy_top-5: 82.5240\n2023-01-01 22:45:34,233 - mmcls - INFO - Epoch [49][100/5005]\tlr: 1.000e-02, eta: 6:24:43, time: 0.113, data_time: 0.037, memory: 1224, loss: 1.9590\n2023-01-01 22:45:43,009 - mmcls - INFO - Epoch [49][200/5005]\tlr: 1.000e-02, eta: 6:24:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9365\n2023-01-01 22:45:51,777 - mmcls - INFO - Epoch [49][300/5005]\tlr: 1.000e-02, eta: 6:24:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9842\n2023-01-01 22:46:00,795 - mmcls - INFO - Epoch [49][400/5005]\tlr: 1.000e-02, eta: 6:24:16, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9669\n2023-01-01 22:46:09,775 - mmcls - INFO - Epoch [49][500/5005]\tlr: 1.000e-02, eta: 6:24:07, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.9644\n2023-01-01 22:46:18,750 - mmcls - INFO - Epoch [49][600/5005]\tlr: 1.000e-02, eta: 6:23:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9721\n2023-01-01 22:46:27,265 - mmcls - INFO - Epoch [49][700/5005]\tlr: 1.000e-02, eta: 6:23:49, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9596\n2023-01-01 22:46:36,172 - mmcls - INFO - Epoch [49][800/5005]\tlr: 1.000e-02, eta: 6:23:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9360\n2023-01-01 22:46:44,998 - mmcls - INFO - Epoch [49][900/5005]\tlr: 1.000e-02, eta: 6:23:32, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9588\n2023-01-01 22:46:53,959 - mmcls - INFO - Epoch [49][1000/5005]\tlr: 1.000e-02, eta: 6:23:23, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9641\n2023-01-01 22:47:02,735 - mmcls - INFO - Epoch [49][1100/5005]\tlr: 1.000e-02, eta: 6:23:14, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9313\n2023-01-01 22:47:11,702 - mmcls - INFO - Epoch [49][1200/5005]\tlr: 1.000e-02, eta: 6:23:05, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9711\n2023-01-01 22:47:20,887 - mmcls - INFO - Epoch [49][1300/5005]\tlr: 1.000e-02, eta: 6:22:56, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.9933\n2023-01-01 22:47:29,394 - mmcls - INFO - Epoch [49][1400/5005]\tlr: 1.000e-02, eta: 6:22:47, time: 0.085, data_time: 0.021, memory: 1224, loss: 1.9661\n2023-01-01 22:47:38,184 - mmcls - INFO - Epoch [49][1500/5005]\tlr: 1.000e-02, eta: 6:22:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9583\n2023-01-01 22:47:46,879 - mmcls - INFO - Epoch [49][1600/5005]\tlr: 1.000e-02, eta: 6:22:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9995\n2023-01-01 22:47:55,477 - mmcls - INFO - Epoch [49][1700/5005]\tlr: 1.000e-02, eta: 6:22:20, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9741\n2023-01-01 22:48:04,409 - mmcls - INFO - Epoch [49][1800/5005]\tlr: 1.000e-02, eta: 6:22:11, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9743\n2023-01-01 22:48:12,960 - mmcls - INFO - Epoch [49][1900/5005]\tlr: 1.000e-02, eta: 6:22:02, time: 0.085, data_time: 0.000, memory: 1224, loss: 1.9863\n2023-01-01 22:48:21,960 - mmcls - INFO - Epoch [49][2000/5005]\tlr: 1.000e-02, eta: 6:21:53, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9886\n2023-01-01 22:48:30,848 - mmcls - INFO - Epoch [49][2100/5005]\tlr: 1.000e-02, eta: 6:21:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9472\n2023-01-01 22:48:39,764 - mmcls - INFO - Epoch [49][2200/5005]\tlr: 1.000e-02, eta: 6:21:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9635\n2023-01-01 22:48:48,486 - mmcls - INFO - Epoch [49][2300/5005]\tlr: 1.000e-02, eta: 6:21:27, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9913\n2023-01-01 22:48:57,090 - mmcls - INFO - Epoch [49][2400/5005]\tlr: 1.000e-02, eta: 6:21:17, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9958\n2023-01-01 22:49:06,123 - mmcls - INFO - Epoch [49][2500/5005]\tlr: 1.000e-02, eta: 6:21:09, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9927\n2023-01-01 22:49:14,552 - mmcls - INFO - Epoch [49][2600/5005]\tlr: 1.000e-02, eta: 6:20:59, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.9975\n2023-01-01 22:49:23,286 - mmcls - INFO - Epoch [49][2700/5005]\tlr: 1.000e-02, eta: 6:20:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9829\n2023-01-01 22:49:32,120 - mmcls - INFO - Epoch [49][2800/5005]\tlr: 1.000e-02, eta: 6:20:41, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0035\n2023-01-01 22:49:40,694 - mmcls - INFO - Epoch [49][2900/5005]\tlr: 1.000e-02, eta: 6:20:32, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.0016\n2023-01-01 22:49:49,378 - mmcls - INFO - Epoch [49][3000/5005]\tlr: 1.000e-02, eta: 6:20:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0061\n2023-01-01 22:49:57,875 - mmcls - INFO - Epoch [49][3100/5005]\tlr: 1.000e-02, eta: 6:20:14, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.0095\n2023-01-01 22:50:06,999 - mmcls - INFO - Epoch [49][3200/5005]\tlr: 1.000e-02, eta: 6:20:05, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.9940\n2023-01-01 22:50:15,738 - mmcls - INFO - Epoch [49][3300/5005]\tlr: 1.000e-02, eta: 6:19:56, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9881\n2023-01-01 22:50:24,655 - mmcls - INFO - Epoch [49][3400/5005]\tlr: 1.000e-02, eta: 6:19:47, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.0057\n2023-01-01 22:50:33,297 - mmcls - INFO - Epoch [49][3500/5005]\tlr: 1.000e-02, eta: 6:19:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9741\n2023-01-01 22:50:42,534 - mmcls - INFO - Epoch [49][3600/5005]\tlr: 1.000e-02, eta: 6:19:30, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.0054\n2023-01-01 22:50:51,366 - mmcls - INFO - Epoch [49][3700/5005]\tlr: 1.000e-02, eta: 6:19:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9649\n2023-01-01 22:51:00,176 - mmcls - INFO - Epoch [49][3800/5005]\tlr: 1.000e-02, eta: 6:19:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0115\n2023-01-01 22:51:08,801 - mmcls - INFO - Epoch [49][3900/5005]\tlr: 1.000e-02, eta: 6:19:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9832\n2023-01-01 22:51:17,793 - mmcls - INFO - Epoch [49][4000/5005]\tlr: 1.000e-02, eta: 6:18:54, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.9944\n2023-01-01 22:51:26,685 - mmcls - INFO - Epoch [49][4100/5005]\tlr: 1.000e-02, eta: 6:18:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9732\n2023-01-01 22:51:35,656 - mmcls - INFO - Epoch [49][4200/5005]\tlr: 1.000e-02, eta: 6:18:36, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9783\n2023-01-01 22:51:44,301 - mmcls - INFO - Epoch [49][4300/5005]\tlr: 1.000e-02, eta: 6:18:27, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9744\n2023-01-01 22:51:52,975 - mmcls - INFO - Epoch [49][4400/5005]\tlr: 1.000e-02, eta: 6:18:18, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9992\n2023-01-01 22:52:01,717 - mmcls - INFO - Epoch [49][4500/5005]\tlr: 1.000e-02, eta: 6:18:09, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9815\n2023-01-01 22:52:10,751 - mmcls - INFO - Epoch [49][4600/5005]\tlr: 1.000e-02, eta: 6:18:01, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0049\n2023-01-01 22:52:19,712 - mmcls - INFO - Epoch [49][4700/5005]\tlr: 1.000e-02, eta: 6:17:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9965\n2023-01-01 22:52:28,503 - mmcls - INFO - Epoch [49][4800/5005]\tlr: 1.000e-02, eta: 6:17:43, time: 0.088, data_time: 0.034, memory: 1224, loss: 1.9909\n2023-01-01 22:52:37,348 - mmcls - INFO - Epoch [49][4900/5005]\tlr: 1.000e-02, eta: 6:17:34, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9894\n2023-01-01 22:52:46,416 - mmcls - INFO - Epoch [49][5000/5005]\tlr: 1.000e-02, eta: 6:17:25, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.9886\n2023-01-01 22:53:09,217 - mmcls - INFO - Epoch(val) [49][196]\taccuracy_top-1: 60.4660, accuracy_top-5: 82.5460\n2023-01-01 22:53:20,115 - mmcls - INFO - Epoch [50][100/5005]\tlr: 1.000e-02, eta: 6:17:18, time: 0.109, data_time: 0.029, memory: 1224, loss: 1.9053\n2023-01-01 22:53:28,984 - mmcls - INFO - Epoch [50][200/5005]\tlr: 1.000e-02, eta: 6:17:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9643\n2023-01-01 22:53:37,736 - mmcls - INFO - Epoch [50][300/5005]\tlr: 1.000e-02, eta: 6:17:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9354\n2023-01-01 22:53:46,298 - mmcls - INFO - Epoch [50][400/5005]\tlr: 1.000e-02, eta: 6:16:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9214\n2023-01-01 22:53:55,159 - mmcls - INFO - Epoch [50][500/5005]\tlr: 1.000e-02, eta: 6:16:42, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9215\n2023-01-01 22:54:04,086 - mmcls - INFO - Epoch [50][600/5005]\tlr: 1.000e-02, eta: 6:16:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9283\n2023-01-01 22:54:12,977 - mmcls - INFO - Epoch [50][700/5005]\tlr: 1.000e-02, eta: 6:16:24, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9781\n2023-01-01 22:54:21,840 - mmcls - INFO - Epoch [50][800/5005]\tlr: 1.000e-02, eta: 6:16:15, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.9918\n2023-01-01 22:54:30,482 - mmcls - INFO - Epoch [50][900/5005]\tlr: 1.000e-02, eta: 6:16:06, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9887\n2023-01-01 22:54:39,604 - mmcls - INFO - Epoch [50][1000/5005]\tlr: 1.000e-02, eta: 6:15:57, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9611\n2023-01-01 22:54:48,545 - mmcls - INFO - Epoch [50][1100/5005]\tlr: 1.000e-02, eta: 6:15:49, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9711\n2023-01-01 22:54:57,208 - mmcls - INFO - Epoch [50][1200/5005]\tlr: 1.000e-02, eta: 6:15:39, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9708\n2023-01-01 22:55:06,139 - mmcls - INFO - Epoch [50][1300/5005]\tlr: 1.000e-02, eta: 6:15:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9643\n2023-01-01 22:55:15,003 - mmcls - INFO - Epoch [50][1400/5005]\tlr: 1.000e-02, eta: 6:15:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9589\n2023-01-01 22:55:23,952 - mmcls - INFO - Epoch [50][1500/5005]\tlr: 1.000e-02, eta: 6:15:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9685\n2023-01-01 22:55:32,700 - mmcls - INFO - Epoch [50][1600/5005]\tlr: 1.000e-02, eta: 6:15:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9579\n2023-01-01 22:55:41,595 - mmcls - INFO - Epoch [50][1700/5005]\tlr: 1.000e-02, eta: 6:14:55, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9651\n2023-01-01 22:55:50,134 - mmcls - INFO - Epoch [50][1800/5005]\tlr: 1.000e-02, eta: 6:14:46, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.9523\n2023-01-01 22:55:59,034 - mmcls - INFO - Epoch [50][1900/5005]\tlr: 1.000e-02, eta: 6:14:37, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9808\n2023-01-01 22:56:08,004 - mmcls - INFO - Epoch [50][2000/5005]\tlr: 1.000e-02, eta: 6:14:28, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9632\n2023-01-01 22:56:16,794 - mmcls - INFO - Epoch [50][2100/5005]\tlr: 1.000e-02, eta: 6:14:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9929\n2023-01-01 22:56:25,752 - mmcls - INFO - Epoch [50][2200/5005]\tlr: 1.000e-02, eta: 6:14:11, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9801\n2023-01-01 22:56:34,557 - mmcls - INFO - Epoch [50][2300/5005]\tlr: 1.000e-02, eta: 6:14:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9645\n2023-01-01 22:56:43,693 - mmcls - INFO - Epoch [50][2400/5005]\tlr: 1.000e-02, eta: 6:13:53, time: 0.091, data_time: 0.033, memory: 1224, loss: 1.9535\n2023-01-01 22:56:52,252 - mmcls - INFO - Epoch [50][2500/5005]\tlr: 1.000e-02, eta: 6:13:44, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9919\n2023-01-01 22:57:01,345 - mmcls - INFO - Epoch [50][2600/5005]\tlr: 1.000e-02, eta: 6:13:35, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9938\n2023-01-01 22:57:10,106 - mmcls - INFO - Epoch [50][2700/5005]\tlr: 1.000e-02, eta: 6:13:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0055\n2023-01-01 22:57:18,656 - mmcls - INFO - Epoch [50][2800/5005]\tlr: 1.000e-02, eta: 6:13:17, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9890\n2023-01-01 22:57:27,964 - mmcls - INFO - Epoch [50][2900/5005]\tlr: 1.000e-02, eta: 6:13:09, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.9726\n2023-01-01 22:57:37,049 - mmcls - INFO - Epoch [50][3000/5005]\tlr: 1.000e-02, eta: 6:13:00, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9728\n2023-01-01 22:57:46,085 - mmcls - INFO - Epoch [50][3100/5005]\tlr: 1.000e-02, eta: 6:12:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9635\n2023-01-01 22:57:54,935 - mmcls - INFO - Epoch [50][3200/5005]\tlr: 1.000e-02, eta: 6:12:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9718\n2023-01-01 22:58:03,626 - mmcls - INFO - Epoch [50][3300/5005]\tlr: 1.000e-02, eta: 6:12:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0128\n2023-01-01 22:58:12,180 - mmcls - INFO - Epoch [50][3400/5005]\tlr: 1.000e-02, eta: 6:12:24, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.0037\n2023-01-01 22:58:20,966 - mmcls - INFO - Epoch [50][3500/5005]\tlr: 1.000e-02, eta: 6:12:15, time: 0.088, data_time: 0.031, memory: 1224, loss: 1.9911\n2023-01-01 22:58:29,845 - mmcls - INFO - Epoch [50][3600/5005]\tlr: 1.000e-02, eta: 6:12:06, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9765\n2023-01-01 22:58:38,962 - mmcls - INFO - Epoch [50][3700/5005]\tlr: 1.000e-02, eta: 6:11:58, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.9714\n2023-01-01 22:58:47,909 - mmcls - INFO - Epoch [50][3800/5005]\tlr: 1.000e-02, eta: 6:11:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9541\n2023-01-01 22:58:56,956 - mmcls - INFO - Epoch [50][3900/5005]\tlr: 1.000e-02, eta: 6:11:40, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9923\n2023-01-01 22:59:05,810 - mmcls - INFO - Epoch [50][4000/5005]\tlr: 1.000e-02, eta: 6:11:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0144\n2023-01-01 22:59:14,638 - mmcls - INFO - Epoch [50][4100/5005]\tlr: 1.000e-02, eta: 6:11:22, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.9716\n2023-01-01 22:59:23,424 - mmcls - INFO - Epoch [50][4200/5005]\tlr: 1.000e-02, eta: 6:11:13, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9847\n2023-01-01 22:59:32,090 - mmcls - INFO - Epoch [50][4300/5005]\tlr: 1.000e-02, eta: 6:11:04, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9828\n2023-01-01 22:59:40,795 - mmcls - INFO - Epoch [50][4400/5005]\tlr: 1.000e-02, eta: 6:10:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9959\n2023-01-01 22:59:49,756 - mmcls - INFO - Epoch [50][4500/5005]\tlr: 1.000e-02, eta: 6:10:46, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9908\n2023-01-01 22:59:58,285 - mmcls - INFO - Epoch [50][4600/5005]\tlr: 1.000e-02, eta: 6:10:37, time: 0.085, data_time: 0.000, memory: 1224, loss: 1.9705\n2023-01-01 23:00:07,045 - mmcls - INFO - Epoch [50][4700/5005]\tlr: 1.000e-02, eta: 6:10:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0253\n2023-01-01 23:00:15,828 - mmcls - INFO - Epoch [50][4800/5005]\tlr: 1.000e-02, eta: 6:10:19, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0062\n2023-01-01 23:00:24,713 - mmcls - INFO - Epoch [50][4900/5005]\tlr: 1.000e-02, eta: 6:10:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9883\n2023-01-01 23:00:33,528 - mmcls - INFO - Epoch [50][5000/5005]\tlr: 1.000e-02, eta: 6:10:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9957\n2023-01-01 23:00:33,918 - mmcls - INFO - Saving checkpoint at 50 epochs\n2023-01-01 23:00:55,815 - mmcls - INFO - Epoch(val) [50][196]\taccuracy_top-1: 60.4500, accuracy_top-5: 82.4180\n2023-01-01 23:01:06,759 - mmcls - INFO - Epoch [51][100/5005]\tlr: 1.000e-02, eta: 6:09:54, time: 0.109, data_time: 0.045, memory: 1224, loss: 1.9902\n2023-01-01 23:01:15,721 - mmcls - INFO - Epoch [51][200/5005]\tlr: 1.000e-02, eta: 6:09:45, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.9386\n2023-01-01 23:01:24,587 - mmcls - INFO - Epoch [51][300/5005]\tlr: 1.000e-02, eta: 6:09:36, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9356\n2023-01-01 23:01:33,226 - mmcls - INFO - Epoch [51][400/5005]\tlr: 1.000e-02, eta: 6:09:27, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9235\n2023-01-01 23:01:42,174 - mmcls - INFO - Epoch [51][500/5005]\tlr: 1.000e-02, eta: 6:09:18, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9672\n2023-01-01 23:01:50,962 - mmcls - INFO - Epoch [51][600/5005]\tlr: 1.000e-02, eta: 6:09:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9666\n2023-01-01 23:01:59,628 - mmcls - INFO - Epoch [51][700/5005]\tlr: 1.000e-02, eta: 6:09:00, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9581\n2023-01-01 23:02:08,664 - mmcls - INFO - Epoch [51][800/5005]\tlr: 1.000e-02, eta: 6:08:51, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9895\n2023-01-01 23:02:17,253 - mmcls - INFO - Epoch [51][900/5005]\tlr: 1.000e-02, eta: 6:08:42, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.9840\n2023-01-01 23:02:26,043 - mmcls - INFO - Epoch [51][1000/5005]\tlr: 1.000e-02, eta: 6:08:33, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9704\n2023-01-01 23:02:34,929 - mmcls - INFO - Epoch [51][1100/5005]\tlr: 1.000e-02, eta: 6:08:24, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.9425\n2023-01-01 23:02:43,486 - mmcls - INFO - Epoch [51][1200/5005]\tlr: 1.000e-02, eta: 6:08:15, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.9574\n2023-01-01 23:02:52,444 - mmcls - INFO - Epoch [51][1300/5005]\tlr: 1.000e-02, eta: 6:08:07, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9460\n2023-01-01 23:03:01,010 - mmcls - INFO - Epoch [51][1400/5005]\tlr: 1.000e-02, eta: 6:07:57, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.9506\n2023-01-01 23:03:09,935 - mmcls - INFO - Epoch [51][1500/5005]\tlr: 1.000e-02, eta: 6:07:49, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9457\n2023-01-01 23:03:18,474 - mmcls - INFO - Epoch [51][1600/5005]\tlr: 1.000e-02, eta: 6:07:39, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9663\n2023-01-01 23:03:27,404 - mmcls - INFO - Epoch [51][1700/5005]\tlr: 1.000e-02, eta: 6:07:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9675\n2023-01-01 23:03:36,201 - mmcls - INFO - Epoch [51][1800/5005]\tlr: 1.000e-02, eta: 6:07:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9832\n2023-01-01 23:03:45,069 - mmcls - INFO - Epoch [51][1900/5005]\tlr: 1.000e-02, eta: 6:07:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9577\n2023-01-01 23:03:54,077 - mmcls - INFO - Epoch [51][2000/5005]\tlr: 1.000e-02, eta: 6:07:04, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9649\n2023-01-01 23:04:02,668 - mmcls - INFO - Epoch [51][2100/5005]\tlr: 1.000e-02, eta: 6:06:55, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9540\n2023-01-01 23:04:11,109 - mmcls - INFO - Epoch [51][2200/5005]\tlr: 1.000e-02, eta: 6:06:45, time: 0.084, data_time: 0.012, memory: 1224, loss: 1.9609\n2023-01-01 23:04:19,840 - mmcls - INFO - Epoch [51][2300/5005]\tlr: 1.000e-02, eta: 6:06:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9726\n2023-01-01 23:04:28,693 - mmcls - INFO - Epoch [51][2400/5005]\tlr: 1.000e-02, eta: 6:06:28, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9788\n2023-01-01 23:04:37,358 - mmcls - INFO - Epoch [51][2500/5005]\tlr: 1.000e-02, eta: 6:06:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9953\n2023-01-01 23:04:46,149 - mmcls - INFO - Epoch [51][2600/5005]\tlr: 1.000e-02, eta: 6:06:10, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9816\n2023-01-01 23:04:54,955 - mmcls - INFO - Epoch [51][2700/5005]\tlr: 1.000e-02, eta: 6:06:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9756\n2023-01-01 23:05:03,585 - mmcls - INFO - Epoch [51][2800/5005]\tlr: 1.000e-02, eta: 6:05:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9689\n2023-01-01 23:05:12,285 - mmcls - INFO - Epoch [51][2900/5005]\tlr: 1.000e-02, eta: 6:05:42, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9691\n2023-01-01 23:05:20,919 - mmcls - INFO - Epoch [51][3000/5005]\tlr: 1.000e-02, eta: 6:05:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9731\n2023-01-01 23:05:29,915 - mmcls - INFO - Epoch [51][3100/5005]\tlr: 1.000e-02, eta: 6:05:25, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.9700\n2023-01-01 23:05:38,581 - mmcls - INFO - Epoch [51][3200/5005]\tlr: 1.000e-02, eta: 6:05:16, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9703\n2023-01-01 23:05:47,404 - mmcls - INFO - Epoch [51][3300/5005]\tlr: 1.000e-02, eta: 6:05:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0010\n2023-01-01 23:05:56,064 - mmcls - INFO - Epoch [51][3400/5005]\tlr: 1.000e-02, eta: 6:04:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9789\n2023-01-01 23:06:04,704 - mmcls - INFO - Epoch [51][3500/5005]\tlr: 1.000e-02, eta: 6:04:48, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9957\n2023-01-01 23:06:13,257 - mmcls - INFO - Epoch [51][3600/5005]\tlr: 1.000e-02, eta: 6:04:39, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.0024\n2023-01-01 23:06:22,066 - mmcls - INFO - Epoch [51][3700/5005]\tlr: 1.000e-02, eta: 6:04:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.0008\n2023-01-01 23:06:30,965 - mmcls - INFO - Epoch [51][3800/5005]\tlr: 1.000e-02, eta: 6:04:21, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9906\n2023-01-01 23:06:39,888 - mmcls - INFO - Epoch [51][3900/5005]\tlr: 1.000e-02, eta: 6:04:13, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9992\n2023-01-01 23:06:48,927 - mmcls - INFO - Epoch [51][4000/5005]\tlr: 1.000e-02, eta: 6:04:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9849\n2023-01-01 23:06:57,523 - mmcls - INFO - Epoch [51][4100/5005]\tlr: 1.000e-02, eta: 6:03:55, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9672\n2023-01-01 23:07:06,085 - mmcls - INFO - Epoch [51][4200/5005]\tlr: 1.000e-02, eta: 6:03:46, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9998\n2023-01-01 23:07:14,660 - mmcls - INFO - Epoch [51][4300/5005]\tlr: 1.000e-02, eta: 6:03:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9718\n2023-01-01 23:07:23,702 - mmcls - INFO - Epoch [51][4400/5005]\tlr: 1.000e-02, eta: 6:03:28, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9671\n2023-01-01 23:07:32,488 - mmcls - INFO - Epoch [51][4500/5005]\tlr: 1.000e-02, eta: 6:03:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9783\n2023-01-01 23:07:41,458 - mmcls - INFO - Epoch [51][4600/5005]\tlr: 1.000e-02, eta: 6:03:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9917\n2023-01-01 23:07:50,230 - mmcls - INFO - Epoch [51][4700/5005]\tlr: 1.000e-02, eta: 6:03:01, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0263\n2023-01-01 23:07:58,986 - mmcls - INFO - Epoch [51][4800/5005]\tlr: 1.000e-02, eta: 6:02:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0047\n2023-01-01 23:08:07,821 - mmcls - INFO - Epoch [51][4900/5005]\tlr: 1.000e-02, eta: 6:02:43, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.0004\n2023-01-01 23:08:16,665 - mmcls - INFO - Epoch [51][5000/5005]\tlr: 1.000e-02, eta: 6:02:34, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0038\n2023-01-01 23:08:39,283 - mmcls - INFO - Epoch(val) [51][196]\taccuracy_top-1: 60.3500, accuracy_top-5: 82.4160\n2023-01-01 23:08:50,230 - mmcls - INFO - Epoch [52][100/5005]\tlr: 1.000e-02, eta: 6:02:27, time: 0.109, data_time: 0.036, memory: 1224, loss: 1.9442\n2023-01-01 23:08:59,044 - mmcls - INFO - Epoch [52][200/5005]\tlr: 1.000e-02, eta: 6:02:18, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9450\n2023-01-01 23:09:07,449 - mmcls - INFO - Epoch [52][300/5005]\tlr: 1.000e-02, eta: 6:02:08, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.9275\n2023-01-01 23:09:16,379 - mmcls - INFO - Epoch [52][400/5005]\tlr: 1.000e-02, eta: 6:01:59, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9607\n2023-01-01 23:09:25,386 - mmcls - INFO - Epoch [52][500/5005]\tlr: 1.000e-02, eta: 6:01:51, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9659\n2023-01-01 23:09:34,201 - mmcls - INFO - Epoch [52][600/5005]\tlr: 1.000e-02, eta: 6:01:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9346\n2023-01-01 23:09:43,242 - mmcls - INFO - Epoch [52][700/5005]\tlr: 1.000e-02, eta: 6:01:33, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9507\n2023-01-01 23:09:52,161 - mmcls - INFO - Epoch [52][800/5005]\tlr: 1.000e-02, eta: 6:01:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9510\n2023-01-01 23:10:01,032 - mmcls - INFO - Epoch [52][900/5005]\tlr: 1.000e-02, eta: 6:01:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9474\n2023-01-01 23:10:09,702 - mmcls - INFO - Epoch [52][1000/5005]\tlr: 1.000e-02, eta: 6:01:06, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.9502\n2023-01-01 23:10:18,325 - mmcls - INFO - Epoch [52][1100/5005]\tlr: 1.000e-02, eta: 6:00:57, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9494\n2023-01-01 23:10:27,159 - mmcls - INFO - Epoch [52][1200/5005]\tlr: 1.000e-02, eta: 6:00:48, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9298\n2023-01-01 23:10:35,663 - mmcls - INFO - Epoch [52][1300/5005]\tlr: 1.000e-02, eta: 6:00:39, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.9670\n2023-01-01 23:10:44,434 - mmcls - INFO - Epoch [52][1400/5005]\tlr: 1.000e-02, eta: 6:00:30, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9452\n2023-01-01 23:10:53,368 - mmcls - INFO - Epoch [52][1500/5005]\tlr: 1.000e-02, eta: 6:00:21, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9566\n2023-01-01 23:11:02,203 - mmcls - INFO - Epoch [52][1600/5005]\tlr: 1.000e-02, eta: 6:00:12, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9508\n2023-01-01 23:11:10,788 - mmcls - INFO - Epoch [52][1700/5005]\tlr: 1.000e-02, eta: 6:00:03, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9594\n2023-01-01 23:11:19,579 - mmcls - INFO - Epoch [52][1800/5005]\tlr: 1.000e-02, eta: 5:59:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9651\n2023-01-01 23:11:28,160 - mmcls - INFO - Epoch [52][1900/5005]\tlr: 1.000e-02, eta: 5:59:45, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9805\n2023-01-01 23:11:36,852 - mmcls - INFO - Epoch [52][2000/5005]\tlr: 1.000e-02, eta: 5:59:36, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9467\n2023-01-01 23:11:45,507 - mmcls - INFO - Epoch [52][2100/5005]\tlr: 1.000e-02, eta: 5:59:27, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9596\n2023-01-01 23:11:54,319 - mmcls - INFO - Epoch [52][2200/5005]\tlr: 1.000e-02, eta: 5:59:18, time: 0.088, data_time: 0.034, memory: 1224, loss: 1.9626\n2023-01-01 23:12:02,939 - mmcls - INFO - Epoch [52][2300/5005]\tlr: 1.000e-02, eta: 5:59:09, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9408\n2023-01-01 23:12:12,036 - mmcls - INFO - Epoch [52][2400/5005]\tlr: 1.000e-02, eta: 5:59:00, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.9816\n2023-01-01 23:12:20,801 - mmcls - INFO - Epoch [52][2500/5005]\tlr: 1.000e-02, eta: 5:58:51, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9964\n2023-01-01 23:12:29,812 - mmcls - INFO - Epoch [52][2600/5005]\tlr: 1.000e-02, eta: 5:58:43, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0016\n2023-01-01 23:12:38,748 - mmcls - INFO - Epoch [52][2700/5005]\tlr: 1.000e-02, eta: 5:58:34, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9971\n2023-01-01 23:12:47,554 - mmcls - INFO - Epoch [52][2800/5005]\tlr: 1.000e-02, eta: 5:58:25, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9741\n2023-01-01 23:12:56,736 - mmcls - INFO - Epoch [52][2900/5005]\tlr: 1.000e-02, eta: 5:58:16, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.9694\n2023-01-01 23:13:05,679 - mmcls - INFO - Epoch [52][3000/5005]\tlr: 1.000e-02, eta: 5:58:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9855\n2023-01-01 23:13:14,502 - mmcls - INFO - Epoch [52][3100/5005]\tlr: 1.000e-02, eta: 5:57:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9754\n2023-01-01 23:13:23,105 - mmcls - INFO - Epoch [52][3200/5005]\tlr: 1.000e-02, eta: 5:57:50, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9758\n2023-01-01 23:13:31,874 - mmcls - INFO - Epoch [52][3300/5005]\tlr: 1.000e-02, eta: 5:57:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0068\n2023-01-01 23:13:40,765 - mmcls - INFO - Epoch [52][3400/5005]\tlr: 1.000e-02, eta: 5:57:32, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9642\n2023-01-01 23:13:49,766 - mmcls - INFO - Epoch [52][3500/5005]\tlr: 1.000e-02, eta: 5:57:23, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9672\n2023-01-01 23:13:58,416 - mmcls - INFO - Epoch [52][3600/5005]\tlr: 1.000e-02, eta: 5:57:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9794\n2023-01-01 23:14:07,003 - mmcls - INFO - Epoch [52][3700/5005]\tlr: 1.000e-02, eta: 5:57:05, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9601\n2023-01-01 23:14:15,841 - mmcls - INFO - Epoch [52][3800/5005]\tlr: 1.000e-02, eta: 5:56:56, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9840\n2023-01-01 23:14:24,741 - mmcls - INFO - Epoch [52][3900/5005]\tlr: 1.000e-02, eta: 5:56:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9789\n2023-01-01 23:14:33,547 - mmcls - INFO - Epoch [52][4000/5005]\tlr: 1.000e-02, eta: 5:56:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9911\n2023-01-01 23:14:42,566 - mmcls - INFO - Epoch [52][4100/5005]\tlr: 1.000e-02, eta: 5:56:29, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9941\n2023-01-01 23:14:51,427 - mmcls - INFO - Epoch [52][4200/5005]\tlr: 1.000e-02, eta: 5:56:20, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.9979\n2023-01-01 23:15:00,019 - mmcls - INFO - Epoch [52][4300/5005]\tlr: 1.000e-02, eta: 5:56:11, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9922\n2023-01-01 23:15:08,586 - mmcls - INFO - Epoch [52][4400/5005]\tlr: 1.000e-02, eta: 5:56:02, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.9759\n2023-01-01 23:15:17,244 - mmcls - INFO - Epoch [52][4500/5005]\tlr: 1.000e-02, eta: 5:55:53, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0211\n2023-01-01 23:15:25,828 - mmcls - INFO - Epoch [52][4600/5005]\tlr: 1.000e-02, eta: 5:55:44, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.0107\n2023-01-01 23:15:34,725 - mmcls - INFO - Epoch [52][4700/5005]\tlr: 1.000e-02, eta: 5:55:35, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0002\n2023-01-01 23:15:43,771 - mmcls - INFO - Epoch [52][4800/5005]\tlr: 1.000e-02, eta: 5:55:26, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.0086\n2023-01-01 23:15:52,653 - mmcls - INFO - Epoch [52][4900/5005]\tlr: 1.000e-02, eta: 5:55:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9902\n2023-01-01 23:16:01,884 - mmcls - INFO - Epoch [52][5000/5005]\tlr: 1.000e-02, eta: 5:55:09, time: 0.092, data_time: 0.015, memory: 1224, loss: 2.0084\n2023-01-01 23:16:24,583 - mmcls - INFO - Epoch(val) [52][196]\taccuracy_top-1: 60.3560, accuracy_top-5: 82.5160\n2023-01-01 23:16:35,960 - mmcls - INFO - Epoch [53][100/5005]\tlr: 1.000e-02, eta: 5:55:02, time: 0.113, data_time: 0.044, memory: 1224, loss: 1.9439\n2023-01-01 23:16:45,274 - mmcls - INFO - Epoch [53][200/5005]\tlr: 1.000e-02, eta: 5:54:53, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.9322\n2023-01-01 23:16:54,452 - mmcls - INFO - Epoch [53][300/5005]\tlr: 1.000e-02, eta: 5:54:45, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9497\n2023-01-01 23:17:03,263 - mmcls - INFO - Epoch [53][400/5005]\tlr: 1.000e-02, eta: 5:54:36, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9246\n2023-01-01 23:17:12,070 - mmcls - INFO - Epoch [53][500/5005]\tlr: 1.000e-02, eta: 5:54:27, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9265\n2023-01-01 23:17:20,847 - mmcls - INFO - Epoch [53][600/5005]\tlr: 1.000e-02, eta: 5:54:18, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9401\n2023-01-01 23:17:29,418 - mmcls - INFO - Epoch [53][700/5005]\tlr: 1.000e-02, eta: 5:54:09, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9408\n2023-01-01 23:17:38,253 - mmcls - INFO - Epoch [53][800/5005]\tlr: 1.000e-02, eta: 5:54:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9448\n2023-01-01 23:17:47,000 - mmcls - INFO - Epoch [53][900/5005]\tlr: 1.000e-02, eta: 5:53:51, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9678\n2023-01-01 23:17:55,626 - mmcls - INFO - Epoch [53][1000/5005]\tlr: 1.000e-02, eta: 5:53:42, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.9499\n2023-01-01 23:18:04,277 - mmcls - INFO - Epoch [53][1100/5005]\tlr: 1.000e-02, eta: 5:53:33, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9389\n2023-01-01 23:18:13,092 - mmcls - INFO - Epoch [53][1200/5005]\tlr: 1.000e-02, eta: 5:53:24, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9629\n2023-01-01 23:18:22,466 - mmcls - INFO - Epoch [53][1300/5005]\tlr: 1.000e-02, eta: 5:53:15, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.9607\n2023-01-01 23:18:31,189 - mmcls - INFO - Epoch [53][1400/5005]\tlr: 1.000e-02, eta: 5:53:06, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.9813\n2023-01-01 23:18:39,981 - mmcls - INFO - Epoch [53][1500/5005]\tlr: 1.000e-02, eta: 5:52:57, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9438\n2023-01-01 23:18:48,545 - mmcls - INFO - Epoch [53][1600/5005]\tlr: 1.000e-02, eta: 5:52:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9869\n2023-01-01 23:18:57,923 - mmcls - INFO - Epoch [53][1700/5005]\tlr: 1.000e-02, eta: 5:52:40, time: 0.094, data_time: 0.009, memory: 1224, loss: 1.9917\n2023-01-01 23:19:06,869 - mmcls - INFO - Epoch [53][1800/5005]\tlr: 1.000e-02, eta: 5:52:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9626\n2023-01-01 23:19:15,678 - mmcls - INFO - Epoch [53][1900/5005]\tlr: 1.000e-02, eta: 5:52:22, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9658\n2023-01-01 23:19:24,420 - mmcls - INFO - Epoch [53][2000/5005]\tlr: 1.000e-02, eta: 5:52:13, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9756\n2023-01-01 23:19:33,395 - mmcls - INFO - Epoch [53][2100/5005]\tlr: 1.000e-02, eta: 5:52:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9810\n2023-01-01 23:19:42,031 - mmcls - INFO - Epoch [53][2200/5005]\tlr: 1.000e-02, eta: 5:51:55, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9498\n2023-01-01 23:19:50,782 - mmcls - INFO - Epoch [53][2300/5005]\tlr: 1.000e-02, eta: 5:51:46, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9575\n2023-01-01 23:19:59,538 - mmcls - INFO - Epoch [53][2400/5005]\tlr: 1.000e-02, eta: 5:51:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9760\n2023-01-01 23:20:08,626 - mmcls - INFO - Epoch [53][2500/5005]\tlr: 1.000e-02, eta: 5:51:28, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.9933\n2023-01-01 23:20:17,610 - mmcls - INFO - Epoch [53][2600/5005]\tlr: 1.000e-02, eta: 5:51:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.0084\n2023-01-01 23:20:26,272 - mmcls - INFO - Epoch [53][2700/5005]\tlr: 1.000e-02, eta: 5:51:11, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9976\n2023-01-01 23:20:35,009 - mmcls - INFO - Epoch [53][2800/5005]\tlr: 1.000e-02, eta: 5:51:02, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9739\n2023-01-01 23:20:43,787 - mmcls - INFO - Epoch [53][2900/5005]\tlr: 1.000e-02, eta: 5:50:53, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.9752\n2023-01-01 23:20:52,841 - mmcls - INFO - Epoch [53][3000/5005]\tlr: 1.000e-02, eta: 5:50:44, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9975\n2023-01-01 23:21:01,688 - mmcls - INFO - Epoch [53][3100/5005]\tlr: 1.000e-02, eta: 5:50:35, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9853\n2023-01-01 23:21:10,361 - mmcls - INFO - Epoch [53][3200/5005]\tlr: 1.000e-02, eta: 5:50:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9867\n2023-01-01 23:21:19,098 - mmcls - INFO - Epoch [53][3300/5005]\tlr: 1.000e-02, eta: 5:50:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9835\n2023-01-01 23:21:27,859 - mmcls - INFO - Epoch [53][3400/5005]\tlr: 1.000e-02, eta: 5:50:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9696\n2023-01-01 23:21:36,675 - mmcls - INFO - Epoch [53][3500/5005]\tlr: 1.000e-02, eta: 5:49:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9884\n2023-01-01 23:21:45,723 - mmcls - INFO - Epoch [53][3600/5005]\tlr: 1.000e-02, eta: 5:49:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9787\n2023-01-01 23:21:54,432 - mmcls - INFO - Epoch [53][3700/5005]\tlr: 1.000e-02, eta: 5:49:41, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0000\n2023-01-01 23:22:03,365 - mmcls - INFO - Epoch [53][3800/5005]\tlr: 1.000e-02, eta: 5:49:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9714\n2023-01-01 23:22:11,993 - mmcls - INFO - Epoch [53][3900/5005]\tlr: 1.000e-02, eta: 5:49:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0106\n2023-01-01 23:22:20,741 - mmcls - INFO - Epoch [53][4000/5005]\tlr: 1.000e-02, eta: 5:49:15, time: 0.087, data_time: 0.037, memory: 1224, loss: 1.9760\n2023-01-01 23:22:29,430 - mmcls - INFO - Epoch [53][4100/5005]\tlr: 1.000e-02, eta: 5:49:06, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0002\n2023-01-01 23:22:38,223 - mmcls - INFO - Epoch [53][4200/5005]\tlr: 1.000e-02, eta: 5:48:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0037\n2023-01-01 23:22:47,155 - mmcls - INFO - Epoch [53][4300/5005]\tlr: 1.000e-02, eta: 5:48:48, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0106\n2023-01-01 23:22:55,782 - mmcls - INFO - Epoch [53][4400/5005]\tlr: 1.000e-02, eta: 5:48:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9896\n2023-01-01 23:23:04,446 - mmcls - INFO - Epoch [53][4500/5005]\tlr: 1.000e-02, eta: 5:48:30, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9885\n2023-01-01 23:23:13,620 - mmcls - INFO - Epoch [53][4600/5005]\tlr: 1.000e-02, eta: 5:48:21, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.9877\n2023-01-01 23:23:22,211 - mmcls - INFO - Epoch [53][4700/5005]\tlr: 1.000e-02, eta: 5:48:12, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9855\n2023-01-01 23:23:30,691 - mmcls - INFO - Epoch [53][4800/5005]\tlr: 1.000e-02, eta: 5:48:03, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.0039\n2023-01-01 23:23:39,523 - mmcls - INFO - Epoch [53][4900/5005]\tlr: 1.000e-02, eta: 5:47:54, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9756\n2023-01-01 23:23:47,996 - mmcls - INFO - Epoch [53][5000/5005]\tlr: 1.000e-02, eta: 5:47:45, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9730\n2023-01-01 23:24:10,754 - mmcls - INFO - Epoch(val) [53][196]\taccuracy_top-1: 60.4640, accuracy_top-5: 82.5920\n2023-01-01 23:24:21,526 - mmcls - INFO - Epoch [54][100/5005]\tlr: 1.000e-02, eta: 5:47:37, time: 0.108, data_time: 0.063, memory: 1224, loss: 1.9007\n2023-01-01 23:24:30,422 - mmcls - INFO - Epoch [54][200/5005]\tlr: 1.000e-02, eta: 5:47:28, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9210\n2023-01-01 23:24:39,636 - mmcls - INFO - Epoch [54][300/5005]\tlr: 1.000e-02, eta: 5:47:19, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.9149\n2023-01-01 23:24:48,573 - mmcls - INFO - Epoch [54][400/5005]\tlr: 1.000e-02, eta: 5:47:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9380\n2023-01-01 23:24:57,444 - mmcls - INFO - Epoch [54][500/5005]\tlr: 1.000e-02, eta: 5:47:01, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.9498\n2023-01-01 23:25:06,183 - mmcls - INFO - Epoch [54][600/5005]\tlr: 1.000e-02, eta: 5:46:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9195\n2023-01-01 23:25:14,835 - mmcls - INFO - Epoch [54][700/5005]\tlr: 1.000e-02, eta: 5:46:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9607\n2023-01-01 23:25:23,909 - mmcls - INFO - Epoch [54][800/5005]\tlr: 1.000e-02, eta: 5:46:35, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9674\n2023-01-01 23:25:32,410 - mmcls - INFO - Epoch [54][900/5005]\tlr: 1.000e-02, eta: 5:46:25, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9722\n2023-01-01 23:25:41,218 - mmcls - INFO - Epoch [54][1000/5005]\tlr: 1.000e-02, eta: 5:46:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9337\n2023-01-01 23:25:50,264 - mmcls - INFO - Epoch [54][1100/5005]\tlr: 1.000e-02, eta: 5:46:08, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.9355\n2023-01-01 23:25:59,291 - mmcls - INFO - Epoch [54][1200/5005]\tlr: 1.000e-02, eta: 5:45:59, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9701\n2023-01-01 23:26:08,483 - mmcls - INFO - Epoch [54][1300/5005]\tlr: 1.000e-02, eta: 5:45:51, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.9463\n2023-01-01 23:26:17,211 - mmcls - INFO - Epoch [54][1400/5005]\tlr: 1.000e-02, eta: 5:45:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9365\n2023-01-01 23:26:26,104 - mmcls - INFO - Epoch [54][1500/5005]\tlr: 1.000e-02, eta: 5:45:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9635\n2023-01-01 23:26:34,849 - mmcls - INFO - Epoch [54][1600/5005]\tlr: 1.000e-02, eta: 5:45:24, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9673\n2023-01-01 23:26:43,791 - mmcls - INFO - Epoch [54][1700/5005]\tlr: 1.000e-02, eta: 5:45:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9819\n2023-01-01 23:26:52,537 - mmcls - INFO - Epoch [54][1800/5005]\tlr: 1.000e-02, eta: 5:45:06, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9681\n2023-01-01 23:27:01,097 - mmcls - INFO - Epoch [54][1900/5005]\tlr: 1.000e-02, eta: 5:44:57, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9457\n2023-01-01 23:27:10,074 - mmcls - INFO - Epoch [54][2000/5005]\tlr: 1.000e-02, eta: 5:44:48, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9531\n2023-01-01 23:27:19,027 - mmcls - INFO - Epoch [54][2100/5005]\tlr: 1.000e-02, eta: 5:44:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9516\n2023-01-01 23:27:28,017 - mmcls - INFO - Epoch [54][2200/5005]\tlr: 1.000e-02, eta: 5:44:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9666\n2023-01-01 23:27:36,544 - mmcls - INFO - Epoch [54][2300/5005]\tlr: 1.000e-02, eta: 5:44:21, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.9493\n2023-01-01 23:27:45,302 - mmcls - INFO - Epoch [54][2400/5005]\tlr: 1.000e-02, eta: 5:44:12, time: 0.088, data_time: 0.040, memory: 1224, loss: 1.9935\n2023-01-01 23:27:54,005 - mmcls - INFO - Epoch [54][2500/5005]\tlr: 1.000e-02, eta: 5:44:03, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9701\n2023-01-01 23:28:02,447 - mmcls - INFO - Epoch [54][2600/5005]\tlr: 1.000e-02, eta: 5:43:54, time: 0.084, data_time: 0.026, memory: 1224, loss: 1.9684\n2023-01-01 23:28:11,305 - mmcls - INFO - Epoch [54][2700/5005]\tlr: 1.000e-02, eta: 5:43:45, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.9862\n2023-01-01 23:28:20,332 - mmcls - INFO - Epoch [54][2800/5005]\tlr: 1.000e-02, eta: 5:43:36, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9579\n2023-01-01 23:28:29,223 - mmcls - INFO - Epoch [54][2900/5005]\tlr: 1.000e-02, eta: 5:43:28, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9606\n2023-01-01 23:28:38,202 - mmcls - INFO - Epoch [54][3000/5005]\tlr: 1.000e-02, eta: 5:43:19, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9595\n2023-01-01 23:28:46,925 - mmcls - INFO - Epoch [54][3100/5005]\tlr: 1.000e-02, eta: 5:43:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9758\n2023-01-01 23:28:55,529 - mmcls - INFO - Epoch [54][3200/5005]\tlr: 1.000e-02, eta: 5:43:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9922\n2023-01-01 23:29:04,298 - mmcls - INFO - Epoch [54][3300/5005]\tlr: 1.000e-02, eta: 5:42:52, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9732\n2023-01-01 23:29:13,090 - mmcls - INFO - Epoch [54][3400/5005]\tlr: 1.000e-02, eta: 5:42:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9945\n2023-01-01 23:29:22,114 - mmcls - INFO - Epoch [54][3500/5005]\tlr: 1.000e-02, eta: 5:42:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9569\n2023-01-01 23:29:31,293 - mmcls - INFO - Epoch [54][3600/5005]\tlr: 1.000e-02, eta: 5:42:25, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.9692\n2023-01-01 23:29:39,900 - mmcls - INFO - Epoch [54][3700/5005]\tlr: 1.000e-02, eta: 5:42:16, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9779\n2023-01-01 23:29:48,592 - mmcls - INFO - Epoch [54][3800/5005]\tlr: 1.000e-02, eta: 5:42:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9864\n2023-01-01 23:29:57,405 - mmcls - INFO - Epoch [54][3900/5005]\tlr: 1.000e-02, eta: 5:41:58, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9920\n2023-01-01 23:30:06,231 - mmcls - INFO - Epoch [54][4000/5005]\tlr: 1.000e-02, eta: 5:41:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0015\n2023-01-01 23:30:14,930 - mmcls - INFO - Epoch [54][4100/5005]\tlr: 1.000e-02, eta: 5:41:40, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9724\n2023-01-01 23:30:23,534 - mmcls - INFO - Epoch [54][4200/5005]\tlr: 1.000e-02, eta: 5:41:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9972\n2023-01-01 23:30:32,296 - mmcls - INFO - Epoch [54][4300/5005]\tlr: 1.000e-02, eta: 5:41:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9598\n2023-01-01 23:30:41,099 - mmcls - INFO - Epoch [54][4400/5005]\tlr: 1.000e-02, eta: 5:41:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9832\n2023-01-01 23:30:50,145 - mmcls - INFO - Epoch [54][4500/5005]\tlr: 1.000e-02, eta: 5:41:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9775\n2023-01-01 23:30:58,913 - mmcls - INFO - Epoch [54][4600/5005]\tlr: 1.000e-02, eta: 5:40:56, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.9709\n2023-01-01 23:31:07,940 - mmcls - INFO - Epoch [54][4700/5005]\tlr: 1.000e-02, eta: 5:40:47, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9934\n2023-01-01 23:31:16,460 - mmcls - INFO - Epoch [54][4800/5005]\tlr: 1.000e-02, eta: 5:40:38, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.9702\n2023-01-01 23:31:25,163 - mmcls - INFO - Epoch [54][4900/5005]\tlr: 1.000e-02, eta: 5:40:29, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.9986\n2023-01-01 23:31:33,835 - mmcls - INFO - Epoch [54][5000/5005]\tlr: 1.000e-02, eta: 5:40:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9940\n2023-01-01 23:31:56,392 - mmcls - INFO - Epoch(val) [54][196]\taccuracy_top-1: 59.8220, accuracy_top-5: 82.2980\n2023-01-01 23:32:07,444 - mmcls - INFO - Epoch [55][100/5005]\tlr: 1.000e-02, eta: 5:40:12, time: 0.110, data_time: 0.037, memory: 1224, loss: 1.9251\n2023-01-01 23:32:16,173 - mmcls - INFO - Epoch [55][200/5005]\tlr: 1.000e-02, eta: 5:40:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9289\n2023-01-01 23:32:25,087 - mmcls - INFO - Epoch [55][300/5005]\tlr: 1.000e-02, eta: 5:39:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9177\n2023-01-01 23:32:33,576 - mmcls - INFO - Epoch [55][400/5005]\tlr: 1.000e-02, eta: 5:39:45, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9380\n2023-01-01 23:32:42,157 - mmcls - INFO - Epoch [55][500/5005]\tlr: 1.000e-02, eta: 5:39:36, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9301\n2023-01-01 23:32:50,909 - mmcls - INFO - Epoch [55][600/5005]\tlr: 1.000e-02, eta: 5:39:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9528\n2023-01-01 23:32:59,684 - mmcls - INFO - Epoch [55][700/5005]\tlr: 1.000e-02, eta: 5:39:18, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9635\n2023-01-01 23:33:08,627 - mmcls - INFO - Epoch [55][800/5005]\tlr: 1.000e-02, eta: 5:39:09, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.9654\n2023-01-01 23:33:17,538 - mmcls - INFO - Epoch [55][900/5005]\tlr: 1.000e-02, eta: 5:39:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9696\n2023-01-01 23:33:26,223 - mmcls - INFO - Epoch [55][1000/5005]\tlr: 1.000e-02, eta: 5:38:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9458\n2023-01-01 23:33:35,045 - mmcls - INFO - Epoch [55][1100/5005]\tlr: 1.000e-02, eta: 5:38:42, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9634\n2023-01-01 23:33:44,092 - mmcls - INFO - Epoch [55][1200/5005]\tlr: 1.000e-02, eta: 5:38:34, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.9432\n2023-01-01 23:33:52,838 - mmcls - INFO - Epoch [55][1300/5005]\tlr: 1.000e-02, eta: 5:38:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9665\n2023-01-01 23:34:01,363 - mmcls - INFO - Epoch [55][1400/5005]\tlr: 1.000e-02, eta: 5:38:16, time: 0.085, data_time: 0.035, memory: 1224, loss: 1.9750\n2023-01-01 23:34:10,096 - mmcls - INFO - Epoch [55][1500/5005]\tlr: 1.000e-02, eta: 5:38:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9723\n2023-01-01 23:34:18,748 - mmcls - INFO - Epoch [55][1600/5005]\tlr: 1.000e-02, eta: 5:37:57, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9510\n2023-01-01 23:34:27,374 - mmcls - INFO - Epoch [55][1700/5005]\tlr: 1.000e-02, eta: 5:37:48, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9776\n2023-01-01 23:34:36,374 - mmcls - INFO - Epoch [55][1800/5005]\tlr: 1.000e-02, eta: 5:37:40, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9527\n2023-01-01 23:34:45,207 - mmcls - INFO - Epoch [55][1900/5005]\tlr: 1.000e-02, eta: 5:37:31, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9494\n2023-01-01 23:34:54,024 - mmcls - INFO - Epoch [55][2000/5005]\tlr: 1.000e-02, eta: 5:37:22, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9609\n2023-01-01 23:35:02,937 - mmcls - INFO - Epoch [55][2100/5005]\tlr: 1.000e-02, eta: 5:37:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9703\n2023-01-01 23:35:11,963 - mmcls - INFO - Epoch [55][2200/5005]\tlr: 1.000e-02, eta: 5:37:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9657\n2023-01-01 23:35:20,798 - mmcls - INFO - Epoch [55][2300/5005]\tlr: 1.000e-02, eta: 5:36:55, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9634\n2023-01-01 23:35:30,130 - mmcls - INFO - Epoch [55][2400/5005]\tlr: 1.000e-02, eta: 5:36:47, time: 0.093, data_time: 0.014, memory: 1224, loss: 1.9779\n2023-01-01 23:35:38,872 - mmcls - INFO - Epoch [55][2500/5005]\tlr: 1.000e-02, eta: 5:36:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9875\n2023-01-01 23:35:47,629 - mmcls - INFO - Epoch [55][2600/5005]\tlr: 1.000e-02, eta: 5:36:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9420\n2023-01-01 23:35:56,444 - mmcls - INFO - Epoch [55][2700/5005]\tlr: 1.000e-02, eta: 5:36:20, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9780\n2023-01-01 23:36:05,345 - mmcls - INFO - Epoch [55][2800/5005]\tlr: 1.000e-02, eta: 5:36:11, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9553\n2023-01-01 23:36:14,190 - mmcls - INFO - Epoch [55][2900/5005]\tlr: 1.000e-02, eta: 5:36:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9740\n2023-01-01 23:36:22,886 - mmcls - INFO - Epoch [55][3000/5005]\tlr: 1.000e-02, eta: 5:35:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9821\n2023-01-01 23:36:31,760 - mmcls - INFO - Epoch [55][3100/5005]\tlr: 1.000e-02, eta: 5:35:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9630\n2023-01-01 23:36:40,310 - mmcls - INFO - Epoch [55][3200/5005]\tlr: 1.000e-02, eta: 5:35:35, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9881\n2023-01-01 23:36:49,258 - mmcls - INFO - Epoch [55][3300/5005]\tlr: 1.000e-02, eta: 5:35:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9664\n2023-01-01 23:36:57,981 - mmcls - INFO - Epoch [55][3400/5005]\tlr: 1.000e-02, eta: 5:35:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9676\n2023-01-01 23:37:06,876 - mmcls - INFO - Epoch [55][3500/5005]\tlr: 1.000e-02, eta: 5:35:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9821\n2023-01-01 23:37:15,851 - mmcls - INFO - Epoch [55][3600/5005]\tlr: 1.000e-02, eta: 5:35:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9795\n2023-01-01 23:37:24,831 - mmcls - INFO - Epoch [55][3700/5005]\tlr: 1.000e-02, eta: 5:34:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9922\n2023-01-01 23:37:33,680 - mmcls - INFO - Epoch [55][3800/5005]\tlr: 1.000e-02, eta: 5:34:42, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9836\n2023-01-01 23:37:42,260 - mmcls - INFO - Epoch [55][3900/5005]\tlr: 1.000e-02, eta: 5:34:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9958\n2023-01-01 23:37:51,191 - mmcls - INFO - Epoch [55][4000/5005]\tlr: 1.000e-02, eta: 5:34:24, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.9751\n2023-01-01 23:37:59,980 - mmcls - INFO - Epoch [55][4100/5005]\tlr: 1.000e-02, eta: 5:34:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9581\n2023-01-01 23:38:08,790 - mmcls - INFO - Epoch [55][4200/5005]\tlr: 1.000e-02, eta: 5:34:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9701\n2023-01-01 23:38:17,588 - mmcls - INFO - Epoch [55][4300/5005]\tlr: 1.000e-02, eta: 5:33:57, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9718\n2023-01-01 23:38:26,175 - mmcls - INFO - Epoch [55][4400/5005]\tlr: 1.000e-02, eta: 5:33:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9879\n2023-01-01 23:38:35,460 - mmcls - INFO - Epoch [55][4500/5005]\tlr: 1.000e-02, eta: 5:33:40, time: 0.093, data_time: 0.018, memory: 1224, loss: 1.9645\n2023-01-01 23:38:44,179 - mmcls - INFO - Epoch [55][4600/5005]\tlr: 1.000e-02, eta: 5:33:31, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9695\n2023-01-01 23:38:53,143 - mmcls - INFO - Epoch [55][4700/5005]\tlr: 1.000e-02, eta: 5:33:22, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9872\n2023-01-01 23:39:01,786 - mmcls - INFO - Epoch [55][4800/5005]\tlr: 1.000e-02, eta: 5:33:13, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9957\n2023-01-01 23:39:10,552 - mmcls - INFO - Epoch [55][4900/5005]\tlr: 1.000e-02, eta: 5:33:04, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0027\n2023-01-01 23:39:19,250 - mmcls - INFO - Epoch [55][5000/5005]\tlr: 1.000e-02, eta: 5:32:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9940\n2023-01-01 23:39:41,916 - mmcls - INFO - Epoch(val) [55][196]\taccuracy_top-1: 59.9000, accuracy_top-5: 82.1740\n2023-01-01 23:39:53,060 - mmcls - INFO - Epoch [56][100/5005]\tlr: 1.000e-02, eta: 5:32:47, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.9604\n2023-01-01 23:40:01,862 - mmcls - INFO - Epoch [56][200/5005]\tlr: 1.000e-02, eta: 5:32:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9316\n2023-01-01 23:40:10,596 - mmcls - INFO - Epoch [56][300/5005]\tlr: 1.000e-02, eta: 5:32:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9207\n2023-01-01 23:40:19,619 - mmcls - INFO - Epoch [56][400/5005]\tlr: 1.000e-02, eta: 5:32:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9428\n2023-01-01 23:40:28,412 - mmcls - INFO - Epoch [56][500/5005]\tlr: 1.000e-02, eta: 5:32:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9413\n2023-01-01 23:40:37,165 - mmcls - INFO - Epoch [56][600/5005]\tlr: 1.000e-02, eta: 5:32:03, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.9519\n2023-01-01 23:40:46,047 - mmcls - INFO - Epoch [56][700/5005]\tlr: 1.000e-02, eta: 5:31:54, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9277\n2023-01-01 23:40:54,468 - mmcls - INFO - Epoch [56][800/5005]\tlr: 1.000e-02, eta: 5:31:45, time: 0.084, data_time: 0.013, memory: 1224, loss: 1.9339\n2023-01-01 23:41:03,253 - mmcls - INFO - Epoch [56][900/5005]\tlr: 1.000e-02, eta: 5:31:36, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9453\n2023-01-01 23:41:12,086 - mmcls - INFO - Epoch [56][1000/5005]\tlr: 1.000e-02, eta: 5:31:27, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9467\n2023-01-01 23:41:20,876 - mmcls - INFO - Epoch [56][1100/5005]\tlr: 1.000e-02, eta: 5:31:18, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9340\n2023-01-01 23:41:29,675 - mmcls - INFO - Epoch [56][1200/5005]\tlr: 1.000e-02, eta: 5:31:09, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9470\n2023-01-01 23:41:38,392 - mmcls - INFO - Epoch [56][1300/5005]\tlr: 1.000e-02, eta: 5:31:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9588\n2023-01-01 23:41:47,417 - mmcls - INFO - Epoch [56][1400/5005]\tlr: 1.000e-02, eta: 5:30:51, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.9516\n2023-01-01 23:41:56,119 - mmcls - INFO - Epoch [56][1500/5005]\tlr: 1.000e-02, eta: 5:30:42, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9847\n2023-01-01 23:42:05,070 - mmcls - INFO - Epoch [56][1600/5005]\tlr: 1.000e-02, eta: 5:30:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9604\n2023-01-01 23:42:13,875 - mmcls - INFO - Epoch [56][1700/5005]\tlr: 1.000e-02, eta: 5:30:24, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9832\n2023-01-01 23:42:22,631 - mmcls - INFO - Epoch [56][1800/5005]\tlr: 1.000e-02, eta: 5:30:15, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9917\n2023-01-01 23:42:31,754 - mmcls - INFO - Epoch [56][1900/5005]\tlr: 1.000e-02, eta: 5:30:07, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9624\n2023-01-01 23:42:40,284 - mmcls - INFO - Epoch [56][2000/5005]\tlr: 1.000e-02, eta: 5:29:58, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.0073\n2023-01-01 23:42:49,088 - mmcls - INFO - Epoch [56][2100/5005]\tlr: 1.000e-02, eta: 5:29:49, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9772\n2023-01-01 23:42:57,994 - mmcls - INFO - Epoch [56][2200/5005]\tlr: 1.000e-02, eta: 5:29:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9847\n2023-01-01 23:43:06,857 - mmcls - INFO - Epoch [56][2300/5005]\tlr: 1.000e-02, eta: 5:29:31, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9547\n2023-01-01 23:43:15,602 - mmcls - INFO - Epoch [56][2400/5005]\tlr: 1.000e-02, eta: 5:29:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9606\n2023-01-01 23:43:24,413 - mmcls - INFO - Epoch [56][2500/5005]\tlr: 1.000e-02, eta: 5:29:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9787\n2023-01-01 23:43:33,280 - mmcls - INFO - Epoch [56][2600/5005]\tlr: 1.000e-02, eta: 5:29:04, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9614\n2023-01-01 23:43:42,090 - mmcls - INFO - Epoch [56][2700/5005]\tlr: 1.000e-02, eta: 5:28:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9709\n2023-01-01 23:43:50,734 - mmcls - INFO - Epoch [56][2800/5005]\tlr: 1.000e-02, eta: 5:28:46, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9784\n2023-01-01 23:43:59,676 - mmcls - INFO - Epoch [56][2900/5005]\tlr: 1.000e-02, eta: 5:28:37, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9675\n2023-01-01 23:44:08,673 - mmcls - INFO - Epoch [56][3000/5005]\tlr: 1.000e-02, eta: 5:28:29, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9499\n2023-01-01 23:44:17,271 - mmcls - INFO - Epoch [56][3100/5005]\tlr: 1.000e-02, eta: 5:28:20, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.9749\n2023-01-01 23:44:26,213 - mmcls - INFO - Epoch [56][3200/5005]\tlr: 1.000e-02, eta: 5:28:11, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.9659\n2023-01-01 23:44:34,920 - mmcls - INFO - Epoch [56][3300/5005]\tlr: 1.000e-02, eta: 5:28:02, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9621\n2023-01-01 23:44:43,600 - mmcls - INFO - Epoch [56][3400/5005]\tlr: 1.000e-02, eta: 5:27:53, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.9684\n2023-01-01 23:44:52,401 - mmcls - INFO - Epoch [56][3500/5005]\tlr: 1.000e-02, eta: 5:27:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9570\n2023-01-01 23:45:00,982 - mmcls - INFO - Epoch [56][3600/5005]\tlr: 1.000e-02, eta: 5:27:35, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9890\n2023-01-01 23:45:09,678 - mmcls - INFO - Epoch [56][3700/5005]\tlr: 1.000e-02, eta: 5:27:26, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9764\n2023-01-01 23:45:18,707 - mmcls - INFO - Epoch [56][3800/5005]\tlr: 1.000e-02, eta: 5:27:17, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0242\n2023-01-01 23:45:27,492 - mmcls - INFO - Epoch [56][3900/5005]\tlr: 1.000e-02, eta: 5:27:08, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9535\n2023-01-01 23:45:36,210 - mmcls - INFO - Epoch [56][4000/5005]\tlr: 1.000e-02, eta: 5:26:59, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9592\n2023-01-01 23:45:44,915 - mmcls - INFO - Epoch [56][4100/5005]\tlr: 1.000e-02, eta: 5:26:50, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9798\n2023-01-01 23:45:53,995 - mmcls - INFO - Epoch [56][4200/5005]\tlr: 1.000e-02, eta: 5:26:41, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9516\n2023-01-01 23:46:02,615 - mmcls - INFO - Epoch [56][4300/5005]\tlr: 1.000e-02, eta: 5:26:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.0050\n2023-01-01 23:46:11,332 - mmcls - INFO - Epoch [56][4400/5005]\tlr: 1.000e-02, eta: 5:26:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9950\n2023-01-01 23:46:19,891 - mmcls - INFO - Epoch [56][4500/5005]\tlr: 1.000e-02, eta: 5:26:14, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9840\n2023-01-01 23:46:29,040 - mmcls - INFO - Epoch [56][4600/5005]\tlr: 1.000e-02, eta: 5:26:06, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.9503\n2023-01-01 23:46:38,040 - mmcls - INFO - Epoch [56][4700/5005]\tlr: 1.000e-02, eta: 5:25:57, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9671\n2023-01-01 23:46:46,684 - mmcls - INFO - Epoch [56][4800/5005]\tlr: 1.000e-02, eta: 5:25:48, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9789\n2023-01-01 23:46:55,266 - mmcls - INFO - Epoch [56][4900/5005]\tlr: 1.000e-02, eta: 5:25:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.0100\n2023-01-01 23:47:03,995 - mmcls - INFO - Epoch [56][5000/5005]\tlr: 1.000e-02, eta: 5:25:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9936\n2023-01-01 23:47:26,752 - mmcls - INFO - Epoch(val) [56][196]\taccuracy_top-1: 60.0100, accuracy_top-5: 82.2900\n2023-01-01 23:47:37,578 - mmcls - INFO - Epoch [57][100/5005]\tlr: 1.000e-02, eta: 5:25:22, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.9029\n2023-01-01 23:47:46,464 - mmcls - INFO - Epoch [57][200/5005]\tlr: 1.000e-02, eta: 5:25:13, time: 0.089, data_time: 0.041, memory: 1224, loss: 1.9220\n2023-01-01 23:47:55,291 - mmcls - INFO - Epoch [57][300/5005]\tlr: 1.000e-02, eta: 5:25:04, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9311\n2023-01-01 23:48:03,941 - mmcls - INFO - Epoch [57][400/5005]\tlr: 1.000e-02, eta: 5:24:55, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9484\n2023-01-01 23:48:12,532 - mmcls - INFO - Epoch [57][500/5005]\tlr: 1.000e-02, eta: 5:24:46, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9811\n2023-01-01 23:48:21,212 - mmcls - INFO - Epoch [57][600/5005]\tlr: 1.000e-02, eta: 5:24:37, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9505\n2023-01-01 23:48:29,989 - mmcls - INFO - Epoch [57][700/5005]\tlr: 1.000e-02, eta: 5:24:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9381\n2023-01-01 23:48:38,616 - mmcls - INFO - Epoch [57][800/5005]\tlr: 1.000e-02, eta: 5:24:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9467\n2023-01-01 23:48:47,329 - mmcls - INFO - Epoch [57][900/5005]\tlr: 1.000e-02, eta: 5:24:10, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9402\n2023-01-01 23:48:56,208 - mmcls - INFO - Epoch [57][1000/5005]\tlr: 1.000e-02, eta: 5:24:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9511\n2023-01-01 23:49:05,049 - mmcls - INFO - Epoch [57][1100/5005]\tlr: 1.000e-02, eta: 5:23:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9444\n2023-01-01 23:49:14,222 - mmcls - INFO - Epoch [57][1200/5005]\tlr: 1.000e-02, eta: 5:23:43, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9449\n2023-01-01 23:49:22,904 - mmcls - INFO - Epoch [57][1300/5005]\tlr: 1.000e-02, eta: 5:23:34, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9206\n2023-01-01 23:49:31,785 - mmcls - INFO - Epoch [57][1400/5005]\tlr: 1.000e-02, eta: 5:23:25, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9394\n2023-01-01 23:49:40,533 - mmcls - INFO - Epoch [57][1500/5005]\tlr: 1.000e-02, eta: 5:23:16, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9435\n2023-01-01 23:49:49,399 - mmcls - INFO - Epoch [57][1600/5005]\tlr: 1.000e-02, eta: 5:23:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9652\n2023-01-01 23:49:58,273 - mmcls - INFO - Epoch [57][1700/5005]\tlr: 1.000e-02, eta: 5:22:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9777\n2023-01-01 23:50:07,118 - mmcls - INFO - Epoch [57][1800/5005]\tlr: 1.000e-02, eta: 5:22:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9491\n2023-01-01 23:50:15,744 - mmcls - INFO - Epoch [57][1900/5005]\tlr: 1.000e-02, eta: 5:22:41, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.9614\n2023-01-01 23:50:24,706 - mmcls - INFO - Epoch [57][2000/5005]\tlr: 1.000e-02, eta: 5:22:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9497\n2023-01-01 23:50:33,547 - mmcls - INFO - Epoch [57][2100/5005]\tlr: 1.000e-02, eta: 5:22:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9541\n2023-01-01 23:50:42,408 - mmcls - INFO - Epoch [57][2200/5005]\tlr: 1.000e-02, eta: 5:22:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9720\n2023-01-01 23:50:51,300 - mmcls - INFO - Epoch [57][2300/5005]\tlr: 1.000e-02, eta: 5:22:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9684\n2023-01-01 23:51:00,125 - mmcls - INFO - Epoch [57][2400/5005]\tlr: 1.000e-02, eta: 5:21:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9524\n2023-01-01 23:51:08,745 - mmcls - INFO - Epoch [57][2500/5005]\tlr: 1.000e-02, eta: 5:21:47, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9289\n2023-01-01 23:51:17,503 - mmcls - INFO - Epoch [57][2600/5005]\tlr: 1.000e-02, eta: 5:21:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9657\n2023-01-01 23:51:26,183 - mmcls - INFO - Epoch [57][2700/5005]\tlr: 1.000e-02, eta: 5:21:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9670\n2023-01-01 23:51:35,001 - mmcls - INFO - Epoch [57][2800/5005]\tlr: 1.000e-02, eta: 5:21:20, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9581\n2023-01-01 23:51:43,933 - mmcls - INFO - Epoch [57][2900/5005]\tlr: 1.000e-02, eta: 5:21:12, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9701\n2023-01-01 23:51:52,691 - mmcls - INFO - Epoch [57][3000/5005]\tlr: 1.000e-02, eta: 5:21:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9180\n2023-01-01 23:52:01,937 - mmcls - INFO - Epoch [57][3100/5005]\tlr: 1.000e-02, eta: 5:20:54, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.9738\n2023-01-01 23:52:10,978 - mmcls - INFO - Epoch [57][3200/5005]\tlr: 1.000e-02, eta: 5:20:45, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0045\n2023-01-01 23:52:19,744 - mmcls - INFO - Epoch [57][3300/5005]\tlr: 1.000e-02, eta: 5:20:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9814\n2023-01-01 23:52:28,808 - mmcls - INFO - Epoch [57][3400/5005]\tlr: 1.000e-02, eta: 5:20:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9877\n2023-01-01 23:52:37,571 - mmcls - INFO - Epoch [57][3500/5005]\tlr: 1.000e-02, eta: 5:20:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9752\n2023-01-01 23:52:46,369 - mmcls - INFO - Epoch [57][3600/5005]\tlr: 1.000e-02, eta: 5:20:10, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9691\n2023-01-01 23:52:55,258 - mmcls - INFO - Epoch [57][3700/5005]\tlr: 1.000e-02, eta: 5:20:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9870\n2023-01-01 23:53:04,006 - mmcls - INFO - Epoch [57][3800/5005]\tlr: 1.000e-02, eta: 5:19:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9613\n2023-01-01 23:53:12,725 - mmcls - INFO - Epoch [57][3900/5005]\tlr: 1.000e-02, eta: 5:19:43, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9951\n2023-01-01 23:53:21,277 - mmcls - INFO - Epoch [57][4000/5005]\tlr: 1.000e-02, eta: 5:19:34, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9677\n2023-01-01 23:53:30,168 - mmcls - INFO - Epoch [57][4100/5005]\tlr: 1.000e-02, eta: 5:19:25, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9630\n2023-01-01 23:53:38,805 - mmcls - INFO - Epoch [57][4200/5005]\tlr: 1.000e-02, eta: 5:19:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9701\n2023-01-01 23:53:47,381 - mmcls - INFO - Epoch [57][4300/5005]\tlr: 1.000e-02, eta: 5:19:07, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9697\n2023-01-01 23:53:56,142 - mmcls - INFO - Epoch [57][4400/5005]\tlr: 1.000e-02, eta: 5:18:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9967\n2023-01-01 23:54:04,874 - mmcls - INFO - Epoch [57][4500/5005]\tlr: 1.000e-02, eta: 5:18:49, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9818\n2023-01-01 23:54:14,146 - mmcls - INFO - Epoch [57][4600/5005]\tlr: 1.000e-02, eta: 5:18:40, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.9735\n2023-01-01 23:54:22,985 - mmcls - INFO - Epoch [57][4700/5005]\tlr: 1.000e-02, eta: 5:18:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9558\n2023-01-01 23:54:31,902 - mmcls - INFO - Epoch [57][4800/5005]\tlr: 1.000e-02, eta: 5:18:23, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9609\n2023-01-01 23:54:40,714 - mmcls - INFO - Epoch [57][4900/5005]\tlr: 1.000e-02, eta: 5:18:14, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9717\n2023-01-01 23:54:49,707 - mmcls - INFO - Epoch [57][5000/5005]\tlr: 1.000e-02, eta: 5:18:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9461\n2023-01-01 23:55:12,430 - mmcls - INFO - Epoch(val) [57][196]\taccuracy_top-1: 60.8100, accuracy_top-5: 82.8580\n2023-01-01 23:55:23,683 - mmcls - INFO - Epoch [58][100/5005]\tlr: 1.000e-02, eta: 5:17:57, time: 0.112, data_time: 0.040, memory: 1224, loss: 1.9078\n2023-01-01 23:55:32,616 - mmcls - INFO - Epoch [58][200/5005]\tlr: 1.000e-02, eta: 5:17:48, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9148\n2023-01-01 23:55:41,188 - mmcls - INFO - Epoch [58][300/5005]\tlr: 1.000e-02, eta: 5:17:39, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9173\n2023-01-01 23:55:49,911 - mmcls - INFO - Epoch [58][400/5005]\tlr: 1.000e-02, eta: 5:17:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9447\n2023-01-01 23:55:58,724 - mmcls - INFO - Epoch [58][500/5005]\tlr: 1.000e-02, eta: 5:17:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9073\n2023-01-01 23:56:07,580 - mmcls - INFO - Epoch [58][600/5005]\tlr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9209\n2023-01-01 23:56:16,648 - mmcls - INFO - Epoch [58][700/5005]\tlr: 1.000e-02, eta: 5:17:04, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.9310\n2023-01-01 23:56:25,406 - mmcls - INFO - Epoch [58][800/5005]\tlr: 1.000e-02, eta: 5:16:55, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9270\n2023-01-01 23:56:34,061 - mmcls - INFO - Epoch [58][900/5005]\tlr: 1.000e-02, eta: 5:16:46, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9307\n2023-01-01 23:56:42,999 - mmcls - INFO - Epoch [58][1000/5005]\tlr: 1.000e-02, eta: 5:16:37, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.9624\n2023-01-01 23:56:51,849 - mmcls - INFO - Epoch [58][1100/5005]\tlr: 1.000e-02, eta: 5:16:28, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9164\n2023-01-01 23:57:00,539 - mmcls - INFO - Epoch [58][1200/5005]\tlr: 1.000e-02, eta: 5:16:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9248\n2023-01-01 23:57:09,534 - mmcls - INFO - Epoch [58][1300/5005]\tlr: 1.000e-02, eta: 5:16:10, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9220\n2023-01-01 23:57:18,620 - mmcls - INFO - Epoch [58][1400/5005]\tlr: 1.000e-02, eta: 5:16:02, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9386\n2023-01-01 23:57:27,449 - mmcls - INFO - Epoch [58][1500/5005]\tlr: 1.000e-02, eta: 5:15:53, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9557\n2023-01-01 23:57:36,212 - mmcls - INFO - Epoch [58][1600/5005]\tlr: 1.000e-02, eta: 5:15:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9446\n2023-01-01 23:57:45,332 - mmcls - INFO - Epoch [58][1700/5005]\tlr: 1.000e-02, eta: 5:15:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9507\n2023-01-01 23:57:54,298 - mmcls - INFO - Epoch [58][1800/5005]\tlr: 1.000e-02, eta: 5:15:26, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9426\n2023-01-01 23:58:02,886 - mmcls - INFO - Epoch [58][1900/5005]\tlr: 1.000e-02, eta: 5:15:17, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9548\n2023-01-01 23:58:11,978 - mmcls - INFO - Epoch [58][2000/5005]\tlr: 1.000e-02, eta: 5:15:09, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9404\n2023-01-01 23:58:20,730 - mmcls - INFO - Epoch [58][2100/5005]\tlr: 1.000e-02, eta: 5:15:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9487\n2023-01-01 23:58:29,512 - mmcls - INFO - Epoch [58][2200/5005]\tlr: 1.000e-02, eta: 5:14:51, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9441\n2023-01-01 23:58:38,247 - mmcls - INFO - Epoch [58][2300/5005]\tlr: 1.000e-02, eta: 5:14:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9436\n2023-01-01 23:58:46,915 - mmcls - INFO - Epoch [58][2400/5005]\tlr: 1.000e-02, eta: 5:14:33, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9804\n2023-01-01 23:58:55,617 - mmcls - INFO - Epoch [58][2500/5005]\tlr: 1.000e-02, eta: 5:14:24, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9624\n2023-01-01 23:59:04,543 - mmcls - INFO - Epoch [58][2600/5005]\tlr: 1.000e-02, eta: 5:14:15, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9527\n2023-01-01 23:59:13,355 - mmcls - INFO - Epoch [58][2700/5005]\tlr: 1.000e-02, eta: 5:14:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9588\n2023-01-01 23:59:21,914 - mmcls - INFO - Epoch [58][2800/5005]\tlr: 1.000e-02, eta: 5:13:57, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.9967\n2023-01-01 23:59:30,799 - mmcls - INFO - Epoch [58][2900/5005]\tlr: 1.000e-02, eta: 5:13:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9711\n2023-01-01 23:59:39,484 - mmcls - INFO - Epoch [58][3000/5005]\tlr: 1.000e-02, eta: 5:13:39, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9582\n2023-01-01 23:59:48,300 - mmcls - INFO - Epoch [58][3100/5005]\tlr: 1.000e-02, eta: 5:13:30, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0012\n2023-01-01 23:59:57,412 - mmcls - INFO - Epoch [58][3200/5005]\tlr: 1.000e-02, eta: 5:13:21, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9729\n2023-01-02 00:00:06,071 - mmcls - INFO - Epoch [58][3300/5005]\tlr: 1.000e-02, eta: 5:13:12, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9747\n2023-01-02 00:00:14,747 - mmcls - INFO - Epoch [58][3400/5005]\tlr: 1.000e-02, eta: 5:13:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9839\n2023-01-02 00:00:23,505 - mmcls - INFO - Epoch [58][3500/5005]\tlr: 1.000e-02, eta: 5:12:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9786\n2023-01-02 00:00:32,269 - mmcls - INFO - Epoch [58][3600/5005]\tlr: 1.000e-02, eta: 5:12:45, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9650\n2023-01-02 00:00:41,037 - mmcls - INFO - Epoch [58][3700/5005]\tlr: 1.000e-02, eta: 5:12:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9763\n2023-01-02 00:00:49,872 - mmcls - INFO - Epoch [58][3800/5005]\tlr: 1.000e-02, eta: 5:12:28, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9741\n2023-01-02 00:00:58,701 - mmcls - INFO - Epoch [58][3900/5005]\tlr: 1.000e-02, eta: 5:12:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9837\n2023-01-02 00:01:07,855 - mmcls - INFO - Epoch [58][4000/5005]\tlr: 1.000e-02, eta: 5:12:10, time: 0.092, data_time: 0.033, memory: 1224, loss: 1.9610\n2023-01-02 00:01:16,450 - mmcls - INFO - Epoch [58][4100/5005]\tlr: 1.000e-02, eta: 5:12:01, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9943\n2023-01-02 00:01:25,206 - mmcls - INFO - Epoch [58][4200/5005]\tlr: 1.000e-02, eta: 5:11:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9469\n2023-01-02 00:01:33,997 - mmcls - INFO - Epoch [58][4300/5005]\tlr: 1.000e-02, eta: 5:11:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9775\n2023-01-02 00:01:43,181 - mmcls - INFO - Epoch [58][4400/5005]\tlr: 1.000e-02, eta: 5:11:34, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9804\n2023-01-02 00:01:52,123 - mmcls - INFO - Epoch [58][4500/5005]\tlr: 1.000e-02, eta: 5:11:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9873\n2023-01-02 00:02:00,882 - mmcls - INFO - Epoch [58][4600/5005]\tlr: 1.000e-02, eta: 5:11:17, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9756\n2023-01-02 00:02:09,486 - mmcls - INFO - Epoch [58][4700/5005]\tlr: 1.000e-02, eta: 5:11:08, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9695\n2023-01-02 00:02:18,299 - mmcls - INFO - Epoch [58][4800/5005]\tlr: 1.000e-02, eta: 5:10:59, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9530\n2023-01-02 00:02:27,125 - mmcls - INFO - Epoch [58][4900/5005]\tlr: 1.000e-02, eta: 5:10:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9811\n2023-01-02 00:02:35,758 - mmcls - INFO - Epoch [58][5000/5005]\tlr: 1.000e-02, eta: 5:10:41, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.0103\n2023-01-02 00:02:58,623 - mmcls - INFO - Epoch(val) [58][196]\taccuracy_top-1: 56.9520, accuracy_top-5: 79.4000\n2023-01-02 00:03:09,615 - mmcls - INFO - Epoch [59][100/5005]\tlr: 1.000e-02, eta: 5:10:33, time: 0.110, data_time: 0.064, memory: 1224, loss: 1.9117\n2023-01-02 00:03:18,425 - mmcls - INFO - Epoch [59][200/5005]\tlr: 1.000e-02, eta: 5:10:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.8996\n2023-01-02 00:03:27,171 - mmcls - INFO - Epoch [59][300/5005]\tlr: 1.000e-02, eta: 5:10:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9168\n2023-01-02 00:03:35,883 - mmcls - INFO - Epoch [59][400/5005]\tlr: 1.000e-02, eta: 5:10:06, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9250\n2023-01-02 00:03:44,862 - mmcls - INFO - Epoch [59][500/5005]\tlr: 1.000e-02, eta: 5:09:57, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9460\n2023-01-02 00:03:53,861 - mmcls - INFO - Epoch [59][600/5005]\tlr: 1.000e-02, eta: 5:09:48, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.9422\n2023-01-02 00:04:02,555 - mmcls - INFO - Epoch [59][700/5005]\tlr: 1.000e-02, eta: 5:09:39, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9282\n2023-01-02 00:04:11,128 - mmcls - INFO - Epoch [59][800/5005]\tlr: 1.000e-02, eta: 5:09:30, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9409\n2023-01-02 00:04:20,441 - mmcls - INFO - Epoch [59][900/5005]\tlr: 1.000e-02, eta: 5:09:22, time: 0.093, data_time: 0.015, memory: 1224, loss: 1.9482\n2023-01-02 00:04:29,471 - mmcls - INFO - Epoch [59][1000/5005]\tlr: 1.000e-02, eta: 5:09:13, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9492\n2023-01-02 00:04:38,611 - mmcls - INFO - Epoch [59][1100/5005]\tlr: 1.000e-02, eta: 5:09:04, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9584\n2023-01-02 00:04:47,434 - mmcls - INFO - Epoch [59][1200/5005]\tlr: 1.000e-02, eta: 5:08:55, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9287\n2023-01-02 00:04:55,934 - mmcls - INFO - Epoch [59][1300/5005]\tlr: 1.000e-02, eta: 5:08:46, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9319\n2023-01-02 00:05:04,804 - mmcls - INFO - Epoch [59][1400/5005]\tlr: 1.000e-02, eta: 5:08:37, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9566\n2023-01-02 00:05:13,270 - mmcls - INFO - Epoch [59][1500/5005]\tlr: 1.000e-02, eta: 5:08:28, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.9677\n2023-01-02 00:05:22,122 - mmcls - INFO - Epoch [59][1600/5005]\tlr: 1.000e-02, eta: 5:08:19, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9819\n2023-01-02 00:05:31,095 - mmcls - INFO - Epoch [59][1700/5005]\tlr: 1.000e-02, eta: 5:08:10, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9480\n2023-01-02 00:05:39,664 - mmcls - INFO - Epoch [59][1800/5005]\tlr: 1.000e-02, eta: 5:08:01, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9359\n2023-01-02 00:05:48,428 - mmcls - INFO - Epoch [59][1900/5005]\tlr: 1.000e-02, eta: 5:07:52, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9358\n2023-01-02 00:05:57,188 - mmcls - INFO - Epoch [59][2000/5005]\tlr: 1.000e-02, eta: 5:07:44, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9420\n2023-01-02 00:06:06,115 - mmcls - INFO - Epoch [59][2100/5005]\tlr: 1.000e-02, eta: 5:07:35, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9679\n2023-01-02 00:06:15,108 - mmcls - INFO - Epoch [59][2200/5005]\tlr: 1.000e-02, eta: 5:07:26, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9563\n2023-01-02 00:06:24,046 - mmcls - INFO - Epoch [59][2300/5005]\tlr: 1.000e-02, eta: 5:07:17, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9378\n2023-01-02 00:06:32,541 - mmcls - INFO - Epoch [59][2400/5005]\tlr: 1.000e-02, eta: 5:07:08, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.0141\n2023-01-02 00:06:41,178 - mmcls - INFO - Epoch [59][2500/5005]\tlr: 1.000e-02, eta: 5:06:59, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9545\n2023-01-02 00:06:50,047 - mmcls - INFO - Epoch [59][2600/5005]\tlr: 1.000e-02, eta: 5:06:50, time: 0.089, data_time: 0.039, memory: 1224, loss: 1.9822\n2023-01-02 00:06:58,817 - mmcls - INFO - Epoch [59][2700/5005]\tlr: 1.000e-02, eta: 5:06:41, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9363\n2023-01-02 00:07:07,781 - mmcls - INFO - Epoch [59][2800/5005]\tlr: 1.000e-02, eta: 5:06:32, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9343\n2023-01-02 00:07:16,459 - mmcls - INFO - Epoch [59][2900/5005]\tlr: 1.000e-02, eta: 5:06:23, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9619\n2023-01-02 00:07:25,473 - mmcls - INFO - Epoch [59][3000/5005]\tlr: 1.000e-02, eta: 5:06:15, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.9568\n2023-01-02 00:07:34,290 - mmcls - INFO - Epoch [59][3100/5005]\tlr: 1.000e-02, eta: 5:06:06, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9952\n2023-01-02 00:07:43,150 - mmcls - INFO - Epoch [59][3200/5005]\tlr: 1.000e-02, eta: 5:05:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9841\n2023-01-02 00:07:52,134 - mmcls - INFO - Epoch [59][3300/5005]\tlr: 1.000e-02, eta: 5:05:48, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.9759\n2023-01-02 00:08:00,973 - mmcls - INFO - Epoch [59][3400/5005]\tlr: 1.000e-02, eta: 5:05:39, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9833\n2023-01-02 00:08:09,769 - mmcls - INFO - Epoch [59][3500/5005]\tlr: 1.000e-02, eta: 5:05:30, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9412\n2023-01-02 00:08:18,586 - mmcls - INFO - Epoch [59][3600/5005]\tlr: 1.000e-02, eta: 5:05:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9659\n2023-01-02 00:08:27,360 - mmcls - INFO - Epoch [59][3700/5005]\tlr: 1.000e-02, eta: 5:05:12, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.9628\n2023-01-02 00:08:36,293 - mmcls - INFO - Epoch [59][3800/5005]\tlr: 1.000e-02, eta: 5:05:04, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9757\n2023-01-02 00:08:45,247 - mmcls - INFO - Epoch [59][3900/5005]\tlr: 1.000e-02, eta: 5:04:55, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.9837\n2023-01-02 00:08:53,979 - mmcls - INFO - Epoch [59][4000/5005]\tlr: 1.000e-02, eta: 5:04:46, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0063\n2023-01-02 00:09:03,042 - mmcls - INFO - Epoch [59][4100/5005]\tlr: 1.000e-02, eta: 5:04:37, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9886\n2023-01-02 00:09:11,794 - mmcls - INFO - Epoch [59][4200/5005]\tlr: 1.000e-02, eta: 5:04:28, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9907\n2023-01-02 00:09:20,583 - mmcls - INFO - Epoch [59][4300/5005]\tlr: 1.000e-02, eta: 5:04:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9440\n2023-01-02 00:09:29,264 - mmcls - INFO - Epoch [59][4400/5005]\tlr: 1.000e-02, eta: 5:04:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9738\n2023-01-02 00:09:38,066 - mmcls - INFO - Epoch [59][4500/5005]\tlr: 1.000e-02, eta: 5:04:01, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9361\n2023-01-02 00:09:47,024 - mmcls - INFO - Epoch [59][4600/5005]\tlr: 1.000e-02, eta: 5:03:52, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9244\n2023-01-02 00:09:55,699 - mmcls - INFO - Epoch [59][4700/5005]\tlr: 1.000e-02, eta: 5:03:43, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9512\n2023-01-02 00:10:04,559 - mmcls - INFO - Epoch [59][4800/5005]\tlr: 1.000e-02, eta: 5:03:35, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9739\n2023-01-02 00:10:13,653 - mmcls - INFO - Epoch [59][4900/5005]\tlr: 1.000e-02, eta: 5:03:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9673\n2023-01-02 00:10:22,375 - mmcls - INFO - Epoch [59][5000/5005]\tlr: 1.000e-02, eta: 5:03:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9811\n2023-01-02 00:10:45,349 - mmcls - INFO - Epoch(val) [59][196]\taccuracy_top-1: 60.4760, accuracy_top-5: 82.8280\n2023-01-02 00:10:56,698 - mmcls - INFO - Epoch [60][100/5005]\tlr: 1.000e-02, eta: 5:03:09, time: 0.113, data_time: 0.031, memory: 1224, loss: 1.9168\n2023-01-02 00:11:05,510 - mmcls - INFO - Epoch [60][200/5005]\tlr: 1.000e-02, eta: 5:03:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9079\n2023-01-02 00:11:14,343 - mmcls - INFO - Epoch [60][300/5005]\tlr: 1.000e-02, eta: 5:02:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9226\n2023-01-02 00:11:23,059 - mmcls - INFO - Epoch [60][400/5005]\tlr: 1.000e-02, eta: 5:02:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9461\n2023-01-02 00:11:31,824 - mmcls - INFO - Epoch [60][500/5005]\tlr: 1.000e-02, eta: 5:02:33, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.8977\n2023-01-02 00:11:40,625 - mmcls - INFO - Epoch [60][600/5005]\tlr: 1.000e-02, eta: 5:02:24, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9101\n2023-01-02 00:11:49,454 - mmcls - INFO - Epoch [60][700/5005]\tlr: 1.000e-02, eta: 5:02:15, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9490\n2023-01-02 00:11:58,144 - mmcls - INFO - Epoch [60][800/5005]\tlr: 1.000e-02, eta: 5:02:06, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9041\n2023-01-02 00:12:06,753 - mmcls - INFO - Epoch [60][900/5005]\tlr: 1.000e-02, eta: 5:01:57, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9200\n2023-01-02 00:12:15,584 - mmcls - INFO - Epoch [60][1000/5005]\tlr: 1.000e-02, eta: 5:01:49, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9350\n2023-01-02 00:12:24,363 - mmcls - INFO - Epoch [60][1100/5005]\tlr: 1.000e-02, eta: 5:01:40, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9376\n2023-01-02 00:12:33,124 - mmcls - INFO - Epoch [60][1200/5005]\tlr: 1.000e-02, eta: 5:01:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9267\n2023-01-02 00:12:41,713 - mmcls - INFO - Epoch [60][1300/5005]\tlr: 1.000e-02, eta: 5:01:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9515\n2023-01-02 00:12:50,488 - mmcls - INFO - Epoch [60][1400/5005]\tlr: 1.000e-02, eta: 5:01:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9413\n2023-01-02 00:12:59,013 - mmcls - INFO - Epoch [60][1500/5005]\tlr: 1.000e-02, eta: 5:01:04, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9373\n2023-01-02 00:13:07,561 - mmcls - INFO - Epoch [60][1600/5005]\tlr: 1.000e-02, eta: 5:00:54, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9342\n2023-01-02 00:13:16,531 - mmcls - INFO - Epoch [60][1700/5005]\tlr: 1.000e-02, eta: 5:00:46, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9626\n2023-01-02 00:13:25,113 - mmcls - INFO - Epoch [60][1800/5005]\tlr: 1.000e-02, eta: 5:00:37, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9630\n2023-01-02 00:13:33,895 - mmcls - INFO - Epoch [60][1900/5005]\tlr: 1.000e-02, eta: 5:00:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9427\n2023-01-02 00:13:43,165 - mmcls - INFO - Epoch [60][2000/5005]\tlr: 1.000e-02, eta: 5:00:19, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.9498\n2023-01-02 00:13:52,200 - mmcls - INFO - Epoch [60][2100/5005]\tlr: 1.000e-02, eta: 5:00:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9610\n2023-01-02 00:14:01,189 - mmcls - INFO - Epoch [60][2200/5005]\tlr: 1.000e-02, eta: 5:00:02, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9394\n2023-01-02 00:14:09,763 - mmcls - INFO - Epoch [60][2300/5005]\tlr: 1.000e-02, eta: 4:59:52, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9616\n2023-01-02 00:14:18,626 - mmcls - INFO - Epoch [60][2400/5005]\tlr: 1.000e-02, eta: 4:59:44, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9696\n2023-01-02 00:14:27,547 - mmcls - INFO - Epoch [60][2500/5005]\tlr: 1.000e-02, eta: 4:59:35, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.9580\n2023-01-02 00:14:36,444 - mmcls - INFO - Epoch [60][2600/5005]\tlr: 1.000e-02, eta: 4:59:26, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9644\n2023-01-02 00:14:45,182 - mmcls - INFO - Epoch [60][2700/5005]\tlr: 1.000e-02, eta: 4:59:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9729\n2023-01-02 00:14:54,129 - mmcls - INFO - Epoch [60][2800/5005]\tlr: 1.000e-02, eta: 4:59:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9495\n2023-01-02 00:15:02,897 - mmcls - INFO - Epoch [60][2900/5005]\tlr: 1.000e-02, eta: 4:58:59, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9447\n2023-01-02 00:15:11,619 - mmcls - INFO - Epoch [60][3000/5005]\tlr: 1.000e-02, eta: 4:58:50, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9519\n2023-01-02 00:15:20,520 - mmcls - INFO - Epoch [60][3100/5005]\tlr: 1.000e-02, eta: 4:58:41, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.9379\n2023-01-02 00:15:29,374 - mmcls - INFO - Epoch [60][3200/5005]\tlr: 1.000e-02, eta: 4:58:33, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9709\n2023-01-02 00:15:38,460 - mmcls - INFO - Epoch [60][3300/5005]\tlr: 1.000e-02, eta: 4:58:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.9814\n2023-01-02 00:15:47,105 - mmcls - INFO - Epoch [60][3400/5005]\tlr: 1.000e-02, eta: 4:58:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9842\n2023-01-02 00:15:56,054 - mmcls - INFO - Epoch [60][3500/5005]\tlr: 1.000e-02, eta: 4:58:06, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9720\n2023-01-02 00:16:04,746 - mmcls - INFO - Epoch [60][3600/5005]\tlr: 1.000e-02, eta: 4:57:57, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9735\n2023-01-02 00:16:13,589 - mmcls - INFO - Epoch [60][3700/5005]\tlr: 1.000e-02, eta: 4:57:48, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9772\n2023-01-02 00:16:22,212 - mmcls - INFO - Epoch [60][3800/5005]\tlr: 1.000e-02, eta: 4:57:39, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9566\n2023-01-02 00:16:31,134 - mmcls - INFO - Epoch [60][3900/5005]\tlr: 1.000e-02, eta: 4:57:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9619\n2023-01-02 00:16:40,115 - mmcls - INFO - Epoch [60][4000/5005]\tlr: 1.000e-02, eta: 4:57:21, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9997\n2023-01-02 00:16:48,867 - mmcls - INFO - Epoch [60][4100/5005]\tlr: 1.000e-02, eta: 4:57:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9996\n2023-01-02 00:16:57,757 - mmcls - INFO - Epoch [60][4200/5005]\tlr: 1.000e-02, eta: 4:57:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.9872\n2023-01-02 00:17:06,552 - mmcls - INFO - Epoch [60][4300/5005]\tlr: 1.000e-02, eta: 4:56:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9667\n2023-01-02 00:17:15,240 - mmcls - INFO - Epoch [60][4400/5005]\tlr: 1.000e-02, eta: 4:56:46, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9829\n2023-01-02 00:17:24,090 - mmcls - INFO - Epoch [60][4500/5005]\tlr: 1.000e-02, eta: 4:56:37, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9650\n2023-01-02 00:17:32,691 - mmcls - INFO - Epoch [60][4600/5005]\tlr: 1.000e-02, eta: 4:56:28, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.9467\n2023-01-02 00:17:41,642 - mmcls - INFO - Epoch [60][4700/5005]\tlr: 1.000e-02, eta: 4:56:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9647\n2023-01-02 00:17:50,716 - mmcls - INFO - Epoch [60][4800/5005]\tlr: 1.000e-02, eta: 4:56:10, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9752\n2023-01-02 00:17:59,725 - mmcls - INFO - Epoch [60][4900/5005]\tlr: 1.000e-02, eta: 4:56:01, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9637\n2023-01-02 00:18:08,310 - mmcls - INFO - Epoch [60][5000/5005]\tlr: 1.000e-02, eta: 4:55:52, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.0175\n2023-01-02 00:18:31,043 - mmcls - INFO - Epoch(val) [60][196]\taccuracy_top-1: 60.1400, accuracy_top-5: 82.4700\n2023-01-02 00:18:41,934 - mmcls - INFO - Epoch [61][100/5005]\tlr: 1.000e-03, eta: 4:55:44, time: 0.109, data_time: 0.037, memory: 1224, loss: 1.8820\n2023-01-02 00:18:50,510 - mmcls - INFO - Epoch [61][200/5005]\tlr: 1.000e-03, eta: 4:55:35, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.8527\n2023-01-02 00:18:59,355 - mmcls - INFO - Epoch [61][300/5005]\tlr: 1.000e-03, eta: 4:55:26, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.8555\n2023-01-02 00:19:07,959 - mmcls - INFO - Epoch [61][400/5005]\tlr: 1.000e-03, eta: 4:55:17, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.8368\n2023-01-02 00:19:16,642 - mmcls - INFO - Epoch [61][500/5005]\tlr: 1.000e-03, eta: 4:55:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.8076\n2023-01-02 00:19:25,408 - mmcls - INFO - Epoch [61][600/5005]\tlr: 1.000e-03, eta: 4:54:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.8417\n2023-01-02 00:19:34,035 - mmcls - INFO - Epoch [61][700/5005]\tlr: 1.000e-03, eta: 4:54:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.8405\n2023-01-02 00:19:43,068 - mmcls - INFO - Epoch [61][800/5005]\tlr: 1.000e-03, eta: 4:54:41, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.8338\n2023-01-02 00:19:51,811 - mmcls - INFO - Epoch [61][900/5005]\tlr: 1.000e-03, eta: 4:54:32, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7922\n2023-01-02 00:20:00,545 - mmcls - INFO - Epoch [61][1000/5005]\tlr: 1.000e-03, eta: 4:54:24, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.8000\n2023-01-02 00:20:09,501 - mmcls - INFO - Epoch [61][1100/5005]\tlr: 1.000e-03, eta: 4:54:15, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.8239\n2023-01-02 00:20:18,313 - mmcls - INFO - Epoch [61][1200/5005]\tlr: 1.000e-03, eta: 4:54:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7871\n2023-01-02 00:20:27,140 - mmcls - INFO - Epoch [61][1300/5005]\tlr: 1.000e-03, eta: 4:53:57, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.8189\n2023-01-02 00:20:35,801 - mmcls - INFO - Epoch [61][1400/5005]\tlr: 1.000e-03, eta: 4:53:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7826\n2023-01-02 00:20:44,506 - mmcls - INFO - Epoch [61][1500/5005]\tlr: 1.000e-03, eta: 4:53:39, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7758\n2023-01-02 00:20:53,411 - mmcls - INFO - Epoch [61][1600/5005]\tlr: 1.000e-03, eta: 4:53:30, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.7833\n2023-01-02 00:21:01,953 - mmcls - INFO - Epoch [61][1700/5005]\tlr: 1.000e-03, eta: 4:53:21, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7730\n2023-01-02 00:21:10,652 - mmcls - INFO - Epoch [61][1800/5005]\tlr: 1.000e-03, eta: 4:53:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7827\n2023-01-02 00:21:19,427 - mmcls - INFO - Epoch [61][1900/5005]\tlr: 1.000e-03, eta: 4:53:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7971\n2023-01-02 00:21:28,259 - mmcls - INFO - Epoch [61][2000/5005]\tlr: 1.000e-03, eta: 4:52:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7989\n2023-01-02 00:21:37,001 - mmcls - INFO - Epoch [61][2100/5005]\tlr: 1.000e-03, eta: 4:52:45, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7766\n2023-01-02 00:21:45,593 - mmcls - INFO - Epoch [61][2200/5005]\tlr: 1.000e-03, eta: 4:52:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7625\n2023-01-02 00:21:54,317 - mmcls - INFO - Epoch [61][2300/5005]\tlr: 1.000e-03, eta: 4:52:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7761\n2023-01-02 00:22:02,946 - mmcls - INFO - Epoch [61][2400/5005]\tlr: 1.000e-03, eta: 4:52:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7801\n2023-01-02 00:22:12,026 - mmcls - INFO - Epoch [61][2500/5005]\tlr: 1.000e-03, eta: 4:52:10, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.7562\n2023-01-02 00:22:21,313 - mmcls - INFO - Epoch [61][2600/5005]\tlr: 1.000e-03, eta: 4:52:01, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.7806\n2023-01-02 00:22:29,864 - mmcls - INFO - Epoch [61][2700/5005]\tlr: 1.000e-03, eta: 4:51:52, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.7608\n2023-01-02 00:22:38,638 - mmcls - INFO - Epoch [61][2800/5005]\tlr: 1.000e-03, eta: 4:51:43, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7528\n2023-01-02 00:22:47,355 - mmcls - INFO - Epoch [61][2900/5005]\tlr: 1.000e-03, eta: 4:51:34, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7833\n2023-01-02 00:22:55,944 - mmcls - INFO - Epoch [61][3000/5005]\tlr: 1.000e-03, eta: 4:51:25, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.7765\n2023-01-02 00:23:04,689 - mmcls - INFO - Epoch [61][3100/5005]\tlr: 1.000e-03, eta: 4:51:16, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7819\n2023-01-02 00:23:13,358 - mmcls - INFO - Epoch [61][3200/5005]\tlr: 1.000e-03, eta: 4:51:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7715\n2023-01-02 00:23:22,286 - mmcls - INFO - Epoch [61][3300/5005]\tlr: 1.000e-03, eta: 4:50:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.7649\n2023-01-02 00:23:30,853 - mmcls - INFO - Epoch [61][3400/5005]\tlr: 1.000e-03, eta: 4:50:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7554\n2023-01-02 00:23:39,594 - mmcls - INFO - Epoch [61][3500/5005]\tlr: 1.000e-03, eta: 4:50:40, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7664\n2023-01-02 00:23:48,459 - mmcls - INFO - Epoch [61][3600/5005]\tlr: 1.000e-03, eta: 4:50:31, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7816\n2023-01-02 00:23:57,804 - mmcls - INFO - Epoch [61][3700/5005]\tlr: 1.000e-03, eta: 4:50:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.7710\n2023-01-02 00:24:06,560 - mmcls - INFO - Epoch [61][3800/5005]\tlr: 1.000e-03, eta: 4:50:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7570\n2023-01-02 00:24:15,796 - mmcls - INFO - Epoch [61][3900/5005]\tlr: 1.000e-03, eta: 4:50:05, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.7719\n2023-01-02 00:24:24,427 - mmcls - INFO - Epoch [61][4000/5005]\tlr: 1.000e-03, eta: 4:49:56, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7801\n2023-01-02 00:24:33,282 - mmcls - INFO - Epoch [61][4100/5005]\tlr: 1.000e-03, eta: 4:49:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7731\n2023-01-02 00:24:42,301 - mmcls - INFO - Epoch [61][4200/5005]\tlr: 1.000e-03, eta: 4:49:38, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7713\n2023-01-02 00:24:51,105 - mmcls - INFO - Epoch [61][4300/5005]\tlr: 1.000e-03, eta: 4:49:30, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.7974\n2023-01-02 00:24:59,838 - mmcls - INFO - Epoch [61][4400/5005]\tlr: 1.000e-03, eta: 4:49:21, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.7858\n2023-01-02 00:25:08,471 - mmcls - INFO - Epoch [61][4500/5005]\tlr: 1.000e-03, eta: 4:49:12, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7479\n2023-01-02 00:25:17,242 - mmcls - INFO - Epoch [61][4600/5005]\tlr: 1.000e-03, eta: 4:49:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7596\n2023-01-02 00:25:26,272 - mmcls - INFO - Epoch [61][4700/5005]\tlr: 1.000e-03, eta: 4:48:54, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7585\n2023-01-02 00:25:35,163 - mmcls - INFO - Epoch [61][4800/5005]\tlr: 1.000e-03, eta: 4:48:45, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.7735\n2023-01-02 00:25:43,953 - mmcls - INFO - Epoch [61][4900/5005]\tlr: 1.000e-03, eta: 4:48:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7423\n2023-01-02 00:25:52,622 - mmcls - INFO - Epoch [61][5000/5005]\tlr: 1.000e-03, eta: 4:48:27, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7735\n2023-01-02 00:26:15,217 - mmcls - INFO - Epoch(val) [61][196]\taccuracy_top-1: 63.4000, accuracy_top-5: 84.7080\n2023-01-02 00:26:26,570 - mmcls - INFO - Epoch [62][100/5005]\tlr: 1.000e-03, eta: 4:48:19, time: 0.113, data_time: 0.050, memory: 1224, loss: 1.7562\n2023-01-02 00:26:35,545 - mmcls - INFO - Epoch [62][200/5005]\tlr: 1.000e-03, eta: 4:48:10, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.7317\n2023-01-02 00:26:44,257 - mmcls - INFO - Epoch [62][300/5005]\tlr: 1.000e-03, eta: 4:48:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7547\n2023-01-02 00:26:52,952 - mmcls - INFO - Epoch [62][400/5005]\tlr: 1.000e-03, eta: 4:47:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.7504\n2023-01-02 00:27:01,671 - mmcls - INFO - Epoch [62][500/5005]\tlr: 1.000e-03, eta: 4:47:43, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7419\n2023-01-02 00:27:10,679 - mmcls - INFO - Epoch [62][600/5005]\tlr: 1.000e-03, eta: 4:47:35, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.7638\n2023-01-02 00:27:19,824 - mmcls - INFO - Epoch [62][700/5005]\tlr: 1.000e-03, eta: 4:47:26, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.7359\n2023-01-02 00:27:28,545 - mmcls - INFO - Epoch [62][800/5005]\tlr: 1.000e-03, eta: 4:47:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7453\n2023-01-02 00:27:37,475 - mmcls - INFO - Epoch [62][900/5005]\tlr: 1.000e-03, eta: 4:47:08, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.7696\n2023-01-02 00:27:46,458 - mmcls - INFO - Epoch [62][1000/5005]\tlr: 1.000e-03, eta: 4:46:59, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7331\n2023-01-02 00:27:55,215 - mmcls - INFO - Epoch [62][1100/5005]\tlr: 1.000e-03, eta: 4:46:50, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.7344\n2023-01-02 00:28:04,467 - mmcls - INFO - Epoch [62][1200/5005]\tlr: 1.000e-03, eta: 4:46:42, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7395\n2023-01-02 00:28:13,362 - mmcls - INFO - Epoch [62][1300/5005]\tlr: 1.000e-03, eta: 4:46:33, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.7224\n2023-01-02 00:28:22,141 - mmcls - INFO - Epoch [62][1400/5005]\tlr: 1.000e-03, eta: 4:46:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7502\n2023-01-02 00:28:31,030 - mmcls - INFO - Epoch [62][1500/5005]\tlr: 1.000e-03, eta: 4:46:15, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7508\n2023-01-02 00:28:39,814 - mmcls - INFO - Epoch [62][1600/5005]\tlr: 1.000e-03, eta: 4:46:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7293\n2023-01-02 00:28:48,573 - mmcls - INFO - Epoch [62][1700/5005]\tlr: 1.000e-03, eta: 4:45:57, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.7367\n2023-01-02 00:28:57,618 - mmcls - INFO - Epoch [62][1800/5005]\tlr: 1.000e-03, eta: 4:45:49, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.7374\n2023-01-02 00:29:06,211 - mmcls - INFO - Epoch [62][1900/5005]\tlr: 1.000e-03, eta: 4:45:40, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7651\n2023-01-02 00:29:15,054 - mmcls - INFO - Epoch [62][2000/5005]\tlr: 1.000e-03, eta: 4:45:31, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7330\n2023-01-02 00:29:23,897 - mmcls - INFO - Epoch [62][2100/5005]\tlr: 1.000e-03, eta: 4:45:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7282\n2023-01-02 00:29:32,911 - mmcls - INFO - Epoch [62][2200/5005]\tlr: 1.000e-03, eta: 4:45:13, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7437\n2023-01-02 00:29:41,837 - mmcls - INFO - Epoch [62][2300/5005]\tlr: 1.000e-03, eta: 4:45:04, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7374\n2023-01-02 00:29:50,498 - mmcls - INFO - Epoch [62][2400/5005]\tlr: 1.000e-03, eta: 4:44:55, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7464\n2023-01-02 00:29:59,163 - mmcls - INFO - Epoch [62][2500/5005]\tlr: 1.000e-03, eta: 4:44:46, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7222\n2023-01-02 00:30:07,947 - mmcls - INFO - Epoch [62][2600/5005]\tlr: 1.000e-03, eta: 4:44:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7390\n2023-01-02 00:30:16,723 - mmcls - INFO - Epoch [62][2700/5005]\tlr: 1.000e-03, eta: 4:44:28, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.7374\n2023-01-02 00:30:25,600 - mmcls - INFO - Epoch [62][2800/5005]\tlr: 1.000e-03, eta: 4:44:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7184\n2023-01-02 00:30:34,369 - mmcls - INFO - Epoch [62][2900/5005]\tlr: 1.000e-03, eta: 4:44:11, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7384\n2023-01-02 00:30:43,090 - mmcls - INFO - Epoch [62][3000/5005]\tlr: 1.000e-03, eta: 4:44:02, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7386\n2023-01-02 00:30:51,800 - mmcls - INFO - Epoch [62][3100/5005]\tlr: 1.000e-03, eta: 4:43:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7306\n2023-01-02 00:31:00,783 - mmcls - INFO - Epoch [62][3200/5005]\tlr: 1.000e-03, eta: 4:43:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7306\n2023-01-02 00:31:09,599 - mmcls - INFO - Epoch [62][3300/5005]\tlr: 1.000e-03, eta: 4:43:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7566\n2023-01-02 00:31:18,228 - mmcls - INFO - Epoch [62][3400/5005]\tlr: 1.000e-03, eta: 4:43:26, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.7256\n2023-01-02 00:31:26,893 - mmcls - INFO - Epoch [62][3500/5005]\tlr: 1.000e-03, eta: 4:43:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7558\n2023-01-02 00:31:35,935 - mmcls - INFO - Epoch [62][3600/5005]\tlr: 1.000e-03, eta: 4:43:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7439\n2023-01-02 00:31:44,713 - mmcls - INFO - Epoch [62][3700/5005]\tlr: 1.000e-03, eta: 4:42:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7288\n2023-01-02 00:31:53,625 - mmcls - INFO - Epoch [62][3800/5005]\tlr: 1.000e-03, eta: 4:42:50, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7522\n2023-01-02 00:32:02,503 - mmcls - INFO - Epoch [62][3900/5005]\tlr: 1.000e-03, eta: 4:42:41, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.7438\n2023-01-02 00:32:11,556 - mmcls - INFO - Epoch [62][4000/5005]\tlr: 1.000e-03, eta: 4:42:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7464\n2023-01-02 00:32:20,699 - mmcls - INFO - Epoch [62][4100/5005]\tlr: 1.000e-03, eta: 4:42:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7346\n2023-01-02 00:32:29,269 - mmcls - INFO - Epoch [62][4200/5005]\tlr: 1.000e-03, eta: 4:42:15, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7246\n2023-01-02 00:32:38,550 - mmcls - INFO - Epoch [62][4300/5005]\tlr: 1.000e-03, eta: 4:42:06, time: 0.093, data_time: 0.021, memory: 1224, loss: 1.7417\n2023-01-02 00:32:47,355 - mmcls - INFO - Epoch [62][4400/5005]\tlr: 1.000e-03, eta: 4:41:57, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7725\n2023-01-02 00:32:56,153 - mmcls - INFO - Epoch [62][4500/5005]\tlr: 1.000e-03, eta: 4:41:49, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7568\n2023-01-02 00:33:04,740 - mmcls - INFO - Epoch [62][4600/5005]\tlr: 1.000e-03, eta: 4:41:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7234\n2023-01-02 00:33:13,589 - mmcls - INFO - Epoch [62][4700/5005]\tlr: 1.000e-03, eta: 4:41:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7368\n2023-01-02 00:33:22,239 - mmcls - INFO - Epoch [62][4800/5005]\tlr: 1.000e-03, eta: 4:41:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7351\n2023-01-02 00:33:30,814 - mmcls - INFO - Epoch [62][4900/5005]\tlr: 1.000e-03, eta: 4:41:13, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.7086\n2023-01-02 00:33:39,890 - mmcls - INFO - Epoch [62][5000/5005]\tlr: 1.000e-03, eta: 4:41:04, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.7516\n2023-01-02 00:34:02,672 - mmcls - INFO - Epoch(val) [62][196]\taccuracy_top-1: 63.7500, accuracy_top-5: 84.8740\n2023-01-02 00:34:14,064 - mmcls - INFO - Epoch [63][100/5005]\tlr: 1.000e-03, eta: 4:40:56, time: 0.114, data_time: 0.060, memory: 1224, loss: 1.7324\n2023-01-02 00:34:22,623 - mmcls - INFO - Epoch [63][200/5005]\tlr: 1.000e-03, eta: 4:40:47, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.7129\n2023-01-02 00:34:31,240 - mmcls - INFO - Epoch [63][300/5005]\tlr: 1.000e-03, eta: 4:40:38, time: 0.086, data_time: 0.029, memory: 1224, loss: 1.7143\n2023-01-02 00:34:39,818 - mmcls - INFO - Epoch [63][400/5005]\tlr: 1.000e-03, eta: 4:40:29, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.7287\n2023-01-02 00:34:49,028 - mmcls - INFO - Epoch [63][500/5005]\tlr: 1.000e-03, eta: 4:40:20, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.6999\n2023-01-02 00:34:57,823 - mmcls - INFO - Epoch [63][600/5005]\tlr: 1.000e-03, eta: 4:40:11, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7223\n2023-01-02 00:35:06,546 - mmcls - INFO - Epoch [63][700/5005]\tlr: 1.000e-03, eta: 4:40:02, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7066\n2023-01-02 00:35:15,184 - mmcls - INFO - Epoch [63][800/5005]\tlr: 1.000e-03, eta: 4:39:53, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.7540\n2023-01-02 00:35:24,208 - mmcls - INFO - Epoch [63][900/5005]\tlr: 1.000e-03, eta: 4:39:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7181\n2023-01-02 00:35:33,187 - mmcls - INFO - Epoch [63][1000/5005]\tlr: 1.000e-03, eta: 4:39:36, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6915\n2023-01-02 00:35:41,865 - mmcls - INFO - Epoch [63][1100/5005]\tlr: 1.000e-03, eta: 4:39:27, time: 0.087, data_time: 0.041, memory: 1224, loss: 1.6967\n2023-01-02 00:35:50,641 - mmcls - INFO - Epoch [63][1200/5005]\tlr: 1.000e-03, eta: 4:39:18, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.7325\n2023-01-02 00:35:59,930 - mmcls - INFO - Epoch [63][1300/5005]\tlr: 1.000e-03, eta: 4:39:09, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.7361\n2023-01-02 00:36:08,777 - mmcls - INFO - Epoch [63][1400/5005]\tlr: 1.000e-03, eta: 4:39:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7241\n2023-01-02 00:36:17,636 - mmcls - INFO - Epoch [63][1500/5005]\tlr: 1.000e-03, eta: 4:38:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7078\n2023-01-02 00:36:26,373 - mmcls - INFO - Epoch [63][1600/5005]\tlr: 1.000e-03, eta: 4:38:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7141\n2023-01-02 00:36:35,656 - mmcls - INFO - Epoch [63][1700/5005]\tlr: 1.000e-03, eta: 4:38:34, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.7339\n2023-01-02 00:36:44,850 - mmcls - INFO - Epoch [63][1800/5005]\tlr: 1.000e-03, eta: 4:38:25, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.7298\n2023-01-02 00:36:53,710 - mmcls - INFO - Epoch [63][1900/5005]\tlr: 1.000e-03, eta: 4:38:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7242\n2023-01-02 00:37:03,094 - mmcls - INFO - Epoch [63][2000/5005]\tlr: 1.000e-03, eta: 4:38:08, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.7478\n2023-01-02 00:37:11,771 - mmcls - INFO - Epoch [63][2100/5005]\tlr: 1.000e-03, eta: 4:37:59, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7286\n2023-01-02 00:37:20,457 - mmcls - INFO - Epoch [63][2200/5005]\tlr: 1.000e-03, eta: 4:37:50, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7278\n2023-01-02 00:37:29,302 - mmcls - INFO - Epoch [63][2300/5005]\tlr: 1.000e-03, eta: 4:37:41, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7049\n2023-01-02 00:37:38,008 - mmcls - INFO - Epoch [63][2400/5005]\tlr: 1.000e-03, eta: 4:37:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7216\n2023-01-02 00:37:46,860 - mmcls - INFO - Epoch [63][2500/5005]\tlr: 1.000e-03, eta: 4:37:23, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.7545\n2023-01-02 00:37:55,646 - mmcls - INFO - Epoch [63][2600/5005]\tlr: 1.000e-03, eta: 4:37:14, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.7257\n2023-01-02 00:38:04,645 - mmcls - INFO - Epoch [63][2700/5005]\tlr: 1.000e-03, eta: 4:37:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7139\n2023-01-02 00:38:13,527 - mmcls - INFO - Epoch [63][2800/5005]\tlr: 1.000e-03, eta: 4:36:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7207\n2023-01-02 00:38:22,291 - mmcls - INFO - Epoch [63][2900/5005]\tlr: 1.000e-03, eta: 4:36:47, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7143\n2023-01-02 00:38:30,760 - mmcls - INFO - Epoch [63][3000/5005]\tlr: 1.000e-03, eta: 4:36:38, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.7008\n2023-01-02 00:38:39,626 - mmcls - INFO - Epoch [63][3100/5005]\tlr: 1.000e-03, eta: 4:36:29, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.7040\n2023-01-02 00:38:48,335 - mmcls - INFO - Epoch [63][3200/5005]\tlr: 1.000e-03, eta: 4:36:21, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.7217\n2023-01-02 00:38:57,044 - mmcls - INFO - Epoch [63][3300/5005]\tlr: 1.000e-03, eta: 4:36:12, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.7239\n2023-01-02 00:39:05,692 - mmcls - INFO - Epoch [63][3400/5005]\tlr: 1.000e-03, eta: 4:36:03, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7269\n2023-01-02 00:39:14,899 - mmcls - INFO - Epoch [63][3500/5005]\tlr: 1.000e-03, eta: 4:35:54, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.7219\n2023-01-02 00:39:23,281 - mmcls - INFO - Epoch [63][3600/5005]\tlr: 1.000e-03, eta: 4:35:45, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.7269\n2023-01-02 00:39:32,382 - mmcls - INFO - Epoch [63][3700/5005]\tlr: 1.000e-03, eta: 4:35:36, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.7252\n2023-01-02 00:39:41,190 - mmcls - INFO - Epoch [63][3800/5005]\tlr: 1.000e-03, eta: 4:35:27, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7348\n2023-01-02 00:39:49,732 - mmcls - INFO - Epoch [63][3900/5005]\tlr: 1.000e-03, eta: 4:35:18, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7398\n2023-01-02 00:39:58,312 - mmcls - INFO - Epoch [63][4000/5005]\tlr: 1.000e-03, eta: 4:35:09, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7196\n2023-01-02 00:40:06,975 - mmcls - INFO - Epoch [63][4100/5005]\tlr: 1.000e-03, eta: 4:35:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7086\n2023-01-02 00:40:16,039 - mmcls - INFO - Epoch [63][4200/5005]\tlr: 1.000e-03, eta: 4:34:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.7408\n2023-01-02 00:40:24,827 - mmcls - INFO - Epoch [63][4300/5005]\tlr: 1.000e-03, eta: 4:34:42, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6949\n2023-01-02 00:40:33,822 - mmcls - INFO - Epoch [63][4400/5005]\tlr: 1.000e-03, eta: 4:34:34, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6958\n2023-01-02 00:40:42,790 - mmcls - INFO - Epoch [63][4500/5005]\tlr: 1.000e-03, eta: 4:34:25, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.7311\n2023-01-02 00:40:51,479 - mmcls - INFO - Epoch [63][4600/5005]\tlr: 1.000e-03, eta: 4:34:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7082\n2023-01-02 00:41:00,603 - mmcls - INFO - Epoch [63][4700/5005]\tlr: 1.000e-03, eta: 4:34:07, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.7212\n2023-01-02 00:41:09,443 - mmcls - INFO - Epoch [63][4800/5005]\tlr: 1.000e-03, eta: 4:33:58, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7030\n2023-01-02 00:41:18,413 - mmcls - INFO - Epoch [63][4900/5005]\tlr: 1.000e-03, eta: 4:33:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7478\n2023-01-02 00:41:27,362 - mmcls - INFO - Epoch [63][5000/5005]\tlr: 1.000e-03, eta: 4:33:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7395\n2023-01-02 00:41:50,084 - mmcls - INFO - Epoch(val) [63][196]\taccuracy_top-1: 63.9220, accuracy_top-5: 84.8700\n2023-01-02 00:42:01,208 - mmcls - INFO - Epoch [64][100/5005]\tlr: 1.000e-03, eta: 4:33:32, time: 0.111, data_time: 0.046, memory: 1224, loss: 1.7199\n2023-01-02 00:42:10,654 - mmcls - INFO - Epoch [64][200/5005]\tlr: 1.000e-03, eta: 4:33:24, time: 0.094, data_time: 0.016, memory: 1224, loss: 1.6922\n2023-01-02 00:42:19,447 - mmcls - INFO - Epoch [64][300/5005]\tlr: 1.000e-03, eta: 4:33:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7291\n2023-01-02 00:42:28,274 - mmcls - INFO - Epoch [64][400/5005]\tlr: 1.000e-03, eta: 4:33:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7209\n2023-01-02 00:42:36,801 - mmcls - INFO - Epoch [64][500/5005]\tlr: 1.000e-03, eta: 4:32:57, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.7217\n2023-01-02 00:42:45,622 - mmcls - INFO - Epoch [64][600/5005]\tlr: 1.000e-03, eta: 4:32:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7147\n2023-01-02 00:42:54,575 - mmcls - INFO - Epoch [64][700/5005]\tlr: 1.000e-03, eta: 4:32:39, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6951\n2023-01-02 00:43:03,574 - mmcls - INFO - Epoch [64][800/5005]\tlr: 1.000e-03, eta: 4:32:30, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7191\n2023-01-02 00:43:12,444 - mmcls - INFO - Epoch [64][900/5005]\tlr: 1.000e-03, eta: 4:32:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7158\n2023-01-02 00:43:21,315 - mmcls - INFO - Epoch [64][1000/5005]\tlr: 1.000e-03, eta: 4:32:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7232\n2023-01-02 00:43:30,299 - mmcls - INFO - Epoch [64][1100/5005]\tlr: 1.000e-03, eta: 4:32:04, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.7378\n2023-01-02 00:43:39,239 - mmcls - INFO - Epoch [64][1200/5005]\tlr: 1.000e-03, eta: 4:31:55, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6919\n2023-01-02 00:43:47,975 - mmcls - INFO - Epoch [64][1300/5005]\tlr: 1.000e-03, eta: 4:31:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7110\n2023-01-02 00:43:56,976 - mmcls - INFO - Epoch [64][1400/5005]\tlr: 1.000e-03, eta: 4:31:37, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.7197\n2023-01-02 00:44:05,620 - mmcls - INFO - Epoch [64][1500/5005]\tlr: 1.000e-03, eta: 4:31:28, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.7023\n2023-01-02 00:44:14,241 - mmcls - INFO - Epoch [64][1600/5005]\tlr: 1.000e-03, eta: 4:31:19, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6917\n2023-01-02 00:44:22,789 - mmcls - INFO - Epoch [64][1700/5005]\tlr: 1.000e-03, eta: 4:31:10, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.7164\n2023-01-02 00:44:31,404 - mmcls - INFO - Epoch [64][1800/5005]\tlr: 1.000e-03, eta: 4:31:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7182\n2023-01-02 00:44:40,266 - mmcls - INFO - Epoch [64][1900/5005]\tlr: 1.000e-03, eta: 4:30:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7189\n2023-01-02 00:44:48,956 - mmcls - INFO - Epoch [64][2000/5005]\tlr: 1.000e-03, eta: 4:30:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7191\n2023-01-02 00:44:57,662 - mmcls - INFO - Epoch [64][2100/5005]\tlr: 1.000e-03, eta: 4:30:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6991\n2023-01-02 00:45:06,578 - mmcls - INFO - Epoch [64][2200/5005]\tlr: 1.000e-03, eta: 4:30:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6976\n2023-01-02 00:45:15,386 - mmcls - INFO - Epoch [64][2300/5005]\tlr: 1.000e-03, eta: 4:30:17, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.7166\n2023-01-02 00:45:24,029 - mmcls - INFO - Epoch [64][2400/5005]\tlr: 1.000e-03, eta: 4:30:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7090\n2023-01-02 00:45:33,018 - mmcls - INFO - Epoch [64][2500/5005]\tlr: 1.000e-03, eta: 4:29:59, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7140\n2023-01-02 00:45:41,827 - mmcls - INFO - Epoch [64][2600/5005]\tlr: 1.000e-03, eta: 4:29:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7193\n2023-01-02 00:45:50,496 - mmcls - INFO - Epoch [64][2700/5005]\tlr: 1.000e-03, eta: 4:29:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7171\n2023-01-02 00:45:59,805 - mmcls - INFO - Epoch [64][2800/5005]\tlr: 1.000e-03, eta: 4:29:32, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.7181\n2023-01-02 00:46:08,633 - mmcls - INFO - Epoch [64][2900/5005]\tlr: 1.000e-03, eta: 4:29:23, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6892\n2023-01-02 00:46:17,752 - mmcls - INFO - Epoch [64][3000/5005]\tlr: 1.000e-03, eta: 4:29:15, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.7280\n2023-01-02 00:46:26,407 - mmcls - INFO - Epoch [64][3100/5005]\tlr: 1.000e-03, eta: 4:29:06, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6794\n2023-01-02 00:46:35,358 - mmcls - INFO - Epoch [64][3200/5005]\tlr: 1.000e-03, eta: 4:28:57, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7045\n2023-01-02 00:46:44,196 - mmcls - INFO - Epoch [64][3300/5005]\tlr: 1.000e-03, eta: 4:28:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6967\n2023-01-02 00:46:52,990 - mmcls - INFO - Epoch [64][3400/5005]\tlr: 1.000e-03, eta: 4:28:39, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.7226\n2023-01-02 00:47:01,867 - mmcls - INFO - Epoch [64][3500/5005]\tlr: 1.000e-03, eta: 4:28:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7216\n2023-01-02 00:47:10,483 - mmcls - INFO - Epoch [64][3600/5005]\tlr: 1.000e-03, eta: 4:28:21, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.6961\n2023-01-02 00:47:19,352 - mmcls - INFO - Epoch [64][3700/5005]\tlr: 1.000e-03, eta: 4:28:12, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.7148\n2023-01-02 00:47:28,313 - mmcls - INFO - Epoch [64][3800/5005]\tlr: 1.000e-03, eta: 4:28:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7057\n2023-01-02 00:47:36,852 - mmcls - INFO - Epoch [64][3900/5005]\tlr: 1.000e-03, eta: 4:27:55, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.7260\n2023-01-02 00:47:45,695 - mmcls - INFO - Epoch [64][4000/5005]\tlr: 1.000e-03, eta: 4:27:46, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.7151\n2023-01-02 00:47:55,060 - mmcls - INFO - Epoch [64][4100/5005]\tlr: 1.000e-03, eta: 4:27:37, time: 0.094, data_time: 0.006, memory: 1224, loss: 1.7103\n2023-01-02 00:48:04,068 - mmcls - INFO - Epoch [64][4200/5005]\tlr: 1.000e-03, eta: 4:27:28, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6918\n2023-01-02 00:48:13,061 - mmcls - INFO - Epoch [64][4300/5005]\tlr: 1.000e-03, eta: 4:27:19, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7182\n2023-01-02 00:48:21,811 - mmcls - INFO - Epoch [64][4400/5005]\tlr: 1.000e-03, eta: 4:27:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7046\n2023-01-02 00:48:30,470 - mmcls - INFO - Epoch [64][4500/5005]\tlr: 1.000e-03, eta: 4:27:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6854\n2023-01-02 00:48:39,149 - mmcls - INFO - Epoch [64][4600/5005]\tlr: 1.000e-03, eta: 4:26:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7232\n2023-01-02 00:48:47,864 - mmcls - INFO - Epoch [64][4700/5005]\tlr: 1.000e-03, eta: 4:26:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6985\n2023-01-02 00:48:56,728 - mmcls - INFO - Epoch [64][4800/5005]\tlr: 1.000e-03, eta: 4:26:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6980\n2023-01-02 00:49:05,355 - mmcls - INFO - Epoch [64][4900/5005]\tlr: 1.000e-03, eta: 4:26:26, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6964\n2023-01-02 00:49:14,126 - mmcls - INFO - Epoch [64][5000/5005]\tlr: 1.000e-03, eta: 4:26:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6856\n2023-01-02 00:49:36,924 - mmcls - INFO - Epoch(val) [64][196]\taccuracy_top-1: 64.1920, accuracy_top-5: 85.0520\n2023-01-02 00:49:47,959 - mmcls - INFO - Epoch [65][100/5005]\tlr: 1.000e-03, eta: 4:26:08, time: 0.110, data_time: 0.042, memory: 1224, loss: 1.6809\n2023-01-02 00:49:56,659 - mmcls - INFO - Epoch [65][200/5005]\tlr: 1.000e-03, eta: 4:26:00, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.7185\n2023-01-02 00:50:05,316 - mmcls - INFO - Epoch [65][300/5005]\tlr: 1.000e-03, eta: 4:25:51, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7126\n2023-01-02 00:50:14,198 - mmcls - INFO - Epoch [65][400/5005]\tlr: 1.000e-03, eta: 4:25:42, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6935\n2023-01-02 00:50:23,165 - mmcls - INFO - Epoch [65][500/5005]\tlr: 1.000e-03, eta: 4:25:33, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6753\n2023-01-02 00:50:32,034 - mmcls - INFO - Epoch [65][600/5005]\tlr: 1.000e-03, eta: 4:25:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6816\n2023-01-02 00:50:40,866 - mmcls - INFO - Epoch [65][700/5005]\tlr: 1.000e-03, eta: 4:25:15, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6834\n2023-01-02 00:50:49,668 - mmcls - INFO - Epoch [65][800/5005]\tlr: 1.000e-03, eta: 4:25:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7122\n2023-01-02 00:50:58,440 - mmcls - INFO - Epoch [65][900/5005]\tlr: 1.000e-03, eta: 4:24:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6984\n2023-01-02 00:51:07,198 - mmcls - INFO - Epoch [65][1000/5005]\tlr: 1.000e-03, eta: 4:24:48, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.7013\n2023-01-02 00:51:16,102 - mmcls - INFO - Epoch [65][1100/5005]\tlr: 1.000e-03, eta: 4:24:40, time: 0.089, data_time: 0.040, memory: 1224, loss: 1.6874\n2023-01-02 00:51:25,144 - mmcls - INFO - Epoch [65][1200/5005]\tlr: 1.000e-03, eta: 4:24:31, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6911\n2023-01-02 00:51:34,081 - mmcls - INFO - Epoch [65][1300/5005]\tlr: 1.000e-03, eta: 4:24:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7010\n2023-01-02 00:51:42,804 - mmcls - INFO - Epoch [65][1400/5005]\tlr: 1.000e-03, eta: 4:24:13, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6869\n2023-01-02 00:51:51,353 - mmcls - INFO - Epoch [65][1500/5005]\tlr: 1.000e-03, eta: 4:24:04, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.7006\n2023-01-02 00:52:00,043 - mmcls - INFO - Epoch [65][1600/5005]\tlr: 1.000e-03, eta: 4:23:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7286\n2023-01-02 00:52:09,033 - mmcls - INFO - Epoch [65][1700/5005]\tlr: 1.000e-03, eta: 4:23:46, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6817\n2023-01-02 00:52:17,669 - mmcls - INFO - Epoch [65][1800/5005]\tlr: 1.000e-03, eta: 4:23:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6969\n2023-01-02 00:52:26,515 - mmcls - INFO - Epoch [65][1900/5005]\tlr: 1.000e-03, eta: 4:23:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7046\n2023-01-02 00:52:35,211 - mmcls - INFO - Epoch [65][2000/5005]\tlr: 1.000e-03, eta: 4:23:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6816\n2023-01-02 00:52:44,121 - mmcls - INFO - Epoch [65][2100/5005]\tlr: 1.000e-03, eta: 4:23:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7016\n2023-01-02 00:52:52,829 - mmcls - INFO - Epoch [65][2200/5005]\tlr: 1.000e-03, eta: 4:23:02, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6911\n2023-01-02 00:53:01,560 - mmcls - INFO - Epoch [65][2300/5005]\tlr: 1.000e-03, eta: 4:22:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6998\n2023-01-02 00:53:10,501 - mmcls - INFO - Epoch [65][2400/5005]\tlr: 1.000e-03, eta: 4:22:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6974\n2023-01-02 00:53:19,409 - mmcls - INFO - Epoch [65][2500/5005]\tlr: 1.000e-03, eta: 4:22:35, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6855\n2023-01-02 00:53:28,093 - mmcls - INFO - Epoch [65][2600/5005]\tlr: 1.000e-03, eta: 4:22:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6924\n2023-01-02 00:53:37,185 - mmcls - INFO - Epoch [65][2700/5005]\tlr: 1.000e-03, eta: 4:22:17, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.7119\n2023-01-02 00:53:46,068 - mmcls - INFO - Epoch [65][2800/5005]\tlr: 1.000e-03, eta: 4:22:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6988\n2023-01-02 00:53:54,613 - mmcls - INFO - Epoch [65][2900/5005]\tlr: 1.000e-03, eta: 4:21:59, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6922\n2023-01-02 00:54:03,414 - mmcls - INFO - Epoch [65][3000/5005]\tlr: 1.000e-03, eta: 4:21:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6957\n2023-01-02 00:54:12,233 - mmcls - INFO - Epoch [65][3100/5005]\tlr: 1.000e-03, eta: 4:21:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7057\n2023-01-02 00:54:20,854 - mmcls - INFO - Epoch [65][3200/5005]\tlr: 1.000e-03, eta: 4:21:32, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6762\n2023-01-02 00:54:29,570 - mmcls - INFO - Epoch [65][3300/5005]\tlr: 1.000e-03, eta: 4:21:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6960\n2023-01-02 00:54:38,474 - mmcls - INFO - Epoch [65][3400/5005]\tlr: 1.000e-03, eta: 4:21:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6967\n2023-01-02 00:54:47,216 - mmcls - INFO - Epoch [65][3500/5005]\tlr: 1.000e-03, eta: 4:21:06, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6950\n2023-01-02 00:54:56,095 - mmcls - INFO - Epoch [65][3600/5005]\tlr: 1.000e-03, eta: 4:20:57, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6994\n2023-01-02 00:55:05,182 - mmcls - INFO - Epoch [65][3700/5005]\tlr: 1.000e-03, eta: 4:20:48, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.7220\n2023-01-02 00:55:14,002 - mmcls - INFO - Epoch [65][3800/5005]\tlr: 1.000e-03, eta: 4:20:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7116\n2023-01-02 00:55:22,841 - mmcls - INFO - Epoch [65][3900/5005]\tlr: 1.000e-03, eta: 4:20:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6953\n2023-01-02 00:55:31,547 - mmcls - INFO - Epoch [65][4000/5005]\tlr: 1.000e-03, eta: 4:20:21, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7120\n2023-01-02 00:55:40,345 - mmcls - INFO - Epoch [65][4100/5005]\tlr: 1.000e-03, eta: 4:20:13, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.7140\n2023-01-02 00:55:49,204 - mmcls - INFO - Epoch [65][4200/5005]\tlr: 1.000e-03, eta: 4:20:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7037\n2023-01-02 00:55:58,000 - mmcls - INFO - Epoch [65][4300/5005]\tlr: 1.000e-03, eta: 4:19:55, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.7186\n2023-01-02 00:56:06,869 - mmcls - INFO - Epoch [65][4400/5005]\tlr: 1.000e-03, eta: 4:19:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6833\n2023-01-02 00:56:15,773 - mmcls - INFO - Epoch [65][4500/5005]\tlr: 1.000e-03, eta: 4:19:37, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6920\n2023-01-02 00:56:24,524 - mmcls - INFO - Epoch [65][4600/5005]\tlr: 1.000e-03, eta: 4:19:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7033\n2023-01-02 00:56:33,163 - mmcls - INFO - Epoch [65][4700/5005]\tlr: 1.000e-03, eta: 4:19:19, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7033\n2023-01-02 00:56:42,018 - mmcls - INFO - Epoch [65][4800/5005]\tlr: 1.000e-03, eta: 4:19:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7082\n2023-01-02 00:56:50,849 - mmcls - INFO - Epoch [65][4900/5005]\tlr: 1.000e-03, eta: 4:19:01, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.7053\n2023-01-02 00:56:59,705 - mmcls - INFO - Epoch [65][5000/5005]\tlr: 1.000e-03, eta: 4:18:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7081\n2023-01-02 00:57:22,730 - mmcls - INFO - Epoch(val) [65][196]\taccuracy_top-1: 64.3040, accuracy_top-5: 85.0380\n2023-01-02 00:57:33,826 - mmcls - INFO - Epoch [66][100/5005]\tlr: 1.000e-03, eta: 4:18:44, time: 0.111, data_time: 0.047, memory: 1224, loss: 1.6958\n2023-01-02 00:57:42,492 - mmcls - INFO - Epoch [66][200/5005]\tlr: 1.000e-03, eta: 4:18:35, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6867\n2023-01-02 00:57:51,317 - mmcls - INFO - Epoch [66][300/5005]\tlr: 1.000e-03, eta: 4:18:26, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6807\n2023-01-02 00:58:00,198 - mmcls - INFO - Epoch [66][400/5005]\tlr: 1.000e-03, eta: 4:18:17, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6799\n2023-01-02 00:58:09,087 - mmcls - INFO - Epoch [66][500/5005]\tlr: 1.000e-03, eta: 4:18:09, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6830\n2023-01-02 00:58:17,658 - mmcls - INFO - Epoch [66][600/5005]\tlr: 1.000e-03, eta: 4:17:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6903\n2023-01-02 00:58:26,279 - mmcls - INFO - Epoch [66][700/5005]\tlr: 1.000e-03, eta: 4:17:50, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6476\n2023-01-02 00:58:35,387 - mmcls - INFO - Epoch [66][800/5005]\tlr: 1.000e-03, eta: 4:17:42, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6739\n2023-01-02 00:58:44,467 - mmcls - INFO - Epoch [66][900/5005]\tlr: 1.000e-03, eta: 4:17:33, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.7020\n2023-01-02 00:58:53,301 - mmcls - INFO - Epoch [66][1000/5005]\tlr: 1.000e-03, eta: 4:17:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6838\n2023-01-02 00:59:01,932 - mmcls - INFO - Epoch [66][1100/5005]\tlr: 1.000e-03, eta: 4:17:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6850\n2023-01-02 00:59:10,798 - mmcls - INFO - Epoch [66][1200/5005]\tlr: 1.000e-03, eta: 4:17:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6927\n2023-01-02 00:59:19,745 - mmcls - INFO - Epoch [66][1300/5005]\tlr: 1.000e-03, eta: 4:16:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7167\n2023-01-02 00:59:28,302 - mmcls - INFO - Epoch [66][1400/5005]\tlr: 1.000e-03, eta: 4:16:48, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6824\n2023-01-02 00:59:37,237 - mmcls - INFO - Epoch [66][1500/5005]\tlr: 1.000e-03, eta: 4:16:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6992\n2023-01-02 00:59:46,532 - mmcls - INFO - Epoch [66][1600/5005]\tlr: 1.000e-03, eta: 4:16:31, time: 0.093, data_time: 0.022, memory: 1224, loss: 1.6906\n2023-01-02 00:59:55,162 - mmcls - INFO - Epoch [66][1700/5005]\tlr: 1.000e-03, eta: 4:16:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6911\n2023-01-02 01:00:03,998 - mmcls - INFO - Epoch [66][1800/5005]\tlr: 1.000e-03, eta: 4:16:13, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6864\n2023-01-02 01:00:12,630 - mmcls - INFO - Epoch [66][1900/5005]\tlr: 1.000e-03, eta: 4:16:04, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6926\n2023-01-02 01:00:21,847 - mmcls - INFO - Epoch [66][2000/5005]\tlr: 1.000e-03, eta: 4:15:55, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6866\n2023-01-02 01:00:30,932 - mmcls - INFO - Epoch [66][2100/5005]\tlr: 1.000e-03, eta: 4:15:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7071\n2023-01-02 01:00:39,672 - mmcls - INFO - Epoch [66][2200/5005]\tlr: 1.000e-03, eta: 4:15:38, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6880\n2023-01-02 01:00:48,386 - mmcls - INFO - Epoch [66][2300/5005]\tlr: 1.000e-03, eta: 4:15:29, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6951\n2023-01-02 01:00:57,136 - mmcls - INFO - Epoch [66][2400/5005]\tlr: 1.000e-03, eta: 4:15:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7029\n2023-01-02 01:01:06,129 - mmcls - INFO - Epoch [66][2500/5005]\tlr: 1.000e-03, eta: 4:15:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6883\n2023-01-02 01:01:14,773 - mmcls - INFO - Epoch [66][2600/5005]\tlr: 1.000e-03, eta: 4:15:02, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7018\n2023-01-02 01:01:23,458 - mmcls - INFO - Epoch [66][2700/5005]\tlr: 1.000e-03, eta: 4:14:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.7005\n2023-01-02 01:01:32,318 - mmcls - INFO - Epoch [66][2800/5005]\tlr: 1.000e-03, eta: 4:14:44, time: 0.089, data_time: 0.046, memory: 1224, loss: 1.6815\n2023-01-02 01:01:40,801 - mmcls - INFO - Epoch [66][2900/5005]\tlr: 1.000e-03, eta: 4:14:35, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6831\n2023-01-02 01:01:49,968 - mmcls - INFO - Epoch [66][3000/5005]\tlr: 1.000e-03, eta: 4:14:26, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6863\n2023-01-02 01:01:58,575 - mmcls - INFO - Epoch [66][3100/5005]\tlr: 1.000e-03, eta: 4:14:17, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6879\n2023-01-02 01:02:07,428 - mmcls - INFO - Epoch [66][3200/5005]\tlr: 1.000e-03, eta: 4:14:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7086\n2023-01-02 01:02:16,206 - mmcls - INFO - Epoch [66][3300/5005]\tlr: 1.000e-03, eta: 4:14:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6964\n2023-01-02 01:02:25,005 - mmcls - INFO - Epoch [66][3400/5005]\tlr: 1.000e-03, eta: 4:13:51, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6805\n2023-01-02 01:02:34,038 - mmcls - INFO - Epoch [66][3500/5005]\tlr: 1.000e-03, eta: 4:13:42, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6867\n2023-01-02 01:02:42,485 - mmcls - INFO - Epoch [66][3600/5005]\tlr: 1.000e-03, eta: 4:13:33, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.7007\n2023-01-02 01:02:51,144 - mmcls - INFO - Epoch [66][3700/5005]\tlr: 1.000e-03, eta: 4:13:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7042\n2023-01-02 01:03:00,168 - mmcls - INFO - Epoch [66][3800/5005]\tlr: 1.000e-03, eta: 4:13:15, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.7126\n2023-01-02 01:03:08,617 - mmcls - INFO - Epoch [66][3900/5005]\tlr: 1.000e-03, eta: 4:13:06, time: 0.084, data_time: 0.021, memory: 1224, loss: 1.6920\n2023-01-02 01:03:17,473 - mmcls - INFO - Epoch [66][4000/5005]\tlr: 1.000e-03, eta: 4:12:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6750\n2023-01-02 01:03:26,041 - mmcls - INFO - Epoch [66][4100/5005]\tlr: 1.000e-03, eta: 4:12:48, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6644\n2023-01-02 01:03:34,903 - mmcls - INFO - Epoch [66][4200/5005]\tlr: 1.000e-03, eta: 4:12:39, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6792\n2023-01-02 01:03:43,816 - mmcls - INFO - Epoch [66][4300/5005]\tlr: 1.000e-03, eta: 4:12:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6979\n2023-01-02 01:03:53,092 - mmcls - INFO - Epoch [66][4400/5005]\tlr: 1.000e-03, eta: 4:12:22, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.7070\n2023-01-02 01:04:02,049 - mmcls - INFO - Epoch [66][4500/5005]\tlr: 1.000e-03, eta: 4:12:13, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6889\n2023-01-02 01:04:10,687 - mmcls - INFO - Epoch [66][4600/5005]\tlr: 1.000e-03, eta: 4:12:04, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7059\n2023-01-02 01:04:19,492 - mmcls - INFO - Epoch [66][4700/5005]\tlr: 1.000e-03, eta: 4:11:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6909\n2023-01-02 01:04:28,330 - mmcls - INFO - Epoch [66][4800/5005]\tlr: 1.000e-03, eta: 4:11:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6824\n2023-01-02 01:04:37,122 - mmcls - INFO - Epoch [66][4900/5005]\tlr: 1.000e-03, eta: 4:11:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6736\n2023-01-02 01:04:45,977 - mmcls - INFO - Epoch [66][5000/5005]\tlr: 1.000e-03, eta: 4:11:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7062\n2023-01-02 01:05:08,807 - mmcls - INFO - Epoch(val) [66][196]\taccuracy_top-1: 64.4140, accuracy_top-5: 85.1420\n2023-01-02 01:05:19,511 - mmcls - INFO - Epoch [67][100/5005]\tlr: 1.000e-03, eta: 4:11:20, time: 0.107, data_time: 0.053, memory: 1224, loss: 1.6863\n2023-01-02 01:05:28,450 - mmcls - INFO - Epoch [67][200/5005]\tlr: 1.000e-03, eta: 4:11:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6887\n2023-01-02 01:05:37,128 - mmcls - INFO - Epoch [67][300/5005]\tlr: 1.000e-03, eta: 4:11:02, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6875\n2023-01-02 01:05:45,902 - mmcls - INFO - Epoch [67][400/5005]\tlr: 1.000e-03, eta: 4:10:53, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6689\n2023-01-02 01:05:54,945 - mmcls - INFO - Epoch [67][500/5005]\tlr: 1.000e-03, eta: 4:10:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6756\n2023-01-02 01:06:03,736 - mmcls - INFO - Epoch [67][600/5005]\tlr: 1.000e-03, eta: 4:10:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6866\n2023-01-02 01:06:12,174 - mmcls - INFO - Epoch [67][700/5005]\tlr: 1.000e-03, eta: 4:10:26, time: 0.084, data_time: 0.026, memory: 1224, loss: 1.6816\n2023-01-02 01:06:21,050 - mmcls - INFO - Epoch [67][800/5005]\tlr: 1.000e-03, eta: 4:10:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6757\n2023-01-02 01:06:29,924 - mmcls - INFO - Epoch [67][900/5005]\tlr: 1.000e-03, eta: 4:10:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6872\n2023-01-02 01:06:38,574 - mmcls - INFO - Epoch [67][1000/5005]\tlr: 1.000e-03, eta: 4:10:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6825\n2023-01-02 01:06:47,321 - mmcls - INFO - Epoch [67][1100/5005]\tlr: 1.000e-03, eta: 4:09:51, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.6786\n2023-01-02 01:06:56,396 - mmcls - INFO - Epoch [67][1200/5005]\tlr: 1.000e-03, eta: 4:09:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6924\n2023-01-02 01:07:05,590 - mmcls - INFO - Epoch [67][1300/5005]\tlr: 1.000e-03, eta: 4:09:33, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6858\n2023-01-02 01:07:14,431 - mmcls - INFO - Epoch [67][1400/5005]\tlr: 1.000e-03, eta: 4:09:24, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6790\n2023-01-02 01:07:23,456 - mmcls - INFO - Epoch [67][1500/5005]\tlr: 1.000e-03, eta: 4:09:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6688\n2023-01-02 01:07:32,170 - mmcls - INFO - Epoch [67][1600/5005]\tlr: 1.000e-03, eta: 4:09:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6819\n2023-01-02 01:07:40,817 - mmcls - INFO - Epoch [67][1700/5005]\tlr: 1.000e-03, eta: 4:08:58, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6902\n2023-01-02 01:07:49,832 - mmcls - INFO - Epoch [67][1800/5005]\tlr: 1.000e-03, eta: 4:08:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6909\n2023-01-02 01:07:58,655 - mmcls - INFO - Epoch [67][1900/5005]\tlr: 1.000e-03, eta: 4:08:40, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6995\n2023-01-02 01:08:07,187 - mmcls - INFO - Epoch [67][2000/5005]\tlr: 1.000e-03, eta: 4:08:31, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7030\n2023-01-02 01:08:15,781 - mmcls - INFO - Epoch [67][2100/5005]\tlr: 1.000e-03, eta: 4:08:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6760\n2023-01-02 01:08:24,530 - mmcls - INFO - Epoch [67][2200/5005]\tlr: 1.000e-03, eta: 4:08:13, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6890\n2023-01-02 01:08:33,312 - mmcls - INFO - Epoch [67][2300/5005]\tlr: 1.000e-03, eta: 4:08:04, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.7103\n2023-01-02 01:08:42,175 - mmcls - INFO - Epoch [67][2400/5005]\tlr: 1.000e-03, eta: 4:07:55, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6902\n2023-01-02 01:08:50,826 - mmcls - INFO - Epoch [67][2500/5005]\tlr: 1.000e-03, eta: 4:07:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6792\n2023-01-02 01:08:59,672 - mmcls - INFO - Epoch [67][2600/5005]\tlr: 1.000e-03, eta: 4:07:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6927\n2023-01-02 01:09:08,569 - mmcls - INFO - Epoch [67][2700/5005]\tlr: 1.000e-03, eta: 4:07:28, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6664\n2023-01-02 01:09:17,453 - mmcls - INFO - Epoch [67][2800/5005]\tlr: 1.000e-03, eta: 4:07:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6738\n2023-01-02 01:09:25,986 - mmcls - INFO - Epoch [67][2900/5005]\tlr: 1.000e-03, eta: 4:07:11, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6923\n2023-01-02 01:09:34,596 - mmcls - INFO - Epoch [67][3000/5005]\tlr: 1.000e-03, eta: 4:07:02, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6875\n2023-01-02 01:09:43,269 - mmcls - INFO - Epoch [67][3100/5005]\tlr: 1.000e-03, eta: 4:06:53, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6847\n2023-01-02 01:09:52,083 - mmcls - INFO - Epoch [67][3200/5005]\tlr: 1.000e-03, eta: 4:06:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6707\n2023-01-02 01:10:00,769 - mmcls - INFO - Epoch [67][3300/5005]\tlr: 1.000e-03, eta: 4:06:35, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.6749\n2023-01-02 01:10:09,647 - mmcls - INFO - Epoch [67][3400/5005]\tlr: 1.000e-03, eta: 4:06:26, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6992\n2023-01-02 01:10:18,405 - mmcls - INFO - Epoch [67][3500/5005]\tlr: 1.000e-03, eta: 4:06:17, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6992\n2023-01-02 01:10:27,283 - mmcls - INFO - Epoch [67][3600/5005]\tlr: 1.000e-03, eta: 4:06:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6905\n2023-01-02 01:10:36,476 - mmcls - INFO - Epoch [67][3700/5005]\tlr: 1.000e-03, eta: 4:05:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7022\n2023-01-02 01:10:45,281 - mmcls - INFO - Epoch [67][3800/5005]\tlr: 1.000e-03, eta: 4:05:51, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6847\n2023-01-02 01:10:54,248 - mmcls - INFO - Epoch [67][3900/5005]\tlr: 1.000e-03, eta: 4:05:42, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6821\n2023-01-02 01:11:03,140 - mmcls - INFO - Epoch [67][4000/5005]\tlr: 1.000e-03, eta: 4:05:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6847\n2023-01-02 01:11:11,936 - mmcls - INFO - Epoch [67][4100/5005]\tlr: 1.000e-03, eta: 4:05:24, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6834\n2023-01-02 01:11:20,771 - mmcls - INFO - Epoch [67][4200/5005]\tlr: 1.000e-03, eta: 4:05:15, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6999\n2023-01-02 01:11:29,558 - mmcls - INFO - Epoch [67][4300/5005]\tlr: 1.000e-03, eta: 4:05:06, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.7061\n2023-01-02 01:11:38,523 - mmcls - INFO - Epoch [67][4400/5005]\tlr: 1.000e-03, eta: 4:04:57, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7076\n2023-01-02 01:11:47,031 - mmcls - INFO - Epoch [67][4500/5005]\tlr: 1.000e-03, eta: 4:04:48, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6935\n2023-01-02 01:11:55,759 - mmcls - INFO - Epoch [67][4600/5005]\tlr: 1.000e-03, eta: 4:04:39, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6782\n2023-01-02 01:12:04,645 - mmcls - INFO - Epoch [67][4700/5005]\tlr: 1.000e-03, eta: 4:04:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6836\n2023-01-02 01:12:13,365 - mmcls - INFO - Epoch [67][4800/5005]\tlr: 1.000e-03, eta: 4:04:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6669\n2023-01-02 01:12:22,225 - mmcls - INFO - Epoch [67][4900/5005]\tlr: 1.000e-03, eta: 4:04:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7084\n2023-01-02 01:12:31,111 - mmcls - INFO - Epoch [67][5000/5005]\tlr: 1.000e-03, eta: 4:04:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6826\n2023-01-02 01:12:53,802 - mmcls - INFO - Epoch(val) [67][196]\taccuracy_top-1: 64.5880, accuracy_top-5: 85.2920\n2023-01-02 01:13:05,335 - mmcls - INFO - Epoch [68][100/5005]\tlr: 1.000e-03, eta: 4:03:56, time: 0.115, data_time: 0.039, memory: 1224, loss: 1.6635\n2023-01-02 01:13:13,859 - mmcls - INFO - Epoch [68][200/5005]\tlr: 1.000e-03, eta: 4:03:47, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6835\n2023-01-02 01:13:22,653 - mmcls - INFO - Epoch [68][300/5005]\tlr: 1.000e-03, eta: 4:03:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6799\n2023-01-02 01:13:31,368 - mmcls - INFO - Epoch [68][400/5005]\tlr: 1.000e-03, eta: 4:03:29, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6648\n2023-01-02 01:13:40,402 - mmcls - INFO - Epoch [68][500/5005]\tlr: 1.000e-03, eta: 4:03:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6831\n2023-01-02 01:13:49,291 - mmcls - INFO - Epoch [68][600/5005]\tlr: 1.000e-03, eta: 4:03:11, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6559\n2023-01-02 01:13:57,836 - mmcls - INFO - Epoch [68][700/5005]\tlr: 1.000e-03, eta: 4:03:02, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6819\n2023-01-02 01:14:06,463 - mmcls - INFO - Epoch [68][800/5005]\tlr: 1.000e-03, eta: 4:02:53, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6876\n2023-01-02 01:14:15,276 - mmcls - INFO - Epoch [68][900/5005]\tlr: 1.000e-03, eta: 4:02:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6838\n2023-01-02 01:14:24,199 - mmcls - INFO - Epoch [68][1000/5005]\tlr: 1.000e-03, eta: 4:02:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6662\n2023-01-02 01:14:32,931 - mmcls - INFO - Epoch [68][1100/5005]\tlr: 1.000e-03, eta: 4:02:26, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6699\n2023-01-02 01:14:41,572 - mmcls - INFO - Epoch [68][1200/5005]\tlr: 1.000e-03, eta: 4:02:17, time: 0.086, data_time: 0.033, memory: 1224, loss: 1.6819\n2023-01-02 01:14:50,453 - mmcls - INFO - Epoch [68][1300/5005]\tlr: 1.000e-03, eta: 4:02:09, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6742\n2023-01-02 01:14:59,828 - mmcls - INFO - Epoch [68][1400/5005]\tlr: 1.000e-03, eta: 4:02:00, time: 0.094, data_time: 0.017, memory: 1224, loss: 1.6806\n2023-01-02 01:15:08,654 - mmcls - INFO - Epoch [68][1500/5005]\tlr: 1.000e-03, eta: 4:01:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6630\n2023-01-02 01:15:17,610 - mmcls - INFO - Epoch [68][1600/5005]\tlr: 1.000e-03, eta: 4:01:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6583\n2023-01-02 01:15:26,393 - mmcls - INFO - Epoch [68][1700/5005]\tlr: 1.000e-03, eta: 4:01:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6845\n2023-01-02 01:15:35,150 - mmcls - INFO - Epoch [68][1800/5005]\tlr: 1.000e-03, eta: 4:01:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6755\n2023-01-02 01:15:43,750 - mmcls - INFO - Epoch [68][1900/5005]\tlr: 1.000e-03, eta: 4:01:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6648\n2023-01-02 01:15:52,375 - mmcls - INFO - Epoch [68][2000/5005]\tlr: 1.000e-03, eta: 4:01:06, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6742\n2023-01-02 01:16:01,385 - mmcls - INFO - Epoch [68][2100/5005]\tlr: 1.000e-03, eta: 4:00:58, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6701\n2023-01-02 01:16:10,317 - mmcls - INFO - Epoch [68][2200/5005]\tlr: 1.000e-03, eta: 4:00:49, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6594\n2023-01-02 01:16:18,812 - mmcls - INFO - Epoch [68][2300/5005]\tlr: 1.000e-03, eta: 4:00:40, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6517\n2023-01-02 01:16:27,804 - mmcls - INFO - Epoch [68][2400/5005]\tlr: 1.000e-03, eta: 4:00:31, time: 0.090, data_time: 0.027, memory: 1224, loss: 1.6736\n2023-01-02 01:16:36,789 - mmcls - INFO - Epoch [68][2500/5005]\tlr: 1.000e-03, eta: 4:00:22, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6684\n2023-01-02 01:16:45,399 - mmcls - INFO - Epoch [68][2600/5005]\tlr: 1.000e-03, eta: 4:00:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6716\n2023-01-02 01:16:54,332 - mmcls - INFO - Epoch [68][2700/5005]\tlr: 1.000e-03, eta: 4:00:04, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6749\n2023-01-02 01:17:03,004 - mmcls - INFO - Epoch [68][2800/5005]\tlr: 1.000e-03, eta: 3:59:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6995\n2023-01-02 01:17:11,979 - mmcls - INFO - Epoch [68][2900/5005]\tlr: 1.000e-03, eta: 3:59:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6824\n2023-01-02 01:17:20,572 - mmcls - INFO - Epoch [68][3000/5005]\tlr: 1.000e-03, eta: 3:59:38, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6690\n2023-01-02 01:17:29,374 - mmcls - INFO - Epoch [68][3100/5005]\tlr: 1.000e-03, eta: 3:59:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6809\n2023-01-02 01:17:38,094 - mmcls - INFO - Epoch [68][3200/5005]\tlr: 1.000e-03, eta: 3:59:20, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6746\n2023-01-02 01:17:46,975 - mmcls - INFO - Epoch [68][3300/5005]\tlr: 1.000e-03, eta: 3:59:11, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6742\n2023-01-02 01:17:55,496 - mmcls - INFO - Epoch [68][3400/5005]\tlr: 1.000e-03, eta: 3:59:02, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6904\n2023-01-02 01:18:04,928 - mmcls - INFO - Epoch [68][3500/5005]\tlr: 1.000e-03, eta: 3:58:53, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6839\n2023-01-02 01:18:13,988 - mmcls - INFO - Epoch [68][3600/5005]\tlr: 1.000e-03, eta: 3:58:44, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.7108\n2023-01-02 01:18:22,897 - mmcls - INFO - Epoch [68][3700/5005]\tlr: 1.000e-03, eta: 3:58:36, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.7027\n2023-01-02 01:18:31,580 - mmcls - INFO - Epoch [68][3800/5005]\tlr: 1.000e-03, eta: 3:58:27, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6787\n2023-01-02 01:18:40,490 - mmcls - INFO - Epoch [68][3900/5005]\tlr: 1.000e-03, eta: 3:58:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6815\n2023-01-02 01:18:49,083 - mmcls - INFO - Epoch [68][4000/5005]\tlr: 1.000e-03, eta: 3:58:09, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6746\n2023-01-02 01:18:57,931 - mmcls - INFO - Epoch [68][4100/5005]\tlr: 1.000e-03, eta: 3:58:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6864\n2023-01-02 01:19:07,335 - mmcls - INFO - Epoch [68][4200/5005]\tlr: 1.000e-03, eta: 3:57:51, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6643\n2023-01-02 01:19:15,840 - mmcls - INFO - Epoch [68][4300/5005]\tlr: 1.000e-03, eta: 3:57:42, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6880\n2023-01-02 01:19:24,782 - mmcls - INFO - Epoch [68][4400/5005]\tlr: 1.000e-03, eta: 3:57:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6906\n2023-01-02 01:19:33,525 - mmcls - INFO - Epoch [68][4500/5005]\tlr: 1.000e-03, eta: 3:57:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6853\n2023-01-02 01:19:42,514 - mmcls - INFO - Epoch [68][4600/5005]\tlr: 1.000e-03, eta: 3:57:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6891\n2023-01-02 01:19:51,404 - mmcls - INFO - Epoch [68][4700/5005]\tlr: 1.000e-03, eta: 3:57:07, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6801\n2023-01-02 01:20:00,244 - mmcls - INFO - Epoch [68][4800/5005]\tlr: 1.000e-03, eta: 3:56:58, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6816\n2023-01-02 01:20:09,305 - mmcls - INFO - Epoch [68][4900/5005]\tlr: 1.000e-03, eta: 3:56:49, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.7069\n2023-01-02 01:20:18,703 - mmcls - INFO - Epoch [68][5000/5005]\tlr: 1.000e-03, eta: 3:56:41, time: 0.094, data_time: 0.003, memory: 1224, loss: 1.6925\n2023-01-02 01:20:41,232 - mmcls - INFO - Epoch(val) [68][196]\taccuracy_top-1: 64.4880, accuracy_top-5: 85.2320\n2023-01-02 01:20:52,100 - mmcls - INFO - Epoch [69][100/5005]\tlr: 1.000e-03, eta: 3:56:32, time: 0.108, data_time: 0.060, memory: 1224, loss: 1.6487\n2023-01-02 01:21:01,010 - mmcls - INFO - Epoch [69][200/5005]\tlr: 1.000e-03, eta: 3:56:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6684\n2023-01-02 01:21:09,756 - mmcls - INFO - Epoch [69][300/5005]\tlr: 1.000e-03, eta: 3:56:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6616\n2023-01-02 01:21:18,714 - mmcls - INFO - Epoch [69][400/5005]\tlr: 1.000e-03, eta: 3:56:05, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6857\n2023-01-02 01:21:27,502 - mmcls - INFO - Epoch [69][500/5005]\tlr: 1.000e-03, eta: 3:55:56, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6711\n2023-01-02 01:21:36,457 - mmcls - INFO - Epoch [69][600/5005]\tlr: 1.000e-03, eta: 3:55:48, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6804\n2023-01-02 01:21:45,463 - mmcls - INFO - Epoch [69][700/5005]\tlr: 1.000e-03, eta: 3:55:39, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6636\n2023-01-02 01:21:53,959 - mmcls - INFO - Epoch [69][800/5005]\tlr: 1.000e-03, eta: 3:55:30, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6976\n2023-01-02 01:22:02,815 - mmcls - INFO - Epoch [69][900/5005]\tlr: 1.000e-03, eta: 3:55:21, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6465\n2023-01-02 01:22:11,682 - mmcls - INFO - Epoch [69][1000/5005]\tlr: 1.000e-03, eta: 3:55:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6644\n2023-01-02 01:22:20,631 - mmcls - INFO - Epoch [69][1100/5005]\tlr: 1.000e-03, eta: 3:55:03, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6934\n2023-01-02 01:22:29,406 - mmcls - INFO - Epoch [69][1200/5005]\tlr: 1.000e-03, eta: 3:54:54, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6755\n2023-01-02 01:22:38,071 - mmcls - INFO - Epoch [69][1300/5005]\tlr: 1.000e-03, eta: 3:54:45, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6610\n2023-01-02 01:22:47,008 - mmcls - INFO - Epoch [69][1400/5005]\tlr: 1.000e-03, eta: 3:54:37, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6674\n2023-01-02 01:22:55,804 - mmcls - INFO - Epoch [69][1500/5005]\tlr: 1.000e-03, eta: 3:54:28, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6784\n2023-01-02 01:23:04,860 - mmcls - INFO - Epoch [69][1600/5005]\tlr: 1.000e-03, eta: 3:54:19, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6534\n2023-01-02 01:23:13,443 - mmcls - INFO - Epoch [69][1700/5005]\tlr: 1.000e-03, eta: 3:54:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6687\n2023-01-02 01:23:22,225 - mmcls - INFO - Epoch [69][1800/5005]\tlr: 1.000e-03, eta: 3:54:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6923\n2023-01-02 01:23:31,064 - mmcls - INFO - Epoch [69][1900/5005]\tlr: 1.000e-03, eta: 3:53:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6382\n2023-01-02 01:23:39,929 - mmcls - INFO - Epoch [69][2000/5005]\tlr: 1.000e-03, eta: 3:53:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6740\n2023-01-02 01:23:48,663 - mmcls - INFO - Epoch [69][2100/5005]\tlr: 1.000e-03, eta: 3:53:34, time: 0.087, data_time: 0.040, memory: 1224, loss: 1.6674\n2023-01-02 01:23:57,370 - mmcls - INFO - Epoch [69][2200/5005]\tlr: 1.000e-03, eta: 3:53:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6528\n2023-01-02 01:24:05,914 - mmcls - INFO - Epoch [69][2300/5005]\tlr: 1.000e-03, eta: 3:53:16, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6647\n2023-01-02 01:24:14,615 - mmcls - INFO - Epoch [69][2400/5005]\tlr: 1.000e-03, eta: 3:53:07, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6856\n2023-01-02 01:24:23,281 - mmcls - INFO - Epoch [69][2500/5005]\tlr: 1.000e-03, eta: 3:52:58, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6625\n2023-01-02 01:24:32,251 - mmcls - INFO - Epoch [69][2600/5005]\tlr: 1.000e-03, eta: 3:52:50, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6619\n2023-01-02 01:24:41,290 - mmcls - INFO - Epoch [69][2700/5005]\tlr: 1.000e-03, eta: 3:52:41, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.6768\n2023-01-02 01:24:50,162 - mmcls - INFO - Epoch [69][2800/5005]\tlr: 1.000e-03, eta: 3:52:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6634\n2023-01-02 01:24:59,027 - mmcls - INFO - Epoch [69][2900/5005]\tlr: 1.000e-03, eta: 3:52:23, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6883\n2023-01-02 01:25:07,537 - mmcls - INFO - Epoch [69][3000/5005]\tlr: 1.000e-03, eta: 3:52:14, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6758\n2023-01-02 01:25:16,383 - mmcls - INFO - Epoch [69][3100/5005]\tlr: 1.000e-03, eta: 3:52:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6606\n2023-01-02 01:25:25,138 - mmcls - INFO - Epoch [69][3200/5005]\tlr: 1.000e-03, eta: 3:51:56, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6844\n2023-01-02 01:25:34,001 - mmcls - INFO - Epoch [69][3300/5005]\tlr: 1.000e-03, eta: 3:51:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6663\n2023-01-02 01:25:42,730 - mmcls - INFO - Epoch [69][3400/5005]\tlr: 1.000e-03, eta: 3:51:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6638\n2023-01-02 01:25:51,582 - mmcls - INFO - Epoch [69][3500/5005]\tlr: 1.000e-03, eta: 3:51:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6831\n2023-01-02 01:26:00,399 - mmcls - INFO - Epoch [69][3600/5005]\tlr: 1.000e-03, eta: 3:51:21, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6898\n2023-01-02 01:26:09,079 - mmcls - INFO - Epoch [69][3700/5005]\tlr: 1.000e-03, eta: 3:51:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6674\n2023-01-02 01:26:18,170 - mmcls - INFO - Epoch [69][3800/5005]\tlr: 1.000e-03, eta: 3:51:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6751\n2023-01-02 01:26:26,685 - mmcls - INFO - Epoch [69][3900/5005]\tlr: 1.000e-03, eta: 3:50:54, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6717\n2023-01-02 01:26:35,545 - mmcls - INFO - Epoch [69][4000/5005]\tlr: 1.000e-03, eta: 3:50:45, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6580\n2023-01-02 01:26:44,341 - mmcls - INFO - Epoch [69][4100/5005]\tlr: 1.000e-03, eta: 3:50:36, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6577\n2023-01-02 01:26:53,376 - mmcls - INFO - Epoch [69][4200/5005]\tlr: 1.000e-03, eta: 3:50:27, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6903\n2023-01-02 01:27:02,000 - mmcls - INFO - Epoch [69][4300/5005]\tlr: 1.000e-03, eta: 3:50:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6955\n2023-01-02 01:27:10,785 - mmcls - INFO - Epoch [69][4400/5005]\tlr: 1.000e-03, eta: 3:50:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6778\n2023-01-02 01:27:19,379 - mmcls - INFO - Epoch [69][4500/5005]\tlr: 1.000e-03, eta: 3:50:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7026\n2023-01-02 01:27:28,076 - mmcls - INFO - Epoch [69][4600/5005]\tlr: 1.000e-03, eta: 3:49:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6926\n2023-01-02 01:27:37,128 - mmcls - INFO - Epoch [69][4700/5005]\tlr: 1.000e-03, eta: 3:49:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6871\n2023-01-02 01:27:46,227 - mmcls - INFO - Epoch [69][4800/5005]\tlr: 1.000e-03, eta: 3:49:34, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6773\n2023-01-02 01:27:54,839 - mmcls - INFO - Epoch [69][4900/5005]\tlr: 1.000e-03, eta: 3:49:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6454\n2023-01-02 01:28:03,946 - mmcls - INFO - Epoch [69][5000/5005]\tlr: 1.000e-03, eta: 3:49:16, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6695\n2023-01-02 01:28:26,915 - mmcls - INFO - Epoch(val) [69][196]\taccuracy_top-1: 64.4920, accuracy_top-5: 85.3100\n2023-01-02 01:28:38,235 - mmcls - INFO - Epoch [70][100/5005]\tlr: 1.000e-03, eta: 3:49:08, time: 0.113, data_time: 0.034, memory: 1224, loss: 1.6800\n2023-01-02 01:28:47,169 - mmcls - INFO - Epoch [70][200/5005]\tlr: 1.000e-03, eta: 3:48:59, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6601\n2023-01-02 01:28:55,879 - mmcls - INFO - Epoch [70][300/5005]\tlr: 1.000e-03, eta: 3:48:50, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6576\n2023-01-02 01:29:04,550 - mmcls - INFO - Epoch [70][400/5005]\tlr: 1.000e-03, eta: 3:48:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6754\n2023-01-02 01:29:13,517 - mmcls - INFO - Epoch [70][500/5005]\tlr: 1.000e-03, eta: 3:48:32, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6503\n2023-01-02 01:29:22,342 - mmcls - INFO - Epoch [70][600/5005]\tlr: 1.000e-03, eta: 3:48:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6655\n2023-01-02 01:29:31,310 - mmcls - INFO - Epoch [70][700/5005]\tlr: 1.000e-03, eta: 3:48:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6776\n2023-01-02 01:29:40,091 - mmcls - INFO - Epoch [70][800/5005]\tlr: 1.000e-03, eta: 3:48:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6534\n2023-01-02 01:29:48,817 - mmcls - INFO - Epoch [70][900/5005]\tlr: 1.000e-03, eta: 3:47:57, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6852\n2023-01-02 01:29:57,705 - mmcls - INFO - Epoch [70][1000/5005]\tlr: 1.000e-03, eta: 3:47:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6570\n2023-01-02 01:30:06,534 - mmcls - INFO - Epoch [70][1100/5005]\tlr: 1.000e-03, eta: 3:47:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6439\n2023-01-02 01:30:15,477 - mmcls - INFO - Epoch [70][1200/5005]\tlr: 1.000e-03, eta: 3:47:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6567\n2023-01-02 01:30:24,667 - mmcls - INFO - Epoch [70][1300/5005]\tlr: 1.000e-03, eta: 3:47:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6457\n2023-01-02 01:30:33,760 - mmcls - INFO - Epoch [70][1400/5005]\tlr: 1.000e-03, eta: 3:47:13, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6743\n2023-01-02 01:30:42,527 - mmcls - INFO - Epoch [70][1500/5005]\tlr: 1.000e-03, eta: 3:47:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6579\n2023-01-02 01:30:51,509 - mmcls - INFO - Epoch [70][1600/5005]\tlr: 1.000e-03, eta: 3:46:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6561\n2023-01-02 01:31:00,457 - mmcls - INFO - Epoch [70][1700/5005]\tlr: 1.000e-03, eta: 3:46:46, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6707\n2023-01-02 01:31:09,228 - mmcls - INFO - Epoch [70][1800/5005]\tlr: 1.000e-03, eta: 3:46:37, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6721\n2023-01-02 01:31:18,098 - mmcls - INFO - Epoch [70][1900/5005]\tlr: 1.000e-03, eta: 3:46:28, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6501\n2023-01-02 01:31:27,316 - mmcls - INFO - Epoch [70][2000/5005]\tlr: 1.000e-03, eta: 3:46:20, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6706\n2023-01-02 01:31:35,916 - mmcls - INFO - Epoch [70][2100/5005]\tlr: 1.000e-03, eta: 3:46:11, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6736\n2023-01-02 01:31:44,453 - mmcls - INFO - Epoch [70][2200/5005]\tlr: 1.000e-03, eta: 3:46:02, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6761\n2023-01-02 01:31:53,199 - mmcls - INFO - Epoch [70][2300/5005]\tlr: 1.000e-03, eta: 3:45:53, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6487\n2023-01-02 01:32:02,135 - mmcls - INFO - Epoch [70][2400/5005]\tlr: 1.000e-03, eta: 3:45:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6540\n2023-01-02 01:32:10,861 - mmcls - INFO - Epoch [70][2500/5005]\tlr: 1.000e-03, eta: 3:45:35, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6615\n2023-01-02 01:32:19,515 - mmcls - INFO - Epoch [70][2600/5005]\tlr: 1.000e-03, eta: 3:45:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6754\n2023-01-02 01:32:28,055 - mmcls - INFO - Epoch [70][2700/5005]\tlr: 1.000e-03, eta: 3:45:17, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6790\n2023-01-02 01:32:36,927 - mmcls - INFO - Epoch [70][2800/5005]\tlr: 1.000e-03, eta: 3:45:08, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6742\n2023-01-02 01:32:45,793 - mmcls - INFO - Epoch [70][2900/5005]\tlr: 1.000e-03, eta: 3:44:59, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.6670\n2023-01-02 01:32:54,369 - mmcls - INFO - Epoch [70][3000/5005]\tlr: 1.000e-03, eta: 3:44:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6738\n2023-01-02 01:33:03,224 - mmcls - INFO - Epoch [70][3100/5005]\tlr: 1.000e-03, eta: 3:44:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6801\n2023-01-02 01:33:12,054 - mmcls - INFO - Epoch [70][3200/5005]\tlr: 1.000e-03, eta: 3:44:33, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6255\n2023-01-02 01:33:20,923 - mmcls - INFO - Epoch [70][3300/5005]\tlr: 1.000e-03, eta: 3:44:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6929\n2023-01-02 01:33:29,648 - mmcls - INFO - Epoch [70][3400/5005]\tlr: 1.000e-03, eta: 3:44:15, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6856\n2023-01-02 01:33:38,463 - mmcls - INFO - Epoch [70][3500/5005]\tlr: 1.000e-03, eta: 3:44:06, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6802\n2023-01-02 01:33:46,856 - mmcls - INFO - Epoch [70][3600/5005]\tlr: 1.000e-03, eta: 3:43:57, time: 0.084, data_time: 0.012, memory: 1224, loss: 1.6571\n2023-01-02 01:33:55,591 - mmcls - INFO - Epoch [70][3700/5005]\tlr: 1.000e-03, eta: 3:43:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6963\n2023-01-02 01:34:04,271 - mmcls - INFO - Epoch [70][3800/5005]\tlr: 1.000e-03, eta: 3:43:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7006\n2023-01-02 01:34:12,760 - mmcls - INFO - Epoch [70][3900/5005]\tlr: 1.000e-03, eta: 3:43:30, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.6734\n2023-01-02 01:34:21,413 - mmcls - INFO - Epoch [70][4000/5005]\tlr: 1.000e-03, eta: 3:43:21, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.6754\n2023-01-02 01:34:30,513 - mmcls - INFO - Epoch [70][4100/5005]\tlr: 1.000e-03, eta: 3:43:12, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6884\n2023-01-02 01:34:39,285 - mmcls - INFO - Epoch [70][4200/5005]\tlr: 1.000e-03, eta: 3:43:03, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.7046\n2023-01-02 01:34:47,862 - mmcls - INFO - Epoch [70][4300/5005]\tlr: 1.000e-03, eta: 3:42:54, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6692\n2023-01-02 01:34:57,229 - mmcls - INFO - Epoch [70][4400/5005]\tlr: 1.000e-03, eta: 3:42:46, time: 0.094, data_time: 0.014, memory: 1224, loss: 1.6704\n2023-01-02 01:35:05,809 - mmcls - INFO - Epoch [70][4500/5005]\tlr: 1.000e-03, eta: 3:42:37, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6599\n2023-01-02 01:35:14,685 - mmcls - INFO - Epoch [70][4600/5005]\tlr: 1.000e-03, eta: 3:42:28, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6787\n2023-01-02 01:35:23,603 - mmcls - INFO - Epoch [70][4700/5005]\tlr: 1.000e-03, eta: 3:42:19, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6588\n2023-01-02 01:35:32,241 - mmcls - INFO - Epoch [70][4800/5005]\tlr: 1.000e-03, eta: 3:42:10, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6694\n2023-01-02 01:35:41,252 - mmcls - INFO - Epoch [70][4900/5005]\tlr: 1.000e-03, eta: 3:42:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6818\n2023-01-02 01:35:49,975 - mmcls - INFO - Epoch [70][5000/5005]\tlr: 1.000e-03, eta: 3:41:52, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6671\n2023-01-02 01:36:12,806 - mmcls - INFO - Epoch(val) [70][196]\taccuracy_top-1: 64.3920, accuracy_top-5: 85.1680\n2023-01-02 01:36:23,835 - mmcls - INFO - Epoch [71][100/5005]\tlr: 1.000e-03, eta: 3:41:44, time: 0.110, data_time: 0.054, memory: 1224, loss: 1.6545\n2023-01-02 01:36:32,528 - mmcls - INFO - Epoch [71][200/5005]\tlr: 1.000e-03, eta: 3:41:35, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6564\n2023-01-02 01:36:41,188 - mmcls - INFO - Epoch [71][300/5005]\tlr: 1.000e-03, eta: 3:41:26, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6765\n2023-01-02 01:36:50,275 - mmcls - INFO - Epoch [71][400/5005]\tlr: 1.000e-03, eta: 3:41:17, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6735\n2023-01-02 01:36:58,802 - mmcls - INFO - Epoch [71][500/5005]\tlr: 1.000e-03, eta: 3:41:08, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6747\n2023-01-02 01:37:07,413 - mmcls - INFO - Epoch [71][600/5005]\tlr: 1.000e-03, eta: 3:40:59, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6539\n2023-01-02 01:37:16,152 - mmcls - INFO - Epoch [71][700/5005]\tlr: 1.000e-03, eta: 3:40:50, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.7041\n2023-01-02 01:37:24,998 - mmcls - INFO - Epoch [71][800/5005]\tlr: 1.000e-03, eta: 3:40:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6488\n2023-01-02 01:37:33,917 - mmcls - INFO - Epoch [71][900/5005]\tlr: 1.000e-03, eta: 3:40:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6669\n2023-01-02 01:37:42,903 - mmcls - INFO - Epoch [71][1000/5005]\tlr: 1.000e-03, eta: 3:40:24, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6692\n2023-01-02 01:37:51,623 - mmcls - INFO - Epoch [71][1100/5005]\tlr: 1.000e-03, eta: 3:40:15, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6602\n2023-01-02 01:38:00,506 - mmcls - INFO - Epoch [71][1200/5005]\tlr: 1.000e-03, eta: 3:40:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6696\n2023-01-02 01:38:09,200 - mmcls - INFO - Epoch [71][1300/5005]\tlr: 1.000e-03, eta: 3:39:57, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6636\n2023-01-02 01:38:17,977 - mmcls - INFO - Epoch [71][1400/5005]\tlr: 1.000e-03, eta: 3:39:48, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6644\n2023-01-02 01:38:26,829 - mmcls - INFO - Epoch [71][1500/5005]\tlr: 1.000e-03, eta: 3:39:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6543\n2023-01-02 01:38:35,631 - mmcls - INFO - Epoch [71][1600/5005]\tlr: 1.000e-03, eta: 3:39:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6776\n2023-01-02 01:38:44,313 - mmcls - INFO - Epoch [71][1700/5005]\tlr: 1.000e-03, eta: 3:39:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6518\n2023-01-02 01:38:52,774 - mmcls - INFO - Epoch [71][1800/5005]\tlr: 1.000e-03, eta: 3:39:12, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6491\n2023-01-02 01:39:01,376 - mmcls - INFO - Epoch [71][1900/5005]\tlr: 1.000e-03, eta: 3:39:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6675\n2023-01-02 01:39:10,129 - mmcls - INFO - Epoch [71][2000/5005]\tlr: 1.000e-03, eta: 3:38:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6631\n2023-01-02 01:39:18,923 - mmcls - INFO - Epoch [71][2100/5005]\tlr: 1.000e-03, eta: 3:38:45, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6699\n2023-01-02 01:39:27,928 - mmcls - INFO - Epoch [71][2200/5005]\tlr: 1.000e-03, eta: 3:38:37, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6699\n2023-01-02 01:39:36,521 - mmcls - INFO - Epoch [71][2300/5005]\tlr: 1.000e-03, eta: 3:38:28, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6562\n2023-01-02 01:39:45,327 - mmcls - INFO - Epoch [71][2400/5005]\tlr: 1.000e-03, eta: 3:38:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6726\n2023-01-02 01:39:53,997 - mmcls - INFO - Epoch [71][2500/5005]\tlr: 1.000e-03, eta: 3:38:10, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6769\n2023-01-02 01:40:02,660 - mmcls - INFO - Epoch [71][2600/5005]\tlr: 1.000e-03, eta: 3:38:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6810\n2023-01-02 01:40:11,270 - mmcls - INFO - Epoch [71][2700/5005]\tlr: 1.000e-03, eta: 3:37:52, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6612\n2023-01-02 01:40:20,299 - mmcls - INFO - Epoch [71][2800/5005]\tlr: 1.000e-03, eta: 3:37:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6437\n2023-01-02 01:40:29,004 - mmcls - INFO - Epoch [71][2900/5005]\tlr: 1.000e-03, eta: 3:37:34, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6828\n2023-01-02 01:40:37,806 - mmcls - INFO - Epoch [71][3000/5005]\tlr: 1.000e-03, eta: 3:37:25, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6392\n2023-01-02 01:40:46,862 - mmcls - INFO - Epoch [71][3100/5005]\tlr: 1.000e-03, eta: 3:37:16, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6615\n2023-01-02 01:40:55,733 - mmcls - INFO - Epoch [71][3200/5005]\tlr: 1.000e-03, eta: 3:37:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6847\n2023-01-02 01:41:04,600 - mmcls - INFO - Epoch [71][3300/5005]\tlr: 1.000e-03, eta: 3:36:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6754\n2023-01-02 01:41:13,515 - mmcls - INFO - Epoch [71][3400/5005]\tlr: 1.000e-03, eta: 3:36:50, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6631\n2023-01-02 01:41:22,306 - mmcls - INFO - Epoch [71][3500/5005]\tlr: 1.000e-03, eta: 3:36:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6982\n2023-01-02 01:41:30,940 - mmcls - INFO - Epoch [71][3600/5005]\tlr: 1.000e-03, eta: 3:36:32, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6564\n2023-01-02 01:41:39,840 - mmcls - INFO - Epoch [71][3700/5005]\tlr: 1.000e-03, eta: 3:36:23, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.6909\n2023-01-02 01:41:48,520 - mmcls - INFO - Epoch [71][3800/5005]\tlr: 1.000e-03, eta: 3:36:14, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6866\n2023-01-02 01:41:57,558 - mmcls - INFO - Epoch [71][3900/5005]\tlr: 1.000e-03, eta: 3:36:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6668\n2023-01-02 01:42:06,246 - mmcls - INFO - Epoch [71][4000/5005]\tlr: 1.000e-03, eta: 3:35:56, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6633\n2023-01-02 01:42:14,965 - mmcls - INFO - Epoch [71][4100/5005]\tlr: 1.000e-03, eta: 3:35:48, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6888\n2023-01-02 01:42:23,588 - mmcls - INFO - Epoch [71][4200/5005]\tlr: 1.000e-03, eta: 3:35:39, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6479\n2023-01-02 01:42:32,408 - mmcls - INFO - Epoch [71][4300/5005]\tlr: 1.000e-03, eta: 3:35:30, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6642\n2023-01-02 01:42:41,109 - mmcls - INFO - Epoch [71][4400/5005]\tlr: 1.000e-03, eta: 3:35:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6527\n2023-01-02 01:42:50,115 - mmcls - INFO - Epoch [71][4500/5005]\tlr: 1.000e-03, eta: 3:35:12, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6695\n2023-01-02 01:42:59,172 - mmcls - INFO - Epoch [71][4600/5005]\tlr: 1.000e-03, eta: 3:35:03, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.7000\n2023-01-02 01:43:08,543 - mmcls - INFO - Epoch [71][4700/5005]\tlr: 1.000e-03, eta: 3:34:54, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6625\n2023-01-02 01:43:17,199 - mmcls - INFO - Epoch [71][4800/5005]\tlr: 1.000e-03, eta: 3:34:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6756\n2023-01-02 01:43:26,403 - mmcls - INFO - Epoch [71][4900/5005]\tlr: 1.000e-03, eta: 3:34:37, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.6341\n2023-01-02 01:43:35,185 - mmcls - INFO - Epoch [71][5000/5005]\tlr: 1.000e-03, eta: 3:34:28, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.6799\n2023-01-02 01:43:58,018 - mmcls - INFO - Epoch(val) [71][196]\taccuracy_top-1: 64.4880, accuracy_top-5: 85.2800\n2023-01-02 01:44:08,935 - mmcls - INFO - Epoch [72][100/5005]\tlr: 1.000e-03, eta: 3:34:19, time: 0.109, data_time: 0.033, memory: 1224, loss: 1.6588\n2023-01-02 01:44:17,679 - mmcls - INFO - Epoch [72][200/5005]\tlr: 1.000e-03, eta: 3:34:10, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6466\n2023-01-02 01:44:26,924 - mmcls - INFO - Epoch [72][300/5005]\tlr: 1.000e-03, eta: 3:34:02, time: 0.092, data_time: 0.030, memory: 1224, loss: 1.6582\n2023-01-02 01:44:35,556 - mmcls - INFO - Epoch [72][400/5005]\tlr: 1.000e-03, eta: 3:33:53, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6470\n2023-01-02 01:44:44,339 - mmcls - INFO - Epoch [72][500/5005]\tlr: 1.000e-03, eta: 3:33:44, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6440\n2023-01-02 01:44:53,235 - mmcls - INFO - Epoch [72][600/5005]\tlr: 1.000e-03, eta: 3:33:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6604\n2023-01-02 01:45:02,008 - mmcls - INFO - Epoch [72][700/5005]\tlr: 1.000e-03, eta: 3:33:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6362\n2023-01-02 01:45:11,056 - mmcls - INFO - Epoch [72][800/5005]\tlr: 1.000e-03, eta: 3:33:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6405\n2023-01-02 01:45:20,380 - mmcls - INFO - Epoch [72][900/5005]\tlr: 1.000e-03, eta: 3:33:09, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.6459\n2023-01-02 01:45:29,267 - mmcls - INFO - Epoch [72][1000/5005]\tlr: 1.000e-03, eta: 3:33:00, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6395\n2023-01-02 01:45:38,395 - mmcls - INFO - Epoch [72][1100/5005]\tlr: 1.000e-03, eta: 3:32:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6626\n2023-01-02 01:45:47,382 - mmcls - INFO - Epoch [72][1200/5005]\tlr: 1.000e-03, eta: 3:32:42, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6599\n2023-01-02 01:45:56,424 - mmcls - INFO - Epoch [72][1300/5005]\tlr: 1.000e-03, eta: 3:32:33, time: 0.091, data_time: 0.035, memory: 1224, loss: 1.6603\n2023-01-02 01:46:05,296 - mmcls - INFO - Epoch [72][1400/5005]\tlr: 1.000e-03, eta: 3:32:24, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6722\n2023-01-02 01:46:13,830 - mmcls - INFO - Epoch [72][1500/5005]\tlr: 1.000e-03, eta: 3:32:15, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6734\n2023-01-02 01:46:22,710 - mmcls - INFO - Epoch [72][1600/5005]\tlr: 1.000e-03, eta: 3:32:07, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6552\n2023-01-02 01:46:31,501 - mmcls - INFO - Epoch [72][1700/5005]\tlr: 1.000e-03, eta: 3:31:58, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6686\n2023-01-02 01:46:40,472 - mmcls - INFO - Epoch [72][1800/5005]\tlr: 1.000e-03, eta: 3:31:49, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6474\n2023-01-02 01:46:49,526 - mmcls - INFO - Epoch [72][1900/5005]\tlr: 1.000e-03, eta: 3:31:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6467\n2023-01-02 01:46:58,162 - mmcls - INFO - Epoch [72][2000/5005]\tlr: 1.000e-03, eta: 3:31:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6518\n2023-01-02 01:47:06,900 - mmcls - INFO - Epoch [72][2100/5005]\tlr: 1.000e-03, eta: 3:31:22, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6597\n2023-01-02 01:47:15,981 - mmcls - INFO - Epoch [72][2200/5005]\tlr: 1.000e-03, eta: 3:31:13, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6499\n2023-01-02 01:47:24,987 - mmcls - INFO - Epoch [72][2300/5005]\tlr: 1.000e-03, eta: 3:31:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6636\n2023-01-02 01:47:33,661 - mmcls - INFO - Epoch [72][2400/5005]\tlr: 1.000e-03, eta: 3:30:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6523\n2023-01-02 01:47:42,486 - mmcls - INFO - Epoch [72][2500/5005]\tlr: 1.000e-03, eta: 3:30:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6475\n2023-01-02 01:47:51,314 - mmcls - INFO - Epoch [72][2600/5005]\tlr: 1.000e-03, eta: 3:30:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6503\n2023-01-02 01:48:00,012 - mmcls - INFO - Epoch [72][2700/5005]\tlr: 1.000e-03, eta: 3:30:29, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6764\n2023-01-02 01:48:08,719 - mmcls - INFO - Epoch [72][2800/5005]\tlr: 1.000e-03, eta: 3:30:20, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6594\n2023-01-02 01:48:17,482 - mmcls - INFO - Epoch [72][2900/5005]\tlr: 1.000e-03, eta: 3:30:11, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6766\n2023-01-02 01:48:26,464 - mmcls - INFO - Epoch [72][3000/5005]\tlr: 1.000e-03, eta: 3:30:02, time: 0.090, data_time: 0.035, memory: 1224, loss: 1.6521\n2023-01-02 01:48:35,321 - mmcls - INFO - Epoch [72][3100/5005]\tlr: 1.000e-03, eta: 3:29:53, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6695\n2023-01-02 01:48:43,993 - mmcls - INFO - Epoch [72][3200/5005]\tlr: 1.000e-03, eta: 3:29:44, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6745\n2023-01-02 01:48:53,002 - mmcls - INFO - Epoch [72][3300/5005]\tlr: 1.000e-03, eta: 3:29:36, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6536\n2023-01-02 01:49:01,700 - mmcls - INFO - Epoch [72][3400/5005]\tlr: 1.000e-03, eta: 3:29:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6704\n2023-01-02 01:49:10,417 - mmcls - INFO - Epoch [72][3500/5005]\tlr: 1.000e-03, eta: 3:29:18, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6579\n2023-01-02 01:49:19,139 - mmcls - INFO - Epoch [72][3600/5005]\tlr: 1.000e-03, eta: 3:29:09, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6661\n2023-01-02 01:49:27,729 - mmcls - INFO - Epoch [72][3700/5005]\tlr: 1.000e-03, eta: 3:29:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6740\n2023-01-02 01:49:36,334 - mmcls - INFO - Epoch [72][3800/5005]\tlr: 1.000e-03, eta: 3:28:51, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6817\n2023-01-02 01:49:45,818 - mmcls - INFO - Epoch [72][3900/5005]\tlr: 1.000e-03, eta: 3:28:42, time: 0.095, data_time: 0.003, memory: 1224, loss: 1.6595\n2023-01-02 01:49:54,339 - mmcls - INFO - Epoch [72][4000/5005]\tlr: 1.000e-03, eta: 3:28:33, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6894\n2023-01-02 01:50:03,131 - mmcls - INFO - Epoch [72][4100/5005]\tlr: 1.000e-03, eta: 3:28:24, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6483\n2023-01-02 01:50:11,991 - mmcls - INFO - Epoch [72][4200/5005]\tlr: 1.000e-03, eta: 3:28:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6694\n2023-01-02 01:50:20,731 - mmcls - INFO - Epoch [72][4300/5005]\tlr: 1.000e-03, eta: 3:28:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6819\n2023-01-02 01:50:29,506 - mmcls - INFO - Epoch [72][4400/5005]\tlr: 1.000e-03, eta: 3:27:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6645\n2023-01-02 01:50:38,529 - mmcls - INFO - Epoch [72][4500/5005]\tlr: 1.000e-03, eta: 3:27:49, time: 0.090, data_time: 0.036, memory: 1224, loss: 1.6746\n2023-01-02 01:50:47,442 - mmcls - INFO - Epoch [72][4600/5005]\tlr: 1.000e-03, eta: 3:27:40, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6723\n2023-01-02 01:50:56,362 - mmcls - INFO - Epoch [72][4700/5005]\tlr: 1.000e-03, eta: 3:27:31, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.6717\n2023-01-02 01:51:05,157 - mmcls - INFO - Epoch [72][4800/5005]\tlr: 1.000e-03, eta: 3:27:22, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6654\n2023-01-02 01:51:14,052 - mmcls - INFO - Epoch [72][4900/5005]\tlr: 1.000e-03, eta: 3:27:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6709\n2023-01-02 01:51:22,847 - mmcls - INFO - Epoch [72][5000/5005]\tlr: 1.000e-03, eta: 3:27:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6777\n2023-01-02 01:51:45,508 - mmcls - INFO - Epoch(val) [72][196]\taccuracy_top-1: 64.5620, accuracy_top-5: 85.2540\n2023-01-02 01:51:56,457 - mmcls - INFO - Epoch [73][100/5005]\tlr: 1.000e-03, eta: 3:26:56, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.6394\n2023-01-02 01:52:05,219 - mmcls - INFO - Epoch [73][200/5005]\tlr: 1.000e-03, eta: 3:26:47, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6593\n2023-01-02 01:52:14,320 - mmcls - INFO - Epoch [73][300/5005]\tlr: 1.000e-03, eta: 3:26:38, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6557\n2023-01-02 01:52:23,041 - mmcls - INFO - Epoch [73][400/5005]\tlr: 1.000e-03, eta: 3:26:29, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6482\n2023-01-02 01:52:32,018 - mmcls - INFO - Epoch [73][500/5005]\tlr: 1.000e-03, eta: 3:26:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6511\n2023-01-02 01:52:41,122 - mmcls - INFO - Epoch [73][600/5005]\tlr: 1.000e-03, eta: 3:26:12, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6414\n2023-01-02 01:52:49,985 - mmcls - INFO - Epoch [73][700/5005]\tlr: 1.000e-03, eta: 3:26:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6353\n2023-01-02 01:52:58,496 - mmcls - INFO - Epoch [73][800/5005]\tlr: 1.000e-03, eta: 3:25:54, time: 0.085, data_time: 0.019, memory: 1224, loss: 1.6358\n2023-01-02 01:53:07,178 - mmcls - INFO - Epoch [73][900/5005]\tlr: 1.000e-03, eta: 3:25:45, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6560\n2023-01-02 01:53:16,344 - mmcls - INFO - Epoch [73][1000/5005]\tlr: 1.000e-03, eta: 3:25:36, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6619\n2023-01-02 01:53:25,102 - mmcls - INFO - Epoch [73][1100/5005]\tlr: 1.000e-03, eta: 3:25:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6360\n2023-01-02 01:53:34,084 - mmcls - INFO - Epoch [73][1200/5005]\tlr: 1.000e-03, eta: 3:25:18, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6421\n2023-01-02 01:53:43,102 - mmcls - INFO - Epoch [73][1300/5005]\tlr: 1.000e-03, eta: 3:25:10, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6628\n2023-01-02 01:53:51,946 - mmcls - INFO - Epoch [73][1400/5005]\tlr: 1.000e-03, eta: 3:25:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6603\n2023-01-02 01:54:01,146 - mmcls - INFO - Epoch [73][1500/5005]\tlr: 1.000e-03, eta: 3:24:52, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.6448\n2023-01-02 01:54:09,984 - mmcls - INFO - Epoch [73][1600/5005]\tlr: 1.000e-03, eta: 3:24:43, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6542\n2023-01-02 01:54:18,778 - mmcls - INFO - Epoch [73][1700/5005]\tlr: 1.000e-03, eta: 3:24:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6626\n2023-01-02 01:54:27,379 - mmcls - INFO - Epoch [73][1800/5005]\tlr: 1.000e-03, eta: 3:24:25, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6520\n2023-01-02 01:54:36,162 - mmcls - INFO - Epoch [73][1900/5005]\tlr: 1.000e-03, eta: 3:24:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6539\n2023-01-02 01:54:44,833 - mmcls - INFO - Epoch [73][2000/5005]\tlr: 1.000e-03, eta: 3:24:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6347\n2023-01-02 01:54:53,552 - mmcls - INFO - Epoch [73][2100/5005]\tlr: 1.000e-03, eta: 3:23:58, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6582\n2023-01-02 01:55:02,331 - mmcls - INFO - Epoch [73][2200/5005]\tlr: 1.000e-03, eta: 3:23:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6488\n2023-01-02 01:55:11,092 - mmcls - INFO - Epoch [73][2300/5005]\tlr: 1.000e-03, eta: 3:23:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6731\n2023-01-02 01:55:19,743 - mmcls - INFO - Epoch [73][2400/5005]\tlr: 1.000e-03, eta: 3:23:32, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6582\n2023-01-02 01:55:28,371 - mmcls - INFO - Epoch [73][2500/5005]\tlr: 1.000e-03, eta: 3:23:23, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6524\n2023-01-02 01:55:36,914 - mmcls - INFO - Epoch [73][2600/5005]\tlr: 1.000e-03, eta: 3:23:14, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6496\n2023-01-02 01:55:45,691 - mmcls - INFO - Epoch [73][2700/5005]\tlr: 1.000e-03, eta: 3:23:05, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6708\n2023-01-02 01:55:54,466 - mmcls - INFO - Epoch [73][2800/5005]\tlr: 1.000e-03, eta: 3:22:56, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6409\n2023-01-02 01:56:03,450 - mmcls - INFO - Epoch [73][2900/5005]\tlr: 1.000e-03, eta: 3:22:47, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6881\n2023-01-02 01:56:12,286 - mmcls - INFO - Epoch [73][3000/5005]\tlr: 1.000e-03, eta: 3:22:38, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6582\n2023-01-02 01:56:20,972 - mmcls - INFO - Epoch [73][3100/5005]\tlr: 1.000e-03, eta: 3:22:29, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6645\n2023-01-02 01:56:30,122 - mmcls - INFO - Epoch [73][3200/5005]\tlr: 1.000e-03, eta: 3:22:21, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6620\n2023-01-02 01:56:39,032 - mmcls - INFO - Epoch [73][3300/5005]\tlr: 1.000e-03, eta: 3:22:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6495\n2023-01-02 01:56:48,004 - mmcls - INFO - Epoch [73][3400/5005]\tlr: 1.000e-03, eta: 3:22:03, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6473\n2023-01-02 01:56:56,941 - mmcls - INFO - Epoch [73][3500/5005]\tlr: 1.000e-03, eta: 3:21:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6762\n2023-01-02 01:57:05,549 - mmcls - INFO - Epoch [73][3600/5005]\tlr: 1.000e-03, eta: 3:21:45, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6502\n2023-01-02 01:57:14,475 - mmcls - INFO - Epoch [73][3700/5005]\tlr: 1.000e-03, eta: 3:21:36, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6602\n2023-01-02 01:57:23,297 - mmcls - INFO - Epoch [73][3800/5005]\tlr: 1.000e-03, eta: 3:21:27, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6797\n2023-01-02 01:57:32,040 - mmcls - INFO - Epoch [73][3900/5005]\tlr: 1.000e-03, eta: 3:21:18, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6614\n2023-01-02 01:57:41,001 - mmcls - INFO - Epoch [73][4000/5005]\tlr: 1.000e-03, eta: 3:21:10, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6614\n2023-01-02 01:57:49,789 - mmcls - INFO - Epoch [73][4100/5005]\tlr: 1.000e-03, eta: 3:21:01, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6638\n2023-01-02 01:57:58,849 - mmcls - INFO - Epoch [73][4200/5005]\tlr: 1.000e-03, eta: 3:20:52, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6873\n2023-01-02 01:58:07,788 - mmcls - INFO - Epoch [73][4300/5005]\tlr: 1.000e-03, eta: 3:20:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6423\n2023-01-02 01:58:16,679 - mmcls - INFO - Epoch [73][4400/5005]\tlr: 1.000e-03, eta: 3:20:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6651\n2023-01-02 01:58:25,509 - mmcls - INFO - Epoch [73][4500/5005]\tlr: 1.000e-03, eta: 3:20:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6417\n2023-01-02 01:58:34,380 - mmcls - INFO - Epoch [73][4600/5005]\tlr: 1.000e-03, eta: 3:20:17, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6825\n2023-01-02 01:58:43,245 - mmcls - INFO - Epoch [73][4700/5005]\tlr: 1.000e-03, eta: 3:20:08, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6676\n2023-01-02 01:58:52,398 - mmcls - INFO - Epoch [73][4800/5005]\tlr: 1.000e-03, eta: 3:19:59, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6828\n2023-01-02 01:59:01,030 - mmcls - INFO - Epoch [73][4900/5005]\tlr: 1.000e-03, eta: 3:19:50, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6738\n2023-01-02 01:59:09,666 - mmcls - INFO - Epoch [73][5000/5005]\tlr: 1.000e-03, eta: 3:19:41, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6659\n2023-01-02 01:59:32,338 - mmcls - INFO - Epoch(val) [73][196]\taccuracy_top-1: 64.4780, accuracy_top-5: 85.3260\n2023-01-02 01:59:43,311 - mmcls - INFO - Epoch [74][100/5005]\tlr: 1.000e-03, eta: 3:19:32, time: 0.109, data_time: 0.042, memory: 1224, loss: 1.6189\n2023-01-02 01:59:52,109 - mmcls - INFO - Epoch [74][200/5005]\tlr: 1.000e-03, eta: 3:19:23, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6083\n2023-01-02 02:00:01,011 - mmcls - INFO - Epoch [74][300/5005]\tlr: 1.000e-03, eta: 3:19:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6489\n2023-01-02 02:00:10,017 - mmcls - INFO - Epoch [74][400/5005]\tlr: 1.000e-03, eta: 3:19:06, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6528\n2023-01-02 02:00:18,906 - mmcls - INFO - Epoch [74][500/5005]\tlr: 1.000e-03, eta: 3:18:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6340\n2023-01-02 02:00:27,904 - mmcls - INFO - Epoch [74][600/5005]\tlr: 1.000e-03, eta: 3:18:48, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6665\n2023-01-02 02:00:36,550 - mmcls - INFO - Epoch [74][700/5005]\tlr: 1.000e-03, eta: 3:18:39, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.6248\n2023-01-02 02:00:45,206 - mmcls - INFO - Epoch [74][800/5005]\tlr: 1.000e-03, eta: 3:18:30, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.6679\n2023-01-02 02:00:53,968 - mmcls - INFO - Epoch [74][900/5005]\tlr: 1.000e-03, eta: 3:18:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6257\n2023-01-02 02:01:03,145 - mmcls - INFO - Epoch [74][1000/5005]\tlr: 1.000e-03, eta: 3:18:12, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6523\n2023-01-02 02:01:11,786 - mmcls - INFO - Epoch [74][1100/5005]\tlr: 1.000e-03, eta: 3:18:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6585\n2023-01-02 02:01:20,682 - mmcls - INFO - Epoch [74][1200/5005]\tlr: 1.000e-03, eta: 3:17:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6472\n2023-01-02 02:01:29,353 - mmcls - INFO - Epoch [74][1300/5005]\tlr: 1.000e-03, eta: 3:17:46, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6540\n2023-01-02 02:01:38,312 - mmcls - INFO - Epoch [74][1400/5005]\tlr: 1.000e-03, eta: 3:17:37, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6513\n2023-01-02 02:01:47,089 - mmcls - INFO - Epoch [74][1500/5005]\tlr: 1.000e-03, eta: 3:17:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6479\n2023-01-02 02:01:55,918 - mmcls - INFO - Epoch [74][1600/5005]\tlr: 1.000e-03, eta: 3:17:19, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6451\n2023-01-02 02:02:05,013 - mmcls - INFO - Epoch [74][1700/5005]\tlr: 1.000e-03, eta: 3:17:10, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6666\n2023-01-02 02:02:13,768 - mmcls - INFO - Epoch [74][1800/5005]\tlr: 1.000e-03, eta: 3:17:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6817\n2023-01-02 02:02:22,264 - mmcls - INFO - Epoch [74][1900/5005]\tlr: 1.000e-03, eta: 3:16:52, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6360\n2023-01-02 02:02:31,008 - mmcls - INFO - Epoch [74][2000/5005]\tlr: 1.000e-03, eta: 3:16:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6621\n2023-01-02 02:02:39,726 - mmcls - INFO - Epoch [74][2100/5005]\tlr: 1.000e-03, eta: 3:16:35, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6434\n2023-01-02 02:02:48,479 - mmcls - INFO - Epoch [74][2200/5005]\tlr: 1.000e-03, eta: 3:16:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6680\n2023-01-02 02:02:56,972 - mmcls - INFO - Epoch [74][2300/5005]\tlr: 1.000e-03, eta: 3:16:17, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6434\n2023-01-02 02:03:05,538 - mmcls - INFO - Epoch [74][2400/5005]\tlr: 1.000e-03, eta: 3:16:08, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6570\n2023-01-02 02:03:14,121 - mmcls - INFO - Epoch [74][2500/5005]\tlr: 1.000e-03, eta: 3:15:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6698\n2023-01-02 02:03:22,927 - mmcls - INFO - Epoch [74][2600/5005]\tlr: 1.000e-03, eta: 3:15:50, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6586\n2023-01-02 02:03:31,940 - mmcls - INFO - Epoch [74][2700/5005]\tlr: 1.000e-03, eta: 3:15:41, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6596\n2023-01-02 02:03:40,580 - mmcls - INFO - Epoch [74][2800/5005]\tlr: 1.000e-03, eta: 3:15:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6665\n2023-01-02 02:03:49,571 - mmcls - INFO - Epoch [74][2900/5005]\tlr: 1.000e-03, eta: 3:15:23, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6440\n2023-01-02 02:03:58,289 - mmcls - INFO - Epoch [74][3000/5005]\tlr: 1.000e-03, eta: 3:15:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6591\n2023-01-02 02:04:07,118 - mmcls - INFO - Epoch [74][3100/5005]\tlr: 1.000e-03, eta: 3:15:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6433\n2023-01-02 02:04:15,720 - mmcls - INFO - Epoch [74][3200/5005]\tlr: 1.000e-03, eta: 3:14:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6416\n2023-01-02 02:04:24,495 - mmcls - INFO - Epoch [74][3300/5005]\tlr: 1.000e-03, eta: 3:14:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6576\n2023-01-02 02:04:33,277 - mmcls - INFO - Epoch [74][3400/5005]\tlr: 1.000e-03, eta: 3:14:39, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6720\n2023-01-02 02:04:42,039 - mmcls - INFO - Epoch [74][3500/5005]\tlr: 1.000e-03, eta: 3:14:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6230\n2023-01-02 02:04:51,207 - mmcls - INFO - Epoch [74][3600/5005]\tlr: 1.000e-03, eta: 3:14:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6528\n2023-01-02 02:05:00,534 - mmcls - INFO - Epoch [74][3700/5005]\tlr: 1.000e-03, eta: 3:14:12, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.6531\n2023-01-02 02:05:09,321 - mmcls - INFO - Epoch [74][3800/5005]\tlr: 1.000e-03, eta: 3:14:03, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6491\n2023-01-02 02:05:18,333 - mmcls - INFO - Epoch [74][3900/5005]\tlr: 1.000e-03, eta: 3:13:55, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6832\n2023-01-02 02:05:27,010 - mmcls - INFO - Epoch [74][4000/5005]\tlr: 1.000e-03, eta: 3:13:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6640\n2023-01-02 02:05:35,756 - mmcls - INFO - Epoch [74][4100/5005]\tlr: 1.000e-03, eta: 3:13:37, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6359\n2023-01-02 02:05:44,452 - mmcls - INFO - Epoch [74][4200/5005]\tlr: 1.000e-03, eta: 3:13:28, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.6542\n2023-01-02 02:05:53,164 - mmcls - INFO - Epoch [74][4300/5005]\tlr: 1.000e-03, eta: 3:13:19, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6446\n2023-01-02 02:06:02,188 - mmcls - INFO - Epoch [74][4400/5005]\tlr: 1.000e-03, eta: 3:13:10, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6475\n2023-01-02 02:06:11,090 - mmcls - INFO - Epoch [74][4500/5005]\tlr: 1.000e-03, eta: 3:13:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6565\n2023-01-02 02:06:19,863 - mmcls - INFO - Epoch [74][4600/5005]\tlr: 1.000e-03, eta: 3:12:52, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6714\n2023-01-02 02:06:28,775 - mmcls - INFO - Epoch [74][4700/5005]\tlr: 1.000e-03, eta: 3:12:44, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6768\n2023-01-02 02:06:37,579 - mmcls - INFO - Epoch [74][4800/5005]\tlr: 1.000e-03, eta: 3:12:35, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6465\n2023-01-02 02:06:46,407 - mmcls - INFO - Epoch [74][4900/5005]\tlr: 1.000e-03, eta: 3:12:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6763\n2023-01-02 02:06:55,098 - mmcls - INFO - Epoch [74][5000/5005]\tlr: 1.000e-03, eta: 3:12:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6879\n2023-01-02 02:07:17,716 - mmcls - INFO - Epoch(val) [74][196]\taccuracy_top-1: 64.5000, accuracy_top-5: 85.2900\n2023-01-02 02:07:28,733 - mmcls - INFO - Epoch [75][100/5005]\tlr: 1.000e-03, eta: 3:12:08, time: 0.110, data_time: 0.035, memory: 1224, loss: 1.6405\n2023-01-02 02:07:38,101 - mmcls - INFO - Epoch [75][200/5005]\tlr: 1.000e-03, eta: 3:11:59, time: 0.094, data_time: 0.010, memory: 1224, loss: 1.6473\n2023-01-02 02:07:47,005 - mmcls - INFO - Epoch [75][300/5005]\tlr: 1.000e-03, eta: 3:11:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6463\n2023-01-02 02:07:55,811 - mmcls - INFO - Epoch [75][400/5005]\tlr: 1.000e-03, eta: 3:11:42, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6376\n2023-01-02 02:08:04,767 - mmcls - INFO - Epoch [75][500/5005]\tlr: 1.000e-03, eta: 3:11:33, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6369\n2023-01-02 02:08:13,748 - mmcls - INFO - Epoch [75][600/5005]\tlr: 1.000e-03, eta: 3:11:24, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6329\n2023-01-02 02:08:22,582 - mmcls - INFO - Epoch [75][700/5005]\tlr: 1.000e-03, eta: 3:11:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6493\n2023-01-02 02:08:31,224 - mmcls - INFO - Epoch [75][800/5005]\tlr: 1.000e-03, eta: 3:11:06, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6557\n2023-01-02 02:08:40,211 - mmcls - INFO - Epoch [75][900/5005]\tlr: 1.000e-03, eta: 3:10:57, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6370\n2023-01-02 02:08:49,215 - mmcls - INFO - Epoch [75][1000/5005]\tlr: 1.000e-03, eta: 3:10:49, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6433\n2023-01-02 02:08:57,795 - mmcls - INFO - Epoch [75][1100/5005]\tlr: 1.000e-03, eta: 3:10:40, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6311\n2023-01-02 02:09:06,953 - mmcls - INFO - Epoch [75][1200/5005]\tlr: 1.000e-03, eta: 3:10:31, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6464\n2023-01-02 02:09:15,855 - mmcls - INFO - Epoch [75][1300/5005]\tlr: 1.000e-03, eta: 3:10:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6633\n2023-01-02 02:09:24,593 - mmcls - INFO - Epoch [75][1400/5005]\tlr: 1.000e-03, eta: 3:10:13, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6635\n2023-01-02 02:09:33,377 - mmcls - INFO - Epoch [75][1500/5005]\tlr: 1.000e-03, eta: 3:10:04, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6496\n2023-01-02 02:09:42,297 - mmcls - INFO - Epoch [75][1600/5005]\tlr: 1.000e-03, eta: 3:09:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6532\n2023-01-02 02:09:50,688 - mmcls - INFO - Epoch [75][1700/5005]\tlr: 1.000e-03, eta: 3:09:46, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.6716\n2023-01-02 02:09:59,481 - mmcls - INFO - Epoch [75][1800/5005]\tlr: 1.000e-03, eta: 3:09:37, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6585\n2023-01-02 02:10:08,769 - mmcls - INFO - Epoch [75][1900/5005]\tlr: 1.000e-03, eta: 3:09:29, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.6439\n2023-01-02 02:10:17,473 - mmcls - INFO - Epoch [75][2000/5005]\tlr: 1.000e-03, eta: 3:09:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6634\n2023-01-02 02:10:26,026 - mmcls - INFO - Epoch [75][2100/5005]\tlr: 1.000e-03, eta: 3:09:11, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6598\n2023-01-02 02:10:34,799 - mmcls - INFO - Epoch [75][2200/5005]\tlr: 1.000e-03, eta: 3:09:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6690\n2023-01-02 02:10:43,599 - mmcls - INFO - Epoch [75][2300/5005]\tlr: 1.000e-03, eta: 3:08:53, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6524\n2023-01-02 02:10:52,208 - mmcls - INFO - Epoch [75][2400/5005]\tlr: 1.000e-03, eta: 3:08:44, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6402\n2023-01-02 02:11:01,002 - mmcls - INFO - Epoch [75][2500/5005]\tlr: 1.000e-03, eta: 3:08:35, time: 0.088, data_time: 0.032, memory: 1224, loss: 1.6424\n2023-01-02 02:11:10,158 - mmcls - INFO - Epoch [75][2600/5005]\tlr: 1.000e-03, eta: 3:08:26, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6600\n2023-01-02 02:11:18,707 - mmcls - INFO - Epoch [75][2700/5005]\tlr: 1.000e-03, eta: 3:08:17, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6560\n2023-01-02 02:11:27,523 - mmcls - INFO - Epoch [75][2800/5005]\tlr: 1.000e-03, eta: 3:08:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6430\n2023-01-02 02:11:36,915 - mmcls - INFO - Epoch [75][2900/5005]\tlr: 1.000e-03, eta: 3:08:00, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6505\n2023-01-02 02:11:45,736 - mmcls - INFO - Epoch [75][3000/5005]\tlr: 1.000e-03, eta: 3:07:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6621\n2023-01-02 02:11:54,206 - mmcls - INFO - Epoch [75][3100/5005]\tlr: 1.000e-03, eta: 3:07:42, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6498\n2023-01-02 02:12:03,046 - mmcls - INFO - Epoch [75][3200/5005]\tlr: 1.000e-03, eta: 3:07:33, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6357\n2023-01-02 02:12:11,758 - mmcls - INFO - Epoch [75][3300/5005]\tlr: 1.000e-03, eta: 3:07:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6458\n2023-01-02 02:12:20,215 - mmcls - INFO - Epoch [75][3400/5005]\tlr: 1.000e-03, eta: 3:07:15, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.6638\n2023-01-02 02:12:29,008 - mmcls - INFO - Epoch [75][3500/5005]\tlr: 1.000e-03, eta: 3:07:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6484\n2023-01-02 02:12:37,644 - mmcls - INFO - Epoch [75][3600/5005]\tlr: 1.000e-03, eta: 3:06:57, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6506\n2023-01-02 02:12:46,517 - mmcls - INFO - Epoch [75][3700/5005]\tlr: 1.000e-03, eta: 3:06:48, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6588\n2023-01-02 02:12:55,295 - mmcls - INFO - Epoch [75][3800/5005]\tlr: 1.000e-03, eta: 3:06:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6354\n2023-01-02 02:13:04,143 - mmcls - INFO - Epoch [75][3900/5005]\tlr: 1.000e-03, eta: 3:06:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6408\n2023-01-02 02:13:12,971 - mmcls - INFO - Epoch [75][4000/5005]\tlr: 1.000e-03, eta: 3:06:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6589\n2023-01-02 02:13:21,695 - mmcls - INFO - Epoch [75][4100/5005]\tlr: 1.000e-03, eta: 3:06:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6581\n2023-01-02 02:13:30,699 - mmcls - INFO - Epoch [75][4200/5005]\tlr: 1.000e-03, eta: 3:06:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6431\n2023-01-02 02:13:39,255 - mmcls - INFO - Epoch [75][4300/5005]\tlr: 1.000e-03, eta: 3:05:55, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.6700\n2023-01-02 02:13:48,044 - mmcls - INFO - Epoch [75][4400/5005]\tlr: 1.000e-03, eta: 3:05:46, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.6501\n2023-01-02 02:13:56,970 - mmcls - INFO - Epoch [75][4500/5005]\tlr: 1.000e-03, eta: 3:05:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6618\n2023-01-02 02:14:05,910 - mmcls - INFO - Epoch [75][4600/5005]\tlr: 1.000e-03, eta: 3:05:29, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6434\n2023-01-02 02:14:14,657 - mmcls - INFO - Epoch [75][4700/5005]\tlr: 1.000e-03, eta: 3:05:20, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6764\n2023-01-02 02:14:23,271 - mmcls - INFO - Epoch [75][4800/5005]\tlr: 1.000e-03, eta: 3:05:11, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6609\n2023-01-02 02:14:32,042 - mmcls - INFO - Epoch [75][4900/5005]\tlr: 1.000e-03, eta: 3:05:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6544\n2023-01-02 02:14:41,078 - mmcls - INFO - Epoch [75][5000/5005]\tlr: 1.000e-03, eta: 3:04:53, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6636\n2023-01-02 02:15:03,748 - mmcls - INFO - Epoch(val) [75][196]\taccuracy_top-1: 64.6880, accuracy_top-5: 85.3680\n2023-01-02 02:15:14,598 - mmcls - INFO - Epoch [76][100/5005]\tlr: 1.000e-03, eta: 3:04:44, time: 0.108, data_time: 0.071, memory: 1224, loss: 1.6514\n2023-01-02 02:15:23,457 - mmcls - INFO - Epoch [76][200/5005]\tlr: 1.000e-03, eta: 3:04:35, time: 0.089, data_time: 0.029, memory: 1224, loss: 1.6374\n2023-01-02 02:15:32,086 - mmcls - INFO - Epoch [76][300/5005]\tlr: 1.000e-03, eta: 3:04:26, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6517\n2023-01-02 02:15:40,851 - mmcls - INFO - Epoch [76][400/5005]\tlr: 1.000e-03, eta: 3:04:17, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6260\n2023-01-02 02:15:49,842 - mmcls - INFO - Epoch [76][500/5005]\tlr: 1.000e-03, eta: 3:04:09, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6501\n2023-01-02 02:15:58,631 - mmcls - INFO - Epoch [76][600/5005]\tlr: 1.000e-03, eta: 3:04:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6224\n2023-01-02 02:16:07,971 - mmcls - INFO - Epoch [76][700/5005]\tlr: 1.000e-03, eta: 3:03:51, time: 0.093, data_time: 0.010, memory: 1224, loss: 1.6331\n2023-01-02 02:16:16,577 - mmcls - INFO - Epoch [76][800/5005]\tlr: 1.000e-03, eta: 3:03:42, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6422\n2023-01-02 02:16:25,495 - mmcls - INFO - Epoch [76][900/5005]\tlr: 1.000e-03, eta: 3:03:33, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6299\n2023-01-02 02:16:34,121 - mmcls - INFO - Epoch [76][1000/5005]\tlr: 1.000e-03, eta: 3:03:24, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6363\n2023-01-02 02:16:42,867 - mmcls - INFO - Epoch [76][1100/5005]\tlr: 1.000e-03, eta: 3:03:15, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6486\n2023-01-02 02:16:51,693 - mmcls - INFO - Epoch [76][1200/5005]\tlr: 1.000e-03, eta: 3:03:07, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6539\n2023-01-02 02:17:00,280 - mmcls - INFO - Epoch [76][1300/5005]\tlr: 1.000e-03, eta: 3:02:58, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6531\n2023-01-02 02:17:08,861 - mmcls - INFO - Epoch [76][1400/5005]\tlr: 1.000e-03, eta: 3:02:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6712\n2023-01-02 02:17:17,876 - mmcls - INFO - Epoch [76][1500/5005]\tlr: 1.000e-03, eta: 3:02:40, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6538\n2023-01-02 02:17:26,727 - mmcls - INFO - Epoch [76][1600/5005]\tlr: 1.000e-03, eta: 3:02:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6369\n2023-01-02 02:17:35,538 - mmcls - INFO - Epoch [76][1700/5005]\tlr: 1.000e-03, eta: 3:02:22, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6471\n2023-01-02 02:17:44,442 - mmcls - INFO - Epoch [76][1800/5005]\tlr: 1.000e-03, eta: 3:02:13, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6373\n2023-01-02 02:17:53,098 - mmcls - INFO - Epoch [76][1900/5005]\tlr: 1.000e-03, eta: 3:02:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6583\n2023-01-02 02:18:02,055 - mmcls - INFO - Epoch [76][2000/5005]\tlr: 1.000e-03, eta: 3:01:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6576\n2023-01-02 02:18:11,216 - mmcls - INFO - Epoch [76][2100/5005]\tlr: 1.000e-03, eta: 3:01:47, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6393\n2023-01-02 02:18:19,895 - mmcls - INFO - Epoch [76][2200/5005]\tlr: 1.000e-03, eta: 3:01:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6475\n2023-01-02 02:18:28,778 - mmcls - INFO - Epoch [76][2300/5005]\tlr: 1.000e-03, eta: 3:01:29, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.6565\n2023-01-02 02:18:37,704 - mmcls - INFO - Epoch [76][2400/5005]\tlr: 1.000e-03, eta: 3:01:20, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6395\n2023-01-02 02:18:46,471 - mmcls - INFO - Epoch [76][2500/5005]\tlr: 1.000e-03, eta: 3:01:11, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6432\n2023-01-02 02:18:55,495 - mmcls - INFO - Epoch [76][2600/5005]\tlr: 1.000e-03, eta: 3:01:02, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6732\n2023-01-02 02:19:04,454 - mmcls - INFO - Epoch [76][2700/5005]\tlr: 1.000e-03, eta: 3:00:53, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6519\n2023-01-02 02:19:13,244 - mmcls - INFO - Epoch [76][2800/5005]\tlr: 1.000e-03, eta: 3:00:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6434\n2023-01-02 02:19:22,186 - mmcls - INFO - Epoch [76][2900/5005]\tlr: 1.000e-03, eta: 3:00:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6466\n2023-01-02 02:19:31,172 - mmcls - INFO - Epoch [76][3000/5005]\tlr: 1.000e-03, eta: 3:00:27, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6414\n2023-01-02 02:19:39,689 - mmcls - INFO - Epoch [76][3100/5005]\tlr: 1.000e-03, eta: 3:00:18, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6676\n2023-01-02 02:19:48,873 - mmcls - INFO - Epoch [76][3200/5005]\tlr: 1.000e-03, eta: 3:00:09, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6302\n2023-01-02 02:19:57,654 - mmcls - INFO - Epoch [76][3300/5005]\tlr: 1.000e-03, eta: 3:00:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6568\n2023-01-02 02:20:06,259 - mmcls - INFO - Epoch [76][3400/5005]\tlr: 1.000e-03, eta: 2:59:51, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6747\n2023-01-02 02:20:15,398 - mmcls - INFO - Epoch [76][3500/5005]\tlr: 1.000e-03, eta: 2:59:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6639\n2023-01-02 02:20:24,239 - mmcls - INFO - Epoch [76][3600/5005]\tlr: 1.000e-03, eta: 2:59:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6526\n2023-01-02 02:20:32,861 - mmcls - INFO - Epoch [76][3700/5005]\tlr: 1.000e-03, eta: 2:59:25, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6565\n2023-01-02 02:20:41,575 - mmcls - INFO - Epoch [76][3800/5005]\tlr: 1.000e-03, eta: 2:59:16, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6565\n2023-01-02 02:20:50,474 - mmcls - INFO - Epoch [76][3900/5005]\tlr: 1.000e-03, eta: 2:59:07, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6613\n2023-01-02 02:20:59,167 - mmcls - INFO - Epoch [76][4000/5005]\tlr: 1.000e-03, eta: 2:58:58, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6607\n2023-01-02 02:21:08,126 - mmcls - INFO - Epoch [76][4100/5005]\tlr: 1.000e-03, eta: 2:58:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6326\n2023-01-02 02:21:16,990 - mmcls - INFO - Epoch [76][4200/5005]\tlr: 1.000e-03, eta: 2:58:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6547\n2023-01-02 02:21:25,468 - mmcls - INFO - Epoch [76][4300/5005]\tlr: 1.000e-03, eta: 2:58:31, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.6326\n2023-01-02 02:21:34,566 - mmcls - INFO - Epoch [76][4400/5005]\tlr: 1.000e-03, eta: 2:58:23, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6556\n2023-01-02 02:21:43,568 - mmcls - INFO - Epoch [76][4500/5005]\tlr: 1.000e-03, eta: 2:58:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6529\n2023-01-02 02:21:52,341 - mmcls - INFO - Epoch [76][4600/5005]\tlr: 1.000e-03, eta: 2:58:05, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6711\n2023-01-02 02:22:01,333 - mmcls - INFO - Epoch [76][4700/5005]\tlr: 1.000e-03, eta: 2:57:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6281\n2023-01-02 02:22:10,181 - mmcls - INFO - Epoch [76][4800/5005]\tlr: 1.000e-03, eta: 2:57:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6621\n2023-01-02 02:22:18,707 - mmcls - INFO - Epoch [76][4900/5005]\tlr: 1.000e-03, eta: 2:57:38, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6577\n2023-01-02 02:22:27,340 - mmcls - INFO - Epoch [76][5000/5005]\tlr: 1.000e-03, eta: 2:57:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6469\n2023-01-02 02:22:50,257 - mmcls - INFO - Epoch(val) [76][196]\taccuracy_top-1: 64.5620, accuracy_top-5: 85.4020\n2023-01-02 02:23:01,602 - mmcls - INFO - Epoch [77][100/5005]\tlr: 1.000e-03, eta: 2:57:21, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.6337\n2023-01-02 02:23:10,323 - mmcls - INFO - Epoch [77][200/5005]\tlr: 1.000e-03, eta: 2:57:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6391\n2023-01-02 02:23:19,162 - mmcls - INFO - Epoch [77][300/5005]\tlr: 1.000e-03, eta: 2:57:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6470\n2023-01-02 02:23:27,939 - mmcls - INFO - Epoch [77][400/5005]\tlr: 1.000e-03, eta: 2:56:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6414\n2023-01-02 02:23:36,742 - mmcls - INFO - Epoch [77][500/5005]\tlr: 1.000e-03, eta: 2:56:45, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.6534\n2023-01-02 02:23:45,576 - mmcls - INFO - Epoch [77][600/5005]\tlr: 1.000e-03, eta: 2:56:36, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6267\n2023-01-02 02:23:54,358 - mmcls - INFO - Epoch [77][700/5005]\tlr: 1.000e-03, eta: 2:56:27, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6326\n2023-01-02 02:24:02,958 - mmcls - INFO - Epoch [77][800/5005]\tlr: 1.000e-03, eta: 2:56:18, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6443\n2023-01-02 02:24:11,783 - mmcls - INFO - Epoch [77][900/5005]\tlr: 1.000e-03, eta: 2:56:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6682\n2023-01-02 02:24:20,626 - mmcls - INFO - Epoch [77][1000/5005]\tlr: 1.000e-03, eta: 2:56:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6443\n2023-01-02 02:24:29,415 - mmcls - INFO - Epoch [77][1100/5005]\tlr: 1.000e-03, eta: 2:55:52, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6372\n2023-01-02 02:24:38,174 - mmcls - INFO - Epoch [77][1200/5005]\tlr: 1.000e-03, eta: 2:55:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6335\n2023-01-02 02:24:46,632 - mmcls - INFO - Epoch [77][1300/5005]\tlr: 1.000e-03, eta: 2:55:34, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6226\n2023-01-02 02:24:55,755 - mmcls - INFO - Epoch [77][1400/5005]\tlr: 1.000e-03, eta: 2:55:25, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6364\n2023-01-02 02:25:04,488 - mmcls - INFO - Epoch [77][1500/5005]\tlr: 1.000e-03, eta: 2:55:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6514\n2023-01-02 02:25:13,328 - mmcls - INFO - Epoch [77][1600/5005]\tlr: 1.000e-03, eta: 2:55:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6670\n2023-01-02 02:25:22,289 - mmcls - INFO - Epoch [77][1700/5005]\tlr: 1.000e-03, eta: 2:54:58, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.6403\n2023-01-02 02:25:30,927 - mmcls - INFO - Epoch [77][1800/5005]\tlr: 1.000e-03, eta: 2:54:49, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6348\n2023-01-02 02:25:39,958 - mmcls - INFO - Epoch [77][1900/5005]\tlr: 1.000e-03, eta: 2:54:41, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6340\n2023-01-02 02:25:49,089 - mmcls - INFO - Epoch [77][2000/5005]\tlr: 1.000e-03, eta: 2:54:32, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.6297\n2023-01-02 02:25:57,876 - mmcls - INFO - Epoch [77][2100/5005]\tlr: 1.000e-03, eta: 2:54:23, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6470\n2023-01-02 02:26:06,749 - mmcls - INFO - Epoch [77][2200/5005]\tlr: 1.000e-03, eta: 2:54:14, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6332\n2023-01-02 02:26:15,599 - mmcls - INFO - Epoch [77][2300/5005]\tlr: 1.000e-03, eta: 2:54:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6414\n2023-01-02 02:26:24,158 - mmcls - INFO - Epoch [77][2400/5005]\tlr: 1.000e-03, eta: 2:53:56, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6495\n2023-01-02 02:26:33,046 - mmcls - INFO - Epoch [77][2500/5005]\tlr: 1.000e-03, eta: 2:53:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6550\n2023-01-02 02:26:42,194 - mmcls - INFO - Epoch [77][2600/5005]\tlr: 1.000e-03, eta: 2:53:39, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6516\n2023-01-02 02:26:50,992 - mmcls - INFO - Epoch [77][2700/5005]\tlr: 1.000e-03, eta: 2:53:30, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6304\n2023-01-02 02:26:59,613 - mmcls - INFO - Epoch [77][2800/5005]\tlr: 1.000e-03, eta: 2:53:21, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6446\n2023-01-02 02:27:08,402 - mmcls - INFO - Epoch [77][2900/5005]\tlr: 1.000e-03, eta: 2:53:12, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6553\n2023-01-02 02:27:17,347 - mmcls - INFO - Epoch [77][3000/5005]\tlr: 1.000e-03, eta: 2:53:03, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6612\n2023-01-02 02:27:26,105 - mmcls - INFO - Epoch [77][3100/5005]\tlr: 1.000e-03, eta: 2:52:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6626\n2023-01-02 02:27:35,103 - mmcls - INFO - Epoch [77][3200/5005]\tlr: 1.000e-03, eta: 2:52:45, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6463\n2023-01-02 02:27:43,860 - mmcls - INFO - Epoch [77][3300/5005]\tlr: 1.000e-03, eta: 2:52:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6446\n2023-01-02 02:27:52,649 - mmcls - INFO - Epoch [77][3400/5005]\tlr: 1.000e-03, eta: 2:52:27, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6370\n2023-01-02 02:28:01,160 - mmcls - INFO - Epoch [77][3500/5005]\tlr: 1.000e-03, eta: 2:52:18, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6583\n2023-01-02 02:28:10,454 - mmcls - INFO - Epoch [77][3600/5005]\tlr: 1.000e-03, eta: 2:52:10, time: 0.093, data_time: 0.007, memory: 1224, loss: 1.6465\n2023-01-02 02:28:19,120 - mmcls - INFO - Epoch [77][3700/5005]\tlr: 1.000e-03, eta: 2:52:01, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6361\n2023-01-02 02:28:27,891 - mmcls - INFO - Epoch [77][3800/5005]\tlr: 1.000e-03, eta: 2:51:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6469\n2023-01-02 02:28:36,548 - mmcls - INFO - Epoch [77][3900/5005]\tlr: 1.000e-03, eta: 2:51:43, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6383\n2023-01-02 02:28:45,239 - mmcls - INFO - Epoch [77][4000/5005]\tlr: 1.000e-03, eta: 2:51:34, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6557\n2023-01-02 02:28:54,035 - mmcls - INFO - Epoch [77][4100/5005]\tlr: 1.000e-03, eta: 2:51:25, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6504\n2023-01-02 02:29:02,884 - mmcls - INFO - Epoch [77][4200/5005]\tlr: 1.000e-03, eta: 2:51:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6502\n2023-01-02 02:29:11,593 - mmcls - INFO - Epoch [77][4300/5005]\tlr: 1.000e-03, eta: 2:51:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6432\n2023-01-02 02:29:20,271 - mmcls - INFO - Epoch [77][4400/5005]\tlr: 1.000e-03, eta: 2:50:59, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6534\n2023-01-02 02:29:29,426 - mmcls - INFO - Epoch [77][4500/5005]\tlr: 1.000e-03, eta: 2:50:50, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6534\n2023-01-02 02:29:38,449 - mmcls - INFO - Epoch [77][4600/5005]\tlr: 1.000e-03, eta: 2:50:41, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6632\n2023-01-02 02:29:47,338 - mmcls - INFO - Epoch [77][4700/5005]\tlr: 1.000e-03, eta: 2:50:32, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.6348\n2023-01-02 02:29:56,126 - mmcls - INFO - Epoch [77][4800/5005]\tlr: 1.000e-03, eta: 2:50:23, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6625\n2023-01-02 02:30:04,828 - mmcls - INFO - Epoch [77][4900/5005]\tlr: 1.000e-03, eta: 2:50:14, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6594\n2023-01-02 02:30:13,383 - mmcls - INFO - Epoch [77][5000/5005]\tlr: 1.000e-03, eta: 2:50:05, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.6586\n2023-01-02 02:30:36,061 - mmcls - INFO - Epoch(val) [77][196]\taccuracy_top-1: 64.4820, accuracy_top-5: 85.3660\n2023-01-02 02:30:47,125 - mmcls - INFO - Epoch [78][100/5005]\tlr: 1.000e-03, eta: 2:49:56, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.6397\n2023-01-02 02:30:55,905 - mmcls - INFO - Epoch [78][200/5005]\tlr: 1.000e-03, eta: 2:49:48, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6362\n2023-01-02 02:31:04,989 - mmcls - INFO - Epoch [78][300/5005]\tlr: 1.000e-03, eta: 2:49:39, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6528\n2023-01-02 02:31:13,735 - mmcls - INFO - Epoch [78][400/5005]\tlr: 1.000e-03, eta: 2:49:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6210\n2023-01-02 02:31:22,676 - mmcls - INFO - Epoch [78][500/5005]\tlr: 1.000e-03, eta: 2:49:21, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6315\n2023-01-02 02:31:31,381 - mmcls - INFO - Epoch [78][600/5005]\tlr: 1.000e-03, eta: 2:49:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6387\n2023-01-02 02:31:40,286 - mmcls - INFO - Epoch [78][700/5005]\tlr: 1.000e-03, eta: 2:49:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6425\n2023-01-02 02:31:49,080 - mmcls - INFO - Epoch [78][800/5005]\tlr: 1.000e-03, eta: 2:48:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6421\n2023-01-02 02:31:57,834 - mmcls - INFO - Epoch [78][900/5005]\tlr: 1.000e-03, eta: 2:48:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6205\n2023-01-02 02:32:06,510 - mmcls - INFO - Epoch [78][1000/5005]\tlr: 1.000e-03, eta: 2:48:37, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6588\n2023-01-02 02:32:15,460 - mmcls - INFO - Epoch [78][1100/5005]\tlr: 1.000e-03, eta: 2:48:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6440\n2023-01-02 02:32:24,130 - mmcls - INFO - Epoch [78][1200/5005]\tlr: 1.000e-03, eta: 2:48:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6213\n2023-01-02 02:32:32,939 - mmcls - INFO - Epoch [78][1300/5005]\tlr: 1.000e-03, eta: 2:48:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6334\n2023-01-02 02:32:41,590 - mmcls - INFO - Epoch [78][1400/5005]\tlr: 1.000e-03, eta: 2:48:01, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6337\n2023-01-02 02:32:50,241 - mmcls - INFO - Epoch [78][1500/5005]\tlr: 1.000e-03, eta: 2:47:52, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6349\n2023-01-02 02:32:59,147 - mmcls - INFO - Epoch [78][1600/5005]\tlr: 1.000e-03, eta: 2:47:43, time: 0.089, data_time: 0.042, memory: 1224, loss: 1.6534\n2023-01-02 02:33:08,272 - mmcls - INFO - Epoch [78][1700/5005]\tlr: 1.000e-03, eta: 2:47:34, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6444\n2023-01-02 02:33:17,004 - mmcls - INFO - Epoch [78][1800/5005]\tlr: 1.000e-03, eta: 2:47:26, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6550\n2023-01-02 02:33:25,608 - mmcls - INFO - Epoch [78][1900/5005]\tlr: 1.000e-03, eta: 2:47:17, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6465\n2023-01-02 02:33:34,531 - mmcls - INFO - Epoch [78][2000/5005]\tlr: 1.000e-03, eta: 2:47:08, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6382\n2023-01-02 02:33:43,695 - mmcls - INFO - Epoch [78][2100/5005]\tlr: 1.000e-03, eta: 2:46:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6534\n2023-01-02 02:33:52,358 - mmcls - INFO - Epoch [78][2200/5005]\tlr: 1.000e-03, eta: 2:46:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6524\n2023-01-02 02:34:01,289 - mmcls - INFO - Epoch [78][2300/5005]\tlr: 1.000e-03, eta: 2:46:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6532\n2023-01-02 02:34:10,130 - mmcls - INFO - Epoch [78][2400/5005]\tlr: 1.000e-03, eta: 2:46:32, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6472\n2023-01-02 02:34:18,970 - mmcls - INFO - Epoch [78][2500/5005]\tlr: 1.000e-03, eta: 2:46:23, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6267\n2023-01-02 02:34:28,067 - mmcls - INFO - Epoch [78][2600/5005]\tlr: 1.000e-03, eta: 2:46:15, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6578\n2023-01-02 02:34:36,754 - mmcls - INFO - Epoch [78][2700/5005]\tlr: 1.000e-03, eta: 2:46:06, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6583\n2023-01-02 02:34:45,644 - mmcls - INFO - Epoch [78][2800/5005]\tlr: 1.000e-03, eta: 2:45:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6322\n2023-01-02 02:34:54,313 - mmcls - INFO - Epoch [78][2900/5005]\tlr: 1.000e-03, eta: 2:45:48, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6630\n2023-01-02 02:35:03,459 - mmcls - INFO - Epoch [78][3000/5005]\tlr: 1.000e-03, eta: 2:45:39, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6678\n2023-01-02 02:35:12,254 - mmcls - INFO - Epoch [78][3100/5005]\tlr: 1.000e-03, eta: 2:45:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6627\n2023-01-02 02:35:21,050 - mmcls - INFO - Epoch [78][3200/5005]\tlr: 1.000e-03, eta: 2:45:21, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6499\n2023-01-02 02:35:30,096 - mmcls - INFO - Epoch [78][3300/5005]\tlr: 1.000e-03, eta: 2:45:13, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6427\n2023-01-02 02:35:39,257 - mmcls - INFO - Epoch [78][3400/5005]\tlr: 1.000e-03, eta: 2:45:04, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6359\n2023-01-02 02:35:48,012 - mmcls - INFO - Epoch [78][3500/5005]\tlr: 1.000e-03, eta: 2:44:55, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.6383\n2023-01-02 02:35:56,990 - mmcls - INFO - Epoch [78][3600/5005]\tlr: 1.000e-03, eta: 2:44:46, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6310\n2023-01-02 02:36:05,936 - mmcls - INFO - Epoch [78][3700/5005]\tlr: 1.000e-03, eta: 2:44:37, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6517\n2023-01-02 02:36:14,745 - mmcls - INFO - Epoch [78][3800/5005]\tlr: 1.000e-03, eta: 2:44:28, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6672\n2023-01-02 02:36:23,674 - mmcls - INFO - Epoch [78][3900/5005]\tlr: 1.000e-03, eta: 2:44:19, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6526\n2023-01-02 02:36:32,597 - mmcls - INFO - Epoch [78][4000/5005]\tlr: 1.000e-03, eta: 2:44:11, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6275\n2023-01-02 02:36:42,109 - mmcls - INFO - Epoch [78][4100/5005]\tlr: 1.000e-03, eta: 2:44:02, time: 0.095, data_time: 0.036, memory: 1224, loss: 1.6562\n2023-01-02 02:36:50,990 - mmcls - INFO - Epoch [78][4200/5005]\tlr: 1.000e-03, eta: 2:43:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6776\n2023-01-02 02:36:59,755 - mmcls - INFO - Epoch [78][4300/5005]\tlr: 1.000e-03, eta: 2:43:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6406\n2023-01-02 02:37:08,668 - mmcls - INFO - Epoch [78][4400/5005]\tlr: 1.000e-03, eta: 2:43:35, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6405\n2023-01-02 02:37:17,290 - mmcls - INFO - Epoch [78][4500/5005]\tlr: 1.000e-03, eta: 2:43:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6527\n2023-01-02 02:37:26,118 - mmcls - INFO - Epoch [78][4600/5005]\tlr: 1.000e-03, eta: 2:43:18, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6456\n2023-01-02 02:37:34,849 - mmcls - INFO - Epoch [78][4700/5005]\tlr: 1.000e-03, eta: 2:43:09, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6610\n2023-01-02 02:37:43,607 - mmcls - INFO - Epoch [78][4800/5005]\tlr: 1.000e-03, eta: 2:43:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6619\n2023-01-02 02:37:52,389 - mmcls - INFO - Epoch [78][4900/5005]\tlr: 1.000e-03, eta: 2:42:51, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6634\n2023-01-02 02:38:01,347 - mmcls - INFO - Epoch [78][5000/5005]\tlr: 1.000e-03, eta: 2:42:42, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6602\n2023-01-02 02:38:24,368 - mmcls - INFO - Epoch(val) [78][196]\taccuracy_top-1: 64.9080, accuracy_top-5: 85.4420\n2023-01-02 02:38:35,407 - mmcls - INFO - Epoch [79][100/5005]\tlr: 1.000e-03, eta: 2:42:33, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.6334\n2023-01-02 02:38:44,061 - mmcls - INFO - Epoch [79][200/5005]\tlr: 1.000e-03, eta: 2:42:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6303\n2023-01-02 02:38:52,831 - mmcls - INFO - Epoch [79][300/5005]\tlr: 1.000e-03, eta: 2:42:15, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6311\n2023-01-02 02:39:01,604 - mmcls - INFO - Epoch [79][400/5005]\tlr: 1.000e-03, eta: 2:42:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6188\n2023-01-02 02:39:10,296 - mmcls - INFO - Epoch [79][500/5005]\tlr: 1.000e-03, eta: 2:41:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6389\n2023-01-02 02:39:19,030 - mmcls - INFO - Epoch [79][600/5005]\tlr: 1.000e-03, eta: 2:41:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6555\n2023-01-02 02:39:27,778 - mmcls - INFO - Epoch [79][700/5005]\tlr: 1.000e-03, eta: 2:41:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6223\n2023-01-02 02:39:36,540 - mmcls - INFO - Epoch [79][800/5005]\tlr: 1.000e-03, eta: 2:41:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6330\n2023-01-02 02:39:45,129 - mmcls - INFO - Epoch [79][900/5005]\tlr: 1.000e-03, eta: 2:41:22, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6589\n2023-01-02 02:39:53,931 - mmcls - INFO - Epoch [79][1000/5005]\tlr: 1.000e-03, eta: 2:41:13, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.6567\n2023-01-02 02:40:02,907 - mmcls - INFO - Epoch [79][1100/5005]\tlr: 1.000e-03, eta: 2:41:04, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6412\n2023-01-02 02:40:11,712 - mmcls - INFO - Epoch [79][1200/5005]\tlr: 1.000e-03, eta: 2:40:55, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6393\n2023-01-02 02:40:20,660 - mmcls - INFO - Epoch [79][1300/5005]\tlr: 1.000e-03, eta: 2:40:46, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6442\n2023-01-02 02:40:29,354 - mmcls - INFO - Epoch [79][1400/5005]\tlr: 1.000e-03, eta: 2:40:38, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6294\n2023-01-02 02:40:38,069 - mmcls - INFO - Epoch [79][1500/5005]\tlr: 1.000e-03, eta: 2:40:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6506\n2023-01-02 02:40:46,630 - mmcls - INFO - Epoch [79][1600/5005]\tlr: 1.000e-03, eta: 2:40:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6607\n2023-01-02 02:40:55,449 - mmcls - INFO - Epoch [79][1700/5005]\tlr: 1.000e-03, eta: 2:40:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6465\n2023-01-02 02:41:04,284 - mmcls - INFO - Epoch [79][1800/5005]\tlr: 1.000e-03, eta: 2:40:02, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6384\n2023-01-02 02:41:13,194 - mmcls - INFO - Epoch [79][1900/5005]\tlr: 1.000e-03, eta: 2:39:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6691\n2023-01-02 02:41:21,977 - mmcls - INFO - Epoch [79][2000/5005]\tlr: 1.000e-03, eta: 2:39:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6531\n2023-01-02 02:41:30,781 - mmcls - INFO - Epoch [79][2100/5005]\tlr: 1.000e-03, eta: 2:39:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6431\n2023-01-02 02:41:39,419 - mmcls - INFO - Epoch [79][2200/5005]\tlr: 1.000e-03, eta: 2:39:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6493\n2023-01-02 02:41:48,516 - mmcls - INFO - Epoch [79][2300/5005]\tlr: 1.000e-03, eta: 2:39:18, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.6454\n2023-01-02 02:41:57,171 - mmcls - INFO - Epoch [79][2400/5005]\tlr: 1.000e-03, eta: 2:39:09, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6183\n2023-01-02 02:42:06,334 - mmcls - INFO - Epoch [79][2500/5005]\tlr: 1.000e-03, eta: 2:39:00, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6473\n2023-01-02 02:42:15,116 - mmcls - INFO - Epoch [79][2600/5005]\tlr: 1.000e-03, eta: 2:38:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6279\n2023-01-02 02:42:24,392 - mmcls - INFO - Epoch [79][2700/5005]\tlr: 1.000e-03, eta: 2:38:42, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6471\n2023-01-02 02:42:33,361 - mmcls - INFO - Epoch [79][2800/5005]\tlr: 1.000e-03, eta: 2:38:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6453\n2023-01-02 02:42:41,966 - mmcls - INFO - Epoch [79][2900/5005]\tlr: 1.000e-03, eta: 2:38:24, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6473\n2023-01-02 02:42:50,740 - mmcls - INFO - Epoch [79][3000/5005]\tlr: 1.000e-03, eta: 2:38:16, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6614\n2023-01-02 02:42:59,528 - mmcls - INFO - Epoch [79][3100/5005]\tlr: 1.000e-03, eta: 2:38:07, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.6064\n2023-01-02 02:43:08,401 - mmcls - INFO - Epoch [79][3200/5005]\tlr: 1.000e-03, eta: 2:37:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6371\n2023-01-02 02:43:17,239 - mmcls - INFO - Epoch [79][3300/5005]\tlr: 1.000e-03, eta: 2:37:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6198\n2023-01-02 02:43:26,564 - mmcls - INFO - Epoch [79][3400/5005]\tlr: 1.000e-03, eta: 2:37:40, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6446\n2023-01-02 02:43:35,327 - mmcls - INFO - Epoch [79][3500/5005]\tlr: 1.000e-03, eta: 2:37:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6433\n2023-01-02 02:43:44,444 - mmcls - INFO - Epoch [79][3600/5005]\tlr: 1.000e-03, eta: 2:37:23, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6436\n2023-01-02 02:43:53,178 - mmcls - INFO - Epoch [79][3700/5005]\tlr: 1.000e-03, eta: 2:37:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6646\n2023-01-02 02:44:02,011 - mmcls - INFO - Epoch [79][3800/5005]\tlr: 1.000e-03, eta: 2:37:05, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6518\n2023-01-02 02:44:10,780 - mmcls - INFO - Epoch [79][3900/5005]\tlr: 1.000e-03, eta: 2:36:56, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6390\n2023-01-02 02:44:19,609 - mmcls - INFO - Epoch [79][4000/5005]\tlr: 1.000e-03, eta: 2:36:47, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6332\n2023-01-02 02:44:28,196 - mmcls - INFO - Epoch [79][4100/5005]\tlr: 1.000e-03, eta: 2:36:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6265\n2023-01-02 02:44:37,138 - mmcls - INFO - Epoch [79][4200/5005]\tlr: 1.000e-03, eta: 2:36:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6560\n2023-01-02 02:44:45,569 - mmcls - INFO - Epoch [79][4300/5005]\tlr: 1.000e-03, eta: 2:36:20, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.6292\n2023-01-02 02:44:54,536 - mmcls - INFO - Epoch [79][4400/5005]\tlr: 1.000e-03, eta: 2:36:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6675\n2023-01-02 02:45:03,194 - mmcls - INFO - Epoch [79][4500/5005]\tlr: 1.000e-03, eta: 2:36:02, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6532\n2023-01-02 02:45:11,794 - mmcls - INFO - Epoch [79][4600/5005]\tlr: 1.000e-03, eta: 2:35:53, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6593\n2023-01-02 02:45:20,436 - mmcls - INFO - Epoch [79][4700/5005]\tlr: 1.000e-03, eta: 2:35:45, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6395\n2023-01-02 02:45:29,208 - mmcls - INFO - Epoch [79][4800/5005]\tlr: 1.000e-03, eta: 2:35:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6708\n2023-01-02 02:45:38,121 - mmcls - INFO - Epoch [79][4900/5005]\tlr: 1.000e-03, eta: 2:35:27, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6629\n2023-01-02 02:45:47,081 - mmcls - INFO - Epoch [79][5000/5005]\tlr: 1.000e-03, eta: 2:35:18, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6542\n2023-01-02 02:46:09,852 - mmcls - INFO - Epoch(val) [79][196]\taccuracy_top-1: 64.7960, accuracy_top-5: 85.3960\n2023-01-02 02:46:20,796 - mmcls - INFO - Epoch [80][100/5005]\tlr: 1.000e-03, eta: 2:35:09, time: 0.109, data_time: 0.058, memory: 1224, loss: 1.6095\n2023-01-02 02:46:29,936 - mmcls - INFO - Epoch [80][200/5005]\tlr: 1.000e-03, eta: 2:35:00, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6244\n2023-01-02 02:46:38,697 - mmcls - INFO - Epoch [80][300/5005]\tlr: 1.000e-03, eta: 2:34:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6340\n2023-01-02 02:46:47,497 - mmcls - INFO - Epoch [80][400/5005]\tlr: 1.000e-03, eta: 2:34:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6367\n2023-01-02 02:46:56,194 - mmcls - INFO - Epoch [80][500/5005]\tlr: 1.000e-03, eta: 2:34:34, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6170\n2023-01-02 02:47:05,041 - mmcls - INFO - Epoch [80][600/5005]\tlr: 1.000e-03, eta: 2:34:25, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6076\n2023-01-02 02:47:13,870 - mmcls - INFO - Epoch [80][700/5005]\tlr: 1.000e-03, eta: 2:34:16, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6186\n2023-01-02 02:47:22,851 - mmcls - INFO - Epoch [80][800/5005]\tlr: 1.000e-03, eta: 2:34:07, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6352\n2023-01-02 02:47:31,632 - mmcls - INFO - Epoch [80][900/5005]\tlr: 1.000e-03, eta: 2:33:58, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6583\n2023-01-02 02:47:40,243 - mmcls - INFO - Epoch [80][1000/5005]\tlr: 1.000e-03, eta: 2:33:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6287\n2023-01-02 02:47:48,948 - mmcls - INFO - Epoch [80][1100/5005]\tlr: 1.000e-03, eta: 2:33:40, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6334\n2023-01-02 02:47:57,586 - mmcls - INFO - Epoch [80][1200/5005]\tlr: 1.000e-03, eta: 2:33:31, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6460\n2023-01-02 02:48:06,082 - mmcls - INFO - Epoch [80][1300/5005]\tlr: 1.000e-03, eta: 2:33:22, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.6395\n2023-01-02 02:48:14,683 - mmcls - INFO - Epoch [80][1400/5005]\tlr: 1.000e-03, eta: 2:33:13, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6480\n2023-01-02 02:48:23,555 - mmcls - INFO - Epoch [80][1500/5005]\tlr: 1.000e-03, eta: 2:33:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6480\n2023-01-02 02:48:32,425 - mmcls - INFO - Epoch [80][1600/5005]\tlr: 1.000e-03, eta: 2:32:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6292\n2023-01-02 02:48:41,113 - mmcls - INFO - Epoch [80][1700/5005]\tlr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6351\n2023-01-02 02:48:49,695 - mmcls - INFO - Epoch [80][1800/5005]\tlr: 1.000e-03, eta: 2:32:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6414\n2023-01-02 02:48:58,498 - mmcls - INFO - Epoch [80][1900/5005]\tlr: 1.000e-03, eta: 2:32:29, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6448\n2023-01-02 02:49:07,334 - mmcls - INFO - Epoch [80][2000/5005]\tlr: 1.000e-03, eta: 2:32:20, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6487\n2023-01-02 02:49:16,644 - mmcls - INFO - Epoch [80][2100/5005]\tlr: 1.000e-03, eta: 2:32:11, time: 0.093, data_time: 0.036, memory: 1224, loss: 1.6332\n2023-01-02 02:49:25,492 - mmcls - INFO - Epoch [80][2200/5005]\tlr: 1.000e-03, eta: 2:32:03, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6415\n2023-01-02 02:49:34,242 - mmcls - INFO - Epoch [80][2300/5005]\tlr: 1.000e-03, eta: 2:31:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6443\n2023-01-02 02:49:43,092 - mmcls - INFO - Epoch [80][2400/5005]\tlr: 1.000e-03, eta: 2:31:45, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6342\n2023-01-02 02:49:51,827 - mmcls - INFO - Epoch [80][2500/5005]\tlr: 1.000e-03, eta: 2:31:36, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6440\n2023-01-02 02:50:00,591 - mmcls - INFO - Epoch [80][2600/5005]\tlr: 1.000e-03, eta: 2:31:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6401\n2023-01-02 02:50:09,433 - mmcls - INFO - Epoch [80][2700/5005]\tlr: 1.000e-03, eta: 2:31:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6377\n2023-01-02 02:50:18,147 - mmcls - INFO - Epoch [80][2800/5005]\tlr: 1.000e-03, eta: 2:31:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6345\n2023-01-02 02:50:26,901 - mmcls - INFO - Epoch [80][2900/5005]\tlr: 1.000e-03, eta: 2:31:00, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6287\n2023-01-02 02:50:35,733 - mmcls - INFO - Epoch [80][3000/5005]\tlr: 1.000e-03, eta: 2:30:51, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6362\n2023-01-02 02:50:44,537 - mmcls - INFO - Epoch [80][3100/5005]\tlr: 1.000e-03, eta: 2:30:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6277\n2023-01-02 02:50:53,147 - mmcls - INFO - Epoch [80][3200/5005]\tlr: 1.000e-03, eta: 2:30:34, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6614\n2023-01-02 02:51:01,856 - mmcls - INFO - Epoch [80][3300/5005]\tlr: 1.000e-03, eta: 2:30:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6436\n2023-01-02 02:51:10,812 - mmcls - INFO - Epoch [80][3400/5005]\tlr: 1.000e-03, eta: 2:30:16, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6193\n2023-01-02 02:51:19,458 - mmcls - INFO - Epoch [80][3500/5005]\tlr: 1.000e-03, eta: 2:30:07, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6391\n2023-01-02 02:51:28,657 - mmcls - INFO - Epoch [80][3600/5005]\tlr: 1.000e-03, eta: 2:29:58, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6531\n2023-01-02 02:51:37,505 - mmcls - INFO - Epoch [80][3700/5005]\tlr: 1.000e-03, eta: 2:29:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6316\n2023-01-02 02:51:46,237 - mmcls - INFO - Epoch [80][3800/5005]\tlr: 1.000e-03, eta: 2:29:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6341\n2023-01-02 02:51:54,934 - mmcls - INFO - Epoch [80][3900/5005]\tlr: 1.000e-03, eta: 2:29:31, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6356\n2023-01-02 02:52:03,444 - mmcls - INFO - Epoch [80][4000/5005]\tlr: 1.000e-03, eta: 2:29:23, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.6417\n2023-01-02 02:52:12,094 - mmcls - INFO - Epoch [80][4100/5005]\tlr: 1.000e-03, eta: 2:29:14, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6648\n2023-01-02 02:52:20,945 - mmcls - INFO - Epoch [80][4200/5005]\tlr: 1.000e-03, eta: 2:29:05, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6548\n2023-01-02 02:52:29,877 - mmcls - INFO - Epoch [80][4300/5005]\tlr: 1.000e-03, eta: 2:28:56, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6191\n2023-01-02 02:52:38,675 - mmcls - INFO - Epoch [80][4400/5005]\tlr: 1.000e-03, eta: 2:28:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6240\n2023-01-02 02:52:47,542 - mmcls - INFO - Epoch [80][4500/5005]\tlr: 1.000e-03, eta: 2:28:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6477\n2023-01-02 02:52:56,368 - mmcls - INFO - Epoch [80][4600/5005]\tlr: 1.000e-03, eta: 2:28:29, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6724\n2023-01-02 02:53:05,818 - mmcls - INFO - Epoch [80][4700/5005]\tlr: 1.000e-03, eta: 2:28:21, time: 0.095, data_time: 0.024, memory: 1224, loss: 1.6841\n2023-01-02 02:53:14,708 - mmcls - INFO - Epoch [80][4800/5005]\tlr: 1.000e-03, eta: 2:28:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6294\n2023-01-02 02:53:23,450 - mmcls - INFO - Epoch [80][4900/5005]\tlr: 1.000e-03, eta: 2:28:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6432\n2023-01-02 02:53:32,162 - mmcls - INFO - Epoch [80][5000/5005]\tlr: 1.000e-03, eta: 2:27:54, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6389\n2023-01-02 02:53:54,854 - mmcls - INFO - Epoch(val) [80][196]\taccuracy_top-1: 64.7060, accuracy_top-5: 85.4100\n2023-01-02 02:54:05,802 - mmcls - INFO - Epoch [81][100/5005]\tlr: 1.000e-03, eta: 2:27:45, time: 0.109, data_time: 0.044, memory: 1224, loss: 1.6285\n2023-01-02 02:54:14,529 - mmcls - INFO - Epoch [81][200/5005]\tlr: 1.000e-03, eta: 2:27:36, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6256\n2023-01-02 02:54:23,133 - mmcls - INFO - Epoch [81][300/5005]\tlr: 1.000e-03, eta: 2:27:27, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6204\n2023-01-02 02:54:32,114 - mmcls - INFO - Epoch [81][400/5005]\tlr: 1.000e-03, eta: 2:27:18, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6244\n2023-01-02 02:54:41,094 - mmcls - INFO - Epoch [81][500/5005]\tlr: 1.000e-03, eta: 2:27:09, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6042\n2023-01-02 02:54:50,063 - mmcls - INFO - Epoch [81][600/5005]\tlr: 1.000e-03, eta: 2:27:01, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6270\n2023-01-02 02:54:58,937 - mmcls - INFO - Epoch [81][700/5005]\tlr: 1.000e-03, eta: 2:26:52, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6326\n2023-01-02 02:55:07,712 - mmcls - INFO - Epoch [81][800/5005]\tlr: 1.000e-03, eta: 2:26:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6456\n2023-01-02 02:55:16,850 - mmcls - INFO - Epoch [81][900/5005]\tlr: 1.000e-03, eta: 2:26:34, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6217\n2023-01-02 02:55:25,463 - mmcls - INFO - Epoch [81][1000/5005]\tlr: 1.000e-03, eta: 2:26:25, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6359\n2023-01-02 02:55:34,055 - mmcls - INFO - Epoch [81][1100/5005]\tlr: 1.000e-03, eta: 2:26:16, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6336\n2023-01-02 02:55:42,892 - mmcls - INFO - Epoch [81][1200/5005]\tlr: 1.000e-03, eta: 2:26:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6184\n2023-01-02 02:55:51,756 - mmcls - INFO - Epoch [81][1300/5005]\tlr: 1.000e-03, eta: 2:25:58, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6289\n2023-01-02 02:56:00,622 - mmcls - INFO - Epoch [81][1400/5005]\tlr: 1.000e-03, eta: 2:25:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6403\n2023-01-02 02:56:09,694 - mmcls - INFO - Epoch [81][1500/5005]\tlr: 1.000e-03, eta: 2:25:41, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6451\n2023-01-02 02:56:18,361 - mmcls - INFO - Epoch [81][1600/5005]\tlr: 1.000e-03, eta: 2:25:32, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6444\n2023-01-02 02:56:27,087 - mmcls - INFO - Epoch [81][1700/5005]\tlr: 1.000e-03, eta: 2:25:23, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6491\n2023-01-02 02:56:35,802 - mmcls - INFO - Epoch [81][1800/5005]\tlr: 1.000e-03, eta: 2:25:14, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6005\n2023-01-02 02:56:44,900 - mmcls - INFO - Epoch [81][1900/5005]\tlr: 1.000e-03, eta: 2:25:05, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6395\n2023-01-02 02:56:53,645 - mmcls - INFO - Epoch [81][2000/5005]\tlr: 1.000e-03, eta: 2:24:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6403\n2023-01-02 02:57:02,496 - mmcls - INFO - Epoch [81][2100/5005]\tlr: 1.000e-03, eta: 2:24:48, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6303\n2023-01-02 02:57:11,015 - mmcls - INFO - Epoch [81][2200/5005]\tlr: 1.000e-03, eta: 2:24:39, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6268\n2023-01-02 02:57:20,194 - mmcls - INFO - Epoch [81][2300/5005]\tlr: 1.000e-03, eta: 2:24:30, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6412\n2023-01-02 02:57:29,023 - mmcls - INFO - Epoch [81][2400/5005]\tlr: 1.000e-03, eta: 2:24:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6555\n2023-01-02 02:57:37,882 - mmcls - INFO - Epoch [81][2500/5005]\tlr: 1.000e-03, eta: 2:24:12, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6249\n2023-01-02 02:57:46,994 - mmcls - INFO - Epoch [81][2600/5005]\tlr: 1.000e-03, eta: 2:24:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6266\n2023-01-02 02:57:55,900 - mmcls - INFO - Epoch [81][2700/5005]\tlr: 1.000e-03, eta: 2:23:54, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6337\n2023-01-02 02:58:04,760 - mmcls - INFO - Epoch [81][2800/5005]\tlr: 1.000e-03, eta: 2:23:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6085\n2023-01-02 02:58:13,592 - mmcls - INFO - Epoch [81][2900/5005]\tlr: 1.000e-03, eta: 2:23:37, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6353\n2023-01-02 02:58:22,538 - mmcls - INFO - Epoch [81][3000/5005]\tlr: 1.000e-03, eta: 2:23:28, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6525\n2023-01-02 02:58:31,362 - mmcls - INFO - Epoch [81][3100/5005]\tlr: 1.000e-03, eta: 2:23:19, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6143\n2023-01-02 02:58:39,982 - mmcls - INFO - Epoch [81][3200/5005]\tlr: 1.000e-03, eta: 2:23:10, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6477\n2023-01-02 02:58:48,674 - mmcls - INFO - Epoch [81][3300/5005]\tlr: 1.000e-03, eta: 2:23:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6525\n2023-01-02 02:58:57,323 - mmcls - INFO - Epoch [81][3400/5005]\tlr: 1.000e-03, eta: 2:22:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6313\n2023-01-02 02:59:06,238 - mmcls - INFO - Epoch [81][3500/5005]\tlr: 1.000e-03, eta: 2:22:43, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6293\n2023-01-02 02:59:15,177 - mmcls - INFO - Epoch [81][3600/5005]\tlr: 1.000e-03, eta: 2:22:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6677\n2023-01-02 02:59:23,970 - mmcls - INFO - Epoch [81][3700/5005]\tlr: 1.000e-03, eta: 2:22:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6272\n2023-01-02 02:59:32,743 - mmcls - INFO - Epoch [81][3800/5005]\tlr: 1.000e-03, eta: 2:22:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6554\n2023-01-02 02:59:41,392 - mmcls - INFO - Epoch [81][3900/5005]\tlr: 1.000e-03, eta: 2:22:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6137\n2023-01-02 02:59:49,973 - mmcls - INFO - Epoch [81][4000/5005]\tlr: 1.000e-03, eta: 2:21:59, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6514\n2023-01-02 02:59:58,762 - mmcls - INFO - Epoch [81][4100/5005]\tlr: 1.000e-03, eta: 2:21:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6536\n2023-01-02 03:00:07,817 - mmcls - INFO - Epoch [81][4200/5005]\tlr: 1.000e-03, eta: 2:21:41, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.6399\n2023-01-02 03:00:16,575 - mmcls - INFO - Epoch [81][4300/5005]\tlr: 1.000e-03, eta: 2:21:32, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6394\n2023-01-02 03:00:25,261 - mmcls - INFO - Epoch [81][4400/5005]\tlr: 1.000e-03, eta: 2:21:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6468\n2023-01-02 03:00:34,094 - mmcls - INFO - Epoch [81][4500/5005]\tlr: 1.000e-03, eta: 2:21:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6427\n2023-01-02 03:00:43,174 - mmcls - INFO - Epoch [81][4600/5005]\tlr: 1.000e-03, eta: 2:21:06, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.6276\n2023-01-02 03:00:51,746 - mmcls - INFO - Epoch [81][4700/5005]\tlr: 1.000e-03, eta: 2:20:57, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6528\n2023-01-02 03:01:00,370 - mmcls - INFO - Epoch [81][4800/5005]\tlr: 1.000e-03, eta: 2:20:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6386\n2023-01-02 03:01:09,094 - mmcls - INFO - Epoch [81][4900/5005]\tlr: 1.000e-03, eta: 2:20:39, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6545\n2023-01-02 03:01:17,845 - mmcls - INFO - Epoch [81][5000/5005]\tlr: 1.000e-03, eta: 2:20:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6526\n2023-01-02 03:01:40,521 - mmcls - INFO - Epoch(val) [81][196]\taccuracy_top-1: 64.7660, accuracy_top-5: 85.3940\n2023-01-02 03:01:51,569 - mmcls - INFO - Epoch [82][100/5005]\tlr: 1.000e-03, eta: 2:20:21, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.6287\n2023-01-02 03:02:01,080 - mmcls - INFO - Epoch [82][200/5005]\tlr: 1.000e-03, eta: 2:20:12, time: 0.095, data_time: 0.010, memory: 1224, loss: 1.6244\n2023-01-02 03:02:09,981 - mmcls - INFO - Epoch [82][300/5005]\tlr: 1.000e-03, eta: 2:20:04, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6210\n2023-01-02 03:02:18,790 - mmcls - INFO - Epoch [82][400/5005]\tlr: 1.000e-03, eta: 2:19:55, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6265\n2023-01-02 03:02:27,695 - mmcls - INFO - Epoch [82][500/5005]\tlr: 1.000e-03, eta: 2:19:46, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6180\n2023-01-02 03:02:36,212 - mmcls - INFO - Epoch [82][600/5005]\tlr: 1.000e-03, eta: 2:19:37, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6230\n2023-01-02 03:02:44,940 - mmcls - INFO - Epoch [82][700/5005]\tlr: 1.000e-03, eta: 2:19:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6253\n2023-01-02 03:02:53,829 - mmcls - INFO - Epoch [82][800/5005]\tlr: 1.000e-03, eta: 2:19:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6295\n2023-01-02 03:03:02,423 - mmcls - INFO - Epoch [82][900/5005]\tlr: 1.000e-03, eta: 2:19:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6210\n2023-01-02 03:03:10,947 - mmcls - INFO - Epoch [82][1000/5005]\tlr: 1.000e-03, eta: 2:19:01, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6474\n2023-01-02 03:03:19,825 - mmcls - INFO - Epoch [82][1100/5005]\tlr: 1.000e-03, eta: 2:18:52, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6163\n2023-01-02 03:03:28,798 - mmcls - INFO - Epoch [82][1200/5005]\tlr: 1.000e-03, eta: 2:18:44, time: 0.090, data_time: 0.031, memory: 1224, loss: 1.6447\n2023-01-02 03:03:37,485 - mmcls - INFO - Epoch [82][1300/5005]\tlr: 1.000e-03, eta: 2:18:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6268\n2023-01-02 03:03:46,295 - mmcls - INFO - Epoch [82][1400/5005]\tlr: 1.000e-03, eta: 2:18:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6671\n2023-01-02 03:03:55,016 - mmcls - INFO - Epoch [82][1500/5005]\tlr: 1.000e-03, eta: 2:18:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6387\n2023-01-02 03:04:03,904 - mmcls - INFO - Epoch [82][1600/5005]\tlr: 1.000e-03, eta: 2:18:08, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6320\n2023-01-02 03:04:12,835 - mmcls - INFO - Epoch [82][1700/5005]\tlr: 1.000e-03, eta: 2:17:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6382\n2023-01-02 03:04:21,672 - mmcls - INFO - Epoch [82][1800/5005]\tlr: 1.000e-03, eta: 2:17:50, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6317\n2023-01-02 03:04:30,430 - mmcls - INFO - Epoch [82][1900/5005]\tlr: 1.000e-03, eta: 2:17:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6297\n2023-01-02 03:04:39,288 - mmcls - INFO - Epoch [82][2000/5005]\tlr: 1.000e-03, eta: 2:17:33, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6219\n2023-01-02 03:04:47,931 - mmcls - INFO - Epoch [82][2100/5005]\tlr: 1.000e-03, eta: 2:17:24, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6114\n2023-01-02 03:04:56,648 - mmcls - INFO - Epoch [82][2200/5005]\tlr: 1.000e-03, eta: 2:17:15, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6294\n2023-01-02 03:05:05,421 - mmcls - INFO - Epoch [82][2300/5005]\tlr: 1.000e-03, eta: 2:17:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6349\n2023-01-02 03:05:14,173 - mmcls - INFO - Epoch [82][2400/5005]\tlr: 1.000e-03, eta: 2:16:57, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6478\n2023-01-02 03:05:23,290 - mmcls - INFO - Epoch [82][2500/5005]\tlr: 1.000e-03, eta: 2:16:48, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6432\n2023-01-02 03:05:31,966 - mmcls - INFO - Epoch [82][2600/5005]\tlr: 1.000e-03, eta: 2:16:39, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6333\n2023-01-02 03:05:40,878 - mmcls - INFO - Epoch [82][2700/5005]\tlr: 1.000e-03, eta: 2:16:30, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.6463\n2023-01-02 03:05:49,629 - mmcls - INFO - Epoch [82][2800/5005]\tlr: 1.000e-03, eta: 2:16:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6385\n2023-01-02 03:05:58,352 - mmcls - INFO - Epoch [82][2900/5005]\tlr: 1.000e-03, eta: 2:16:13, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6387\n2023-01-02 03:06:07,166 - mmcls - INFO - Epoch [82][3000/5005]\tlr: 1.000e-03, eta: 2:16:04, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6479\n2023-01-02 03:06:16,497 - mmcls - INFO - Epoch [82][3100/5005]\tlr: 1.000e-03, eta: 2:15:55, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6351\n2023-01-02 03:06:25,018 - mmcls - INFO - Epoch [82][3200/5005]\tlr: 1.000e-03, eta: 2:15:46, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6401\n2023-01-02 03:06:34,160 - mmcls - INFO - Epoch [82][3300/5005]\tlr: 1.000e-03, eta: 2:15:37, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6333\n2023-01-02 03:06:42,932 - mmcls - INFO - Epoch [82][3400/5005]\tlr: 1.000e-03, eta: 2:15:28, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6251\n2023-01-02 03:06:51,621 - mmcls - INFO - Epoch [82][3500/5005]\tlr: 1.000e-03, eta: 2:15:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6405\n2023-01-02 03:07:00,405 - mmcls - INFO - Epoch [82][3600/5005]\tlr: 1.000e-03, eta: 2:15:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6261\n2023-01-02 03:07:09,231 - mmcls - INFO - Epoch [82][3700/5005]\tlr: 1.000e-03, eta: 2:15:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6268\n2023-01-02 03:07:18,012 - mmcls - INFO - Epoch [82][3800/5005]\tlr: 1.000e-03, eta: 2:14:53, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6182\n2023-01-02 03:07:26,857 - mmcls - INFO - Epoch [82][3900/5005]\tlr: 1.000e-03, eta: 2:14:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6366\n2023-01-02 03:07:35,506 - mmcls - INFO - Epoch [82][4000/5005]\tlr: 1.000e-03, eta: 2:14:35, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6623\n2023-01-02 03:07:44,244 - mmcls - INFO - Epoch [82][4100/5005]\tlr: 1.000e-03, eta: 2:14:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6393\n2023-01-02 03:07:52,944 - mmcls - INFO - Epoch [82][4200/5005]\tlr: 1.000e-03, eta: 2:14:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6479\n2023-01-02 03:08:01,897 - mmcls - INFO - Epoch [82][4300/5005]\tlr: 1.000e-03, eta: 2:14:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6416\n2023-01-02 03:08:10,522 - mmcls - INFO - Epoch [82][4400/5005]\tlr: 1.000e-03, eta: 2:13:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6580\n2023-01-02 03:08:19,233 - mmcls - INFO - Epoch [82][4500/5005]\tlr: 1.000e-03, eta: 2:13:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6385\n2023-01-02 03:08:28,342 - mmcls - INFO - Epoch [82][4600/5005]\tlr: 1.000e-03, eta: 2:13:42, time: 0.091, data_time: 0.049, memory: 1224, loss: 1.6286\n2023-01-02 03:08:37,191 - mmcls - INFO - Epoch [82][4700/5005]\tlr: 1.000e-03, eta: 2:13:33, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6670\n2023-01-02 03:08:45,918 - mmcls - INFO - Epoch [82][4800/5005]\tlr: 1.000e-03, eta: 2:13:24, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6585\n2023-01-02 03:08:54,763 - mmcls - INFO - Epoch [82][4900/5005]\tlr: 1.000e-03, eta: 2:13:15, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6501\n2023-01-02 03:09:03,758 - mmcls - INFO - Epoch [82][5000/5005]\tlr: 1.000e-03, eta: 2:13:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6276\n2023-01-02 03:09:26,393 - mmcls - INFO - Epoch(val) [82][196]\taccuracy_top-1: 64.7200, accuracy_top-5: 85.4040\n2023-01-02 03:09:37,539 - mmcls - INFO - Epoch [83][100/5005]\tlr: 1.000e-03, eta: 2:12:57, time: 0.111, data_time: 0.036, memory: 1224, loss: 1.6035\n2023-01-02 03:09:46,280 - mmcls - INFO - Epoch [83][200/5005]\tlr: 1.000e-03, eta: 2:12:48, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6344\n2023-01-02 03:09:55,355 - mmcls - INFO - Epoch [83][300/5005]\tlr: 1.000e-03, eta: 2:12:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6015\n2023-01-02 03:10:04,171 - mmcls - INFO - Epoch [83][400/5005]\tlr: 1.000e-03, eta: 2:12:31, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6067\n2023-01-02 03:10:12,813 - mmcls - INFO - Epoch [83][500/5005]\tlr: 1.000e-03, eta: 2:12:22, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6088\n2023-01-02 03:10:21,774 - mmcls - INFO - Epoch [83][600/5005]\tlr: 1.000e-03, eta: 2:12:13, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.6605\n2023-01-02 03:10:30,427 - mmcls - INFO - Epoch [83][700/5005]\tlr: 1.000e-03, eta: 2:12:04, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6228\n2023-01-02 03:10:39,119 - mmcls - INFO - Epoch [83][800/5005]\tlr: 1.000e-03, eta: 2:11:55, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6322\n2023-01-02 03:10:47,985 - mmcls - INFO - Epoch [83][900/5005]\tlr: 1.000e-03, eta: 2:11:46, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6273\n2023-01-02 03:10:56,536 - mmcls - INFO - Epoch [83][1000/5005]\tlr: 1.000e-03, eta: 2:11:37, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6261\n2023-01-02 03:11:05,575 - mmcls - INFO - Epoch [83][1100/5005]\tlr: 1.000e-03, eta: 2:11:29, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6184\n2023-01-02 03:11:14,424 - mmcls - INFO - Epoch [83][1200/5005]\tlr: 1.000e-03, eta: 2:11:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6226\n2023-01-02 03:11:23,424 - mmcls - INFO - Epoch [83][1300/5005]\tlr: 1.000e-03, eta: 2:11:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6296\n2023-01-02 03:11:32,220 - mmcls - INFO - Epoch [83][1400/5005]\tlr: 1.000e-03, eta: 2:11:02, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6185\n2023-01-02 03:11:41,155 - mmcls - INFO - Epoch [83][1500/5005]\tlr: 1.000e-03, eta: 2:10:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6359\n2023-01-02 03:11:49,933 - mmcls - INFO - Epoch [83][1600/5005]\tlr: 1.000e-03, eta: 2:10:44, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6410\n2023-01-02 03:11:58,955 - mmcls - INFO - Epoch [83][1700/5005]\tlr: 1.000e-03, eta: 2:10:35, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6258\n2023-01-02 03:12:07,780 - mmcls - INFO - Epoch [83][1800/5005]\tlr: 1.000e-03, eta: 2:10:26, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6239\n2023-01-02 03:12:16,830 - mmcls - INFO - Epoch [83][1900/5005]\tlr: 1.000e-03, eta: 2:10:18, time: 0.090, data_time: 0.038, memory: 1224, loss: 1.6425\n2023-01-02 03:12:25,587 - mmcls - INFO - Epoch [83][2000/5005]\tlr: 1.000e-03, eta: 2:10:09, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6225\n2023-01-02 03:12:34,385 - mmcls - INFO - Epoch [83][2100/5005]\tlr: 1.000e-03, eta: 2:10:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6415\n2023-01-02 03:12:43,028 - mmcls - INFO - Epoch [83][2200/5005]\tlr: 1.000e-03, eta: 2:09:51, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6649\n2023-01-02 03:12:51,713 - mmcls - INFO - Epoch [83][2300/5005]\tlr: 1.000e-03, eta: 2:09:42, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6377\n2023-01-02 03:13:00,386 - mmcls - INFO - Epoch [83][2400/5005]\tlr: 1.000e-03, eta: 2:09:33, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6514\n2023-01-02 03:13:09,432 - mmcls - INFO - Epoch [83][2500/5005]\tlr: 1.000e-03, eta: 2:09:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6385\n2023-01-02 03:13:18,454 - mmcls - INFO - Epoch [83][2600/5005]\tlr: 1.000e-03, eta: 2:09:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6263\n2023-01-02 03:13:27,243 - mmcls - INFO - Epoch [83][2700/5005]\tlr: 1.000e-03, eta: 2:09:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6349\n2023-01-02 03:13:35,932 - mmcls - INFO - Epoch [83][2800/5005]\tlr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6305\n2023-01-02 03:13:45,139 - mmcls - INFO - Epoch [83][2900/5005]\tlr: 1.000e-03, eta: 2:08:49, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6445\n2023-01-02 03:13:53,729 - mmcls - INFO - Epoch [83][3000/5005]\tlr: 1.000e-03, eta: 2:08:40, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6402\n2023-01-02 03:14:02,961 - mmcls - INFO - Epoch [83][3100/5005]\tlr: 1.000e-03, eta: 2:08:31, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6125\n2023-01-02 03:14:11,652 - mmcls - INFO - Epoch [83][3200/5005]\tlr: 1.000e-03, eta: 2:08:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6359\n2023-01-02 03:14:20,216 - mmcls - INFO - Epoch [83][3300/5005]\tlr: 1.000e-03, eta: 2:08:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6348\n2023-01-02 03:14:28,935 - mmcls - INFO - Epoch [83][3400/5005]\tlr: 1.000e-03, eta: 2:08:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6638\n2023-01-02 03:14:37,735 - mmcls - INFO - Epoch [83][3500/5005]\tlr: 1.000e-03, eta: 2:07:56, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6436\n2023-01-02 03:14:46,952 - mmcls - INFO - Epoch [83][3600/5005]\tlr: 1.000e-03, eta: 2:07:47, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6437\n2023-01-02 03:14:55,993 - mmcls - INFO - Epoch [83][3700/5005]\tlr: 1.000e-03, eta: 2:07:38, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6509\n2023-01-02 03:15:04,540 - mmcls - INFO - Epoch [83][3800/5005]\tlr: 1.000e-03, eta: 2:07:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6014\n2023-01-02 03:15:13,273 - mmcls - INFO - Epoch [83][3900/5005]\tlr: 1.000e-03, eta: 2:07:20, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6522\n2023-01-02 03:15:22,040 - mmcls - INFO - Epoch [83][4000/5005]\tlr: 1.000e-03, eta: 2:07:11, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6459\n2023-01-02 03:15:30,782 - mmcls - INFO - Epoch [83][4100/5005]\tlr: 1.000e-03, eta: 2:07:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6224\n2023-01-02 03:15:39,387 - mmcls - INFO - Epoch [83][4200/5005]\tlr: 1.000e-03, eta: 2:06:53, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6678\n2023-01-02 03:15:48,521 - mmcls - INFO - Epoch [83][4300/5005]\tlr: 1.000e-03, eta: 2:06:45, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6276\n2023-01-02 03:15:57,607 - mmcls - INFO - Epoch [83][4400/5005]\tlr: 1.000e-03, eta: 2:06:36, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6343\n2023-01-02 03:16:06,468 - mmcls - INFO - Epoch [83][4500/5005]\tlr: 1.000e-03, eta: 2:06:27, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6312\n2023-01-02 03:16:15,147 - mmcls - INFO - Epoch [83][4600/5005]\tlr: 1.000e-03, eta: 2:06:18, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6414\n2023-01-02 03:16:24,105 - mmcls - INFO - Epoch [83][4700/5005]\tlr: 1.000e-03, eta: 2:06:09, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.6499\n2023-01-02 03:16:32,656 - mmcls - INFO - Epoch [83][4800/5005]\tlr: 1.000e-03, eta: 2:06:00, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.6213\n2023-01-02 03:16:41,717 - mmcls - INFO - Epoch [83][4900/5005]\tlr: 1.000e-03, eta: 2:05:51, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6405\n2023-01-02 03:16:50,655 - mmcls - INFO - Epoch [83][5000/5005]\tlr: 1.000e-03, eta: 2:05:43, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6274\n2023-01-02 03:17:13,408 - mmcls - INFO - Epoch(val) [83][196]\taccuracy_top-1: 64.7820, accuracy_top-5: 85.3960\n2023-01-02 03:17:24,365 - mmcls - INFO - Epoch [84][100/5005]\tlr: 1.000e-03, eta: 2:05:34, time: 0.109, data_time: 0.057, memory: 1224, loss: 1.6256\n2023-01-02 03:17:33,379 - mmcls - INFO - Epoch [84][200/5005]\tlr: 1.000e-03, eta: 2:05:25, time: 0.090, data_time: 0.045, memory: 1224, loss: 1.6095\n2023-01-02 03:17:42,072 - mmcls - INFO - Epoch [84][300/5005]\tlr: 1.000e-03, eta: 2:05:16, time: 0.087, data_time: 0.039, memory: 1224, loss: 1.6195\n2023-01-02 03:17:51,207 - mmcls - INFO - Epoch [84][400/5005]\tlr: 1.000e-03, eta: 2:05:07, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6344\n2023-01-02 03:17:59,979 - mmcls - INFO - Epoch [84][500/5005]\tlr: 1.000e-03, eta: 2:04:58, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6251\n2023-01-02 03:18:08,947 - mmcls - INFO - Epoch [84][600/5005]\tlr: 1.000e-03, eta: 2:04:49, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6377\n2023-01-02 03:18:17,823 - mmcls - INFO - Epoch [84][700/5005]\tlr: 1.000e-03, eta: 2:04:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6492\n2023-01-02 03:18:26,563 - mmcls - INFO - Epoch [84][800/5005]\tlr: 1.000e-03, eta: 2:04:32, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6326\n2023-01-02 03:18:35,542 - mmcls - INFO - Epoch [84][900/5005]\tlr: 1.000e-03, eta: 2:04:23, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6143\n2023-01-02 03:18:44,457 - mmcls - INFO - Epoch [84][1000/5005]\tlr: 1.000e-03, eta: 2:04:14, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.6289\n2023-01-02 03:18:53,079 - mmcls - INFO - Epoch [84][1100/5005]\tlr: 1.000e-03, eta: 2:04:05, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6356\n2023-01-02 03:19:02,098 - mmcls - INFO - Epoch [84][1200/5005]\tlr: 1.000e-03, eta: 2:03:56, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6286\n2023-01-02 03:19:10,812 - mmcls - INFO - Epoch [84][1300/5005]\tlr: 1.000e-03, eta: 2:03:47, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6279\n2023-01-02 03:19:19,762 - mmcls - INFO - Epoch [84][1400/5005]\tlr: 1.000e-03, eta: 2:03:38, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6307\n2023-01-02 03:19:28,447 - mmcls - INFO - Epoch [84][1500/5005]\tlr: 1.000e-03, eta: 2:03:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5950\n2023-01-02 03:19:37,255 - mmcls - INFO - Epoch [84][1600/5005]\tlr: 1.000e-03, eta: 2:03:21, time: 0.088, data_time: 0.040, memory: 1224, loss: 1.6349\n2023-01-02 03:19:46,311 - mmcls - INFO - Epoch [84][1700/5005]\tlr: 1.000e-03, eta: 2:03:12, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6271\n2023-01-02 03:19:54,934 - mmcls - INFO - Epoch [84][1800/5005]\tlr: 1.000e-03, eta: 2:03:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6100\n2023-01-02 03:20:03,686 - mmcls - INFO - Epoch [84][1900/5005]\tlr: 1.000e-03, eta: 2:02:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6276\n2023-01-02 03:20:12,958 - mmcls - INFO - Epoch [84][2000/5005]\tlr: 1.000e-03, eta: 2:02:45, time: 0.093, data_time: 0.010, memory: 1224, loss: 1.6232\n2023-01-02 03:20:21,710 - mmcls - INFO - Epoch [84][2100/5005]\tlr: 1.000e-03, eta: 2:02:36, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6354\n2023-01-02 03:20:30,260 - mmcls - INFO - Epoch [84][2200/5005]\tlr: 1.000e-03, eta: 2:02:27, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6251\n2023-01-02 03:20:39,318 - mmcls - INFO - Epoch [84][2300/5005]\tlr: 1.000e-03, eta: 2:02:19, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6279\n2023-01-02 03:20:48,256 - mmcls - INFO - Epoch [84][2400/5005]\tlr: 1.000e-03, eta: 2:02:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6301\n2023-01-02 03:20:56,828 - mmcls - INFO - Epoch [84][2500/5005]\tlr: 1.000e-03, eta: 2:02:01, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6175\n2023-01-02 03:21:05,657 - mmcls - INFO - Epoch [84][2600/5005]\tlr: 1.000e-03, eta: 2:01:52, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6281\n2023-01-02 03:21:14,574 - mmcls - INFO - Epoch [84][2700/5005]\tlr: 1.000e-03, eta: 2:01:43, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6403\n2023-01-02 03:21:23,417 - mmcls - INFO - Epoch [84][2800/5005]\tlr: 1.000e-03, eta: 2:01:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6109\n2023-01-02 03:21:32,352 - mmcls - INFO - Epoch [84][2900/5005]\tlr: 1.000e-03, eta: 2:01:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6357\n2023-01-02 03:21:41,144 - mmcls - INFO - Epoch [84][3000/5005]\tlr: 1.000e-03, eta: 2:01:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6242\n2023-01-02 03:21:50,067 - mmcls - INFO - Epoch [84][3100/5005]\tlr: 1.000e-03, eta: 2:01:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6193\n2023-01-02 03:21:58,810 - mmcls - INFO - Epoch [84][3200/5005]\tlr: 1.000e-03, eta: 2:00:59, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6314\n2023-01-02 03:22:07,598 - mmcls - INFO - Epoch [84][3300/5005]\tlr: 1.000e-03, eta: 2:00:50, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6499\n2023-01-02 03:22:16,495 - mmcls - INFO - Epoch [84][3400/5005]\tlr: 1.000e-03, eta: 2:00:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6393\n2023-01-02 03:22:25,221 - mmcls - INFO - Epoch [84][3500/5005]\tlr: 1.000e-03, eta: 2:00:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6380\n2023-01-02 03:22:34,007 - mmcls - INFO - Epoch [84][3600/5005]\tlr: 1.000e-03, eta: 2:00:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6168\n2023-01-02 03:22:42,814 - mmcls - INFO - Epoch [84][3700/5005]\tlr: 1.000e-03, eta: 2:00:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6440\n2023-01-02 03:22:51,690 - mmcls - INFO - Epoch [84][3800/5005]\tlr: 1.000e-03, eta: 2:00:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6370\n2023-01-02 03:23:00,389 - mmcls - INFO - Epoch [84][3900/5005]\tlr: 1.000e-03, eta: 1:59:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6380\n2023-01-02 03:23:09,107 - mmcls - INFO - Epoch [84][4000/5005]\tlr: 1.000e-03, eta: 1:59:48, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6297\n2023-01-02 03:23:17,985 - mmcls - INFO - Epoch [84][4100/5005]\tlr: 1.000e-03, eta: 1:59:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6399\n2023-01-02 03:23:26,701 - mmcls - INFO - Epoch [84][4200/5005]\tlr: 1.000e-03, eta: 1:59:30, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6387\n2023-01-02 03:23:35,435 - mmcls - INFO - Epoch [84][4300/5005]\tlr: 1.000e-03, eta: 1:59:21, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6486\n2023-01-02 03:23:43,887 - mmcls - INFO - Epoch [84][4400/5005]\tlr: 1.000e-03, eta: 1:59:12, time: 0.084, data_time: 0.021, memory: 1224, loss: 1.6440\n2023-01-02 03:23:52,636 - mmcls - INFO - Epoch [84][4500/5005]\tlr: 1.000e-03, eta: 1:59:03, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6533\n2023-01-02 03:24:01,546 - mmcls - INFO - Epoch [84][4600/5005]\tlr: 1.000e-03, eta: 1:58:54, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6531\n2023-01-02 03:24:10,475 - mmcls - INFO - Epoch [84][4700/5005]\tlr: 1.000e-03, eta: 1:58:46, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.6394\n2023-01-02 03:24:19,137 - mmcls - INFO - Epoch [84][4800/5005]\tlr: 1.000e-03, eta: 1:58:37, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6741\n2023-01-02 03:24:28,074 - mmcls - INFO - Epoch [84][4900/5005]\tlr: 1.000e-03, eta: 1:58:28, time: 0.089, data_time: 0.033, memory: 1224, loss: 1.6522\n2023-01-02 03:24:36,808 - mmcls - INFO - Epoch [84][5000/5005]\tlr: 1.000e-03, eta: 1:58:19, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.6601\n2023-01-02 03:24:59,423 - mmcls - INFO - Epoch(val) [84][196]\taccuracy_top-1: 64.8040, accuracy_top-5: 85.4840\n2023-01-02 03:25:10,826 - mmcls - INFO - Epoch [85][100/5005]\tlr: 1.000e-03, eta: 1:58:10, time: 0.114, data_time: 0.046, memory: 1224, loss: 1.6264\n2023-01-02 03:25:19,726 - mmcls - INFO - Epoch [85][200/5005]\tlr: 1.000e-03, eta: 1:58:01, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6372\n2023-01-02 03:25:28,404 - mmcls - INFO - Epoch [85][300/5005]\tlr: 1.000e-03, eta: 1:57:52, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6223\n2023-01-02 03:25:37,117 - mmcls - INFO - Epoch [85][400/5005]\tlr: 1.000e-03, eta: 1:57:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6292\n2023-01-02 03:25:46,015 - mmcls - INFO - Epoch [85][500/5005]\tlr: 1.000e-03, eta: 1:57:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6118\n2023-01-02 03:25:55,015 - mmcls - INFO - Epoch [85][600/5005]\tlr: 1.000e-03, eta: 1:57:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6333\n2023-01-02 03:26:03,626 - mmcls - INFO - Epoch [85][700/5005]\tlr: 1.000e-03, eta: 1:57:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6364\n2023-01-02 03:26:12,026 - mmcls - INFO - Epoch [85][800/5005]\tlr: 1.000e-03, eta: 1:57:08, time: 0.084, data_time: 0.028, memory: 1224, loss: 1.6245\n2023-01-02 03:26:20,774 - mmcls - INFO - Epoch [85][900/5005]\tlr: 1.000e-03, eta: 1:56:59, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6473\n2023-01-02 03:26:29,751 - mmcls - INFO - Epoch [85][1000/5005]\tlr: 1.000e-03, eta: 1:56:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6301\n2023-01-02 03:26:38,350 - mmcls - INFO - Epoch [85][1100/5005]\tlr: 1.000e-03, eta: 1:56:41, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6122\n2023-01-02 03:26:47,202 - mmcls - INFO - Epoch [85][1200/5005]\tlr: 1.000e-03, eta: 1:56:32, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6311\n2023-01-02 03:26:55,867 - mmcls - INFO - Epoch [85][1300/5005]\tlr: 1.000e-03, eta: 1:56:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6205\n2023-01-02 03:27:05,042 - mmcls - INFO - Epoch [85][1400/5005]\tlr: 1.000e-03, eta: 1:56:15, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6253\n2023-01-02 03:27:13,692 - mmcls - INFO - Epoch [85][1500/5005]\tlr: 1.000e-03, eta: 1:56:06, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.6197\n2023-01-02 03:27:22,637 - mmcls - INFO - Epoch [85][1600/5005]\tlr: 1.000e-03, eta: 1:55:57, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6158\n2023-01-02 03:27:31,326 - mmcls - INFO - Epoch [85][1700/5005]\tlr: 1.000e-03, eta: 1:55:48, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6096\n2023-01-02 03:27:40,136 - mmcls - INFO - Epoch [85][1800/5005]\tlr: 1.000e-03, eta: 1:55:39, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6421\n2023-01-02 03:27:49,105 - mmcls - INFO - Epoch [85][1900/5005]\tlr: 1.000e-03, eta: 1:55:30, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6188\n2023-01-02 03:27:58,313 - mmcls - INFO - Epoch [85][2000/5005]\tlr: 1.000e-03, eta: 1:55:21, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6057\n2023-01-02 03:28:06,993 - mmcls - INFO - Epoch [85][2100/5005]\tlr: 1.000e-03, eta: 1:55:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6161\n2023-01-02 03:28:15,786 - mmcls - INFO - Epoch [85][2200/5005]\tlr: 1.000e-03, eta: 1:55:04, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6206\n2023-01-02 03:28:24,673 - mmcls - INFO - Epoch [85][2300/5005]\tlr: 1.000e-03, eta: 1:54:55, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6135\n2023-01-02 03:28:33,584 - mmcls - INFO - Epoch [85][2400/5005]\tlr: 1.000e-03, eta: 1:54:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6386\n2023-01-02 03:28:42,486 - mmcls - INFO - Epoch [85][2500/5005]\tlr: 1.000e-03, eta: 1:54:37, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6435\n2023-01-02 03:28:51,610 - mmcls - INFO - Epoch [85][2600/5005]\tlr: 1.000e-03, eta: 1:54:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6115\n2023-01-02 03:29:00,343 - mmcls - INFO - Epoch [85][2700/5005]\tlr: 1.000e-03, eta: 1:54:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6262\n2023-01-02 03:29:08,936 - mmcls - INFO - Epoch [85][2800/5005]\tlr: 1.000e-03, eta: 1:54:10, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6350\n2023-01-02 03:29:17,725 - mmcls - INFO - Epoch [85][2900/5005]\tlr: 1.000e-03, eta: 1:54:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6267\n2023-01-02 03:29:26,515 - mmcls - INFO - Epoch [85][3000/5005]\tlr: 1.000e-03, eta: 1:53:53, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6248\n2023-01-02 03:29:35,766 - mmcls - INFO - Epoch [85][3100/5005]\tlr: 1.000e-03, eta: 1:53:44, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6391\n2023-01-02 03:29:44,462 - mmcls - INFO - Epoch [85][3200/5005]\tlr: 1.000e-03, eta: 1:53:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6309\n2023-01-02 03:29:53,301 - mmcls - INFO - Epoch [85][3300/5005]\tlr: 1.000e-03, eta: 1:53:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6522\n2023-01-02 03:30:02,049 - mmcls - INFO - Epoch [85][3400/5005]\tlr: 1.000e-03, eta: 1:53:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6286\n2023-01-02 03:30:10,826 - mmcls - INFO - Epoch [85][3500/5005]\tlr: 1.000e-03, eta: 1:53:08, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6298\n2023-01-02 03:30:19,587 - mmcls - INFO - Epoch [85][3600/5005]\tlr: 1.000e-03, eta: 1:52:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6658\n2023-01-02 03:30:28,283 - mmcls - INFO - Epoch [85][3700/5005]\tlr: 1.000e-03, eta: 1:52:51, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.5881\n2023-01-02 03:30:37,069 - mmcls - INFO - Epoch [85][3800/5005]\tlr: 1.000e-03, eta: 1:52:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6531\n2023-01-02 03:30:46,020 - mmcls - INFO - Epoch [85][3900/5005]\tlr: 1.000e-03, eta: 1:52:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6282\n2023-01-02 03:30:54,946 - mmcls - INFO - Epoch [85][4000/5005]\tlr: 1.000e-03, eta: 1:52:24, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6228\n2023-01-02 03:31:03,926 - mmcls - INFO - Epoch [85][4100/5005]\tlr: 1.000e-03, eta: 1:52:15, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6527\n2023-01-02 03:31:12,734 - mmcls - INFO - Epoch [85][4200/5005]\tlr: 1.000e-03, eta: 1:52:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6218\n2023-01-02 03:31:21,449 - mmcls - INFO - Epoch [85][4300/5005]\tlr: 1.000e-03, eta: 1:51:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6377\n2023-01-02 03:31:30,066 - mmcls - INFO - Epoch [85][4400/5005]\tlr: 1.000e-03, eta: 1:51:48, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6373\n2023-01-02 03:31:38,799 - mmcls - INFO - Epoch [85][4500/5005]\tlr: 1.000e-03, eta: 1:51:40, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6357\n2023-01-02 03:31:47,573 - mmcls - INFO - Epoch [85][4600/5005]\tlr: 1.000e-03, eta: 1:51:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6368\n2023-01-02 03:31:56,596 - mmcls - INFO - Epoch [85][4700/5005]\tlr: 1.000e-03, eta: 1:51:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6516\n2023-01-02 03:32:05,458 - mmcls - INFO - Epoch [85][4800/5005]\tlr: 1.000e-03, eta: 1:51:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6316\n2023-01-02 03:32:14,235 - mmcls - INFO - Epoch [85][4900/5005]\tlr: 1.000e-03, eta: 1:51:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6407\n2023-01-02 03:32:22,770 - mmcls - INFO - Epoch [85][5000/5005]\tlr: 1.000e-03, eta: 1:50:55, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.6545\n2023-01-02 03:32:45,345 - mmcls - INFO - Epoch(val) [85][196]\taccuracy_top-1: 64.6760, accuracy_top-5: 85.3460\n2023-01-02 03:32:56,112 - mmcls - INFO - Epoch [86][100/5005]\tlr: 1.000e-03, eta: 1:50:46, time: 0.107, data_time: 0.044, memory: 1224, loss: 1.6182\n2023-01-02 03:33:05,423 - mmcls - INFO - Epoch [86][200/5005]\tlr: 1.000e-03, eta: 1:50:37, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6036\n2023-01-02 03:33:14,182 - mmcls - INFO - Epoch [86][300/5005]\tlr: 1.000e-03, eta: 1:50:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5935\n2023-01-02 03:33:22,985 - mmcls - INFO - Epoch [86][400/5005]\tlr: 1.000e-03, eta: 1:50:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6237\n2023-01-02 03:33:31,783 - mmcls - INFO - Epoch [86][500/5005]\tlr: 1.000e-03, eta: 1:50:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6152\n2023-01-02 03:33:40,531 - mmcls - INFO - Epoch [86][600/5005]\tlr: 1.000e-03, eta: 1:50:02, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6259\n2023-01-02 03:33:49,141 - mmcls - INFO - Epoch [86][700/5005]\tlr: 1.000e-03, eta: 1:49:53, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6190\n2023-01-02 03:33:57,935 - mmcls - INFO - Epoch [86][800/5005]\tlr: 1.000e-03, eta: 1:49:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6406\n2023-01-02 03:34:06,762 - mmcls - INFO - Epoch [86][900/5005]\tlr: 1.000e-03, eta: 1:49:35, time: 0.088, data_time: 0.035, memory: 1224, loss: 1.6405\n2023-01-02 03:34:15,756 - mmcls - INFO - Epoch [86][1000/5005]\tlr: 1.000e-03, eta: 1:49:26, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6300\n2023-01-02 03:34:24,690 - mmcls - INFO - Epoch [86][1100/5005]\tlr: 1.000e-03, eta: 1:49:17, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6489\n2023-01-02 03:34:33,422 - mmcls - INFO - Epoch [86][1200/5005]\tlr: 1.000e-03, eta: 1:49:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6330\n2023-01-02 03:34:42,512 - mmcls - INFO - Epoch [86][1300/5005]\tlr: 1.000e-03, eta: 1:49:00, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6103\n2023-01-02 03:34:51,169 - mmcls - INFO - Epoch [86][1400/5005]\tlr: 1.000e-03, eta: 1:48:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6165\n2023-01-02 03:35:00,144 - mmcls - INFO - Epoch [86][1500/5005]\tlr: 1.000e-03, eta: 1:48:42, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6112\n2023-01-02 03:35:09,048 - mmcls - INFO - Epoch [86][1600/5005]\tlr: 1.000e-03, eta: 1:48:33, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6368\n2023-01-02 03:35:18,012 - mmcls - INFO - Epoch [86][1700/5005]\tlr: 1.000e-03, eta: 1:48:24, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6291\n2023-01-02 03:35:26,609 - mmcls - INFO - Epoch [86][1800/5005]\tlr: 1.000e-03, eta: 1:48:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6429\n2023-01-02 03:35:35,431 - mmcls - INFO - Epoch [86][1900/5005]\tlr: 1.000e-03, eta: 1:48:06, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6050\n2023-01-02 03:35:43,839 - mmcls - INFO - Epoch [86][2000/5005]\tlr: 1.000e-03, eta: 1:47:58, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.6483\n2023-01-02 03:35:52,472 - mmcls - INFO - Epoch [86][2100/5005]\tlr: 1.000e-03, eta: 1:47:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6312\n2023-01-02 03:36:01,387 - mmcls - INFO - Epoch [86][2200/5005]\tlr: 1.000e-03, eta: 1:47:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6331\n2023-01-02 03:36:09,887 - mmcls - INFO - Epoch [86][2300/5005]\tlr: 1.000e-03, eta: 1:47:31, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6254\n2023-01-02 03:36:18,854 - mmcls - INFO - Epoch [86][2400/5005]\tlr: 1.000e-03, eta: 1:47:22, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6195\n2023-01-02 03:36:27,501 - mmcls - INFO - Epoch [86][2500/5005]\tlr: 1.000e-03, eta: 1:47:13, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6041\n2023-01-02 03:36:36,182 - mmcls - INFO - Epoch [86][2600/5005]\tlr: 1.000e-03, eta: 1:47:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6239\n2023-01-02 03:36:45,245 - mmcls - INFO - Epoch [86][2700/5005]\tlr: 1.000e-03, eta: 1:46:55, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6075\n2023-01-02 03:36:54,106 - mmcls - INFO - Epoch [86][2800/5005]\tlr: 1.000e-03, eta: 1:46:46, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6036\n2023-01-02 03:37:02,781 - mmcls - INFO - Epoch [86][2900/5005]\tlr: 1.000e-03, eta: 1:46:38, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6160\n2023-01-02 03:37:11,491 - mmcls - INFO - Epoch [86][3000/5005]\tlr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6185\n2023-01-02 03:37:20,573 - mmcls - INFO - Epoch [86][3100/5005]\tlr: 1.000e-03, eta: 1:46:20, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6314\n2023-01-02 03:37:29,706 - mmcls - INFO - Epoch [86][3200/5005]\tlr: 1.000e-03, eta: 1:46:11, time: 0.091, data_time: 0.043, memory: 1224, loss: 1.6319\n2023-01-02 03:37:38,676 - mmcls - INFO - Epoch [86][3300/5005]\tlr: 1.000e-03, eta: 1:46:02, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6534\n2023-01-02 03:37:47,795 - mmcls - INFO - Epoch [86][3400/5005]\tlr: 1.000e-03, eta: 1:45:53, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6218\n2023-01-02 03:37:56,893 - mmcls - INFO - Epoch [86][3500/5005]\tlr: 1.000e-03, eta: 1:45:45, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6167\n2023-01-02 03:38:05,689 - mmcls - INFO - Epoch [86][3600/5005]\tlr: 1.000e-03, eta: 1:45:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6315\n2023-01-02 03:38:14,503 - mmcls - INFO - Epoch [86][3700/5005]\tlr: 1.000e-03, eta: 1:45:27, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6218\n2023-01-02 03:38:23,263 - mmcls - INFO - Epoch [86][3800/5005]\tlr: 1.000e-03, eta: 1:45:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6315\n2023-01-02 03:38:32,241 - mmcls - INFO - Epoch [86][3900/5005]\tlr: 1.000e-03, eta: 1:45:09, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6203\n2023-01-02 03:38:40,864 - mmcls - INFO - Epoch [86][4000/5005]\tlr: 1.000e-03, eta: 1:45:00, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6344\n2023-01-02 03:38:49,818 - mmcls - INFO - Epoch [86][4100/5005]\tlr: 1.000e-03, eta: 1:44:51, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6265\n2023-01-02 03:38:58,635 - mmcls - INFO - Epoch [86][4200/5005]\tlr: 1.000e-03, eta: 1:44:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6435\n2023-01-02 03:39:07,567 - mmcls - INFO - Epoch [86][4300/5005]\tlr: 1.000e-03, eta: 1:44:34, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6176\n2023-01-02 03:39:16,377 - mmcls - INFO - Epoch [86][4400/5005]\tlr: 1.000e-03, eta: 1:44:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6227\n2023-01-02 03:39:25,230 - mmcls - INFO - Epoch [86][4500/5005]\tlr: 1.000e-03, eta: 1:44:16, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6387\n2023-01-02 03:39:34,019 - mmcls - INFO - Epoch [86][4600/5005]\tlr: 1.000e-03, eta: 1:44:07, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6336\n2023-01-02 03:39:43,073 - mmcls - INFO - Epoch [86][4700/5005]\tlr: 1.000e-03, eta: 1:43:58, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6261\n2023-01-02 03:39:51,851 - mmcls - INFO - Epoch [86][4800/5005]\tlr: 1.000e-03, eta: 1:43:49, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6544\n2023-01-02 03:40:00,490 - mmcls - INFO - Epoch [86][4900/5005]\tlr: 1.000e-03, eta: 1:43:40, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6334\n2023-01-02 03:40:09,315 - mmcls - INFO - Epoch [86][5000/5005]\tlr: 1.000e-03, eta: 1:43:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6547\n2023-01-02 03:40:32,107 - mmcls - INFO - Epoch(val) [86][196]\taccuracy_top-1: 64.8300, accuracy_top-5: 85.5140\n2023-01-02 03:40:43,568 - mmcls - INFO - Epoch [87][100/5005]\tlr: 1.000e-03, eta: 1:43:23, time: 0.114, data_time: 0.066, memory: 1224, loss: 1.6390\n2023-01-02 03:40:52,588 - mmcls - INFO - Epoch [87][200/5005]\tlr: 1.000e-03, eta: 1:43:14, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6155\n2023-01-02 03:41:01,186 - mmcls - INFO - Epoch [87][300/5005]\tlr: 1.000e-03, eta: 1:43:05, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6164\n2023-01-02 03:41:10,422 - mmcls - INFO - Epoch [87][400/5005]\tlr: 1.000e-03, eta: 1:42:56, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.5939\n2023-01-02 03:41:19,715 - mmcls - INFO - Epoch [87][500/5005]\tlr: 1.000e-03, eta: 1:42:47, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.5971\n2023-01-02 03:41:28,574 - mmcls - INFO - Epoch [87][600/5005]\tlr: 1.000e-03, eta: 1:42:38, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6329\n2023-01-02 03:41:37,517 - mmcls - INFO - Epoch [87][700/5005]\tlr: 1.000e-03, eta: 1:42:29, time: 0.089, data_time: 0.033, memory: 1224, loss: 1.6233\n2023-01-02 03:41:46,722 - mmcls - INFO - Epoch [87][800/5005]\tlr: 1.000e-03, eta: 1:42:21, time: 0.092, data_time: 0.027, memory: 1224, loss: 1.6266\n2023-01-02 03:41:55,213 - mmcls - INFO - Epoch [87][900/5005]\tlr: 1.000e-03, eta: 1:42:12, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.5927\n2023-01-02 03:42:04,232 - mmcls - INFO - Epoch [87][1000/5005]\tlr: 1.000e-03, eta: 1:42:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6217\n2023-01-02 03:42:12,802 - mmcls - INFO - Epoch [87][1100/5005]\tlr: 1.000e-03, eta: 1:41:54, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6086\n2023-01-02 03:42:21,385 - mmcls - INFO - Epoch [87][1200/5005]\tlr: 1.000e-03, eta: 1:41:45, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6155\n2023-01-02 03:42:30,105 - mmcls - INFO - Epoch [87][1300/5005]\tlr: 1.000e-03, eta: 1:41:36, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6112\n2023-01-02 03:42:38,966 - mmcls - INFO - Epoch [87][1400/5005]\tlr: 1.000e-03, eta: 1:41:27, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6367\n2023-01-02 03:42:48,026 - mmcls - INFO - Epoch [87][1500/5005]\tlr: 1.000e-03, eta: 1:41:18, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6289\n2023-01-02 03:42:56,676 - mmcls - INFO - Epoch [87][1600/5005]\tlr: 1.000e-03, eta: 1:41:10, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.6031\n2023-01-02 03:43:05,436 - mmcls - INFO - Epoch [87][1700/5005]\tlr: 1.000e-03, eta: 1:41:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6283\n2023-01-02 03:43:14,241 - mmcls - INFO - Epoch [87][1800/5005]\tlr: 1.000e-03, eta: 1:40:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6005\n2023-01-02 03:43:23,050 - mmcls - INFO - Epoch [87][1900/5005]\tlr: 1.000e-03, eta: 1:40:43, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6392\n2023-01-02 03:43:31,731 - mmcls - INFO - Epoch [87][2000/5005]\tlr: 1.000e-03, eta: 1:40:34, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6363\n2023-01-02 03:43:40,727 - mmcls - INFO - Epoch [87][2100/5005]\tlr: 1.000e-03, eta: 1:40:25, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.6290\n2023-01-02 03:43:49,651 - mmcls - INFO - Epoch [87][2200/5005]\tlr: 1.000e-03, eta: 1:40:16, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6458\n2023-01-02 03:43:58,168 - mmcls - INFO - Epoch [87][2300/5005]\tlr: 1.000e-03, eta: 1:40:07, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6235\n2023-01-02 03:44:06,852 - mmcls - INFO - Epoch [87][2400/5005]\tlr: 1.000e-03, eta: 1:39:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6187\n2023-01-02 03:44:15,325 - mmcls - INFO - Epoch [87][2500/5005]\tlr: 1.000e-03, eta: 1:39:50, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6374\n2023-01-02 03:44:24,130 - mmcls - INFO - Epoch [87][2600/5005]\tlr: 1.000e-03, eta: 1:39:41, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6459\n2023-01-02 03:44:32,962 - mmcls - INFO - Epoch [87][2700/5005]\tlr: 1.000e-03, eta: 1:39:32, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6277\n2023-01-02 03:44:41,705 - mmcls - INFO - Epoch [87][2800/5005]\tlr: 1.000e-03, eta: 1:39:23, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6293\n2023-01-02 03:44:50,492 - mmcls - INFO - Epoch [87][2900/5005]\tlr: 1.000e-03, eta: 1:39:14, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6218\n2023-01-02 03:44:58,931 - mmcls - INFO - Epoch [87][3000/5005]\tlr: 1.000e-03, eta: 1:39:05, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.6287\n2023-01-02 03:45:07,549 - mmcls - INFO - Epoch [87][3100/5005]\tlr: 1.000e-03, eta: 1:38:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6323\n2023-01-02 03:45:16,663 - mmcls - INFO - Epoch [87][3200/5005]\tlr: 1.000e-03, eta: 1:38:47, time: 0.091, data_time: 0.000, memory: 1224, loss: 1.6347\n2023-01-02 03:45:25,615 - mmcls - INFO - Epoch [87][3300/5005]\tlr: 1.000e-03, eta: 1:38:39, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6454\n2023-01-02 03:45:34,322 - mmcls - INFO - Epoch [87][3400/5005]\tlr: 1.000e-03, eta: 1:38:30, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6359\n2023-01-02 03:45:43,060 - mmcls - INFO - Epoch [87][3500/5005]\tlr: 1.000e-03, eta: 1:38:21, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6326\n2023-01-02 03:45:51,773 - mmcls - INFO - Epoch [87][3600/5005]\tlr: 1.000e-03, eta: 1:38:12, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6297\n2023-01-02 03:46:00,522 - mmcls - INFO - Epoch [87][3700/5005]\tlr: 1.000e-03, eta: 1:38:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6535\n2023-01-02 03:46:09,390 - mmcls - INFO - Epoch [87][3800/5005]\tlr: 1.000e-03, eta: 1:37:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6396\n2023-01-02 03:46:18,331 - mmcls - INFO - Epoch [87][3900/5005]\tlr: 1.000e-03, eta: 1:37:45, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6049\n2023-01-02 03:46:27,051 - mmcls - INFO - Epoch [87][4000/5005]\tlr: 1.000e-03, eta: 1:37:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6137\n2023-01-02 03:46:36,081 - mmcls - INFO - Epoch [87][4100/5005]\tlr: 1.000e-03, eta: 1:37:28, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6409\n2023-01-02 03:46:44,777 - mmcls - INFO - Epoch [87][4200/5005]\tlr: 1.000e-03, eta: 1:37:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6333\n2023-01-02 03:46:53,432 - mmcls - INFO - Epoch [87][4300/5005]\tlr: 1.000e-03, eta: 1:37:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6294\n2023-01-02 03:47:02,554 - mmcls - INFO - Epoch [87][4400/5005]\tlr: 1.000e-03, eta: 1:37:01, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6417\n2023-01-02 03:47:11,596 - mmcls - INFO - Epoch [87][4500/5005]\tlr: 1.000e-03, eta: 1:36:52, time: 0.090, data_time: 0.026, memory: 1224, loss: 1.6374\n2023-01-02 03:47:20,410 - mmcls - INFO - Epoch [87][4600/5005]\tlr: 1.000e-03, eta: 1:36:43, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6533\n2023-01-02 03:47:29,231 - mmcls - INFO - Epoch [87][4700/5005]\tlr: 1.000e-03, eta: 1:36:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6336\n2023-01-02 03:47:38,049 - mmcls - INFO - Epoch [87][4800/5005]\tlr: 1.000e-03, eta: 1:36:26, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6341\n2023-01-02 03:47:46,887 - mmcls - INFO - Epoch [87][4900/5005]\tlr: 1.000e-03, eta: 1:36:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6224\n2023-01-02 03:47:55,640 - mmcls - INFO - Epoch [87][5000/5005]\tlr: 1.000e-03, eta: 1:36:08, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6125\n2023-01-02 03:48:18,495 - mmcls - INFO - Epoch(val) [87][196]\taccuracy_top-1: 64.8440, accuracy_top-5: 85.4340\n2023-01-02 03:48:29,441 - mmcls - INFO - Epoch [88][100/5005]\tlr: 1.000e-03, eta: 1:35:59, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.6147\n2023-01-02 03:48:38,359 - mmcls - INFO - Epoch [88][200/5005]\tlr: 1.000e-03, eta: 1:35:50, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6271\n2023-01-02 03:48:47,233 - mmcls - INFO - Epoch [88][300/5005]\tlr: 1.000e-03, eta: 1:35:41, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6137\n2023-01-02 03:48:56,156 - mmcls - INFO - Epoch [88][400/5005]\tlr: 1.000e-03, eta: 1:35:32, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6088\n2023-01-02 03:49:05,139 - mmcls - INFO - Epoch [88][500/5005]\tlr: 1.000e-03, eta: 1:35:23, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6013\n2023-01-02 03:49:14,286 - mmcls - INFO - Epoch [88][600/5005]\tlr: 1.000e-03, eta: 1:35:14, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6222\n2023-01-02 03:49:23,109 - mmcls - INFO - Epoch [88][700/5005]\tlr: 1.000e-03, eta: 1:35:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6275\n2023-01-02 03:49:31,915 - mmcls - INFO - Epoch [88][800/5005]\tlr: 1.000e-03, eta: 1:34:57, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6033\n2023-01-02 03:49:40,775 - mmcls - INFO - Epoch [88][900/5005]\tlr: 1.000e-03, eta: 1:34:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6276\n2023-01-02 03:49:49,223 - mmcls - INFO - Epoch [88][1000/5005]\tlr: 1.000e-03, eta: 1:34:39, time: 0.084, data_time: 0.029, memory: 1224, loss: 1.5990\n2023-01-02 03:49:58,115 - mmcls - INFO - Epoch [88][1100/5005]\tlr: 1.000e-03, eta: 1:34:30, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6177\n2023-01-02 03:50:07,181 - mmcls - INFO - Epoch [88][1200/5005]\tlr: 1.000e-03, eta: 1:34:21, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6035\n2023-01-02 03:50:15,647 - mmcls - INFO - Epoch [88][1300/5005]\tlr: 1.000e-03, eta: 1:34:12, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6467\n2023-01-02 03:50:24,253 - mmcls - INFO - Epoch [88][1400/5005]\tlr: 1.000e-03, eta: 1:34:03, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6272\n2023-01-02 03:50:33,657 - mmcls - INFO - Epoch [88][1500/5005]\tlr: 1.000e-03, eta: 1:33:55, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6210\n2023-01-02 03:50:42,918 - mmcls - INFO - Epoch [88][1600/5005]\tlr: 1.000e-03, eta: 1:33:46, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6090\n2023-01-02 03:50:51,925 - mmcls - INFO - Epoch [88][1700/5005]\tlr: 1.000e-03, eta: 1:33:37, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6366\n2023-01-02 03:51:00,533 - mmcls - INFO - Epoch [88][1800/5005]\tlr: 1.000e-03, eta: 1:33:28, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6163\n2023-01-02 03:51:09,289 - mmcls - INFO - Epoch [88][1900/5005]\tlr: 1.000e-03, eta: 1:33:19, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6291\n2023-01-02 03:51:17,873 - mmcls - INFO - Epoch [88][2000/5005]\tlr: 1.000e-03, eta: 1:33:10, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6118\n2023-01-02 03:51:26,609 - mmcls - INFO - Epoch [88][2100/5005]\tlr: 1.000e-03, eta: 1:33:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6161\n2023-01-02 03:51:35,486 - mmcls - INFO - Epoch [88][2200/5005]\tlr: 1.000e-03, eta: 1:32:53, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6355\n2023-01-02 03:51:44,295 - mmcls - INFO - Epoch [88][2300/5005]\tlr: 1.000e-03, eta: 1:32:44, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6298\n2023-01-02 03:51:53,039 - mmcls - INFO - Epoch [88][2400/5005]\tlr: 1.000e-03, eta: 1:32:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6318\n2023-01-02 03:52:01,940 - mmcls - INFO - Epoch [88][2500/5005]\tlr: 1.000e-03, eta: 1:32:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6274\n2023-01-02 03:52:10,649 - mmcls - INFO - Epoch [88][2600/5005]\tlr: 1.000e-03, eta: 1:32:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6173\n2023-01-02 03:52:19,421 - mmcls - INFO - Epoch [88][2700/5005]\tlr: 1.000e-03, eta: 1:32:08, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6363\n2023-01-02 03:52:28,078 - mmcls - INFO - Epoch [88][2800/5005]\tlr: 1.000e-03, eta: 1:31:59, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6391\n2023-01-02 03:52:36,847 - mmcls - INFO - Epoch [88][2900/5005]\tlr: 1.000e-03, eta: 1:31:50, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6440\n2023-01-02 03:52:45,652 - mmcls - INFO - Epoch [88][3000/5005]\tlr: 1.000e-03, eta: 1:31:41, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6450\n2023-01-02 03:52:54,538 - mmcls - INFO - Epoch [88][3100/5005]\tlr: 1.000e-03, eta: 1:31:33, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6170\n2023-01-02 03:53:03,071 - mmcls - INFO - Epoch [88][3200/5005]\tlr: 1.000e-03, eta: 1:31:24, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6171\n2023-01-02 03:53:11,831 - mmcls - INFO - Epoch [88][3300/5005]\tlr: 1.000e-03, eta: 1:31:15, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6371\n2023-01-02 03:53:20,647 - mmcls - INFO - Epoch [88][3400/5005]\tlr: 1.000e-03, eta: 1:31:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6130\n2023-01-02 03:53:29,366 - mmcls - INFO - Epoch [88][3500/5005]\tlr: 1.000e-03, eta: 1:30:57, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6215\n2023-01-02 03:53:38,092 - mmcls - INFO - Epoch [88][3600/5005]\tlr: 1.000e-03, eta: 1:30:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6302\n2023-01-02 03:53:46,917 - mmcls - INFO - Epoch [88][3700/5005]\tlr: 1.000e-03, eta: 1:30:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6153\n2023-01-02 03:53:55,880 - mmcls - INFO - Epoch [88][3800/5005]\tlr: 1.000e-03, eta: 1:30:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6319\n2023-01-02 03:54:04,747 - mmcls - INFO - Epoch [88][3900/5005]\tlr: 1.000e-03, eta: 1:30:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6244\n2023-01-02 03:54:13,681 - mmcls - INFO - Epoch [88][4000/5005]\tlr: 1.000e-03, eta: 1:30:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6320\n2023-01-02 03:54:22,450 - mmcls - INFO - Epoch [88][4100/5005]\tlr: 1.000e-03, eta: 1:30:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6179\n2023-01-02 03:54:31,117 - mmcls - INFO - Epoch [88][4200/5005]\tlr: 1.000e-03, eta: 1:29:55, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6181\n2023-01-02 03:54:40,146 - mmcls - INFO - Epoch [88][4300/5005]\tlr: 1.000e-03, eta: 1:29:46, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6282\n2023-01-02 03:54:48,798 - mmcls - INFO - Epoch [88][4400/5005]\tlr: 1.000e-03, eta: 1:29:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6219\n2023-01-02 03:54:57,697 - mmcls - INFO - Epoch [88][4500/5005]\tlr: 1.000e-03, eta: 1:29:28, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6024\n2023-01-02 03:55:06,328 - mmcls - INFO - Epoch [88][4600/5005]\tlr: 1.000e-03, eta: 1:29:19, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6448\n2023-01-02 03:55:15,017 - mmcls - INFO - Epoch [88][4700/5005]\tlr: 1.000e-03, eta: 1:29:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6119\n2023-01-02 03:55:23,793 - mmcls - INFO - Epoch [88][4800/5005]\tlr: 1.000e-03, eta: 1:29:02, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6168\n2023-01-02 03:55:32,634 - mmcls - INFO - Epoch [88][4900/5005]\tlr: 1.000e-03, eta: 1:28:53, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6306\n2023-01-02 03:55:41,462 - mmcls - INFO - Epoch [88][5000/5005]\tlr: 1.000e-03, eta: 1:28:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6359\n2023-01-02 03:56:04,023 - mmcls - INFO - Epoch(val) [88][196]\taccuracy_top-1: 64.8560, accuracy_top-5: 85.4560\n2023-01-02 03:56:15,221 - mmcls - INFO - Epoch [89][100/5005]\tlr: 1.000e-03, eta: 1:28:35, time: 0.112, data_time: 0.054, memory: 1224, loss: 1.6215\n2023-01-02 03:56:23,895 - mmcls - INFO - Epoch [89][200/5005]\tlr: 1.000e-03, eta: 1:28:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6146\n2023-01-02 03:56:32,856 - mmcls - INFO - Epoch [89][300/5005]\tlr: 1.000e-03, eta: 1:28:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5773\n2023-01-02 03:56:41,922 - mmcls - INFO - Epoch [89][400/5005]\tlr: 1.000e-03, eta: 1:28:08, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.6194\n2023-01-02 03:56:51,050 - mmcls - INFO - Epoch [89][500/5005]\tlr: 1.000e-03, eta: 1:28:00, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6205\n2023-01-02 03:57:00,014 - mmcls - INFO - Epoch [89][600/5005]\tlr: 1.000e-03, eta: 1:27:51, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6079\n2023-01-02 03:57:08,719 - mmcls - INFO - Epoch [89][700/5005]\tlr: 1.000e-03, eta: 1:27:42, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6030\n2023-01-02 03:57:17,623 - mmcls - INFO - Epoch [89][800/5005]\tlr: 1.000e-03, eta: 1:27:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6213\n2023-01-02 03:57:26,307 - mmcls - INFO - Epoch [89][900/5005]\tlr: 1.000e-03, eta: 1:27:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6282\n2023-01-02 03:57:35,297 - mmcls - INFO - Epoch [89][1000/5005]\tlr: 1.000e-03, eta: 1:27:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6086\n2023-01-02 03:57:44,239 - mmcls - INFO - Epoch [89][1100/5005]\tlr: 1.000e-03, eta: 1:27:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6112\n2023-01-02 03:57:53,403 - mmcls - INFO - Epoch [89][1200/5005]\tlr: 1.000e-03, eta: 1:26:58, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6256\n2023-01-02 03:58:02,151 - mmcls - INFO - Epoch [89][1300/5005]\tlr: 1.000e-03, eta: 1:26:49, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5982\n2023-01-02 03:58:11,413 - mmcls - INFO - Epoch [89][1400/5005]\tlr: 1.000e-03, eta: 1:26:40, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.6181\n2023-01-02 03:58:20,270 - mmcls - INFO - Epoch [89][1500/5005]\tlr: 1.000e-03, eta: 1:26:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6503\n2023-01-02 03:58:28,933 - mmcls - INFO - Epoch [89][1600/5005]\tlr: 1.000e-03, eta: 1:26:22, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6307\n2023-01-02 03:58:37,744 - mmcls - INFO - Epoch [89][1700/5005]\tlr: 1.000e-03, eta: 1:26:13, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6325\n2023-01-02 03:58:46,507 - mmcls - INFO - Epoch [89][1800/5005]\tlr: 1.000e-03, eta: 1:26:04, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6128\n2023-01-02 03:58:55,232 - mmcls - INFO - Epoch [89][1900/5005]\tlr: 1.000e-03, eta: 1:25:55, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6164\n2023-01-02 03:59:03,897 - mmcls - INFO - Epoch [89][2000/5005]\tlr: 1.000e-03, eta: 1:25:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6215\n2023-01-02 03:59:12,868 - mmcls - INFO - Epoch [89][2100/5005]\tlr: 1.000e-03, eta: 1:25:38, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6160\n2023-01-02 03:59:21,330 - mmcls - INFO - Epoch [89][2200/5005]\tlr: 1.000e-03, eta: 1:25:29, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6232\n2023-01-02 03:59:30,265 - mmcls - INFO - Epoch [89][2300/5005]\tlr: 1.000e-03, eta: 1:25:20, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6421\n2023-01-02 03:59:38,988 - mmcls - INFO - Epoch [89][2400/5005]\tlr: 1.000e-03, eta: 1:25:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5982\n2023-01-02 03:59:47,742 - mmcls - INFO - Epoch [89][2500/5005]\tlr: 1.000e-03, eta: 1:25:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6337\n2023-01-02 03:59:56,451 - mmcls - INFO - Epoch [89][2600/5005]\tlr: 1.000e-03, eta: 1:24:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6182\n2023-01-02 04:00:05,352 - mmcls - INFO - Epoch [89][2700/5005]\tlr: 1.000e-03, eta: 1:24:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6491\n2023-01-02 04:00:14,448 - mmcls - INFO - Epoch [89][2800/5005]\tlr: 1.000e-03, eta: 1:24:36, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6143\n2023-01-02 04:00:23,188 - mmcls - INFO - Epoch [89][2900/5005]\tlr: 1.000e-03, eta: 1:24:27, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6374\n2023-01-02 04:00:32,154 - mmcls - INFO - Epoch [89][3000/5005]\tlr: 1.000e-03, eta: 1:24:18, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6470\n2023-01-02 04:00:40,950 - mmcls - INFO - Epoch [89][3100/5005]\tlr: 1.000e-03, eta: 1:24:09, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6456\n2023-01-02 04:00:49,472 - mmcls - INFO - Epoch [89][3200/5005]\tlr: 1.000e-03, eta: 1:24:00, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.6066\n2023-01-02 04:00:58,418 - mmcls - INFO - Epoch [89][3300/5005]\tlr: 1.000e-03, eta: 1:23:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6121\n2023-01-02 04:01:07,059 - mmcls - INFO - Epoch [89][3400/5005]\tlr: 1.000e-03, eta: 1:23:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6303\n2023-01-02 04:01:16,036 - mmcls - INFO - Epoch [89][3500/5005]\tlr: 1.000e-03, eta: 1:23:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6189\n2023-01-02 04:01:24,732 - mmcls - INFO - Epoch [89][3600/5005]\tlr: 1.000e-03, eta: 1:23:25, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6163\n2023-01-02 04:01:33,445 - mmcls - INFO - Epoch [89][3700/5005]\tlr: 1.000e-03, eta: 1:23:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6161\n2023-01-02 04:01:42,096 - mmcls - INFO - Epoch [89][3800/5005]\tlr: 1.000e-03, eta: 1:23:07, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6234\n2023-01-02 04:01:50,921 - mmcls - INFO - Epoch [89][3900/5005]\tlr: 1.000e-03, eta: 1:22:58, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6289\n2023-01-02 04:02:00,387 - mmcls - INFO - Epoch [89][4000/5005]\tlr: 1.000e-03, eta: 1:22:49, time: 0.095, data_time: 0.002, memory: 1224, loss: 1.6307\n2023-01-02 04:02:09,156 - mmcls - INFO - Epoch [89][4100/5005]\tlr: 1.000e-03, eta: 1:22:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6293\n2023-01-02 04:02:17,730 - mmcls - INFO - Epoch [89][4200/5005]\tlr: 1.000e-03, eta: 1:22:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6420\n2023-01-02 04:02:26,635 - mmcls - INFO - Epoch [89][4300/5005]\tlr: 1.000e-03, eta: 1:22:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6380\n2023-01-02 04:02:35,366 - mmcls - INFO - Epoch [89][4400/5005]\tlr: 1.000e-03, eta: 1:22:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6479\n2023-01-02 04:02:44,306 - mmcls - INFO - Epoch [89][4500/5005]\tlr: 1.000e-03, eta: 1:22:05, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6467\n2023-01-02 04:02:52,924 - mmcls - INFO - Epoch [89][4600/5005]\tlr: 1.000e-03, eta: 1:21:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6544\n2023-01-02 04:03:02,015 - mmcls - INFO - Epoch [89][4700/5005]\tlr: 1.000e-03, eta: 1:21:47, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6275\n2023-01-02 04:03:10,793 - mmcls - INFO - Epoch [89][4800/5005]\tlr: 1.000e-03, eta: 1:21:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6371\n2023-01-02 04:03:19,233 - mmcls - INFO - Epoch [89][4900/5005]\tlr: 1.000e-03, eta: 1:21:29, time: 0.084, data_time: 0.007, memory: 1224, loss: 1.6205\n2023-01-02 04:03:28,040 - mmcls - INFO - Epoch [89][5000/5005]\tlr: 1.000e-03, eta: 1:21:20, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6316\n2023-01-02 04:03:50,748 - mmcls - INFO - Epoch(val) [89][196]\taccuracy_top-1: 64.7040, accuracy_top-5: 85.5680\n2023-01-02 04:04:01,760 - mmcls - INFO - Epoch [90][100/5005]\tlr: 1.000e-03, eta: 1:21:11, time: 0.110, data_time: 0.059, memory: 1224, loss: 1.6009\n2023-01-02 04:04:10,833 - mmcls - INFO - Epoch [90][200/5005]\tlr: 1.000e-03, eta: 1:21:02, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6269\n2023-01-02 04:04:19,662 - mmcls - INFO - Epoch [90][300/5005]\tlr: 1.000e-03, eta: 1:20:54, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6164\n2023-01-02 04:04:28,340 - mmcls - INFO - Epoch [90][400/5005]\tlr: 1.000e-03, eta: 1:20:45, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6004\n2023-01-02 04:04:37,327 - mmcls - INFO - Epoch [90][500/5005]\tlr: 1.000e-03, eta: 1:20:36, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6168\n2023-01-02 04:04:46,946 - mmcls - INFO - Epoch [90][600/5005]\tlr: 1.000e-03, eta: 1:20:27, time: 0.096, data_time: 0.003, memory: 1224, loss: 1.6270\n2023-01-02 04:04:56,168 - mmcls - INFO - Epoch [90][700/5005]\tlr: 1.000e-03, eta: 1:20:18, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6005\n2023-01-02 04:05:04,959 - mmcls - INFO - Epoch [90][800/5005]\tlr: 1.000e-03, eta: 1:20:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6297\n2023-01-02 04:05:13,963 - mmcls - INFO - Epoch [90][900/5005]\tlr: 1.000e-03, eta: 1:20:00, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6098\n2023-01-02 04:05:22,752 - mmcls - INFO - Epoch [90][1000/5005]\tlr: 1.000e-03, eta: 1:19:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6125\n2023-01-02 04:05:31,410 - mmcls - INFO - Epoch [90][1100/5005]\tlr: 1.000e-03, eta: 1:19:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6152\n2023-01-02 04:05:40,036 - mmcls - INFO - Epoch [90][1200/5005]\tlr: 1.000e-03, eta: 1:19:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6190\n2023-01-02 04:05:49,054 - mmcls - INFO - Epoch [90][1300/5005]\tlr: 1.000e-03, eta: 1:19:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6190\n2023-01-02 04:05:57,690 - mmcls - INFO - Epoch [90][1400/5005]\tlr: 1.000e-03, eta: 1:19:16, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6333\n2023-01-02 04:06:06,417 - mmcls - INFO - Epoch [90][1500/5005]\tlr: 1.000e-03, eta: 1:19:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6514\n2023-01-02 04:06:15,348 - mmcls - INFO - Epoch [90][1600/5005]\tlr: 1.000e-03, eta: 1:18:58, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6293\n2023-01-02 04:06:24,729 - mmcls - INFO - Epoch [90][1700/5005]\tlr: 1.000e-03, eta: 1:18:50, time: 0.094, data_time: 0.008, memory: 1224, loss: 1.6333\n2023-01-02 04:06:33,505 - mmcls - INFO - Epoch [90][1800/5005]\tlr: 1.000e-03, eta: 1:18:41, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6184\n2023-01-02 04:06:42,497 - mmcls - INFO - Epoch [90][1900/5005]\tlr: 1.000e-03, eta: 1:18:32, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6176\n2023-01-02 04:06:51,318 - mmcls - INFO - Epoch [90][2000/5005]\tlr: 1.000e-03, eta: 1:18:23, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5938\n2023-01-02 04:06:59,815 - mmcls - INFO - Epoch [90][2100/5005]\tlr: 1.000e-03, eta: 1:18:14, time: 0.085, data_time: 0.029, memory: 1224, loss: 1.6198\n2023-01-02 04:07:08,525 - mmcls - INFO - Epoch [90][2200/5005]\tlr: 1.000e-03, eta: 1:18:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6320\n2023-01-02 04:07:17,480 - mmcls - INFO - Epoch [90][2300/5005]\tlr: 1.000e-03, eta: 1:17:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5892\n2023-01-02 04:07:26,276 - mmcls - INFO - Epoch [90][2400/5005]\tlr: 1.000e-03, eta: 1:17:47, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6075\n2023-01-02 04:07:35,183 - mmcls - INFO - Epoch [90][2500/5005]\tlr: 1.000e-03, eta: 1:17:39, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6547\n2023-01-02 04:07:44,325 - mmcls - INFO - Epoch [90][2600/5005]\tlr: 1.000e-03, eta: 1:17:30, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.5934\n2023-01-02 04:07:53,029 - mmcls - INFO - Epoch [90][2700/5005]\tlr: 1.000e-03, eta: 1:17:21, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6224\n2023-01-02 04:08:01,864 - mmcls - INFO - Epoch [90][2800/5005]\tlr: 1.000e-03, eta: 1:17:12, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5985\n2023-01-02 04:08:10,448 - mmcls - INFO - Epoch [90][2900/5005]\tlr: 1.000e-03, eta: 1:17:03, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6205\n2023-01-02 04:08:19,158 - mmcls - INFO - Epoch [90][3000/5005]\tlr: 1.000e-03, eta: 1:16:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6098\n2023-01-02 04:08:28,057 - mmcls - INFO - Epoch [90][3100/5005]\tlr: 1.000e-03, eta: 1:16:45, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6221\n2023-01-02 04:08:36,724 - mmcls - INFO - Epoch [90][3200/5005]\tlr: 1.000e-03, eta: 1:16:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6514\n2023-01-02 04:08:45,453 - mmcls - INFO - Epoch [90][3300/5005]\tlr: 1.000e-03, eta: 1:16:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6459\n2023-01-02 04:08:54,047 - mmcls - INFO - Epoch [90][3400/5005]\tlr: 1.000e-03, eta: 1:16:19, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6386\n2023-01-02 04:09:02,847 - mmcls - INFO - Epoch [90][3500/5005]\tlr: 1.000e-03, eta: 1:16:10, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6051\n2023-01-02 04:09:11,614 - mmcls - INFO - Epoch [90][3600/5005]\tlr: 1.000e-03, eta: 1:16:01, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6448\n2023-01-02 04:09:20,355 - mmcls - INFO - Epoch [90][3700/5005]\tlr: 1.000e-03, eta: 1:15:52, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6139\n2023-01-02 04:09:29,301 - mmcls - INFO - Epoch [90][3800/5005]\tlr: 1.000e-03, eta: 1:15:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6283\n2023-01-02 04:09:38,004 - mmcls - INFO - Epoch [90][3900/5005]\tlr: 1.000e-03, eta: 1:15:34, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6429\n2023-01-02 04:09:46,652 - mmcls - INFO - Epoch [90][4000/5005]\tlr: 1.000e-03, eta: 1:15:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6207\n2023-01-02 04:09:55,539 - mmcls - INFO - Epoch [90][4100/5005]\tlr: 1.000e-03, eta: 1:15:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6471\n2023-01-02 04:10:04,329 - mmcls - INFO - Epoch [90][4200/5005]\tlr: 1.000e-03, eta: 1:15:08, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6315\n2023-01-02 04:10:12,911 - mmcls - INFO - Epoch [90][4300/5005]\tlr: 1.000e-03, eta: 1:14:59, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6541\n2023-01-02 04:10:21,857 - mmcls - INFO - Epoch [90][4400/5005]\tlr: 1.000e-03, eta: 1:14:50, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.6321\n2023-01-02 04:10:30,540 - mmcls - INFO - Epoch [90][4500/5005]\tlr: 1.000e-03, eta: 1:14:41, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6301\n2023-01-02 04:10:39,430 - mmcls - INFO - Epoch [90][4600/5005]\tlr: 1.000e-03, eta: 1:14:32, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6198\n2023-01-02 04:10:48,135 - mmcls - INFO - Epoch [90][4700/5005]\tlr: 1.000e-03, eta: 1:14:23, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6209\n2023-01-02 04:10:57,362 - mmcls - INFO - Epoch [90][4800/5005]\tlr: 1.000e-03, eta: 1:14:14, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.6212\n2023-01-02 04:11:06,266 - mmcls - INFO - Epoch [90][4900/5005]\tlr: 1.000e-03, eta: 1:14:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6150\n2023-01-02 04:11:15,746 - mmcls - INFO - Epoch [90][5000/5005]\tlr: 1.000e-03, eta: 1:13:57, time: 0.095, data_time: 0.004, memory: 1224, loss: 1.6168\n2023-01-02 04:11:38,285 - mmcls - INFO - Epoch(val) [90][196]\taccuracy_top-1: 64.8200, accuracy_top-5: 85.5240\n2023-01-02 04:11:49,036 - mmcls - INFO - Epoch [91][100/5005]\tlr: 1.000e-04, eta: 1:13:48, time: 0.107, data_time: 0.033, memory: 1224, loss: 1.6003\n2023-01-02 04:11:58,015 - mmcls - INFO - Epoch [91][200/5005]\tlr: 1.000e-04, eta: 1:13:39, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.5916\n2023-01-02 04:12:06,545 - mmcls - INFO - Epoch [91][300/5005]\tlr: 1.000e-04, eta: 1:13:30, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.5975\n2023-01-02 04:12:15,304 - mmcls - INFO - Epoch [91][400/5005]\tlr: 1.000e-04, eta: 1:13:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6271\n2023-01-02 04:12:24,134 - mmcls - INFO - Epoch [91][500/5005]\tlr: 1.000e-04, eta: 1:13:12, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6159\n2023-01-02 04:12:32,968 - mmcls - INFO - Epoch [91][600/5005]\tlr: 1.000e-04, eta: 1:13:03, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6006\n2023-01-02 04:12:41,619 - mmcls - INFO - Epoch [91][700/5005]\tlr: 1.000e-04, eta: 1:12:54, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6018\n2023-01-02 04:12:50,321 - mmcls - INFO - Epoch [91][800/5005]\tlr: 1.000e-04, eta: 1:12:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5878\n2023-01-02 04:12:59,516 - mmcls - INFO - Epoch [91][900/5005]\tlr: 1.000e-04, eta: 1:12:37, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.5952\n2023-01-02 04:13:08,597 - mmcls - INFO - Epoch [91][1000/5005]\tlr: 1.000e-04, eta: 1:12:28, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.5767\n2023-01-02 04:13:17,346 - mmcls - INFO - Epoch [91][1100/5005]\tlr: 1.000e-04, eta: 1:12:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.5858\n2023-01-02 04:13:26,200 - mmcls - INFO - Epoch [91][1200/5005]\tlr: 1.000e-04, eta: 1:12:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5897\n2023-01-02 04:13:34,953 - mmcls - INFO - Epoch [91][1300/5005]\tlr: 1.000e-04, eta: 1:12:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5803\n2023-01-02 04:13:43,956 - mmcls - INFO - Epoch [91][1400/5005]\tlr: 1.000e-04, eta: 1:11:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5765\n2023-01-02 04:13:52,773 - mmcls - INFO - Epoch [91][1500/5005]\tlr: 1.000e-04, eta: 1:11:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5779\n2023-01-02 04:14:01,566 - mmcls - INFO - Epoch [91][1600/5005]\tlr: 1.000e-04, eta: 1:11:35, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5794\n2023-01-02 04:14:10,284 - mmcls - INFO - Epoch [91][1700/5005]\tlr: 1.000e-04, eta: 1:11:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.5815\n2023-01-02 04:14:19,115 - mmcls - INFO - Epoch [91][1800/5005]\tlr: 1.000e-04, eta: 1:11:17, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6153\n2023-01-02 04:14:27,965 - mmcls - INFO - Epoch [91][1900/5005]\tlr: 1.000e-04, eta: 1:11:08, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6108\n2023-01-02 04:14:36,742 - mmcls - INFO - Epoch [91][2000/5005]\tlr: 1.000e-04, eta: 1:10:59, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6157\n2023-01-02 04:14:45,770 - mmcls - INFO - Epoch [91][2100/5005]\tlr: 1.000e-04, eta: 1:10:50, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6066\n2023-01-02 04:14:54,380 - mmcls - INFO - Epoch [91][2200/5005]\tlr: 1.000e-04, eta: 1:10:41, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.5990\n2023-01-02 04:15:03,141 - mmcls - INFO - Epoch [91][2300/5005]\tlr: 1.000e-04, eta: 1:10:33, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.5947\n2023-01-02 04:15:12,162 - mmcls - INFO - Epoch [91][2400/5005]\tlr: 1.000e-04, eta: 1:10:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5756\n2023-01-02 04:15:20,994 - mmcls - INFO - Epoch [91][2500/5005]\tlr: 1.000e-04, eta: 1:10:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5945\n2023-01-02 04:15:29,683 - mmcls - INFO - Epoch [91][2600/5005]\tlr: 1.000e-04, eta: 1:10:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5855\n2023-01-02 04:15:38,473 - mmcls - INFO - Epoch [91][2700/5005]\tlr: 1.000e-04, eta: 1:09:57, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6078\n2023-01-02 04:15:47,009 - mmcls - INFO - Epoch [91][2800/5005]\tlr: 1.000e-04, eta: 1:09:48, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6010\n2023-01-02 04:15:55,771 - mmcls - INFO - Epoch [91][2900/5005]\tlr: 1.000e-04, eta: 1:09:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5921\n2023-01-02 04:16:04,647 - mmcls - INFO - Epoch [91][3000/5005]\tlr: 1.000e-04, eta: 1:09:30, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6065\n2023-01-02 04:16:13,189 - mmcls - INFO - Epoch [91][3100/5005]\tlr: 1.000e-04, eta: 1:09:22, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.5978\n2023-01-02 04:16:22,134 - mmcls - INFO - Epoch [91][3200/5005]\tlr: 1.000e-04, eta: 1:09:13, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.5893\n2023-01-02 04:16:30,894 - mmcls - INFO - Epoch [91][3300/5005]\tlr: 1.000e-04, eta: 1:09:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5849\n2023-01-02 04:16:39,398 - mmcls - INFO - Epoch [91][3400/5005]\tlr: 1.000e-04, eta: 1:08:55, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.5782\n2023-01-02 04:16:48,195 - mmcls - INFO - Epoch [91][3500/5005]\tlr: 1.000e-04, eta: 1:08:46, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5843\n2023-01-02 04:16:57,056 - mmcls - INFO - Epoch [91][3600/5005]\tlr: 1.000e-04, eta: 1:08:37, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.5896\n2023-01-02 04:17:05,841 - mmcls - INFO - Epoch [91][3700/5005]\tlr: 1.000e-04, eta: 1:08:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6016\n2023-01-02 04:17:14,853 - mmcls - INFO - Epoch [91][3800/5005]\tlr: 1.000e-04, eta: 1:08:19, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.5912\n2023-01-02 04:17:23,849 - mmcls - INFO - Epoch [91][3900/5005]\tlr: 1.000e-04, eta: 1:08:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5921\n2023-01-02 04:17:32,708 - mmcls - INFO - Epoch [91][4000/5005]\tlr: 1.000e-04, eta: 1:08:02, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5821\n2023-01-02 04:17:41,757 - mmcls - INFO - Epoch [91][4100/5005]\tlr: 1.000e-04, eta: 1:07:53, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5976\n2023-01-02 04:17:50,565 - mmcls - INFO - Epoch [91][4200/5005]\tlr: 1.000e-04, eta: 1:07:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5821\n2023-01-02 04:17:59,419 - mmcls - INFO - Epoch [91][4300/5005]\tlr: 1.000e-04, eta: 1:07:35, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.5931\n2023-01-02 04:18:08,373 - mmcls - INFO - Epoch [91][4400/5005]\tlr: 1.000e-04, eta: 1:07:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5833\n2023-01-02 04:18:17,392 - mmcls - INFO - Epoch [91][4500/5005]\tlr: 1.000e-04, eta: 1:07:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5982\n2023-01-02 04:18:26,026 - mmcls - INFO - Epoch [91][4600/5005]\tlr: 1.000e-04, eta: 1:07:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5872\n2023-01-02 04:18:34,964 - mmcls - INFO - Epoch [91][4700/5005]\tlr: 1.000e-04, eta: 1:07:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5848\n2023-01-02 04:18:43,511 - mmcls - INFO - Epoch [91][4800/5005]\tlr: 1.000e-04, eta: 1:06:51, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.5911\n2023-01-02 04:18:52,171 - mmcls - INFO - Epoch [91][4900/5005]\tlr: 1.000e-04, eta: 1:06:42, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5775\n2023-01-02 04:19:01,041 - mmcls - INFO - Epoch [91][5000/5005]\tlr: 1.000e-04, eta: 1:06:33, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6061\n2023-01-02 04:19:23,827 - mmcls - INFO - Epoch(val) [91][196]\taccuracy_top-1: 65.2800, accuracy_top-5: 85.6880\n2023-01-02 04:19:34,739 - mmcls - INFO - Epoch [92][100/5005]\tlr: 1.000e-04, eta: 1:06:24, time: 0.109, data_time: 0.031, memory: 1224, loss: 1.5935\n2023-01-02 04:19:43,735 - mmcls - INFO - Epoch [92][200/5005]\tlr: 1.000e-04, eta: 1:06:15, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5738\n2023-01-02 04:19:52,225 - mmcls - INFO - Epoch [92][300/5005]\tlr: 1.000e-04, eta: 1:06:06, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.5890\n2023-01-02 04:20:01,154 - mmcls - INFO - Epoch [92][400/5005]\tlr: 1.000e-04, eta: 1:05:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5823\n2023-01-02 04:20:09,867 - mmcls - INFO - Epoch [92][500/5005]\tlr: 1.000e-04, eta: 1:05:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5852\n2023-01-02 04:20:18,824 - mmcls - INFO - Epoch [92][600/5005]\tlr: 1.000e-04, eta: 1:05:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5940\n2023-01-02 04:20:27,772 - mmcls - INFO - Epoch [92][700/5005]\tlr: 1.000e-04, eta: 1:05:31, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6046\n2023-01-02 04:20:36,417 - mmcls - INFO - Epoch [92][800/5005]\tlr: 1.000e-04, eta: 1:05:22, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5787\n2023-01-02 04:20:45,201 - mmcls - INFO - Epoch [92][900/5005]\tlr: 1.000e-04, eta: 1:05:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5699\n2023-01-02 04:20:54,390 - mmcls - INFO - Epoch [92][1000/5005]\tlr: 1.000e-04, eta: 1:05:04, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.5703\n2023-01-02 04:21:03,112 - mmcls - INFO - Epoch [92][1100/5005]\tlr: 1.000e-04, eta: 1:04:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5859\n2023-01-02 04:21:11,782 - mmcls - INFO - Epoch [92][1200/5005]\tlr: 1.000e-04, eta: 1:04:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5946\n2023-01-02 04:21:20,622 - mmcls - INFO - Epoch [92][1300/5005]\tlr: 1.000e-04, eta: 1:04:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5979\n2023-01-02 04:21:29,787 - mmcls - INFO - Epoch [92][1400/5005]\tlr: 1.000e-04, eta: 1:04:29, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5935\n2023-01-02 04:21:38,386 - mmcls - INFO - Epoch [92][1500/5005]\tlr: 1.000e-04, eta: 1:04:20, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.5707\n2023-01-02 04:21:47,645 - mmcls - INFO - Epoch [92][1600/5005]\tlr: 1.000e-04, eta: 1:04:11, time: 0.093, data_time: 0.037, memory: 1224, loss: 1.6059\n2023-01-02 04:21:56,831 - mmcls - INFO - Epoch [92][1700/5005]\tlr: 1.000e-04, eta: 1:04:02, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.5819\n2023-01-02 04:22:05,422 - mmcls - INFO - Epoch [92][1800/5005]\tlr: 1.000e-04, eta: 1:03:53, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.5893\n2023-01-02 04:22:14,327 - mmcls - INFO - Epoch [92][1900/5005]\tlr: 1.000e-04, eta: 1:03:44, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.5783\n2023-01-02 04:22:23,153 - mmcls - INFO - Epoch [92][2000/5005]\tlr: 1.000e-04, eta: 1:03:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5875\n2023-01-02 04:22:31,848 - mmcls - INFO - Epoch [92][2100/5005]\tlr: 1.000e-04, eta: 1:03:27, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.5725\n2023-01-02 04:22:40,528 - mmcls - INFO - Epoch [92][2200/5005]\tlr: 1.000e-04, eta: 1:03:18, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5976\n2023-01-02 04:22:49,313 - mmcls - INFO - Epoch [92][2300/5005]\tlr: 1.000e-04, eta: 1:03:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6083\n2023-01-02 04:22:57,916 - mmcls - INFO - Epoch [92][2400/5005]\tlr: 1.000e-04, eta: 1:03:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5774\n2023-01-02 04:23:06,841 - mmcls - INFO - Epoch [92][2500/5005]\tlr: 1.000e-04, eta: 1:02:51, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5920\n2023-01-02 04:23:15,761 - mmcls - INFO - Epoch [92][2600/5005]\tlr: 1.000e-04, eta: 1:02:42, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5907\n2023-01-02 04:23:24,890 - mmcls - INFO - Epoch [92][2700/5005]\tlr: 1.000e-04, eta: 1:02:33, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5794\n2023-01-02 04:23:33,815 - mmcls - INFO - Epoch [92][2800/5005]\tlr: 1.000e-04, eta: 1:02:24, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5769\n2023-01-02 04:23:42,415 - mmcls - INFO - Epoch [92][2900/5005]\tlr: 1.000e-04, eta: 1:02:16, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.5604\n2023-01-02 04:23:51,207 - mmcls - INFO - Epoch [92][3000/5005]\tlr: 1.000e-04, eta: 1:02:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6091\n2023-01-02 04:23:59,949 - mmcls - INFO - Epoch [92][3100/5005]\tlr: 1.000e-04, eta: 1:01:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5842\n2023-01-02 04:24:08,742 - mmcls - INFO - Epoch [92][3200/5005]\tlr: 1.000e-04, eta: 1:01:49, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.5825\n2023-01-02 04:24:17,522 - mmcls - INFO - Epoch [92][3300/5005]\tlr: 1.000e-04, eta: 1:01:40, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6103\n2023-01-02 04:24:25,993 - mmcls - INFO - Epoch [92][3400/5005]\tlr: 1.000e-04, eta: 1:01:31, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.5906\n2023-01-02 04:24:35,116 - mmcls - INFO - Epoch [92][3500/5005]\tlr: 1.000e-04, eta: 1:01:22, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.5936\n2023-01-02 04:24:44,070 - mmcls - INFO - Epoch [92][3600/5005]\tlr: 1.000e-04, eta: 1:01:14, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6065\n2023-01-02 04:24:52,929 - mmcls - INFO - Epoch [92][3700/5005]\tlr: 1.000e-04, eta: 1:01:05, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.5935\n2023-01-02 04:25:01,915 - mmcls - INFO - Epoch [92][3800/5005]\tlr: 1.000e-04, eta: 1:00:56, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5824\n2023-01-02 04:25:10,761 - mmcls - INFO - Epoch [92][3900/5005]\tlr: 1.000e-04, eta: 1:00:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5802\n2023-01-02 04:25:19,530 - mmcls - INFO - Epoch [92][4000/5005]\tlr: 1.000e-04, eta: 1:00:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5792\n2023-01-02 04:25:28,379 - mmcls - INFO - Epoch [92][4100/5005]\tlr: 1.000e-04, eta: 1:00:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5786\n2023-01-02 04:25:37,338 - mmcls - INFO - Epoch [92][4200/5005]\tlr: 1.000e-04, eta: 1:00:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5899\n2023-01-02 04:25:46,018 - mmcls - INFO - Epoch [92][4300/5005]\tlr: 1.000e-04, eta: 1:00:11, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.5971\n2023-01-02 04:25:54,825 - mmcls - INFO - Epoch [92][4400/5005]\tlr: 1.000e-04, eta: 1:00:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6025\n2023-01-02 04:26:03,608 - mmcls - INFO - Epoch [92][4500/5005]\tlr: 1.000e-04, eta: 0:59:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5887\n2023-01-02 04:26:12,240 - mmcls - INFO - Epoch [92][4600/5005]\tlr: 1.000e-04, eta: 0:59:45, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.5719\n2023-01-02 04:26:20,793 - mmcls - INFO - Epoch [92][4700/5005]\tlr: 1.000e-04, eta: 0:59:36, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.5831\n2023-01-02 04:26:29,480 - mmcls - INFO - Epoch [92][4800/5005]\tlr: 1.000e-04, eta: 0:59:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5925\n2023-01-02 04:26:38,371 - mmcls - INFO - Epoch [92][4900/5005]\tlr: 1.000e-04, eta: 0:59:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5827\n2023-01-02 04:26:47,327 - mmcls - INFO - Epoch [92][5000/5005]\tlr: 1.000e-04, eta: 0:59:09, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5784\n2023-01-02 04:27:10,071 - mmcls - INFO - Epoch(val) [92][196]\taccuracy_top-1: 65.2480, accuracy_top-5: 85.6640\n2023-01-02 04:27:21,351 - mmcls - INFO - Epoch [93][100/5005]\tlr: 1.000e-04, eta: 0:59:00, time: 0.113, data_time: 0.064, memory: 1224, loss: 1.5837\n2023-01-02 04:27:30,175 - mmcls - INFO - Epoch [93][200/5005]\tlr: 1.000e-04, eta: 0:58:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5890\n2023-01-02 04:27:39,083 - mmcls - INFO - Epoch [93][300/5005]\tlr: 1.000e-04, eta: 0:58:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5653\n2023-01-02 04:27:47,834 - mmcls - INFO - Epoch [93][400/5005]\tlr: 1.000e-04, eta: 0:58:34, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5835\n2023-01-02 04:27:56,337 - mmcls - INFO - Epoch [93][500/5005]\tlr: 1.000e-04, eta: 0:58:25, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.6042\n2023-01-02 04:28:05,141 - mmcls - INFO - Epoch [93][600/5005]\tlr: 1.000e-04, eta: 0:58:16, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6196\n2023-01-02 04:28:13,947 - mmcls - INFO - Epoch [93][700/5005]\tlr: 1.000e-04, eta: 0:58:07, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5924\n2023-01-02 04:28:22,782 - mmcls - INFO - Epoch [93][800/5005]\tlr: 1.000e-04, eta: 0:57:58, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5679\n2023-01-02 04:28:31,712 - mmcls - INFO - Epoch [93][900/5005]\tlr: 1.000e-04, eta: 0:57:49, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6009\n2023-01-02 04:28:40,463 - mmcls - INFO - Epoch [93][1000/5005]\tlr: 1.000e-04, eta: 0:57:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5829\n2023-01-02 04:28:49,386 - mmcls - INFO - Epoch [93][1100/5005]\tlr: 1.000e-04, eta: 0:57:31, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5886\n2023-01-02 04:28:58,223 - mmcls - INFO - Epoch [93][1200/5005]\tlr: 1.000e-04, eta: 0:57:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5727\n2023-01-02 04:29:06,883 - mmcls - INFO - Epoch [93][1300/5005]\tlr: 1.000e-04, eta: 0:57:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5750\n2023-01-02 04:29:15,895 - mmcls - INFO - Epoch [93][1400/5005]\tlr: 1.000e-04, eta: 0:57:05, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.5660\n2023-01-02 04:29:24,872 - mmcls - INFO - Epoch [93][1500/5005]\tlr: 1.000e-04, eta: 0:56:56, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.5803\n2023-01-02 04:29:33,752 - mmcls - INFO - Epoch [93][1600/5005]\tlr: 1.000e-04, eta: 0:56:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.5935\n2023-01-02 04:29:42,332 - mmcls - INFO - Epoch [93][1700/5005]\tlr: 1.000e-04, eta: 0:56:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.5759\n2023-01-02 04:29:51,286 - mmcls - INFO - Epoch [93][1800/5005]\tlr: 1.000e-04, eta: 0:56:29, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.5750\n2023-01-02 04:30:00,010 - mmcls - INFO - Epoch [93][1900/5005]\tlr: 1.000e-04, eta: 0:56:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5682\n2023-01-02 04:30:08,831 - mmcls - INFO - Epoch [93][2000/5005]\tlr: 1.000e-04, eta: 0:56:12, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5921\n2023-01-02 04:30:17,842 - mmcls - INFO - Epoch [93][2100/5005]\tlr: 1.000e-04, eta: 0:56:03, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5806\n2023-01-02 04:30:26,219 - mmcls - INFO - Epoch [93][2200/5005]\tlr: 1.000e-04, eta: 0:55:54, time: 0.084, data_time: 0.014, memory: 1224, loss: 1.5755\n2023-01-02 04:30:34,889 - mmcls - INFO - Epoch [93][2300/5005]\tlr: 1.000e-04, eta: 0:55:45, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5887\n2023-01-02 04:30:43,571 - mmcls - INFO - Epoch [93][2400/5005]\tlr: 1.000e-04, eta: 0:55:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5927\n2023-01-02 04:30:52,425 - mmcls - INFO - Epoch [93][2500/5005]\tlr: 1.000e-04, eta: 0:55:27, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5886\n2023-01-02 04:31:01,117 - mmcls - INFO - Epoch [93][2600/5005]\tlr: 1.000e-04, eta: 0:55:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5782\n2023-01-02 04:31:09,982 - mmcls - INFO - Epoch [93][2700/5005]\tlr: 1.000e-04, eta: 0:55:10, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6072\n2023-01-02 04:31:18,742 - mmcls - INFO - Epoch [93][2800/5005]\tlr: 1.000e-04, eta: 0:55:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5624\n2023-01-02 04:31:27,383 - mmcls - INFO - Epoch [93][2900/5005]\tlr: 1.000e-04, eta: 0:54:52, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.5895\n2023-01-02 04:31:36,071 - mmcls - INFO - Epoch [93][3000/5005]\tlr: 1.000e-04, eta: 0:54:43, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5859\n2023-01-02 04:31:44,799 - mmcls - INFO - Epoch [93][3100/5005]\tlr: 1.000e-04, eta: 0:54:34, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5829\n2023-01-02 04:31:53,777 - mmcls - INFO - Epoch [93][3200/5005]\tlr: 1.000e-04, eta: 0:54:25, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5764\n2023-01-02 04:32:02,437 - mmcls - INFO - Epoch [93][3300/5005]\tlr: 1.000e-04, eta: 0:54:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5989\n2023-01-02 04:32:11,324 - mmcls - INFO - Epoch [93][3400/5005]\tlr: 1.000e-04, eta: 0:54:07, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6012\n2023-01-02 04:32:19,957 - mmcls - INFO - Epoch [93][3500/5005]\tlr: 1.000e-04, eta: 0:53:59, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.5876\n2023-01-02 04:32:28,918 - mmcls - INFO - Epoch [93][3600/5005]\tlr: 1.000e-04, eta: 0:53:50, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.5864\n2023-01-02 04:32:37,790 - mmcls - INFO - Epoch [93][3700/5005]\tlr: 1.000e-04, eta: 0:53:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5774\n2023-01-02 04:32:46,479 - mmcls - INFO - Epoch [93][3800/5005]\tlr: 1.000e-04, eta: 0:53:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5766\n2023-01-02 04:32:55,796 - mmcls - INFO - Epoch [93][3900/5005]\tlr: 1.000e-04, eta: 0:53:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.5645\n2023-01-02 04:33:04,800 - mmcls - INFO - Epoch [93][4000/5005]\tlr: 1.000e-04, eta: 0:53:14, time: 0.090, data_time: 0.037, memory: 1224, loss: 1.5929\n2023-01-02 04:33:13,850 - mmcls - INFO - Epoch [93][4100/5005]\tlr: 1.000e-04, eta: 0:53:05, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.5589\n2023-01-02 04:33:22,731 - mmcls - INFO - Epoch [93][4200/5005]\tlr: 1.000e-04, eta: 0:52:57, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5754\n2023-01-02 04:33:31,368 - mmcls - INFO - Epoch [93][4300/5005]\tlr: 1.000e-04, eta: 0:52:48, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.5949\n2023-01-02 04:33:40,103 - mmcls - INFO - Epoch [93][4400/5005]\tlr: 1.000e-04, eta: 0:52:39, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6140\n2023-01-02 04:33:48,835 - mmcls - INFO - Epoch [93][4500/5005]\tlr: 1.000e-04, eta: 0:52:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5927\n2023-01-02 04:33:58,068 - mmcls - INFO - Epoch [93][4600/5005]\tlr: 1.000e-04, eta: 0:52:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5967\n2023-01-02 04:34:07,091 - mmcls - INFO - Epoch [93][4700/5005]\tlr: 1.000e-04, eta: 0:52:12, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5925\n2023-01-02 04:34:15,665 - mmcls - INFO - Epoch [93][4800/5005]\tlr: 1.000e-04, eta: 0:52:03, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5844\n2023-01-02 04:34:24,564 - mmcls - INFO - Epoch [93][4900/5005]\tlr: 1.000e-04, eta: 0:51:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5934\n2023-01-02 04:34:33,390 - mmcls - INFO - Epoch [93][5000/5005]\tlr: 1.000e-04, eta: 0:51:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5982\n2023-01-02 04:34:56,325 - mmcls - INFO - Epoch(val) [93][196]\taccuracy_top-1: 65.2780, accuracy_top-5: 85.6780\n2023-01-02 04:35:07,165 - mmcls - INFO - Epoch [94][100/5005]\tlr: 1.000e-04, eta: 0:51:36, time: 0.108, data_time: 0.054, memory: 1224, loss: 1.6063\n2023-01-02 04:35:15,837 - mmcls - INFO - Epoch [94][200/5005]\tlr: 1.000e-04, eta: 0:51:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5751\n2023-01-02 04:35:24,565 - mmcls - INFO - Epoch [94][300/5005]\tlr: 1.000e-04, eta: 0:51:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5738\n2023-01-02 04:35:33,254 - mmcls - INFO - Epoch [94][400/5005]\tlr: 1.000e-04, eta: 0:51:10, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5644\n2023-01-02 04:35:42,438 - mmcls - INFO - Epoch [94][500/5005]\tlr: 1.000e-04, eta: 0:51:01, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.5660\n2023-01-02 04:35:51,154 - mmcls - INFO - Epoch [94][600/5005]\tlr: 1.000e-04, eta: 0:50:52, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5648\n2023-01-02 04:36:00,096 - mmcls - INFO - Epoch [94][700/5005]\tlr: 1.000e-04, eta: 0:50:43, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.5752\n2023-01-02 04:36:08,835 - mmcls - INFO - Epoch [94][800/5005]\tlr: 1.000e-04, eta: 0:50:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5792\n2023-01-02 04:36:17,868 - mmcls - INFO - Epoch [94][900/5005]\tlr: 1.000e-04, eta: 0:50:25, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5738\n2023-01-02 04:36:26,632 - mmcls - INFO - Epoch [94][1000/5005]\tlr: 1.000e-04, eta: 0:50:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5689\n2023-01-02 04:36:35,601 - mmcls - INFO - Epoch [94][1100/5005]\tlr: 1.000e-04, eta: 0:50:08, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5923\n2023-01-02 04:36:44,760 - mmcls - INFO - Epoch [94][1200/5005]\tlr: 1.000e-04, eta: 0:49:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5914\n2023-01-02 04:36:53,407 - mmcls - INFO - Epoch [94][1300/5005]\tlr: 1.000e-04, eta: 0:49:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5871\n2023-01-02 04:37:02,338 - mmcls - INFO - Epoch [94][1400/5005]\tlr: 1.000e-04, eta: 0:49:41, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5817\n2023-01-02 04:37:11,215 - mmcls - INFO - Epoch [94][1500/5005]\tlr: 1.000e-04, eta: 0:49:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5675\n2023-01-02 04:37:20,059 - mmcls - INFO - Epoch [94][1600/5005]\tlr: 1.000e-04, eta: 0:49:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5887\n2023-01-02 04:37:28,932 - mmcls - INFO - Epoch [94][1700/5005]\tlr: 1.000e-04, eta: 0:49:15, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6058\n2023-01-02 04:37:37,685 - mmcls - INFO - Epoch [94][1800/5005]\tlr: 1.000e-04, eta: 0:49:06, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6017\n2023-01-02 04:37:46,569 - mmcls - INFO - Epoch [94][1900/5005]\tlr: 1.000e-04, eta: 0:48:57, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5719\n2023-01-02 04:37:55,333 - mmcls - INFO - Epoch [94][2000/5005]\tlr: 1.000e-04, eta: 0:48:48, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5952\n2023-01-02 04:38:04,065 - mmcls - INFO - Epoch [94][2100/5005]\tlr: 1.000e-04, eta: 0:48:39, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5829\n2023-01-02 04:38:13,042 - mmcls - INFO - Epoch [94][2200/5005]\tlr: 1.000e-04, eta: 0:48:30, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.5722\n2023-01-02 04:38:21,747 - mmcls - INFO - Epoch [94][2300/5005]\tlr: 1.000e-04, eta: 0:48:21, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.5950\n2023-01-02 04:38:30,593 - mmcls - INFO - Epoch [94][2400/5005]\tlr: 1.000e-04, eta: 0:48:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5826\n2023-01-02 04:38:39,301 - mmcls - INFO - Epoch [94][2500/5005]\tlr: 1.000e-04, eta: 0:48:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5730\n2023-01-02 04:38:48,117 - mmcls - INFO - Epoch [94][2600/5005]\tlr: 1.000e-04, eta: 0:47:55, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5834\n2023-01-02 04:38:57,185 - mmcls - INFO - Epoch [94][2700/5005]\tlr: 1.000e-04, eta: 0:47:46, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.5700\n2023-01-02 04:39:05,943 - mmcls - INFO - Epoch [94][2800/5005]\tlr: 1.000e-04, eta: 0:47:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5845\n2023-01-02 04:39:14,559 - mmcls - INFO - Epoch [94][2900/5005]\tlr: 1.000e-04, eta: 0:47:28, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5917\n2023-01-02 04:39:23,153 - mmcls - INFO - Epoch [94][3000/5005]\tlr: 1.000e-04, eta: 0:47:19, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6038\n2023-01-02 04:39:31,982 - mmcls - INFO - Epoch [94][3100/5005]\tlr: 1.000e-04, eta: 0:47:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5760\n2023-01-02 04:39:40,624 - mmcls - INFO - Epoch [94][3200/5005]\tlr: 1.000e-04, eta: 0:47:02, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.5763\n2023-01-02 04:39:49,500 - mmcls - INFO - Epoch [94][3300/5005]\tlr: 1.000e-04, eta: 0:46:53, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6078\n2023-01-02 04:39:58,319 - mmcls - INFO - Epoch [94][3400/5005]\tlr: 1.000e-04, eta: 0:46:44, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5581\n2023-01-02 04:40:07,383 - mmcls - INFO - Epoch [94][3500/5005]\tlr: 1.000e-04, eta: 0:46:35, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.5817\n2023-01-02 04:40:16,683 - mmcls - INFO - Epoch [94][3600/5005]\tlr: 1.000e-04, eta: 0:46:26, time: 0.093, data_time: 0.017, memory: 1224, loss: 1.5856\n2023-01-02 04:40:25,304 - mmcls - INFO - Epoch [94][3700/5005]\tlr: 1.000e-04, eta: 0:46:17, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.5846\n2023-01-02 04:40:33,970 - mmcls - INFO - Epoch [94][3800/5005]\tlr: 1.000e-04, eta: 0:46:08, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5624\n2023-01-02 04:40:43,139 - mmcls - INFO - Epoch [94][3900/5005]\tlr: 1.000e-04, eta: 0:46:00, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.5912\n2023-01-02 04:40:52,119 - mmcls - INFO - Epoch [94][4000/5005]\tlr: 1.000e-04, eta: 0:45:51, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.5975\n2023-01-02 04:41:00,882 - mmcls - INFO - Epoch [94][4100/5005]\tlr: 1.000e-04, eta: 0:45:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6021\n2023-01-02 04:41:09,855 - mmcls - INFO - Epoch [94][4200/5005]\tlr: 1.000e-04, eta: 0:45:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5860\n2023-01-02 04:41:18,577 - mmcls - INFO - Epoch [94][4300/5005]\tlr: 1.000e-04, eta: 0:45:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5926\n2023-01-02 04:41:27,300 - mmcls - INFO - Epoch [94][4400/5005]\tlr: 1.000e-04, eta: 0:45:15, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.5916\n2023-01-02 04:41:36,615 - mmcls - INFO - Epoch [94][4500/5005]\tlr: 1.000e-04, eta: 0:45:06, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.5859\n2023-01-02 04:41:45,401 - mmcls - INFO - Epoch [94][4600/5005]\tlr: 1.000e-04, eta: 0:44:57, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5569\n2023-01-02 04:41:54,037 - mmcls - INFO - Epoch [94][4700/5005]\tlr: 1.000e-04, eta: 0:44:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5745\n2023-01-02 04:42:03,207 - mmcls - INFO - Epoch [94][4800/5005]\tlr: 1.000e-04, eta: 0:44:40, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.5786\n2023-01-02 04:42:11,975 - mmcls - INFO - Epoch [94][4900/5005]\tlr: 1.000e-04, eta: 0:44:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.5861\n2023-01-02 04:42:20,967 - mmcls - INFO - Epoch [94][5000/5005]\tlr: 1.000e-04, eta: 0:44:22, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5854\n2023-01-02 04:42:43,696 - mmcls - INFO - Epoch(val) [94][196]\taccuracy_top-1: 65.3460, accuracy_top-5: 85.7620\n2023-01-02 04:42:55,064 - mmcls - INFO - Epoch [95][100/5005]\tlr: 1.000e-04, eta: 0:44:13, time: 0.113, data_time: 0.056, memory: 1224, loss: 1.5675\n2023-01-02 04:43:03,695 - mmcls - INFO - Epoch [95][200/5005]\tlr: 1.000e-04, eta: 0:44:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5733\n2023-01-02 04:43:12,425 - mmcls - INFO - Epoch [95][300/5005]\tlr: 1.000e-04, eta: 0:43:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5899\n2023-01-02 04:43:21,397 - mmcls - INFO - Epoch [95][400/5005]\tlr: 1.000e-04, eta: 0:43:46, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.5941\n2023-01-02 04:43:30,186 - mmcls - INFO - Epoch [95][500/5005]\tlr: 1.000e-04, eta: 0:43:37, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.5965\n2023-01-02 04:43:38,895 - mmcls - INFO - Epoch [95][600/5005]\tlr: 1.000e-04, eta: 0:43:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5927\n2023-01-02 04:43:47,798 - mmcls - INFO - Epoch [95][700/5005]\tlr: 1.000e-04, eta: 0:43:20, time: 0.089, data_time: 0.044, memory: 1224, loss: 1.5806\n2023-01-02 04:43:56,666 - mmcls - INFO - Epoch [95][800/5005]\tlr: 1.000e-04, eta: 0:43:11, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5814\n2023-01-02 04:44:05,306 - mmcls - INFO - Epoch [95][900/5005]\tlr: 1.000e-04, eta: 0:43:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.5772\n2023-01-02 04:44:14,220 - mmcls - INFO - Epoch [95][1000/5005]\tlr: 1.000e-04, eta: 0:42:53, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5922\n2023-01-02 04:44:23,224 - mmcls - INFO - Epoch [95][1100/5005]\tlr: 1.000e-04, eta: 0:42:44, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.5767\n2023-01-02 04:44:31,768 - mmcls - INFO - Epoch [95][1200/5005]\tlr: 1.000e-04, eta: 0:42:35, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.5576\n2023-01-02 04:44:40,440 - mmcls - INFO - Epoch [95][1300/5005]\tlr: 1.000e-04, eta: 0:42:26, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.5602\n2023-01-02 04:44:49,255 - mmcls - INFO - Epoch [95][1400/5005]\tlr: 1.000e-04, eta: 0:42:18, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5986\n2023-01-02 04:44:58,030 - mmcls - INFO - Epoch [95][1500/5005]\tlr: 1.000e-04, eta: 0:42:09, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5686\n2023-01-02 04:45:06,973 - mmcls - INFO - Epoch [95][1600/5005]\tlr: 1.000e-04, eta: 0:42:00, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.5829\n2023-01-02 04:45:15,933 - mmcls - INFO - Epoch [95][1700/5005]\tlr: 1.000e-04, eta: 0:41:51, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.5762\n2023-01-02 04:45:24,830 - mmcls - INFO - Epoch [95][1800/5005]\tlr: 1.000e-04, eta: 0:41:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5664\n2023-01-02 04:45:33,474 - mmcls - INFO - Epoch [95][1900/5005]\tlr: 1.000e-04, eta: 0:41:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5717\n2023-01-02 04:45:42,248 - mmcls - INFO - Epoch [95][2000/5005]\tlr: 1.000e-04, eta: 0:41:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5699\n2023-01-02 04:45:50,934 - mmcls - INFO - Epoch [95][2100/5005]\tlr: 1.000e-04, eta: 0:41:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5873\n2023-01-02 04:45:59,523 - mmcls - INFO - Epoch [95][2200/5005]\tlr: 1.000e-04, eta: 0:41:07, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5870\n2023-01-02 04:46:08,066 - mmcls - INFO - Epoch [95][2300/5005]\tlr: 1.000e-04, eta: 0:40:58, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.5792\n2023-01-02 04:46:16,825 - mmcls - INFO - Epoch [95][2400/5005]\tlr: 1.000e-04, eta: 0:40:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5804\n2023-01-02 04:46:25,552 - mmcls - INFO - Epoch [95][2500/5005]\tlr: 1.000e-04, eta: 0:40:40, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5681\n2023-01-02 04:46:34,445 - mmcls - INFO - Epoch [95][2600/5005]\tlr: 1.000e-04, eta: 0:40:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5868\n2023-01-02 04:46:43,308 - mmcls - INFO - Epoch [95][2700/5005]\tlr: 1.000e-04, eta: 0:40:22, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5860\n2023-01-02 04:46:52,096 - mmcls - INFO - Epoch [95][2800/5005]\tlr: 1.000e-04, eta: 0:40:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5782\n2023-01-02 04:47:01,096 - mmcls - INFO - Epoch [95][2900/5005]\tlr: 1.000e-04, eta: 0:40:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.5784\n2023-01-02 04:47:09,908 - mmcls - INFO - Epoch [95][3000/5005]\tlr: 1.000e-04, eta: 0:39:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5770\n2023-01-02 04:47:19,011 - mmcls - INFO - Epoch [95][3100/5005]\tlr: 1.000e-04, eta: 0:39:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5802\n2023-01-02 04:47:28,066 - mmcls - INFO - Epoch [95][3200/5005]\tlr: 1.000e-04, eta: 0:39:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5827\n2023-01-02 04:47:36,760 - mmcls - INFO - Epoch [95][3300/5005]\tlr: 1.000e-04, eta: 0:39:29, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.5739\n2023-01-02 04:47:45,536 - mmcls - INFO - Epoch [95][3400/5005]\tlr: 1.000e-04, eta: 0:39:20, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.5796\n2023-01-02 04:47:54,413 - mmcls - INFO - Epoch [95][3500/5005]\tlr: 1.000e-04, eta: 0:39:11, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5825\n2023-01-02 04:48:03,406 - mmcls - INFO - Epoch [95][3600/5005]\tlr: 1.000e-04, eta: 0:39:02, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5778\n2023-01-02 04:48:12,120 - mmcls - INFO - Epoch [95][3700/5005]\tlr: 1.000e-04, eta: 0:38:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5867\n2023-01-02 04:48:20,940 - mmcls - INFO - Epoch [95][3800/5005]\tlr: 1.000e-04, eta: 0:38:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6139\n2023-01-02 04:48:30,004 - mmcls - INFO - Epoch [95][3900/5005]\tlr: 1.000e-04, eta: 0:38:36, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5872\n2023-01-02 04:48:39,222 - mmcls - INFO - Epoch [95][4000/5005]\tlr: 1.000e-04, eta: 0:38:27, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5785\n2023-01-02 04:48:48,281 - mmcls - INFO - Epoch [95][4100/5005]\tlr: 1.000e-04, eta: 0:38:18, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5895\n2023-01-02 04:48:56,904 - mmcls - INFO - Epoch [95][4200/5005]\tlr: 1.000e-04, eta: 0:38:09, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6094\n2023-01-02 04:49:05,903 - mmcls - INFO - Epoch [95][4300/5005]\tlr: 1.000e-04, eta: 0:38:00, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.5677\n2023-01-02 04:49:14,829 - mmcls - INFO - Epoch [95][4400/5005]\tlr: 1.000e-04, eta: 0:37:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6011\n2023-01-02 04:49:23,368 - mmcls - INFO - Epoch [95][4500/5005]\tlr: 1.000e-04, eta: 0:37:43, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.5661\n2023-01-02 04:49:32,144 - mmcls - INFO - Epoch [95][4600/5005]\tlr: 1.000e-04, eta: 0:37:34, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.5934\n2023-01-02 04:49:41,197 - mmcls - INFO - Epoch [95][4700/5005]\tlr: 1.000e-04, eta: 0:37:25, time: 0.091, data_time: 0.031, memory: 1224, loss: 1.5740\n2023-01-02 04:49:50,063 - mmcls - INFO - Epoch [95][4800/5005]\tlr: 1.000e-04, eta: 0:37:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5845\n2023-01-02 04:49:58,841 - mmcls - INFO - Epoch [95][4900/5005]\tlr: 1.000e-04, eta: 0:37:07, time: 0.088, data_time: 0.044, memory: 1224, loss: 1.5835\n2023-01-02 04:50:07,672 - mmcls - INFO - Epoch [95][5000/5005]\tlr: 1.000e-04, eta: 0:36:58, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5867\n2023-01-02 04:50:30,329 - mmcls - INFO - Epoch(val) [95][196]\taccuracy_top-1: 65.3760, accuracy_top-5: 85.7900\n2023-01-02 04:50:41,245 - mmcls - INFO - Epoch [96][100/5005]\tlr: 1.000e-04, eta: 0:36:49, time: 0.109, data_time: 0.051, memory: 1224, loss: 1.5557\n2023-01-02 04:50:49,791 - mmcls - INFO - Epoch [96][200/5005]\tlr: 1.000e-04, eta: 0:36:40, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.5764\n2023-01-02 04:50:58,392 - mmcls - INFO - Epoch [96][300/5005]\tlr: 1.000e-04, eta: 0:36:31, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.5813\n2023-01-02 04:51:07,274 - mmcls - INFO - Epoch [96][400/5005]\tlr: 1.000e-04, eta: 0:36:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5697\n2023-01-02 04:51:16,255 - mmcls - INFO - Epoch [96][500/5005]\tlr: 1.000e-04, eta: 0:36:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5877\n2023-01-02 04:51:25,138 - mmcls - INFO - Epoch [96][600/5005]\tlr: 1.000e-04, eta: 0:36:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5761\n2023-01-02 04:51:34,011 - mmcls - INFO - Epoch [96][700/5005]\tlr: 1.000e-04, eta: 0:35:56, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5665\n2023-01-02 04:51:43,150 - mmcls - INFO - Epoch [96][800/5005]\tlr: 1.000e-04, eta: 0:35:47, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.5855\n2023-01-02 04:51:51,784 - mmcls - INFO - Epoch [96][900/5005]\tlr: 1.000e-04, eta: 0:35:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5790\n2023-01-02 04:52:00,861 - mmcls - INFO - Epoch [96][1000/5005]\tlr: 1.000e-04, eta: 0:35:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6128\n2023-01-02 04:52:09,578 - mmcls - INFO - Epoch [96][1100/5005]\tlr: 1.000e-04, eta: 0:35:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5575\n2023-01-02 04:52:18,268 - mmcls - INFO - Epoch [96][1200/5005]\tlr: 1.000e-04, eta: 0:35:12, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5917\n2023-01-02 04:52:27,367 - mmcls - INFO - Epoch [96][1300/5005]\tlr: 1.000e-04, eta: 0:35:03, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.5866\n2023-01-02 04:52:35,892 - mmcls - INFO - Epoch [96][1400/5005]\tlr: 1.000e-04, eta: 0:34:54, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.5878\n2023-01-02 04:52:44,735 - mmcls - INFO - Epoch [96][1500/5005]\tlr: 1.000e-04, eta: 0:34:45, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5796\n2023-01-02 04:52:53,350 - mmcls - INFO - Epoch [96][1600/5005]\tlr: 1.000e-04, eta: 0:34:36, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5801\n2023-01-02 04:53:01,991 - mmcls - INFO - Epoch [96][1700/5005]\tlr: 1.000e-04, eta: 0:34:27, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5766\n2023-01-02 04:53:10,799 - mmcls - INFO - Epoch [96][1800/5005]\tlr: 1.000e-04, eta: 0:34:18, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.5864\n2023-01-02 04:53:19,681 - mmcls - INFO - Epoch [96][1900/5005]\tlr: 1.000e-04, eta: 0:34:09, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.5858\n2023-01-02 04:53:28,739 - mmcls - INFO - Epoch [96][2000/5005]\tlr: 1.000e-04, eta: 0:34:01, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5910\n2023-01-02 04:53:37,375 - mmcls - INFO - Epoch [96][2100/5005]\tlr: 1.000e-04, eta: 0:33:52, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5842\n2023-01-02 04:53:46,346 - mmcls - INFO - Epoch [96][2200/5005]\tlr: 1.000e-04, eta: 0:33:43, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5741\n2023-01-02 04:53:55,210 - mmcls - INFO - Epoch [96][2300/5005]\tlr: 1.000e-04, eta: 0:33:34, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5737\n2023-01-02 04:54:03,992 - mmcls - INFO - Epoch [96][2400/5005]\tlr: 1.000e-04, eta: 0:33:25, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5792\n2023-01-02 04:54:12,700 - mmcls - INFO - Epoch [96][2500/5005]\tlr: 1.000e-04, eta: 0:33:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5855\n2023-01-02 04:54:21,737 - mmcls - INFO - Epoch [96][2600/5005]\tlr: 1.000e-04, eta: 0:33:07, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5881\n2023-01-02 04:54:30,659 - mmcls - INFO - Epoch [96][2700/5005]\tlr: 1.000e-04, eta: 0:32:59, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5764\n2023-01-02 04:54:39,296 - mmcls - INFO - Epoch [96][2800/5005]\tlr: 1.000e-04, eta: 0:32:50, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5720\n2023-01-02 04:54:48,170 - mmcls - INFO - Epoch [96][2900/5005]\tlr: 1.000e-04, eta: 0:32:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5871\n2023-01-02 04:54:57,067 - mmcls - INFO - Epoch [96][3000/5005]\tlr: 1.000e-04, eta: 0:32:32, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5751\n2023-01-02 04:55:05,783 - mmcls - INFO - Epoch [96][3100/5005]\tlr: 1.000e-04, eta: 0:32:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5813\n2023-01-02 04:55:14,803 - mmcls - INFO - Epoch [96][3200/5005]\tlr: 1.000e-04, eta: 0:32:14, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5897\n2023-01-02 04:55:23,929 - mmcls - INFO - Epoch [96][3300/5005]\tlr: 1.000e-04, eta: 0:32:05, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.5860\n2023-01-02 04:55:32,787 - mmcls - INFO - Epoch [96][3400/5005]\tlr: 1.000e-04, eta: 0:31:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5886\n2023-01-02 04:55:41,398 - mmcls - INFO - Epoch [96][3500/5005]\tlr: 1.000e-04, eta: 0:31:48, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5938\n2023-01-02 04:55:50,377 - mmcls - INFO - Epoch [96][3600/5005]\tlr: 1.000e-04, eta: 0:31:39, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.5729\n2023-01-02 04:55:58,978 - mmcls - INFO - Epoch [96][3700/5005]\tlr: 1.000e-04, eta: 0:31:30, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.5848\n2023-01-02 04:56:07,830 - mmcls - INFO - Epoch [96][3800/5005]\tlr: 1.000e-04, eta: 0:31:21, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5828\n2023-01-02 04:56:16,273 - mmcls - INFO - Epoch [96][3900/5005]\tlr: 1.000e-04, eta: 0:31:12, time: 0.084, data_time: 0.024, memory: 1224, loss: 1.5719\n2023-01-02 04:56:25,169 - mmcls - INFO - Epoch [96][4000/5005]\tlr: 1.000e-04, eta: 0:31:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5766\n2023-01-02 04:56:34,216 - mmcls - INFO - Epoch [96][4100/5005]\tlr: 1.000e-04, eta: 0:30:54, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5759\n2023-01-02 04:56:43,151 - mmcls - INFO - Epoch [96][4200/5005]\tlr: 1.000e-04, eta: 0:30:46, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.5854\n2023-01-02 04:56:52,225 - mmcls - INFO - Epoch [96][4300/5005]\tlr: 1.000e-04, eta: 0:30:37, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.5789\n2023-01-02 04:57:00,792 - mmcls - INFO - Epoch [96][4400/5005]\tlr: 1.000e-04, eta: 0:30:28, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5734\n2023-01-02 04:57:09,698 - mmcls - INFO - Epoch [96][4500/5005]\tlr: 1.000e-04, eta: 0:30:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5678\n2023-01-02 04:57:18,666 - mmcls - INFO - Epoch [96][4600/5005]\tlr: 1.000e-04, eta: 0:30:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5761\n2023-01-02 04:57:27,577 - mmcls - INFO - Epoch [96][4700/5005]\tlr: 1.000e-04, eta: 0:30:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5873\n2023-01-02 04:57:36,328 - mmcls - INFO - Epoch [96][4800/5005]\tlr: 1.000e-04, eta: 0:29:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.5818\n2023-01-02 04:57:45,061 - mmcls - INFO - Epoch [96][4900/5005]\tlr: 1.000e-04, eta: 0:29:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5916\n2023-01-02 04:57:53,744 - mmcls - INFO - Epoch [96][5000/5005]\tlr: 1.000e-04, eta: 0:29:35, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.5729\n2023-01-02 04:58:16,383 - mmcls - INFO - Epoch(val) [96][196]\taccuracy_top-1: 65.4020, accuracy_top-5: 85.7920\n2023-01-02 04:58:27,557 - mmcls - INFO - Epoch [97][100/5005]\tlr: 1.000e-04, eta: 0:29:25, time: 0.111, data_time: 0.039, memory: 1224, loss: 1.5681\n2023-01-02 04:58:36,458 - mmcls - INFO - Epoch [97][200/5005]\tlr: 1.000e-04, eta: 0:29:17, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.5833\n2023-01-02 04:58:45,350 - mmcls - INFO - Epoch [97][300/5005]\tlr: 1.000e-04, eta: 0:29:08, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.5962\n2023-01-02 04:58:54,166 - mmcls - INFO - Epoch [97][400/5005]\tlr: 1.000e-04, eta: 0:28:59, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6109\n2023-01-02 04:59:03,038 - mmcls - INFO - Epoch [97][500/5005]\tlr: 1.000e-04, eta: 0:28:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5786\n2023-01-02 04:59:11,532 - mmcls - INFO - Epoch [97][600/5005]\tlr: 1.000e-04, eta: 0:28:41, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.5891\n2023-01-02 04:59:20,504 - mmcls - INFO - Epoch [97][700/5005]\tlr: 1.000e-04, eta: 0:28:32, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5744\n2023-01-02 04:59:29,669 - mmcls - INFO - Epoch [97][800/5005]\tlr: 1.000e-04, eta: 0:28:23, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5586\n2023-01-02 04:59:38,558 - mmcls - INFO - Epoch [97][900/5005]\tlr: 1.000e-04, eta: 0:28:15, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.5872\n2023-01-02 04:59:47,174 - mmcls - INFO - Epoch [97][1000/5005]\tlr: 1.000e-04, eta: 0:28:06, time: 0.086, data_time: 0.029, memory: 1224, loss: 1.5386\n2023-01-02 04:59:55,762 - mmcls - INFO - Epoch [97][1100/5005]\tlr: 1.000e-04, eta: 0:27:57, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.5718\n2023-01-02 05:00:04,482 - mmcls - INFO - Epoch [97][1200/5005]\tlr: 1.000e-04, eta: 0:27:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5551\n2023-01-02 05:00:13,083 - mmcls - INFO - Epoch [97][1300/5005]\tlr: 1.000e-04, eta: 0:27:39, time: 0.086, data_time: 0.039, memory: 1224, loss: 1.5914\n2023-01-02 05:00:21,789 - mmcls - INFO - Epoch [97][1400/5005]\tlr: 1.000e-04, eta: 0:27:30, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.5902\n2023-01-02 05:00:30,546 - mmcls - INFO - Epoch [97][1500/5005]\tlr: 1.000e-04, eta: 0:27:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5716\n2023-01-02 05:00:39,716 - mmcls - INFO - Epoch [97][1600/5005]\tlr: 1.000e-04, eta: 0:27:12, time: 0.092, data_time: 0.027, memory: 1224, loss: 1.5884\n2023-01-02 05:00:48,866 - mmcls - INFO - Epoch [97][1700/5005]\tlr: 1.000e-04, eta: 0:27:04, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.5615\n2023-01-02 05:00:57,439 - mmcls - INFO - Epoch [97][1800/5005]\tlr: 1.000e-04, eta: 0:26:55, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.5816\n2023-01-02 05:01:06,116 - mmcls - INFO - Epoch [97][1900/5005]\tlr: 1.000e-04, eta: 0:26:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5735\n2023-01-02 05:01:14,958 - mmcls - INFO - Epoch [97][2000/5005]\tlr: 1.000e-04, eta: 0:26:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5757\n2023-01-02 05:01:23,612 - mmcls - INFO - Epoch [97][2100/5005]\tlr: 1.000e-04, eta: 0:26:28, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.5884\n2023-01-02 05:01:32,534 - mmcls - INFO - Epoch [97][2200/5005]\tlr: 1.000e-04, eta: 0:26:19, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.5661\n2023-01-02 05:01:41,446 - mmcls - INFO - Epoch [97][2300/5005]\tlr: 1.000e-04, eta: 0:26:10, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5846\n2023-01-02 05:01:50,141 - mmcls - INFO - Epoch [97][2400/5005]\tlr: 1.000e-04, eta: 0:26:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5757\n2023-01-02 05:01:59,007 - mmcls - INFO - Epoch [97][2500/5005]\tlr: 1.000e-04, eta: 0:25:53, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5769\n2023-01-02 05:02:07,792 - mmcls - INFO - Epoch [97][2600/5005]\tlr: 1.000e-04, eta: 0:25:44, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5935\n2023-01-02 05:02:16,589 - mmcls - INFO - Epoch [97][2700/5005]\tlr: 1.000e-04, eta: 0:25:35, time: 0.088, data_time: 0.041, memory: 1224, loss: 1.5904\n2023-01-02 05:02:25,829 - mmcls - INFO - Epoch [97][2800/5005]\tlr: 1.000e-04, eta: 0:25:26, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.5790\n2023-01-02 05:02:34,687 - mmcls - INFO - Epoch [97][2900/5005]\tlr: 1.000e-04, eta: 0:25:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5663\n2023-01-02 05:02:43,349 - mmcls - INFO - Epoch [97][3000/5005]\tlr: 1.000e-04, eta: 0:25:08, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.5921\n2023-01-02 05:02:52,269 - mmcls - INFO - Epoch [97][3100/5005]\tlr: 1.000e-04, eta: 0:24:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5785\n2023-01-02 05:03:00,906 - mmcls - INFO - Epoch [97][3200/5005]\tlr: 1.000e-04, eta: 0:24:51, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5922\n2023-01-02 05:03:09,658 - mmcls - INFO - Epoch [97][3300/5005]\tlr: 1.000e-04, eta: 0:24:42, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.5664\n2023-01-02 05:03:18,667 - mmcls - INFO - Epoch [97][3400/5005]\tlr: 1.000e-04, eta: 0:24:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5941\n2023-01-02 05:03:27,392 - mmcls - INFO - Epoch [97][3500/5005]\tlr: 1.000e-04, eta: 0:24:24, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.5735\n2023-01-02 05:03:36,424 - mmcls - INFO - Epoch [97][3600/5005]\tlr: 1.000e-04, eta: 0:24:15, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6001\n2023-01-02 05:03:45,023 - mmcls - INFO - Epoch [97][3700/5005]\tlr: 1.000e-04, eta: 0:24:06, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6003\n2023-01-02 05:03:53,911 - mmcls - INFO - Epoch [97][3800/5005]\tlr: 1.000e-04, eta: 0:23:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.5592\n2023-01-02 05:04:02,644 - mmcls - INFO - Epoch [97][3900/5005]\tlr: 1.000e-04, eta: 0:23:48, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.5670\n2023-01-02 05:04:11,577 - mmcls - INFO - Epoch [97][4000/5005]\tlr: 1.000e-04, eta: 0:23:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5689\n2023-01-02 05:04:20,572 - mmcls - INFO - Epoch [97][4100/5005]\tlr: 1.000e-04, eta: 0:23:31, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.5810\n2023-01-02 05:04:29,429 - mmcls - INFO - Epoch [97][4200/5005]\tlr: 1.000e-04, eta: 0:23:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5756\n2023-01-02 05:04:38,027 - mmcls - INFO - Epoch [97][4300/5005]\tlr: 1.000e-04, eta: 0:23:13, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.5470\n2023-01-02 05:04:46,970 - mmcls - INFO - Epoch [97][4400/5005]\tlr: 1.000e-04, eta: 0:23:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5823\n2023-01-02 05:04:55,945 - mmcls - INFO - Epoch [97][4500/5005]\tlr: 1.000e-04, eta: 0:22:55, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.5798\n2023-01-02 05:05:04,834 - mmcls - INFO - Epoch [97][4600/5005]\tlr: 1.000e-04, eta: 0:22:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6110\n2023-01-02 05:05:13,542 - mmcls - INFO - Epoch [97][4700/5005]\tlr: 1.000e-04, eta: 0:22:38, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.5613\n2023-01-02 05:05:22,378 - mmcls - INFO - Epoch [97][4800/5005]\tlr: 1.000e-04, eta: 0:22:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5628\n2023-01-02 05:05:31,154 - mmcls - INFO - Epoch [97][4900/5005]\tlr: 1.000e-04, eta: 0:22:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5461\n2023-01-02 05:05:40,095 - mmcls - INFO - Epoch [97][5000/5005]\tlr: 1.000e-04, eta: 0:22:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5682\n2023-01-02 05:06:02,820 - mmcls - INFO - Epoch(val) [97][196]\taccuracy_top-1: 65.3600, accuracy_top-5: 85.7300\n2023-01-02 05:06:14,233 - mmcls - INFO - Epoch [98][100/5005]\tlr: 1.000e-04, eta: 0:22:02, time: 0.114, data_time: 0.053, memory: 1224, loss: 1.5806\n2023-01-02 05:06:22,902 - mmcls - INFO - Epoch [98][200/5005]\tlr: 1.000e-04, eta: 0:21:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5645\n2023-01-02 05:06:31,714 - mmcls - INFO - Epoch [98][300/5005]\tlr: 1.000e-04, eta: 0:21:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5616\n2023-01-02 05:06:40,404 - mmcls - INFO - Epoch [98][400/5005]\tlr: 1.000e-04, eta: 0:21:35, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.5705\n2023-01-02 05:06:48,979 - mmcls - INFO - Epoch [98][500/5005]\tlr: 1.000e-04, eta: 0:21:26, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.5688\n2023-01-02 05:06:57,872 - mmcls - INFO - Epoch [98][600/5005]\tlr: 1.000e-04, eta: 0:21:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5775\n2023-01-02 05:07:06,525 - mmcls - INFO - Epoch [98][700/5005]\tlr: 1.000e-04, eta: 0:21:09, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5709\n2023-01-02 05:07:15,538 - mmcls - INFO - Epoch [98][800/5005]\tlr: 1.000e-04, eta: 0:21:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5743\n2023-01-02 05:07:24,307 - mmcls - INFO - Epoch [98][900/5005]\tlr: 1.000e-04, eta: 0:20:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.5915\n2023-01-02 05:07:32,891 - mmcls - INFO - Epoch [98][1000/5005]\tlr: 1.000e-04, eta: 0:20:42, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.5666\n2023-01-02 05:07:41,478 - mmcls - INFO - Epoch [98][1100/5005]\tlr: 1.000e-04, eta: 0:20:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5965\n2023-01-02 05:07:50,466 - mmcls - INFO - Epoch [98][1200/5005]\tlr: 1.000e-04, eta: 0:20:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5781\n2023-01-02 05:07:59,517 - mmcls - INFO - Epoch [98][1300/5005]\tlr: 1.000e-04, eta: 0:20:15, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5721\n2023-01-02 05:08:08,671 - mmcls - INFO - Epoch [98][1400/5005]\tlr: 1.000e-04, eta: 0:20:06, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.5852\n2023-01-02 05:08:17,371 - mmcls - INFO - Epoch [98][1500/5005]\tlr: 1.000e-04, eta: 0:19:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5790\n2023-01-02 05:08:26,104 - mmcls - INFO - Epoch [98][1600/5005]\tlr: 1.000e-04, eta: 0:19:49, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5722\n2023-01-02 05:08:34,721 - mmcls - INFO - Epoch [98][1700/5005]\tlr: 1.000e-04, eta: 0:19:40, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6041\n2023-01-02 05:08:43,525 - mmcls - INFO - Epoch [98][1800/5005]\tlr: 1.000e-04, eta: 0:19:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5810\n2023-01-02 05:08:52,287 - mmcls - INFO - Epoch [98][1900/5005]\tlr: 1.000e-04, eta: 0:19:22, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5709\n2023-01-02 05:09:01,057 - mmcls - INFO - Epoch [98][2000/5005]\tlr: 1.000e-04, eta: 0:19:13, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5842\n2023-01-02 05:09:09,786 - mmcls - INFO - Epoch [98][2100/5005]\tlr: 1.000e-04, eta: 0:19:04, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.5804\n2023-01-02 05:09:18,978 - mmcls - INFO - Epoch [98][2200/5005]\tlr: 1.000e-04, eta: 0:18:56, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.5472\n2023-01-02 05:09:27,918 - mmcls - INFO - Epoch [98][2300/5005]\tlr: 1.000e-04, eta: 0:18:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5772\n2023-01-02 05:09:36,662 - mmcls - INFO - Epoch [98][2400/5005]\tlr: 1.000e-04, eta: 0:18:38, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.5903\n2023-01-02 05:09:45,268 - mmcls - INFO - Epoch [98][2500/5005]\tlr: 1.000e-04, eta: 0:18:29, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6041\n2023-01-02 05:09:54,361 - mmcls - INFO - Epoch [98][2600/5005]\tlr: 1.000e-04, eta: 0:18:20, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.5762\n2023-01-02 05:10:03,308 - mmcls - INFO - Epoch [98][2700/5005]\tlr: 1.000e-04, eta: 0:18:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5596\n2023-01-02 05:10:12,103 - mmcls - INFO - Epoch [98][2800/5005]\tlr: 1.000e-04, eta: 0:18:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5693\n2023-01-02 05:10:20,800 - mmcls - INFO - Epoch [98][2900/5005]\tlr: 1.000e-04, eta: 0:17:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5662\n2023-01-02 05:10:29,501 - mmcls - INFO - Epoch [98][3000/5005]\tlr: 1.000e-04, eta: 0:17:45, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.5730\n2023-01-02 05:10:38,404 - mmcls - INFO - Epoch [98][3100/5005]\tlr: 1.000e-04, eta: 0:17:36, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5897\n2023-01-02 05:10:47,157 - mmcls - INFO - Epoch [98][3200/5005]\tlr: 1.000e-04, eta: 0:17:27, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.5691\n2023-01-02 05:10:55,705 - mmcls - INFO - Epoch [98][3300/5005]\tlr: 1.000e-04, eta: 0:17:18, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.5719\n2023-01-02 05:11:04,550 - mmcls - INFO - Epoch [98][3400/5005]\tlr: 1.000e-04, eta: 0:17:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5854\n2023-01-02 05:11:13,337 - mmcls - INFO - Epoch [98][3500/5005]\tlr: 1.000e-04, eta: 0:17:00, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5969\n2023-01-02 05:11:22,338 - mmcls - INFO - Epoch [98][3600/5005]\tlr: 1.000e-04, eta: 0:16:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5729\n2023-01-02 05:11:30,960 - mmcls - INFO - Epoch [98][3700/5005]\tlr: 1.000e-04, eta: 0:16:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5956\n2023-01-02 05:11:39,470 - mmcls - INFO - Epoch [98][3800/5005]\tlr: 1.000e-04, eta: 0:16:34, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.5842\n2023-01-02 05:11:48,180 - mmcls - INFO - Epoch [98][3900/5005]\tlr: 1.000e-04, eta: 0:16:25, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.5665\n2023-01-02 05:11:57,008 - mmcls - INFO - Epoch [98][4000/5005]\tlr: 1.000e-04, eta: 0:16:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5642\n2023-01-02 05:12:05,770 - mmcls - INFO - Epoch [98][4100/5005]\tlr: 1.000e-04, eta: 0:16:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6066\n2023-01-02 05:12:14,451 - mmcls - INFO - Epoch [98][4200/5005]\tlr: 1.000e-04, eta: 0:15:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5847\n2023-01-02 05:12:23,621 - mmcls - INFO - Epoch [98][4300/5005]\tlr: 1.000e-04, eta: 0:15:49, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5872\n2023-01-02 05:12:32,429 - mmcls - INFO - Epoch [98][4400/5005]\tlr: 1.000e-04, eta: 0:15:40, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5678\n2023-01-02 05:12:41,454 - mmcls - INFO - Epoch [98][4500/5005]\tlr: 1.000e-04, eta: 0:15:32, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.5910\n2023-01-02 05:12:50,219 - mmcls - INFO - Epoch [98][4600/5005]\tlr: 1.000e-04, eta: 0:15:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5602\n2023-01-02 05:12:58,993 - mmcls - INFO - Epoch [98][4700/5005]\tlr: 1.000e-04, eta: 0:15:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5951\n2023-01-02 05:13:07,831 - mmcls - INFO - Epoch [98][4800/5005]\tlr: 1.000e-04, eta: 0:15:05, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5786\n2023-01-02 05:13:16,675 - mmcls - INFO - Epoch [98][4900/5005]\tlr: 1.000e-04, eta: 0:14:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5910\n2023-01-02 05:13:25,592 - mmcls - INFO - Epoch [98][5000/5005]\tlr: 1.000e-04, eta: 0:14:47, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.5727\n2023-01-02 05:13:48,367 - mmcls - INFO - Epoch(val) [98][196]\taccuracy_top-1: 65.3100, accuracy_top-5: 85.7620\n2023-01-02 05:13:59,478 - mmcls - INFO - Epoch [99][100/5005]\tlr: 1.000e-04, eta: 0:14:38, time: 0.111, data_time: 0.029, memory: 1224, loss: 1.5952\n2023-01-02 05:14:08,192 - mmcls - INFO - Epoch [99][200/5005]\tlr: 1.000e-04, eta: 0:14:29, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5776\n2023-01-02 05:14:17,008 - mmcls - INFO - Epoch [99][300/5005]\tlr: 1.000e-04, eta: 0:14:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5773\n2023-01-02 05:14:25,792 - mmcls - INFO - Epoch [99][400/5005]\tlr: 1.000e-04, eta: 0:14:11, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5783\n2023-01-02 05:14:34,594 - mmcls - INFO - Epoch [99][500/5005]\tlr: 1.000e-04, eta: 0:14:03, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5777\n2023-01-02 05:14:43,660 - mmcls - INFO - Epoch [99][600/5005]\tlr: 1.000e-04, eta: 0:13:54, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.5802\n2023-01-02 05:14:52,399 - mmcls - INFO - Epoch [99][700/5005]\tlr: 1.000e-04, eta: 0:13:45, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5844\n2023-01-02 05:15:01,367 - mmcls - INFO - Epoch [99][800/5005]\tlr: 1.000e-04, eta: 0:13:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5671\n2023-01-02 05:15:10,046 - mmcls - INFO - Epoch [99][900/5005]\tlr: 1.000e-04, eta: 0:13:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5708\n2023-01-02 05:15:18,777 - mmcls - INFO - Epoch [99][1000/5005]\tlr: 1.000e-04, eta: 0:13:18, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.5947\n2023-01-02 05:15:27,427 - mmcls - INFO - Epoch [99][1100/5005]\tlr: 1.000e-04, eta: 0:13:09, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5742\n2023-01-02 05:15:36,323 - mmcls - INFO - Epoch [99][1200/5005]\tlr: 1.000e-04, eta: 0:13:00, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5808\n2023-01-02 05:15:45,127 - mmcls - INFO - Epoch [99][1300/5005]\tlr: 1.000e-04, eta: 0:12:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5850\n2023-01-02 05:15:53,837 - mmcls - INFO - Epoch [99][1400/5005]\tlr: 1.000e-04, eta: 0:12:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5833\n2023-01-02 05:16:02,534 - mmcls - INFO - Epoch [99][1500/5005]\tlr: 1.000e-04, eta: 0:12:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5725\n2023-01-02 05:16:11,495 - mmcls - INFO - Epoch [99][1600/5005]\tlr: 1.000e-04, eta: 0:12:25, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.5656\n2023-01-02 05:16:20,448 - mmcls - INFO - Epoch [99][1700/5005]\tlr: 1.000e-04, eta: 0:12:16, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.5889\n2023-01-02 05:16:29,219 - mmcls - INFO - Epoch [99][1800/5005]\tlr: 1.000e-04, eta: 0:12:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5870\n2023-01-02 05:16:38,108 - mmcls - INFO - Epoch [99][1900/5005]\tlr: 1.000e-04, eta: 0:11:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5605\n2023-01-02 05:16:47,035 - mmcls - INFO - Epoch [99][2000/5005]\tlr: 1.000e-04, eta: 0:11:50, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5767\n2023-01-02 05:16:55,814 - mmcls - INFO - Epoch [99][2100/5005]\tlr: 1.000e-04, eta: 0:11:41, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5535\n2023-01-02 05:17:04,380 - mmcls - INFO - Epoch [99][2200/5005]\tlr: 1.000e-04, eta: 0:11:32, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.5794\n2023-01-02 05:17:13,424 - mmcls - INFO - Epoch [99][2300/5005]\tlr: 1.000e-04, eta: 0:11:23, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5709\n2023-01-02 05:17:22,292 - mmcls - INFO - Epoch [99][2400/5005]\tlr: 1.000e-04, eta: 0:11:14, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.5686\n2023-01-02 05:17:31,463 - mmcls - INFO - Epoch [99][2500/5005]\tlr: 1.000e-04, eta: 0:11:05, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5923\n2023-01-02 05:17:40,264 - mmcls - INFO - Epoch [99][2600/5005]\tlr: 1.000e-04, eta: 0:10:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5554\n2023-01-02 05:17:48,864 - mmcls - INFO - Epoch [99][2700/5005]\tlr: 1.000e-04, eta: 0:10:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5728\n2023-01-02 05:17:57,578 - mmcls - INFO - Epoch [99][2800/5005]\tlr: 1.000e-04, eta: 0:10:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5723\n2023-01-02 05:18:06,488 - mmcls - INFO - Epoch [99][2900/5005]\tlr: 1.000e-04, eta: 0:10:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5436\n2023-01-02 05:18:15,319 - mmcls - INFO - Epoch [99][3000/5005]\tlr: 1.000e-04, eta: 0:10:21, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5868\n2023-01-02 05:18:24,163 - mmcls - INFO - Epoch [99][3100/5005]\tlr: 1.000e-04, eta: 0:10:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5692\n2023-01-02 05:18:33,037 - mmcls - INFO - Epoch [99][3200/5005]\tlr: 1.000e-04, eta: 0:10:03, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.5730\n2023-01-02 05:18:41,871 - mmcls - INFO - Epoch [99][3300/5005]\tlr: 1.000e-04, eta: 0:09:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5882\n2023-01-02 05:18:50,476 - mmcls - INFO - Epoch [99][3400/5005]\tlr: 1.000e-04, eta: 0:09:45, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5885\n2023-01-02 05:18:59,279 - mmcls - INFO - Epoch [99][3500/5005]\tlr: 1.000e-04, eta: 0:09:37, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.5993\n2023-01-02 05:19:08,216 - mmcls - INFO - Epoch [99][3600/5005]\tlr: 1.000e-04, eta: 0:09:28, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.5715\n2023-01-02 05:19:16,852 - mmcls - INFO - Epoch [99][3700/5005]\tlr: 1.000e-04, eta: 0:09:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5743\n2023-01-02 05:19:26,033 - mmcls - INFO - Epoch [99][3800/5005]\tlr: 1.000e-04, eta: 0:09:10, time: 0.092, data_time: 0.044, memory: 1224, loss: 1.5668\n2023-01-02 05:19:34,808 - mmcls - INFO - Epoch [99][3900/5005]\tlr: 1.000e-04, eta: 0:09:01, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.5769\n2023-01-02 05:19:43,752 - mmcls - INFO - Epoch [99][4000/5005]\tlr: 1.000e-04, eta: 0:08:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5396\n2023-01-02 05:19:52,361 - mmcls - INFO - Epoch [99][4100/5005]\tlr: 1.000e-04, eta: 0:08:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5724\n2023-01-02 05:20:01,172 - mmcls - INFO - Epoch [99][4200/5005]\tlr: 1.000e-04, eta: 0:08:35, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5776\n2023-01-02 05:20:10,107 - mmcls - INFO - Epoch [99][4300/5005]\tlr: 1.000e-04, eta: 0:08:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5851\n2023-01-02 05:20:19,129 - mmcls - INFO - Epoch [99][4400/5005]\tlr: 1.000e-04, eta: 0:08:17, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.5845\n2023-01-02 05:20:27,902 - mmcls - INFO - Epoch [99][4500/5005]\tlr: 1.000e-04, eta: 0:08:08, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5589\n2023-01-02 05:20:36,774 - mmcls - INFO - Epoch [99][4600/5005]\tlr: 1.000e-04, eta: 0:07:59, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5616\n2023-01-02 05:20:45,642 - mmcls - INFO - Epoch [99][4700/5005]\tlr: 1.000e-04, eta: 0:07:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5876\n2023-01-02 05:20:54,242 - mmcls - INFO - Epoch [99][4800/5005]\tlr: 1.000e-04, eta: 0:07:41, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6037\n2023-01-02 05:21:03,181 - mmcls - INFO - Epoch [99][4900/5005]\tlr: 1.000e-04, eta: 0:07:32, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5876\n2023-01-02 05:21:11,924 - mmcls - INFO - Epoch [99][5000/5005]\tlr: 1.000e-04, eta: 0:07:24, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5844\n2023-01-02 05:21:34,694 - mmcls - INFO - Epoch(val) [99][196]\taccuracy_top-1: 65.4180, accuracy_top-5: 85.7900\n2023-01-02 05:21:45,488 - mmcls - INFO - Epoch [100][100/5005]\tlr: 1.000e-04, eta: 0:07:14, time: 0.108, data_time: 0.039, memory: 1224, loss: 1.5828\n2023-01-02 05:21:54,328 - mmcls - INFO - Epoch [100][200/5005]\tlr: 1.000e-04, eta: 0:07:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.5668\n2023-01-02 05:22:02,958 - mmcls - INFO - Epoch [100][300/5005]\tlr: 1.000e-04, eta: 0:06:57, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5605\n2023-01-02 05:22:12,085 - mmcls - INFO - Epoch [100][400/5005]\tlr: 1.000e-04, eta: 0:06:48, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.5737\n2023-01-02 05:22:20,695 - mmcls - INFO - Epoch [100][500/5005]\tlr: 1.000e-04, eta: 0:06:39, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5879\n2023-01-02 05:22:29,342 - mmcls - INFO - Epoch [100][600/5005]\tlr: 1.000e-04, eta: 0:06:30, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5756\n2023-01-02 05:22:38,262 - mmcls - INFO - Epoch [100][700/5005]\tlr: 1.000e-04, eta: 0:06:21, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5823\n2023-01-02 05:22:47,470 - mmcls - INFO - Epoch [100][800/5005]\tlr: 1.000e-04, eta: 0:06:12, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.5769\n2023-01-02 05:22:56,258 - mmcls - INFO - Epoch [100][900/5005]\tlr: 1.000e-04, eta: 0:06:03, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5704\n2023-01-02 05:23:05,225 - mmcls - INFO - Epoch [100][1000/5005]\tlr: 1.000e-04, eta: 0:05:55, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.5853\n2023-01-02 05:23:13,941 - mmcls - INFO - Epoch [100][1100/5005]\tlr: 1.000e-04, eta: 0:05:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5654\n2023-01-02 05:23:22,624 - mmcls - INFO - Epoch [100][1200/5005]\tlr: 1.000e-04, eta: 0:05:37, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.5782\n2023-01-02 05:23:31,406 - mmcls - INFO - Epoch [100][1300/5005]\tlr: 1.000e-04, eta: 0:05:28, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.5709\n2023-01-02 05:23:40,115 - mmcls - INFO - Epoch [100][1400/5005]\tlr: 1.000e-04, eta: 0:05:19, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.5866\n2023-01-02 05:23:48,751 - mmcls - INFO - Epoch [100][1500/5005]\tlr: 1.000e-04, eta: 0:05:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5704\n2023-01-02 05:23:57,689 - mmcls - INFO - Epoch [100][1600/5005]\tlr: 1.000e-04, eta: 0:05:01, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5837\n2023-01-02 05:24:06,370 - mmcls - INFO - Epoch [100][1700/5005]\tlr: 1.000e-04, eta: 0:04:52, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5736\n2023-01-02 05:24:15,264 - mmcls - INFO - Epoch [100][1800/5005]\tlr: 1.000e-04, eta: 0:04:44, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5853\n2023-01-02 05:24:23,923 - mmcls - INFO - Epoch [100][1900/5005]\tlr: 1.000e-04, eta: 0:04:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.5694\n2023-01-02 05:24:32,855 - mmcls - INFO - Epoch [100][2000/5005]\tlr: 1.000e-04, eta: 0:04:26, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5935\n2023-01-02 05:24:41,667 - mmcls - INFO - Epoch [100][2100/5005]\tlr: 1.000e-04, eta: 0:04:17, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.5708\n2023-01-02 05:24:50,456 - mmcls - INFO - Epoch [100][2200/5005]\tlr: 1.000e-04, eta: 0:04:08, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.5762\n2023-01-02 05:24:59,258 - mmcls - INFO - Epoch [100][2300/5005]\tlr: 1.000e-04, eta: 0:03:59, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5510\n2023-01-02 05:25:08,024 - mmcls - INFO - Epoch [100][2400/5005]\tlr: 1.000e-04, eta: 0:03:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5720\n2023-01-02 05:25:17,077 - mmcls - INFO - Epoch [100][2500/5005]\tlr: 1.000e-04, eta: 0:03:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5912\n2023-01-02 05:25:25,866 - mmcls - INFO - Epoch [100][2600/5005]\tlr: 1.000e-04, eta: 0:03:33, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.5695\n2023-01-02 05:25:34,639 - mmcls - INFO - Epoch [100][2700/5005]\tlr: 1.000e-04, eta: 0:03:24, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.5668\n2023-01-02 05:25:43,456 - mmcls - INFO - Epoch [100][2800/5005]\tlr: 1.000e-04, eta: 0:03:15, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5741\n2023-01-02 05:25:52,142 - mmcls - INFO - Epoch [100][2900/5005]\tlr: 1.000e-04, eta: 0:03:06, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5732\n2023-01-02 05:26:00,763 - mmcls - INFO - Epoch [100][3000/5005]\tlr: 1.000e-04, eta: 0:02:57, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.5811\n2023-01-02 05:26:09,707 - mmcls - INFO - Epoch [100][3100/5005]\tlr: 1.000e-04, eta: 0:02:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5964\n2023-01-02 05:26:18,639 - mmcls - INFO - Epoch [100][3200/5005]\tlr: 1.000e-04, eta: 0:02:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.5928\n2023-01-02 05:26:27,503 - mmcls - INFO - Epoch [100][3300/5005]\tlr: 1.000e-04, eta: 0:02:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5621\n2023-01-02 05:26:35,935 - mmcls - INFO - Epoch [100][3400/5005]\tlr: 1.000e-04, eta: 0:02:22, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.5775\n2023-01-02 05:26:44,864 - mmcls - INFO - Epoch [100][3500/5005]\tlr: 1.000e-04, eta: 0:02:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5721\n2023-01-02 05:26:53,689 - mmcls - INFO - Epoch [100][3600/5005]\tlr: 1.000e-04, eta: 0:02:04, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5808\n2023-01-02 05:27:02,382 - mmcls - INFO - Epoch [100][3700/5005]\tlr: 1.000e-04, eta: 0:01:55, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.5721\n2023-01-02 05:27:11,198 - mmcls - INFO - Epoch [100][3800/5005]\tlr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6036\n2023-01-02 05:27:19,968 - mmcls - INFO - Epoch [100][3900/5005]\tlr: 1.000e-04, eta: 0:01:37, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5860\n2023-01-02 05:27:28,549 - mmcls - INFO - Epoch [100][4000/5005]\tlr: 1.000e-04, eta: 0:01:29, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5708\n2023-01-02 05:27:37,287 - mmcls - INFO - Epoch [100][4100/5005]\tlr: 1.000e-04, eta: 0:01:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5870\n2023-01-02 05:27:46,162 - mmcls - INFO - Epoch [100][4200/5005]\tlr: 1.000e-04, eta: 0:01:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5577\n2023-01-02 05:27:54,969 - mmcls - INFO - Epoch [100][4300/5005]\tlr: 1.000e-04, eta: 0:01:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5683\n2023-01-02 05:28:03,770 - mmcls - INFO - Epoch [100][4400/5005]\tlr: 1.000e-04, eta: 0:00:53, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5880\n2023-01-02 05:28:13,064 - mmcls - INFO - Epoch [100][4500/5005]\tlr: 1.000e-04, eta: 0:00:44, time: 0.093, data_time: 0.030, memory: 1224, loss: 1.5715\n2023-01-02 05:28:21,743 - mmcls - INFO - Epoch [100][4600/5005]\tlr: 1.000e-04, eta: 0:00:35, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5760\n2023-01-02 05:28:31,001 - mmcls - INFO - Epoch [100][4700/5005]\tlr: 1.000e-04, eta: 0:00:27, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.5630\n2023-01-02 05:28:39,656 - mmcls - INFO - Epoch [100][4800/5005]\tlr: 1.000e-04, eta: 0:00:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5797\n2023-01-02 05:28:48,438 - mmcls - INFO - Epoch [100][4900/5005]\tlr: 1.000e-04, eta: 0:00:09, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5830\n2023-01-02 05:28:57,236 - mmcls - INFO - Epoch [100][5000/5005]\tlr: 1.000e-04, eta: 0:00:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6048\n2023-01-02 05:28:57,585 - mmcls - INFO - Saving checkpoint at 100 epochs\n2023-01-02 05:29:19,997 - mmcls - INFO - Epoch(val) [100][196]\taccuracy_top-1: 65.3060, accuracy_top-5: 85.8300\n"
  },
  {
    "path": "classification-NO-activation-function/logs/pinet_1x1_relu.log",
    "content": "2022-12-31 14:18:36,463 - mmcls - INFO - Environment info:\n------------------------------------------------------------\nsys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 10.2, V10.2.8\nGCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 10.2\nMMClassification: 0.24.0+91b85bb\n------------------------------------------------------------\n\n2022-12-31 14:18:36,464 - mmcls - INFO - Distributed training: True\n2022-12-31 14:18:36,596 - mmcls - INFO - Config:\nmodel = dict(\n    type='ImageClassifier',\n    backbone=dict(\n        type='ResNet',\n        depth=18,\n        num_stages=4,\n        out_indices=(3, ),\n        style='pytorch'),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=1000,\n        in_channels=512,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1)),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=32,\n    workers_per_gpu=2,\n    train=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[30, 60, 90])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=50)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/resnet18_8xb32_in1k_pi'\ngpu_ids = range(0, 8)\n\n2022-12-31 14:18:42,027 - mmcls - INFO - Set random seed to 1565778062, deterministic: False\n2022-12-31 14:18:42,212 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n2022-12-31 14:18:42,302 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\nName of parameter - Initialization information\n\nbackbone.conv1.weight - torch.Size([64, 3, 7, 7]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.0.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv3.weight - torch.Size([64, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.1.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv3.weight - torch.Size([64, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.0.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv3.weight - torch.Size([128, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.downsample.1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.1.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv3.weight - torch.Size([128, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.0.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv3.weight - torch.Size([256, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.downsample.1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.1.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv3.weight - torch.Size([256, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.0.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv3.weight - torch.Size([512, 512, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.downsample.1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.1.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv3.weight - torch.Size([512, 512, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nhead.fc.weight - torch.Size([1000, 512]): \nNormalInit: mean=0, std=0.01, bias=0 \n\nhead.fc.bias - torch.Size([1000]): \nNormalInit: mean=0, std=0.01, bias=0 \n2022-12-31 14:19:10,732 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi\n2022-12-31 14:19:10,733 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) DistOptimizerHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2022-12-31 14:19:10,733 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs\n2022-12-31 14:19:10,733 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend.\n2022-12-31 14:19:22,898 - mmcls - INFO - Epoch [1][100/5005]\tlr: 1.000e-01, eta: 16:52:13, time: 0.121, data_time: 0.031, memory: 1224, loss: 6.9145\n2022-12-31 14:19:31,850 - mmcls - INFO - Epoch [1][200/5005]\tlr: 1.000e-01, eta: 14:39:06, time: 0.089, data_time: 0.004, memory: 1224, loss: 6.8518\n2022-12-31 14:19:41,054 - mmcls - INFO - Epoch [1][300/5005]\tlr: 1.000e-01, eta: 14:01:42, time: 0.092, data_time: 0.001, memory: 1224, loss: 6.7409\n2022-12-31 14:19:49,951 - mmcls - INFO - Epoch [1][400/5005]\tlr: 1.000e-01, eta: 13:36:37, time: 0.089, data_time: 0.003, memory: 1224, loss: 6.6521\n2022-12-31 14:19:58,856 - mmcls - INFO - Epoch [1][500/5005]\tlr: 1.000e-01, eta: 13:21:33, time: 0.089, data_time: 0.007, memory: 1224, loss: 6.5536\n2022-12-31 14:20:07,854 - mmcls - INFO - Epoch [1][600/5005]\tlr: 1.000e-01, eta: 13:12:45, time: 0.090, data_time: 0.016, memory: 1224, loss: 6.4413\n2022-12-31 14:20:16,790 - mmcls - INFO - Epoch [1][700/5005]\tlr: 1.000e-01, eta: 13:05:42, time: 0.089, data_time: 0.011, memory: 1224, loss: 6.3203\n2022-12-31 14:20:25,666 - mmcls - INFO - Epoch [1][800/5005]\tlr: 1.000e-01, eta: 12:59:45, time: 0.089, data_time: 0.020, memory: 1224, loss: 6.2093\n2022-12-31 14:20:35,032 - mmcls - INFO - Epoch [1][900/5005]\tlr: 1.000e-01, eta: 12:59:36, time: 0.094, data_time: 0.013, memory: 1224, loss: 6.1252\n2022-12-31 14:20:43,830 - mmcls - INFO - Epoch [1][1000/5005]\tlr: 1.000e-01, eta: 12:54:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 6.0635\n2022-12-31 14:20:52,938 - mmcls - INFO - Epoch [1][1100/5005]\tlr: 1.000e-01, eta: 12:53:05, time: 0.091, data_time: 0.001, memory: 1224, loss: 5.9582\n2022-12-31 14:21:02,051 - mmcls - INFO - Epoch [1][1200/5005]\tlr: 1.000e-01, eta: 12:51:42, time: 0.091, data_time: 0.004, memory: 1224, loss: 5.9076\n2022-12-31 14:21:11,011 - mmcls - INFO - Epoch [1][1300/5005]\tlr: 1.000e-01, eta: 12:49:34, time: 0.090, data_time: 0.003, memory: 1224, loss: 5.8187\n2022-12-31 14:21:19,851 - mmcls - INFO - Epoch [1][1400/5005]\tlr: 1.000e-01, eta: 12:46:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 5.7441\n2022-12-31 14:21:28,545 - mmcls - INFO - Epoch [1][1500/5005]\tlr: 1.000e-01, eta: 12:43:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 5.6767\n2022-12-31 14:21:37,376 - mmcls - INFO - Epoch [1][1600/5005]\tlr: 1.000e-01, eta: 12:41:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 5.5923\n2022-12-31 14:21:46,032 - mmcls - INFO - Epoch [1][1700/5005]\tlr: 1.000e-01, eta: 12:39:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 5.5427\n2022-12-31 14:21:54,978 - mmcls - INFO - Epoch [1][1800/5005]\tlr: 1.000e-01, eta: 12:38:12, time: 0.089, data_time: 0.008, memory: 1224, loss: 5.4727\n2022-12-31 14:22:03,883 - mmcls - INFO - Epoch [1][1900/5005]\tlr: 1.000e-01, eta: 12:37:06, time: 0.089, data_time: 0.003, memory: 1224, loss: 5.4028\n2022-12-31 14:22:13,080 - mmcls - INFO - Epoch [1][2000/5005]\tlr: 1.000e-01, eta: 12:37:19, time: 0.092, data_time: 0.004, memory: 1224, loss: 5.3603\n2022-12-31 14:22:22,170 - mmcls - INFO - Epoch [1][2100/5005]\tlr: 1.000e-01, eta: 12:37:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 5.2915\n2022-12-31 14:22:31,340 - mmcls - INFO - Epoch [1][2200/5005]\tlr: 1.000e-01, eta: 12:37:05, time: 0.092, data_time: 0.006, memory: 1224, loss: 5.2445\n2022-12-31 14:22:40,014 - mmcls - INFO - Epoch [1][2300/5005]\tlr: 1.000e-01, eta: 12:35:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 5.1960\n2022-12-31 14:22:49,307 - mmcls - INFO - Epoch [1][2400/5005]\tlr: 1.000e-01, eta: 12:35:53, time: 0.093, data_time: 0.018, memory: 1224, loss: 5.1426\n2022-12-31 14:22:58,326 - mmcls - INFO - Epoch [1][2500/5005]\tlr: 1.000e-01, eta: 12:35:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 5.1003\n2022-12-31 14:23:07,195 - mmcls - INFO - Epoch [1][2600/5005]\tlr: 1.000e-01, eta: 12:34:34, time: 0.089, data_time: 0.022, memory: 1224, loss: 5.0493\n2022-12-31 14:23:15,877 - mmcls - INFO - Epoch [1][2700/5005]\tlr: 1.000e-01, eta: 12:33:07, time: 0.087, data_time: 0.023, memory: 1224, loss: 4.9949\n2022-12-31 14:23:24,754 - mmcls - INFO - Epoch [1][2800/5005]\tlr: 1.000e-01, eta: 12:32:23, time: 0.089, data_time: 0.044, memory: 1224, loss: 4.9564\n2022-12-31 14:23:33,581 - mmcls - INFO - Epoch [1][2900/5005]\tlr: 1.000e-01, eta: 12:31:32, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.9208\n2022-12-31 14:23:42,518 - mmcls - INFO - Epoch [1][3000/5005]\tlr: 1.000e-01, eta: 12:31:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 4.8527\n2022-12-31 14:23:51,224 - mmcls - INFO - Epoch [1][3100/5005]\tlr: 1.000e-01, eta: 12:29:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.8234\n2022-12-31 14:23:59,964 - mmcls - INFO - Epoch [1][3200/5005]\tlr: 1.000e-01, eta: 12:29:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 4.7827\n2022-12-31 14:24:08,866 - mmcls - INFO - Epoch [1][3300/5005]\tlr: 1.000e-01, eta: 12:28:30, time: 0.089, data_time: 0.008, memory: 1224, loss: 4.7637\n2022-12-31 14:24:17,668 - mmcls - INFO - Epoch [1][3400/5005]\tlr: 1.000e-01, eta: 12:27:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.6780\n2022-12-31 14:24:26,718 - mmcls - INFO - Epoch [1][3500/5005]\tlr: 1.000e-01, eta: 12:27:41, time: 0.090, data_time: 0.017, memory: 1224, loss: 4.6568\n2022-12-31 14:24:35,579 - mmcls - INFO - Epoch [1][3600/5005]\tlr: 1.000e-01, eta: 12:27:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 4.6368\n2022-12-31 14:24:44,300 - mmcls - INFO - Epoch [1][3700/5005]\tlr: 1.000e-01, eta: 12:26:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.5910\n2022-12-31 14:24:53,198 - mmcls - INFO - Epoch [1][3800/5005]\tlr: 1.000e-01, eta: 12:25:55, time: 0.089, data_time: 0.015, memory: 1224, loss: 4.5157\n2022-12-31 14:25:02,149 - mmcls - INFO - Epoch [1][3900/5005]\tlr: 1.000e-01, eta: 12:25:38, time: 0.089, data_time: 0.004, memory: 1224, loss: 4.5032\n2022-12-31 14:25:11,094 - mmcls - INFO - Epoch [1][4000/5005]\tlr: 1.000e-01, eta: 12:25:22, time: 0.090, data_time: 0.002, memory: 1224, loss: 4.4981\n2022-12-31 14:25:20,282 - mmcls - INFO - Epoch [1][4100/5005]\tlr: 1.000e-01, eta: 12:25:35, time: 0.092, data_time: 0.006, memory: 1224, loss: 4.4685\n2022-12-31 14:25:29,050 - mmcls - INFO - Epoch [1][4200/5005]\tlr: 1.000e-01, eta: 12:24:56, time: 0.088, data_time: 0.030, memory: 1224, loss: 4.4190\n2022-12-31 14:25:38,232 - mmcls - INFO - Epoch [1][4300/5005]\tlr: 1.000e-01, eta: 12:25:08, time: 0.092, data_time: 0.001, memory: 1224, loss: 4.3722\n2022-12-31 14:25:46,963 - mmcls - INFO - Epoch [1][4400/5005]\tlr: 1.000e-01, eta: 12:24:28, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.3420\n2022-12-31 14:25:55,527 - mmcls - INFO - Epoch [1][4500/5005]\tlr: 1.000e-01, eta: 12:23:29, time: 0.086, data_time: 0.013, memory: 1224, loss: 4.3088\n2022-12-31 14:26:04,728 - mmcls - INFO - Epoch [1][4600/5005]\tlr: 1.000e-01, eta: 12:23:43, time: 0.092, data_time: 0.004, memory: 1224, loss: 4.2734\n2022-12-31 14:26:13,559 - mmcls - INFO - Epoch [1][4700/5005]\tlr: 1.000e-01, eta: 12:23:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 4.2628\n2022-12-31 14:26:22,360 - mmcls - INFO - Epoch [1][4800/5005]\tlr: 1.000e-01, eta: 12:22:47, time: 0.088, data_time: 0.014, memory: 1224, loss: 4.2119\n2022-12-31 14:26:31,331 - mmcls - INFO - Epoch [1][4900/5005]\tlr: 1.000e-01, eta: 12:22:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 4.2229\n2022-12-31 14:26:40,473 - mmcls - INFO - Epoch [1][5000/5005]\tlr: 1.000e-01, eta: 12:22:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.1944\n2022-12-31 14:27:03,974 - mmcls - INFO - Epoch(val) [1][196]\taccuracy_top-1: 20.7600, accuracy_top-5: 43.2760\n2022-12-31 14:27:15,074 - mmcls - INFO - Epoch [2][100/5005]\tlr: 1.000e-01, eta: 12:25:12, time: 0.111, data_time: 0.039, memory: 1224, loss: 4.1041\n2022-12-31 14:27:24,030 - mmcls - INFO - Epoch [2][200/5005]\tlr: 1.000e-01, eta: 12:24:55, time: 0.089, data_time: 0.002, memory: 1224, loss: 4.0855\n2022-12-31 14:27:32,472 - mmcls - INFO - Epoch [2][300/5005]\tlr: 1.000e-01, eta: 12:23:53, time: 0.085, data_time: 0.001, memory: 1224, loss: 4.0948\n2022-12-31 14:27:41,188 - mmcls - INFO - Epoch [2][400/5005]\tlr: 1.000e-01, eta: 12:23:15, time: 0.087, data_time: 0.029, memory: 1224, loss: 4.0651\n2022-12-31 14:27:49,794 - mmcls - INFO - Epoch [2][500/5005]\tlr: 1.000e-01, eta: 12:22:30, time: 0.086, data_time: 0.008, memory: 1224, loss: 4.0343\n2022-12-31 14:27:58,571 - mmcls - INFO - Epoch [2][600/5005]\tlr: 1.000e-01, eta: 12:22:01, time: 0.088, data_time: 0.014, memory: 1224, loss: 4.0408\n2022-12-31 14:28:07,228 - mmcls - INFO - Epoch [2][700/5005]\tlr: 1.000e-01, eta: 12:21:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 3.9827\n2022-12-31 14:28:16,123 - mmcls - INFO - Epoch [2][800/5005]\tlr: 1.000e-01, eta: 12:21:06, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.9743\n2022-12-31 14:28:24,918 - mmcls - INFO - Epoch [2][900/5005]\tlr: 1.000e-01, eta: 12:20:41, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.9803\n2022-12-31 14:28:33,967 - mmcls - INFO - Epoch [2][1000/5005]\tlr: 1.000e-01, eta: 12:20:37, time: 0.090, data_time: 0.018, memory: 1224, loss: 3.9164\n2022-12-31 14:28:42,561 - mmcls - INFO - Epoch [2][1100/5005]\tlr: 1.000e-01, eta: 12:19:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.9209\n2022-12-31 14:28:51,197 - mmcls - INFO - Epoch [2][1200/5005]\tlr: 1.000e-01, eta: 12:19:20, time: 0.086, data_time: 0.021, memory: 1224, loss: 3.8949\n2022-12-31 14:28:59,852 - mmcls - INFO - Epoch [2][1300/5005]\tlr: 1.000e-01, eta: 12:18:46, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.8661\n2022-12-31 14:29:08,620 - mmcls - INFO - Epoch [2][1400/5005]\tlr: 1.000e-01, eta: 12:18:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.8579\n2022-12-31 14:29:17,517 - mmcls - INFO - Epoch [2][1500/5005]\tlr: 1.000e-01, eta: 12:18:06, time: 0.089, data_time: 0.015, memory: 1224, loss: 3.8111\n2022-12-31 14:29:26,524 - mmcls - INFO - Epoch [2][1600/5005]\tlr: 1.000e-01, eta: 12:18:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 3.8050\n2022-12-31 14:29:35,640 - mmcls - INFO - Epoch [2][1700/5005]\tlr: 1.000e-01, eta: 12:18:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.8072\n2022-12-31 14:29:44,325 - mmcls - INFO - Epoch [2][1800/5005]\tlr: 1.000e-01, eta: 12:17:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.7828\n2022-12-31 14:29:53,069 - mmcls - INFO - Epoch [2][1900/5005]\tlr: 1.000e-01, eta: 12:17:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.7542\n2022-12-31 14:30:01,949 - mmcls - INFO - Epoch [2][2000/5005]\tlr: 1.000e-01, eta: 12:16:53, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.7437\n2022-12-31 14:30:10,410 - mmcls - INFO - Epoch [2][2100/5005]\tlr: 1.000e-01, eta: 12:16:10, time: 0.085, data_time: 0.008, memory: 1224, loss: 3.7587\n2022-12-31 14:30:19,379 - mmcls - INFO - Epoch [2][2200/5005]\tlr: 1.000e-01, eta: 12:16:02, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.7093\n2022-12-31 14:30:27,992 - mmcls - INFO - Epoch [2][2300/5005]\tlr: 1.000e-01, eta: 12:15:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.6989\n2022-12-31 14:30:36,778 - mmcls - INFO - Epoch [2][2400/5005]\tlr: 1.000e-01, eta: 12:15:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.6647\n2022-12-31 14:30:45,653 - mmcls - INFO - Epoch [2][2500/5005]\tlr: 1.000e-01, eta: 12:14:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.6560\n2022-12-31 14:30:54,536 - mmcls - INFO - Epoch [2][2600/5005]\tlr: 1.000e-01, eta: 12:14:44, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.6465\n2022-12-31 14:31:03,396 - mmcls - INFO - Epoch [2][2700/5005]\tlr: 1.000e-01, eta: 12:14:29, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.6454\n2022-12-31 14:31:12,052 - mmcls - INFO - Epoch [2][2800/5005]\tlr: 1.000e-01, eta: 12:14:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.6156\n2022-12-31 14:31:20,950 - mmcls - INFO - Epoch [2][2900/5005]\tlr: 1.000e-01, eta: 12:13:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.6129\n2022-12-31 14:31:29,712 - mmcls - INFO - Epoch [2][3000/5005]\tlr: 1.000e-01, eta: 12:13:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.5735\n2022-12-31 14:31:38,610 - mmcls - INFO - Epoch [2][3100/5005]\tlr: 1.000e-01, eta: 12:13:19, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.5776\n2022-12-31 14:31:47,386 - mmcls - INFO - Epoch [2][3200/5005]\tlr: 1.000e-01, eta: 12:13:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5465\n2022-12-31 14:31:56,301 - mmcls - INFO - Epoch [2][3300/5005]\tlr: 1.000e-01, eta: 12:12:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.5932\n2022-12-31 14:32:05,080 - mmcls - INFO - Epoch [2][3400/5005]\tlr: 1.000e-01, eta: 12:12:33, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5164\n2022-12-31 14:32:13,837 - mmcls - INFO - Epoch [2][3500/5005]\tlr: 1.000e-01, eta: 12:12:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5148\n2022-12-31 14:32:22,707 - mmcls - INFO - Epoch [2][3600/5005]\tlr: 1.000e-01, eta: 12:12:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.5002\n2022-12-31 14:32:31,334 - mmcls - INFO - Epoch [2][3700/5005]\tlr: 1.000e-01, eta: 12:11:35, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.4858\n2022-12-31 14:32:39,921 - mmcls - INFO - Epoch [2][3800/5005]\tlr: 1.000e-01, eta: 12:11:07, time: 0.086, data_time: 0.009, memory: 1224, loss: 3.4673\n2022-12-31 14:32:48,669 - mmcls - INFO - Epoch [2][3900/5005]\tlr: 1.000e-01, eta: 12:10:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.4662\n2022-12-31 14:32:57,538 - mmcls - INFO - Epoch [2][4000/5005]\tlr: 1.000e-01, eta: 12:10:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.4286\n2022-12-31 14:33:06,331 - mmcls - INFO - Epoch [2][4100/5005]\tlr: 1.000e-01, eta: 12:10:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.4441\n2022-12-31 14:33:14,985 - mmcls - INFO - Epoch [2][4200/5005]\tlr: 1.000e-01, eta: 12:09:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.4411\n2022-12-31 14:33:23,761 - mmcls - INFO - Epoch [2][4300/5005]\tlr: 1.000e-01, eta: 12:09:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.4258\n2022-12-31 14:33:32,343 - mmcls - INFO - Epoch [2][4400/5005]\tlr: 1.000e-01, eta: 12:09:16, time: 0.086, data_time: 0.018, memory: 1224, loss: 3.4391\n2022-12-31 14:33:41,417 - mmcls - INFO - Epoch [2][4500/5005]\tlr: 1.000e-01, eta: 12:09:15, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.4259\n2022-12-31 14:33:50,320 - mmcls - INFO - Epoch [2][4600/5005]\tlr: 1.000e-01, eta: 12:09:06, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.3824\n2022-12-31 14:33:59,398 - mmcls - INFO - Epoch [2][4700/5005]\tlr: 1.000e-01, eta: 12:09:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.4116\n2022-12-31 14:34:07,923 - mmcls - INFO - Epoch [2][4800/5005]\tlr: 1.000e-01, eta: 12:08:37, time: 0.085, data_time: 0.001, memory: 1224, loss: 3.3384\n2022-12-31 14:34:16,735 - mmcls - INFO - Epoch [2][4900/5005]\tlr: 1.000e-01, eta: 12:08:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.3673\n2022-12-31 14:34:25,571 - mmcls - INFO - Epoch [2][5000/5005]\tlr: 1.000e-01, eta: 12:08:11, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.3482\n2022-12-31 14:34:48,688 - mmcls - INFO - Epoch(val) [2][196]\taccuracy_top-1: 34.1100, accuracy_top-5: 60.4580\n2022-12-31 14:34:59,706 - mmcls - INFO - Epoch [3][100/5005]\tlr: 1.000e-01, eta: 12:09:21, time: 0.110, data_time: 0.048, memory: 1224, loss: 3.3193\n2022-12-31 14:35:08,315 - mmcls - INFO - Epoch [3][200/5005]\tlr: 1.000e-01, eta: 12:08:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.2824\n2022-12-31 14:35:17,258 - mmcls - INFO - Epoch [3][300/5005]\tlr: 1.000e-01, eta: 12:08:49, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.2799\n2022-12-31 14:35:26,022 - mmcls - INFO - Epoch [3][400/5005]\tlr: 1.000e-01, eta: 12:08:32, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.3000\n2022-12-31 14:35:34,512 - mmcls - INFO - Epoch [3][500/5005]\tlr: 1.000e-01, eta: 12:08:04, time: 0.085, data_time: 0.012, memory: 1224, loss: 3.2747\n2022-12-31 14:35:43,298 - mmcls - INFO - Epoch [3][600/5005]\tlr: 1.000e-01, eta: 12:07:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.2599\n2022-12-31 14:35:52,057 - mmcls - INFO - Epoch [3][700/5005]\tlr: 1.000e-01, eta: 12:07:33, time: 0.088, data_time: 0.007, memory: 1224, loss: 3.2774\n2022-12-31 14:36:00,936 - mmcls - INFO - Epoch [3][800/5005]\tlr: 1.000e-01, eta: 12:07:23, time: 0.089, data_time: 0.015, memory: 1224, loss: 3.2444\n2022-12-31 14:36:09,834 - mmcls - INFO - Epoch [3][900/5005]\tlr: 1.000e-01, eta: 12:07:12, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.2548\n2022-12-31 14:36:18,689 - mmcls - INFO - Epoch [3][1000/5005]\tlr: 1.000e-01, eta: 12:07:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 3.2401\n2022-12-31 14:36:27,459 - mmcls - INFO - Epoch [3][1100/5005]\tlr: 1.000e-01, eta: 12:06:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.2274\n2022-12-31 14:36:36,026 - mmcls - INFO - Epoch [3][1200/5005]\tlr: 1.000e-01, eta: 12:06:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.2263\n2022-12-31 14:36:44,692 - mmcls - INFO - Epoch [3][1300/5005]\tlr: 1.000e-01, eta: 12:06:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.2057\n2022-12-31 14:36:53,390 - mmcls - INFO - Epoch [3][1400/5005]\tlr: 1.000e-01, eta: 12:05:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.2175\n2022-12-31 14:37:02,083 - mmcls - INFO - Epoch [3][1500/5005]\tlr: 1.000e-01, eta: 12:05:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.2217\n2022-12-31 14:37:10,809 - mmcls - INFO - Epoch [3][1600/5005]\tlr: 1.000e-01, eta: 12:05:11, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.2073\n2022-12-31 14:37:19,821 - mmcls - INFO - Epoch [3][1700/5005]\tlr: 1.000e-01, eta: 12:05:06, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.1729\n2022-12-31 14:37:28,486 - mmcls - INFO - Epoch [3][1800/5005]\tlr: 1.000e-01, eta: 12:04:47, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.1837\n2022-12-31 14:37:37,577 - mmcls - INFO - Epoch [3][1900/5005]\tlr: 1.000e-01, eta: 12:04:47, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.1651\n2022-12-31 14:37:46,480 - mmcls - INFO - Epoch [3][2000/5005]\tlr: 1.000e-01, eta: 12:04:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.2049\n2022-12-31 14:37:55,285 - mmcls - INFO - Epoch [3][2100/5005]\tlr: 1.000e-01, eta: 12:04:25, time: 0.088, data_time: 0.000, memory: 1224, loss: 3.1770\n2022-12-31 14:38:04,212 - mmcls - INFO - Epoch [3][2200/5005]\tlr: 1.000e-01, eta: 12:04:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1993\n2022-12-31 14:38:13,021 - mmcls - INFO - Epoch [3][2300/5005]\tlr: 1.000e-01, eta: 12:04:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1848\n2022-12-31 14:38:21,785 - mmcls - INFO - Epoch [3][2400/5005]\tlr: 1.000e-01, eta: 12:03:50, time: 0.088, data_time: 0.013, memory: 1224, loss: 3.1480\n2022-12-31 14:38:30,312 - mmcls - INFO - Epoch [3][2500/5005]\tlr: 1.000e-01, eta: 12:03:27, time: 0.085, data_time: 0.001, memory: 1224, loss: 3.1263\n2022-12-31 14:38:39,520 - mmcls - INFO - Epoch [3][2600/5005]\tlr: 1.000e-01, eta: 12:03:30, time: 0.092, data_time: 0.006, memory: 1224, loss: 3.1352\n2022-12-31 14:38:48,295 - mmcls - INFO - Epoch [3][2700/5005]\tlr: 1.000e-01, eta: 12:03:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.1117\n2022-12-31 14:38:56,926 - mmcls - INFO - Epoch [3][2800/5005]\tlr: 1.000e-01, eta: 12:02:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1322\n2022-12-31 14:39:05,884 - mmcls - INFO - Epoch [3][2900/5005]\tlr: 1.000e-01, eta: 12:02:51, time: 0.090, data_time: 0.006, memory: 1224, loss: 3.0916\n2022-12-31 14:39:14,710 - mmcls - INFO - Epoch [3][3000/5005]\tlr: 1.000e-01, eta: 12:02:39, time: 0.088, data_time: 0.013, memory: 1224, loss: 3.1319\n2022-12-31 14:39:23,457 - mmcls - INFO - Epoch [3][3100/5005]\tlr: 1.000e-01, eta: 12:02:25, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.1089\n2022-12-31 14:39:32,203 - mmcls - INFO - Epoch [3][3200/5005]\tlr: 1.000e-01, eta: 12:02:11, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0940\n2022-12-31 14:39:40,901 - mmcls - INFO - Epoch [3][3300/5005]\tlr: 1.000e-01, eta: 12:01:55, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.0975\n2022-12-31 14:39:49,652 - mmcls - INFO - Epoch [3][3400/5005]\tlr: 1.000e-01, eta: 12:01:41, time: 0.087, data_time: 0.003, memory: 1224, loss: 3.1034\n2022-12-31 14:39:58,465 - mmcls - INFO - Epoch [3][3500/5005]\tlr: 1.000e-01, eta: 12:01:29, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0861\n2022-12-31 14:40:07,112 - mmcls - INFO - Epoch [3][3600/5005]\tlr: 1.000e-01, eta: 12:01:11, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1162\n2022-12-31 14:40:15,997 - mmcls - INFO - Epoch [3][3700/5005]\tlr: 1.000e-01, eta: 12:01:02, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0855\n2022-12-31 14:40:24,984 - mmcls - INFO - Epoch [3][3800/5005]\tlr: 1.000e-01, eta: 12:00:57, time: 0.090, data_time: 0.010, memory: 1224, loss: 3.0695\n2022-12-31 14:40:33,785 - mmcls - INFO - Epoch [3][3900/5005]\tlr: 1.000e-01, eta: 12:00:45, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0792\n2022-12-31 14:40:42,800 - mmcls - INFO - Epoch [3][4000/5005]\tlr: 1.000e-01, eta: 12:00:40, time: 0.090, data_time: 0.022, memory: 1224, loss: 3.0494\n2022-12-31 14:40:51,394 - mmcls - INFO - Epoch [3][4100/5005]\tlr: 1.000e-01, eta: 12:00:21, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0773\n2022-12-31 14:41:00,124 - mmcls - INFO - Epoch [3][4200/5005]\tlr: 1.000e-01, eta: 12:00:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.0690\n2022-12-31 14:41:08,985 - mmcls - INFO - Epoch [3][4300/5005]\tlr: 1.000e-01, eta: 11:59:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0531\n2022-12-31 14:41:17,959 - mmcls - INFO - Epoch [3][4400/5005]\tlr: 1.000e-01, eta: 11:59:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0540\n2022-12-31 14:41:26,784 - mmcls - INFO - Epoch [3][4500/5005]\tlr: 1.000e-01, eta: 11:59:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0270\n2022-12-31 14:41:35,514 - mmcls - INFO - Epoch [3][4600/5005]\tlr: 1.000e-01, eta: 11:59:26, time: 0.087, data_time: 0.010, memory: 1224, loss: 3.0466\n2022-12-31 14:41:44,141 - mmcls - INFO - Epoch [3][4700/5005]\tlr: 1.000e-01, eta: 11:59:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.0467\n2022-12-31 14:41:53,496 - mmcls - INFO - Epoch [3][4800/5005]\tlr: 1.000e-01, eta: 11:59:15, time: 0.093, data_time: 0.001, memory: 1224, loss: 3.0222\n2022-12-31 14:42:02,267 - mmcls - INFO - Epoch [3][4900/5005]\tlr: 1.000e-01, eta: 11:59:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.0122\n2022-12-31 14:42:11,093 - mmcls - INFO - Epoch [3][5000/5005]\tlr: 1.000e-01, eta: 11:58:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.0146\n2022-12-31 14:42:33,775 - mmcls - INFO - Epoch(val) [3][196]\taccuracy_top-1: 40.5460, accuracy_top-5: 67.2300\n2022-12-31 14:42:44,699 - mmcls - INFO - Epoch [4][100/5005]\tlr: 1.000e-01, eta: 11:59:33, time: 0.109, data_time: 0.037, memory: 1224, loss: 2.9622\n2022-12-31 14:42:53,712 - mmcls - INFO - Epoch [4][200/5005]\tlr: 1.000e-01, eta: 11:59:28, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.9904\n2022-12-31 14:43:02,304 - mmcls - INFO - Epoch [4][300/5005]\tlr: 1.000e-01, eta: 11:59:09, time: 0.086, data_time: 0.036, memory: 1224, loss: 2.9785\n2022-12-31 14:43:11,166 - mmcls - INFO - Epoch [4][400/5005]\tlr: 1.000e-01, eta: 11:58:59, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.9614\n2022-12-31 14:43:19,750 - mmcls - INFO - Epoch [4][500/5005]\tlr: 1.000e-01, eta: 11:58:41, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.9235\n2022-12-31 14:43:28,751 - mmcls - INFO - Epoch [4][600/5005]\tlr: 1.000e-01, eta: 11:58:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9435\n2022-12-31 14:43:37,699 - mmcls - INFO - Epoch [4][700/5005]\tlr: 1.000e-01, eta: 11:58:28, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9521\n2022-12-31 14:43:46,633 - mmcls - INFO - Epoch [4][800/5005]\tlr: 1.000e-01, eta: 11:58:21, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.9636\n2022-12-31 14:43:55,344 - mmcls - INFO - Epoch [4][900/5005]\tlr: 1.000e-01, eta: 11:58:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9487\n2022-12-31 14:44:03,986 - mmcls - INFO - Epoch [4][1000/5005]\tlr: 1.000e-01, eta: 11:57:50, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.9723\n2022-12-31 14:44:13,065 - mmcls - INFO - Epoch [4][1100/5005]\tlr: 1.000e-01, eta: 11:57:47, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.9366\n2022-12-31 14:44:22,207 - mmcls - INFO - Epoch [4][1200/5005]\tlr: 1.000e-01, eta: 11:57:45, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.9551\n2022-12-31 14:44:31,170 - mmcls - INFO - Epoch [4][1300/5005]\tlr: 1.000e-01, eta: 11:57:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.9588\n2022-12-31 14:44:40,013 - mmcls - INFO - Epoch [4][1400/5005]\tlr: 1.000e-01, eta: 11:57:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9379\n2022-12-31 14:44:48,722 - mmcls - INFO - Epoch [4][1500/5005]\tlr: 1.000e-01, eta: 11:57:14, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.9477\n2022-12-31 14:44:57,518 - mmcls - INFO - Epoch [4][1600/5005]\tlr: 1.000e-01, eta: 11:57:02, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9251\n2022-12-31 14:45:06,534 - mmcls - INFO - Epoch [4][1700/5005]\tlr: 1.000e-01, eta: 11:56:57, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.9208\n2022-12-31 14:45:15,869 - mmcls - INFO - Epoch [4][1800/5005]\tlr: 1.000e-01, eta: 11:57:00, time: 0.093, data_time: 0.022, memory: 1224, loss: 2.9051\n2022-12-31 14:45:24,612 - mmcls - INFO - Epoch [4][1900/5005]\tlr: 1.000e-01, eta: 11:56:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9243\n2022-12-31 14:45:33,615 - mmcls - INFO - Epoch [4][2000/5005]\tlr: 1.000e-01, eta: 11:56:41, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9177\n2022-12-31 14:45:42,033 - mmcls - INFO - Epoch [4][2100/5005]\tlr: 1.000e-01, eta: 11:56:19, time: 0.084, data_time: 0.006, memory: 1224, loss: 2.9285\n2022-12-31 14:45:50,687 - mmcls - INFO - Epoch [4][2200/5005]\tlr: 1.000e-01, eta: 11:56:04, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.9136\n2022-12-31 14:45:59,385 - mmcls - INFO - Epoch [4][2300/5005]\tlr: 1.000e-01, eta: 11:55:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9264\n2022-12-31 14:46:07,965 - mmcls - INFO - Epoch [4][2400/5005]\tlr: 1.000e-01, eta: 11:55:32, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9109\n2022-12-31 14:46:16,653 - mmcls - INFO - Epoch [4][2500/5005]\tlr: 1.000e-01, eta: 11:55:17, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8960\n2022-12-31 14:46:25,443 - mmcls - INFO - Epoch [4][2600/5005]\tlr: 1.000e-01, eta: 11:55:06, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.8966\n2022-12-31 14:46:34,328 - mmcls - INFO - Epoch [4][2700/5005]\tlr: 1.000e-01, eta: 11:54:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9077\n2022-12-31 14:46:43,320 - mmcls - INFO - Epoch [4][2800/5005]\tlr: 1.000e-01, eta: 11:54:51, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9034\n2022-12-31 14:46:51,938 - mmcls - INFO - Epoch [4][2900/5005]\tlr: 1.000e-01, eta: 11:54:35, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8933\n2022-12-31 14:47:01,156 - mmcls - INFO - Epoch [4][3000/5005]\tlr: 1.000e-01, eta: 11:54:35, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.9160\n2022-12-31 14:47:10,069 - mmcls - INFO - Epoch [4][3100/5005]\tlr: 1.000e-01, eta: 11:54:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8961\n2022-12-31 14:47:19,292 - mmcls - INFO - Epoch [4][3200/5005]\tlr: 1.000e-01, eta: 11:54:27, time: 0.092, data_time: 0.016, memory: 1224, loss: 2.9042\n2022-12-31 14:47:28,153 - mmcls - INFO - Epoch [4][3300/5005]\tlr: 1.000e-01, eta: 11:54:17, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8993\n2022-12-31 14:47:36,882 - mmcls - INFO - Epoch [4][3400/5005]\tlr: 1.000e-01, eta: 11:54:04, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8810\n2022-12-31 14:47:45,988 - mmcls - INFO - Epoch [4][3500/5005]\tlr: 1.000e-01, eta: 11:54:01, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8713\n2022-12-31 14:47:54,759 - mmcls - INFO - Epoch [4][3600/5005]\tlr: 1.000e-01, eta: 11:53:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8889\n2022-12-31 14:48:03,586 - mmcls - INFO - Epoch [4][3700/5005]\tlr: 1.000e-01, eta: 11:53:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8625\n2022-12-31 14:48:12,573 - mmcls - INFO - Epoch [4][3800/5005]\tlr: 1.000e-01, eta: 11:53:32, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.9068\n2022-12-31 14:48:21,363 - mmcls - INFO - Epoch [4][3900/5005]\tlr: 1.000e-01, eta: 11:53:20, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.8967\n2022-12-31 14:48:30,150 - mmcls - INFO - Epoch [4][4000/5005]\tlr: 1.000e-01, eta: 11:53:09, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8667\n2022-12-31 14:48:38,921 - mmcls - INFO - Epoch [4][4100/5005]\tlr: 1.000e-01, eta: 11:52:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8725\n2022-12-31 14:48:48,044 - mmcls - INFO - Epoch [4][4200/5005]\tlr: 1.000e-01, eta: 11:52:54, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8794\n2022-12-31 14:48:57,116 - mmcls - INFO - Epoch [4][4300/5005]\tlr: 1.000e-01, eta: 11:52:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8764\n2022-12-31 14:49:06,187 - mmcls - INFO - Epoch [4][4400/5005]\tlr: 1.000e-01, eta: 11:52:45, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.8564\n2022-12-31 14:49:14,621 - mmcls - INFO - Epoch [4][4500/5005]\tlr: 1.000e-01, eta: 11:52:25, time: 0.084, data_time: 0.002, memory: 1224, loss: 2.8397\n2022-12-31 14:49:23,222 - mmcls - INFO - Epoch [4][4600/5005]\tlr: 1.000e-01, eta: 11:52:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8573\n2022-12-31 14:49:32,068 - mmcls - INFO - Epoch [4][4700/5005]\tlr: 1.000e-01, eta: 11:51:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8272\n2022-12-31 14:49:40,763 - mmcls - INFO - Epoch [4][4800/5005]\tlr: 1.000e-01, eta: 11:51:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8633\n2022-12-31 14:49:49,579 - mmcls - INFO - Epoch [4][4900/5005]\tlr: 1.000e-01, eta: 11:51:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8223\n2022-12-31 14:49:58,350 - mmcls - INFO - Epoch [4][5000/5005]\tlr: 1.000e-01, eta: 11:51:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8250\n2022-12-31 14:50:21,161 - mmcls - INFO - Epoch(val) [4][196]\taccuracy_top-1: 41.2460, accuracy_top-5: 67.6780\n2022-12-31 14:50:31,966 - mmcls - INFO - Epoch [5][100/5005]\tlr: 1.000e-01, eta: 11:51:49, time: 0.108, data_time: 0.033, memory: 1224, loss: 2.7883\n2022-12-31 14:50:40,973 - mmcls - INFO - Epoch [5][200/5005]\tlr: 1.000e-01, eta: 11:51:43, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8051\n2022-12-31 14:50:49,854 - mmcls - INFO - Epoch [5][300/5005]\tlr: 1.000e-01, eta: 11:51:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7634\n2022-12-31 14:50:58,444 - mmcls - INFO - Epoch [5][400/5005]\tlr: 1.000e-01, eta: 11:51:18, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8121\n2022-12-31 14:51:07,186 - mmcls - INFO - Epoch [5][500/5005]\tlr: 1.000e-01, eta: 11:51:05, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7923\n2022-12-31 14:51:15,860 - mmcls - INFO - Epoch [5][600/5005]\tlr: 1.000e-01, eta: 11:50:51, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7920\n2022-12-31 14:51:24,831 - mmcls - INFO - Epoch [5][700/5005]\tlr: 1.000e-01, eta: 11:50:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8065\n2022-12-31 14:51:33,743 - mmcls - INFO - Epoch [5][800/5005]\tlr: 1.000e-01, eta: 11:50:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7872\n2022-12-31 14:51:42,547 - mmcls - INFO - Epoch [5][900/5005]\tlr: 1.000e-01, eta: 11:50:25, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.8043\n2022-12-31 14:51:51,374 - mmcls - INFO - Epoch [5][1000/5005]\tlr: 1.000e-01, eta: 11:50:15, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7884\n2022-12-31 14:52:00,312 - mmcls - INFO - Epoch [5][1100/5005]\tlr: 1.000e-01, eta: 11:50:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8122\n2022-12-31 14:52:09,317 - mmcls - INFO - Epoch [5][1200/5005]\tlr: 1.000e-01, eta: 11:50:01, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7952\n2022-12-31 14:52:18,275 - mmcls - INFO - Epoch [5][1300/5005]\tlr: 1.000e-01, eta: 11:49:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7806\n2022-12-31 14:52:26,986 - mmcls - INFO - Epoch [5][1400/5005]\tlr: 1.000e-01, eta: 11:49:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7990\n2022-12-31 14:52:35,689 - mmcls - INFO - Epoch [5][1500/5005]\tlr: 1.000e-01, eta: 11:49:28, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8061\n2022-12-31 14:52:44,465 - mmcls - INFO - Epoch [5][1600/5005]\tlr: 1.000e-01, eta: 11:49:16, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7924\n2022-12-31 14:52:53,376 - mmcls - INFO - Epoch [5][1700/5005]\tlr: 1.000e-01, eta: 11:49:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8078\n2022-12-31 14:53:02,157 - mmcls - INFO - Epoch [5][1800/5005]\tlr: 1.000e-01, eta: 11:48:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7638\n2022-12-31 14:53:11,319 - mmcls - INFO - Epoch [5][1900/5005]\tlr: 1.000e-01, eta: 11:48:54, time: 0.092, data_time: 0.011, memory: 1224, loss: 2.7742\n2022-12-31 14:53:20,154 - mmcls - INFO - Epoch [5][2000/5005]\tlr: 1.000e-01, eta: 11:48:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7921\n2022-12-31 14:53:28,881 - mmcls - INFO - Epoch [5][2100/5005]\tlr: 1.000e-01, eta: 11:48:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7961\n2022-12-31 14:53:37,583 - mmcls - INFO - Epoch [5][2200/5005]\tlr: 1.000e-01, eta: 11:48:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7684\n2022-12-31 14:53:46,082 - mmcls - INFO - Epoch [5][2300/5005]\tlr: 1.000e-01, eta: 11:48:01, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7883\n2022-12-31 14:53:54,858 - mmcls - INFO - Epoch [5][2400/5005]\tlr: 1.000e-01, eta: 11:47:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7898\n2022-12-31 14:54:04,019 - mmcls - INFO - Epoch [5][2500/5005]\tlr: 1.000e-01, eta: 11:47:47, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8040\n2022-12-31 14:54:13,033 - mmcls - INFO - Epoch [5][2600/5005]\tlr: 1.000e-01, eta: 11:47:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7966\n2022-12-31 14:54:21,878 - mmcls - INFO - Epoch [5][2700/5005]\tlr: 1.000e-01, eta: 11:47:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7814\n2022-12-31 14:54:30,733 - mmcls - INFO - Epoch [5][2800/5005]\tlr: 1.000e-01, eta: 11:47:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7720\n2022-12-31 14:54:39,710 - mmcls - INFO - Epoch [5][2900/5005]\tlr: 1.000e-01, eta: 11:47:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7566\n2022-12-31 14:54:48,358 - mmcls - INFO - Epoch [5][3000/5005]\tlr: 1.000e-01, eta: 11:47:01, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7665\n2022-12-31 14:54:57,349 - mmcls - INFO - Epoch [5][3100/5005]\tlr: 1.000e-01, eta: 11:46:54, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8099\n2022-12-31 14:55:05,981 - mmcls - INFO - Epoch [5][3200/5005]\tlr: 1.000e-01, eta: 11:46:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7850\n2022-12-31 14:55:14,880 - mmcls - INFO - Epoch [5][3300/5005]\tlr: 1.000e-01, eta: 11:46:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7789\n2022-12-31 14:55:23,765 - mmcls - INFO - Epoch [5][3400/5005]\tlr: 1.000e-01, eta: 11:46:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7536\n2022-12-31 14:55:32,453 - mmcls - INFO - Epoch [5][3500/5005]\tlr: 1.000e-01, eta: 11:46:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7590\n2022-12-31 14:55:41,428 - mmcls - INFO - Epoch [5][3600/5005]\tlr: 1.000e-01, eta: 11:46:02, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7605\n2022-12-31 14:55:50,242 - mmcls - INFO - Epoch [5][3700/5005]\tlr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7922\n2022-12-31 14:55:59,221 - mmcls - INFO - Epoch [5][3800/5005]\tlr: 1.000e-01, eta: 11:45:45, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7962\n2022-12-31 14:56:08,001 - mmcls - INFO - Epoch [5][3900/5005]\tlr: 1.000e-01, eta: 11:45:34, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7540\n2022-12-31 14:56:16,467 - mmcls - INFO - Epoch [5][4000/5005]\tlr: 1.000e-01, eta: 11:45:17, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7862\n2022-12-31 14:56:25,141 - mmcls - INFO - Epoch [5][4100/5005]\tlr: 1.000e-01, eta: 11:45:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7692\n2022-12-31 14:56:33,732 - mmcls - INFO - Epoch [5][4200/5005]\tlr: 1.000e-01, eta: 11:44:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7535\n2022-12-31 14:56:42,554 - mmcls - INFO - Epoch [5][4300/5005]\tlr: 1.000e-01, eta: 11:44:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7215\n2022-12-31 14:56:51,176 - mmcls - INFO - Epoch [5][4400/5005]\tlr: 1.000e-01, eta: 11:44:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7596\n2022-12-31 14:56:59,998 - mmcls - INFO - Epoch [5][4500/5005]\tlr: 1.000e-01, eta: 11:44:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7443\n2022-12-31 14:57:09,124 - mmcls - INFO - Epoch [5][4600/5005]\tlr: 1.000e-01, eta: 11:44:11, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.7595\n2022-12-31 14:57:17,754 - mmcls - INFO - Epoch [5][4700/5005]\tlr: 1.000e-01, eta: 11:43:58, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.7294\n2022-12-31 14:57:26,792 - mmcls - INFO - Epoch [5][4800/5005]\tlr: 1.000e-01, eta: 11:43:52, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7469\n2022-12-31 14:57:35,358 - mmcls - INFO - Epoch [5][4900/5005]\tlr: 1.000e-01, eta: 11:43:37, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.7681\n2022-12-31 14:57:44,150 - mmcls - INFO - Epoch [5][5000/5005]\tlr: 1.000e-01, eta: 11:43:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7587\n2022-12-31 14:58:06,839 - mmcls - INFO - Epoch(val) [5][196]\taccuracy_top-1: 43.8300, accuracy_top-5: 70.7380\n2022-12-31 14:58:17,741 - mmcls - INFO - Epoch [6][100/5005]\tlr: 1.000e-01, eta: 11:43:47, time: 0.109, data_time: 0.049, memory: 1224, loss: 2.6893\n2022-12-31 14:58:27,059 - mmcls - INFO - Epoch [6][200/5005]\tlr: 1.000e-01, eta: 11:43:46, time: 0.093, data_time: 0.013, memory: 1224, loss: 2.7007\n2022-12-31 14:58:36,238 - mmcls - INFO - Epoch [6][300/5005]\tlr: 1.000e-01, eta: 11:43:43, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.6929\n2022-12-31 14:58:44,827 - mmcls - INFO - Epoch [6][400/5005]\tlr: 1.000e-01, eta: 11:43:28, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.6818\n2022-12-31 14:58:53,437 - mmcls - INFO - Epoch [6][500/5005]\tlr: 1.000e-01, eta: 11:43:14, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.6739\n2022-12-31 14:59:02,054 - mmcls - INFO - Epoch [6][600/5005]\tlr: 1.000e-01, eta: 11:43:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6953\n2022-12-31 14:59:10,913 - mmcls - INFO - Epoch [6][700/5005]\tlr: 1.000e-01, eta: 11:42:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7038\n2022-12-31 14:59:19,337 - mmcls - INFO - Epoch [6][800/5005]\tlr: 1.000e-01, eta: 11:42:34, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.7204\n2022-12-31 14:59:28,286 - mmcls - INFO - Epoch [6][900/5005]\tlr: 1.000e-01, eta: 11:42:26, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.6825\n2022-12-31 14:59:37,057 - mmcls - INFO - Epoch [6][1000/5005]\tlr: 1.000e-01, eta: 11:42:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6981\n2022-12-31 14:59:46,071 - mmcls - INFO - Epoch [6][1100/5005]\tlr: 1.000e-01, eta: 11:42:09, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7227\n2022-12-31 14:59:54,952 - mmcls - INFO - Epoch [6][1200/5005]\tlr: 1.000e-01, eta: 11:42:00, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7004\n2022-12-31 15:00:03,477 - mmcls - INFO - Epoch [6][1300/5005]\tlr: 1.000e-01, eta: 11:41:44, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7428\n2022-12-31 15:00:12,375 - mmcls - INFO - Epoch [6][1400/5005]\tlr: 1.000e-01, eta: 11:41:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6873\n2022-12-31 15:00:21,187 - mmcls - INFO - Epoch [6][1500/5005]\tlr: 1.000e-01, eta: 11:41:26, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7018\n2022-12-31 15:00:29,874 - mmcls - INFO - Epoch [6][1600/5005]\tlr: 1.000e-01, eta: 11:41:13, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7077\n2022-12-31 15:00:38,932 - mmcls - INFO - Epoch [6][1700/5005]\tlr: 1.000e-01, eta: 11:41:08, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.6731\n2022-12-31 15:00:47,751 - mmcls - INFO - Epoch [6][1800/5005]\tlr: 1.000e-01, eta: 11:40:57, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7253\n2022-12-31 15:00:56,605 - mmcls - INFO - Epoch [6][1900/5005]\tlr: 1.000e-01, eta: 11:40:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6952\n2022-12-31 15:01:05,967 - mmcls - INFO - Epoch [6][2000/5005]\tlr: 1.000e-01, eta: 11:40:48, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.6848\n2022-12-31 15:01:14,806 - mmcls - INFO - Epoch [6][2100/5005]\tlr: 1.000e-01, eta: 11:40:38, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.6948\n2022-12-31 15:01:23,604 - mmcls - INFO - Epoch [6][2200/5005]\tlr: 1.000e-01, eta: 11:40:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7245\n2022-12-31 15:01:32,163 - mmcls - INFO - Epoch [6][2300/5005]\tlr: 1.000e-01, eta: 11:40:13, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.6792\n2022-12-31 15:01:40,865 - mmcls - INFO - Epoch [6][2400/5005]\tlr: 1.000e-01, eta: 11:40:02, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7051\n2022-12-31 15:01:49,355 - mmcls - INFO - Epoch [6][2500/5005]\tlr: 1.000e-01, eta: 11:39:46, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.6977\n2022-12-31 15:01:58,195 - mmcls - INFO - Epoch [6][2600/5005]\tlr: 1.000e-01, eta: 11:39:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.6944\n2022-12-31 15:02:07,045 - mmcls - INFO - Epoch [6][2700/5005]\tlr: 1.000e-01, eta: 11:39:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7474\n2022-12-31 15:02:15,818 - mmcls - INFO - Epoch [6][2800/5005]\tlr: 1.000e-01, eta: 11:39:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6692\n2022-12-31 15:02:24,673 - mmcls - INFO - Epoch [6][2900/5005]\tlr: 1.000e-01, eta: 11:39:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6828\n2022-12-31 15:02:33,455 - mmcls - INFO - Epoch [6][3000/5005]\tlr: 1.000e-01, eta: 11:38:57, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7192\n2022-12-31 15:02:42,292 - mmcls - INFO - Epoch [6][3100/5005]\tlr: 1.000e-01, eta: 11:38:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7162\n2022-12-31 15:02:50,966 - mmcls - INFO - Epoch [6][3200/5005]\tlr: 1.000e-01, eta: 11:38:35, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6855\n2022-12-31 15:02:59,857 - mmcls - INFO - Epoch [6][3300/5005]\tlr: 1.000e-01, eta: 11:38:26, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7006\n2022-12-31 15:03:08,603 - mmcls - INFO - Epoch [6][3400/5005]\tlr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6787\n2022-12-31 15:03:17,278 - mmcls - INFO - Epoch [6][3500/5005]\tlr: 1.000e-01, eta: 11:38:03, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.6887\n2022-12-31 15:03:26,136 - mmcls - INFO - Epoch [6][3600/5005]\tlr: 1.000e-01, eta: 11:37:54, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6949\n2022-12-31 15:03:34,877 - mmcls - INFO - Epoch [6][3700/5005]\tlr: 1.000e-01, eta: 11:37:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.6962\n2022-12-31 15:03:43,675 - mmcls - INFO - Epoch [6][3800/5005]\tlr: 1.000e-01, eta: 11:37:33, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.6899\n2022-12-31 15:03:52,594 - mmcls - INFO - Epoch [6][3900/5005]\tlr: 1.000e-01, eta: 11:37:25, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.6670\n2022-12-31 15:04:01,373 - mmcls - INFO - Epoch [6][4000/5005]\tlr: 1.000e-01, eta: 11:37:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6850\n2022-12-31 15:04:10,062 - mmcls - INFO - Epoch [6][4100/5005]\tlr: 1.000e-01, eta: 11:37:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6992\n2022-12-31 15:04:18,661 - mmcls - INFO - Epoch [6][4200/5005]\tlr: 1.000e-01, eta: 11:36:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6771\n2022-12-31 15:04:27,584 - mmcls - INFO - Epoch [6][4300/5005]\tlr: 1.000e-01, eta: 11:36:41, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6620\n2022-12-31 15:04:36,582 - mmcls - INFO - Epoch [6][4400/5005]\tlr: 1.000e-01, eta: 11:36:34, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.6976\n2022-12-31 15:04:45,206 - mmcls - INFO - Epoch [6][4500/5005]\tlr: 1.000e-01, eta: 11:36:21, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.6796\n2022-12-31 15:04:54,207 - mmcls - INFO - Epoch [6][4600/5005]\tlr: 1.000e-01, eta: 11:36:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.6723\n2022-12-31 15:05:02,929 - mmcls - INFO - Epoch [6][4700/5005]\tlr: 1.000e-01, eta: 11:36:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6702\n2022-12-31 15:05:11,662 - mmcls - INFO - Epoch [6][4800/5005]\tlr: 1.000e-01, eta: 11:35:52, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7053\n2022-12-31 15:05:20,317 - mmcls - INFO - Epoch [6][4900/5005]\tlr: 1.000e-01, eta: 11:35:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.6731\n2022-12-31 15:05:29,118 - mmcls - INFO - Epoch [6][5000/5005]\tlr: 1.000e-01, eta: 11:35:30, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.6835\n2022-12-31 15:05:52,166 - mmcls - INFO - Epoch(val) [6][196]\taccuracy_top-1: 47.2820, accuracy_top-5: 73.3220\n2022-12-31 15:06:03,536 - mmcls - INFO - Epoch [7][100/5005]\tlr: 1.000e-01, eta: 11:35:52, time: 0.113, data_time: 0.040, memory: 1224, loss: 2.6088\n2022-12-31 15:06:12,436 - mmcls - INFO - Epoch [7][200/5005]\tlr: 1.000e-01, eta: 11:35:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6177\n2022-12-31 15:06:21,117 - mmcls - INFO - Epoch [7][300/5005]\tlr: 1.000e-01, eta: 11:35:32, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.6288\n2022-12-31 15:06:29,694 - mmcls - INFO - Epoch [7][400/5005]\tlr: 1.000e-01, eta: 11:35:18, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.6409\n2022-12-31 15:06:38,435 - mmcls - INFO - Epoch [7][500/5005]\tlr: 1.000e-01, eta: 11:35:07, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.6469\n2022-12-31 15:06:46,994 - mmcls - INFO - Epoch [7][600/5005]\tlr: 1.000e-01, eta: 11:34:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6223\n2022-12-31 15:06:55,795 - mmcls - INFO - Epoch [7][700/5005]\tlr: 1.000e-01, eta: 11:34:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6451\n2022-12-31 15:07:04,489 - mmcls - INFO - Epoch [7][800/5005]\tlr: 1.000e-01, eta: 11:34:32, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.6186\n2022-12-31 15:07:13,359 - mmcls - INFO - Epoch [7][900/5005]\tlr: 1.000e-01, eta: 11:34:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6461\n2022-12-31 15:07:22,223 - mmcls - INFO - Epoch [7][1000/5005]\tlr: 1.000e-01, eta: 11:34:14, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6442\n2022-12-31 15:07:31,095 - mmcls - INFO - Epoch [7][1100/5005]\tlr: 1.000e-01, eta: 11:34:05, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.6460\n2022-12-31 15:07:39,884 - mmcls - INFO - Epoch [7][1200/5005]\tlr: 1.000e-01, eta: 11:33:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.6180\n2022-12-31 15:07:48,622 - mmcls - INFO - Epoch [7][1300/5005]\tlr: 1.000e-01, eta: 11:33:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6479\n2022-12-31 15:07:57,505 - mmcls - INFO - Epoch [7][1400/5005]\tlr: 1.000e-01, eta: 11:33:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6648\n2022-12-31 15:08:06,316 - mmcls - INFO - Epoch [7][1500/5005]\tlr: 1.000e-01, eta: 11:33:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.6383\n2022-12-31 15:08:15,064 - mmcls - INFO - Epoch [7][1600/5005]\tlr: 1.000e-01, eta: 11:33:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6210\n2022-12-31 15:08:23,752 - mmcls - INFO - Epoch [7][1700/5005]\tlr: 1.000e-01, eta: 11:33:03, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.6263\n2022-12-31 15:08:32,420 - mmcls - INFO - Epoch [7][1800/5005]\tlr: 1.000e-01, eta: 11:32:51, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.6527\n2022-12-31 15:08:41,319 - mmcls - INFO - Epoch [7][1900/5005]\tlr: 1.000e-01, eta: 11:32:43, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.6263\n2022-12-31 15:08:50,053 - mmcls - INFO - Epoch [7][2000/5005]\tlr: 1.000e-01, eta: 11:32:32, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.6357\n2022-12-31 15:08:58,744 - mmcls - INFO - Epoch [7][2100/5005]\tlr: 1.000e-01, eta: 11:32:20, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.6575\n2022-12-31 15:09:07,201 - mmcls - INFO - Epoch [7][2200/5005]\tlr: 1.000e-01, eta: 11:32:05, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.6480\n2022-12-31 15:09:15,981 - mmcls - INFO - Epoch [7][2300/5005]\tlr: 1.000e-01, eta: 11:31:55, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.6372\n2022-12-31 15:09:25,050 - mmcls - INFO - Epoch [7][2400/5005]\tlr: 1.000e-01, eta: 11:31:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.6518\n2022-12-31 15:09:33,603 - mmcls - INFO - Epoch [7][2500/5005]\tlr: 1.000e-01, eta: 11:31:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6611\n2022-12-31 15:09:42,451 - mmcls - INFO - Epoch [7][2600/5005]\tlr: 1.000e-01, eta: 11:31:27, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.6516\n2022-12-31 15:09:51,369 - mmcls - INFO - Epoch [7][2700/5005]\tlr: 1.000e-01, eta: 11:31:19, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.6536\n2022-12-31 15:10:00,007 - mmcls - INFO - Epoch [7][2800/5005]\tlr: 1.000e-01, eta: 11:31:06, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.6542\n2022-12-31 15:10:08,936 - mmcls - INFO - Epoch [7][2900/5005]\tlr: 1.000e-01, eta: 11:30:58, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.6261\n2022-12-31 15:10:17,586 - mmcls - INFO - Epoch [7][3000/5005]\tlr: 1.000e-01, eta: 11:30:47, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.6405\n2022-12-31 15:10:26,191 - mmcls - INFO - Epoch [7][3100/5005]\tlr: 1.000e-01, eta: 11:30:34, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.6436\n2022-12-31 15:10:35,221 - mmcls - INFO - Epoch [7][3200/5005]\tlr: 1.000e-01, eta: 11:30:27, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.6491\n2022-12-31 15:10:43,968 - mmcls - INFO - Epoch [7][3300/5005]\tlr: 1.000e-01, eta: 11:30:17, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6373\n2022-12-31 15:10:52,707 - mmcls - INFO - Epoch [7][3400/5005]\tlr: 1.000e-01, eta: 11:30:06, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.6456\n2022-12-31 15:11:01,714 - mmcls - INFO - Epoch [7][3500/5005]\tlr: 1.000e-01, eta: 11:29:59, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.6242\n2022-12-31 15:11:10,532 - mmcls - INFO - Epoch [7][3600/5005]\tlr: 1.000e-01, eta: 11:29:50, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.6193\n2022-12-31 15:11:19,335 - mmcls - INFO - Epoch [7][3700/5005]\tlr: 1.000e-01, eta: 11:29:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.6401\n2022-12-31 15:11:28,194 - mmcls - INFO - Epoch [7][3800/5005]\tlr: 1.000e-01, eta: 11:29:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.6497\n2022-12-31 15:11:37,225 - mmcls - INFO - Epoch [7][3900/5005]\tlr: 1.000e-01, eta: 11:29:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.6341\n2022-12-31 15:11:46,056 - mmcls - INFO - Epoch [7][4000/5005]\tlr: 1.000e-01, eta: 11:29:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.6525\n2022-12-31 15:11:54,579 - mmcls - INFO - Epoch [7][4100/5005]\tlr: 1.000e-01, eta: 11:29:01, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.6422\n2022-12-31 15:12:03,264 - mmcls - INFO - Epoch [7][4200/5005]\tlr: 1.000e-01, eta: 11:28:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.6371\n2022-12-31 15:12:12,138 - mmcls - INFO - Epoch [7][4300/5005]\tlr: 1.000e-01, eta: 11:28:42, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.6161\n2022-12-31 15:12:20,750 - mmcls - INFO - Epoch [7][4400/5005]\tlr: 1.000e-01, eta: 11:28:29, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.6268\n2022-12-31 15:12:29,645 - mmcls - INFO - Epoch [7][4500/5005]\tlr: 1.000e-01, eta: 11:28:21, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.6604\n2022-12-31 15:12:38,824 - mmcls - INFO - Epoch [7][4600/5005]\tlr: 1.000e-01, eta: 11:28:16, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.5926\n2022-12-31 15:12:47,392 - mmcls - INFO - Epoch [7][4700/5005]\tlr: 1.000e-01, eta: 11:28:03, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.6255\n2022-12-31 15:12:56,519 - mmcls - INFO - Epoch [7][4800/5005]\tlr: 1.000e-01, eta: 11:27:58, time: 0.091, data_time: 0.031, memory: 1224, loss: 2.6187\n2022-12-31 15:13:05,079 - mmcls - INFO - Epoch [7][4900/5005]\tlr: 1.000e-01, eta: 11:27:45, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.6444\n2022-12-31 15:13:13,626 - mmcls - INFO - Epoch [7][5000/5005]\tlr: 1.000e-01, eta: 11:27:32, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.6461\n2022-12-31 15:13:36,207 - mmcls - INFO - Epoch(val) [7][196]\taccuracy_top-1: 48.1260, accuracy_top-5: 74.4340\n2022-12-31 15:13:46,896 - mmcls - INFO - Epoch [8][100/5005]\tlr: 1.000e-01, eta: 11:27:41, time: 0.107, data_time: 0.055, memory: 1224, loss: 2.5821\n2022-12-31 15:13:55,808 - mmcls - INFO - Epoch [8][200/5005]\tlr: 1.000e-01, eta: 11:27:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.5662\n2022-12-31 15:14:05,003 - mmcls - INFO - Epoch [8][300/5005]\tlr: 1.000e-01, eta: 11:27:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5782\n2022-12-31 15:14:14,089 - mmcls - INFO - Epoch [8][400/5005]\tlr: 1.000e-01, eta: 11:27:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5682\n2022-12-31 15:14:22,969 - mmcls - INFO - Epoch [8][500/5005]\tlr: 1.000e-01, eta: 11:27:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5902\n2022-12-31 15:14:31,934 - mmcls - INFO - Epoch [8][600/5005]\tlr: 1.000e-01, eta: 11:27:05, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.5969\n2022-12-31 15:14:41,007 - mmcls - INFO - Epoch [8][700/5005]\tlr: 1.000e-01, eta: 11:26:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5988\n2022-12-31 15:14:49,747 - mmcls - INFO - Epoch [8][800/5005]\tlr: 1.000e-01, eta: 11:26:49, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.5951\n2022-12-31 15:14:58,760 - mmcls - INFO - Epoch [8][900/5005]\tlr: 1.000e-01, eta: 11:26:42, time: 0.090, data_time: 0.035, memory: 1224, loss: 2.5924\n2022-12-31 15:15:07,801 - mmcls - INFO - Epoch [8][1000/5005]\tlr: 1.000e-01, eta: 11:26:35, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.5824\n2022-12-31 15:15:16,549 - mmcls - INFO - Epoch [8][1100/5005]\tlr: 1.000e-01, eta: 11:26:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.5937\n2022-12-31 15:15:25,130 - mmcls - INFO - Epoch [8][1200/5005]\tlr: 1.000e-01, eta: 11:26:12, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.6002\n2022-12-31 15:15:33,976 - mmcls - INFO - Epoch [8][1300/5005]\tlr: 1.000e-01, eta: 11:26:03, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5670\n2022-12-31 15:15:42,749 - mmcls - INFO - Epoch [8][1400/5005]\tlr: 1.000e-01, eta: 11:25:53, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5978\n2022-12-31 15:15:51,703 - mmcls - INFO - Epoch [8][1500/5005]\tlr: 1.000e-01, eta: 11:25:45, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.6279\n2022-12-31 15:16:00,268 - mmcls - INFO - Epoch [8][1600/5005]\tlr: 1.000e-01, eta: 11:25:32, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.6054\n2022-12-31 15:16:09,050 - mmcls - INFO - Epoch [8][1700/5005]\tlr: 1.000e-01, eta: 11:25:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.6068\n2022-12-31 15:16:17,808 - mmcls - INFO - Epoch [8][1800/5005]\tlr: 1.000e-01, eta: 11:25:12, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.6008\n2022-12-31 15:16:26,545 - mmcls - INFO - Epoch [8][1900/5005]\tlr: 1.000e-01, eta: 11:25:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6054\n2022-12-31 15:16:35,240 - mmcls - INFO - Epoch [8][2000/5005]\tlr: 1.000e-01, eta: 11:24:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6303\n2022-12-31 15:16:44,033 - mmcls - INFO - Epoch [8][2100/5005]\tlr: 1.000e-01, eta: 11:24:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5889\n2022-12-31 15:16:52,729 - mmcls - INFO - Epoch [8][2200/5005]\tlr: 1.000e-01, eta: 11:24:30, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.5894\n2022-12-31 15:17:01,658 - mmcls - INFO - Epoch [8][2300/5005]\tlr: 1.000e-01, eta: 11:24:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5924\n2022-12-31 15:17:10,256 - mmcls - INFO - Epoch [8][2400/5005]\tlr: 1.000e-01, eta: 11:24:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.5895\n2022-12-31 15:17:19,099 - mmcls - INFO - Epoch [8][2500/5005]\tlr: 1.000e-01, eta: 11:24:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.6159\n2022-12-31 15:17:27,649 - mmcls - INFO - Epoch [8][2600/5005]\tlr: 1.000e-01, eta: 11:23:48, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5980\n2022-12-31 15:17:36,415 - mmcls - INFO - Epoch [8][2700/5005]\tlr: 1.000e-01, eta: 11:23:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5956\n2022-12-31 15:17:45,711 - mmcls - INFO - Epoch [8][2800/5005]\tlr: 1.000e-01, eta: 11:23:34, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.5685\n2022-12-31 15:17:54,315 - mmcls - INFO - Epoch [8][2900/5005]\tlr: 1.000e-01, eta: 11:23:22, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5870\n2022-12-31 15:18:02,880 - mmcls - INFO - Epoch [8][3000/5005]\tlr: 1.000e-01, eta: 11:23:09, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.5852\n2022-12-31 15:18:11,716 - mmcls - INFO - Epoch [8][3100/5005]\tlr: 1.000e-01, eta: 11:23:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6227\n2022-12-31 15:18:20,183 - mmcls - INFO - Epoch [8][3200/5005]\tlr: 1.000e-01, eta: 11:22:46, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.6005\n2022-12-31 15:18:28,992 - mmcls - INFO - Epoch [8][3300/5005]\tlr: 1.000e-01, eta: 11:22:37, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.6146\n2022-12-31 15:18:38,003 - mmcls - INFO - Epoch [8][3400/5005]\tlr: 1.000e-01, eta: 11:22:30, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5997\n2022-12-31 15:18:47,050 - mmcls - INFO - Epoch [8][3500/5005]\tlr: 1.000e-01, eta: 11:22:23, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5990\n2022-12-31 15:18:55,798 - mmcls - INFO - Epoch [8][3600/5005]\tlr: 1.000e-01, eta: 11:22:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5693\n2022-12-31 15:19:04,646 - mmcls - INFO - Epoch [8][3700/5005]\tlr: 1.000e-01, eta: 11:22:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5968\n2022-12-31 15:19:13,483 - mmcls - INFO - Epoch [8][3800/5005]\tlr: 1.000e-01, eta: 11:21:55, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5945\n2022-12-31 15:19:21,952 - mmcls - INFO - Epoch [8][3900/5005]\tlr: 1.000e-01, eta: 11:21:41, time: 0.085, data_time: 0.027, memory: 1224, loss: 2.6096\n2022-12-31 15:19:30,564 - mmcls - INFO - Epoch [8][4000/5005]\tlr: 1.000e-01, eta: 11:21:29, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.5969\n2022-12-31 15:19:39,441 - mmcls - INFO - Epoch [8][4100/5005]\tlr: 1.000e-01, eta: 11:21:20, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5996\n2022-12-31 15:19:48,421 - mmcls - INFO - Epoch [8][4200/5005]\tlr: 1.000e-01, eta: 11:21:13, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.6211\n2022-12-31 15:19:57,227 - mmcls - INFO - Epoch [8][4300/5005]\tlr: 1.000e-01, eta: 11:21:04, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.5814\n2022-12-31 15:20:06,146 - mmcls - INFO - Epoch [8][4400/5005]\tlr: 1.000e-01, eta: 11:20:55, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.5784\n2022-12-31 15:20:15,131 - mmcls - INFO - Epoch [8][4500/5005]\tlr: 1.000e-01, eta: 11:20:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5960\n2022-12-31 15:20:23,748 - mmcls - INFO - Epoch [8][4600/5005]\tlr: 1.000e-01, eta: 11:20:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.6127\n2022-12-31 15:20:32,807 - mmcls - INFO - Epoch [8][4700/5005]\tlr: 1.000e-01, eta: 11:20:30, time: 0.091, data_time: 0.026, memory: 1224, loss: 2.6233\n2022-12-31 15:20:41,335 - mmcls - INFO - Epoch [8][4800/5005]\tlr: 1.000e-01, eta: 11:20:17, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.5825\n2022-12-31 15:20:50,055 - mmcls - INFO - Epoch [8][4900/5005]\tlr: 1.000e-01, eta: 11:20:07, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.5849\n2022-12-31 15:20:58,832 - mmcls - INFO - Epoch [8][5000/5005]\tlr: 1.000e-01, eta: 11:19:57, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.5951\n2022-12-31 15:21:21,664 - mmcls - INFO - Epoch(val) [8][196]\taccuracy_top-1: 47.0460, accuracy_top-5: 73.6240\n2022-12-31 15:21:32,664 - mmcls - INFO - Epoch [9][100/5005]\tlr: 1.000e-01, eta: 11:20:07, time: 0.110, data_time: 0.069, memory: 1224, loss: 2.5470\n2022-12-31 15:21:41,336 - mmcls - INFO - Epoch [9][200/5005]\tlr: 1.000e-01, eta: 11:19:55, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5385\n2022-12-31 15:21:50,290 - mmcls - INFO - Epoch [9][300/5005]\tlr: 1.000e-01, eta: 11:19:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5646\n2022-12-31 15:21:59,242 - mmcls - INFO - Epoch [9][400/5005]\tlr: 1.000e-01, eta: 11:19:40, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.5224\n2022-12-31 15:22:07,846 - mmcls - INFO - Epoch [9][500/5005]\tlr: 1.000e-01, eta: 11:19:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5394\n2022-12-31 15:22:16,576 - mmcls - INFO - Epoch [9][600/5005]\tlr: 1.000e-01, eta: 11:19:18, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5607\n2022-12-31 15:22:25,330 - mmcls - INFO - Epoch [9][700/5005]\tlr: 1.000e-01, eta: 11:19:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.5482\n2022-12-31 15:22:34,246 - mmcls - INFO - Epoch [9][800/5005]\tlr: 1.000e-01, eta: 11:18:59, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.5662\n2022-12-31 15:22:43,081 - mmcls - INFO - Epoch [9][900/5005]\tlr: 1.000e-01, eta: 11:18:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5844\n2022-12-31 15:22:51,893 - mmcls - INFO - Epoch [9][1000/5005]\tlr: 1.000e-01, eta: 11:18:40, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.5427\n2022-12-31 15:23:00,583 - mmcls - INFO - Epoch [9][1100/5005]\tlr: 1.000e-01, eta: 11:18:30, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.5482\n2022-12-31 15:23:09,238 - mmcls - INFO - Epoch [9][1200/5005]\tlr: 1.000e-01, eta: 11:18:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.5804\n2022-12-31 15:23:17,901 - mmcls - INFO - Epoch [9][1300/5005]\tlr: 1.000e-01, eta: 11:18:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.5432\n2022-12-31 15:23:26,748 - mmcls - INFO - Epoch [9][1400/5005]\tlr: 1.000e-01, eta: 11:17:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5675\n2022-12-31 15:23:35,676 - mmcls - INFO - Epoch [9][1500/5005]\tlr: 1.000e-01, eta: 11:17:50, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.5535\n2022-12-31 15:23:44,702 - mmcls - INFO - Epoch [9][1600/5005]\tlr: 1.000e-01, eta: 11:17:43, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.5630\n2022-12-31 15:23:53,905 - mmcls - INFO - Epoch [9][1700/5005]\tlr: 1.000e-01, eta: 11:17:38, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5722\n2022-12-31 15:24:02,539 - mmcls - INFO - Epoch [9][1800/5005]\tlr: 1.000e-01, eta: 11:17:27, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.5382\n2022-12-31 15:24:11,466 - mmcls - INFO - Epoch [9][1900/5005]\tlr: 1.000e-01, eta: 11:17:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5764\n2022-12-31 15:24:20,359 - mmcls - INFO - Epoch [9][2000/5005]\tlr: 1.000e-01, eta: 11:17:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5691\n2022-12-31 15:24:29,692 - mmcls - INFO - Epoch [9][2100/5005]\tlr: 1.000e-01, eta: 11:17:06, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.5539\n2022-12-31 15:24:38,446 - mmcls - INFO - Epoch [9][2200/5005]\tlr: 1.000e-01, eta: 11:16:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5631\n2022-12-31 15:24:47,273 - mmcls - INFO - Epoch [9][2300/5005]\tlr: 1.000e-01, eta: 11:16:47, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5956\n2022-12-31 15:24:56,202 - mmcls - INFO - Epoch [9][2400/5005]\tlr: 1.000e-01, eta: 11:16:39, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5459\n2022-12-31 15:25:04,881 - mmcls - INFO - Epoch [9][2500/5005]\tlr: 1.000e-01, eta: 11:16:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5778\n2022-12-31 15:25:13,756 - mmcls - INFO - Epoch [9][2600/5005]\tlr: 1.000e-01, eta: 11:16:19, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5624\n2022-12-31 15:25:22,634 - mmcls - INFO - Epoch [9][2700/5005]\tlr: 1.000e-01, eta: 11:16:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5659\n2022-12-31 15:25:31,172 - mmcls - INFO - Epoch [9][2800/5005]\tlr: 1.000e-01, eta: 11:15:58, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.5674\n2022-12-31 15:25:39,702 - mmcls - INFO - Epoch [9][2900/5005]\tlr: 1.000e-01, eta: 11:15:46, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5895\n2022-12-31 15:25:48,304 - mmcls - INFO - Epoch [9][3000/5005]\tlr: 1.000e-01, eta: 11:15:34, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.5504\n2022-12-31 15:25:57,198 - mmcls - INFO - Epoch [9][3100/5005]\tlr: 1.000e-01, eta: 11:15:26, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.5824\n2022-12-31 15:26:06,288 - mmcls - INFO - Epoch [9][3200/5005]\tlr: 1.000e-01, eta: 11:15:19, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.5560\n2022-12-31 15:26:15,103 - mmcls - INFO - Epoch [9][3300/5005]\tlr: 1.000e-01, eta: 11:15:10, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.5586\n2022-12-31 15:26:23,676 - mmcls - INFO - Epoch [9][3400/5005]\tlr: 1.000e-01, eta: 11:14:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5429\n2022-12-31 15:26:32,276 - mmcls - INFO - Epoch [9][3500/5005]\tlr: 1.000e-01, eta: 11:14:46, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5699\n2022-12-31 15:26:40,847 - mmcls - INFO - Epoch [9][3600/5005]\tlr: 1.000e-01, eta: 11:14:34, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.5654\n2022-12-31 15:26:49,897 - mmcls - INFO - Epoch [9][3700/5005]\tlr: 1.000e-01, eta: 11:14:27, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.5844\n2022-12-31 15:26:58,832 - mmcls - INFO - Epoch [9][3800/5005]\tlr: 1.000e-01, eta: 11:14:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5626\n2022-12-31 15:27:07,721 - mmcls - INFO - Epoch [9][3900/5005]\tlr: 1.000e-01, eta: 11:14:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5798\n2022-12-31 15:27:16,339 - mmcls - INFO - Epoch [9][4000/5005]\tlr: 1.000e-01, eta: 11:13:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5674\n2022-12-31 15:27:25,036 - mmcls - INFO - Epoch [9][4100/5005]\tlr: 1.000e-01, eta: 11:13:49, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5567\n2022-12-31 15:27:33,674 - mmcls - INFO - Epoch [9][4200/5005]\tlr: 1.000e-01, eta: 11:13:38, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5567\n2022-12-31 15:27:42,516 - mmcls - INFO - Epoch [9][4300/5005]\tlr: 1.000e-01, eta: 11:13:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5704\n2022-12-31 15:27:51,695 - mmcls - INFO - Epoch [9][4400/5005]\tlr: 1.000e-01, eta: 11:13:23, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.5449\n2022-12-31 15:28:00,734 - mmcls - INFO - Epoch [9][4500/5005]\tlr: 1.000e-01, eta: 11:13:16, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5831\n2022-12-31 15:28:09,252 - mmcls - INFO - Epoch [9][4600/5005]\tlr: 1.000e-01, eta: 11:13:04, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5550\n2022-12-31 15:28:18,098 - mmcls - INFO - Epoch [9][4700/5005]\tlr: 1.000e-01, eta: 11:12:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.5601\n2022-12-31 15:28:26,761 - mmcls - INFO - Epoch [9][4800/5005]\tlr: 1.000e-01, eta: 11:12:44, time: 0.087, data_time: 0.030, memory: 1224, loss: 2.5357\n2022-12-31 15:28:35,642 - mmcls - INFO - Epoch [9][4900/5005]\tlr: 1.000e-01, eta: 11:12:35, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5600\n2022-12-31 15:28:44,277 - mmcls - INFO - Epoch [9][5000/5005]\tlr: 1.000e-01, eta: 11:12:24, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5707\n2022-12-31 15:29:06,928 - mmcls - INFO - Epoch(val) [9][196]\taccuracy_top-1: 47.8140, accuracy_top-5: 73.7100\n2022-12-31 15:29:17,829 - mmcls - INFO - Epoch [10][100/5005]\tlr: 1.000e-01, eta: 11:12:31, time: 0.109, data_time: 0.047, memory: 1224, loss: 2.5026\n2022-12-31 15:29:26,541 - mmcls - INFO - Epoch [10][200/5005]\tlr: 1.000e-01, eta: 11:12:20, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4951\n2022-12-31 15:29:35,280 - mmcls - INFO - Epoch [10][300/5005]\tlr: 1.000e-01, eta: 11:12:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5498\n2022-12-31 15:29:43,904 - mmcls - INFO - Epoch [10][400/5005]\tlr: 1.000e-01, eta: 11:11:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5160\n2022-12-31 15:29:52,702 - mmcls - INFO - Epoch [10][500/5005]\tlr: 1.000e-01, eta: 11:11:50, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.4854\n2022-12-31 15:30:01,435 - mmcls - INFO - Epoch [10][600/5005]\tlr: 1.000e-01, eta: 11:11:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5491\n2022-12-31 15:30:10,134 - mmcls - INFO - Epoch [10][700/5005]\tlr: 1.000e-01, eta: 11:11:29, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5246\n2022-12-31 15:30:18,781 - mmcls - INFO - Epoch [10][800/5005]\tlr: 1.000e-01, eta: 11:11:18, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.5131\n2022-12-31 15:30:27,621 - mmcls - INFO - Epoch [10][900/5005]\tlr: 1.000e-01, eta: 11:11:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5135\n2022-12-31 15:30:37,177 - mmcls - INFO - Epoch [10][1000/5005]\tlr: 1.000e-01, eta: 11:11:07, time: 0.096, data_time: 0.000, memory: 1224, loss: 2.5489\n2022-12-31 15:30:46,178 - mmcls - INFO - Epoch [10][1100/5005]\tlr: 1.000e-01, eta: 11:10:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5020\n2022-12-31 15:30:54,790 - mmcls - INFO - Epoch [10][1200/5005]\tlr: 1.000e-01, eta: 11:10:48, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.5232\n2022-12-31 15:31:03,487 - mmcls - INFO - Epoch [10][1300/5005]\tlr: 1.000e-01, eta: 11:10:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5584\n2022-12-31 15:31:12,196 - mmcls - INFO - Epoch [10][1400/5005]\tlr: 1.000e-01, eta: 11:10:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5249\n2022-12-31 15:31:20,860 - mmcls - INFO - Epoch [10][1500/5005]\tlr: 1.000e-01, eta: 11:10:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5573\n2022-12-31 15:31:29,509 - mmcls - INFO - Epoch [10][1600/5005]\tlr: 1.000e-01, eta: 11:10:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5528\n2022-12-31 15:31:38,222 - mmcls - INFO - Epoch [10][1700/5005]\tlr: 1.000e-01, eta: 11:09:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5601\n2022-12-31 15:31:46,704 - mmcls - INFO - Epoch [10][1800/5005]\tlr: 1.000e-01, eta: 11:09:43, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.5543\n2022-12-31 15:31:55,521 - mmcls - INFO - Epoch [10][1900/5005]\tlr: 1.000e-01, eta: 11:09:33, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.5153\n2022-12-31 15:32:04,081 - mmcls - INFO - Epoch [10][2000/5005]\tlr: 1.000e-01, eta: 11:09:22, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.5488\n2022-12-31 15:32:12,892 - mmcls - INFO - Epoch [10][2100/5005]\tlr: 1.000e-01, eta: 11:09:12, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5359\n2022-12-31 15:32:21,828 - mmcls - INFO - Epoch [10][2200/5005]\tlr: 1.000e-01, eta: 11:09:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.5381\n2022-12-31 15:32:30,786 - mmcls - INFO - Epoch [10][2300/5005]\tlr: 1.000e-01, eta: 11:08:56, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5366\n2022-12-31 15:32:39,538 - mmcls - INFO - Epoch [10][2400/5005]\tlr: 1.000e-01, eta: 11:08:47, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5205\n2022-12-31 15:32:48,171 - mmcls - INFO - Epoch [10][2500/5005]\tlr: 1.000e-01, eta: 11:08:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5544\n2022-12-31 15:32:56,844 - mmcls - INFO - Epoch [10][2600/5005]\tlr: 1.000e-01, eta: 11:08:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5453\n2022-12-31 15:33:05,989 - mmcls - INFO - Epoch [10][2700/5005]\tlr: 1.000e-01, eta: 11:08:19, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.5416\n2022-12-31 15:33:14,811 - mmcls - INFO - Epoch [10][2800/5005]\tlr: 1.000e-01, eta: 11:08:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5323\n2022-12-31 15:33:23,394 - mmcls - INFO - Epoch [10][2900/5005]\tlr: 1.000e-01, eta: 11:07:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5571\n2022-12-31 15:33:32,036 - mmcls - INFO - Epoch [10][3000/5005]\tlr: 1.000e-01, eta: 11:07:47, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.5412\n2022-12-31 15:33:41,145 - mmcls - INFO - Epoch [10][3100/5005]\tlr: 1.000e-01, eta: 11:07:41, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.5362\n2022-12-31 15:33:49,941 - mmcls - INFO - Epoch [10][3200/5005]\tlr: 1.000e-01, eta: 11:07:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5302\n2022-12-31 15:33:58,775 - mmcls - INFO - Epoch [10][3300/5005]\tlr: 1.000e-01, eta: 11:07:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5480\n2022-12-31 15:34:07,628 - mmcls - INFO - Epoch [10][3400/5005]\tlr: 1.000e-01, eta: 11:07:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5142\n2022-12-31 15:34:16,485 - mmcls - INFO - Epoch [10][3500/5005]\tlr: 1.000e-01, eta: 11:07:05, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.5379\n2022-12-31 15:34:25,012 - mmcls - INFO - Epoch [10][3600/5005]\tlr: 1.000e-01, eta: 11:06:53, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.5709\n2022-12-31 15:34:34,200 - mmcls - INFO - Epoch [10][3700/5005]\tlr: 1.000e-01, eta: 11:06:47, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.5636\n2022-12-31 15:34:43,217 - mmcls - INFO - Epoch [10][3800/5005]\tlr: 1.000e-01, eta: 11:06:39, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5276\n2022-12-31 15:34:52,019 - mmcls - INFO - Epoch [10][3900/5005]\tlr: 1.000e-01, eta: 11:06:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.5123\n2022-12-31 15:35:01,073 - mmcls - INFO - Epoch [10][4000/5005]\tlr: 1.000e-01, eta: 11:06:23, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.5466\n2022-12-31 15:35:10,126 - mmcls - INFO - Epoch [10][4100/5005]\tlr: 1.000e-01, eta: 11:06:16, time: 0.090, data_time: 0.000, memory: 1224, loss: 2.5657\n2022-12-31 15:35:19,016 - mmcls - INFO - Epoch [10][4200/5005]\tlr: 1.000e-01, eta: 11:06:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5342\n2022-12-31 15:35:27,751 - mmcls - INFO - Epoch [10][4300/5005]\tlr: 1.000e-01, eta: 11:05:57, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.5336\n2022-12-31 15:35:36,447 - mmcls - INFO - Epoch [10][4400/5005]\tlr: 1.000e-01, eta: 11:05:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5541\n2022-12-31 15:35:45,489 - mmcls - INFO - Epoch [10][4500/5005]\tlr: 1.000e-01, eta: 11:05:40, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.5669\n2022-12-31 15:35:54,141 - mmcls - INFO - Epoch [10][4600/5005]\tlr: 1.000e-01, eta: 11:05:29, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5311\n2022-12-31 15:36:03,078 - mmcls - INFO - Epoch [10][4700/5005]\tlr: 1.000e-01, eta: 11:05:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5630\n2022-12-31 15:36:12,265 - mmcls - INFO - Epoch [10][4800/5005]\tlr: 1.000e-01, eta: 11:05:15, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5508\n2022-12-31 15:36:21,055 - mmcls - INFO - Epoch [10][4900/5005]\tlr: 1.000e-01, eta: 11:05:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5491\n2022-12-31 15:36:29,952 - mmcls - INFO - Epoch [10][5000/5005]\tlr: 1.000e-01, eta: 11:04:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5393\n2022-12-31 15:36:52,425 - mmcls - INFO - Epoch(val) [10][196]\taccuracy_top-1: 48.2000, accuracy_top-5: 74.2600\n2022-12-31 15:37:03,626 - mmcls - INFO - Epoch [11][100/5005]\tlr: 1.000e-01, eta: 11:05:05, time: 0.112, data_time: 0.044, memory: 1224, loss: 2.4948\n2022-12-31 15:37:12,223 - mmcls - INFO - Epoch [11][200/5005]\tlr: 1.000e-01, eta: 11:04:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4970\n2022-12-31 15:37:21,106 - mmcls - INFO - Epoch [11][300/5005]\tlr: 1.000e-01, eta: 11:04:45, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.4925\n2022-12-31 15:37:29,721 - mmcls - INFO - Epoch [11][400/5005]\tlr: 1.000e-01, eta: 11:04:34, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.5309\n2022-12-31 15:37:38,339 - mmcls - INFO - Epoch [11][500/5005]\tlr: 1.000e-01, eta: 11:04:23, time: 0.086, data_time: 0.030, memory: 1224, loss: 2.5005\n2022-12-31 15:37:47,040 - mmcls - INFO - Epoch [11][600/5005]\tlr: 1.000e-01, eta: 11:04:12, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.5051\n2022-12-31 15:37:56,210 - mmcls - INFO - Epoch [11][700/5005]\tlr: 1.000e-01, eta: 11:04:06, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.4759\n2022-12-31 15:38:05,170 - mmcls - INFO - Epoch [11][800/5005]\tlr: 1.000e-01, eta: 11:03:58, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5091\n2022-12-31 15:38:14,128 - mmcls - INFO - Epoch [11][900/5005]\tlr: 1.000e-01, eta: 11:03:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5044\n2022-12-31 15:38:23,120 - mmcls - INFO - Epoch [11][1000/5005]\tlr: 1.000e-01, eta: 11:03:43, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5219\n2022-12-31 15:38:32,017 - mmcls - INFO - Epoch [11][1100/5005]\tlr: 1.000e-01, eta: 11:03:34, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5270\n2022-12-31 15:38:40,517 - mmcls - INFO - Epoch [11][1200/5005]\tlr: 1.000e-01, eta: 11:03:22, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.5282\n2022-12-31 15:38:49,287 - mmcls - INFO - Epoch [11][1300/5005]\tlr: 1.000e-01, eta: 11:03:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5136\n2022-12-31 15:38:58,197 - mmcls - INFO - Epoch [11][1400/5005]\tlr: 1.000e-01, eta: 11:03:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5117\n2022-12-31 15:39:07,076 - mmcls - INFO - Epoch [11][1500/5005]\tlr: 1.000e-01, eta: 11:02:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4965\n2022-12-31 15:39:15,694 - mmcls - INFO - Epoch [11][1600/5005]\tlr: 1.000e-01, eta: 11:02:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5614\n2022-12-31 15:39:24,629 - mmcls - INFO - Epoch [11][1700/5005]\tlr: 1.000e-01, eta: 11:02:36, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5015\n2022-12-31 15:39:33,671 - mmcls - INFO - Epoch [11][1800/5005]\tlr: 1.000e-01, eta: 11:02:29, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4924\n2022-12-31 15:39:42,444 - mmcls - INFO - Epoch [11][1900/5005]\tlr: 1.000e-01, eta: 11:02:19, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5291\n2022-12-31 15:39:51,079 - mmcls - INFO - Epoch [11][2000/5005]\tlr: 1.000e-01, eta: 11:02:08, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.5520\n2022-12-31 15:39:59,929 - mmcls - INFO - Epoch [11][2100/5005]\tlr: 1.000e-01, eta: 11:01:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5200\n2022-12-31 15:40:09,046 - mmcls - INFO - Epoch [11][2200/5005]\tlr: 1.000e-01, eta: 11:01:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5247\n2022-12-31 15:40:17,987 - mmcls - INFO - Epoch [11][2300/5005]\tlr: 1.000e-01, eta: 11:01:45, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5151\n2022-12-31 15:40:26,669 - mmcls - INFO - Epoch [11][2400/5005]\tlr: 1.000e-01, eta: 11:01:34, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.5131\n2022-12-31 15:40:35,484 - mmcls - INFO - Epoch [11][2500/5005]\tlr: 1.000e-01, eta: 11:01:25, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5215\n2022-12-31 15:40:44,466 - mmcls - INFO - Epoch [11][2600/5005]\tlr: 1.000e-01, eta: 11:01:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.5283\n2022-12-31 15:40:53,050 - mmcls - INFO - Epoch [11][2700/5005]\tlr: 1.000e-01, eta: 11:01:06, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.5431\n2022-12-31 15:41:02,239 - mmcls - INFO - Epoch [11][2800/5005]\tlr: 1.000e-01, eta: 11:01:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5155\n2022-12-31 15:41:10,868 - mmcls - INFO - Epoch [11][2900/5005]\tlr: 1.000e-01, eta: 11:00:49, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5317\n2022-12-31 15:41:19,459 - mmcls - INFO - Epoch [11][3000/5005]\tlr: 1.000e-01, eta: 11:00:38, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.5417\n2022-12-31 15:41:28,265 - mmcls - INFO - Epoch [11][3100/5005]\tlr: 1.000e-01, eta: 11:00:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5413\n2022-12-31 15:41:37,017 - mmcls - INFO - Epoch [11][3200/5005]\tlr: 1.000e-01, eta: 11:00:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.5251\n2022-12-31 15:41:45,823 - mmcls - INFO - Epoch [11][3300/5005]\tlr: 1.000e-01, eta: 11:00:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5115\n2022-12-31 15:41:54,717 - mmcls - INFO - Epoch [11][3400/5005]\tlr: 1.000e-01, eta: 11:00:01, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5483\n2022-12-31 15:42:03,809 - mmcls - INFO - Epoch [11][3500/5005]\tlr: 1.000e-01, eta: 10:59:54, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4943\n2022-12-31 15:42:12,504 - mmcls - INFO - Epoch [11][3600/5005]\tlr: 1.000e-01, eta: 10:59:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5004\n2022-12-31 15:42:21,115 - mmcls - INFO - Epoch [11][3700/5005]\tlr: 1.000e-01, eta: 10:59:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.5196\n2022-12-31 15:42:29,930 - mmcls - INFO - Epoch [11][3800/5005]\tlr: 1.000e-01, eta: 10:59:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5044\n2022-12-31 15:42:39,188 - mmcls - INFO - Epoch [11][3900/5005]\tlr: 1.000e-01, eta: 10:59:18, time: 0.093, data_time: 0.024, memory: 1224, loss: 2.5173\n2022-12-31 15:42:48,217 - mmcls - INFO - Epoch [11][4000/5005]\tlr: 1.000e-01, eta: 10:59:11, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5321\n2022-12-31 15:42:57,074 - mmcls - INFO - Epoch [11][4100/5005]\tlr: 1.000e-01, eta: 10:59:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.5051\n2022-12-31 15:43:05,725 - mmcls - INFO - Epoch [11][4200/5005]\tlr: 1.000e-01, eta: 10:58:51, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.5089\n2022-12-31 15:43:14,285 - mmcls - INFO - Epoch [11][4300/5005]\tlr: 1.000e-01, eta: 10:58:40, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5199\n2022-12-31 15:43:23,154 - mmcls - INFO - Epoch [11][4400/5005]\tlr: 1.000e-01, eta: 10:58:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5150\n2022-12-31 15:43:31,984 - mmcls - INFO - Epoch [11][4500/5005]\tlr: 1.000e-01, eta: 10:58:22, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5278\n2022-12-31 15:43:40,846 - mmcls - INFO - Epoch [11][4600/5005]\tlr: 1.000e-01, eta: 10:58:13, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.5317\n2022-12-31 15:43:49,739 - mmcls - INFO - Epoch [11][4700/5005]\tlr: 1.000e-01, eta: 10:58:05, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.5059\n2022-12-31 15:43:58,323 - mmcls - INFO - Epoch [11][4800/5005]\tlr: 1.000e-01, eta: 10:57:53, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.5094\n2022-12-31 15:44:07,065 - mmcls - INFO - Epoch [11][4900/5005]\tlr: 1.000e-01, eta: 10:57:44, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.5236\n2022-12-31 15:44:15,691 - mmcls - INFO - Epoch [11][5000/5005]\tlr: 1.000e-01, eta: 10:57:33, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.5408\n2022-12-31 15:44:38,248 - mmcls - INFO - Epoch(val) [11][196]\taccuracy_top-1: 50.1380, accuracy_top-5: 75.9240\n2022-12-31 15:44:49,440 - mmcls - INFO - Epoch [12][100/5005]\tlr: 1.000e-01, eta: 10:57:39, time: 0.112, data_time: 0.051, memory: 1224, loss: 2.4657\n2022-12-31 15:44:57,953 - mmcls - INFO - Epoch [12][200/5005]\tlr: 1.000e-01, eta: 10:57:27, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.4795\n2022-12-31 15:45:06,679 - mmcls - INFO - Epoch [12][300/5005]\tlr: 1.000e-01, eta: 10:57:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4873\n2022-12-31 15:45:15,725 - mmcls - INFO - Epoch [12][400/5005]\tlr: 1.000e-01, eta: 10:57:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5003\n2022-12-31 15:45:24,555 - mmcls - INFO - Epoch [12][500/5005]\tlr: 1.000e-01, eta: 10:57:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.4837\n2022-12-31 15:45:33,417 - mmcls - INFO - Epoch [12][600/5005]\tlr: 1.000e-01, eta: 10:56:52, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.4970\n2022-12-31 15:45:42,369 - mmcls - INFO - Epoch [12][700/5005]\tlr: 1.000e-01, eta: 10:56:44, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4768\n2022-12-31 15:45:51,082 - mmcls - INFO - Epoch [12][800/5005]\tlr: 1.000e-01, eta: 10:56:34, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4884\n2022-12-31 15:45:59,850 - mmcls - INFO - Epoch [12][900/5005]\tlr: 1.000e-01, eta: 10:56:24, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4964\n2022-12-31 15:46:08,794 - mmcls - INFO - Epoch [12][1000/5005]\tlr: 1.000e-01, eta: 10:56:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4934\n2022-12-31 15:46:17,643 - mmcls - INFO - Epoch [12][1100/5005]\tlr: 1.000e-01, eta: 10:56:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5017\n2022-12-31 15:46:26,810 - mmcls - INFO - Epoch [12][1200/5005]\tlr: 1.000e-01, eta: 10:56:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4697\n2022-12-31 15:46:35,763 - mmcls - INFO - Epoch [12][1300/5005]\tlr: 1.000e-01, eta: 10:55:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5131\n2022-12-31 15:46:44,465 - mmcls - INFO - Epoch [12][1400/5005]\tlr: 1.000e-01, eta: 10:55:42, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4963\n2022-12-31 15:46:53,592 - mmcls - INFO - Epoch [12][1500/5005]\tlr: 1.000e-01, eta: 10:55:35, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4795\n2022-12-31 15:47:02,411 - mmcls - INFO - Epoch [12][1600/5005]\tlr: 1.000e-01, eta: 10:55:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5083\n2022-12-31 15:47:11,150 - mmcls - INFO - Epoch [12][1700/5005]\tlr: 1.000e-01, eta: 10:55:16, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.5149\n2022-12-31 15:47:19,861 - mmcls - INFO - Epoch [12][1800/5005]\tlr: 1.000e-01, eta: 10:55:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4863\n2022-12-31 15:47:28,674 - mmcls - INFO - Epoch [12][1900/5005]\tlr: 1.000e-01, eta: 10:54:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5122\n2022-12-31 15:47:37,522 - mmcls - INFO - Epoch [12][2000/5005]\tlr: 1.000e-01, eta: 10:54:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5021\n2022-12-31 15:47:46,143 - mmcls - INFO - Epoch [12][2100/5005]\tlr: 1.000e-01, eta: 10:54:37, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4939\n2022-12-31 15:47:54,621 - mmcls - INFO - Epoch [12][2200/5005]\tlr: 1.000e-01, eta: 10:54:26, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5144\n2022-12-31 15:48:03,155 - mmcls - INFO - Epoch [12][2300/5005]\tlr: 1.000e-01, eta: 10:54:14, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.5395\n2022-12-31 15:48:11,749 - mmcls - INFO - Epoch [12][2400/5005]\tlr: 1.000e-01, eta: 10:54:03, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5182\n2022-12-31 15:48:20,552 - mmcls - INFO - Epoch [12][2500/5005]\tlr: 1.000e-01, eta: 10:53:54, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5240\n2022-12-31 15:48:29,389 - mmcls - INFO - Epoch [12][2600/5005]\tlr: 1.000e-01, eta: 10:53:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.5163\n2022-12-31 15:48:38,213 - mmcls - INFO - Epoch [12][2700/5005]\tlr: 1.000e-01, eta: 10:53:36, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5058\n2022-12-31 15:48:46,825 - mmcls - INFO - Epoch [12][2800/5005]\tlr: 1.000e-01, eta: 10:53:25, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5150\n2022-12-31 15:48:55,660 - mmcls - INFO - Epoch [12][2900/5005]\tlr: 1.000e-01, eta: 10:53:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5183\n2022-12-31 15:49:04,590 - mmcls - INFO - Epoch [12][3000/5005]\tlr: 1.000e-01, eta: 10:53:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4974\n2022-12-31 15:49:13,474 - mmcls - INFO - Epoch [12][3100/5005]\tlr: 1.000e-01, eta: 10:52:59, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.4805\n2022-12-31 15:49:22,368 - mmcls - INFO - Epoch [12][3200/5005]\tlr: 1.000e-01, eta: 10:52:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4870\n2022-12-31 15:49:31,013 - mmcls - INFO - Epoch [12][3300/5005]\tlr: 1.000e-01, eta: 10:52:40, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.4991\n2022-12-31 15:49:40,000 - mmcls - INFO - Epoch [12][3400/5005]\tlr: 1.000e-01, eta: 10:52:32, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.4933\n2022-12-31 15:49:48,976 - mmcls - INFO - Epoch [12][3500/5005]\tlr: 1.000e-01, eta: 10:52:24, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.5144\n2022-12-31 15:49:57,831 - mmcls - INFO - Epoch [12][3600/5005]\tlr: 1.000e-01, eta: 10:52:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5007\n2022-12-31 15:50:06,417 - mmcls - INFO - Epoch [12][3700/5005]\tlr: 1.000e-01, eta: 10:52:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4788\n2022-12-31 15:50:15,135 - mmcls - INFO - Epoch [12][3800/5005]\tlr: 1.000e-01, eta: 10:51:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4907\n2022-12-31 15:50:23,809 - mmcls - INFO - Epoch [12][3900/5005]\tlr: 1.000e-01, eta: 10:51:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4866\n2022-12-31 15:50:32,405 - mmcls - INFO - Epoch [12][4000/5005]\tlr: 1.000e-01, eta: 10:51:34, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4777\n2022-12-31 15:50:41,046 - mmcls - INFO - Epoch [12][4100/5005]\tlr: 1.000e-01, eta: 10:51:23, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5136\n2022-12-31 15:50:49,945 - mmcls - INFO - Epoch [12][4200/5005]\tlr: 1.000e-01, eta: 10:51:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5082\n2022-12-31 15:50:58,640 - mmcls - INFO - Epoch [12][4300/5005]\tlr: 1.000e-01, eta: 10:51:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.5092\n2022-12-31 15:51:07,359 - mmcls - INFO - Epoch [12][4400/5005]\tlr: 1.000e-01, eta: 10:50:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5194\n2022-12-31 15:51:16,234 - mmcls - INFO - Epoch [12][4500/5005]\tlr: 1.000e-01, eta: 10:50:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4935\n2022-12-31 15:51:24,716 - mmcls - INFO - Epoch [12][4600/5005]\tlr: 1.000e-01, eta: 10:50:34, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.5198\n2022-12-31 15:51:33,290 - mmcls - INFO - Epoch [12][4700/5005]\tlr: 1.000e-01, eta: 10:50:23, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.5282\n2022-12-31 15:51:42,331 - mmcls - INFO - Epoch [12][4800/5005]\tlr: 1.000e-01, eta: 10:50:16, time: 0.090, data_time: 0.024, memory: 1224, loss: 2.5059\n2022-12-31 15:51:51,471 - mmcls - INFO - Epoch [12][4900/5005]\tlr: 1.000e-01, eta: 10:50:09, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5229\n2022-12-31 15:52:00,101 - mmcls - INFO - Epoch [12][5000/5005]\tlr: 1.000e-01, eta: 10:49:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5254\n2022-12-31 15:52:22,758 - mmcls - INFO - Epoch(val) [12][196]\taccuracy_top-1: 50.3200, accuracy_top-5: 75.9940\n2022-12-31 15:52:34,201 - mmcls - INFO - Epoch [13][100/5005]\tlr: 1.000e-01, eta: 10:50:05, time: 0.114, data_time: 0.047, memory: 1224, loss: 2.4527\n2022-12-31 15:52:42,848 - mmcls - INFO - Epoch [13][200/5005]\tlr: 1.000e-01, eta: 10:49:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4649\n2022-12-31 15:52:51,538 - mmcls - INFO - Epoch [13][300/5005]\tlr: 1.000e-01, eta: 10:49:44, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.4735\n2022-12-31 15:53:01,265 - mmcls - INFO - Epoch [13][400/5005]\tlr: 1.000e-01, eta: 10:49:42, time: 0.097, data_time: 0.001, memory: 1224, loss: 2.4369\n2022-12-31 15:53:10,070 - mmcls - INFO - Epoch [13][500/5005]\tlr: 1.000e-01, eta: 10:49:33, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4861\n2022-12-31 15:53:18,741 - mmcls - INFO - Epoch [13][600/5005]\tlr: 1.000e-01, eta: 10:49:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4640\n2022-12-31 15:53:27,585 - mmcls - INFO - Epoch [13][700/5005]\tlr: 1.000e-01, eta: 10:49:13, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4801\n2022-12-31 15:53:36,581 - mmcls - INFO - Epoch [13][800/5005]\tlr: 1.000e-01, eta: 10:49:05, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.4527\n2022-12-31 15:53:45,295 - mmcls - INFO - Epoch [13][900/5005]\tlr: 1.000e-01, eta: 10:48:56, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4586\n2022-12-31 15:53:53,815 - mmcls - INFO - Epoch [13][1000/5005]\tlr: 1.000e-01, eta: 10:48:44, time: 0.085, data_time: 0.017, memory: 1224, loss: 2.4892\n2022-12-31 15:54:02,420 - mmcls - INFO - Epoch [13][1100/5005]\tlr: 1.000e-01, eta: 10:48:34, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4938\n2022-12-31 15:54:11,308 - mmcls - INFO - Epoch [13][1200/5005]\tlr: 1.000e-01, eta: 10:48:25, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.5020\n2022-12-31 15:54:20,215 - mmcls - INFO - Epoch [13][1300/5005]\tlr: 1.000e-01, eta: 10:48:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4744\n2022-12-31 15:54:28,995 - mmcls - INFO - Epoch [13][1400/5005]\tlr: 1.000e-01, eta: 10:48:07, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4757\n2022-12-31 15:54:37,861 - mmcls - INFO - Epoch [13][1500/5005]\tlr: 1.000e-01, eta: 10:47:58, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4755\n2022-12-31 15:54:46,578 - mmcls - INFO - Epoch [13][1600/5005]\tlr: 1.000e-01, eta: 10:47:48, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4910\n2022-12-31 15:54:55,498 - mmcls - INFO - Epoch [13][1700/5005]\tlr: 1.000e-01, eta: 10:47:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4832\n2022-12-31 15:55:04,451 - mmcls - INFO - Epoch [13][1800/5005]\tlr: 1.000e-01, eta: 10:47:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5114\n2022-12-31 15:55:13,271 - mmcls - INFO - Epoch [13][1900/5005]\tlr: 1.000e-01, eta: 10:47:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4888\n2022-12-31 15:55:22,028 - mmcls - INFO - Epoch [13][2000/5005]\tlr: 1.000e-01, eta: 10:47:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4926\n2022-12-31 15:55:30,704 - mmcls - INFO - Epoch [13][2100/5005]\tlr: 1.000e-01, eta: 10:47:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5128\n2022-12-31 15:55:39,596 - mmcls - INFO - Epoch [13][2200/5005]\tlr: 1.000e-01, eta: 10:46:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4945\n2022-12-31 15:55:48,124 - mmcls - INFO - Epoch [13][2300/5005]\tlr: 1.000e-01, eta: 10:46:43, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.5005\n2022-12-31 15:55:56,745 - mmcls - INFO - Epoch [13][2400/5005]\tlr: 1.000e-01, eta: 10:46:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4693\n2022-12-31 15:56:05,821 - mmcls - INFO - Epoch [13][2500/5005]\tlr: 1.000e-01, eta: 10:46:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4917\n2022-12-31 15:56:14,652 - mmcls - INFO - Epoch [13][2600/5005]\tlr: 1.000e-01, eta: 10:46:16, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4883\n2022-12-31 15:56:23,653 - mmcls - INFO - Epoch [13][2700/5005]\tlr: 1.000e-01, eta: 10:46:08, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.4773\n2022-12-31 15:56:32,483 - mmcls - INFO - Epoch [13][2800/5005]\tlr: 1.000e-01, eta: 10:45:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.4738\n2022-12-31 15:56:41,226 - mmcls - INFO - Epoch [13][2900/5005]\tlr: 1.000e-01, eta: 10:45:50, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5010\n2022-12-31 15:56:50,174 - mmcls - INFO - Epoch [13][3000/5005]\tlr: 1.000e-01, eta: 10:45:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5079\n2022-12-31 15:56:58,687 - mmcls - INFO - Epoch [13][3100/5005]\tlr: 1.000e-01, eta: 10:45:30, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.5016\n2022-12-31 15:57:07,273 - mmcls - INFO - Epoch [13][3200/5005]\tlr: 1.000e-01, eta: 10:45:19, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4800\n2022-12-31 15:57:16,334 - mmcls - INFO - Epoch [13][3300/5005]\tlr: 1.000e-01, eta: 10:45:12, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.4774\n2022-12-31 15:57:25,032 - mmcls - INFO - Epoch [13][3400/5005]\tlr: 1.000e-01, eta: 10:45:02, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.4703\n2022-12-31 15:57:33,773 - mmcls - INFO - Epoch [13][3500/5005]\tlr: 1.000e-01, eta: 10:44:53, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4943\n2022-12-31 15:57:42,713 - mmcls - INFO - Epoch [13][3600/5005]\tlr: 1.000e-01, eta: 10:44:44, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.5187\n2022-12-31 15:57:51,335 - mmcls - INFO - Epoch [13][3700/5005]\tlr: 1.000e-01, eta: 10:44:34, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.4818\n2022-12-31 15:58:00,239 - mmcls - INFO - Epoch [13][3800/5005]\tlr: 1.000e-01, eta: 10:44:25, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.4726\n2022-12-31 15:58:09,220 - mmcls - INFO - Epoch [13][3900/5005]\tlr: 1.000e-01, eta: 10:44:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.5007\n2022-12-31 15:58:18,025 - mmcls - INFO - Epoch [13][4000/5005]\tlr: 1.000e-01, eta: 10:44:08, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.5121\n2022-12-31 15:58:26,876 - mmcls - INFO - Epoch [13][4100/5005]\tlr: 1.000e-01, eta: 10:43:59, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.4899\n2022-12-31 15:58:35,821 - mmcls - INFO - Epoch [13][4200/5005]\tlr: 1.000e-01, eta: 10:43:51, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4990\n2022-12-31 15:58:44,405 - mmcls - INFO - Epoch [13][4300/5005]\tlr: 1.000e-01, eta: 10:43:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4998\n2022-12-31 15:58:53,099 - mmcls - INFO - Epoch [13][4400/5005]\tlr: 1.000e-01, eta: 10:43:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4953\n2022-12-31 15:59:02,065 - mmcls - INFO - Epoch [13][4500/5005]\tlr: 1.000e-01, eta: 10:43:22, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4943\n2022-12-31 15:59:11,238 - mmcls - INFO - Epoch [13][4600/5005]\tlr: 1.000e-01, eta: 10:43:15, time: 0.092, data_time: 0.030, memory: 1224, loss: 2.5301\n2022-12-31 15:59:20,089 - mmcls - INFO - Epoch [13][4700/5005]\tlr: 1.000e-01, eta: 10:43:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.5164\n2022-12-31 15:59:29,122 - mmcls - INFO - Epoch [13][4800/5005]\tlr: 1.000e-01, eta: 10:42:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.4985\n2022-12-31 15:59:37,899 - mmcls - INFO - Epoch [13][4900/5005]\tlr: 1.000e-01, eta: 10:42:49, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4638\n2022-12-31 15:59:46,743 - mmcls - INFO - Epoch [13][5000/5005]\tlr: 1.000e-01, eta: 10:42:40, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.5045\n2022-12-31 16:00:09,620 - mmcls - INFO - Epoch(val) [13][196]\taccuracy_top-1: 51.3240, accuracy_top-5: 77.0860\n2022-12-31 16:00:20,547 - mmcls - INFO - Epoch [14][100/5005]\tlr: 1.000e-01, eta: 10:42:42, time: 0.109, data_time: 0.033, memory: 1224, loss: 2.4495\n2022-12-31 16:00:29,188 - mmcls - INFO - Epoch [14][200/5005]\tlr: 1.000e-01, eta: 10:42:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4230\n2022-12-31 16:00:37,766 - mmcls - INFO - Epoch [14][300/5005]\tlr: 1.000e-01, eta: 10:42:21, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4356\n2022-12-31 16:00:46,336 - mmcls - INFO - Epoch [14][400/5005]\tlr: 1.000e-01, eta: 10:42:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4451\n2022-12-31 16:00:55,070 - mmcls - INFO - Epoch [14][500/5005]\tlr: 1.000e-01, eta: 10:42:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4302\n2022-12-31 16:01:03,678 - mmcls - INFO - Epoch [14][600/5005]\tlr: 1.000e-01, eta: 10:41:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4537\n2022-12-31 16:01:12,149 - mmcls - INFO - Epoch [14][700/5005]\tlr: 1.000e-01, eta: 10:41:38, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4494\n2022-12-31 16:01:20,910 - mmcls - INFO - Epoch [14][800/5005]\tlr: 1.000e-01, eta: 10:41:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4769\n2022-12-31 16:01:29,660 - mmcls - INFO - Epoch [14][900/5005]\tlr: 1.000e-01, eta: 10:41:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4753\n2022-12-31 16:01:38,261 - mmcls - INFO - Epoch [14][1000/5005]\tlr: 1.000e-01, eta: 10:41:09, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4830\n2022-12-31 16:01:47,125 - mmcls - INFO - Epoch [14][1100/5005]\tlr: 1.000e-01, eta: 10:41:00, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4490\n2022-12-31 16:01:56,108 - mmcls - INFO - Epoch [14][1200/5005]\tlr: 1.000e-01, eta: 10:40:52, time: 0.090, data_time: 0.035, memory: 1224, loss: 2.4775\n2022-12-31 16:02:04,778 - mmcls - INFO - Epoch [14][1300/5005]\tlr: 1.000e-01, eta: 10:40:42, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4724\n2022-12-31 16:02:13,343 - mmcls - INFO - Epoch [14][1400/5005]\tlr: 1.000e-01, eta: 10:40:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4791\n2022-12-31 16:02:22,288 - mmcls - INFO - Epoch [14][1500/5005]\tlr: 1.000e-01, eta: 10:40:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4848\n2022-12-31 16:02:30,877 - mmcls - INFO - Epoch [14][1600/5005]\tlr: 1.000e-01, eta: 10:40:12, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4629\n2022-12-31 16:02:39,554 - mmcls - INFO - Epoch [14][1700/5005]\tlr: 1.000e-01, eta: 10:40:02, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4571\n2022-12-31 16:02:48,168 - mmcls - INFO - Epoch [14][1800/5005]\tlr: 1.000e-01, eta: 10:39:52, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4794\n2022-12-31 16:02:56,777 - mmcls - INFO - Epoch [14][1900/5005]\tlr: 1.000e-01, eta: 10:39:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4783\n2022-12-31 16:03:05,563 - mmcls - INFO - Epoch [14][2000/5005]\tlr: 1.000e-01, eta: 10:39:32, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4768\n2022-12-31 16:03:14,397 - mmcls - INFO - Epoch [14][2100/5005]\tlr: 1.000e-01, eta: 10:39:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4768\n2022-12-31 16:03:23,324 - mmcls - INFO - Epoch [14][2200/5005]\tlr: 1.000e-01, eta: 10:39:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4733\n2022-12-31 16:03:31,945 - mmcls - INFO - Epoch [14][2300/5005]\tlr: 1.000e-01, eta: 10:39:04, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4873\n2022-12-31 16:03:41,115 - mmcls - INFO - Epoch [14][2400/5005]\tlr: 1.000e-01, eta: 10:38:58, time: 0.092, data_time: 0.017, memory: 1224, loss: 2.4756\n2022-12-31 16:03:49,873 - mmcls - INFO - Epoch [14][2500/5005]\tlr: 1.000e-01, eta: 10:38:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5116\n2022-12-31 16:03:58,602 - mmcls - INFO - Epoch [14][2600/5005]\tlr: 1.000e-01, eta: 10:38:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4737\n2022-12-31 16:04:07,438 - mmcls - INFO - Epoch [14][2700/5005]\tlr: 1.000e-01, eta: 10:38:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4620\n2022-12-31 16:04:16,376 - mmcls - INFO - Epoch [14][2800/5005]\tlr: 1.000e-01, eta: 10:38:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4822\n2022-12-31 16:04:25,220 - mmcls - INFO - Epoch [14][2900/5005]\tlr: 1.000e-01, eta: 10:38:12, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4850\n2022-12-31 16:04:33,906 - mmcls - INFO - Epoch [14][3000/5005]\tlr: 1.000e-01, eta: 10:38:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4599\n2022-12-31 16:04:42,345 - mmcls - INFO - Epoch [14][3100/5005]\tlr: 1.000e-01, eta: 10:37:51, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.4816\n2022-12-31 16:04:51,193 - mmcls - INFO - Epoch [14][3200/5005]\tlr: 1.000e-01, eta: 10:37:42, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4484\n2022-12-31 16:05:00,116 - mmcls - INFO - Epoch [14][3300/5005]\tlr: 1.000e-01, eta: 10:37:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4535\n2022-12-31 16:05:08,739 - mmcls - INFO - Epoch [14][3400/5005]\tlr: 1.000e-01, eta: 10:37:23, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4599\n2022-12-31 16:05:17,646 - mmcls - INFO - Epoch [14][3500/5005]\tlr: 1.000e-01, eta: 10:37:15, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4854\n2022-12-31 16:05:26,766 - mmcls - INFO - Epoch [14][3600/5005]\tlr: 1.000e-01, eta: 10:37:08, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.4883\n2022-12-31 16:05:35,610 - mmcls - INFO - Epoch [14][3700/5005]\tlr: 1.000e-01, eta: 10:36:59, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4957\n2022-12-31 16:05:44,370 - mmcls - INFO - Epoch [14][3800/5005]\tlr: 1.000e-01, eta: 10:36:49, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.4737\n2022-12-31 16:05:53,143 - mmcls - INFO - Epoch [14][3900/5005]\tlr: 1.000e-01, eta: 10:36:40, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.4808\n2022-12-31 16:06:01,976 - mmcls - INFO - Epoch [14][4000/5005]\tlr: 1.000e-01, eta: 10:36:31, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4637\n2022-12-31 16:06:10,631 - mmcls - INFO - Epoch [14][4100/5005]\tlr: 1.000e-01, eta: 10:36:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4899\n2022-12-31 16:06:19,185 - mmcls - INFO - Epoch [14][4200/5005]\tlr: 1.000e-01, eta: 10:36:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.4630\n2022-12-31 16:06:28,351 - mmcls - INFO - Epoch [14][4300/5005]\tlr: 1.000e-01, eta: 10:36:03, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.5068\n2022-12-31 16:06:37,189 - mmcls - INFO - Epoch [14][4400/5005]\tlr: 1.000e-01, eta: 10:35:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4542\n2022-12-31 16:06:46,059 - mmcls - INFO - Epoch [14][4500/5005]\tlr: 1.000e-01, eta: 10:35:46, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4627\n2022-12-31 16:06:54,811 - mmcls - INFO - Epoch [14][4600/5005]\tlr: 1.000e-01, eta: 10:35:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.5087\n2022-12-31 16:07:03,455 - mmcls - INFO - Epoch [14][4700/5005]\tlr: 1.000e-01, eta: 10:35:26, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.4682\n2022-12-31 16:07:12,175 - mmcls - INFO - Epoch [14][4800/5005]\tlr: 1.000e-01, eta: 10:35:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4871\n2022-12-31 16:07:20,658 - mmcls - INFO - Epoch [14][4900/5005]\tlr: 1.000e-01, eta: 10:35:05, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4842\n2022-12-31 16:07:29,570 - mmcls - INFO - Epoch [14][5000/5005]\tlr: 1.000e-01, eta: 10:34:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4791\n2022-12-31 16:07:52,379 - mmcls - INFO - Epoch(val) [14][196]\taccuracy_top-1: 49.6700, accuracy_top-5: 75.7400\n2022-12-31 16:08:03,845 - mmcls - INFO - Epoch [15][100/5005]\tlr: 1.000e-01, eta: 10:35:01, time: 0.114, data_time: 0.055, memory: 1224, loss: 2.4360\n2022-12-31 16:08:12,697 - mmcls - INFO - Epoch [15][200/5005]\tlr: 1.000e-01, eta: 10:34:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4160\n2022-12-31 16:08:21,286 - mmcls - INFO - Epoch [15][300/5005]\tlr: 1.000e-01, eta: 10:34:41, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4307\n2022-12-31 16:08:30,087 - mmcls - INFO - Epoch [15][400/5005]\tlr: 1.000e-01, eta: 10:34:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4339\n2022-12-31 16:08:38,729 - mmcls - INFO - Epoch [15][500/5005]\tlr: 1.000e-01, eta: 10:34:22, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.4197\n2022-12-31 16:08:47,915 - mmcls - INFO - Epoch [15][600/5005]\tlr: 1.000e-01, eta: 10:34:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4441\n2022-12-31 16:08:56,667 - mmcls - INFO - Epoch [15][700/5005]\tlr: 1.000e-01, eta: 10:34:06, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4648\n2022-12-31 16:09:05,296 - mmcls - INFO - Epoch [15][800/5005]\tlr: 1.000e-01, eta: 10:33:55, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4347\n2022-12-31 16:09:13,930 - mmcls - INFO - Epoch [15][900/5005]\tlr: 1.000e-01, eta: 10:33:45, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4634\n2022-12-31 16:09:22,833 - mmcls - INFO - Epoch [15][1000/5005]\tlr: 1.000e-01, eta: 10:33:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4477\n2022-12-31 16:09:31,711 - mmcls - INFO - Epoch [15][1100/5005]\tlr: 1.000e-01, eta: 10:33:28, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4767\n2022-12-31 16:09:40,635 - mmcls - INFO - Epoch [15][1200/5005]\tlr: 1.000e-01, eta: 10:33:19, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4778\n2022-12-31 16:09:49,522 - mmcls - INFO - Epoch [15][1300/5005]\tlr: 1.000e-01, eta: 10:33:11, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4415\n2022-12-31 16:09:58,181 - mmcls - INFO - Epoch [15][1400/5005]\tlr: 1.000e-01, eta: 10:33:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4740\n2022-12-31 16:10:07,261 - mmcls - INFO - Epoch [15][1500/5005]\tlr: 1.000e-01, eta: 10:32:53, time: 0.091, data_time: 0.008, memory: 1224, loss: 2.4589\n2022-12-31 16:10:16,116 - mmcls - INFO - Epoch [15][1600/5005]\tlr: 1.000e-01, eta: 10:32:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4624\n2022-12-31 16:10:24,794 - mmcls - INFO - Epoch [15][1700/5005]\tlr: 1.000e-01, eta: 10:32:35, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.4836\n2022-12-31 16:10:33,594 - mmcls - INFO - Epoch [15][1800/5005]\tlr: 1.000e-01, eta: 10:32:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4619\n2022-12-31 16:10:42,620 - mmcls - INFO - Epoch [15][1900/5005]\tlr: 1.000e-01, eta: 10:32:18, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4565\n2022-12-31 16:10:51,620 - mmcls - INFO - Epoch [15][2000/5005]\tlr: 1.000e-01, eta: 10:32:10, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4647\n2022-12-31 16:11:00,145 - mmcls - INFO - Epoch [15][2100/5005]\tlr: 1.000e-01, eta: 10:31:59, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4491\n2022-12-31 16:11:08,946 - mmcls - INFO - Epoch [15][2200/5005]\tlr: 1.000e-01, eta: 10:31:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4859\n2022-12-31 16:11:17,866 - mmcls - INFO - Epoch [15][2300/5005]\tlr: 1.000e-01, eta: 10:31:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4829\n2022-12-31 16:11:26,672 - mmcls - INFO - Epoch [15][2400/5005]\tlr: 1.000e-01, eta: 10:31:32, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4378\n2022-12-31 16:11:35,602 - mmcls - INFO - Epoch [15][2500/5005]\tlr: 1.000e-01, eta: 10:31:24, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4747\n2022-12-31 16:11:44,399 - mmcls - INFO - Epoch [15][2600/5005]\tlr: 1.000e-01, eta: 10:31:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4880\n2022-12-31 16:11:53,166 - mmcls - INFO - Epoch [15][2700/5005]\tlr: 1.000e-01, eta: 10:31:05, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4461\n2022-12-31 16:12:02,046 - mmcls - INFO - Epoch [15][2800/5005]\tlr: 1.000e-01, eta: 10:30:56, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4728\n2022-12-31 16:12:11,608 - mmcls - INFO - Epoch [15][2900/5005]\tlr: 1.000e-01, eta: 10:30:52, time: 0.096, data_time: 0.014, memory: 1224, loss: 2.4897\n2022-12-31 16:12:20,364 - mmcls - INFO - Epoch [15][3000/5005]\tlr: 1.000e-01, eta: 10:30:42, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4795\n2022-12-31 16:12:29,026 - mmcls - INFO - Epoch [15][3100/5005]\tlr: 1.000e-01, eta: 10:30:32, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4616\n2022-12-31 16:12:37,784 - mmcls - INFO - Epoch [15][3200/5005]\tlr: 1.000e-01, eta: 10:30:23, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4612\n2022-12-31 16:12:46,519 - mmcls - INFO - Epoch [15][3300/5005]\tlr: 1.000e-01, eta: 10:30:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4841\n2022-12-31 16:12:55,397 - mmcls - INFO - Epoch [15][3400/5005]\tlr: 1.000e-01, eta: 10:30:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4920\n2022-12-31 16:13:03,842 - mmcls - INFO - Epoch [15][3500/5005]\tlr: 1.000e-01, eta: 10:29:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.4691\n2022-12-31 16:13:12,755 - mmcls - INFO - Epoch [15][3600/5005]\tlr: 1.000e-01, eta: 10:29:45, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4988\n2022-12-31 16:13:21,918 - mmcls - INFO - Epoch [15][3700/5005]\tlr: 1.000e-01, eta: 10:29:38, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4449\n2022-12-31 16:13:30,936 - mmcls - INFO - Epoch [15][3800/5005]\tlr: 1.000e-01, eta: 10:29:30, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4774\n2022-12-31 16:13:39,590 - mmcls - INFO - Epoch [15][3900/5005]\tlr: 1.000e-01, eta: 10:29:20, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4776\n2022-12-31 16:13:48,405 - mmcls - INFO - Epoch [15][4000/5005]\tlr: 1.000e-01, eta: 10:29:11, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4812\n2022-12-31 16:13:56,932 - mmcls - INFO - Epoch [15][4100/5005]\tlr: 1.000e-01, eta: 10:29:00, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4739\n2022-12-31 16:14:06,000 - mmcls - INFO - Epoch [15][4200/5005]\tlr: 1.000e-01, eta: 10:28:52, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4637\n2022-12-31 16:14:14,791 - mmcls - INFO - Epoch [15][4300/5005]\tlr: 1.000e-01, eta: 10:28:43, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4776\n2022-12-31 16:14:23,616 - mmcls - INFO - Epoch [15][4400/5005]\tlr: 1.000e-01, eta: 10:28:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4607\n2022-12-31 16:14:32,371 - mmcls - INFO - Epoch [15][4500/5005]\tlr: 1.000e-01, eta: 10:28:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4888\n2022-12-31 16:14:41,505 - mmcls - INFO - Epoch [15][4600/5005]\tlr: 1.000e-01, eta: 10:28:18, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.4546\n2022-12-31 16:14:50,224 - mmcls - INFO - Epoch [15][4700/5005]\tlr: 1.000e-01, eta: 10:28:08, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4723\n2022-12-31 16:14:58,897 - mmcls - INFO - Epoch [15][4800/5005]\tlr: 1.000e-01, eta: 10:27:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4497\n2022-12-31 16:15:07,497 - mmcls - INFO - Epoch [15][4900/5005]\tlr: 1.000e-01, eta: 10:27:48, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4644\n2022-12-31 16:15:16,135 - mmcls - INFO - Epoch [15][5000/5005]\tlr: 1.000e-01, eta: 10:27:38, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4775\n2022-12-31 16:15:38,877 - mmcls - INFO - Epoch(val) [15][196]\taccuracy_top-1: 50.8780, accuracy_top-5: 76.1600\n2022-12-31 16:15:49,872 - mmcls - INFO - Epoch [16][100/5005]\tlr: 1.000e-01, eta: 10:27:38, time: 0.110, data_time: 0.054, memory: 1224, loss: 2.4295\n2022-12-31 16:15:58,678 - mmcls - INFO - Epoch [16][200/5005]\tlr: 1.000e-01, eta: 10:27:29, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4282\n2022-12-31 16:16:07,419 - mmcls - INFO - Epoch [16][300/5005]\tlr: 1.000e-01, eta: 10:27:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4579\n2022-12-31 16:16:16,233 - mmcls - INFO - Epoch [16][400/5005]\tlr: 1.000e-01, eta: 10:27:10, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4480\n2022-12-31 16:16:24,786 - mmcls - INFO - Epoch [16][500/5005]\tlr: 1.000e-01, eta: 10:27:00, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4379\n2022-12-31 16:16:34,244 - mmcls - INFO - Epoch [16][600/5005]\tlr: 1.000e-01, eta: 10:26:54, time: 0.095, data_time: 0.001, memory: 1224, loss: 2.4416\n2022-12-31 16:16:43,724 - mmcls - INFO - Epoch [16][700/5005]\tlr: 1.000e-01, eta: 10:26:49, time: 0.095, data_time: 0.000, memory: 1224, loss: 2.4240\n2022-12-31 16:16:52,454 - mmcls - INFO - Epoch [16][800/5005]\tlr: 1.000e-01, eta: 10:26:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4181\n2022-12-31 16:17:01,252 - mmcls - INFO - Epoch [16][900/5005]\tlr: 1.000e-01, eta: 10:26:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4401\n2022-12-31 16:17:10,037 - mmcls - INFO - Epoch [16][1000/5005]\tlr: 1.000e-01, eta: 10:26:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4630\n2022-12-31 16:17:18,937 - mmcls - INFO - Epoch [16][1100/5005]\tlr: 1.000e-01, eta: 10:26:12, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4704\n2022-12-31 16:17:27,590 - mmcls - INFO - Epoch [16][1200/5005]\tlr: 1.000e-01, eta: 10:26:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.4446\n2022-12-31 16:17:36,814 - mmcls - INFO - Epoch [16][1300/5005]\tlr: 1.000e-01, eta: 10:25:55, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4621\n2022-12-31 16:17:45,686 - mmcls - INFO - Epoch [16][1400/5005]\tlr: 1.000e-01, eta: 10:25:47, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4444\n2022-12-31 16:17:54,426 - mmcls - INFO - Epoch [16][1500/5005]\tlr: 1.000e-01, eta: 10:25:37, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4568\n2022-12-31 16:18:03,050 - mmcls - INFO - Epoch [16][1600/5005]\tlr: 1.000e-01, eta: 10:25:27, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4364\n2022-12-31 16:18:11,803 - mmcls - INFO - Epoch [16][1700/5005]\tlr: 1.000e-01, eta: 10:25:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4721\n2022-12-31 16:18:20,937 - mmcls - INFO - Epoch [16][1800/5005]\tlr: 1.000e-01, eta: 10:25:10, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.4421\n2022-12-31 16:18:30,047 - mmcls - INFO - Epoch [16][1900/5005]\tlr: 1.000e-01, eta: 10:25:03, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4276\n2022-12-31 16:18:38,768 - mmcls - INFO - Epoch [16][2000/5005]\tlr: 1.000e-01, eta: 10:24:53, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4552\n2022-12-31 16:18:47,224 - mmcls - INFO - Epoch [16][2100/5005]\tlr: 1.000e-01, eta: 10:24:42, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.4383\n2022-12-31 16:18:55,896 - mmcls - INFO - Epoch [16][2200/5005]\tlr: 1.000e-01, eta: 10:24:32, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4626\n2022-12-31 16:19:04,524 - mmcls - INFO - Epoch [16][2300/5005]\tlr: 1.000e-01, eta: 10:24:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4739\n2022-12-31 16:19:13,410 - mmcls - INFO - Epoch [16][2400/5005]\tlr: 1.000e-01, eta: 10:24:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4689\n2022-12-31 16:19:21,985 - mmcls - INFO - Epoch [16][2500/5005]\tlr: 1.000e-01, eta: 10:24:03, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4581\n2022-12-31 16:19:31,016 - mmcls - INFO - Epoch [16][2600/5005]\tlr: 1.000e-01, eta: 10:23:55, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.4522\n2022-12-31 16:19:39,691 - mmcls - INFO - Epoch [16][2700/5005]\tlr: 1.000e-01, eta: 10:23:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4334\n2022-12-31 16:19:48,505 - mmcls - INFO - Epoch [16][2800/5005]\tlr: 1.000e-01, eta: 10:23:36, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4147\n2022-12-31 16:19:57,165 - mmcls - INFO - Epoch [16][2900/5005]\tlr: 1.000e-01, eta: 10:23:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4612\n2022-12-31 16:20:06,048 - mmcls - INFO - Epoch [16][3000/5005]\tlr: 1.000e-01, eta: 10:23:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4440\n2022-12-31 16:20:15,533 - mmcls - INFO - Epoch [16][3100/5005]\tlr: 1.000e-01, eta: 10:23:12, time: 0.095, data_time: 0.001, memory: 1224, loss: 2.4526\n2022-12-31 16:20:24,202 - mmcls - INFO - Epoch [16][3200/5005]\tlr: 1.000e-01, eta: 10:23:02, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4696\n2022-12-31 16:20:33,205 - mmcls - INFO - Epoch [16][3300/5005]\tlr: 1.000e-01, eta: 10:22:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4730\n2022-12-31 16:20:42,009 - mmcls - INFO - Epoch [16][3400/5005]\tlr: 1.000e-01, eta: 10:22:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4197\n2022-12-31 16:20:51,012 - mmcls - INFO - Epoch [16][3500/5005]\tlr: 1.000e-01, eta: 10:22:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4504\n2022-12-31 16:21:00,200 - mmcls - INFO - Epoch [16][3600/5005]\tlr: 1.000e-01, eta: 10:22:30, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.4720\n2022-12-31 16:21:08,889 - mmcls - INFO - Epoch [16][3700/5005]\tlr: 1.000e-01, eta: 10:22:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4570\n2022-12-31 16:21:17,621 - mmcls - INFO - Epoch [16][3800/5005]\tlr: 1.000e-01, eta: 10:22:11, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4897\n2022-12-31 16:21:26,107 - mmcls - INFO - Epoch [16][3900/5005]\tlr: 1.000e-01, eta: 10:22:00, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.4628\n2022-12-31 16:21:35,286 - mmcls - INFO - Epoch [16][4000/5005]\tlr: 1.000e-01, eta: 10:21:53, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4913\n2022-12-31 16:21:44,278 - mmcls - INFO - Epoch [16][4100/5005]\tlr: 1.000e-01, eta: 10:21:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4466\n2022-12-31 16:21:52,942 - mmcls - INFO - Epoch [16][4200/5005]\tlr: 1.000e-01, eta: 10:21:35, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.4503\n2022-12-31 16:22:01,777 - mmcls - INFO - Epoch [16][4300/5005]\tlr: 1.000e-01, eta: 10:21:26, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4595\n2022-12-31 16:22:10,573 - mmcls - INFO - Epoch [16][4400/5005]\tlr: 1.000e-01, eta: 10:21:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4270\n2022-12-31 16:22:19,580 - mmcls - INFO - Epoch [16][4500/5005]\tlr: 1.000e-01, eta: 10:21:09, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4610\n2022-12-31 16:22:28,539 - mmcls - INFO - Epoch [16][4600/5005]\tlr: 1.000e-01, eta: 10:21:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4607\n2022-12-31 16:22:37,044 - mmcls - INFO - Epoch [16][4700/5005]\tlr: 1.000e-01, eta: 10:20:50, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4621\n2022-12-31 16:22:45,684 - mmcls - INFO - Epoch [16][4800/5005]\tlr: 1.000e-01, eta: 10:20:40, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4532\n2022-12-31 16:22:54,523 - mmcls - INFO - Epoch [16][4900/5005]\tlr: 1.000e-01, eta: 10:20:31, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4443\n2022-12-31 16:23:03,150 - mmcls - INFO - Epoch [16][5000/5005]\tlr: 1.000e-01, eta: 10:20:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4538\n2022-12-31 16:23:25,962 - mmcls - INFO - Epoch(val) [16][196]\taccuracy_top-1: 51.3140, accuracy_top-5: 77.0000\n2022-12-31 16:23:37,427 - mmcls - INFO - Epoch [17][100/5005]\tlr: 1.000e-01, eta: 10:20:23, time: 0.114, data_time: 0.042, memory: 1224, loss: 2.4110\n2022-12-31 16:23:46,386 - mmcls - INFO - Epoch [17][200/5005]\tlr: 1.000e-01, eta: 10:20:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4273\n2022-12-31 16:23:54,877 - mmcls - INFO - Epoch [17][300/5005]\tlr: 1.000e-01, eta: 10:20:04, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.4095\n2022-12-31 16:24:03,565 - mmcls - INFO - Epoch [17][400/5005]\tlr: 1.000e-01, eta: 10:19:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4213\n2022-12-31 16:24:12,192 - mmcls - INFO - Epoch [17][500/5005]\tlr: 1.000e-01, eta: 10:19:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4095\n2022-12-31 16:24:20,882 - mmcls - INFO - Epoch [17][600/5005]\tlr: 1.000e-01, eta: 10:19:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4084\n2022-12-31 16:24:29,552 - mmcls - INFO - Epoch [17][700/5005]\tlr: 1.000e-01, eta: 10:19:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4505\n2022-12-31 16:24:38,055 - mmcls - INFO - Epoch [17][800/5005]\tlr: 1.000e-01, eta: 10:19:14, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4578\n2022-12-31 16:24:46,938 - mmcls - INFO - Epoch [17][900/5005]\tlr: 1.000e-01, eta: 10:19:05, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4112\n2022-12-31 16:24:55,747 - mmcls - INFO - Epoch [17][1000/5005]\tlr: 1.000e-01, eta: 10:18:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4348\n2022-12-31 16:25:04,285 - mmcls - INFO - Epoch [17][1100/5005]\tlr: 1.000e-01, eta: 10:18:45, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.4318\n2022-12-31 16:25:13,224 - mmcls - INFO - Epoch [17][1200/5005]\tlr: 1.000e-01, eta: 10:18:37, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4055\n2022-12-31 16:25:22,162 - mmcls - INFO - Epoch [17][1300/5005]\tlr: 1.000e-01, eta: 10:18:28, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4424\n2022-12-31 16:25:30,824 - mmcls - INFO - Epoch [17][1400/5005]\tlr: 1.000e-01, eta: 10:18:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4300\n2022-12-31 16:25:39,315 - mmcls - INFO - Epoch [17][1500/5005]\tlr: 1.000e-01, eta: 10:18:08, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4311\n2022-12-31 16:25:47,803 - mmcls - INFO - Epoch [17][1600/5005]\tlr: 1.000e-01, eta: 10:17:57, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4422\n2022-12-31 16:25:56,501 - mmcls - INFO - Epoch [17][1700/5005]\tlr: 1.000e-01, eta: 10:17:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4727\n2022-12-31 16:26:05,254 - mmcls - INFO - Epoch [17][1800/5005]\tlr: 1.000e-01, eta: 10:17:38, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4615\n2022-12-31 16:26:14,146 - mmcls - INFO - Epoch [17][1900/5005]\tlr: 1.000e-01, eta: 10:17:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4689\n2022-12-31 16:26:23,279 - mmcls - INFO - Epoch [17][2000/5005]\tlr: 1.000e-01, eta: 10:17:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4502\n2022-12-31 16:26:32,558 - mmcls - INFO - Epoch [17][2100/5005]\tlr: 1.000e-01, eta: 10:17:15, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.4577\n2022-12-31 16:26:41,426 - mmcls - INFO - Epoch [17][2200/5005]\tlr: 1.000e-01, eta: 10:17:07, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.4593\n2022-12-31 16:26:50,071 - mmcls - INFO - Epoch [17][2300/5005]\tlr: 1.000e-01, eta: 10:16:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4441\n2022-12-31 16:26:58,708 - mmcls - INFO - Epoch [17][2400/5005]\tlr: 1.000e-01, eta: 10:16:47, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4483\n2022-12-31 16:27:07,360 - mmcls - INFO - Epoch [17][2500/5005]\tlr: 1.000e-01, eta: 10:16:37, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4328\n2022-12-31 16:27:16,212 - mmcls - INFO - Epoch [17][2600/5005]\tlr: 1.000e-01, eta: 10:16:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4519\n2022-12-31 16:27:24,741 - mmcls - INFO - Epoch [17][2700/5005]\tlr: 1.000e-01, eta: 10:16:17, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.4450\n2022-12-31 16:27:33,601 - mmcls - INFO - Epoch [17][2800/5005]\tlr: 1.000e-01, eta: 10:16:09, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.4393\n2022-12-31 16:27:42,457 - mmcls - INFO - Epoch [17][2900/5005]\tlr: 1.000e-01, eta: 10:16:00, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.4688\n2022-12-31 16:27:51,196 - mmcls - INFO - Epoch [17][3000/5005]\tlr: 1.000e-01, eta: 10:15:50, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.4609\n2022-12-31 16:27:59,839 - mmcls - INFO - Epoch [17][3100/5005]\tlr: 1.000e-01, eta: 10:15:40, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4464\n2022-12-31 16:28:08,919 - mmcls - INFO - Epoch [17][3200/5005]\tlr: 1.000e-01, eta: 10:15:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4566\n2022-12-31 16:28:17,757 - mmcls - INFO - Epoch [17][3300/5005]\tlr: 1.000e-01, eta: 10:15:24, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4451\n2022-12-31 16:28:26,832 - mmcls - INFO - Epoch [17][3400/5005]\tlr: 1.000e-01, eta: 10:15:16, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4320\n2022-12-31 16:28:35,692 - mmcls - INFO - Epoch [17][3500/5005]\tlr: 1.000e-01, eta: 10:15:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4298\n2022-12-31 16:28:44,534 - mmcls - INFO - Epoch [17][3600/5005]\tlr: 1.000e-01, eta: 10:14:58, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4560\n2022-12-31 16:28:53,506 - mmcls - INFO - Epoch [17][3700/5005]\tlr: 1.000e-01, eta: 10:14:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4484\n2022-12-31 16:29:02,024 - mmcls - INFO - Epoch [17][3800/5005]\tlr: 1.000e-01, eta: 10:14:40, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4678\n2022-12-31 16:29:11,014 - mmcls - INFO - Epoch [17][3900/5005]\tlr: 1.000e-01, eta: 10:14:31, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4560\n2022-12-31 16:29:19,758 - mmcls - INFO - Epoch [17][4000/5005]\tlr: 1.000e-01, eta: 10:14:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4495\n2022-12-31 16:29:28,425 - mmcls - INFO - Epoch [17][4100/5005]\tlr: 1.000e-01, eta: 10:14:12, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4426\n2022-12-31 16:29:37,396 - mmcls - INFO - Epoch [17][4200/5005]\tlr: 1.000e-01, eta: 10:14:04, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4673\n2022-12-31 16:29:46,082 - mmcls - INFO - Epoch [17][4300/5005]\tlr: 1.000e-01, eta: 10:13:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4620\n2022-12-31 16:29:54,889 - mmcls - INFO - Epoch [17][4400/5005]\tlr: 1.000e-01, eta: 10:13:45, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4572\n2022-12-31 16:30:03,605 - mmcls - INFO - Epoch [17][4500/5005]\tlr: 1.000e-01, eta: 10:13:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4669\n2022-12-31 16:30:12,356 - mmcls - INFO - Epoch [17][4600/5005]\tlr: 1.000e-01, eta: 10:13:26, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4609\n2022-12-31 16:30:21,079 - mmcls - INFO - Epoch [17][4700/5005]\tlr: 1.000e-01, eta: 10:13:17, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4526\n2022-12-31 16:30:29,708 - mmcls - INFO - Epoch [17][4800/5005]\tlr: 1.000e-01, eta: 10:13:07, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4543\n2022-12-31 16:30:38,151 - mmcls - INFO - Epoch [17][4900/5005]\tlr: 1.000e-01, eta: 10:12:56, time: 0.084, data_time: 0.010, memory: 1224, loss: 2.4532\n2022-12-31 16:30:47,178 - mmcls - INFO - Epoch [17][5000/5005]\tlr: 1.000e-01, eta: 10:12:48, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4363\n2022-12-31 16:31:09,804 - mmcls - INFO - Epoch(val) [17][196]\taccuracy_top-1: 51.3020, accuracy_top-5: 77.0040\n2022-12-31 16:31:20,963 - mmcls - INFO - Epoch [18][100/5005]\tlr: 1.000e-01, eta: 10:12:48, time: 0.111, data_time: 0.055, memory: 1224, loss: 2.3922\n2022-12-31 16:31:29,814 - mmcls - INFO - Epoch [18][200/5005]\tlr: 1.000e-01, eta: 10:12:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4067\n2022-12-31 16:31:38,745 - mmcls - INFO - Epoch [18][300/5005]\tlr: 1.000e-01, eta: 10:12:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3841\n2022-12-31 16:31:47,510 - mmcls - INFO - Epoch [18][400/5005]\tlr: 1.000e-01, eta: 10:12:21, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4095\n2022-12-31 16:31:56,653 - mmcls - INFO - Epoch [18][500/5005]\tlr: 1.000e-01, eta: 10:12:14, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.4357\n2022-12-31 16:32:05,120 - mmcls - INFO - Epoch [18][600/5005]\tlr: 1.000e-01, eta: 10:12:03, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4257\n2022-12-31 16:32:14,134 - mmcls - INFO - Epoch [18][700/5005]\tlr: 1.000e-01, eta: 10:11:55, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.4274\n2022-12-31 16:32:22,915 - mmcls - INFO - Epoch [18][800/5005]\tlr: 1.000e-01, eta: 10:11:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4438\n2022-12-31 16:32:31,582 - mmcls - INFO - Epoch [18][900/5005]\tlr: 1.000e-01, eta: 10:11:36, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.4166\n2022-12-31 16:32:40,450 - mmcls - INFO - Epoch [18][1000/5005]\tlr: 1.000e-01, eta: 10:11:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4416\n2022-12-31 16:32:49,285 - mmcls - INFO - Epoch [18][1100/5005]\tlr: 1.000e-01, eta: 10:11:18, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4408\n2022-12-31 16:32:57,901 - mmcls - INFO - Epoch [18][1200/5005]\tlr: 1.000e-01, eta: 10:11:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4415\n2022-12-31 16:33:06,391 - mmcls - INFO - Epoch [18][1300/5005]\tlr: 1.000e-01, eta: 10:10:58, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4083\n2022-12-31 16:33:15,240 - mmcls - INFO - Epoch [18][1400/5005]\tlr: 1.000e-01, eta: 10:10:49, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4316\n2022-12-31 16:33:24,107 - mmcls - INFO - Epoch [18][1500/5005]\tlr: 1.000e-01, eta: 10:10:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4415\n2022-12-31 16:33:33,094 - mmcls - INFO - Epoch [18][1600/5005]\tlr: 1.000e-01, eta: 10:10:32, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4307\n2022-12-31 16:33:42,255 - mmcls - INFO - Epoch [18][1700/5005]\tlr: 1.000e-01, eta: 10:10:24, time: 0.092, data_time: 0.018, memory: 1224, loss: 2.4474\n2022-12-31 16:33:50,846 - mmcls - INFO - Epoch [18][1800/5005]\tlr: 1.000e-01, eta: 10:10:14, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.4493\n2022-12-31 16:33:59,634 - mmcls - INFO - Epoch [18][1900/5005]\tlr: 1.000e-01, eta: 10:10:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.4586\n2022-12-31 16:34:08,266 - mmcls - INFO - Epoch [18][2000/5005]\tlr: 1.000e-01, eta: 10:09:55, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4344\n2022-12-31 16:34:17,080 - mmcls - INFO - Epoch [18][2100/5005]\tlr: 1.000e-01, eta: 10:09:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4443\n2022-12-31 16:34:25,913 - mmcls - INFO - Epoch [18][2200/5005]\tlr: 1.000e-01, eta: 10:09:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4438\n2022-12-31 16:34:34,873 - mmcls - INFO - Epoch [18][2300/5005]\tlr: 1.000e-01, eta: 10:09:29, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4581\n2022-12-31 16:34:43,841 - mmcls - INFO - Epoch [18][2400/5005]\tlr: 1.000e-01, eta: 10:09:21, time: 0.090, data_time: 0.025, memory: 1224, loss: 2.4124\n2022-12-31 16:34:52,722 - mmcls - INFO - Epoch [18][2500/5005]\tlr: 1.000e-01, eta: 10:09:12, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4511\n2022-12-31 16:35:01,245 - mmcls - INFO - Epoch [18][2600/5005]\tlr: 1.000e-01, eta: 10:09:01, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.4413\n2022-12-31 16:35:10,025 - mmcls - INFO - Epoch [18][2700/5005]\tlr: 1.000e-01, eta: 10:08:52, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.4294\n2022-12-31 16:35:19,163 - mmcls - INFO - Epoch [18][2800/5005]\tlr: 1.000e-01, eta: 10:08:45, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.4242\n2022-12-31 16:35:28,057 - mmcls - INFO - Epoch [18][2900/5005]\tlr: 1.000e-01, eta: 10:08:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4258\n2022-12-31 16:35:36,722 - mmcls - INFO - Epoch [18][3000/5005]\tlr: 1.000e-01, eta: 10:08:26, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4296\n2022-12-31 16:35:45,346 - mmcls - INFO - Epoch [18][3100/5005]\tlr: 1.000e-01, eta: 10:08:16, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4288\n2022-12-31 16:35:54,055 - mmcls - INFO - Epoch [18][3200/5005]\tlr: 1.000e-01, eta: 10:08:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4463\n2022-12-31 16:36:02,739 - mmcls - INFO - Epoch [18][3300/5005]\tlr: 1.000e-01, eta: 10:07:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4324\n2022-12-31 16:36:11,451 - mmcls - INFO - Epoch [18][3400/5005]\tlr: 1.000e-01, eta: 10:07:48, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4588\n2022-12-31 16:36:20,382 - mmcls - INFO - Epoch [18][3500/5005]\tlr: 1.000e-01, eta: 10:07:39, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.4368\n2022-12-31 16:36:29,057 - mmcls - INFO - Epoch [18][3600/5005]\tlr: 1.000e-01, eta: 10:07:30, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4371\n2022-12-31 16:36:37,771 - mmcls - INFO - Epoch [18][3700/5005]\tlr: 1.000e-01, eta: 10:07:20, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4392\n2022-12-31 16:36:46,474 - mmcls - INFO - Epoch [18][3800/5005]\tlr: 1.000e-01, eta: 10:07:10, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.4464\n2022-12-31 16:36:55,220 - mmcls - INFO - Epoch [18][3900/5005]\tlr: 1.000e-01, eta: 10:07:01, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4410\n2022-12-31 16:37:04,004 - mmcls - INFO - Epoch [18][4000/5005]\tlr: 1.000e-01, eta: 10:06:52, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4610\n2022-12-31 16:37:12,565 - mmcls - INFO - Epoch [18][4100/5005]\tlr: 1.000e-01, eta: 10:06:42, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4517\n2022-12-31 16:37:21,755 - mmcls - INFO - Epoch [18][4200/5005]\tlr: 1.000e-01, eta: 10:06:35, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.4888\n2022-12-31 16:37:30,547 - mmcls - INFO - Epoch [18][4300/5005]\tlr: 1.000e-01, eta: 10:06:25, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4175\n2022-12-31 16:37:39,203 - mmcls - INFO - Epoch [18][4400/5005]\tlr: 1.000e-01, eta: 10:06:16, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.4467\n2022-12-31 16:37:47,800 - mmcls - INFO - Epoch [18][4500/5005]\tlr: 1.000e-01, eta: 10:06:06, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4367\n2022-12-31 16:37:57,266 - mmcls - INFO - Epoch [18][4600/5005]\tlr: 1.000e-01, eta: 10:06:00, time: 0.095, data_time: 0.013, memory: 1224, loss: 2.4568\n2022-12-31 16:38:05,964 - mmcls - INFO - Epoch [18][4700/5005]\tlr: 1.000e-01, eta: 10:05:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4438\n2022-12-31 16:38:14,690 - mmcls - INFO - Epoch [18][4800/5005]\tlr: 1.000e-01, eta: 10:05:41, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4583\n2022-12-31 16:38:23,567 - mmcls - INFO - Epoch [18][4900/5005]\tlr: 1.000e-01, eta: 10:05:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4216\n2022-12-31 16:38:32,325 - mmcls - INFO - Epoch [18][5000/5005]\tlr: 1.000e-01, eta: 10:05:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4393\n2022-12-31 16:38:54,926 - mmcls - INFO - Epoch(val) [18][196]\taccuracy_top-1: 50.4360, accuracy_top-5: 76.0140\n2022-12-31 16:39:06,142 - mmcls - INFO - Epoch [19][100/5005]\tlr: 1.000e-01, eta: 10:05:22, time: 0.112, data_time: 0.030, memory: 1224, loss: 2.3814\n2022-12-31 16:39:14,585 - mmcls - INFO - Epoch [19][200/5005]\tlr: 1.000e-01, eta: 10:05:11, time: 0.084, data_time: 0.002, memory: 1224, loss: 2.3917\n2022-12-31 16:39:23,386 - mmcls - INFO - Epoch [19][300/5005]\tlr: 1.000e-01, eta: 10:05:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3942\n2022-12-31 16:39:32,243 - mmcls - INFO - Epoch [19][400/5005]\tlr: 1.000e-01, eta: 10:04:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4078\n2022-12-31 16:39:41,058 - mmcls - INFO - Epoch [19][500/5005]\tlr: 1.000e-01, eta: 10:04:44, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4249\n2022-12-31 16:39:49,623 - mmcls - INFO - Epoch [19][600/5005]\tlr: 1.000e-01, eta: 10:04:34, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4166\n2022-12-31 16:39:58,394 - mmcls - INFO - Epoch [19][700/5005]\tlr: 1.000e-01, eta: 10:04:25, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4209\n2022-12-31 16:40:07,164 - mmcls - INFO - Epoch [19][800/5005]\tlr: 1.000e-01, eta: 10:04:16, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4274\n2022-12-31 16:40:16,009 - mmcls - INFO - Epoch [19][900/5005]\tlr: 1.000e-01, eta: 10:04:07, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4058\n2022-12-31 16:40:24,814 - mmcls - INFO - Epoch [19][1000/5005]\tlr: 1.000e-01, eta: 10:03:58, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4295\n2022-12-31 16:40:33,508 - mmcls - INFO - Epoch [19][1100/5005]\tlr: 1.000e-01, eta: 10:03:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4149\n2022-12-31 16:40:42,207 - mmcls - INFO - Epoch [19][1200/5005]\tlr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.4176\n2022-12-31 16:40:50,781 - mmcls - INFO - Epoch [19][1300/5005]\tlr: 1.000e-01, eta: 10:03:29, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.4361\n2022-12-31 16:40:59,749 - mmcls - INFO - Epoch [19][1400/5005]\tlr: 1.000e-01, eta: 10:03:20, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.4291\n2022-12-31 16:41:08,661 - mmcls - INFO - Epoch [19][1500/5005]\tlr: 1.000e-01, eta: 10:03:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4213\n2022-12-31 16:41:17,528 - mmcls - INFO - Epoch [19][1600/5005]\tlr: 1.000e-01, eta: 10:03:03, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4203\n2022-12-31 16:41:26,515 - mmcls - INFO - Epoch [19][1700/5005]\tlr: 1.000e-01, eta: 10:02:55, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4231\n2022-12-31 16:41:35,352 - mmcls - INFO - Epoch [19][1800/5005]\tlr: 1.000e-01, eta: 10:02:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4303\n2022-12-31 16:41:44,050 - mmcls - INFO - Epoch [19][1900/5005]\tlr: 1.000e-01, eta: 10:02:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4192\n2022-12-31 16:41:52,634 - mmcls - INFO - Epoch [19][2000/5005]\tlr: 1.000e-01, eta: 10:02:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4425\n2022-12-31 16:42:01,370 - mmcls - INFO - Epoch [19][2100/5005]\tlr: 1.000e-01, eta: 10:02:17, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4497\n2022-12-31 16:42:10,284 - mmcls - INFO - Epoch [19][2200/5005]\tlr: 1.000e-01, eta: 10:02:08, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4436\n2022-12-31 16:42:18,964 - mmcls - INFO - Epoch [19][2300/5005]\tlr: 1.000e-01, eta: 10:01:59, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.4300\n2022-12-31 16:42:27,629 - mmcls - INFO - Epoch [19][2400/5005]\tlr: 1.000e-01, eta: 10:01:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4539\n2022-12-31 16:42:36,658 - mmcls - INFO - Epoch [19][2500/5005]\tlr: 1.000e-01, eta: 10:01:41, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.4141\n2022-12-31 16:42:45,306 - mmcls - INFO - Epoch [19][2600/5005]\tlr: 1.000e-01, eta: 10:01:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4288\n2022-12-31 16:42:53,956 - mmcls - INFO - Epoch [19][2700/5005]\tlr: 1.000e-01, eta: 10:01:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4325\n2022-12-31 16:43:02,909 - mmcls - INFO - Epoch [19][2800/5005]\tlr: 1.000e-01, eta: 10:01:13, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4352\n2022-12-31 16:43:11,824 - mmcls - INFO - Epoch [19][2900/5005]\tlr: 1.000e-01, eta: 10:01:04, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4319\n2022-12-31 16:43:20,668 - mmcls - INFO - Epoch [19][3000/5005]\tlr: 1.000e-01, eta: 10:00:56, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4357\n2022-12-31 16:43:29,423 - mmcls - INFO - Epoch [19][3100/5005]\tlr: 1.000e-01, eta: 10:00:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4106\n2022-12-31 16:43:38,156 - mmcls - INFO - Epoch [19][3200/5005]\tlr: 1.000e-01, eta: 10:00:37, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.4256\n2022-12-31 16:43:46,970 - mmcls - INFO - Epoch [19][3300/5005]\tlr: 1.000e-01, eta: 10:00:28, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4144\n2022-12-31 16:43:55,434 - mmcls - INFO - Epoch [19][3400/5005]\tlr: 1.000e-01, eta: 10:00:17, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4823\n2022-12-31 16:44:04,590 - mmcls - INFO - Epoch [19][3500/5005]\tlr: 1.000e-01, eta: 10:00:10, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.4379\n2022-12-31 16:44:13,305 - mmcls - INFO - Epoch [19][3600/5005]\tlr: 1.000e-01, eta: 10:00:00, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.4609\n2022-12-31 16:44:22,114 - mmcls - INFO - Epoch [19][3700/5005]\tlr: 1.000e-01, eta: 9:59:51, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4340\n2022-12-31 16:44:30,793 - mmcls - INFO - Epoch [19][3800/5005]\tlr: 1.000e-01, eta: 9:59:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.4279\n2022-12-31 16:44:39,516 - mmcls - INFO - Epoch [19][3900/5005]\tlr: 1.000e-01, eta: 9:59:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4380\n2022-12-31 16:44:48,159 - mmcls - INFO - Epoch [19][4000/5005]\tlr: 1.000e-01, eta: 9:59:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4343\n2022-12-31 16:44:56,772 - mmcls - INFO - Epoch [19][4100/5005]\tlr: 1.000e-01, eta: 9:59:13, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4296\n2022-12-31 16:45:05,593 - mmcls - INFO - Epoch [19][4200/5005]\tlr: 1.000e-01, eta: 9:59:04, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.4242\n2022-12-31 16:45:14,336 - mmcls - INFO - Epoch [19][4300/5005]\tlr: 1.000e-01, eta: 9:58:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4300\n2022-12-31 16:45:23,330 - mmcls - INFO - Epoch [19][4400/5005]\tlr: 1.000e-01, eta: 9:58:46, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4497\n2022-12-31 16:45:31,950 - mmcls - INFO - Epoch [19][4500/5005]\tlr: 1.000e-01, eta: 9:58:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4410\n2022-12-31 16:45:40,617 - mmcls - INFO - Epoch [19][4600/5005]\tlr: 1.000e-01, eta: 9:58:27, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4347\n2022-12-31 16:45:49,773 - mmcls - INFO - Epoch [19][4700/5005]\tlr: 1.000e-01, eta: 9:58:19, time: 0.092, data_time: 0.040, memory: 1224, loss: 2.4380\n2022-12-31 16:45:58,605 - mmcls - INFO - Epoch [19][4800/5005]\tlr: 1.000e-01, eta: 9:58:10, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4254\n2022-12-31 16:46:07,364 - mmcls - INFO - Epoch [19][4900/5005]\tlr: 1.000e-01, eta: 9:58:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4393\n2022-12-31 16:46:15,767 - mmcls - INFO - Epoch [19][5000/5005]\tlr: 1.000e-01, eta: 9:57:50, time: 0.084, data_time: 0.001, memory: 1224, loss: 2.4458\n2022-12-31 16:46:38,464 - mmcls - INFO - Epoch(val) [19][196]\taccuracy_top-1: 49.8560, accuracy_top-5: 76.0680\n2022-12-31 16:46:49,880 - mmcls - INFO - Epoch [20][100/5005]\tlr: 1.000e-01, eta: 9:57:50, time: 0.114, data_time: 0.039, memory: 1224, loss: 2.4087\n2022-12-31 16:46:58,628 - mmcls - INFO - Epoch [20][200/5005]\tlr: 1.000e-01, eta: 9:57:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3807\n2022-12-31 16:47:07,538 - mmcls - INFO - Epoch [20][300/5005]\tlr: 1.000e-01, eta: 9:57:32, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4114\n2022-12-31 16:47:16,493 - mmcls - INFO - Epoch [20][400/5005]\tlr: 1.000e-01, eta: 9:57:24, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.3709\n2022-12-31 16:47:25,419 - mmcls - INFO - Epoch [20][500/5005]\tlr: 1.000e-01, eta: 9:57:15, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3984\n2022-12-31 16:47:34,058 - mmcls - INFO - Epoch [20][600/5005]\tlr: 1.000e-01, eta: 9:57:05, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4043\n2022-12-31 16:47:43,636 - mmcls - INFO - Epoch [20][700/5005]\tlr: 1.000e-01, eta: 9:57:00, time: 0.096, data_time: 0.001, memory: 1224, loss: 2.4115\n2022-12-31 16:47:52,518 - mmcls - INFO - Epoch [20][800/5005]\tlr: 1.000e-01, eta: 9:56:51, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3993\n2022-12-31 16:48:01,628 - mmcls - INFO - Epoch [20][900/5005]\tlr: 1.000e-01, eta: 9:56:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3910\n2022-12-31 16:48:10,158 - mmcls - INFO - Epoch [20][1000/5005]\tlr: 1.000e-01, eta: 9:56:33, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.3857\n2022-12-31 16:48:18,744 - mmcls - INFO - Epoch [20][1100/5005]\tlr: 1.000e-01, eta: 9:56:23, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4160\n2022-12-31 16:48:27,330 - mmcls - INFO - Epoch [20][1200/5005]\tlr: 1.000e-01, eta: 9:56:13, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4014\n2022-12-31 16:48:36,096 - mmcls - INFO - Epoch [20][1300/5005]\tlr: 1.000e-01, eta: 9:56:04, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4218\n2022-12-31 16:48:44,734 - mmcls - INFO - Epoch [20][1400/5005]\tlr: 1.000e-01, eta: 9:55:54, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.4293\n2022-12-31 16:48:53,402 - mmcls - INFO - Epoch [20][1500/5005]\tlr: 1.000e-01, eta: 9:55:44, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.3855\n2022-12-31 16:49:02,481 - mmcls - INFO - Epoch [20][1600/5005]\tlr: 1.000e-01, eta: 9:55:37, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4292\n2022-12-31 16:49:11,427 - mmcls - INFO - Epoch [20][1700/5005]\tlr: 1.000e-01, eta: 9:55:28, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4261\n2022-12-31 16:49:20,291 - mmcls - INFO - Epoch [20][1800/5005]\tlr: 1.000e-01, eta: 9:55:19, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4440\n2022-12-31 16:49:29,029 - mmcls - INFO - Epoch [20][1900/5005]\tlr: 1.000e-01, eta: 9:55:10, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4324\n2022-12-31 16:49:37,820 - mmcls - INFO - Epoch [20][2000/5005]\tlr: 1.000e-01, eta: 9:55:01, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4173\n2022-12-31 16:49:46,673 - mmcls - INFO - Epoch [20][2100/5005]\tlr: 1.000e-01, eta: 9:54:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4309\n2022-12-31 16:49:55,633 - mmcls - INFO - Epoch [20][2200/5005]\tlr: 1.000e-01, eta: 9:54:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.4093\n2022-12-31 16:50:05,257 - mmcls - INFO - Epoch [20][2300/5005]\tlr: 1.000e-01, eta: 9:54:38, time: 0.096, data_time: 0.022, memory: 1224, loss: 2.4318\n2022-12-31 16:50:13,914 - mmcls - INFO - Epoch [20][2400/5005]\tlr: 1.000e-01, eta: 9:54:28, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.4461\n2022-12-31 16:50:22,771 - mmcls - INFO - Epoch [20][2500/5005]\tlr: 1.000e-01, eta: 9:54:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4588\n2022-12-31 16:50:31,380 - mmcls - INFO - Epoch [20][2600/5005]\tlr: 1.000e-01, eta: 9:54:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4349\n2022-12-31 16:50:39,988 - mmcls - INFO - Epoch [20][2700/5005]\tlr: 1.000e-01, eta: 9:54:00, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4425\n2022-12-31 16:50:48,686 - mmcls - INFO - Epoch [20][2800/5005]\tlr: 1.000e-01, eta: 9:53:50, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4474\n2022-12-31 16:50:57,478 - mmcls - INFO - Epoch [20][2900/5005]\tlr: 1.000e-01, eta: 9:53:41, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.4355\n2022-12-31 16:51:06,064 - mmcls - INFO - Epoch [20][3000/5005]\tlr: 1.000e-01, eta: 9:53:31, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4260\n2022-12-31 16:51:14,820 - mmcls - INFO - Epoch [20][3100/5005]\tlr: 1.000e-01, eta: 9:53:22, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4023\n2022-12-31 16:51:23,671 - mmcls - INFO - Epoch [20][3200/5005]\tlr: 1.000e-01, eta: 9:53:13, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.4383\n2022-12-31 16:51:32,523 - mmcls - INFO - Epoch [20][3300/5005]\tlr: 1.000e-01, eta: 9:53:04, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4408\n2022-12-31 16:51:41,574 - mmcls - INFO - Epoch [20][3400/5005]\tlr: 1.000e-01, eta: 9:52:56, time: 0.091, data_time: 0.018, memory: 1224, loss: 2.4335\n2022-12-31 16:51:50,364 - mmcls - INFO - Epoch [20][3500/5005]\tlr: 1.000e-01, eta: 9:52:47, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4623\n2022-12-31 16:51:58,968 - mmcls - INFO - Epoch [20][3600/5005]\tlr: 1.000e-01, eta: 9:52:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4328\n2022-12-31 16:52:07,994 - mmcls - INFO - Epoch [20][3700/5005]\tlr: 1.000e-01, eta: 9:52:29, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4229\n2022-12-31 16:52:16,643 - mmcls - INFO - Epoch [20][3800/5005]\tlr: 1.000e-01, eta: 9:52:20, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4239\n2022-12-31 16:52:25,356 - mmcls - INFO - Epoch [20][3900/5005]\tlr: 1.000e-01, eta: 9:52:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4314\n2022-12-31 16:52:34,234 - mmcls - INFO - Epoch [20][4000/5005]\tlr: 1.000e-01, eta: 9:52:01, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4412\n2022-12-31 16:52:42,948 - mmcls - INFO - Epoch [20][4100/5005]\tlr: 1.000e-01, eta: 9:51:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4496\n2022-12-31 16:52:51,731 - mmcls - INFO - Epoch [20][4200/5005]\tlr: 1.000e-01, eta: 9:51:43, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4234\n2022-12-31 16:53:00,471 - mmcls - INFO - Epoch [20][4300/5005]\tlr: 1.000e-01, eta: 9:51:34, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4630\n2022-12-31 16:53:09,160 - mmcls - INFO - Epoch [20][4400/5005]\tlr: 1.000e-01, eta: 9:51:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4374\n2022-12-31 16:53:18,310 - mmcls - INFO - Epoch [20][4500/5005]\tlr: 1.000e-01, eta: 9:51:17, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4297\n2022-12-31 16:53:27,246 - mmcls - INFO - Epoch [20][4600/5005]\tlr: 1.000e-01, eta: 9:51:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4261\n2022-12-31 16:53:35,927 - mmcls - INFO - Epoch [20][4700/5005]\tlr: 1.000e-01, eta: 9:50:58, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.4373\n2022-12-31 16:53:44,535 - mmcls - INFO - Epoch [20][4800/5005]\tlr: 1.000e-01, eta: 9:50:49, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4425\n2022-12-31 16:53:52,991 - mmcls - INFO - Epoch [20][4900/5005]\tlr: 1.000e-01, eta: 9:50:38, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.3891\n2022-12-31 16:54:01,815 - mmcls - INFO - Epoch [20][5000/5005]\tlr: 1.000e-01, eta: 9:50:29, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4467\n2022-12-31 16:54:25,159 - mmcls - INFO - Epoch(val) [20][196]\taccuracy_top-1: 51.2320, accuracy_top-5: 77.1460\n2022-12-31 16:54:35,955 - mmcls - INFO - Epoch [21][100/5005]\tlr: 1.000e-01, eta: 9:50:26, time: 0.108, data_time: 0.039, memory: 1224, loss: 2.4004\n2022-12-31 16:54:44,562 - mmcls - INFO - Epoch [21][200/5005]\tlr: 1.000e-01, eta: 9:50:16, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3831\n2022-12-31 16:54:53,303 - mmcls - INFO - Epoch [21][300/5005]\tlr: 1.000e-01, eta: 9:50:07, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.4050\n2022-12-31 16:55:02,742 - mmcls - INFO - Epoch [21][400/5005]\tlr: 1.000e-01, eta: 9:50:00, time: 0.094, data_time: 0.031, memory: 1224, loss: 2.3921\n2022-12-31 16:55:11,285 - mmcls - INFO - Epoch [21][500/5005]\tlr: 1.000e-01, eta: 9:49:50, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.3578\n2022-12-31 16:55:20,197 - mmcls - INFO - Epoch [21][600/5005]\tlr: 1.000e-01, eta: 9:49:42, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.3926\n2022-12-31 16:55:28,931 - mmcls - INFO - Epoch [21][700/5005]\tlr: 1.000e-01, eta: 9:49:32, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.3955\n2022-12-31 16:55:38,204 - mmcls - INFO - Epoch [21][800/5005]\tlr: 1.000e-01, eta: 9:49:25, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3993\n2022-12-31 16:55:46,959 - mmcls - INFO - Epoch [21][900/5005]\tlr: 1.000e-01, eta: 9:49:16, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.3855\n2022-12-31 16:55:55,901 - mmcls - INFO - Epoch [21][1000/5005]\tlr: 1.000e-01, eta: 9:49:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4207\n2022-12-31 16:56:04,737 - mmcls - INFO - Epoch [21][1100/5005]\tlr: 1.000e-01, eta: 9:48:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4363\n2022-12-31 16:56:13,713 - mmcls - INFO - Epoch [21][1200/5005]\tlr: 1.000e-01, eta: 9:48:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4099\n2022-12-31 16:56:22,541 - mmcls - INFO - Epoch [21][1300/5005]\tlr: 1.000e-01, eta: 9:48:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3977\n2022-12-31 16:56:31,507 - mmcls - INFO - Epoch [21][1400/5005]\tlr: 1.000e-01, eta: 9:48:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4128\n2022-12-31 16:56:40,472 - mmcls - INFO - Epoch [21][1500/5005]\tlr: 1.000e-01, eta: 9:48:24, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4033\n2022-12-31 16:56:49,220 - mmcls - INFO - Epoch [21][1600/5005]\tlr: 1.000e-01, eta: 9:48:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4188\n2022-12-31 16:56:57,888 - mmcls - INFO - Epoch [21][1700/5005]\tlr: 1.000e-01, eta: 9:48:06, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4181\n2022-12-31 16:57:07,016 - mmcls - INFO - Epoch [21][1800/5005]\tlr: 1.000e-01, eta: 9:47:58, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4178\n2022-12-31 16:57:15,705 - mmcls - INFO - Epoch [21][1900/5005]\tlr: 1.000e-01, eta: 9:47:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3970\n2022-12-31 16:57:24,784 - mmcls - INFO - Epoch [21][2000/5005]\tlr: 1.000e-01, eta: 9:47:40, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.4009\n2022-12-31 16:57:33,388 - mmcls - INFO - Epoch [21][2100/5005]\tlr: 1.000e-01, eta: 9:47:30, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.4382\n2022-12-31 16:57:42,399 - mmcls - INFO - Epoch [21][2200/5005]\tlr: 1.000e-01, eta: 9:47:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.3973\n2022-12-31 16:57:51,230 - mmcls - INFO - Epoch [21][2300/5005]\tlr: 1.000e-01, eta: 9:47:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4084\n2022-12-31 16:58:00,561 - mmcls - INFO - Epoch [21][2400/5005]\tlr: 1.000e-01, eta: 9:47:06, time: 0.093, data_time: 0.008, memory: 1224, loss: 2.4492\n2022-12-31 16:58:09,241 - mmcls - INFO - Epoch [21][2500/5005]\tlr: 1.000e-01, eta: 9:46:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4013\n2022-12-31 16:58:17,898 - mmcls - INFO - Epoch [21][2600/5005]\tlr: 1.000e-01, eta: 9:46:47, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4387\n2022-12-31 16:58:27,294 - mmcls - INFO - Epoch [21][2700/5005]\tlr: 1.000e-01, eta: 9:46:40, time: 0.094, data_time: 0.012, memory: 1224, loss: 2.4215\n2022-12-31 16:58:35,970 - mmcls - INFO - Epoch [21][2800/5005]\tlr: 1.000e-01, eta: 9:46:31, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4617\n2022-12-31 16:58:44,721 - mmcls - INFO - Epoch [21][2900/5005]\tlr: 1.000e-01, eta: 9:46:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4199\n2022-12-31 16:58:53,461 - mmcls - INFO - Epoch [21][3000/5005]\tlr: 1.000e-01, eta: 9:46:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4567\n2022-12-31 16:59:02,257 - mmcls - INFO - Epoch [21][3100/5005]\tlr: 1.000e-01, eta: 9:46:03, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4128\n2022-12-31 16:59:11,119 - mmcls - INFO - Epoch [21][3200/5005]\tlr: 1.000e-01, eta: 9:45:55, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4251\n2022-12-31 16:59:19,969 - mmcls - INFO - Epoch [21][3300/5005]\tlr: 1.000e-01, eta: 9:45:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4239\n2022-12-31 16:59:29,165 - mmcls - INFO - Epoch [21][3400/5005]\tlr: 1.000e-01, eta: 9:45:38, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.4322\n2022-12-31 16:59:37,752 - mmcls - INFO - Epoch [21][3500/5005]\tlr: 1.000e-01, eta: 9:45:28, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4340\n2022-12-31 16:59:46,737 - mmcls - INFO - Epoch [21][3600/5005]\tlr: 1.000e-01, eta: 9:45:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4367\n2022-12-31 16:59:55,378 - mmcls - INFO - Epoch [21][3700/5005]\tlr: 1.000e-01, eta: 9:45:10, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4392\n2022-12-31 17:00:04,087 - mmcls - INFO - Epoch [21][3800/5005]\tlr: 1.000e-01, eta: 9:45:01, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.4152\n2022-12-31 17:00:12,701 - mmcls - INFO - Epoch [21][3900/5005]\tlr: 1.000e-01, eta: 9:44:51, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4263\n2022-12-31 17:00:21,428 - mmcls - INFO - Epoch [21][4000/5005]\tlr: 1.000e-01, eta: 9:44:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.4380\n2022-12-31 17:00:30,092 - mmcls - INFO - Epoch [21][4100/5005]\tlr: 1.000e-01, eta: 9:44:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4201\n2022-12-31 17:00:38,948 - mmcls - INFO - Epoch [21][4200/5005]\tlr: 1.000e-01, eta: 9:44:23, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4348\n2022-12-31 17:00:47,698 - mmcls - INFO - Epoch [21][4300/5005]\tlr: 1.000e-01, eta: 9:44:14, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4059\n2022-12-31 17:00:56,543 - mmcls - INFO - Epoch [21][4400/5005]\tlr: 1.000e-01, eta: 9:44:05, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4103\n2022-12-31 17:01:05,272 - mmcls - INFO - Epoch [21][4500/5005]\tlr: 1.000e-01, eta: 9:43:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4195\n2022-12-31 17:01:13,923 - mmcls - INFO - Epoch [21][4600/5005]\tlr: 1.000e-01, eta: 9:43:46, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.4477\n2022-12-31 17:01:22,861 - mmcls - INFO - Epoch [21][4700/5005]\tlr: 1.000e-01, eta: 9:43:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4172\n2022-12-31 17:01:31,625 - mmcls - INFO - Epoch [21][4800/5005]\tlr: 1.000e-01, eta: 9:43:29, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4544\n2022-12-31 17:01:40,213 - mmcls - INFO - Epoch [21][4900/5005]\tlr: 1.000e-01, eta: 9:43:19, time: 0.086, data_time: 0.034, memory: 1224, loss: 2.4045\n2022-12-31 17:01:49,789 - mmcls - INFO - Epoch [21][5000/5005]\tlr: 1.000e-01, eta: 9:43:13, time: 0.096, data_time: 0.012, memory: 1224, loss: 2.4499\n2022-12-31 17:02:12,553 - mmcls - INFO - Epoch(val) [21][196]\taccuracy_top-1: 50.6840, accuracy_top-5: 76.6440\n2022-12-31 17:02:23,555 - mmcls - INFO - Epoch [22][100/5005]\tlr: 1.000e-01, eta: 9:43:10, time: 0.110, data_time: 0.036, memory: 1224, loss: 2.3784\n2022-12-31 17:02:32,349 - mmcls - INFO - Epoch [22][200/5005]\tlr: 1.000e-01, eta: 9:43:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3758\n2022-12-31 17:02:41,673 - mmcls - INFO - Epoch [22][300/5005]\tlr: 1.000e-01, eta: 9:42:54, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3608\n2022-12-31 17:02:50,176 - mmcls - INFO - Epoch [22][400/5005]\tlr: 1.000e-01, eta: 9:42:43, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.3893\n2022-12-31 17:02:58,633 - mmcls - INFO - Epoch [22][500/5005]\tlr: 1.000e-01, eta: 9:42:33, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.3683\n2022-12-31 17:03:07,436 - mmcls - INFO - Epoch [22][600/5005]\tlr: 1.000e-01, eta: 9:42:24, time: 0.088, data_time: 0.032, memory: 1224, loss: 2.3808\n2022-12-31 17:03:16,137 - mmcls - INFO - Epoch [22][700/5005]\tlr: 1.000e-01, eta: 9:42:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3798\n2022-12-31 17:03:24,896 - mmcls - INFO - Epoch [22][800/5005]\tlr: 1.000e-01, eta: 9:42:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4183\n2022-12-31 17:03:33,790 - mmcls - INFO - Epoch [22][900/5005]\tlr: 1.000e-01, eta: 9:41:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4126\n2022-12-31 17:03:42,578 - mmcls - INFO - Epoch [22][1000/5005]\tlr: 1.000e-01, eta: 9:41:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4168\n2022-12-31 17:03:51,276 - mmcls - INFO - Epoch [22][1100/5005]\tlr: 1.000e-01, eta: 9:41:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4084\n2022-12-31 17:03:59,886 - mmcls - INFO - Epoch [22][1200/5005]\tlr: 1.000e-01, eta: 9:41:29, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.3794\n2022-12-31 17:04:09,599 - mmcls - INFO - Epoch [22][1300/5005]\tlr: 1.000e-01, eta: 9:41:23, time: 0.097, data_time: 0.016, memory: 1224, loss: 2.3887\n2022-12-31 17:04:18,366 - mmcls - INFO - Epoch [22][1400/5005]\tlr: 1.000e-01, eta: 9:41:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4217\n2022-12-31 17:04:26,988 - mmcls - INFO - Epoch [22][1500/5005]\tlr: 1.000e-01, eta: 9:41:04, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.3852\n2022-12-31 17:04:36,025 - mmcls - INFO - Epoch [22][1600/5005]\tlr: 1.000e-01, eta: 9:40:56, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.3993\n2022-12-31 17:04:44,810 - mmcls - INFO - Epoch [22][1700/5005]\tlr: 1.000e-01, eta: 9:40:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4189\n2022-12-31 17:04:53,659 - mmcls - INFO - Epoch [22][1800/5005]\tlr: 1.000e-01, eta: 9:40:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4067\n2022-12-31 17:05:02,531 - mmcls - INFO - Epoch [22][1900/5005]\tlr: 1.000e-01, eta: 9:40:29, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3934\n2022-12-31 17:05:11,075 - mmcls - INFO - Epoch [22][2000/5005]\tlr: 1.000e-01, eta: 9:40:19, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.4108\n2022-12-31 17:05:19,742 - mmcls - INFO - Epoch [22][2100/5005]\tlr: 1.000e-01, eta: 9:40:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4290\n2022-12-31 17:05:28,392 - mmcls - INFO - Epoch [22][2200/5005]\tlr: 1.000e-01, eta: 9:40:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4246\n2022-12-31 17:05:37,154 - mmcls - INFO - Epoch [22][2300/5005]\tlr: 1.000e-01, eta: 9:39:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4298\n2022-12-31 17:05:46,037 - mmcls - INFO - Epoch [22][2400/5005]\tlr: 1.000e-01, eta: 9:39:42, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4329\n2022-12-31 17:05:55,077 - mmcls - INFO - Epoch [22][2500/5005]\tlr: 1.000e-01, eta: 9:39:34, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.4167\n2022-12-31 17:06:03,862 - mmcls - INFO - Epoch [22][2600/5005]\tlr: 1.000e-01, eta: 9:39:25, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4319\n2022-12-31 17:06:12,587 - mmcls - INFO - Epoch [22][2700/5005]\tlr: 1.000e-01, eta: 9:39:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4187\n2022-12-31 17:06:21,272 - mmcls - INFO - Epoch [22][2800/5005]\tlr: 1.000e-01, eta: 9:39:06, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4091\n2022-12-31 17:06:29,841 - mmcls - INFO - Epoch [22][2900/5005]\tlr: 1.000e-01, eta: 9:38:56, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4370\n2022-12-31 17:06:38,517 - mmcls - INFO - Epoch [22][3000/5005]\tlr: 1.000e-01, eta: 9:38:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4214\n2022-12-31 17:06:47,561 - mmcls - INFO - Epoch [22][3100/5005]\tlr: 1.000e-01, eta: 9:38:39, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4112\n2022-12-31 17:06:56,522 - mmcls - INFO - Epoch [22][3200/5005]\tlr: 1.000e-01, eta: 9:38:30, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.4457\n2022-12-31 17:07:05,681 - mmcls - INFO - Epoch [22][3300/5005]\tlr: 1.000e-01, eta: 9:38:22, time: 0.092, data_time: 0.000, memory: 1224, loss: 2.4133\n2022-12-31 17:07:14,532 - mmcls - INFO - Epoch [22][3400/5005]\tlr: 1.000e-01, eta: 9:38:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4310\n2022-12-31 17:07:23,174 - mmcls - INFO - Epoch [22][3500/5005]\tlr: 1.000e-01, eta: 9:38:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4269\n2022-12-31 17:07:31,919 - mmcls - INFO - Epoch [22][3600/5005]\tlr: 1.000e-01, eta: 9:37:55, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4319\n2022-12-31 17:07:40,654 - mmcls - INFO - Epoch [22][3700/5005]\tlr: 1.000e-01, eta: 9:37:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3976\n2022-12-31 17:07:49,308 - mmcls - INFO - Epoch [22][3800/5005]\tlr: 1.000e-01, eta: 9:37:36, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4342\n2022-12-31 17:07:57,810 - mmcls - INFO - Epoch [22][3900/5005]\tlr: 1.000e-01, eta: 9:37:26, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4306\n2022-12-31 17:08:06,644 - mmcls - INFO - Epoch [22][4000/5005]\tlr: 1.000e-01, eta: 9:37:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4288\n2022-12-31 17:08:15,357 - mmcls - INFO - Epoch [22][4100/5005]\tlr: 1.000e-01, eta: 9:37:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4065\n2022-12-31 17:08:24,554 - mmcls - INFO - Epoch [22][4200/5005]\tlr: 1.000e-01, eta: 9:37:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4043\n2022-12-31 17:08:33,288 - mmcls - INFO - Epoch [22][4300/5005]\tlr: 1.000e-01, eta: 9:36:51, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.4152\n2022-12-31 17:08:42,189 - mmcls - INFO - Epoch [22][4400/5005]\tlr: 1.000e-01, eta: 9:36:42, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4191\n2022-12-31 17:08:50,759 - mmcls - INFO - Epoch [22][4500/5005]\tlr: 1.000e-01, eta: 9:36:32, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4410\n2022-12-31 17:08:59,549 - mmcls - INFO - Epoch [22][4600/5005]\tlr: 1.000e-01, eta: 9:36:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4127\n2022-12-31 17:09:08,289 - mmcls - INFO - Epoch [22][4700/5005]\tlr: 1.000e-01, eta: 9:36:14, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.4035\n2022-12-31 17:09:17,153 - mmcls - INFO - Epoch [22][4800/5005]\tlr: 1.000e-01, eta: 9:36:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3968\n2022-12-31 17:09:26,342 - mmcls - INFO - Epoch [22][4900/5005]\tlr: 1.000e-01, eta: 9:35:57, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4106\n2022-12-31 17:09:35,096 - mmcls - INFO - Epoch [22][5000/5005]\tlr: 1.000e-01, eta: 9:35:48, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4401\n2022-12-31 17:09:57,766 - mmcls - INFO - Epoch(val) [22][196]\taccuracy_top-1: 51.9980, accuracy_top-5: 77.2480\n2022-12-31 17:10:08,652 - mmcls - INFO - Epoch [23][100/5005]\tlr: 1.000e-01, eta: 9:35:44, time: 0.109, data_time: 0.043, memory: 1224, loss: 2.3654\n2022-12-31 17:10:17,818 - mmcls - INFO - Epoch [23][200/5005]\tlr: 1.000e-01, eta: 9:35:37, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.3778\n2022-12-31 17:10:26,508 - mmcls - INFO - Epoch [23][300/5005]\tlr: 1.000e-01, eta: 9:35:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3672\n2022-12-31 17:10:35,264 - mmcls - INFO - Epoch [23][400/5005]\tlr: 1.000e-01, eta: 9:35:18, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.3438\n2022-12-31 17:10:44,049 - mmcls - INFO - Epoch [23][500/5005]\tlr: 1.000e-01, eta: 9:35:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4034\n2022-12-31 17:10:52,802 - mmcls - INFO - Epoch [23][600/5005]\tlr: 1.000e-01, eta: 9:35:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3626\n2022-12-31 17:11:01,802 - mmcls - INFO - Epoch [23][700/5005]\tlr: 1.000e-01, eta: 9:34:51, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4028\n2022-12-31 17:11:10,487 - mmcls - INFO - Epoch [23][800/5005]\tlr: 1.000e-01, eta: 9:34:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3758\n2022-12-31 17:11:19,399 - mmcls - INFO - Epoch [23][900/5005]\tlr: 1.000e-01, eta: 9:34:33, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3987\n2022-12-31 17:11:28,146 - mmcls - INFO - Epoch [23][1000/5005]\tlr: 1.000e-01, eta: 9:34:24, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4180\n2022-12-31 17:11:37,422 - mmcls - INFO - Epoch [23][1100/5005]\tlr: 1.000e-01, eta: 9:34:17, time: 0.093, data_time: 0.009, memory: 1224, loss: 2.3990\n2022-12-31 17:11:46,275 - mmcls - INFO - Epoch [23][1200/5005]\tlr: 1.000e-01, eta: 9:34:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4134\n2022-12-31 17:11:54,870 - mmcls - INFO - Epoch [23][1300/5005]\tlr: 1.000e-01, eta: 9:33:58, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.3948\n2022-12-31 17:12:03,582 - mmcls - INFO - Epoch [23][1400/5005]\tlr: 1.000e-01, eta: 9:33:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.3761\n2022-12-31 17:12:12,342 - mmcls - INFO - Epoch [23][1500/5005]\tlr: 1.000e-01, eta: 9:33:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3880\n2022-12-31 17:12:21,239 - mmcls - INFO - Epoch [23][1600/5005]\tlr: 1.000e-01, eta: 9:33:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4204\n2022-12-31 17:12:29,885 - mmcls - INFO - Epoch [23][1700/5005]\tlr: 1.000e-01, eta: 9:33:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4298\n2022-12-31 17:12:38,588 - mmcls - INFO - Epoch [23][1800/5005]\tlr: 1.000e-01, eta: 9:33:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4132\n2022-12-31 17:12:47,295 - mmcls - INFO - Epoch [23][1900/5005]\tlr: 1.000e-01, eta: 9:33:03, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4156\n2022-12-31 17:12:55,981 - mmcls - INFO - Epoch [23][2000/5005]\tlr: 1.000e-01, eta: 9:32:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4238\n2022-12-31 17:13:04,645 - mmcls - INFO - Epoch [23][2100/5005]\tlr: 1.000e-01, eta: 9:32:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4358\n2022-12-31 17:13:13,252 - mmcls - INFO - Epoch [23][2200/5005]\tlr: 1.000e-01, eta: 9:32:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3996\n2022-12-31 17:13:22,178 - mmcls - INFO - Epoch [23][2300/5005]\tlr: 1.000e-01, eta: 9:32:25, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4049\n2022-12-31 17:13:30,663 - mmcls - INFO - Epoch [23][2400/5005]\tlr: 1.000e-01, eta: 9:32:15, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4332\n2022-12-31 17:13:39,438 - mmcls - INFO - Epoch [23][2500/5005]\tlr: 1.000e-01, eta: 9:32:06, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4047\n2022-12-31 17:13:47,938 - mmcls - INFO - Epoch [23][2600/5005]\tlr: 1.000e-01, eta: 9:31:56, time: 0.085, data_time: 0.030, memory: 1224, loss: 2.4228\n2022-12-31 17:13:56,844 - mmcls - INFO - Epoch [23][2700/5005]\tlr: 1.000e-01, eta: 9:31:47, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4107\n2022-12-31 17:14:05,724 - mmcls - INFO - Epoch [23][2800/5005]\tlr: 1.000e-01, eta: 9:31:39, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4036\n2022-12-31 17:14:14,503 - mmcls - INFO - Epoch [23][2900/5005]\tlr: 1.000e-01, eta: 9:31:30, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.4210\n2022-12-31 17:14:23,717 - mmcls - INFO - Epoch [23][3000/5005]\tlr: 1.000e-01, eta: 9:31:22, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4177\n2022-12-31 17:14:32,408 - mmcls - INFO - Epoch [23][3100/5005]\tlr: 1.000e-01, eta: 9:31:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4212\n2022-12-31 17:14:40,990 - mmcls - INFO - Epoch [23][3200/5005]\tlr: 1.000e-01, eta: 9:31:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4056\n2022-12-31 17:14:49,952 - mmcls - INFO - Epoch [23][3300/5005]\tlr: 1.000e-01, eta: 9:30:54, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.4216\n2022-12-31 17:14:58,566 - mmcls - INFO - Epoch [23][3400/5005]\tlr: 1.000e-01, eta: 9:30:45, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.4073\n2022-12-31 17:15:07,798 - mmcls - INFO - Epoch [23][3500/5005]\tlr: 1.000e-01, eta: 9:30:37, time: 0.092, data_time: 0.011, memory: 1224, loss: 2.4229\n2022-12-31 17:15:17,183 - mmcls - INFO - Epoch [23][3600/5005]\tlr: 1.000e-01, eta: 9:30:30, time: 0.094, data_time: 0.011, memory: 1224, loss: 2.4299\n2022-12-31 17:15:26,017 - mmcls - INFO - Epoch [23][3700/5005]\tlr: 1.000e-01, eta: 9:30:21, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.4279\n2022-12-31 17:15:34,997 - mmcls - INFO - Epoch [23][3800/5005]\tlr: 1.000e-01, eta: 9:30:13, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4438\n2022-12-31 17:15:43,791 - mmcls - INFO - Epoch [23][3900/5005]\tlr: 1.000e-01, eta: 9:30:04, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.4088\n2022-12-31 17:15:52,926 - mmcls - INFO - Epoch [23][4000/5005]\tlr: 1.000e-01, eta: 9:29:56, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.3956\n2022-12-31 17:16:01,593 - mmcls - INFO - Epoch [23][4100/5005]\tlr: 1.000e-01, eta: 9:29:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4365\n2022-12-31 17:16:10,316 - mmcls - INFO - Epoch [23][4200/5005]\tlr: 1.000e-01, eta: 9:29:37, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.4172\n2022-12-31 17:16:19,026 - mmcls - INFO - Epoch [23][4300/5005]\tlr: 1.000e-01, eta: 9:29:28, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.4074\n2022-12-31 17:16:27,979 - mmcls - INFO - Epoch [23][4400/5005]\tlr: 1.000e-01, eta: 9:29:19, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4107\n2022-12-31 17:16:37,004 - mmcls - INFO - Epoch [23][4500/5005]\tlr: 1.000e-01, eta: 9:29:11, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4120\n2022-12-31 17:16:45,804 - mmcls - INFO - Epoch [23][4600/5005]\tlr: 1.000e-01, eta: 9:29:02, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.4281\n2022-12-31 17:16:54,293 - mmcls - INFO - Epoch [23][4700/5005]\tlr: 1.000e-01, eta: 9:28:52, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.4144\n2022-12-31 17:17:03,507 - mmcls - INFO - Epoch [23][4800/5005]\tlr: 1.000e-01, eta: 9:28:44, time: 0.092, data_time: 0.006, memory: 1224, loss: 2.4171\n2022-12-31 17:17:12,345 - mmcls - INFO - Epoch [23][4900/5005]\tlr: 1.000e-01, eta: 9:28:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4026\n2022-12-31 17:17:20,980 - mmcls - INFO - Epoch [23][5000/5005]\tlr: 1.000e-01, eta: 9:28:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4022\n2022-12-31 17:17:43,795 - mmcls - INFO - Epoch(val) [23][196]\taccuracy_top-1: 50.9380, accuracy_top-5: 76.6960\n2022-12-31 17:17:55,020 - mmcls - INFO - Epoch [24][100/5005]\tlr: 1.000e-01, eta: 9:28:23, time: 0.112, data_time: 0.060, memory: 1224, loss: 2.3756\n2022-12-31 17:18:04,230 - mmcls - INFO - Epoch [24][200/5005]\tlr: 1.000e-01, eta: 9:28:15, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.3690\n2022-12-31 17:18:13,016 - mmcls - INFO - Epoch [24][300/5005]\tlr: 1.000e-01, eta: 9:28:06, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.3634\n2022-12-31 17:18:21,951 - mmcls - INFO - Epoch [24][400/5005]\tlr: 1.000e-01, eta: 9:27:58, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.3946\n2022-12-31 17:18:30,661 - mmcls - INFO - Epoch [24][500/5005]\tlr: 1.000e-01, eta: 9:27:48, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3878\n2022-12-31 17:18:39,622 - mmcls - INFO - Epoch [24][600/5005]\tlr: 1.000e-01, eta: 9:27:40, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.3817\n2022-12-31 17:18:48,428 - mmcls - INFO - Epoch [24][700/5005]\tlr: 1.000e-01, eta: 9:27:31, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.3823\n2022-12-31 17:18:57,170 - mmcls - INFO - Epoch [24][800/5005]\tlr: 1.000e-01, eta: 9:27:22, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.4212\n2022-12-31 17:19:05,862 - mmcls - INFO - Epoch [24][900/5005]\tlr: 1.000e-01, eta: 9:27:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4116\n2022-12-31 17:19:14,442 - mmcls - INFO - Epoch [24][1000/5005]\tlr: 1.000e-01, eta: 9:27:02, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.3980\n2022-12-31 17:19:23,068 - mmcls - INFO - Epoch [24][1100/5005]\tlr: 1.000e-01, eta: 9:26:53, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3945\n2022-12-31 17:19:31,822 - mmcls - INFO - Epoch [24][1200/5005]\tlr: 1.000e-01, eta: 9:26:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3924\n2022-12-31 17:19:40,405 - mmcls - INFO - Epoch [24][1300/5005]\tlr: 1.000e-01, eta: 9:26:34, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.3972\n2022-12-31 17:19:49,608 - mmcls - INFO - Epoch [24][1400/5005]\tlr: 1.000e-01, eta: 9:26:26, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.4048\n2022-12-31 17:19:58,451 - mmcls - INFO - Epoch [24][1500/5005]\tlr: 1.000e-01, eta: 9:26:17, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3952\n2022-12-31 17:20:07,481 - mmcls - INFO - Epoch [24][1600/5005]\tlr: 1.000e-01, eta: 9:26:09, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.3906\n2022-12-31 17:20:16,289 - mmcls - INFO - Epoch [24][1700/5005]\tlr: 1.000e-01, eta: 9:26:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4045\n2022-12-31 17:20:24,878 - mmcls - INFO - Epoch [24][1800/5005]\tlr: 1.000e-01, eta: 9:25:50, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3838\n2022-12-31 17:20:33,623 - mmcls - INFO - Epoch [24][1900/5005]\tlr: 1.000e-01, eta: 9:25:41, time: 0.087, data_time: 0.035, memory: 1224, loss: 2.4226\n2022-12-31 17:20:42,365 - mmcls - INFO - Epoch [24][2000/5005]\tlr: 1.000e-01, eta: 9:25:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3981\n2022-12-31 17:20:51,123 - mmcls - INFO - Epoch [24][2100/5005]\tlr: 1.000e-01, eta: 9:25:23, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4340\n2022-12-31 17:20:59,803 - mmcls - INFO - Epoch [24][2200/5005]\tlr: 1.000e-01, eta: 9:25:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4215\n2022-12-31 17:21:08,882 - mmcls - INFO - Epoch [24][2300/5005]\tlr: 1.000e-01, eta: 9:25:05, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.3891\n2022-12-31 17:21:17,722 - mmcls - INFO - Epoch [24][2400/5005]\tlr: 1.000e-01, eta: 9:24:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4015\n2022-12-31 17:21:26,465 - mmcls - INFO - Epoch [24][2500/5005]\tlr: 1.000e-01, eta: 9:24:47, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4135\n2022-12-31 17:21:35,052 - mmcls - INFO - Epoch [24][2600/5005]\tlr: 1.000e-01, eta: 9:24:37, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3711\n2022-12-31 17:21:43,710 - mmcls - INFO - Epoch [24][2700/5005]\tlr: 1.000e-01, eta: 9:24:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3950\n2022-12-31 17:21:52,338 - mmcls - INFO - Epoch [24][2800/5005]\tlr: 1.000e-01, eta: 9:24:18, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.4010\n2022-12-31 17:22:01,191 - mmcls - INFO - Epoch [24][2900/5005]\tlr: 1.000e-01, eta: 9:24:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4215\n2022-12-31 17:22:10,292 - mmcls - INFO - Epoch [24][3000/5005]\tlr: 1.000e-01, eta: 9:24:02, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4043\n2022-12-31 17:22:19,044 - mmcls - INFO - Epoch [24][3100/5005]\tlr: 1.000e-01, eta: 9:23:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4267\n2022-12-31 17:22:27,758 - mmcls - INFO - Epoch [24][3200/5005]\tlr: 1.000e-01, eta: 9:23:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4006\n2022-12-31 17:22:36,566 - mmcls - INFO - Epoch [24][3300/5005]\tlr: 1.000e-01, eta: 9:23:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3927\n2022-12-31 17:22:45,356 - mmcls - INFO - Epoch [24][3400/5005]\tlr: 1.000e-01, eta: 9:23:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4224\n2022-12-31 17:22:54,529 - mmcls - INFO - Epoch [24][3500/5005]\tlr: 1.000e-01, eta: 9:23:17, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4081\n2022-12-31 17:23:03,150 - mmcls - INFO - Epoch [24][3600/5005]\tlr: 1.000e-01, eta: 9:23:08, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.3924\n2022-12-31 17:23:11,892 - mmcls - INFO - Epoch [24][3700/5005]\tlr: 1.000e-01, eta: 9:22:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4302\n2022-12-31 17:23:21,689 - mmcls - INFO - Epoch [24][3800/5005]\tlr: 1.000e-01, eta: 9:22:53, time: 0.098, data_time: 0.001, memory: 1224, loss: 2.4000\n2022-12-31 17:23:30,328 - mmcls - INFO - Epoch [24][3900/5005]\tlr: 1.000e-01, eta: 9:22:43, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4140\n2022-12-31 17:23:38,925 - mmcls - INFO - Epoch [24][4000/5005]\tlr: 1.000e-01, eta: 9:22:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4255\n2022-12-31 17:23:47,576 - mmcls - INFO - Epoch [24][4100/5005]\tlr: 1.000e-01, eta: 9:22:24, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.3964\n2022-12-31 17:23:56,400 - mmcls - INFO - Epoch [24][4200/5005]\tlr: 1.000e-01, eta: 9:22:15, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4100\n2022-12-31 17:24:05,611 - mmcls - INFO - Epoch [24][4300/5005]\tlr: 1.000e-01, eta: 9:22:07, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.4041\n2022-12-31 17:24:14,260 - mmcls - INFO - Epoch [24][4400/5005]\tlr: 1.000e-01, eta: 9:21:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4333\n2022-12-31 17:24:23,064 - mmcls - INFO - Epoch [24][4500/5005]\tlr: 1.000e-01, eta: 9:21:49, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.3924\n2022-12-31 17:24:31,853 - mmcls - INFO - Epoch [24][4600/5005]\tlr: 1.000e-01, eta: 9:21:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.3991\n2022-12-31 17:24:40,599 - mmcls - INFO - Epoch [24][4700/5005]\tlr: 1.000e-01, eta: 9:21:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3777\n2022-12-31 17:24:49,235 - mmcls - INFO - Epoch [24][4800/5005]\tlr: 1.000e-01, eta: 9:21:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4342\n2022-12-31 17:24:57,756 - mmcls - INFO - Epoch [24][4900/5005]\tlr: 1.000e-01, eta: 9:21:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.4217\n2022-12-31 17:25:06,520 - mmcls - INFO - Epoch [24][5000/5005]\tlr: 1.000e-01, eta: 9:21:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3913\n2022-12-31 17:25:29,430 - mmcls - INFO - Epoch(val) [24][196]\taccuracy_top-1: 52.0580, accuracy_top-5: 77.5200\n2022-12-31 17:25:40,723 - mmcls - INFO - Epoch [25][100/5005]\tlr: 1.000e-01, eta: 9:20:59, time: 0.113, data_time: 0.031, memory: 1224, loss: 2.3601\n2022-12-31 17:25:49,649 - mmcls - INFO - Epoch [25][200/5005]\tlr: 1.000e-01, eta: 9:20:50, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.3385\n2022-12-31 17:25:58,603 - mmcls - INFO - Epoch [25][300/5005]\tlr: 1.000e-01, eta: 9:20:42, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.3693\n2022-12-31 17:26:07,560 - mmcls - INFO - Epoch [25][400/5005]\tlr: 1.000e-01, eta: 9:20:33, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.4133\n2022-12-31 17:26:16,259 - mmcls - INFO - Epoch [25][500/5005]\tlr: 1.000e-01, eta: 9:20:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3695\n2022-12-31 17:26:24,892 - mmcls - INFO - Epoch [25][600/5005]\tlr: 1.000e-01, eta: 9:20:14, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.3636\n2022-12-31 17:26:33,653 - mmcls - INFO - Epoch [25][700/5005]\tlr: 1.000e-01, eta: 9:20:05, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3817\n2022-12-31 17:26:42,283 - mmcls - INFO - Epoch [25][800/5005]\tlr: 1.000e-01, eta: 9:19:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3833\n2022-12-31 17:26:51,474 - mmcls - INFO - Epoch [25][900/5005]\tlr: 1.000e-01, eta: 9:19:48, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3911\n2022-12-31 17:27:00,105 - mmcls - INFO - Epoch [25][1000/5005]\tlr: 1.000e-01, eta: 9:19:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3970\n2022-12-31 17:27:08,877 - mmcls - INFO - Epoch [25][1100/5005]\tlr: 1.000e-01, eta: 9:19:29, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3563\n2022-12-31 17:27:17,721 - mmcls - INFO - Epoch [25][1200/5005]\tlr: 1.000e-01, eta: 9:19:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3900\n2022-12-31 17:27:26,549 - mmcls - INFO - Epoch [25][1300/5005]\tlr: 1.000e-01, eta: 9:19:12, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.3868\n2022-12-31 17:27:35,663 - mmcls - INFO - Epoch [25][1400/5005]\tlr: 1.000e-01, eta: 9:19:04, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.3813\n2022-12-31 17:27:44,226 - mmcls - INFO - Epoch [25][1500/5005]\tlr: 1.000e-01, eta: 9:18:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4028\n2022-12-31 17:27:53,241 - mmcls - INFO - Epoch [25][1600/5005]\tlr: 1.000e-01, eta: 9:18:45, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.3832\n2022-12-31 17:28:01,906 - mmcls - INFO - Epoch [25][1700/5005]\tlr: 1.000e-01, eta: 9:18:36, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4222\n2022-12-31 17:28:10,786 - mmcls - INFO - Epoch [25][1800/5005]\tlr: 1.000e-01, eta: 9:18:27, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4115\n2022-12-31 17:28:19,404 - mmcls - INFO - Epoch [25][1900/5005]\tlr: 1.000e-01, eta: 9:18:18, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.4090\n2022-12-31 17:28:28,223 - mmcls - INFO - Epoch [25][2000/5005]\tlr: 1.000e-01, eta: 9:18:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3943\n2022-12-31 17:28:37,137 - mmcls - INFO - Epoch [25][2100/5005]\tlr: 1.000e-01, eta: 9:18:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4103\n2022-12-31 17:28:45,845 - mmcls - INFO - Epoch [25][2200/5005]\tlr: 1.000e-01, eta: 9:17:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3884\n2022-12-31 17:28:54,506 - mmcls - INFO - Epoch [25][2300/5005]\tlr: 1.000e-01, eta: 9:17:41, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4046\n2022-12-31 17:29:03,252 - mmcls - INFO - Epoch [25][2400/5005]\tlr: 1.000e-01, eta: 9:17:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3909\n2022-12-31 17:29:12,070 - mmcls - INFO - Epoch [25][2500/5005]\tlr: 1.000e-01, eta: 9:17:23, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4184\n2022-12-31 17:29:20,775 - mmcls - INFO - Epoch [25][2600/5005]\tlr: 1.000e-01, eta: 9:17:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3990\n2022-12-31 17:29:29,804 - mmcls - INFO - Epoch [25][2700/5005]\tlr: 1.000e-01, eta: 9:17:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4070\n2022-12-31 17:29:38,506 - mmcls - INFO - Epoch [25][2800/5005]\tlr: 1.000e-01, eta: 9:16:56, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4165\n2022-12-31 17:29:47,305 - mmcls - INFO - Epoch [25][2900/5005]\tlr: 1.000e-01, eta: 9:16:47, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3872\n2022-12-31 17:29:56,070 - mmcls - INFO - Epoch [25][3000/5005]\tlr: 1.000e-01, eta: 9:16:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3808\n2022-12-31 17:30:04,930 - mmcls - INFO - Epoch [25][3100/5005]\tlr: 1.000e-01, eta: 9:16:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4145\n2022-12-31 17:30:13,628 - mmcls - INFO - Epoch [25][3200/5005]\tlr: 1.000e-01, eta: 9:16:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3965\n2022-12-31 17:30:22,418 - mmcls - INFO - Epoch [25][3300/5005]\tlr: 1.000e-01, eta: 9:16:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3930\n2022-12-31 17:30:31,186 - mmcls - INFO - Epoch [25][3400/5005]\tlr: 1.000e-01, eta: 9:16:02, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4074\n2022-12-31 17:30:39,980 - mmcls - INFO - Epoch [25][3500/5005]\tlr: 1.000e-01, eta: 9:15:53, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4185\n2022-12-31 17:30:48,627 - mmcls - INFO - Epoch [25][3600/5005]\tlr: 1.000e-01, eta: 9:15:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4075\n2022-12-31 17:30:57,321 - mmcls - INFO - Epoch [25][3700/5005]\tlr: 1.000e-01, eta: 9:15:34, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.4037\n2022-12-31 17:31:06,027 - mmcls - INFO - Epoch [25][3800/5005]\tlr: 1.000e-01, eta: 9:15:25, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.3940\n2022-12-31 17:31:15,063 - mmcls - INFO - Epoch [25][3900/5005]\tlr: 1.000e-01, eta: 9:15:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4204\n2022-12-31 17:31:23,966 - mmcls - INFO - Epoch [25][4000/5005]\tlr: 1.000e-01, eta: 9:15:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4034\n2022-12-31 17:31:32,585 - mmcls - INFO - Epoch [25][4100/5005]\tlr: 1.000e-01, eta: 9:14:58, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4028\n2022-12-31 17:31:41,336 - mmcls - INFO - Epoch [25][4200/5005]\tlr: 1.000e-01, eta: 9:14:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4247\n2022-12-31 17:31:50,245 - mmcls - INFO - Epoch [25][4300/5005]\tlr: 1.000e-01, eta: 9:14:40, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.4011\n2022-12-31 17:31:58,862 - mmcls - INFO - Epoch [25][4400/5005]\tlr: 1.000e-01, eta: 9:14:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3771\n2022-12-31 17:32:07,577 - mmcls - INFO - Epoch [25][4500/5005]\tlr: 1.000e-01, eta: 9:14:22, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.3915\n2022-12-31 17:32:16,209 - mmcls - INFO - Epoch [25][4600/5005]\tlr: 1.000e-01, eta: 9:14:12, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4332\n2022-12-31 17:32:25,215 - mmcls - INFO - Epoch [25][4700/5005]\tlr: 1.000e-01, eta: 9:14:04, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.4064\n2022-12-31 17:32:34,048 - mmcls - INFO - Epoch [25][4800/5005]\tlr: 1.000e-01, eta: 9:13:55, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4110\n2022-12-31 17:32:43,265 - mmcls - INFO - Epoch [25][4900/5005]\tlr: 1.000e-01, eta: 9:13:47, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4094\n2022-12-31 17:32:52,044 - mmcls - INFO - Epoch [25][5000/5005]\tlr: 1.000e-01, eta: 9:13:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4124\n2022-12-31 17:33:15,028 - mmcls - INFO - Epoch(val) [25][196]\taccuracy_top-1: 52.3140, accuracy_top-5: 77.8040\n2022-12-31 17:33:25,865 - mmcls - INFO - Epoch [26][100/5005]\tlr: 1.000e-01, eta: 9:13:33, time: 0.108, data_time: 0.032, memory: 1224, loss: 2.3777\n2022-12-31 17:33:34,735 - mmcls - INFO - Epoch [26][200/5005]\tlr: 1.000e-01, eta: 9:13:24, time: 0.089, data_time: 0.034, memory: 1224, loss: 2.3646\n2022-12-31 17:33:43,572 - mmcls - INFO - Epoch [26][300/5005]\tlr: 1.000e-01, eta: 9:13:16, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.3617\n2022-12-31 17:33:52,770 - mmcls - INFO - Epoch [26][400/5005]\tlr: 1.000e-01, eta: 9:13:08, time: 0.092, data_time: 0.014, memory: 1224, loss: 2.3656\n2022-12-31 17:34:01,749 - mmcls - INFO - Epoch [26][500/5005]\tlr: 1.000e-01, eta: 9:12:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3654\n2022-12-31 17:34:10,649 - mmcls - INFO - Epoch [26][600/5005]\tlr: 1.000e-01, eta: 9:12:51, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3606\n2022-12-31 17:34:19,308 - mmcls - INFO - Epoch [26][700/5005]\tlr: 1.000e-01, eta: 9:12:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3760\n2022-12-31 17:34:27,954 - mmcls - INFO - Epoch [26][800/5005]\tlr: 1.000e-01, eta: 9:12:32, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.3738\n2022-12-31 17:34:37,107 - mmcls - INFO - Epoch [26][900/5005]\tlr: 1.000e-01, eta: 9:12:24, time: 0.092, data_time: 0.013, memory: 1224, loss: 2.3962\n2022-12-31 17:34:45,917 - mmcls - INFO - Epoch [26][1000/5005]\tlr: 1.000e-01, eta: 9:12:15, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3793\n2022-12-31 17:34:54,744 - mmcls - INFO - Epoch [26][1100/5005]\tlr: 1.000e-01, eta: 9:12:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3894\n2022-12-31 17:35:03,697 - mmcls - INFO - Epoch [26][1200/5005]\tlr: 1.000e-01, eta: 9:11:57, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.3823\n2022-12-31 17:35:12,383 - mmcls - INFO - Epoch [26][1300/5005]\tlr: 1.000e-01, eta: 9:11:48, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.3936\n2022-12-31 17:35:21,137 - mmcls - INFO - Epoch [26][1400/5005]\tlr: 1.000e-01, eta: 9:11:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.3947\n2022-12-31 17:35:30,045 - mmcls - INFO - Epoch [26][1500/5005]\tlr: 1.000e-01, eta: 9:11:30, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3835\n2022-12-31 17:35:38,705 - mmcls - INFO - Epoch [26][1600/5005]\tlr: 1.000e-01, eta: 9:11:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4182\n2022-12-31 17:35:47,910 - mmcls - INFO - Epoch [26][1700/5005]\tlr: 1.000e-01, eta: 9:11:13, time: 0.092, data_time: 0.006, memory: 1224, loss: 2.3727\n2022-12-31 17:35:56,736 - mmcls - INFO - Epoch [26][1800/5005]\tlr: 1.000e-01, eta: 9:11:04, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4019\n2022-12-31 17:36:05,728 - mmcls - INFO - Epoch [26][1900/5005]\tlr: 1.000e-01, eta: 9:10:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3850\n2022-12-31 17:36:14,655 - mmcls - INFO - Epoch [26][2000/5005]\tlr: 1.000e-01, eta: 9:10:47, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4105\n2022-12-31 17:36:23,317 - mmcls - INFO - Epoch [26][2100/5005]\tlr: 1.000e-01, eta: 9:10:37, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4255\n2022-12-31 17:36:32,235 - mmcls - INFO - Epoch [26][2200/5005]\tlr: 1.000e-01, eta: 9:10:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3763\n2022-12-31 17:36:41,283 - mmcls - INFO - Epoch [26][2300/5005]\tlr: 1.000e-01, eta: 9:10:21, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.4173\n2022-12-31 17:36:49,898 - mmcls - INFO - Epoch [26][2400/5005]\tlr: 1.000e-01, eta: 9:10:11, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.3802\n2022-12-31 17:36:58,648 - mmcls - INFO - Epoch [26][2500/5005]\tlr: 1.000e-01, eta: 9:10:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4000\n2022-12-31 17:37:07,264 - mmcls - INFO - Epoch [26][2600/5005]\tlr: 1.000e-01, eta: 9:09:52, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.3873\n2022-12-31 17:37:16,031 - mmcls - INFO - Epoch [26][2700/5005]\tlr: 1.000e-01, eta: 9:09:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4098\n2022-12-31 17:37:24,794 - mmcls - INFO - Epoch [26][2800/5005]\tlr: 1.000e-01, eta: 9:09:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4126\n2022-12-31 17:37:33,658 - mmcls - INFO - Epoch [26][2900/5005]\tlr: 1.000e-01, eta: 9:09:25, time: 0.089, data_time: 0.032, memory: 1224, loss: 2.3944\n2022-12-31 17:37:42,560 - mmcls - INFO - Epoch [26][3000/5005]\tlr: 1.000e-01, eta: 9:09:17, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.4071\n2022-12-31 17:37:50,961 - mmcls - INFO - Epoch [26][3100/5005]\tlr: 1.000e-01, eta: 9:09:06, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.4019\n2022-12-31 17:37:59,832 - mmcls - INFO - Epoch [26][3200/5005]\tlr: 1.000e-01, eta: 9:08:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4167\n2022-12-31 17:38:08,345 - mmcls - INFO - Epoch [26][3300/5005]\tlr: 1.000e-01, eta: 9:08:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4104\n2022-12-31 17:38:16,995 - mmcls - INFO - Epoch [26][3400/5005]\tlr: 1.000e-01, eta: 9:08:38, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4053\n2022-12-31 17:38:25,781 - mmcls - INFO - Epoch [26][3500/5005]\tlr: 1.000e-01, eta: 9:08:29, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4133\n2022-12-31 17:38:34,422 - mmcls - INFO - Epoch [26][3600/5005]\tlr: 1.000e-01, eta: 9:08:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.3939\n2022-12-31 17:38:43,793 - mmcls - INFO - Epoch [26][3700/5005]\tlr: 1.000e-01, eta: 9:08:13, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.4013\n2022-12-31 17:38:52,549 - mmcls - INFO - Epoch [26][3800/5005]\tlr: 1.000e-01, eta: 9:08:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4234\n2022-12-31 17:39:01,441 - mmcls - INFO - Epoch [26][3900/5005]\tlr: 1.000e-01, eta: 9:07:55, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4197\n2022-12-31 17:39:10,317 - mmcls - INFO - Epoch [26][4000/5005]\tlr: 1.000e-01, eta: 9:07:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4166\n2022-12-31 17:39:19,202 - mmcls - INFO - Epoch [26][4100/5005]\tlr: 1.000e-01, eta: 9:07:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3902\n2022-12-31 17:39:27,648 - mmcls - INFO - Epoch [26][4200/5005]\tlr: 1.000e-01, eta: 9:07:27, time: 0.084, data_time: 0.012, memory: 1224, loss: 2.4299\n2022-12-31 17:39:36,558 - mmcls - INFO - Epoch [26][4300/5005]\tlr: 1.000e-01, eta: 9:07:18, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.4112\n2022-12-31 17:39:45,333 - mmcls - INFO - Epoch [26][4400/5005]\tlr: 1.000e-01, eta: 9:07:09, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4024\n2022-12-31 17:39:54,128 - mmcls - INFO - Epoch [26][4500/5005]\tlr: 1.000e-01, eta: 9:07:00, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4097\n2022-12-31 17:40:02,789 - mmcls - INFO - Epoch [26][4600/5005]\tlr: 1.000e-01, eta: 9:06:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4085\n2022-12-31 17:40:11,560 - mmcls - INFO - Epoch [26][4700/5005]\tlr: 1.000e-01, eta: 9:06:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4104\n2022-12-31 17:40:20,514 - mmcls - INFO - Epoch [26][4800/5005]\tlr: 1.000e-01, eta: 9:06:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.4117\n2022-12-31 17:40:29,316 - mmcls - INFO - Epoch [26][4900/5005]\tlr: 1.000e-01, eta: 9:06:24, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.4205\n2022-12-31 17:40:38,078 - mmcls - INFO - Epoch [26][5000/5005]\tlr: 1.000e-01, eta: 9:06:15, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4024\n2022-12-31 17:41:00,723 - mmcls - INFO - Epoch(val) [26][196]\taccuracy_top-1: 51.3820, accuracy_top-5: 77.2600\n2022-12-31 17:41:11,756 - mmcls - INFO - Epoch [27][100/5005]\tlr: 1.000e-01, eta: 9:06:11, time: 0.110, data_time: 0.063, memory: 1224, loss: 2.3570\n2022-12-31 17:41:20,661 - mmcls - INFO - Epoch [27][200/5005]\tlr: 1.000e-01, eta: 9:06:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.3511\n2022-12-31 17:41:29,446 - mmcls - INFO - Epoch [27][300/5005]\tlr: 1.000e-01, eta: 9:05:53, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3544\n2022-12-31 17:41:38,198 - mmcls - INFO - Epoch [27][400/5005]\tlr: 1.000e-01, eta: 9:05:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.3602\n2022-12-31 17:41:46,861 - mmcls - INFO - Epoch [27][500/5005]\tlr: 1.000e-01, eta: 9:05:35, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4103\n2022-12-31 17:41:56,069 - mmcls - INFO - Epoch [27][600/5005]\tlr: 1.000e-01, eta: 9:05:27, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3531\n2022-12-31 17:42:04,731 - mmcls - INFO - Epoch [27][700/5005]\tlr: 1.000e-01, eta: 9:05:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4062\n2022-12-31 17:42:13,735 - mmcls - INFO - Epoch [27][800/5005]\tlr: 1.000e-01, eta: 9:05:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3800\n2022-12-31 17:42:22,503 - mmcls - INFO - Epoch [27][900/5005]\tlr: 1.000e-01, eta: 9:05:00, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3705\n2022-12-31 17:42:31,381 - mmcls - INFO - Epoch [27][1000/5005]\tlr: 1.000e-01, eta: 9:04:51, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3835\n2022-12-31 17:42:40,238 - mmcls - INFO - Epoch [27][1100/5005]\tlr: 1.000e-01, eta: 9:04:42, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3686\n2022-12-31 17:42:49,000 - mmcls - INFO - Epoch [27][1200/5005]\tlr: 1.000e-01, eta: 9:04:33, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3797\n2022-12-31 17:42:57,633 - mmcls - INFO - Epoch [27][1300/5005]\tlr: 1.000e-01, eta: 9:04:24, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.3889\n2022-12-31 17:43:06,309 - mmcls - INFO - Epoch [27][1400/5005]\tlr: 1.000e-01, eta: 9:04:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.3886\n2022-12-31 17:43:15,363 - mmcls - INFO - Epoch [27][1500/5005]\tlr: 1.000e-01, eta: 9:04:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3660\n2022-12-31 17:43:24,055 - mmcls - INFO - Epoch [27][1600/5005]\tlr: 1.000e-01, eta: 9:03:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3836\n2022-12-31 17:43:32,962 - mmcls - INFO - Epoch [27][1700/5005]\tlr: 1.000e-01, eta: 9:03:48, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.3693\n2022-12-31 17:43:41,779 - mmcls - INFO - Epoch [27][1800/5005]\tlr: 1.000e-01, eta: 9:03:39, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.3854\n2022-12-31 17:43:50,627 - mmcls - INFO - Epoch [27][1900/5005]\tlr: 1.000e-01, eta: 9:03:30, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3850\n2022-12-31 17:43:59,189 - mmcls - INFO - Epoch [27][2000/5005]\tlr: 1.000e-01, eta: 9:03:21, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3552\n2022-12-31 17:44:08,088 - mmcls - INFO - Epoch [27][2100/5005]\tlr: 1.000e-01, eta: 9:03:12, time: 0.089, data_time: 0.031, memory: 1224, loss: 2.4041\n2022-12-31 17:44:17,081 - mmcls - INFO - Epoch [27][2200/5005]\tlr: 1.000e-01, eta: 9:03:03, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4191\n2022-12-31 17:44:25,912 - mmcls - INFO - Epoch [27][2300/5005]\tlr: 1.000e-01, eta: 9:02:55, time: 0.088, data_time: 0.029, memory: 1224, loss: 2.3758\n2022-12-31 17:44:35,097 - mmcls - INFO - Epoch [27][2400/5005]\tlr: 1.000e-01, eta: 9:02:47, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.4102\n2022-12-31 17:44:44,027 - mmcls - INFO - Epoch [27][2500/5005]\tlr: 1.000e-01, eta: 9:02:38, time: 0.089, data_time: 0.038, memory: 1224, loss: 2.4004\n2022-12-31 17:44:52,712 - mmcls - INFO - Epoch [27][2600/5005]\tlr: 1.000e-01, eta: 9:02:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3980\n2022-12-31 17:45:01,465 - mmcls - INFO - Epoch [27][2700/5005]\tlr: 1.000e-01, eta: 9:02:20, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4008\n2022-12-31 17:45:09,996 - mmcls - INFO - Epoch [27][2800/5005]\tlr: 1.000e-01, eta: 9:02:10, time: 0.085, data_time: 0.028, memory: 1224, loss: 2.3729\n2022-12-31 17:45:18,742 - mmcls - INFO - Epoch [27][2900/5005]\tlr: 1.000e-01, eta: 9:02:01, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.4121\n2022-12-31 17:45:27,416 - mmcls - INFO - Epoch [27][3000/5005]\tlr: 1.000e-01, eta: 9:01:51, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.4029\n2022-12-31 17:45:36,148 - mmcls - INFO - Epoch [27][3100/5005]\tlr: 1.000e-01, eta: 9:01:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4114\n2022-12-31 17:45:45,116 - mmcls - INFO - Epoch [27][3200/5005]\tlr: 1.000e-01, eta: 9:01:34, time: 0.090, data_time: 0.029, memory: 1224, loss: 2.4123\n2022-12-31 17:45:53,772 - mmcls - INFO - Epoch [27][3300/5005]\tlr: 1.000e-01, eta: 9:01:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4039\n2022-12-31 17:46:02,892 - mmcls - INFO - Epoch [27][3400/5005]\tlr: 1.000e-01, eta: 9:01:16, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.3832\n2022-12-31 17:46:11,642 - mmcls - INFO - Epoch [27][3500/5005]\tlr: 1.000e-01, eta: 9:01:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3976\n2022-12-31 17:46:20,624 - mmcls - INFO - Epoch [27][3600/5005]\tlr: 1.000e-01, eta: 9:00:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4344\n2022-12-31 17:46:29,927 - mmcls - INFO - Epoch [27][3700/5005]\tlr: 1.000e-01, eta: 9:00:51, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.3941\n2022-12-31 17:46:39,012 - mmcls - INFO - Epoch [27][3800/5005]\tlr: 1.000e-01, eta: 9:00:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4113\n2022-12-31 17:46:47,887 - mmcls - INFO - Epoch [27][3900/5005]\tlr: 1.000e-01, eta: 9:00:34, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4207\n2022-12-31 17:46:56,921 - mmcls - INFO - Epoch [27][4000/5005]\tlr: 1.000e-01, eta: 9:00:26, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.4077\n2022-12-31 17:47:05,538 - mmcls - INFO - Epoch [27][4100/5005]\tlr: 1.000e-01, eta: 9:00:16, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4030\n2022-12-31 17:47:14,194 - mmcls - INFO - Epoch [27][4200/5005]\tlr: 1.000e-01, eta: 9:00:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.3738\n2022-12-31 17:47:23,229 - mmcls - INFO - Epoch [27][4300/5005]\tlr: 1.000e-01, eta: 8:59:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3890\n2022-12-31 17:47:32,467 - mmcls - INFO - Epoch [27][4400/5005]\tlr: 1.000e-01, eta: 8:59:51, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3795\n2022-12-31 17:47:41,097 - mmcls - INFO - Epoch [27][4500/5005]\tlr: 1.000e-01, eta: 8:59:41, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.3956\n2022-12-31 17:47:49,815 - mmcls - INFO - Epoch [27][4600/5005]\tlr: 1.000e-01, eta: 8:59:32, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4216\n2022-12-31 17:47:59,059 - mmcls - INFO - Epoch [27][4700/5005]\tlr: 1.000e-01, eta: 8:59:24, time: 0.093, data_time: 0.023, memory: 1224, loss: 2.3826\n2022-12-31 17:48:07,732 - mmcls - INFO - Epoch [27][4800/5005]\tlr: 1.000e-01, eta: 8:59:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3978\n2022-12-31 17:48:16,384 - mmcls - INFO - Epoch [27][4900/5005]\tlr: 1.000e-01, eta: 8:59:05, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3674\n2022-12-31 17:48:25,341 - mmcls - INFO - Epoch [27][5000/5005]\tlr: 1.000e-01, eta: 8:58:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3719\n2022-12-31 17:48:47,905 - mmcls - INFO - Epoch(val) [27][196]\taccuracy_top-1: 52.4000, accuracy_top-5: 77.7660\n2022-12-31 17:48:59,038 - mmcls - INFO - Epoch [28][100/5005]\tlr: 1.000e-01, eta: 8:58:52, time: 0.111, data_time: 0.048, memory: 1224, loss: 2.3523\n2022-12-31 17:49:07,628 - mmcls - INFO - Epoch [28][200/5005]\tlr: 1.000e-01, eta: 8:58:43, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3574\n2022-12-31 17:49:16,389 - mmcls - INFO - Epoch [28][300/5005]\tlr: 1.000e-01, eta: 8:58:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3914\n2022-12-31 17:49:25,331 - mmcls - INFO - Epoch [28][400/5005]\tlr: 1.000e-01, eta: 8:58:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3576\n2022-12-31 17:49:33,821 - mmcls - INFO - Epoch [28][500/5005]\tlr: 1.000e-01, eta: 8:58:15, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.3615\n2022-12-31 17:49:42,706 - mmcls - INFO - Epoch [28][600/5005]\tlr: 1.000e-01, eta: 8:58:07, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.3703\n2022-12-31 17:49:51,927 - mmcls - INFO - Epoch [28][700/5005]\tlr: 1.000e-01, eta: 8:57:59, time: 0.092, data_time: 0.013, memory: 1224, loss: 2.3967\n2022-12-31 17:50:00,605 - mmcls - INFO - Epoch [28][800/5005]\tlr: 1.000e-01, eta: 8:57:49, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3832\n2022-12-31 17:50:09,486 - mmcls - INFO - Epoch [28][900/5005]\tlr: 1.000e-01, eta: 8:57:41, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3628\n2022-12-31 17:50:18,256 - mmcls - INFO - Epoch [28][1000/5005]\tlr: 1.000e-01, eta: 8:57:31, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.3536\n2022-12-31 17:50:27,275 - mmcls - INFO - Epoch [28][1100/5005]\tlr: 1.000e-01, eta: 8:57:23, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3529\n2022-12-31 17:50:36,095 - mmcls - INFO - Epoch [28][1200/5005]\tlr: 1.000e-01, eta: 8:57:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3994\n2022-12-31 17:50:45,094 - mmcls - INFO - Epoch [28][1300/5005]\tlr: 1.000e-01, eta: 8:57:06, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3775\n2022-12-31 17:50:53,979 - mmcls - INFO - Epoch [28][1400/5005]\tlr: 1.000e-01, eta: 8:56:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3704\n2022-12-31 17:51:02,769 - mmcls - INFO - Epoch [28][1500/5005]\tlr: 1.000e-01, eta: 8:56:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3952\n2022-12-31 17:51:11,365 - mmcls - INFO - Epoch [28][1600/5005]\tlr: 1.000e-01, eta: 8:56:38, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3901\n2022-12-31 17:51:20,185 - mmcls - INFO - Epoch [28][1700/5005]\tlr: 1.000e-01, eta: 8:56:29, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3916\n2022-12-31 17:51:29,079 - mmcls - INFO - Epoch [28][1800/5005]\tlr: 1.000e-01, eta: 8:56:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4001\n2022-12-31 17:51:38,047 - mmcls - INFO - Epoch [28][1900/5005]\tlr: 1.000e-01, eta: 8:56:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3902\n2022-12-31 17:51:46,907 - mmcls - INFO - Epoch [28][2000/5005]\tlr: 1.000e-01, eta: 8:56:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3602\n2022-12-31 17:51:55,676 - mmcls - INFO - Epoch [28][2100/5005]\tlr: 1.000e-01, eta: 8:55:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3828\n2022-12-31 17:52:04,348 - mmcls - INFO - Epoch [28][2200/5005]\tlr: 1.000e-01, eta: 8:55:45, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3843\n2022-12-31 17:52:13,616 - mmcls - INFO - Epoch [28][2300/5005]\tlr: 1.000e-01, eta: 8:55:37, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.3889\n2022-12-31 17:52:22,414 - mmcls - INFO - Epoch [28][2400/5005]\tlr: 1.000e-01, eta: 8:55:28, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4056\n2022-12-31 17:52:31,243 - mmcls - INFO - Epoch [28][2500/5005]\tlr: 1.000e-01, eta: 8:55:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3829\n2022-12-31 17:52:39,740 - mmcls - INFO - Epoch [28][2600/5005]\tlr: 1.000e-01, eta: 8:55:09, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.3894\n2022-12-31 17:52:48,447 - mmcls - INFO - Epoch [28][2700/5005]\tlr: 1.000e-01, eta: 8:55:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4108\n2022-12-31 17:52:57,411 - mmcls - INFO - Epoch [28][2800/5005]\tlr: 1.000e-01, eta: 8:54:52, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.3899\n2022-12-31 17:53:06,253 - mmcls - INFO - Epoch [28][2900/5005]\tlr: 1.000e-01, eta: 8:54:43, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.3791\n2022-12-31 17:53:14,866 - mmcls - INFO - Epoch [28][3000/5005]\tlr: 1.000e-01, eta: 8:54:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3775\n2022-12-31 17:53:23,517 - mmcls - INFO - Epoch [28][3100/5005]\tlr: 1.000e-01, eta: 8:54:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4068\n2022-12-31 17:53:32,628 - mmcls - INFO - Epoch [28][3200/5005]\tlr: 1.000e-01, eta: 8:54:16, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.3921\n2022-12-31 17:53:41,542 - mmcls - INFO - Epoch [28][3300/5005]\tlr: 1.000e-01, eta: 8:54:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4241\n2022-12-31 17:53:50,472 - mmcls - INFO - Epoch [28][3400/5005]\tlr: 1.000e-01, eta: 8:53:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3925\n2022-12-31 17:53:59,573 - mmcls - INFO - Epoch [28][3500/5005]\tlr: 1.000e-01, eta: 8:53:50, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.4007\n2022-12-31 17:54:08,187 - mmcls - INFO - Epoch [28][3600/5005]\tlr: 1.000e-01, eta: 8:53:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3955\n2022-12-31 17:54:16,738 - mmcls - INFO - Epoch [28][3700/5005]\tlr: 1.000e-01, eta: 8:53:31, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.4046\n2022-12-31 17:54:25,411 - mmcls - INFO - Epoch [28][3800/5005]\tlr: 1.000e-01, eta: 8:53:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4053\n2022-12-31 17:54:34,202 - mmcls - INFO - Epoch [28][3900/5005]\tlr: 1.000e-01, eta: 8:53:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3989\n2022-12-31 17:54:43,079 - mmcls - INFO - Epoch [28][4000/5005]\tlr: 1.000e-01, eta: 8:53:04, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3956\n2022-12-31 17:54:52,085 - mmcls - INFO - Epoch [28][4100/5005]\tlr: 1.000e-01, eta: 8:52:56, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4032\n2022-12-31 17:55:00,565 - mmcls - INFO - Epoch [28][4200/5005]\tlr: 1.000e-01, eta: 8:52:46, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.3926\n2022-12-31 17:55:09,359 - mmcls - INFO - Epoch [28][4300/5005]\tlr: 1.000e-01, eta: 8:52:37, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4026\n2022-12-31 17:55:17,846 - mmcls - INFO - Epoch [28][4400/5005]\tlr: 1.000e-01, eta: 8:52:27, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4062\n2022-12-31 17:55:26,463 - mmcls - INFO - Epoch [28][4500/5005]\tlr: 1.000e-01, eta: 8:52:17, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.3906\n2022-12-31 17:55:35,358 - mmcls - INFO - Epoch [28][4600/5005]\tlr: 1.000e-01, eta: 8:52:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4192\n2022-12-31 17:55:44,397 - mmcls - INFO - Epoch [28][4700/5005]\tlr: 1.000e-01, eta: 8:52:00, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.3995\n2022-12-31 17:55:53,296 - mmcls - INFO - Epoch [28][4800/5005]\tlr: 1.000e-01, eta: 8:51:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3949\n2022-12-31 17:56:01,959 - mmcls - INFO - Epoch [28][4900/5005]\tlr: 1.000e-01, eta: 8:51:42, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.3981\n2022-12-31 17:56:10,963 - mmcls - INFO - Epoch [28][5000/5005]\tlr: 1.000e-01, eta: 8:51:34, time: 0.090, data_time: 0.031, memory: 1224, loss: 2.4134\n2022-12-31 17:56:33,493 - mmcls - INFO - Epoch(val) [28][196]\taccuracy_top-1: 50.3460, accuracy_top-5: 76.1560\n2022-12-31 17:56:44,648 - mmcls - INFO - Epoch [29][100/5005]\tlr: 1.000e-01, eta: 8:51:29, time: 0.111, data_time: 0.027, memory: 1224, loss: 2.3304\n2022-12-31 17:56:53,596 - mmcls - INFO - Epoch [29][200/5005]\tlr: 1.000e-01, eta: 8:51:21, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.3693\n2022-12-31 17:57:02,450 - mmcls - INFO - Epoch [29][300/5005]\tlr: 1.000e-01, eta: 8:51:12, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3476\n2022-12-31 17:57:11,071 - mmcls - INFO - Epoch [29][400/5005]\tlr: 1.000e-01, eta: 8:51:02, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.3509\n2022-12-31 17:57:20,465 - mmcls - INFO - Epoch [29][500/5005]\tlr: 1.000e-01, eta: 8:50:55, time: 0.094, data_time: 0.006, memory: 1224, loss: 2.3778\n2022-12-31 17:57:29,539 - mmcls - INFO - Epoch [29][600/5005]\tlr: 1.000e-01, eta: 8:50:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3671\n2022-12-31 17:57:38,521 - mmcls - INFO - Epoch [29][700/5005]\tlr: 1.000e-01, eta: 8:50:38, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3619\n2022-12-31 17:57:47,284 - mmcls - INFO - Epoch [29][800/5005]\tlr: 1.000e-01, eta: 8:50:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3616\n2022-12-31 17:57:56,272 - mmcls - INFO - Epoch [29][900/5005]\tlr: 1.000e-01, eta: 8:50:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.3534\n2022-12-31 17:58:05,240 - mmcls - INFO - Epoch [29][1000/5005]\tlr: 1.000e-01, eta: 8:50:12, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3539\n2022-12-31 17:58:14,665 - mmcls - INFO - Epoch [29][1100/5005]\tlr: 1.000e-01, eta: 8:50:04, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.3756\n2022-12-31 17:58:23,649 - mmcls - INFO - Epoch [29][1200/5005]\tlr: 1.000e-01, eta: 8:49:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3431\n2022-12-31 17:58:32,280 - mmcls - INFO - Epoch [29][1300/5005]\tlr: 1.000e-01, eta: 8:49:47, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.3613\n2022-12-31 17:58:40,986 - mmcls - INFO - Epoch [29][1400/5005]\tlr: 1.000e-01, eta: 8:49:37, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.3927\n2022-12-31 17:58:49,596 - mmcls - INFO - Epoch [29][1500/5005]\tlr: 1.000e-01, eta: 8:49:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.3641\n2022-12-31 17:58:58,341 - mmcls - INFO - Epoch [29][1600/5005]\tlr: 1.000e-01, eta: 8:49:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3821\n2022-12-31 17:59:07,211 - mmcls - INFO - Epoch [29][1700/5005]\tlr: 1.000e-01, eta: 8:49:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3641\n2022-12-31 17:59:15,978 - mmcls - INFO - Epoch [29][1800/5005]\tlr: 1.000e-01, eta: 8:49:01, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4052\n2022-12-31 17:59:24,647 - mmcls - INFO - Epoch [29][1900/5005]\tlr: 1.000e-01, eta: 8:48:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.3832\n2022-12-31 17:59:33,406 - mmcls - INFO - Epoch [29][2000/5005]\tlr: 1.000e-01, eta: 8:48:42, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3641\n2022-12-31 17:59:42,529 - mmcls - INFO - Epoch [29][2100/5005]\tlr: 1.000e-01, eta: 8:48:34, time: 0.091, data_time: 0.000, memory: 1224, loss: 2.3607\n2022-12-31 17:59:51,248 - mmcls - INFO - Epoch [29][2200/5005]\tlr: 1.000e-01, eta: 8:48:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3833\n2022-12-31 18:00:00,176 - mmcls - INFO - Epoch [29][2300/5005]\tlr: 1.000e-01, eta: 8:48:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.3675\n2022-12-31 18:00:09,094 - mmcls - INFO - Epoch [29][2400/5005]\tlr: 1.000e-01, eta: 8:48:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4205\n2022-12-31 18:00:17,817 - mmcls - INFO - Epoch [29][2500/5005]\tlr: 1.000e-01, eta: 8:47:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3878\n2022-12-31 18:00:26,909 - mmcls - INFO - Epoch [29][2600/5005]\tlr: 1.000e-01, eta: 8:47:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.3894\n2022-12-31 18:00:35,696 - mmcls - INFO - Epoch [29][2700/5005]\tlr: 1.000e-01, eta: 8:47:41, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4277\n2022-12-31 18:00:44,380 - mmcls - INFO - Epoch [29][2800/5005]\tlr: 1.000e-01, eta: 8:47:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3920\n2022-12-31 18:00:52,980 - mmcls - INFO - Epoch [29][2900/5005]\tlr: 1.000e-01, eta: 8:47:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3986\n2022-12-31 18:01:02,037 - mmcls - INFO - Epoch [29][3000/5005]\tlr: 1.000e-01, eta: 8:47:14, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4229\n2022-12-31 18:01:10,575 - mmcls - INFO - Epoch [29][3100/5005]\tlr: 1.000e-01, eta: 8:47:04, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.4171\n2022-12-31 18:01:19,329 - mmcls - INFO - Epoch [29][3200/5005]\tlr: 1.000e-01, eta: 8:46:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4014\n2022-12-31 18:01:28,301 - mmcls - INFO - Epoch [29][3300/5005]\tlr: 1.000e-01, eta: 8:46:47, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3822\n2022-12-31 18:01:37,260 - mmcls - INFO - Epoch [29][3400/5005]\tlr: 1.000e-01, eta: 8:46:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.3772\n2022-12-31 18:01:46,034 - mmcls - INFO - Epoch [29][3500/5005]\tlr: 1.000e-01, eta: 8:46:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3673\n2022-12-31 18:01:54,903 - mmcls - INFO - Epoch [29][3600/5005]\tlr: 1.000e-01, eta: 8:46:20, time: 0.089, data_time: 0.028, memory: 1224, loss: 2.3963\n2022-12-31 18:02:03,572 - mmcls - INFO - Epoch [29][3700/5005]\tlr: 1.000e-01, eta: 8:46:11, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.3870\n2022-12-31 18:02:12,486 - mmcls - INFO - Epoch [29][3800/5005]\tlr: 1.000e-01, eta: 8:46:02, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3716\n2022-12-31 18:02:20,952 - mmcls - INFO - Epoch [29][3900/5005]\tlr: 1.000e-01, eta: 8:45:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.3957\n2022-12-31 18:02:29,552 - mmcls - INFO - Epoch [29][4000/5005]\tlr: 1.000e-01, eta: 8:45:43, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4138\n2022-12-31 18:02:38,364 - mmcls - INFO - Epoch [29][4100/5005]\tlr: 1.000e-01, eta: 8:45:34, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4098\n2022-12-31 18:02:47,162 - mmcls - INFO - Epoch [29][4200/5005]\tlr: 1.000e-01, eta: 8:45:25, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3983\n2022-12-31 18:02:55,803 - mmcls - INFO - Epoch [29][4300/5005]\tlr: 1.000e-01, eta: 8:45:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4071\n2022-12-31 18:03:04,497 - mmcls - INFO - Epoch [29][4400/5005]\tlr: 1.000e-01, eta: 8:45:07, time: 0.087, data_time: 0.031, memory: 1224, loss: 2.4130\n2022-12-31 18:03:13,332 - mmcls - INFO - Epoch [29][4500/5005]\tlr: 1.000e-01, eta: 8:44:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4052\n2022-12-31 18:03:22,301 - mmcls - INFO - Epoch [29][4600/5005]\tlr: 1.000e-01, eta: 8:44:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4086\n2022-12-31 18:03:31,008 - mmcls - INFO - Epoch [29][4700/5005]\tlr: 1.000e-01, eta: 8:44:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3969\n2022-12-31 18:03:39,771 - mmcls - INFO - Epoch [29][4800/5005]\tlr: 1.000e-01, eta: 8:44:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.3941\n2022-12-31 18:03:48,702 - mmcls - INFO - Epoch [29][4900/5005]\tlr: 1.000e-01, eta: 8:44:22, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3801\n2022-12-31 18:03:57,347 - mmcls - INFO - Epoch [29][5000/5005]\tlr: 1.000e-01, eta: 8:44:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4149\n2022-12-31 18:04:20,026 - mmcls - INFO - Epoch(val) [29][196]\taccuracy_top-1: 51.2260, accuracy_top-5: 76.8380\n2022-12-31 18:04:30,929 - mmcls - INFO - Epoch [30][100/5005]\tlr: 1.000e-01, eta: 8:44:07, time: 0.109, data_time: 0.029, memory: 1224, loss: 2.3656\n2022-12-31 18:04:39,624 - mmcls - INFO - Epoch [30][200/5005]\tlr: 1.000e-01, eta: 8:43:58, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.3131\n2022-12-31 18:04:48,279 - mmcls - INFO - Epoch [30][300/5005]\tlr: 1.000e-01, eta: 8:43:49, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3301\n2022-12-31 18:04:57,177 - mmcls - INFO - Epoch [30][400/5005]\tlr: 1.000e-01, eta: 8:43:40, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3250\n2022-12-31 18:05:06,373 - mmcls - INFO - Epoch [30][500/5005]\tlr: 1.000e-01, eta: 8:43:32, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.3337\n2022-12-31 18:05:15,159 - mmcls - INFO - Epoch [30][600/5005]\tlr: 1.000e-01, eta: 8:43:23, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.3683\n2022-12-31 18:05:23,973 - mmcls - INFO - Epoch [30][700/5005]\tlr: 1.000e-01, eta: 8:43:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3651\n2022-12-31 18:05:32,830 - mmcls - INFO - Epoch [30][800/5005]\tlr: 1.000e-01, eta: 8:43:05, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3384\n2022-12-31 18:05:41,573 - mmcls - INFO - Epoch [30][900/5005]\tlr: 1.000e-01, eta: 8:42:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3636\n2022-12-31 18:05:50,281 - mmcls - INFO - Epoch [30][1000/5005]\tlr: 1.000e-01, eta: 8:42:47, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3892\n2022-12-31 18:05:58,782 - mmcls - INFO - Epoch [30][1100/5005]\tlr: 1.000e-01, eta: 8:42:37, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.3608\n2022-12-31 18:06:07,784 - mmcls - INFO - Epoch [30][1200/5005]\tlr: 1.000e-01, eta: 8:42:29, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.3732\n2022-12-31 18:06:16,435 - mmcls - INFO - Epoch [30][1300/5005]\tlr: 1.000e-01, eta: 8:42:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3888\n2022-12-31 18:06:25,164 - mmcls - INFO - Epoch [30][1400/5005]\tlr: 1.000e-01, eta: 8:42:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3808\n2022-12-31 18:06:33,820 - mmcls - INFO - Epoch [30][1500/5005]\tlr: 1.000e-01, eta: 8:42:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3909\n2022-12-31 18:06:42,368 - mmcls - INFO - Epoch [30][1600/5005]\tlr: 1.000e-01, eta: 8:41:51, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.3648\n2022-12-31 18:06:51,416 - mmcls - INFO - Epoch [30][1700/5005]\tlr: 1.000e-01, eta: 8:41:43, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.3581\n2022-12-31 18:07:00,403 - mmcls - INFO - Epoch [30][1800/5005]\tlr: 1.000e-01, eta: 8:41:34, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3694\n2022-12-31 18:07:09,318 - mmcls - INFO - Epoch [30][1900/5005]\tlr: 1.000e-01, eta: 8:41:26, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.3672\n2022-12-31 18:07:18,164 - mmcls - INFO - Epoch [30][2000/5005]\tlr: 1.000e-01, eta: 8:41:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3870\n2022-12-31 18:07:26,801 - mmcls - INFO - Epoch [30][2100/5005]\tlr: 1.000e-01, eta: 8:41:07, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3942\n2022-12-31 18:07:35,570 - mmcls - INFO - Epoch [30][2200/5005]\tlr: 1.000e-01, eta: 8:40:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4325\n2022-12-31 18:07:44,248 - mmcls - INFO - Epoch [30][2300/5005]\tlr: 1.000e-01, eta: 8:40:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.3797\n2022-12-31 18:07:53,193 - mmcls - INFO - Epoch [30][2400/5005]\tlr: 1.000e-01, eta: 8:40:41, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3728\n2022-12-31 18:08:02,071 - mmcls - INFO - Epoch [30][2500/5005]\tlr: 1.000e-01, eta: 8:40:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.3818\n2022-12-31 18:08:10,876 - mmcls - INFO - Epoch [30][2600/5005]\tlr: 1.000e-01, eta: 8:40:23, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4185\n2022-12-31 18:08:19,968 - mmcls - INFO - Epoch [30][2700/5005]\tlr: 1.000e-01, eta: 8:40:14, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.3837\n2022-12-31 18:08:28,928 - mmcls - INFO - Epoch [30][2800/5005]\tlr: 1.000e-01, eta: 8:40:06, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.3684\n2022-12-31 18:08:37,711 - mmcls - INFO - Epoch [30][2900/5005]\tlr: 1.000e-01, eta: 8:39:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3910\n2022-12-31 18:08:46,777 - mmcls - INFO - Epoch [30][3000/5005]\tlr: 1.000e-01, eta: 8:39:49, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.3943\n2022-12-31 18:08:55,481 - mmcls - INFO - Epoch [30][3100/5005]\tlr: 1.000e-01, eta: 8:39:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4051\n2022-12-31 18:09:04,742 - mmcls - INFO - Epoch [30][3200/5005]\tlr: 1.000e-01, eta: 8:39:31, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.3875\n2022-12-31 18:09:13,779 - mmcls - INFO - Epoch [30][3300/5005]\tlr: 1.000e-01, eta: 8:39:23, time: 0.090, data_time: 0.031, memory: 1224, loss: 2.4099\n2022-12-31 18:09:22,618 - mmcls - INFO - Epoch [30][3400/5005]\tlr: 1.000e-01, eta: 8:39:14, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.3638\n2022-12-31 18:09:31,417 - mmcls - INFO - Epoch [30][3500/5005]\tlr: 1.000e-01, eta: 8:39:05, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3898\n2022-12-31 18:09:40,404 - mmcls - INFO - Epoch [30][3600/5005]\tlr: 1.000e-01, eta: 8:38:57, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.3902\n2022-12-31 18:09:49,065 - mmcls - INFO - Epoch [30][3700/5005]\tlr: 1.000e-01, eta: 8:38:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4024\n2022-12-31 18:09:57,788 - mmcls - INFO - Epoch [30][3800/5005]\tlr: 1.000e-01, eta: 8:38:38, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.3889\n2022-12-31 18:10:06,292 - mmcls - INFO - Epoch [30][3900/5005]\tlr: 1.000e-01, eta: 8:38:29, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.3903\n2022-12-31 18:10:15,201 - mmcls - INFO - Epoch [30][4000/5005]\tlr: 1.000e-01, eta: 8:38:20, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4152\n2022-12-31 18:10:23,596 - mmcls - INFO - Epoch [30][4100/5005]\tlr: 1.000e-01, eta: 8:38:10, time: 0.084, data_time: 0.017, memory: 1224, loss: 2.3904\n2022-12-31 18:10:32,131 - mmcls - INFO - Epoch [30][4200/5005]\tlr: 1.000e-01, eta: 8:38:00, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.3939\n2022-12-31 18:10:40,685 - mmcls - INFO - Epoch [30][4300/5005]\tlr: 1.000e-01, eta: 8:37:51, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3993\n2022-12-31 18:10:49,539 - mmcls - INFO - Epoch [30][4400/5005]\tlr: 1.000e-01, eta: 8:37:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3824\n2022-12-31 18:10:58,392 - mmcls - INFO - Epoch [30][4500/5005]\tlr: 1.000e-01, eta: 8:37:33, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3962\n2022-12-31 18:11:07,506 - mmcls - INFO - Epoch [30][4600/5005]\tlr: 1.000e-01, eta: 8:37:25, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.3824\n2022-12-31 18:11:16,477 - mmcls - INFO - Epoch [30][4700/5005]\tlr: 1.000e-01, eta: 8:37:16, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.3793\n2022-12-31 18:11:25,106 - mmcls - INFO - Epoch [30][4800/5005]\tlr: 1.000e-01, eta: 8:37:07, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.3938\n2022-12-31 18:11:34,098 - mmcls - INFO - Epoch [30][4900/5005]\tlr: 1.000e-01, eta: 8:36:58, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4065\n2022-12-31 18:11:42,688 - mmcls - INFO - Epoch [30][5000/5005]\tlr: 1.000e-01, eta: 8:36:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4086\n2022-12-31 18:12:05,584 - mmcls - INFO - Epoch(val) [30][196]\taccuracy_top-1: 52.4360, accuracy_top-5: 77.7300\n2022-12-31 18:12:16,418 - mmcls - INFO - Epoch [31][100/5005]\tlr: 1.000e-02, eta: 8:36:43, time: 0.108, data_time: 0.044, memory: 1224, loss: 2.2000\n2022-12-31 18:12:25,443 - mmcls - INFO - Epoch [31][200/5005]\tlr: 1.000e-02, eta: 8:36:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0742\n2022-12-31 18:12:34,302 - mmcls - INFO - Epoch [31][300/5005]\tlr: 1.000e-02, eta: 8:36:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0043\n2022-12-31 18:12:42,957 - mmcls - INFO - Epoch [31][400/5005]\tlr: 1.000e-02, eta: 8:36:16, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.0216\n2022-12-31 18:12:51,758 - mmcls - INFO - Epoch [31][500/5005]\tlr: 1.000e-02, eta: 8:36:08, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0123\n2022-12-31 18:13:00,203 - mmcls - INFO - Epoch [31][600/5005]\tlr: 1.000e-02, eta: 8:35:58, time: 0.084, data_time: 0.017, memory: 1224, loss: 2.0126\n2022-12-31 18:13:09,016 - mmcls - INFO - Epoch [31][700/5005]\tlr: 1.000e-02, eta: 8:35:49, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9754\n2022-12-31 18:13:17,746 - mmcls - INFO - Epoch [31][800/5005]\tlr: 1.000e-02, eta: 8:35:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9608\n2022-12-31 18:13:26,641 - mmcls - INFO - Epoch [31][900/5005]\tlr: 1.000e-02, eta: 8:35:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9392\n2022-12-31 18:13:35,849 - mmcls - INFO - Epoch [31][1000/5005]\tlr: 1.000e-02, eta: 8:35:23, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9639\n2022-12-31 18:13:44,634 - mmcls - INFO - Epoch [31][1100/5005]\tlr: 1.000e-02, eta: 8:35:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9418\n2022-12-31 18:13:53,230 - mmcls - INFO - Epoch [31][1200/5005]\tlr: 1.000e-02, eta: 8:35:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9103\n2022-12-31 18:14:02,352 - mmcls - INFO - Epoch [31][1300/5005]\tlr: 1.000e-02, eta: 8:34:56, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.9392\n2022-12-31 18:14:10,926 - mmcls - INFO - Epoch [31][1400/5005]\tlr: 1.000e-02, eta: 8:34:47, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9278\n2022-12-31 18:14:19,934 - mmcls - INFO - Epoch [31][1500/5005]\tlr: 1.000e-02, eta: 8:34:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9195\n2022-12-31 18:14:28,613 - mmcls - INFO - Epoch [31][1600/5005]\tlr: 1.000e-02, eta: 8:34:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9164\n2022-12-31 18:14:37,339 - mmcls - INFO - Epoch [31][1700/5005]\tlr: 1.000e-02, eta: 8:34:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.8955\n2022-12-31 18:14:46,029 - mmcls - INFO - Epoch [31][1800/5005]\tlr: 1.000e-02, eta: 8:34:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9211\n2022-12-31 18:14:54,818 - mmcls - INFO - Epoch [31][1900/5005]\tlr: 1.000e-02, eta: 8:34:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.8903\n2022-12-31 18:15:03,973 - mmcls - INFO - Epoch [31][2000/5005]\tlr: 1.000e-02, eta: 8:33:53, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.8817\n2022-12-31 18:15:12,656 - mmcls - INFO - Epoch [31][2100/5005]\tlr: 1.000e-02, eta: 8:33:44, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9021\n2022-12-31 18:15:21,508 - mmcls - INFO - Epoch [31][2200/5005]\tlr: 1.000e-02, eta: 8:33:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.8682\n2022-12-31 18:15:30,219 - mmcls - INFO - Epoch [31][2300/5005]\tlr: 1.000e-02, eta: 8:33:26, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.8828\n2022-12-31 18:15:38,911 - mmcls - INFO - Epoch [31][2400/5005]\tlr: 1.000e-02, eta: 8:33:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.8530\n2022-12-31 18:15:47,539 - mmcls - INFO - Epoch [31][2500/5005]\tlr: 1.000e-02, eta: 8:33:08, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.8704\n2022-12-31 18:15:56,408 - mmcls - INFO - Epoch [31][2600/5005]\tlr: 1.000e-02, eta: 8:32:59, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.8586\n2022-12-31 18:16:05,290 - mmcls - INFO - Epoch [31][2700/5005]\tlr: 1.000e-02, eta: 8:32:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8601\n2022-12-31 18:16:14,164 - mmcls - INFO - Epoch [31][2800/5005]\tlr: 1.000e-02, eta: 8:32:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8742\n2022-12-31 18:16:22,746 - mmcls - INFO - Epoch [31][2900/5005]\tlr: 1.000e-02, eta: 8:32:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.8539\n2022-12-31 18:16:31,515 - mmcls - INFO - Epoch [31][3000/5005]\tlr: 1.000e-02, eta: 8:32:23, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.8504\n2022-12-31 18:16:40,451 - mmcls - INFO - Epoch [31][3100/5005]\tlr: 1.000e-02, eta: 8:32:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.8531\n2022-12-31 18:16:49,148 - mmcls - INFO - Epoch [31][3200/5005]\tlr: 1.000e-02, eta: 8:32:05, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.8508\n2022-12-31 18:16:58,133 - mmcls - INFO - Epoch [31][3300/5005]\tlr: 1.000e-02, eta: 8:31:56, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.8590\n2022-12-31 18:17:06,946 - mmcls - INFO - Epoch [31][3400/5005]\tlr: 1.000e-02, eta: 8:31:47, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.8539\n2022-12-31 18:17:15,935 - mmcls - INFO - Epoch [31][3500/5005]\tlr: 1.000e-02, eta: 8:31:39, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.8660\n2022-12-31 18:17:24,616 - mmcls - INFO - Epoch [31][3600/5005]\tlr: 1.000e-02, eta: 8:31:30, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.8398\n2022-12-31 18:17:33,193 - mmcls - INFO - Epoch [31][3700/5005]\tlr: 1.000e-02, eta: 8:31:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.8505\n2022-12-31 18:17:42,139 - mmcls - INFO - Epoch [31][3800/5005]\tlr: 1.000e-02, eta: 8:31:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8491\n2022-12-31 18:17:51,098 - mmcls - INFO - Epoch [31][3900/5005]\tlr: 1.000e-02, eta: 8:31:03, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.8441\n2022-12-31 18:17:59,987 - mmcls - INFO - Epoch [31][4000/5005]\tlr: 1.000e-02, eta: 8:30:54, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.8312\n2022-12-31 18:18:08,617 - mmcls - INFO - Epoch [31][4100/5005]\tlr: 1.000e-02, eta: 8:30:45, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.8304\n2022-12-31 18:18:17,209 - mmcls - INFO - Epoch [31][4200/5005]\tlr: 1.000e-02, eta: 8:30:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.8551\n2022-12-31 18:18:25,865 - mmcls - INFO - Epoch [31][4300/5005]\tlr: 1.000e-02, eta: 8:30:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.8363\n2022-12-31 18:18:35,159 - mmcls - INFO - Epoch [31][4400/5005]\tlr: 1.000e-02, eta: 8:30:18, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.8273\n2022-12-31 18:18:44,010 - mmcls - INFO - Epoch [31][4500/5005]\tlr: 1.000e-02, eta: 8:30:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.8143\n2022-12-31 18:18:52,872 - mmcls - INFO - Epoch [31][4600/5005]\tlr: 1.000e-02, eta: 8:30:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.8376\n2022-12-31 18:19:01,958 - mmcls - INFO - Epoch [31][4700/5005]\tlr: 1.000e-02, eta: 8:29:52, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.8384\n2022-12-31 18:19:10,672 - mmcls - INFO - Epoch [31][4800/5005]\tlr: 1.000e-02, eta: 8:29:43, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.8179\n2022-12-31 18:19:19,601 - mmcls - INFO - Epoch [31][4900/5005]\tlr: 1.000e-02, eta: 8:29:34, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.8179\n2022-12-31 18:19:28,425 - mmcls - INFO - Epoch [31][5000/5005]\tlr: 1.000e-02, eta: 8:29:25, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.8281\n2022-12-31 18:19:51,202 - mmcls - INFO - Epoch(val) [31][196]\taccuracy_top-1: 64.1660, accuracy_top-5: 85.8000\n2022-12-31 18:20:02,369 - mmcls - INFO - Epoch [32][100/5005]\tlr: 1.000e-02, eta: 8:29:20, time: 0.111, data_time: 0.029, memory: 1224, loss: 1.7959\n2022-12-31 18:20:11,166 - mmcls - INFO - Epoch [32][200/5005]\tlr: 1.000e-02, eta: 8:29:11, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7959\n2022-12-31 18:20:19,941 - mmcls - INFO - Epoch [32][300/5005]\tlr: 1.000e-02, eta: 8:29:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.7833\n2022-12-31 18:20:28,869 - mmcls - INFO - Epoch [32][400/5005]\tlr: 1.000e-02, eta: 8:28:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7887\n2022-12-31 18:20:37,805 - mmcls - INFO - Epoch [32][500/5005]\tlr: 1.000e-02, eta: 8:28:45, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.8076\n2022-12-31 18:20:46,590 - mmcls - INFO - Epoch [32][600/5005]\tlr: 1.000e-02, eta: 8:28:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7730\n2022-12-31 18:20:55,474 - mmcls - INFO - Epoch [32][700/5005]\tlr: 1.000e-02, eta: 8:28:27, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.7822\n2022-12-31 18:21:04,373 - mmcls - INFO - Epoch [32][800/5005]\tlr: 1.000e-02, eta: 8:28:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7964\n2022-12-31 18:21:12,933 - mmcls - INFO - Epoch [32][900/5005]\tlr: 1.000e-02, eta: 8:28:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.7899\n2022-12-31 18:21:21,826 - mmcls - INFO - Epoch [32][1000/5005]\tlr: 1.000e-02, eta: 8:28:00, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8128\n2022-12-31 18:21:30,638 - mmcls - INFO - Epoch [32][1100/5005]\tlr: 1.000e-02, eta: 8:27:51, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.7668\n2022-12-31 18:21:39,335 - mmcls - INFO - Epoch [32][1200/5005]\tlr: 1.000e-02, eta: 8:27:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7968\n2022-12-31 18:21:47,855 - mmcls - INFO - Epoch [32][1300/5005]\tlr: 1.000e-02, eta: 8:27:32, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.7845\n2022-12-31 18:21:56,765 - mmcls - INFO - Epoch [32][1400/5005]\tlr: 1.000e-02, eta: 8:27:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7592\n2022-12-31 18:22:05,798 - mmcls - INFO - Epoch [32][1500/5005]\tlr: 1.000e-02, eta: 8:27:15, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7805\n2022-12-31 18:22:14,230 - mmcls - INFO - Epoch [32][1600/5005]\tlr: 1.000e-02, eta: 8:27:05, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.7744\n2022-12-31 18:22:22,990 - mmcls - INFO - Epoch [32][1700/5005]\tlr: 1.000e-02, eta: 8:26:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7779\n2022-12-31 18:22:31,798 - mmcls - INFO - Epoch [32][1800/5005]\tlr: 1.000e-02, eta: 8:26:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.7829\n2022-12-31 18:22:40,677 - mmcls - INFO - Epoch [32][1900/5005]\tlr: 1.000e-02, eta: 8:26:39, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.7918\n2022-12-31 18:22:49,320 - mmcls - INFO - Epoch [32][2000/5005]\tlr: 1.000e-02, eta: 8:26:29, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7878\n2022-12-31 18:22:58,289 - mmcls - INFO - Epoch [32][2100/5005]\tlr: 1.000e-02, eta: 8:26:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7773\n2022-12-31 18:23:06,930 - mmcls - INFO - Epoch [32][2200/5005]\tlr: 1.000e-02, eta: 8:26:11, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7646\n2022-12-31 18:23:16,177 - mmcls - INFO - Epoch [32][2300/5005]\tlr: 1.000e-02, eta: 8:26:03, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.7944\n2022-12-31 18:23:25,162 - mmcls - INFO - Epoch [32][2400/5005]\tlr: 1.000e-02, eta: 8:25:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7846\n2022-12-31 18:23:33,846 - mmcls - INFO - Epoch [32][2500/5005]\tlr: 1.000e-02, eta: 8:25:46, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7716\n2022-12-31 18:23:42,693 - mmcls - INFO - Epoch [32][2600/5005]\tlr: 1.000e-02, eta: 8:25:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7998\n2022-12-31 18:23:51,441 - mmcls - INFO - Epoch [32][2700/5005]\tlr: 1.000e-02, eta: 8:25:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.8080\n2022-12-31 18:24:00,649 - mmcls - INFO - Epoch [32][2800/5005]\tlr: 1.000e-02, eta: 8:25:20, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7491\n2022-12-31 18:24:09,642 - mmcls - INFO - Epoch [32][2900/5005]\tlr: 1.000e-02, eta: 8:25:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7656\n2022-12-31 18:24:18,371 - mmcls - INFO - Epoch [32][3000/5005]\tlr: 1.000e-02, eta: 8:25:02, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.7801\n2022-12-31 18:24:27,074 - mmcls - INFO - Epoch [32][3100/5005]\tlr: 1.000e-02, eta: 8:24:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7934\n2022-12-31 18:24:35,760 - mmcls - INFO - Epoch [32][3200/5005]\tlr: 1.000e-02, eta: 8:24:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7832\n2022-12-31 18:24:44,344 - mmcls - INFO - Epoch [32][3300/5005]\tlr: 1.000e-02, eta: 8:24:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7744\n2022-12-31 18:24:53,344 - mmcls - INFO - Epoch [32][3400/5005]\tlr: 1.000e-02, eta: 8:24:26, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.7735\n2022-12-31 18:25:01,972 - mmcls - INFO - Epoch [32][3500/5005]\tlr: 1.000e-02, eta: 8:24:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7713\n2022-12-31 18:25:10,778 - mmcls - INFO - Epoch [32][3600/5005]\tlr: 1.000e-02, eta: 8:24:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7737\n2022-12-31 18:25:19,415 - mmcls - INFO - Epoch [32][3700/5005]\tlr: 1.000e-02, eta: 8:23:58, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.7742\n2022-12-31 18:25:28,274 - mmcls - INFO - Epoch [32][3800/5005]\tlr: 1.000e-02, eta: 8:23:49, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.7522\n2022-12-31 18:25:37,082 - mmcls - INFO - Epoch [32][3900/5005]\tlr: 1.000e-02, eta: 8:23:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7763\n2022-12-31 18:25:45,966 - mmcls - INFO - Epoch [32][4000/5005]\tlr: 1.000e-02, eta: 8:23:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7584\n2022-12-31 18:25:54,667 - mmcls - INFO - Epoch [32][4100/5005]\tlr: 1.000e-02, eta: 8:23:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7511\n2022-12-31 18:26:03,340 - mmcls - INFO - Epoch [32][4200/5005]\tlr: 1.000e-02, eta: 8:23:13, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7572\n2022-12-31 18:26:12,019 - mmcls - INFO - Epoch [32][4300/5005]\tlr: 1.000e-02, eta: 8:23:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7512\n2022-12-31 18:26:20,597 - mmcls - INFO - Epoch [32][4400/5005]\tlr: 1.000e-02, eta: 8:22:54, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7564\n2022-12-31 18:26:29,503 - mmcls - INFO - Epoch [32][4500/5005]\tlr: 1.000e-02, eta: 8:22:46, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7462\n2022-12-31 18:26:38,564 - mmcls - INFO - Epoch [32][4600/5005]\tlr: 1.000e-02, eta: 8:22:37, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.7699\n2022-12-31 18:26:47,609 - mmcls - INFO - Epoch [32][4700/5005]\tlr: 1.000e-02, eta: 8:22:29, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.7689\n2022-12-31 18:26:56,283 - mmcls - INFO - Epoch [32][4800/5005]\tlr: 1.000e-02, eta: 8:22:20, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7575\n2022-12-31 18:27:05,158 - mmcls - INFO - Epoch [32][4900/5005]\tlr: 1.000e-02, eta: 8:22:11, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7552\n2022-12-31 18:27:14,358 - mmcls - INFO - Epoch [32][5000/5005]\tlr: 1.000e-02, eta: 8:22:03, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.7786\n2022-12-31 18:27:37,053 - mmcls - INFO - Epoch(val) [32][196]\taccuracy_top-1: 65.1880, accuracy_top-5: 86.3360\n2022-12-31 18:27:48,063 - mmcls - INFO - Epoch [33][100/5005]\tlr: 1.000e-02, eta: 8:21:57, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.7436\n2022-12-31 18:27:56,866 - mmcls - INFO - Epoch [33][200/5005]\tlr: 1.000e-02, eta: 8:21:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7166\n2022-12-31 18:28:05,469 - mmcls - INFO - Epoch [33][300/5005]\tlr: 1.000e-02, eta: 8:21:39, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7425\n2022-12-31 18:28:14,740 - mmcls - INFO - Epoch [33][400/5005]\tlr: 1.000e-02, eta: 8:21:31, time: 0.093, data_time: 0.019, memory: 1224, loss: 1.7259\n2022-12-31 18:28:23,591 - mmcls - INFO - Epoch [33][500/5005]\tlr: 1.000e-02, eta: 8:21:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7348\n2022-12-31 18:28:32,694 - mmcls - INFO - Epoch [33][600/5005]\tlr: 1.000e-02, eta: 8:21:13, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.7341\n2022-12-31 18:28:41,534 - mmcls - INFO - Epoch [33][700/5005]\tlr: 1.000e-02, eta: 8:21:05, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7379\n2022-12-31 18:28:50,238 - mmcls - INFO - Epoch [33][800/5005]\tlr: 1.000e-02, eta: 8:20:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7513\n2022-12-31 18:28:58,747 - mmcls - INFO - Epoch [33][900/5005]\tlr: 1.000e-02, eta: 8:20:46, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.7262\n2022-12-31 18:29:07,919 - mmcls - INFO - Epoch [33][1000/5005]\tlr: 1.000e-02, eta: 8:20:38, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.6994\n2022-12-31 18:29:16,579 - mmcls - INFO - Epoch [33][1100/5005]\tlr: 1.000e-02, eta: 8:20:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7017\n2022-12-31 18:29:25,318 - mmcls - INFO - Epoch [33][1200/5005]\tlr: 1.000e-02, eta: 8:20:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7344\n2022-12-31 18:29:34,259 - mmcls - INFO - Epoch [33][1300/5005]\tlr: 1.000e-02, eta: 8:20:11, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7551\n2022-12-31 18:29:43,160 - mmcls - INFO - Epoch [33][1400/5005]\tlr: 1.000e-02, eta: 8:20:02, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7143\n2022-12-31 18:29:51,848 - mmcls - INFO - Epoch [33][1500/5005]\tlr: 1.000e-02, eta: 8:19:53, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.7361\n2022-12-31 18:30:01,130 - mmcls - INFO - Epoch [33][1600/5005]\tlr: 1.000e-02, eta: 8:19:45, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.7610\n2022-12-31 18:30:10,105 - mmcls - INFO - Epoch [33][1700/5005]\tlr: 1.000e-02, eta: 8:19:36, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7321\n2022-12-31 18:30:19,221 - mmcls - INFO - Epoch [33][1800/5005]\tlr: 1.000e-02, eta: 8:19:28, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.7265\n2022-12-31 18:30:27,909 - mmcls - INFO - Epoch [33][1900/5005]\tlr: 1.000e-02, eta: 8:19:19, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.7236\n2022-12-31 18:30:36,618 - mmcls - INFO - Epoch [33][2000/5005]\tlr: 1.000e-02, eta: 8:19:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7438\n2022-12-31 18:30:45,355 - mmcls - INFO - Epoch [33][2100/5005]\tlr: 1.000e-02, eta: 8:19:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7444\n2022-12-31 18:30:54,013 - mmcls - INFO - Epoch [33][2200/5005]\tlr: 1.000e-02, eta: 8:18:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7250\n2022-12-31 18:31:02,859 - mmcls - INFO - Epoch [33][2300/5005]\tlr: 1.000e-02, eta: 8:18:42, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7142\n2022-12-31 18:31:12,011 - mmcls - INFO - Epoch [33][2400/5005]\tlr: 1.000e-02, eta: 8:18:34, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.7258\n2022-12-31 18:31:20,896 - mmcls - INFO - Epoch [33][2500/5005]\tlr: 1.000e-02, eta: 8:18:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7291\n2022-12-31 18:31:30,291 - mmcls - INFO - Epoch [33][2600/5005]\tlr: 1.000e-02, eta: 8:18:17, time: 0.094, data_time: 0.011, memory: 1224, loss: 1.7225\n2022-12-31 18:31:38,890 - mmcls - INFO - Epoch [33][2700/5005]\tlr: 1.000e-02, eta: 8:18:08, time: 0.086, data_time: 0.035, memory: 1224, loss: 1.7383\n2022-12-31 18:31:47,929 - mmcls - INFO - Epoch [33][2800/5005]\tlr: 1.000e-02, eta: 8:18:00, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.7441\n2022-12-31 18:31:56,546 - mmcls - INFO - Epoch [33][2900/5005]\tlr: 1.000e-02, eta: 8:17:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.7209\n2022-12-31 18:32:05,231 - mmcls - INFO - Epoch [33][3000/5005]\tlr: 1.000e-02, eta: 8:17:41, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7351\n2022-12-31 18:32:13,978 - mmcls - INFO - Epoch [33][3100/5005]\tlr: 1.000e-02, eta: 8:17:32, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.7307\n2022-12-31 18:32:22,725 - mmcls - INFO - Epoch [33][3200/5005]\tlr: 1.000e-02, eta: 8:17:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7226\n2022-12-31 18:32:31,532 - mmcls - INFO - Epoch [33][3300/5005]\tlr: 1.000e-02, eta: 8:17:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7324\n2022-12-31 18:32:40,662 - mmcls - INFO - Epoch [33][3400/5005]\tlr: 1.000e-02, eta: 8:17:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7260\n2022-12-31 18:32:49,256 - mmcls - INFO - Epoch [33][3500/5005]\tlr: 1.000e-02, eta: 8:16:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7236\n2022-12-31 18:32:57,927 - mmcls - INFO - Epoch [33][3600/5005]\tlr: 1.000e-02, eta: 8:16:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7300\n2022-12-31 18:33:06,700 - mmcls - INFO - Epoch [33][3700/5005]\tlr: 1.000e-02, eta: 8:16:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7268\n2022-12-31 18:33:15,304 - mmcls - INFO - Epoch [33][3800/5005]\tlr: 1.000e-02, eta: 8:16:29, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.7314\n2022-12-31 18:33:23,924 - mmcls - INFO - Epoch [33][3900/5005]\tlr: 1.000e-02, eta: 8:16:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7252\n2022-12-31 18:33:32,648 - mmcls - INFO - Epoch [33][4000/5005]\tlr: 1.000e-02, eta: 8:16:10, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7140\n2022-12-31 18:33:41,583 - mmcls - INFO - Epoch [33][4100/5005]\tlr: 1.000e-02, eta: 8:16:02, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6926\n2022-12-31 18:33:50,456 - mmcls - INFO - Epoch [33][4200/5005]\tlr: 1.000e-02, eta: 8:15:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7276\n2022-12-31 18:33:59,350 - mmcls - INFO - Epoch [33][4300/5005]\tlr: 1.000e-02, eta: 8:15:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7138\n2022-12-31 18:34:08,163 - mmcls - INFO - Epoch [33][4400/5005]\tlr: 1.000e-02, eta: 8:15:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7199\n2022-12-31 18:34:16,939 - mmcls - INFO - Epoch [33][4500/5005]\tlr: 1.000e-02, eta: 8:15:26, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7408\n2022-12-31 18:34:25,696 - mmcls - INFO - Epoch [33][4600/5005]\tlr: 1.000e-02, eta: 8:15:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7341\n2022-12-31 18:34:34,349 - mmcls - INFO - Epoch [33][4700/5005]\tlr: 1.000e-02, eta: 8:15:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7724\n2022-12-31 18:34:43,285 - mmcls - INFO - Epoch [33][4800/5005]\tlr: 1.000e-02, eta: 8:14:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7411\n2022-12-31 18:34:51,872 - mmcls - INFO - Epoch [33][4900/5005]\tlr: 1.000e-02, eta: 8:14:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7271\n2022-12-31 18:35:00,645 - mmcls - INFO - Epoch [33][5000/5005]\tlr: 1.000e-02, eta: 8:14:41, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7071\n2022-12-31 18:35:23,407 - mmcls - INFO - Epoch(val) [33][196]\taccuracy_top-1: 65.8040, accuracy_top-5: 86.8480\n2022-12-31 18:35:34,526 - mmcls - INFO - Epoch [34][100/5005]\tlr: 1.000e-02, eta: 8:14:35, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.6903\n2022-12-31 18:35:43,417 - mmcls - INFO - Epoch [34][200/5005]\tlr: 1.000e-02, eta: 8:14:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6712\n2022-12-31 18:35:52,629 - mmcls - INFO - Epoch [34][300/5005]\tlr: 1.000e-02, eta: 8:14:18, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6885\n2022-12-31 18:36:01,514 - mmcls - INFO - Epoch [34][400/5005]\tlr: 1.000e-02, eta: 8:14:09, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.7061\n2022-12-31 18:36:10,208 - mmcls - INFO - Epoch [34][500/5005]\tlr: 1.000e-02, eta: 8:14:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7066\n2022-12-31 18:36:18,978 - mmcls - INFO - Epoch [34][600/5005]\tlr: 1.000e-02, eta: 8:13:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6967\n2022-12-31 18:36:27,715 - mmcls - INFO - Epoch [34][700/5005]\tlr: 1.000e-02, eta: 8:13:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6960\n2022-12-31 18:36:36,462 - mmcls - INFO - Epoch [34][800/5005]\tlr: 1.000e-02, eta: 8:13:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7075\n2022-12-31 18:36:45,057 - mmcls - INFO - Epoch [34][900/5005]\tlr: 1.000e-02, eta: 8:13:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6996\n2022-12-31 18:36:53,653 - mmcls - INFO - Epoch [34][1000/5005]\tlr: 1.000e-02, eta: 8:13:14, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7062\n2022-12-31 18:37:02,325 - mmcls - INFO - Epoch [34][1100/5005]\tlr: 1.000e-02, eta: 8:13:05, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.7103\n2022-12-31 18:37:11,122 - mmcls - INFO - Epoch [34][1200/5005]\tlr: 1.000e-02, eta: 8:12:56, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.7086\n2022-12-31 18:37:19,920 - mmcls - INFO - Epoch [34][1300/5005]\tlr: 1.000e-02, eta: 8:12:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6714\n2022-12-31 18:37:28,807 - mmcls - INFO - Epoch [34][1400/5005]\tlr: 1.000e-02, eta: 8:12:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6932\n2022-12-31 18:37:37,573 - mmcls - INFO - Epoch [34][1500/5005]\tlr: 1.000e-02, eta: 8:12:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6976\n2022-12-31 18:37:46,198 - mmcls - INFO - Epoch [34][1600/5005]\tlr: 1.000e-02, eta: 8:12:20, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7050\n2022-12-31 18:37:55,106 - mmcls - INFO - Epoch [34][1700/5005]\tlr: 1.000e-02, eta: 8:12:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7054\n2022-12-31 18:38:03,665 - mmcls - INFO - Epoch [34][1800/5005]\tlr: 1.000e-02, eta: 8:12:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6942\n2022-12-31 18:38:12,789 - mmcls - INFO - Epoch [34][1900/5005]\tlr: 1.000e-02, eta: 8:11:54, time: 0.091, data_time: 0.040, memory: 1224, loss: 1.7151\n2022-12-31 18:38:21,440 - mmcls - INFO - Epoch [34][2000/5005]\tlr: 1.000e-02, eta: 8:11:44, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7182\n2022-12-31 18:38:30,001 - mmcls - INFO - Epoch [34][2100/5005]\tlr: 1.000e-02, eta: 8:11:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.7174\n2022-12-31 18:38:38,768 - mmcls - INFO - Epoch [34][2200/5005]\tlr: 1.000e-02, eta: 8:11:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7127\n2022-12-31 18:38:47,738 - mmcls - INFO - Epoch [34][2300/5005]\tlr: 1.000e-02, eta: 8:11:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6925\n2022-12-31 18:38:56,481 - mmcls - INFO - Epoch [34][2400/5005]\tlr: 1.000e-02, eta: 8:11:08, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7044\n2022-12-31 18:39:05,244 - mmcls - INFO - Epoch [34][2500/5005]\tlr: 1.000e-02, eta: 8:10:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7056\n2022-12-31 18:39:13,926 - mmcls - INFO - Epoch [34][2600/5005]\tlr: 1.000e-02, eta: 8:10:50, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.7054\n2022-12-31 18:39:22,874 - mmcls - INFO - Epoch [34][2700/5005]\tlr: 1.000e-02, eta: 8:10:41, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7284\n2022-12-31 18:39:31,670 - mmcls - INFO - Epoch [34][2800/5005]\tlr: 1.000e-02, eta: 8:10:32, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6928\n2022-12-31 18:39:40,871 - mmcls - INFO - Epoch [34][2900/5005]\tlr: 1.000e-02, eta: 8:10:24, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.6969\n2022-12-31 18:39:49,645 - mmcls - INFO - Epoch [34][3000/5005]\tlr: 1.000e-02, eta: 8:10:15, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6811\n2022-12-31 18:39:58,467 - mmcls - INFO - Epoch [34][3100/5005]\tlr: 1.000e-02, eta: 8:10:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6879\n2022-12-31 18:40:06,998 - mmcls - INFO - Epoch [34][3200/5005]\tlr: 1.000e-02, eta: 8:09:57, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6905\n2022-12-31 18:40:16,349 - mmcls - INFO - Epoch [34][3300/5005]\tlr: 1.000e-02, eta: 8:09:49, time: 0.094, data_time: 0.028, memory: 1224, loss: 1.6957\n2022-12-31 18:40:24,978 - mmcls - INFO - Epoch [34][3400/5005]\tlr: 1.000e-02, eta: 8:09:40, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7017\n2022-12-31 18:40:33,976 - mmcls - INFO - Epoch [34][3500/5005]\tlr: 1.000e-02, eta: 8:09:31, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.7219\n2022-12-31 18:40:42,897 - mmcls - INFO - Epoch [34][3600/5005]\tlr: 1.000e-02, eta: 8:09:22, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7015\n2022-12-31 18:40:51,798 - mmcls - INFO - Epoch [34][3700/5005]\tlr: 1.000e-02, eta: 8:09:14, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7170\n2022-12-31 18:41:00,676 - mmcls - INFO - Epoch [34][3800/5005]\tlr: 1.000e-02, eta: 8:09:05, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.7046\n2022-12-31 18:41:09,378 - mmcls - INFO - Epoch [34][3900/5005]\tlr: 1.000e-02, eta: 8:08:56, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7169\n2022-12-31 18:41:18,122 - mmcls - INFO - Epoch [34][4000/5005]\tlr: 1.000e-02, eta: 8:08:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6930\n2022-12-31 18:41:26,762 - mmcls - INFO - Epoch [34][4100/5005]\tlr: 1.000e-02, eta: 8:08:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7137\n2022-12-31 18:41:35,542 - mmcls - INFO - Epoch [34][4200/5005]\tlr: 1.000e-02, eta: 8:08:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7154\n2022-12-31 18:41:44,485 - mmcls - INFO - Epoch [34][4300/5005]\tlr: 1.000e-02, eta: 8:08:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7175\n2022-12-31 18:41:53,067 - mmcls - INFO - Epoch [34][4400/5005]\tlr: 1.000e-02, eta: 8:08:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6991\n2022-12-31 18:42:01,668 - mmcls - INFO - Epoch [34][4500/5005]\tlr: 1.000e-02, eta: 8:08:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6939\n2022-12-31 18:42:10,585 - mmcls - INFO - Epoch [34][4600/5005]\tlr: 1.000e-02, eta: 8:07:52, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6943\n2022-12-31 18:42:19,505 - mmcls - INFO - Epoch [34][4700/5005]\tlr: 1.000e-02, eta: 8:07:43, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.7177\n2022-12-31 18:42:28,444 - mmcls - INFO - Epoch [34][4800/5005]\tlr: 1.000e-02, eta: 8:07:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6961\n2022-12-31 18:42:37,198 - mmcls - INFO - Epoch [34][4900/5005]\tlr: 1.000e-02, eta: 8:07:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7003\n2022-12-31 18:42:46,558 - mmcls - INFO - Epoch [34][5000/5005]\tlr: 1.000e-02, eta: 8:07:18, time: 0.094, data_time: 0.014, memory: 1224, loss: 1.7108\n2022-12-31 18:43:09,599 - mmcls - INFO - Epoch(val) [34][196]\taccuracy_top-1: 66.2280, accuracy_top-5: 87.1200\n2022-12-31 18:43:20,329 - mmcls - INFO - Epoch [35][100/5005]\tlr: 1.000e-02, eta: 8:07:11, time: 0.107, data_time: 0.035, memory: 1224, loss: 1.6794\n2022-12-31 18:43:29,216 - mmcls - INFO - Epoch [35][200/5005]\tlr: 1.000e-02, eta: 8:07:03, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6676\n2022-12-31 18:43:38,234 - mmcls - INFO - Epoch [35][300/5005]\tlr: 1.000e-02, eta: 8:06:54, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6774\n2022-12-31 18:43:46,880 - mmcls - INFO - Epoch [35][400/5005]\tlr: 1.000e-02, eta: 8:06:45, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6544\n2022-12-31 18:43:55,598 - mmcls - INFO - Epoch [35][500/5005]\tlr: 1.000e-02, eta: 8:06:36, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6928\n2022-12-31 18:44:04,240 - mmcls - INFO - Epoch [35][600/5005]\tlr: 1.000e-02, eta: 8:06:26, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6566\n2022-12-31 18:44:12,947 - mmcls - INFO - Epoch [35][700/5005]\tlr: 1.000e-02, eta: 8:06:17, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6814\n2022-12-31 18:44:21,814 - mmcls - INFO - Epoch [35][800/5005]\tlr: 1.000e-02, eta: 8:06:08, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6834\n2022-12-31 18:44:30,581 - mmcls - INFO - Epoch [35][900/5005]\tlr: 1.000e-02, eta: 8:05:59, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6997\n2022-12-31 18:44:39,514 - mmcls - INFO - Epoch [35][1000/5005]\tlr: 1.000e-02, eta: 8:05:51, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6826\n2022-12-31 18:44:48,257 - mmcls - INFO - Epoch [35][1100/5005]\tlr: 1.000e-02, eta: 8:05:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6714\n2022-12-31 18:44:56,879 - mmcls - INFO - Epoch [35][1200/5005]\tlr: 1.000e-02, eta: 8:05:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6812\n2022-12-31 18:45:05,569 - mmcls - INFO - Epoch [35][1300/5005]\tlr: 1.000e-02, eta: 8:05:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6701\n2022-12-31 18:45:14,393 - mmcls - INFO - Epoch [35][1400/5005]\tlr: 1.000e-02, eta: 8:05:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6597\n2022-12-31 18:45:23,077 - mmcls - INFO - Epoch [35][1500/5005]\tlr: 1.000e-02, eta: 8:05:05, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7104\n2022-12-31 18:45:32,081 - mmcls - INFO - Epoch [35][1600/5005]\tlr: 1.000e-02, eta: 8:04:57, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6885\n2022-12-31 18:45:40,951 - mmcls - INFO - Epoch [35][1700/5005]\tlr: 1.000e-02, eta: 8:04:48, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6873\n2022-12-31 18:45:50,252 - mmcls - INFO - Epoch [35][1800/5005]\tlr: 1.000e-02, eta: 8:04:40, time: 0.093, data_time: 0.017, memory: 1224, loss: 1.6750\n2022-12-31 18:45:58,773 - mmcls - INFO - Epoch [35][1900/5005]\tlr: 1.000e-02, eta: 8:04:30, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6811\n2022-12-31 18:46:07,266 - mmcls - INFO - Epoch [35][2000/5005]\tlr: 1.000e-02, eta: 8:04:21, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.7000\n2022-12-31 18:46:16,042 - mmcls - INFO - Epoch [35][2100/5005]\tlr: 1.000e-02, eta: 8:04:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6987\n2022-12-31 18:46:25,025 - mmcls - INFO - Epoch [35][2200/5005]\tlr: 1.000e-02, eta: 8:04:03, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6755\n2022-12-31 18:46:33,521 - mmcls - INFO - Epoch [35][2300/5005]\tlr: 1.000e-02, eta: 8:03:54, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6579\n2022-12-31 18:46:42,278 - mmcls - INFO - Epoch [35][2400/5005]\tlr: 1.000e-02, eta: 8:03:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6759\n2022-12-31 18:46:50,990 - mmcls - INFO - Epoch [35][2500/5005]\tlr: 1.000e-02, eta: 8:03:35, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6895\n2022-12-31 18:47:00,513 - mmcls - INFO - Epoch [35][2600/5005]\tlr: 1.000e-02, eta: 8:03:28, time: 0.095, data_time: 0.006, memory: 1224, loss: 1.6803\n2022-12-31 18:47:09,241 - mmcls - INFO - Epoch [35][2700/5005]\tlr: 1.000e-02, eta: 8:03:19, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6716\n2022-12-31 18:47:18,067 - mmcls - INFO - Epoch [35][2800/5005]\tlr: 1.000e-02, eta: 8:03:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6676\n2022-12-31 18:47:26,738 - mmcls - INFO - Epoch [35][2900/5005]\tlr: 1.000e-02, eta: 8:03:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6966\n2022-12-31 18:47:35,557 - mmcls - INFO - Epoch [35][3000/5005]\tlr: 1.000e-02, eta: 8:02:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6930\n2022-12-31 18:47:44,301 - mmcls - INFO - Epoch [35][3100/5005]\tlr: 1.000e-02, eta: 8:02:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6710\n2022-12-31 18:47:53,159 - mmcls - INFO - Epoch [35][3200/5005]\tlr: 1.000e-02, eta: 8:02:34, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6926\n2022-12-31 18:48:01,676 - mmcls - INFO - Epoch [35][3300/5005]\tlr: 1.000e-02, eta: 8:02:24, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.6967\n2022-12-31 18:48:10,258 - mmcls - INFO - Epoch [35][3400/5005]\tlr: 1.000e-02, eta: 8:02:15, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.7065\n2022-12-31 18:48:19,041 - mmcls - INFO - Epoch [35][3500/5005]\tlr: 1.000e-02, eta: 8:02:06, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6878\n2022-12-31 18:48:27,865 - mmcls - INFO - Epoch [35][3600/5005]\tlr: 1.000e-02, eta: 8:01:57, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6562\n2022-12-31 18:48:36,897 - mmcls - INFO - Epoch [35][3700/5005]\tlr: 1.000e-02, eta: 8:01:49, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6913\n2022-12-31 18:48:45,486 - mmcls - INFO - Epoch [35][3800/5005]\tlr: 1.000e-02, eta: 8:01:39, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7078\n2022-12-31 18:48:54,291 - mmcls - INFO - Epoch [35][3900/5005]\tlr: 1.000e-02, eta: 8:01:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6856\n2022-12-31 18:49:02,868 - mmcls - INFO - Epoch [35][4000/5005]\tlr: 1.000e-02, eta: 8:01:21, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6531\n2022-12-31 18:49:11,546 - mmcls - INFO - Epoch [35][4100/5005]\tlr: 1.000e-02, eta: 8:01:12, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6979\n2022-12-31 18:49:20,144 - mmcls - INFO - Epoch [35][4200/5005]\tlr: 1.000e-02, eta: 8:01:03, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6889\n2022-12-31 18:49:28,831 - mmcls - INFO - Epoch [35][4300/5005]\tlr: 1.000e-02, eta: 8:00:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7054\n2022-12-31 18:49:37,589 - mmcls - INFO - Epoch [35][4400/5005]\tlr: 1.000e-02, eta: 8:00:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6733\n2022-12-31 18:49:46,356 - mmcls - INFO - Epoch [35][4500/5005]\tlr: 1.000e-02, eta: 8:00:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6964\n2022-12-31 18:49:55,116 - mmcls - INFO - Epoch [35][4600/5005]\tlr: 1.000e-02, eta: 8:00:26, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7150\n2022-12-31 18:50:04,094 - mmcls - INFO - Epoch [35][4700/5005]\tlr: 1.000e-02, eta: 8:00:18, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6716\n2022-12-31 18:50:12,836 - mmcls - INFO - Epoch [35][4800/5005]\tlr: 1.000e-02, eta: 8:00:09, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6774\n2022-12-31 18:50:21,692 - mmcls - INFO - Epoch [35][4900/5005]\tlr: 1.000e-02, eta: 8:00:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6771\n2022-12-31 18:50:30,631 - mmcls - INFO - Epoch [35][5000/5005]\tlr: 1.000e-02, eta: 7:59:51, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7025\n2022-12-31 18:50:53,358 - mmcls - INFO - Epoch(val) [35][196]\taccuracy_top-1: 66.1840, accuracy_top-5: 87.1980\n2022-12-31 18:51:04,200 - mmcls - INFO - Epoch [36][100/5005]\tlr: 1.000e-02, eta: 7:59:45, time: 0.108, data_time: 0.045, memory: 1224, loss: 1.6436\n2022-12-31 18:51:13,035 - mmcls - INFO - Epoch [36][200/5005]\tlr: 1.000e-02, eta: 7:59:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6382\n2022-12-31 18:51:21,809 - mmcls - INFO - Epoch [36][300/5005]\tlr: 1.000e-02, eta: 7:59:27, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6703\n2022-12-31 18:51:30,311 - mmcls - INFO - Epoch [36][400/5005]\tlr: 1.000e-02, eta: 7:59:17, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6526\n2022-12-31 18:51:38,998 - mmcls - INFO - Epoch [36][500/5005]\tlr: 1.000e-02, eta: 7:59:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6545\n2022-12-31 18:51:47,785 - mmcls - INFO - Epoch [36][600/5005]\tlr: 1.000e-02, eta: 7:58:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6518\n2022-12-31 18:51:56,713 - mmcls - INFO - Epoch [36][700/5005]\tlr: 1.000e-02, eta: 7:58:50, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6533\n2022-12-31 18:52:05,258 - mmcls - INFO - Epoch [36][800/5005]\tlr: 1.000e-02, eta: 7:58:41, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6668\n2022-12-31 18:52:14,112 - mmcls - INFO - Epoch [36][900/5005]\tlr: 1.000e-02, eta: 7:58:32, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6528\n2022-12-31 18:52:22,865 - mmcls - INFO - Epoch [36][1000/5005]\tlr: 1.000e-02, eta: 7:58:23, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6611\n2022-12-31 18:52:31,716 - mmcls - INFO - Epoch [36][1100/5005]\tlr: 1.000e-02, eta: 7:58:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7029\n2022-12-31 18:52:40,738 - mmcls - INFO - Epoch [36][1200/5005]\tlr: 1.000e-02, eta: 7:58:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6548\n2022-12-31 18:52:49,441 - mmcls - INFO - Epoch [36][1300/5005]\tlr: 1.000e-02, eta: 7:57:57, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6428\n2022-12-31 18:52:58,231 - mmcls - INFO - Epoch [36][1400/5005]\tlr: 1.000e-02, eta: 7:57:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6609\n2022-12-31 18:53:07,168 - mmcls - INFO - Epoch [36][1500/5005]\tlr: 1.000e-02, eta: 7:57:39, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6422\n2022-12-31 18:53:16,194 - mmcls - INFO - Epoch [36][1600/5005]\tlr: 1.000e-02, eta: 7:57:30, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6754\n2022-12-31 18:53:24,957 - mmcls - INFO - Epoch [36][1700/5005]\tlr: 1.000e-02, eta: 7:57:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6768\n2022-12-31 18:53:33,759 - mmcls - INFO - Epoch [36][1800/5005]\tlr: 1.000e-02, eta: 7:57:12, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6592\n2022-12-31 18:53:42,288 - mmcls - INFO - Epoch [36][1900/5005]\tlr: 1.000e-02, eta: 7:57:03, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6679\n2022-12-31 18:53:50,952 - mmcls - INFO - Epoch [36][2000/5005]\tlr: 1.000e-02, eta: 7:56:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6503\n2022-12-31 18:53:59,560 - mmcls - INFO - Epoch [36][2100/5005]\tlr: 1.000e-02, eta: 7:56:45, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6729\n2022-12-31 18:54:08,381 - mmcls - INFO - Epoch [36][2200/5005]\tlr: 1.000e-02, eta: 7:56:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6677\n2022-12-31 18:54:17,254 - mmcls - INFO - Epoch [36][2300/5005]\tlr: 1.000e-02, eta: 7:56:27, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6797\n2022-12-31 18:54:26,412 - mmcls - INFO - Epoch [36][2400/5005]\tlr: 1.000e-02, eta: 7:56:19, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6851\n2022-12-31 18:54:35,184 - mmcls - INFO - Epoch [36][2500/5005]\tlr: 1.000e-02, eta: 7:56:10, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6593\n2022-12-31 18:54:44,060 - mmcls - INFO - Epoch [36][2600/5005]\tlr: 1.000e-02, eta: 7:56:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6638\n2022-12-31 18:54:52,983 - mmcls - INFO - Epoch [36][2700/5005]\tlr: 1.000e-02, eta: 7:55:52, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6676\n2022-12-31 18:55:01,784 - mmcls - INFO - Epoch [36][2800/5005]\tlr: 1.000e-02, eta: 7:55:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6709\n2022-12-31 18:55:11,040 - mmcls - INFO - Epoch [36][2900/5005]\tlr: 1.000e-02, eta: 7:55:35, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.6822\n2022-12-31 18:55:19,641 - mmcls - INFO - Epoch [36][3000/5005]\tlr: 1.000e-02, eta: 7:55:26, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6838\n2022-12-31 18:55:28,170 - mmcls - INFO - Epoch [36][3100/5005]\tlr: 1.000e-02, eta: 7:55:16, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6967\n2022-12-31 18:55:37,229 - mmcls - INFO - Epoch [36][3200/5005]\tlr: 1.000e-02, eta: 7:55:08, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.6674\n2022-12-31 18:55:46,032 - mmcls - INFO - Epoch [36][3300/5005]\tlr: 1.000e-02, eta: 7:54:59, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6660\n2022-12-31 18:55:54,700 - mmcls - INFO - Epoch [36][3400/5005]\tlr: 1.000e-02, eta: 7:54:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6699\n2022-12-31 18:56:03,933 - mmcls - INFO - Epoch [36][3500/5005]\tlr: 1.000e-02, eta: 7:54:42, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6722\n2022-12-31 18:56:12,948 - mmcls - INFO - Epoch [36][3600/5005]\tlr: 1.000e-02, eta: 7:54:33, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.6896\n2022-12-31 18:56:21,652 - mmcls - INFO - Epoch [36][3700/5005]\tlr: 1.000e-02, eta: 7:54:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6672\n2022-12-31 18:56:30,354 - mmcls - INFO - Epoch [36][3800/5005]\tlr: 1.000e-02, eta: 7:54:15, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6725\n2022-12-31 18:56:39,278 - mmcls - INFO - Epoch [36][3900/5005]\tlr: 1.000e-02, eta: 7:54:06, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6844\n2022-12-31 18:56:47,947 - mmcls - INFO - Epoch [36][4000/5005]\tlr: 1.000e-02, eta: 7:53:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6694\n2022-12-31 18:56:56,777 - mmcls - INFO - Epoch [36][4100/5005]\tlr: 1.000e-02, eta: 7:53:48, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6936\n2022-12-31 18:57:05,290 - mmcls - INFO - Epoch [36][4200/5005]\tlr: 1.000e-02, eta: 7:53:38, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6607\n2022-12-31 18:57:14,372 - mmcls - INFO - Epoch [36][4300/5005]\tlr: 1.000e-02, eta: 7:53:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6691\n2022-12-31 18:57:23,031 - mmcls - INFO - Epoch [36][4400/5005]\tlr: 1.000e-02, eta: 7:53:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6710\n2022-12-31 18:57:31,808 - mmcls - INFO - Epoch [36][4500/5005]\tlr: 1.000e-02, eta: 7:53:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6745\n2022-12-31 18:57:40,684 - mmcls - INFO - Epoch [36][4600/5005]\tlr: 1.000e-02, eta: 7:53:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6760\n2022-12-31 18:57:49,435 - mmcls - INFO - Epoch [36][4700/5005]\tlr: 1.000e-02, eta: 7:52:54, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6668\n2022-12-31 18:57:58,363 - mmcls - INFO - Epoch [36][4800/5005]\tlr: 1.000e-02, eta: 7:52:45, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6575\n2022-12-31 18:58:07,532 - mmcls - INFO - Epoch [36][4900/5005]\tlr: 1.000e-02, eta: 7:52:37, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6996\n2022-12-31 18:58:16,134 - mmcls - INFO - Epoch [36][5000/5005]\tlr: 1.000e-02, eta: 7:52:28, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6675\n2022-12-31 18:58:38,767 - mmcls - INFO - Epoch(val) [36][196]\taccuracy_top-1: 66.1900, accuracy_top-5: 87.1380\n2022-12-31 18:58:49,829 - mmcls - INFO - Epoch [37][100/5005]\tlr: 1.000e-02, eta: 7:52:22, time: 0.110, data_time: 0.036, memory: 1224, loss: 1.6474\n2022-12-31 18:58:58,261 - mmcls - INFO - Epoch [37][200/5005]\tlr: 1.000e-02, eta: 7:52:12, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6419\n2022-12-31 18:59:07,032 - mmcls - INFO - Epoch [37][300/5005]\tlr: 1.000e-02, eta: 7:52:03, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6305\n2022-12-31 18:59:15,966 - mmcls - INFO - Epoch [37][400/5005]\tlr: 1.000e-02, eta: 7:51:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6270\n2022-12-31 18:59:24,651 - mmcls - INFO - Epoch [37][500/5005]\tlr: 1.000e-02, eta: 7:51:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6483\n2022-12-31 18:59:33,534 - mmcls - INFO - Epoch [37][600/5005]\tlr: 1.000e-02, eta: 7:51:36, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6265\n2022-12-31 18:59:42,051 - mmcls - INFO - Epoch [37][700/5005]\tlr: 1.000e-02, eta: 7:51:27, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6360\n2022-12-31 18:59:50,761 - mmcls - INFO - Epoch [37][800/5005]\tlr: 1.000e-02, eta: 7:51:18, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6553\n2022-12-31 18:59:59,828 - mmcls - INFO - Epoch [37][900/5005]\tlr: 1.000e-02, eta: 7:51:09, time: 0.091, data_time: 0.031, memory: 1224, loss: 1.6580\n2022-12-31 19:00:09,362 - mmcls - INFO - Epoch [37][1000/5005]\tlr: 1.000e-02, eta: 7:51:02, time: 0.095, data_time: 0.010, memory: 1224, loss: 1.6614\n2022-12-31 19:00:18,307 - mmcls - INFO - Epoch [37][1100/5005]\tlr: 1.000e-02, eta: 7:50:53, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6472\n2022-12-31 19:00:26,821 - mmcls - INFO - Epoch [37][1200/5005]\tlr: 1.000e-02, eta: 7:50:44, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6546\n2022-12-31 19:00:35,500 - mmcls - INFO - Epoch [37][1300/5005]\tlr: 1.000e-02, eta: 7:50:34, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6521\n2022-12-31 19:00:44,661 - mmcls - INFO - Epoch [37][1400/5005]\tlr: 1.000e-02, eta: 7:50:26, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.6692\n2022-12-31 19:00:53,781 - mmcls - INFO - Epoch [37][1500/5005]\tlr: 1.000e-02, eta: 7:50:18, time: 0.091, data_time: 0.018, memory: 1224, loss: 1.6361\n2022-12-31 19:01:02,429 - mmcls - INFO - Epoch [37][1600/5005]\tlr: 1.000e-02, eta: 7:50:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6651\n2022-12-31 19:01:11,060 - mmcls - INFO - Epoch [37][1700/5005]\tlr: 1.000e-02, eta: 7:49:59, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6722\n2022-12-31 19:01:20,012 - mmcls - INFO - Epoch [37][1800/5005]\tlr: 1.000e-02, eta: 7:49:51, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6467\n2022-12-31 19:01:29,070 - mmcls - INFO - Epoch [37][1900/5005]\tlr: 1.000e-02, eta: 7:49:42, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6516\n2022-12-31 19:01:37,687 - mmcls - INFO - Epoch [37][2000/5005]\tlr: 1.000e-02, eta: 7:49:33, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6550\n2022-12-31 19:01:46,342 - mmcls - INFO - Epoch [37][2100/5005]\tlr: 1.000e-02, eta: 7:49:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6526\n2022-12-31 19:01:55,276 - mmcls - INFO - Epoch [37][2200/5005]\tlr: 1.000e-02, eta: 7:49:15, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6659\n2022-12-31 19:02:04,148 - mmcls - INFO - Epoch [37][2300/5005]\tlr: 1.000e-02, eta: 7:49:06, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6720\n2022-12-31 19:02:12,878 - mmcls - INFO - Epoch [37][2400/5005]\tlr: 1.000e-02, eta: 7:48:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6620\n2022-12-31 19:02:21,555 - mmcls - INFO - Epoch [37][2500/5005]\tlr: 1.000e-02, eta: 7:48:48, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6837\n2022-12-31 19:02:29,866 - mmcls - INFO - Epoch [37][2600/5005]\tlr: 1.000e-02, eta: 7:48:38, time: 0.083, data_time: 0.003, memory: 1224, loss: 1.6721\n2022-12-31 19:02:38,988 - mmcls - INFO - Epoch [37][2700/5005]\tlr: 1.000e-02, eta: 7:48:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6829\n2022-12-31 19:02:47,611 - mmcls - INFO - Epoch [37][2800/5005]\tlr: 1.000e-02, eta: 7:48:20, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6620\n2022-12-31 19:02:56,458 - mmcls - INFO - Epoch [37][2900/5005]\tlr: 1.000e-02, eta: 7:48:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6380\n2022-12-31 19:03:05,198 - mmcls - INFO - Epoch [37][3000/5005]\tlr: 1.000e-02, eta: 7:48:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6625\n2022-12-31 19:03:14,107 - mmcls - INFO - Epoch [37][3100/5005]\tlr: 1.000e-02, eta: 7:47:54, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6732\n2022-12-31 19:03:22,755 - mmcls - INFO - Epoch [37][3200/5005]\tlr: 1.000e-02, eta: 7:47:45, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6712\n2022-12-31 19:03:31,970 - mmcls - INFO - Epoch [37][3300/5005]\tlr: 1.000e-02, eta: 7:47:36, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6801\n2022-12-31 19:03:40,586 - mmcls - INFO - Epoch [37][3400/5005]\tlr: 1.000e-02, eta: 7:47:27, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6757\n2022-12-31 19:03:49,162 - mmcls - INFO - Epoch [37][3500/5005]\tlr: 1.000e-02, eta: 7:47:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6712\n2022-12-31 19:03:57,862 - mmcls - INFO - Epoch [37][3600/5005]\tlr: 1.000e-02, eta: 7:47:09, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6979\n2022-12-31 19:04:06,700 - mmcls - INFO - Epoch [37][3700/5005]\tlr: 1.000e-02, eta: 7:47:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6779\n2022-12-31 19:04:15,543 - mmcls - INFO - Epoch [37][3800/5005]\tlr: 1.000e-02, eta: 7:46:51, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6781\n2022-12-31 19:04:24,554 - mmcls - INFO - Epoch [37][3900/5005]\tlr: 1.000e-02, eta: 7:46:42, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6691\n2022-12-31 19:04:33,402 - mmcls - INFO - Epoch [37][4000/5005]\tlr: 1.000e-02, eta: 7:46:34, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.7028\n2022-12-31 19:04:42,344 - mmcls - INFO - Epoch [37][4100/5005]\tlr: 1.000e-02, eta: 7:46:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6731\n2022-12-31 19:04:51,026 - mmcls - INFO - Epoch [37][4200/5005]\tlr: 1.000e-02, eta: 7:46:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6714\n2022-12-31 19:04:59,855 - mmcls - INFO - Epoch [37][4300/5005]\tlr: 1.000e-02, eta: 7:46:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6833\n2022-12-31 19:05:08,798 - mmcls - INFO - Epoch [37][4400/5005]\tlr: 1.000e-02, eta: 7:45:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6478\n2022-12-31 19:05:17,669 - mmcls - INFO - Epoch [37][4500/5005]\tlr: 1.000e-02, eta: 7:45:49, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6481\n2022-12-31 19:05:26,147 - mmcls - INFO - Epoch [37][4600/5005]\tlr: 1.000e-02, eta: 7:45:40, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6736\n2022-12-31 19:05:35,100 - mmcls - INFO - Epoch [37][4700/5005]\tlr: 1.000e-02, eta: 7:45:31, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6868\n2022-12-31 19:05:44,177 - mmcls - INFO - Epoch [37][4800/5005]\tlr: 1.000e-02, eta: 7:45:23, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.6768\n2022-12-31 19:05:52,777 - mmcls - INFO - Epoch [37][4900/5005]\tlr: 1.000e-02, eta: 7:45:13, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6746\n2022-12-31 19:06:02,142 - mmcls - INFO - Epoch [37][5000/5005]\tlr: 1.000e-02, eta: 7:45:05, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6750\n2022-12-31 19:06:24,843 - mmcls - INFO - Epoch(val) [37][196]\taccuracy_top-1: 66.4720, accuracy_top-5: 87.2420\n2022-12-31 19:06:35,795 - mmcls - INFO - Epoch [38][100/5005]\tlr: 1.000e-02, eta: 7:44:59, time: 0.109, data_time: 0.034, memory: 1224, loss: 1.6378\n2022-12-31 19:06:44,570 - mmcls - INFO - Epoch [38][200/5005]\tlr: 1.000e-02, eta: 7:44:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6480\n2022-12-31 19:06:53,706 - mmcls - INFO - Epoch [38][300/5005]\tlr: 1.000e-02, eta: 7:44:42, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6365\n2022-12-31 19:07:02,348 - mmcls - INFO - Epoch [38][400/5005]\tlr: 1.000e-02, eta: 7:44:32, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6323\n2022-12-31 19:07:11,184 - mmcls - INFO - Epoch [38][500/5005]\tlr: 1.000e-02, eta: 7:44:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6411\n2022-12-31 19:07:20,045 - mmcls - INFO - Epoch [38][600/5005]\tlr: 1.000e-02, eta: 7:44:15, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6271\n2022-12-31 19:07:28,980 - mmcls - INFO - Epoch [38][700/5005]\tlr: 1.000e-02, eta: 7:44:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6173\n2022-12-31 19:07:38,134 - mmcls - INFO - Epoch [38][800/5005]\tlr: 1.000e-02, eta: 7:43:58, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.6442\n2022-12-31 19:07:47,138 - mmcls - INFO - Epoch [38][900/5005]\tlr: 1.000e-02, eta: 7:43:49, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6391\n2022-12-31 19:07:55,783 - mmcls - INFO - Epoch [38][1000/5005]\tlr: 1.000e-02, eta: 7:43:40, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6486\n2022-12-31 19:08:04,598 - mmcls - INFO - Epoch [38][1100/5005]\tlr: 1.000e-02, eta: 7:43:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6641\n2022-12-31 19:08:13,313 - mmcls - INFO - Epoch [38][1200/5005]\tlr: 1.000e-02, eta: 7:43:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6721\n2022-12-31 19:08:22,189 - mmcls - INFO - Epoch [38][1300/5005]\tlr: 1.000e-02, eta: 7:43:13, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6491\n2022-12-31 19:08:30,795 - mmcls - INFO - Epoch [38][1400/5005]\tlr: 1.000e-02, eta: 7:43:04, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6634\n2022-12-31 19:08:39,695 - mmcls - INFO - Epoch [38][1500/5005]\tlr: 1.000e-02, eta: 7:42:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6505\n2022-12-31 19:08:48,607 - mmcls - INFO - Epoch [38][1600/5005]\tlr: 1.000e-02, eta: 7:42:46, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6601\n2022-12-31 19:08:57,247 - mmcls - INFO - Epoch [38][1700/5005]\tlr: 1.000e-02, eta: 7:42:37, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6657\n2022-12-31 19:09:05,965 - mmcls - INFO - Epoch [38][1800/5005]\tlr: 1.000e-02, eta: 7:42:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6535\n2022-12-31 19:09:14,405 - mmcls - INFO - Epoch [38][1900/5005]\tlr: 1.000e-02, eta: 7:42:18, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6389\n2022-12-31 19:09:23,532 - mmcls - INFO - Epoch [38][2000/5005]\tlr: 1.000e-02, eta: 7:42:10, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6552\n2022-12-31 19:09:32,238 - mmcls - INFO - Epoch [38][2100/5005]\tlr: 1.000e-02, eta: 7:42:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6353\n2022-12-31 19:09:41,064 - mmcls - INFO - Epoch [38][2200/5005]\tlr: 1.000e-02, eta: 7:41:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6342\n2022-12-31 19:09:49,974 - mmcls - INFO - Epoch [38][2300/5005]\tlr: 1.000e-02, eta: 7:41:43, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6869\n2022-12-31 19:09:58,869 - mmcls - INFO - Epoch [38][2400/5005]\tlr: 1.000e-02, eta: 7:41:34, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6545\n2022-12-31 19:10:07,800 - mmcls - INFO - Epoch [38][2500/5005]\tlr: 1.000e-02, eta: 7:41:26, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6434\n2022-12-31 19:10:16,590 - mmcls - INFO - Epoch [38][2600/5005]\tlr: 1.000e-02, eta: 7:41:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6472\n2022-12-31 19:10:25,265 - mmcls - INFO - Epoch [38][2700/5005]\tlr: 1.000e-02, eta: 7:41:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6653\n2022-12-31 19:10:34,080 - mmcls - INFO - Epoch [38][2800/5005]\tlr: 1.000e-02, eta: 7:40:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6661\n2022-12-31 19:10:43,077 - mmcls - INFO - Epoch [38][2900/5005]\tlr: 1.000e-02, eta: 7:40:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6554\n2022-12-31 19:10:51,946 - mmcls - INFO - Epoch [38][3000/5005]\tlr: 1.000e-02, eta: 7:40:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6698\n2022-12-31 19:11:00,773 - mmcls - INFO - Epoch [38][3100/5005]\tlr: 1.000e-02, eta: 7:40:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6432\n2022-12-31 19:11:09,557 - mmcls - INFO - Epoch [38][3200/5005]\tlr: 1.000e-02, eta: 7:40:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6582\n2022-12-31 19:11:18,375 - mmcls - INFO - Epoch [38][3300/5005]\tlr: 1.000e-02, eta: 7:40:15, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6671\n2022-12-31 19:11:27,666 - mmcls - INFO - Epoch [38][3400/5005]\tlr: 1.000e-02, eta: 7:40:06, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.6490\n2022-12-31 19:11:36,673 - mmcls - INFO - Epoch [38][3500/5005]\tlr: 1.000e-02, eta: 7:39:58, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6921\n2022-12-31 19:11:45,279 - mmcls - INFO - Epoch [38][3600/5005]\tlr: 1.000e-02, eta: 7:39:49, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.6442\n2022-12-31 19:11:54,027 - mmcls - INFO - Epoch [38][3700/5005]\tlr: 1.000e-02, eta: 7:39:40, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6795\n2022-12-31 19:12:02,740 - mmcls - INFO - Epoch [38][3800/5005]\tlr: 1.000e-02, eta: 7:39:30, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6707\n2022-12-31 19:12:12,216 - mmcls - INFO - Epoch [38][3900/5005]\tlr: 1.000e-02, eta: 7:39:23, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.6404\n2022-12-31 19:12:20,616 - mmcls - INFO - Epoch [38][4000/5005]\tlr: 1.000e-02, eta: 7:39:13, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.6817\n2022-12-31 19:12:29,310 - mmcls - INFO - Epoch [38][4100/5005]\tlr: 1.000e-02, eta: 7:39:04, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6766\n2022-12-31 19:12:38,203 - mmcls - INFO - Epoch [38][4200/5005]\tlr: 1.000e-02, eta: 7:38:55, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6699\n2022-12-31 19:12:46,918 - mmcls - INFO - Epoch [38][4300/5005]\tlr: 1.000e-02, eta: 7:38:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6752\n2022-12-31 19:12:55,794 - mmcls - INFO - Epoch [38][4400/5005]\tlr: 1.000e-02, eta: 7:38:37, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6876\n2022-12-31 19:13:04,541 - mmcls - INFO - Epoch [38][4500/5005]\tlr: 1.000e-02, eta: 7:38:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6543\n2022-12-31 19:13:13,256 - mmcls - INFO - Epoch [38][4600/5005]\tlr: 1.000e-02, eta: 7:38:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6868\n2022-12-31 19:13:22,154 - mmcls - INFO - Epoch [38][4700/5005]\tlr: 1.000e-02, eta: 7:38:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6760\n2022-12-31 19:13:30,875 - mmcls - INFO - Epoch [38][4800/5005]\tlr: 1.000e-02, eta: 7:38:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6721\n2022-12-31 19:13:39,569 - mmcls - INFO - Epoch [38][4900/5005]\tlr: 1.000e-02, eta: 7:37:52, time: 0.087, data_time: 0.044, memory: 1224, loss: 1.6694\n2022-12-31 19:13:48,565 - mmcls - INFO - Epoch [38][5000/5005]\tlr: 1.000e-02, eta: 7:37:44, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6707\n2022-12-31 19:14:11,175 - mmcls - INFO - Epoch(val) [38][196]\taccuracy_top-1: 66.4360, accuracy_top-5: 87.3880\n2022-12-31 19:14:21,990 - mmcls - INFO - Epoch [39][100/5005]\tlr: 1.000e-02, eta: 7:37:37, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.6103\n2022-12-31 19:14:30,728 - mmcls - INFO - Epoch [39][200/5005]\tlr: 1.000e-02, eta: 7:37:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6458\n2022-12-31 19:14:39,562 - mmcls - INFO - Epoch [39][300/5005]\tlr: 1.000e-02, eta: 7:37:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6186\n2022-12-31 19:14:48,472 - mmcls - INFO - Epoch [39][400/5005]\tlr: 1.000e-02, eta: 7:37:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6271\n2022-12-31 19:14:57,212 - mmcls - INFO - Epoch [39][500/5005]\tlr: 1.000e-02, eta: 7:37:01, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6261\n2022-12-31 19:15:06,032 - mmcls - INFO - Epoch [39][600/5005]\tlr: 1.000e-02, eta: 7:36:52, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6619\n2022-12-31 19:15:14,924 - mmcls - INFO - Epoch [39][700/5005]\tlr: 1.000e-02, eta: 7:36:43, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6243\n2022-12-31 19:15:23,860 - mmcls - INFO - Epoch [39][800/5005]\tlr: 1.000e-02, eta: 7:36:35, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6471\n2022-12-31 19:15:32,521 - mmcls - INFO - Epoch [39][900/5005]\tlr: 1.000e-02, eta: 7:36:25, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6588\n2022-12-31 19:15:41,106 - mmcls - INFO - Epoch [39][1000/5005]\tlr: 1.000e-02, eta: 7:36:16, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6537\n2022-12-31 19:15:50,229 - mmcls - INFO - Epoch [39][1100/5005]\tlr: 1.000e-02, eta: 7:36:08, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6288\n2022-12-31 19:15:58,963 - mmcls - INFO - Epoch [39][1200/5005]\tlr: 1.000e-02, eta: 7:35:59, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6429\n2022-12-31 19:16:07,962 - mmcls - INFO - Epoch [39][1300/5005]\tlr: 1.000e-02, eta: 7:35:50, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6434\n2022-12-31 19:16:16,670 - mmcls - INFO - Epoch [39][1400/5005]\tlr: 1.000e-02, eta: 7:35:41, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6355\n2022-12-31 19:16:25,471 - mmcls - INFO - Epoch [39][1500/5005]\tlr: 1.000e-02, eta: 7:35:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6450\n2022-12-31 19:16:34,406 - mmcls - INFO - Epoch [39][1600/5005]\tlr: 1.000e-02, eta: 7:35:23, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6743\n2022-12-31 19:16:43,134 - mmcls - INFO - Epoch [39][1700/5005]\tlr: 1.000e-02, eta: 7:35:14, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6672\n2022-12-31 19:16:52,038 - mmcls - INFO - Epoch [39][1800/5005]\tlr: 1.000e-02, eta: 7:35:06, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.6629\n2022-12-31 19:17:00,429 - mmcls - INFO - Epoch [39][1900/5005]\tlr: 1.000e-02, eta: 7:34:56, time: 0.084, data_time: 0.000, memory: 1224, loss: 1.6632\n2022-12-31 19:17:09,337 - mmcls - INFO - Epoch [39][2000/5005]\tlr: 1.000e-02, eta: 7:34:47, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6564\n2022-12-31 19:17:18,067 - mmcls - INFO - Epoch [39][2100/5005]\tlr: 1.000e-02, eta: 7:34:38, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6454\n2022-12-31 19:17:26,824 - mmcls - INFO - Epoch [39][2200/5005]\tlr: 1.000e-02, eta: 7:34:29, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6725\n2022-12-31 19:17:35,566 - mmcls - INFO - Epoch [39][2300/5005]\tlr: 1.000e-02, eta: 7:34:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6407\n2022-12-31 19:17:44,480 - mmcls - INFO - Epoch [39][2400/5005]\tlr: 1.000e-02, eta: 7:34:11, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6456\n2022-12-31 19:17:53,475 - mmcls - INFO - Epoch [39][2500/5005]\tlr: 1.000e-02, eta: 7:34:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6590\n2022-12-31 19:18:02,455 - mmcls - INFO - Epoch [39][2600/5005]\tlr: 1.000e-02, eta: 7:33:54, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6588\n2022-12-31 19:18:11,374 - mmcls - INFO - Epoch [39][2700/5005]\tlr: 1.000e-02, eta: 7:33:45, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6715\n2022-12-31 19:18:20,171 - mmcls - INFO - Epoch [39][2800/5005]\tlr: 1.000e-02, eta: 7:33:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6435\n2022-12-31 19:18:29,171 - mmcls - INFO - Epoch [39][2900/5005]\tlr: 1.000e-02, eta: 7:33:28, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6549\n2022-12-31 19:18:37,906 - mmcls - INFO - Epoch [39][3000/5005]\tlr: 1.000e-02, eta: 7:33:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6575\n2022-12-31 19:18:46,722 - mmcls - INFO - Epoch [39][3100/5005]\tlr: 1.000e-02, eta: 7:33:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6757\n2022-12-31 19:18:55,630 - mmcls - INFO - Epoch [39][3200/5005]\tlr: 1.000e-02, eta: 7:33:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6648\n2022-12-31 19:19:04,716 - mmcls - INFO - Epoch [39][3300/5005]\tlr: 1.000e-02, eta: 7:32:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6477\n2022-12-31 19:19:13,560 - mmcls - INFO - Epoch [39][3400/5005]\tlr: 1.000e-02, eta: 7:32:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6665\n2022-12-31 19:19:22,121 - mmcls - INFO - Epoch [39][3500/5005]\tlr: 1.000e-02, eta: 7:32:35, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6407\n2022-12-31 19:19:30,881 - mmcls - INFO - Epoch [39][3600/5005]\tlr: 1.000e-02, eta: 7:32:25, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6434\n2022-12-31 19:19:39,552 - mmcls - INFO - Epoch [39][3700/5005]\tlr: 1.000e-02, eta: 7:32:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6708\n2022-12-31 19:19:48,248 - mmcls - INFO - Epoch [39][3800/5005]\tlr: 1.000e-02, eta: 7:32:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6577\n2022-12-31 19:19:57,186 - mmcls - INFO - Epoch [39][3900/5005]\tlr: 1.000e-02, eta: 7:31:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6597\n2022-12-31 19:20:06,359 - mmcls - INFO - Epoch [39][4000/5005]\tlr: 1.000e-02, eta: 7:31:50, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.6428\n2022-12-31 19:20:15,112 - mmcls - INFO - Epoch [39][4100/5005]\tlr: 1.000e-02, eta: 7:31:41, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6740\n2022-12-31 19:20:23,808 - mmcls - INFO - Epoch [39][4200/5005]\tlr: 1.000e-02, eta: 7:31:32, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6562\n2022-12-31 19:20:32,514 - mmcls - INFO - Epoch [39][4300/5005]\tlr: 1.000e-02, eta: 7:31:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6778\n2022-12-31 19:20:41,504 - mmcls - INFO - Epoch [39][4400/5005]\tlr: 1.000e-02, eta: 7:31:14, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6728\n2022-12-31 19:20:50,299 - mmcls - INFO - Epoch [39][4500/5005]\tlr: 1.000e-02, eta: 7:31:05, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6895\n2022-12-31 19:20:59,274 - mmcls - INFO - Epoch [39][4600/5005]\tlr: 1.000e-02, eta: 7:30:57, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6777\n2022-12-31 19:21:07,874 - mmcls - INFO - Epoch [39][4700/5005]\tlr: 1.000e-02, eta: 7:30:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6866\n2022-12-31 19:21:16,547 - mmcls - INFO - Epoch [39][4800/5005]\tlr: 1.000e-02, eta: 7:30:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6675\n2022-12-31 19:21:25,382 - mmcls - INFO - Epoch [39][4900/5005]\tlr: 1.000e-02, eta: 7:30:30, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6562\n2022-12-31 19:21:34,271 - mmcls - INFO - Epoch [39][5000/5005]\tlr: 1.000e-02, eta: 7:30:21, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6756\n2022-12-31 19:21:56,905 - mmcls - INFO - Epoch(val) [39][196]\taccuracy_top-1: 66.0640, accuracy_top-5: 87.0240\n2022-12-31 19:22:07,883 - mmcls - INFO - Epoch [40][100/5005]\tlr: 1.000e-02, eta: 7:30:14, time: 0.109, data_time: 0.038, memory: 1224, loss: 1.6272\n2022-12-31 19:22:16,786 - mmcls - INFO - Epoch [40][200/5005]\tlr: 1.000e-02, eta: 7:30:05, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6072\n2022-12-31 19:22:25,609 - mmcls - INFO - Epoch [40][300/5005]\tlr: 1.000e-02, eta: 7:29:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6365\n2022-12-31 19:22:34,234 - mmcls - INFO - Epoch [40][400/5005]\tlr: 1.000e-02, eta: 7:29:47, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6354\n2022-12-31 19:22:42,964 - mmcls - INFO - Epoch [40][500/5005]\tlr: 1.000e-02, eta: 7:29:38, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6307\n2022-12-31 19:22:51,882 - mmcls - INFO - Epoch [40][600/5005]\tlr: 1.000e-02, eta: 7:29:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6458\n2022-12-31 19:23:00,368 - mmcls - INFO - Epoch [40][700/5005]\tlr: 1.000e-02, eta: 7:29:20, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6405\n2022-12-31 19:23:09,104 - mmcls - INFO - Epoch [40][800/5005]\tlr: 1.000e-02, eta: 7:29:11, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6068\n2022-12-31 19:23:18,022 - mmcls - INFO - Epoch [40][900/5005]\tlr: 1.000e-02, eta: 7:29:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6336\n2022-12-31 19:23:26,747 - mmcls - INFO - Epoch [40][1000/5005]\tlr: 1.000e-02, eta: 7:28:53, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6389\n2022-12-31 19:23:35,517 - mmcls - INFO - Epoch [40][1100/5005]\tlr: 1.000e-02, eta: 7:28:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6142\n2022-12-31 19:23:44,272 - mmcls - INFO - Epoch [40][1200/5005]\tlr: 1.000e-02, eta: 7:28:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6524\n2022-12-31 19:23:52,709 - mmcls - INFO - Epoch [40][1300/5005]\tlr: 1.000e-02, eta: 7:28:26, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6586\n2022-12-31 19:24:01,434 - mmcls - INFO - Epoch [40][1400/5005]\tlr: 1.000e-02, eta: 7:28:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6485\n2022-12-31 19:24:10,290 - mmcls - INFO - Epoch [40][1500/5005]\tlr: 1.000e-02, eta: 7:28:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6453\n2022-12-31 19:24:18,857 - mmcls - INFO - Epoch [40][1600/5005]\tlr: 1.000e-02, eta: 7:27:58, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6485\n2022-12-31 19:24:27,547 - mmcls - INFO - Epoch [40][1700/5005]\tlr: 1.000e-02, eta: 7:27:49, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6363\n2022-12-31 19:24:36,311 - mmcls - INFO - Epoch [40][1800/5005]\tlr: 1.000e-02, eta: 7:27:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6586\n2022-12-31 19:24:44,941 - mmcls - INFO - Epoch [40][1900/5005]\tlr: 1.000e-02, eta: 7:27:31, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6588\n2022-12-31 19:24:53,833 - mmcls - INFO - Epoch [40][2000/5005]\tlr: 1.000e-02, eta: 7:27:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6755\n2022-12-31 19:25:02,584 - mmcls - INFO - Epoch [40][2100/5005]\tlr: 1.000e-02, eta: 7:27:13, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6312\n2022-12-31 19:25:11,712 - mmcls - INFO - Epoch [40][2200/5005]\tlr: 1.000e-02, eta: 7:27:05, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6400\n2022-12-31 19:25:20,669 - mmcls - INFO - Epoch [40][2300/5005]\tlr: 1.000e-02, eta: 7:26:56, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6490\n2022-12-31 19:25:29,671 - mmcls - INFO - Epoch [40][2400/5005]\tlr: 1.000e-02, eta: 7:26:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6386\n2022-12-31 19:25:38,351 - mmcls - INFO - Epoch [40][2500/5005]\tlr: 1.000e-02, eta: 7:26:39, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6337\n2022-12-31 19:25:47,068 - mmcls - INFO - Epoch [40][2600/5005]\tlr: 1.000e-02, eta: 7:26:30, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6373\n2022-12-31 19:25:55,677 - mmcls - INFO - Epoch [40][2700/5005]\tlr: 1.000e-02, eta: 7:26:20, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.6527\n2022-12-31 19:26:04,429 - mmcls - INFO - Epoch [40][2800/5005]\tlr: 1.000e-02, eta: 7:26:11, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6727\n2022-12-31 19:26:13,137 - mmcls - INFO - Epoch [40][2900/5005]\tlr: 1.000e-02, eta: 7:26:02, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6434\n2022-12-31 19:26:21,811 - mmcls - INFO - Epoch [40][3000/5005]\tlr: 1.000e-02, eta: 7:25:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6648\n2022-12-31 19:26:31,222 - mmcls - INFO - Epoch [40][3100/5005]\tlr: 1.000e-02, eta: 7:25:45, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6575\n2022-12-31 19:26:40,145 - mmcls - INFO - Epoch [40][3200/5005]\tlr: 1.000e-02, eta: 7:25:36, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6575\n2022-12-31 19:26:48,993 - mmcls - INFO - Epoch [40][3300/5005]\tlr: 1.000e-02, eta: 7:25:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6518\n2022-12-31 19:26:57,580 - mmcls - INFO - Epoch [40][3400/5005]\tlr: 1.000e-02, eta: 7:25:18, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6767\n2022-12-31 19:27:06,302 - mmcls - INFO - Epoch [40][3500/5005]\tlr: 1.000e-02, eta: 7:25:09, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6490\n2022-12-31 19:27:15,028 - mmcls - INFO - Epoch [40][3600/5005]\tlr: 1.000e-02, eta: 7:25:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6650\n2022-12-31 19:27:24,093 - mmcls - INFO - Epoch [40][3700/5005]\tlr: 1.000e-02, eta: 7:24:52, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6537\n2022-12-31 19:27:33,293 - mmcls - INFO - Epoch [40][3800/5005]\tlr: 1.000e-02, eta: 7:24:43, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6666\n2022-12-31 19:27:42,259 - mmcls - INFO - Epoch [40][3900/5005]\tlr: 1.000e-02, eta: 7:24:35, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6448\n2022-12-31 19:27:50,907 - mmcls - INFO - Epoch [40][4000/5005]\tlr: 1.000e-02, eta: 7:24:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6526\n2022-12-31 19:27:59,904 - mmcls - INFO - Epoch [40][4100/5005]\tlr: 1.000e-02, eta: 7:24:17, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6623\n2022-12-31 19:28:08,538 - mmcls - INFO - Epoch [40][4200/5005]\tlr: 1.000e-02, eta: 7:24:08, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6802\n2022-12-31 19:28:17,479 - mmcls - INFO - Epoch [40][4300/5005]\tlr: 1.000e-02, eta: 7:23:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6664\n2022-12-31 19:28:26,248 - mmcls - INFO - Epoch [40][4400/5005]\tlr: 1.000e-02, eta: 7:23:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6594\n2022-12-31 19:28:35,160 - mmcls - INFO - Epoch [40][4500/5005]\tlr: 1.000e-02, eta: 7:23:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6584\n2022-12-31 19:28:43,906 - mmcls - INFO - Epoch [40][4600/5005]\tlr: 1.000e-02, eta: 7:23:32, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6710\n2022-12-31 19:28:52,600 - mmcls - INFO - Epoch [40][4700/5005]\tlr: 1.000e-02, eta: 7:23:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6682\n2022-12-31 19:29:01,317 - mmcls - INFO - Epoch [40][4800/5005]\tlr: 1.000e-02, eta: 7:23:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6676\n2022-12-31 19:29:10,033 - mmcls - INFO - Epoch [40][4900/5005]\tlr: 1.000e-02, eta: 7:23:05, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6622\n2022-12-31 19:29:18,614 - mmcls - INFO - Epoch [40][5000/5005]\tlr: 1.000e-02, eta: 7:22:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6935\n2022-12-31 19:29:41,402 - mmcls - INFO - Epoch(val) [40][196]\taccuracy_top-1: 66.4980, accuracy_top-5: 87.2980\n2022-12-31 19:29:52,542 - mmcls - INFO - Epoch [41][100/5005]\tlr: 1.000e-02, eta: 7:22:49, time: 0.111, data_time: 0.067, memory: 1224, loss: 1.6306\n2022-12-31 19:30:01,180 - mmcls - INFO - Epoch [41][200/5005]\tlr: 1.000e-02, eta: 7:22:40, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5957\n2022-12-31 19:30:09,966 - mmcls - INFO - Epoch [41][300/5005]\tlr: 1.000e-02, eta: 7:22:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6138\n2022-12-31 19:30:18,886 - mmcls - INFO - Epoch [41][400/5005]\tlr: 1.000e-02, eta: 7:22:22, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6436\n2022-12-31 19:30:27,541 - mmcls - INFO - Epoch [41][500/5005]\tlr: 1.000e-02, eta: 7:22:13, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6460\n2022-12-31 19:30:36,344 - mmcls - INFO - Epoch [41][600/5005]\tlr: 1.000e-02, eta: 7:22:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6159\n2022-12-31 19:30:44,963 - mmcls - INFO - Epoch [41][700/5005]\tlr: 1.000e-02, eta: 7:21:55, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6440\n2022-12-31 19:30:53,873 - mmcls - INFO - Epoch [41][800/5005]\tlr: 1.000e-02, eta: 7:21:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6508\n2022-12-31 19:31:02,730 - mmcls - INFO - Epoch [41][900/5005]\tlr: 1.000e-02, eta: 7:21:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6393\n2022-12-31 19:31:11,634 - mmcls - INFO - Epoch [41][1000/5005]\tlr: 1.000e-02, eta: 7:21:29, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6251\n2022-12-31 19:31:20,476 - mmcls - INFO - Epoch [41][1100/5005]\tlr: 1.000e-02, eta: 7:21:20, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6347\n2022-12-31 19:31:29,316 - mmcls - INFO - Epoch [41][1200/5005]\tlr: 1.000e-02, eta: 7:21:11, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6258\n2022-12-31 19:31:38,039 - mmcls - INFO - Epoch [41][1300/5005]\tlr: 1.000e-02, eta: 7:21:02, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6498\n2022-12-31 19:31:46,587 - mmcls - INFO - Epoch [41][1400/5005]\tlr: 1.000e-02, eta: 7:20:53, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6474\n2022-12-31 19:31:55,327 - mmcls - INFO - Epoch [41][1500/5005]\tlr: 1.000e-02, eta: 7:20:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6568\n2022-12-31 19:32:04,068 - mmcls - INFO - Epoch [41][1600/5005]\tlr: 1.000e-02, eta: 7:20:35, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6525\n2022-12-31 19:32:12,867 - mmcls - INFO - Epoch [41][1700/5005]\tlr: 1.000e-02, eta: 7:20:26, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6472\n2022-12-31 19:32:21,661 - mmcls - INFO - Epoch [41][1800/5005]\tlr: 1.000e-02, eta: 7:20:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6440\n2022-12-31 19:32:30,508 - mmcls - INFO - Epoch [41][1900/5005]\tlr: 1.000e-02, eta: 7:20:08, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6744\n2022-12-31 19:32:39,774 - mmcls - INFO - Epoch [41][2000/5005]\tlr: 1.000e-02, eta: 7:20:00, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.6583\n2022-12-31 19:32:48,771 - mmcls - INFO - Epoch [41][2100/5005]\tlr: 1.000e-02, eta: 7:19:51, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6526\n2022-12-31 19:32:57,380 - mmcls - INFO - Epoch [41][2200/5005]\tlr: 1.000e-02, eta: 7:19:42, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6581\n2022-12-31 19:33:06,170 - mmcls - INFO - Epoch [41][2300/5005]\tlr: 1.000e-02, eta: 7:19:33, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6529\n2022-12-31 19:33:14,822 - mmcls - INFO - Epoch [41][2400/5005]\tlr: 1.000e-02, eta: 7:19:24, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6677\n2022-12-31 19:33:24,074 - mmcls - INFO - Epoch [41][2500/5005]\tlr: 1.000e-02, eta: 7:19:15, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.6658\n2022-12-31 19:33:33,048 - mmcls - INFO - Epoch [41][2600/5005]\tlr: 1.000e-02, eta: 7:19:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6723\n2022-12-31 19:33:41,821 - mmcls - INFO - Epoch [41][2700/5005]\tlr: 1.000e-02, eta: 7:18:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6736\n2022-12-31 19:33:50,796 - mmcls - INFO - Epoch [41][2800/5005]\tlr: 1.000e-02, eta: 7:18:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6599\n2022-12-31 19:33:59,980 - mmcls - INFO - Epoch [41][2900/5005]\tlr: 1.000e-02, eta: 7:18:41, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6468\n2022-12-31 19:34:09,013 - mmcls - INFO - Epoch [41][3000/5005]\tlr: 1.000e-02, eta: 7:18:32, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6607\n2022-12-31 19:34:18,012 - mmcls - INFO - Epoch [41][3100/5005]\tlr: 1.000e-02, eta: 7:18:24, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6630\n2022-12-31 19:34:26,703 - mmcls - INFO - Epoch [41][3200/5005]\tlr: 1.000e-02, eta: 7:18:15, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6575\n2022-12-31 19:34:35,485 - mmcls - INFO - Epoch [41][3300/5005]\tlr: 1.000e-02, eta: 7:18:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6677\n2022-12-31 19:34:44,198 - mmcls - INFO - Epoch [41][3400/5005]\tlr: 1.000e-02, eta: 7:17:56, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6578\n2022-12-31 19:34:52,955 - mmcls - INFO - Epoch [41][3500/5005]\tlr: 1.000e-02, eta: 7:17:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6619\n2022-12-31 19:35:01,617 - mmcls - INFO - Epoch [41][3600/5005]\tlr: 1.000e-02, eta: 7:17:38, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6718\n2022-12-31 19:35:10,557 - mmcls - INFO - Epoch [41][3700/5005]\tlr: 1.000e-02, eta: 7:17:30, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6686\n2022-12-31 19:35:19,233 - mmcls - INFO - Epoch [41][3800/5005]\tlr: 1.000e-02, eta: 7:17:21, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6497\n2022-12-31 19:35:28,109 - mmcls - INFO - Epoch [41][3900/5005]\tlr: 1.000e-02, eta: 7:17:12, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6673\n2022-12-31 19:35:36,711 - mmcls - INFO - Epoch [41][4000/5005]\tlr: 1.000e-02, eta: 7:17:03, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6705\n2022-12-31 19:35:45,632 - mmcls - INFO - Epoch [41][4100/5005]\tlr: 1.000e-02, eta: 7:16:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6786\n2022-12-31 19:35:54,240 - mmcls - INFO - Epoch [41][4200/5005]\tlr: 1.000e-02, eta: 7:16:45, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6651\n2022-12-31 19:36:02,916 - mmcls - INFO - Epoch [41][4300/5005]\tlr: 1.000e-02, eta: 7:16:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6764\n2022-12-31 19:36:12,029 - mmcls - INFO - Epoch [41][4400/5005]\tlr: 1.000e-02, eta: 7:16:27, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.6587\n2022-12-31 19:36:20,598 - mmcls - INFO - Epoch [41][4500/5005]\tlr: 1.000e-02, eta: 7:16:18, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6703\n2022-12-31 19:36:29,393 - mmcls - INFO - Epoch [41][4600/5005]\tlr: 1.000e-02, eta: 7:16:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6763\n2022-12-31 19:36:38,336 - mmcls - INFO - Epoch [41][4700/5005]\tlr: 1.000e-02, eta: 7:16:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6586\n2022-12-31 19:36:47,450 - mmcls - INFO - Epoch [41][4800/5005]\tlr: 1.000e-02, eta: 7:15:52, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6639\n2022-12-31 19:36:56,126 - mmcls - INFO - Epoch [41][4900/5005]\tlr: 1.000e-02, eta: 7:15:43, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6755\n2022-12-31 19:37:04,929 - mmcls - INFO - Epoch [41][5000/5005]\tlr: 1.000e-02, eta: 7:15:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6795\n2022-12-31 19:37:27,892 - mmcls - INFO - Epoch(val) [41][196]\taccuracy_top-1: 65.6960, accuracy_top-5: 86.8600\n2022-12-31 19:37:38,852 - mmcls - INFO - Epoch [42][100/5005]\tlr: 1.000e-02, eta: 7:15:27, time: 0.109, data_time: 0.043, memory: 1224, loss: 1.6067\n2022-12-31 19:37:47,600 - mmcls - INFO - Epoch [42][200/5005]\tlr: 1.000e-02, eta: 7:15:18, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6167\n2022-12-31 19:37:56,579 - mmcls - INFO - Epoch [42][300/5005]\tlr: 1.000e-02, eta: 7:15:09, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6260\n2022-12-31 19:38:05,477 - mmcls - INFO - Epoch [42][400/5005]\tlr: 1.000e-02, eta: 7:15:00, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6379\n2022-12-31 19:38:14,543 - mmcls - INFO - Epoch [42][500/5005]\tlr: 1.000e-02, eta: 7:14:52, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6194\n2022-12-31 19:38:23,093 - mmcls - INFO - Epoch [42][600/5005]\tlr: 1.000e-02, eta: 7:14:42, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.5997\n2022-12-31 19:38:31,882 - mmcls - INFO - Epoch [42][700/5005]\tlr: 1.000e-02, eta: 7:14:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6119\n2022-12-31 19:38:40,626 - mmcls - INFO - Epoch [42][800/5005]\tlr: 1.000e-02, eta: 7:14:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6341\n2022-12-31 19:38:49,440 - mmcls - INFO - Epoch [42][900/5005]\tlr: 1.000e-02, eta: 7:14:16, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6255\n2022-12-31 19:38:58,431 - mmcls - INFO - Epoch [42][1000/5005]\tlr: 1.000e-02, eta: 7:14:07, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6159\n2022-12-31 19:39:07,009 - mmcls - INFO - Epoch [42][1100/5005]\tlr: 1.000e-02, eta: 7:13:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6303\n2022-12-31 19:39:15,766 - mmcls - INFO - Epoch [42][1200/5005]\tlr: 1.000e-02, eta: 7:13:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6461\n2022-12-31 19:39:24,648 - mmcls - INFO - Epoch [42][1300/5005]\tlr: 1.000e-02, eta: 7:13:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6418\n2022-12-31 19:39:33,207 - mmcls - INFO - Epoch [42][1400/5005]\tlr: 1.000e-02, eta: 7:13:31, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6652\n2022-12-31 19:39:42,432 - mmcls - INFO - Epoch [42][1500/5005]\tlr: 1.000e-02, eta: 7:13:22, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6372\n2022-12-31 19:39:51,249 - mmcls - INFO - Epoch [42][1600/5005]\tlr: 1.000e-02, eta: 7:13:13, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6562\n2022-12-31 19:39:59,885 - mmcls - INFO - Epoch [42][1700/5005]\tlr: 1.000e-02, eta: 7:13:04, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6526\n2022-12-31 19:40:08,962 - mmcls - INFO - Epoch [42][1800/5005]\tlr: 1.000e-02, eta: 7:12:56, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6324\n2022-12-31 19:40:17,845 - mmcls - INFO - Epoch [42][1900/5005]\tlr: 1.000e-02, eta: 7:12:47, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6459\n2022-12-31 19:40:26,618 - mmcls - INFO - Epoch [42][2000/5005]\tlr: 1.000e-02, eta: 7:12:38, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6461\n2022-12-31 19:40:35,412 - mmcls - INFO - Epoch [42][2100/5005]\tlr: 1.000e-02, eta: 7:12:29, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6596\n2022-12-31 19:40:44,189 - mmcls - INFO - Epoch [42][2200/5005]\tlr: 1.000e-02, eta: 7:12:20, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6706\n2022-12-31 19:40:53,216 - mmcls - INFO - Epoch [42][2300/5005]\tlr: 1.000e-02, eta: 7:12:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6547\n2022-12-31 19:41:01,857 - mmcls - INFO - Epoch [42][2400/5005]\tlr: 1.000e-02, eta: 7:12:02, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6503\n2022-12-31 19:41:10,328 - mmcls - INFO - Epoch [42][2500/5005]\tlr: 1.000e-02, eta: 7:11:53, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6632\n2022-12-31 19:41:19,430 - mmcls - INFO - Epoch [42][2600/5005]\tlr: 1.000e-02, eta: 7:11:44, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6697\n2022-12-31 19:41:28,272 - mmcls - INFO - Epoch [42][2700/5005]\tlr: 1.000e-02, eta: 7:11:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6449\n2022-12-31 19:41:37,200 - mmcls - INFO - Epoch [42][2800/5005]\tlr: 1.000e-02, eta: 7:11:27, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6292\n2022-12-31 19:41:45,684 - mmcls - INFO - Epoch [42][2900/5005]\tlr: 1.000e-02, eta: 7:11:17, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6894\n2022-12-31 19:41:54,338 - mmcls - INFO - Epoch [42][3000/5005]\tlr: 1.000e-02, eta: 7:11:08, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6778\n2022-12-31 19:42:03,135 - mmcls - INFO - Epoch [42][3100/5005]\tlr: 1.000e-02, eta: 7:10:59, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6781\n2022-12-31 19:42:11,998 - mmcls - INFO - Epoch [42][3200/5005]\tlr: 1.000e-02, eta: 7:10:51, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6458\n2022-12-31 19:42:20,932 - mmcls - INFO - Epoch [42][3300/5005]\tlr: 1.000e-02, eta: 7:10:42, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6500\n2022-12-31 19:42:29,555 - mmcls - INFO - Epoch [42][3400/5005]\tlr: 1.000e-02, eta: 7:10:33, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6516\n2022-12-31 19:42:38,083 - mmcls - INFO - Epoch [42][3500/5005]\tlr: 1.000e-02, eta: 7:10:23, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6697\n2022-12-31 19:42:46,774 - mmcls - INFO - Epoch [42][3600/5005]\tlr: 1.000e-02, eta: 7:10:14, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6563\n2022-12-31 19:42:55,327 - mmcls - INFO - Epoch [42][3700/5005]\tlr: 1.000e-02, eta: 7:10:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6642\n2022-12-31 19:43:04,066 - mmcls - INFO - Epoch [42][3800/5005]\tlr: 1.000e-02, eta: 7:09:56, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6714\n2022-12-31 19:43:12,883 - mmcls - INFO - Epoch [42][3900/5005]\tlr: 1.000e-02, eta: 7:09:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6669\n2022-12-31 19:43:21,595 - mmcls - INFO - Epoch [42][4000/5005]\tlr: 1.000e-02, eta: 7:09:38, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6328\n2022-12-31 19:43:30,361 - mmcls - INFO - Epoch [42][4100/5005]\tlr: 1.000e-02, eta: 7:09:29, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6801\n2022-12-31 19:43:39,519 - mmcls - INFO - Epoch [42][4200/5005]\tlr: 1.000e-02, eta: 7:09:21, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.6545\n2022-12-31 19:43:48,428 - mmcls - INFO - Epoch [42][4300/5005]\tlr: 1.000e-02, eta: 7:09:12, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6535\n2022-12-31 19:43:57,519 - mmcls - INFO - Epoch [42][4400/5005]\tlr: 1.000e-02, eta: 7:09:03, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6709\n2022-12-31 19:44:06,075 - mmcls - INFO - Epoch [42][4500/5005]\tlr: 1.000e-02, eta: 7:08:54, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6624\n2022-12-31 19:44:15,039 - mmcls - INFO - Epoch [42][4600/5005]\tlr: 1.000e-02, eta: 7:08:45, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.6792\n2022-12-31 19:44:24,006 - mmcls - INFO - Epoch [42][4700/5005]\tlr: 1.000e-02, eta: 7:08:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6720\n2022-12-31 19:44:32,818 - mmcls - INFO - Epoch [42][4800/5005]\tlr: 1.000e-02, eta: 7:08:28, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6815\n2022-12-31 19:44:41,500 - mmcls - INFO - Epoch [42][4900/5005]\tlr: 1.000e-02, eta: 7:08:19, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6603\n2022-12-31 19:44:50,214 - mmcls - INFO - Epoch [42][5000/5005]\tlr: 1.000e-02, eta: 7:08:10, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6679\n2022-12-31 19:45:12,924 - mmcls - INFO - Epoch(val) [42][196]\taccuracy_top-1: 65.5240, accuracy_top-5: 86.8380\n2022-12-31 19:45:24,126 - mmcls - INFO - Epoch [43][100/5005]\tlr: 1.000e-02, eta: 7:08:03, time: 0.112, data_time: 0.057, memory: 1224, loss: 1.6401\n2022-12-31 19:45:32,657 - mmcls - INFO - Epoch [43][200/5005]\tlr: 1.000e-02, eta: 7:07:54, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6057\n2022-12-31 19:45:41,475 - mmcls - INFO - Epoch [43][300/5005]\tlr: 1.000e-02, eta: 7:07:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6359\n2022-12-31 19:45:50,189 - mmcls - INFO - Epoch [43][400/5005]\tlr: 1.000e-02, eta: 7:07:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6369\n2022-12-31 19:45:59,007 - mmcls - INFO - Epoch [43][500/5005]\tlr: 1.000e-02, eta: 7:07:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6381\n2022-12-31 19:46:07,898 - mmcls - INFO - Epoch [43][600/5005]\tlr: 1.000e-02, eta: 7:07:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6334\n2022-12-31 19:46:17,267 - mmcls - INFO - Epoch [43][700/5005]\tlr: 1.000e-02, eta: 7:07:10, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6310\n2022-12-31 19:46:25,976 - mmcls - INFO - Epoch [43][800/5005]\tlr: 1.000e-02, eta: 7:07:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6383\n2022-12-31 19:46:34,797 - mmcls - INFO - Epoch [43][900/5005]\tlr: 1.000e-02, eta: 7:06:52, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6190\n2022-12-31 19:46:43,684 - mmcls - INFO - Epoch [43][1000/5005]\tlr: 1.000e-02, eta: 7:06:43, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6603\n2022-12-31 19:46:52,408 - mmcls - INFO - Epoch [43][1100/5005]\tlr: 1.000e-02, eta: 7:06:34, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6253\n2022-12-31 19:47:01,302 - mmcls - INFO - Epoch [43][1200/5005]\tlr: 1.000e-02, eta: 7:06:25, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6624\n2022-12-31 19:47:10,021 - mmcls - INFO - Epoch [43][1300/5005]\tlr: 1.000e-02, eta: 7:06:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6600\n2022-12-31 19:47:18,634 - mmcls - INFO - Epoch [43][1400/5005]\tlr: 1.000e-02, eta: 7:06:07, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6606\n2022-12-31 19:47:27,626 - mmcls - INFO - Epoch [43][1500/5005]\tlr: 1.000e-02, eta: 7:05:59, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6428\n2022-12-31 19:47:36,558 - mmcls - INFO - Epoch [43][1600/5005]\tlr: 1.000e-02, eta: 7:05:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6561\n2022-12-31 19:47:45,306 - mmcls - INFO - Epoch [43][1700/5005]\tlr: 1.000e-02, eta: 7:05:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6417\n2022-12-31 19:47:54,061 - mmcls - INFO - Epoch [43][1800/5005]\tlr: 1.000e-02, eta: 7:05:32, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6425\n2022-12-31 19:48:02,861 - mmcls - INFO - Epoch [43][1900/5005]\tlr: 1.000e-02, eta: 7:05:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6574\n2022-12-31 19:48:11,639 - mmcls - INFO - Epoch [43][2000/5005]\tlr: 1.000e-02, eta: 7:05:14, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6442\n2022-12-31 19:48:20,271 - mmcls - INFO - Epoch [43][2100/5005]\tlr: 1.000e-02, eta: 7:05:05, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6425\n2022-12-31 19:48:28,973 - mmcls - INFO - Epoch [43][2200/5005]\tlr: 1.000e-02, eta: 7:04:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6627\n2022-12-31 19:48:37,919 - mmcls - INFO - Epoch [43][2300/5005]\tlr: 1.000e-02, eta: 7:04:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6454\n2022-12-31 19:48:46,625 - mmcls - INFO - Epoch [43][2400/5005]\tlr: 1.000e-02, eta: 7:04:38, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6503\n2022-12-31 19:48:55,326 - mmcls - INFO - Epoch [43][2500/5005]\tlr: 1.000e-02, eta: 7:04:29, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6477\n2022-12-31 19:49:03,958 - mmcls - INFO - Epoch [43][2600/5005]\tlr: 1.000e-02, eta: 7:04:20, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6671\n2022-12-31 19:49:12,830 - mmcls - INFO - Epoch [43][2700/5005]\tlr: 1.000e-02, eta: 7:04:11, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6303\n2022-12-31 19:49:21,675 - mmcls - INFO - Epoch [43][2800/5005]\tlr: 1.000e-02, eta: 7:04:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6666\n2022-12-31 19:49:30,612 - mmcls - INFO - Epoch [43][2900/5005]\tlr: 1.000e-02, eta: 7:03:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6781\n2022-12-31 19:49:39,417 - mmcls - INFO - Epoch [43][3000/5005]\tlr: 1.000e-02, eta: 7:03:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6774\n2022-12-31 19:49:47,925 - mmcls - INFO - Epoch [43][3100/5005]\tlr: 1.000e-02, eta: 7:03:35, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6741\n2022-12-31 19:49:56,669 - mmcls - INFO - Epoch [43][3200/5005]\tlr: 1.000e-02, eta: 7:03:26, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6605\n2022-12-31 19:50:05,394 - mmcls - INFO - Epoch [43][3300/5005]\tlr: 1.000e-02, eta: 7:03:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6599\n2022-12-31 19:50:14,671 - mmcls - INFO - Epoch [43][3400/5005]\tlr: 1.000e-02, eta: 7:03:09, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6612\n2022-12-31 19:50:23,186 - mmcls - INFO - Epoch [43][3500/5005]\tlr: 1.000e-02, eta: 7:03:00, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6628\n2022-12-31 19:50:31,831 - mmcls - INFO - Epoch [43][3600/5005]\tlr: 1.000e-02, eta: 7:02:50, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6617\n2022-12-31 19:50:40,472 - mmcls - INFO - Epoch [43][3700/5005]\tlr: 1.000e-02, eta: 7:02:41, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6580\n2022-12-31 19:50:49,374 - mmcls - INFO - Epoch [43][3800/5005]\tlr: 1.000e-02, eta: 7:02:33, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6481\n2022-12-31 19:50:58,304 - mmcls - INFO - Epoch [43][3900/5005]\tlr: 1.000e-02, eta: 7:02:24, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6644\n2022-12-31 19:51:06,926 - mmcls - INFO - Epoch [43][4000/5005]\tlr: 1.000e-02, eta: 7:02:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6582\n2022-12-31 19:51:15,692 - mmcls - INFO - Epoch [43][4100/5005]\tlr: 1.000e-02, eta: 7:02:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6516\n2022-12-31 19:51:24,617 - mmcls - INFO - Epoch [43][4200/5005]\tlr: 1.000e-02, eta: 7:01:57, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6771\n2022-12-31 19:51:33,439 - mmcls - INFO - Epoch [43][4300/5005]\tlr: 1.000e-02, eta: 7:01:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6629\n2022-12-31 19:51:42,079 - mmcls - INFO - Epoch [43][4400/5005]\tlr: 1.000e-02, eta: 7:01:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6435\n2022-12-31 19:51:50,855 - mmcls - INFO - Epoch [43][4500/5005]\tlr: 1.000e-02, eta: 7:01:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6584\n2022-12-31 19:51:59,744 - mmcls - INFO - Epoch [43][4600/5005]\tlr: 1.000e-02, eta: 7:01:21, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6864\n2022-12-31 19:52:08,421 - mmcls - INFO - Epoch [43][4700/5005]\tlr: 1.000e-02, eta: 7:01:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6585\n2022-12-31 19:52:16,992 - mmcls - INFO - Epoch [43][4800/5005]\tlr: 1.000e-02, eta: 7:01:03, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6583\n2022-12-31 19:52:26,124 - mmcls - INFO - Epoch [43][4900/5005]\tlr: 1.000e-02, eta: 7:00:54, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6634\n2022-12-31 19:52:34,813 - mmcls - INFO - Epoch [43][5000/5005]\tlr: 1.000e-02, eta: 7:00:45, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6581\n2022-12-31 19:52:57,625 - mmcls - INFO - Epoch(val) [43][196]\taccuracy_top-1: 66.1840, accuracy_top-5: 87.1460\n2022-12-31 19:53:08,794 - mmcls - INFO - Epoch [44][100/5005]\tlr: 1.000e-02, eta: 7:00:38, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.6342\n2022-12-31 19:53:17,396 - mmcls - INFO - Epoch [44][200/5005]\tlr: 1.000e-02, eta: 7:00:29, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6201\n2022-12-31 19:53:26,741 - mmcls - INFO - Epoch [44][300/5005]\tlr: 1.000e-02, eta: 7:00:21, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6425\n2022-12-31 19:53:35,766 - mmcls - INFO - Epoch [44][400/5005]\tlr: 1.000e-02, eta: 7:00:12, time: 0.090, data_time: 0.031, memory: 1224, loss: 1.6217\n2022-12-31 19:53:44,941 - mmcls - INFO - Epoch [44][500/5005]\tlr: 1.000e-02, eta: 7:00:04, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.6309\n2022-12-31 19:53:53,740 - mmcls - INFO - Epoch [44][600/5005]\tlr: 1.000e-02, eta: 6:59:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6160\n2022-12-31 19:54:02,485 - mmcls - INFO - Epoch [44][700/5005]\tlr: 1.000e-02, eta: 6:59:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6138\n2022-12-31 19:54:10,994 - mmcls - INFO - Epoch [44][800/5005]\tlr: 1.000e-02, eta: 6:59:37, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6341\n2022-12-31 19:54:19,856 - mmcls - INFO - Epoch [44][900/5005]\tlr: 1.000e-02, eta: 6:59:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6568\n2022-12-31 19:54:28,797 - mmcls - INFO - Epoch [44][1000/5005]\tlr: 1.000e-02, eta: 6:59:19, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6387\n2022-12-31 19:54:37,579 - mmcls - INFO - Epoch [44][1100/5005]\tlr: 1.000e-02, eta: 6:59:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6432\n2022-12-31 19:54:46,331 - mmcls - INFO - Epoch [44][1200/5005]\tlr: 1.000e-02, eta: 6:59:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6539\n2022-12-31 19:54:55,110 - mmcls - INFO - Epoch [44][1300/5005]\tlr: 1.000e-02, eta: 6:58:52, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6618\n2022-12-31 19:55:03,885 - mmcls - INFO - Epoch [44][1400/5005]\tlr: 1.000e-02, eta: 6:58:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6369\n2022-12-31 19:55:13,039 - mmcls - INFO - Epoch [44][1500/5005]\tlr: 1.000e-02, eta: 6:58:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6325\n2022-12-31 19:55:21,727 - mmcls - INFO - Epoch [44][1600/5005]\tlr: 1.000e-02, eta: 6:58:26, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6434\n2022-12-31 19:55:30,632 - mmcls - INFO - Epoch [44][1700/5005]\tlr: 1.000e-02, eta: 6:58:17, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6444\n2022-12-31 19:55:39,481 - mmcls - INFO - Epoch [44][1800/5005]\tlr: 1.000e-02, eta: 6:58:08, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6583\n2022-12-31 19:55:48,203 - mmcls - INFO - Epoch [44][1900/5005]\tlr: 1.000e-02, eta: 6:57:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6387\n2022-12-31 19:55:57,392 - mmcls - INFO - Epoch [44][2000/5005]\tlr: 1.000e-02, eta: 6:57:51, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6551\n2022-12-31 19:56:06,255 - mmcls - INFO - Epoch [44][2100/5005]\tlr: 1.000e-02, eta: 6:57:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6447\n2022-12-31 19:56:15,072 - mmcls - INFO - Epoch [44][2200/5005]\tlr: 1.000e-02, eta: 6:57:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6794\n2022-12-31 19:56:23,869 - mmcls - INFO - Epoch [44][2300/5005]\tlr: 1.000e-02, eta: 6:57:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6652\n2022-12-31 19:56:33,063 - mmcls - INFO - Epoch [44][2400/5005]\tlr: 1.000e-02, eta: 6:57:16, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.6680\n2022-12-31 19:56:41,897 - mmcls - INFO - Epoch [44][2500/5005]\tlr: 1.000e-02, eta: 6:57:07, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6529\n2022-12-31 19:56:50,649 - mmcls - INFO - Epoch [44][2600/5005]\tlr: 1.000e-02, eta: 6:56:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6596\n2022-12-31 19:56:59,599 - mmcls - INFO - Epoch [44][2700/5005]\tlr: 1.000e-02, eta: 6:56:49, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6574\n2022-12-31 19:57:08,634 - mmcls - INFO - Epoch [44][2800/5005]\tlr: 1.000e-02, eta: 6:56:41, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6582\n2022-12-31 19:57:17,134 - mmcls - INFO - Epoch [44][2900/5005]\tlr: 1.000e-02, eta: 6:56:31, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6388\n2022-12-31 19:57:25,814 - mmcls - INFO - Epoch [44][3000/5005]\tlr: 1.000e-02, eta: 6:56:22, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6587\n2022-12-31 19:57:34,645 - mmcls - INFO - Epoch [44][3100/5005]\tlr: 1.000e-02, eta: 6:56:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6462\n2022-12-31 19:57:43,497 - mmcls - INFO - Epoch [44][3200/5005]\tlr: 1.000e-02, eta: 6:56:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6283\n2022-12-31 19:57:52,214 - mmcls - INFO - Epoch [44][3300/5005]\tlr: 1.000e-02, eta: 6:55:56, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6516\n2022-12-31 19:58:00,909 - mmcls - INFO - Epoch [44][3400/5005]\tlr: 1.000e-02, eta: 6:55:46, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6509\n2022-12-31 19:58:09,584 - mmcls - INFO - Epoch [44][3500/5005]\tlr: 1.000e-02, eta: 6:55:37, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6598\n2022-12-31 19:58:18,373 - mmcls - INFO - Epoch [44][3600/5005]\tlr: 1.000e-02, eta: 6:55:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6707\n2022-12-31 19:58:27,574 - mmcls - INFO - Epoch [44][3700/5005]\tlr: 1.000e-02, eta: 6:55:20, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6572\n2022-12-31 19:58:36,326 - mmcls - INFO - Epoch [44][3800/5005]\tlr: 1.000e-02, eta: 6:55:11, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6506\n2022-12-31 19:58:44,999 - mmcls - INFO - Epoch [44][3900/5005]\tlr: 1.000e-02, eta: 6:55:02, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6615\n2022-12-31 19:58:53,775 - mmcls - INFO - Epoch [44][4000/5005]\tlr: 1.000e-02, eta: 6:54:53, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6694\n2022-12-31 19:59:02,741 - mmcls - INFO - Epoch [44][4100/5005]\tlr: 1.000e-02, eta: 6:54:44, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.6580\n2022-12-31 19:59:11,325 - mmcls - INFO - Epoch [44][4200/5005]\tlr: 1.000e-02, eta: 6:54:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6669\n2022-12-31 19:59:20,042 - mmcls - INFO - Epoch [44][4300/5005]\tlr: 1.000e-02, eta: 6:54:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6841\n2022-12-31 19:59:29,013 - mmcls - INFO - Epoch [44][4400/5005]\tlr: 1.000e-02, eta: 6:54:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6647\n2022-12-31 19:59:37,762 - mmcls - INFO - Epoch [44][4500/5005]\tlr: 1.000e-02, eta: 6:54:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6635\n2022-12-31 19:59:46,392 - mmcls - INFO - Epoch [44][4600/5005]\tlr: 1.000e-02, eta: 6:53:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6379\n2022-12-31 19:59:55,206 - mmcls - INFO - Epoch [44][4700/5005]\tlr: 1.000e-02, eta: 6:53:50, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6721\n2022-12-31 20:00:04,232 - mmcls - INFO - Epoch [44][4800/5005]\tlr: 1.000e-02, eta: 6:53:42, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6827\n2022-12-31 20:00:13,239 - mmcls - INFO - Epoch [44][4900/5005]\tlr: 1.000e-02, eta: 6:53:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6713\n2022-12-31 20:00:22,031 - mmcls - INFO - Epoch [44][5000/5005]\tlr: 1.000e-02, eta: 6:53:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6843\n2022-12-31 20:00:44,783 - mmcls - INFO - Epoch(val) [44][196]\taccuracy_top-1: 65.8820, accuracy_top-5: 87.0060\n2022-12-31 20:00:55,801 - mmcls - INFO - Epoch [45][100/5005]\tlr: 1.000e-02, eta: 6:53:17, time: 0.110, data_time: 0.050, memory: 1224, loss: 1.6251\n2022-12-31 20:01:04,382 - mmcls - INFO - Epoch [45][200/5005]\tlr: 1.000e-02, eta: 6:53:08, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6050\n2022-12-31 20:01:13,129 - mmcls - INFO - Epoch [45][300/5005]\tlr: 1.000e-02, eta: 6:52:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6304\n2022-12-31 20:01:21,743 - mmcls - INFO - Epoch [45][400/5005]\tlr: 1.000e-02, eta: 6:52:50, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6159\n2022-12-31 20:01:30,566 - mmcls - INFO - Epoch [45][500/5005]\tlr: 1.000e-02, eta: 6:52:41, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6383\n2022-12-31 20:01:39,493 - mmcls - INFO - Epoch [45][600/5005]\tlr: 1.000e-02, eta: 6:52:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6335\n2022-12-31 20:01:48,537 - mmcls - INFO - Epoch [45][700/5005]\tlr: 1.000e-02, eta: 6:52:24, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6303\n2022-12-31 20:01:57,602 - mmcls - INFO - Epoch [45][800/5005]\tlr: 1.000e-02, eta: 6:52:15, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6350\n2022-12-31 20:02:06,470 - mmcls - INFO - Epoch [45][900/5005]\tlr: 1.000e-02, eta: 6:52:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6391\n2022-12-31 20:02:15,262 - mmcls - INFO - Epoch [45][1000/5005]\tlr: 1.000e-02, eta: 6:51:57, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6398\n2022-12-31 20:02:24,141 - mmcls - INFO - Epoch [45][1100/5005]\tlr: 1.000e-02, eta: 6:51:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6507\n2022-12-31 20:02:32,850 - mmcls - INFO - Epoch [45][1200/5005]\tlr: 1.000e-02, eta: 6:51:39, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6309\n2022-12-31 20:02:41,513 - mmcls - INFO - Epoch [45][1300/5005]\tlr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6340\n2022-12-31 20:02:50,482 - mmcls - INFO - Epoch [45][1400/5005]\tlr: 1.000e-02, eta: 6:51:22, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.6377\n2022-12-31 20:02:59,139 - mmcls - INFO - Epoch [45][1500/5005]\tlr: 1.000e-02, eta: 6:51:12, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6289\n2022-12-31 20:03:08,062 - mmcls - INFO - Epoch [45][1600/5005]\tlr: 1.000e-02, eta: 6:51:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6720\n2022-12-31 20:03:16,810 - mmcls - INFO - Epoch [45][1700/5005]\tlr: 1.000e-02, eta: 6:50:55, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6330\n2022-12-31 20:03:25,602 - mmcls - INFO - Epoch [45][1800/5005]\tlr: 1.000e-02, eta: 6:50:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6475\n2022-12-31 20:03:34,640 - mmcls - INFO - Epoch [45][1900/5005]\tlr: 1.000e-02, eta: 6:50:37, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6656\n2022-12-31 20:03:43,689 - mmcls - INFO - Epoch [45][2000/5005]\tlr: 1.000e-02, eta: 6:50:29, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6512\n2022-12-31 20:03:52,677 - mmcls - INFO - Epoch [45][2100/5005]\tlr: 1.000e-02, eta: 6:50:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6633\n2022-12-31 20:04:01,313 - mmcls - INFO - Epoch [45][2200/5005]\tlr: 1.000e-02, eta: 6:50:11, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6553\n2022-12-31 20:04:10,137 - mmcls - INFO - Epoch [45][2300/5005]\tlr: 1.000e-02, eta: 6:50:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6456\n2022-12-31 20:04:19,002 - mmcls - INFO - Epoch [45][2400/5005]\tlr: 1.000e-02, eta: 6:49:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6670\n2022-12-31 20:04:27,574 - mmcls - INFO - Epoch [45][2500/5005]\tlr: 1.000e-02, eta: 6:49:44, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6478\n2022-12-31 20:04:36,636 - mmcls - INFO - Epoch [45][2600/5005]\tlr: 1.000e-02, eta: 6:49:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6578\n2022-12-31 20:04:45,218 - mmcls - INFO - Epoch [45][2700/5005]\tlr: 1.000e-02, eta: 6:49:26, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6598\n2022-12-31 20:04:53,949 - mmcls - INFO - Epoch [45][2800/5005]\tlr: 1.000e-02, eta: 6:49:17, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6612\n2022-12-31 20:05:02,605 - mmcls - INFO - Epoch [45][2900/5005]\tlr: 1.000e-02, eta: 6:49:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6722\n2022-12-31 20:05:11,679 - mmcls - INFO - Epoch [45][3000/5005]\tlr: 1.000e-02, eta: 6:48:59, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.6603\n2022-12-31 20:05:20,438 - mmcls - INFO - Epoch [45][3100/5005]\tlr: 1.000e-02, eta: 6:48:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6654\n2022-12-31 20:05:29,197 - mmcls - INFO - Epoch [45][3200/5005]\tlr: 1.000e-02, eta: 6:48:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6528\n2022-12-31 20:05:37,913 - mmcls - INFO - Epoch [45][3300/5005]\tlr: 1.000e-02, eta: 6:48:32, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6419\n2022-12-31 20:05:46,849 - mmcls - INFO - Epoch [45][3400/5005]\tlr: 1.000e-02, eta: 6:48:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6624\n2022-12-31 20:05:55,651 - mmcls - INFO - Epoch [45][3500/5005]\tlr: 1.000e-02, eta: 6:48:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6559\n2022-12-31 20:06:04,652 - mmcls - INFO - Epoch [45][3600/5005]\tlr: 1.000e-02, eta: 6:48:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6601\n2022-12-31 20:06:13,387 - mmcls - INFO - Epoch [45][3700/5005]\tlr: 1.000e-02, eta: 6:47:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6733\n2022-12-31 20:06:22,226 - mmcls - INFO - Epoch [45][3800/5005]\tlr: 1.000e-02, eta: 6:47:48, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6628\n2022-12-31 20:06:30,654 - mmcls - INFO - Epoch [45][3900/5005]\tlr: 1.000e-02, eta: 6:47:39, time: 0.084, data_time: 0.009, memory: 1224, loss: 1.6608\n2022-12-31 20:06:39,537 - mmcls - INFO - Epoch [45][4000/5005]\tlr: 1.000e-02, eta: 6:47:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6653\n2022-12-31 20:06:48,666 - mmcls - INFO - Epoch [45][4100/5005]\tlr: 1.000e-02, eta: 6:47:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6427\n2022-12-31 20:06:57,322 - mmcls - INFO - Epoch [45][4200/5005]\tlr: 1.000e-02, eta: 6:47:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6390\n2022-12-31 20:07:06,234 - mmcls - INFO - Epoch [45][4300/5005]\tlr: 1.000e-02, eta: 6:47:04, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6718\n2022-12-31 20:07:15,433 - mmcls - INFO - Epoch [45][4400/5005]\tlr: 1.000e-02, eta: 6:46:55, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6808\n2022-12-31 20:07:24,007 - mmcls - INFO - Epoch [45][4500/5005]\tlr: 1.000e-02, eta: 6:46:46, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6730\n2022-12-31 20:07:32,841 - mmcls - INFO - Epoch [45][4600/5005]\tlr: 1.000e-02, eta: 6:46:37, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6558\n2022-12-31 20:07:41,508 - mmcls - INFO - Epoch [45][4700/5005]\tlr: 1.000e-02, eta: 6:46:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6623\n2022-12-31 20:07:50,716 - mmcls - INFO - Epoch [45][4800/5005]\tlr: 1.000e-02, eta: 6:46:20, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6641\n2022-12-31 20:07:59,425 - mmcls - INFO - Epoch [45][4900/5005]\tlr: 1.000e-02, eta: 6:46:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6706\n2022-12-31 20:08:08,273 - mmcls - INFO - Epoch [45][5000/5005]\tlr: 1.000e-02, eta: 6:46:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6731\n2022-12-31 20:08:30,993 - mmcls - INFO - Epoch(val) [45][196]\taccuracy_top-1: 65.6440, accuracy_top-5: 86.9980\n2022-12-31 20:08:42,005 - mmcls - INFO - Epoch [46][100/5005]\tlr: 1.000e-02, eta: 6:45:55, time: 0.110, data_time: 0.041, memory: 1224, loss: 1.6267\n2022-12-31 20:08:50,792 - mmcls - INFO - Epoch [46][200/5005]\tlr: 1.000e-02, eta: 6:45:46, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6179\n2022-12-31 20:08:59,339 - mmcls - INFO - Epoch [46][300/5005]\tlr: 1.000e-02, eta: 6:45:37, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6384\n2022-12-31 20:09:08,177 - mmcls - INFO - Epoch [46][400/5005]\tlr: 1.000e-02, eta: 6:45:28, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6170\n2022-12-31 20:09:16,896 - mmcls - INFO - Epoch [46][500/5005]\tlr: 1.000e-02, eta: 6:45:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6287\n2022-12-31 20:09:25,671 - mmcls - INFO - Epoch [46][600/5005]\tlr: 1.000e-02, eta: 6:45:10, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6402\n2022-12-31 20:09:34,461 - mmcls - INFO - Epoch [46][700/5005]\tlr: 1.000e-02, eta: 6:45:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6271\n2022-12-31 20:09:43,327 - mmcls - INFO - Epoch [46][800/5005]\tlr: 1.000e-02, eta: 6:44:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6559\n2022-12-31 20:09:51,991 - mmcls - INFO - Epoch [46][900/5005]\tlr: 1.000e-02, eta: 6:44:43, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6444\n2022-12-31 20:10:00,975 - mmcls - INFO - Epoch [46][1000/5005]\tlr: 1.000e-02, eta: 6:44:34, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6304\n2022-12-31 20:10:09,982 - mmcls - INFO - Epoch [46][1100/5005]\tlr: 1.000e-02, eta: 6:44:26, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6532\n2022-12-31 20:10:18,871 - mmcls - INFO - Epoch [46][1200/5005]\tlr: 1.000e-02, eta: 6:44:17, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6506\n2022-12-31 20:10:27,799 - mmcls - INFO - Epoch [46][1300/5005]\tlr: 1.000e-02, eta: 6:44:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6311\n2022-12-31 20:10:36,614 - mmcls - INFO - Epoch [46][1400/5005]\tlr: 1.000e-02, eta: 6:43:59, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6463\n2022-12-31 20:10:45,546 - mmcls - INFO - Epoch [46][1500/5005]\tlr: 1.000e-02, eta: 6:43:50, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6315\n2022-12-31 20:10:54,291 - mmcls - INFO - Epoch [46][1600/5005]\tlr: 1.000e-02, eta: 6:43:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6733\n2022-12-31 20:11:02,921 - mmcls - INFO - Epoch [46][1700/5005]\tlr: 1.000e-02, eta: 6:43:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6654\n2022-12-31 20:11:12,398 - mmcls - INFO - Epoch [46][1800/5005]\tlr: 1.000e-02, eta: 6:43:24, time: 0.095, data_time: 0.011, memory: 1224, loss: 1.6373\n2022-12-31 20:11:21,266 - mmcls - INFO - Epoch [46][1900/5005]\tlr: 1.000e-02, eta: 6:43:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6635\n2022-12-31 20:11:30,153 - mmcls - INFO - Epoch [46][2000/5005]\tlr: 1.000e-02, eta: 6:43:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6527\n2022-12-31 20:11:39,270 - mmcls - INFO - Epoch [46][2100/5005]\tlr: 1.000e-02, eta: 6:42:58, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6438\n2022-12-31 20:11:47,959 - mmcls - INFO - Epoch [46][2200/5005]\tlr: 1.000e-02, eta: 6:42:49, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6542\n2022-12-31 20:11:56,974 - mmcls - INFO - Epoch [46][2300/5005]\tlr: 1.000e-02, eta: 6:42:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6433\n2022-12-31 20:12:05,746 - mmcls - INFO - Epoch [46][2400/5005]\tlr: 1.000e-02, eta: 6:42:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6494\n2022-12-31 20:12:14,492 - mmcls - INFO - Epoch [46][2500/5005]\tlr: 1.000e-02, eta: 6:42:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6467\n2022-12-31 20:12:23,293 - mmcls - INFO - Epoch [46][2600/5005]\tlr: 1.000e-02, eta: 6:42:13, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6925\n2022-12-31 20:12:32,232 - mmcls - INFO - Epoch [46][2700/5005]\tlr: 1.000e-02, eta: 6:42:05, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6420\n2022-12-31 20:12:41,285 - mmcls - INFO - Epoch [46][2800/5005]\tlr: 1.000e-02, eta: 6:41:56, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6397\n2022-12-31 20:12:50,223 - mmcls - INFO - Epoch [46][2900/5005]\tlr: 1.000e-02, eta: 6:41:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6533\n2022-12-31 20:12:59,105 - mmcls - INFO - Epoch [46][3000/5005]\tlr: 1.000e-02, eta: 6:41:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6509\n2022-12-31 20:13:07,988 - mmcls - INFO - Epoch [46][3100/5005]\tlr: 1.000e-02, eta: 6:41:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6493\n2022-12-31 20:13:17,375 - mmcls - INFO - Epoch [46][3200/5005]\tlr: 1.000e-02, eta: 6:41:21, time: 0.094, data_time: 0.012, memory: 1224, loss: 1.6720\n2022-12-31 20:13:26,620 - mmcls - INFO - Epoch [46][3300/5005]\tlr: 1.000e-02, eta: 6:41:13, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6703\n2022-12-31 20:13:35,472 - mmcls - INFO - Epoch [46][3400/5005]\tlr: 1.000e-02, eta: 6:41:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6301\n2022-12-31 20:13:43,935 - mmcls - INFO - Epoch [46][3500/5005]\tlr: 1.000e-02, eta: 6:40:55, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6671\n2022-12-31 20:13:52,533 - mmcls - INFO - Epoch [46][3600/5005]\tlr: 1.000e-02, eta: 6:40:46, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6413\n2022-12-31 20:14:01,343 - mmcls - INFO - Epoch [46][3700/5005]\tlr: 1.000e-02, eta: 6:40:37, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6731\n2022-12-31 20:14:10,071 - mmcls - INFO - Epoch [46][3800/5005]\tlr: 1.000e-02, eta: 6:40:28, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6688\n2022-12-31 20:14:19,025 - mmcls - INFO - Epoch [46][3900/5005]\tlr: 1.000e-02, eta: 6:40:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6585\n2022-12-31 20:14:28,117 - mmcls - INFO - Epoch [46][4000/5005]\tlr: 1.000e-02, eta: 6:40:11, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6441\n2022-12-31 20:14:36,667 - mmcls - INFO - Epoch [46][4100/5005]\tlr: 1.000e-02, eta: 6:40:01, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6834\n2022-12-31 20:14:45,523 - mmcls - INFO - Epoch [46][4200/5005]\tlr: 1.000e-02, eta: 6:39:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6664\n2022-12-31 20:14:54,279 - mmcls - INFO - Epoch [46][4300/5005]\tlr: 1.000e-02, eta: 6:39:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6649\n2022-12-31 20:15:03,100 - mmcls - INFO - Epoch [46][4400/5005]\tlr: 1.000e-02, eta: 6:39:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6885\n2022-12-31 20:15:11,986 - mmcls - INFO - Epoch [46][4500/5005]\tlr: 1.000e-02, eta: 6:39:26, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6785\n2022-12-31 20:15:20,700 - mmcls - INFO - Epoch [46][4600/5005]\tlr: 1.000e-02, eta: 6:39:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6804\n2022-12-31 20:15:29,387 - mmcls - INFO - Epoch [46][4700/5005]\tlr: 1.000e-02, eta: 6:39:08, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6722\n2022-12-31 20:15:38,146 - mmcls - INFO - Epoch [46][4800/5005]\tlr: 1.000e-02, eta: 6:38:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6813\n2022-12-31 20:15:46,790 - mmcls - INFO - Epoch [46][4900/5005]\tlr: 1.000e-02, eta: 6:38:50, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6786\n2022-12-31 20:15:55,730 - mmcls - INFO - Epoch [46][5000/5005]\tlr: 1.000e-02, eta: 6:38:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6889\n2022-12-31 20:16:18,373 - mmcls - INFO - Epoch(val) [46][196]\taccuracy_top-1: 65.6980, accuracy_top-5: 86.9320\n2022-12-31 20:16:29,572 - mmcls - INFO - Epoch [47][100/5005]\tlr: 1.000e-02, eta: 6:38:34, time: 0.112, data_time: 0.042, memory: 1224, loss: 1.6542\n2022-12-31 20:16:38,254 - mmcls - INFO - Epoch [47][200/5005]\tlr: 1.000e-02, eta: 6:38:25, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.5992\n2022-12-31 20:16:47,192 - mmcls - INFO - Epoch [47][300/5005]\tlr: 1.000e-02, eta: 6:38:16, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6223\n2022-12-31 20:16:55,952 - mmcls - INFO - Epoch [47][400/5005]\tlr: 1.000e-02, eta: 6:38:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6113\n2022-12-31 20:17:04,860 - mmcls - INFO - Epoch [47][500/5005]\tlr: 1.000e-02, eta: 6:37:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6440\n2022-12-31 20:17:13,457 - mmcls - INFO - Epoch [47][600/5005]\tlr: 1.000e-02, eta: 6:37:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6187\n2022-12-31 20:17:22,400 - mmcls - INFO - Epoch [47][700/5005]\tlr: 1.000e-02, eta: 6:37:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6267\n2022-12-31 20:17:31,231 - mmcls - INFO - Epoch [47][800/5005]\tlr: 1.000e-02, eta: 6:37:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6213\n2022-12-31 20:17:39,894 - mmcls - INFO - Epoch [47][900/5005]\tlr: 1.000e-02, eta: 6:37:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6214\n2022-12-31 20:17:48,529 - mmcls - INFO - Epoch [47][1000/5005]\tlr: 1.000e-02, eta: 6:37:13, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6380\n2022-12-31 20:17:57,242 - mmcls - INFO - Epoch [47][1100/5005]\tlr: 1.000e-02, eta: 6:37:04, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6359\n2022-12-31 20:18:06,050 - mmcls - INFO - Epoch [47][1200/5005]\tlr: 1.000e-02, eta: 6:36:55, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6684\n2022-12-31 20:18:14,727 - mmcls - INFO - Epoch [47][1300/5005]\tlr: 1.000e-02, eta: 6:36:46, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6626\n2022-12-31 20:18:23,620 - mmcls - INFO - Epoch [47][1400/5005]\tlr: 1.000e-02, eta: 6:36:38, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6550\n2022-12-31 20:18:32,241 - mmcls - INFO - Epoch [47][1500/5005]\tlr: 1.000e-02, eta: 6:36:28, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6360\n2022-12-31 20:18:40,935 - mmcls - INFO - Epoch [47][1600/5005]\tlr: 1.000e-02, eta: 6:36:19, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6515\n2022-12-31 20:18:49,665 - mmcls - INFO - Epoch [47][1700/5005]\tlr: 1.000e-02, eta: 6:36:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6341\n2022-12-31 20:18:58,452 - mmcls - INFO - Epoch [47][1800/5005]\tlr: 1.000e-02, eta: 6:36:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6478\n2022-12-31 20:19:07,241 - mmcls - INFO - Epoch [47][1900/5005]\tlr: 1.000e-02, eta: 6:35:53, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6842\n2022-12-31 20:19:16,033 - mmcls - INFO - Epoch [47][2000/5005]\tlr: 1.000e-02, eta: 6:35:44, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6339\n2022-12-31 20:19:24,863 - mmcls - INFO - Epoch [47][2100/5005]\tlr: 1.000e-02, eta: 6:35:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6400\n2022-12-31 20:19:33,668 - mmcls - INFO - Epoch [47][2200/5005]\tlr: 1.000e-02, eta: 6:35:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6718\n2022-12-31 20:19:42,621 - mmcls - INFO - Epoch [47][2300/5005]\tlr: 1.000e-02, eta: 6:35:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6711\n2022-12-31 20:19:51,860 - mmcls - INFO - Epoch [47][2400/5005]\tlr: 1.000e-02, eta: 6:35:09, time: 0.092, data_time: 0.021, memory: 1224, loss: 1.6484\n2022-12-31 20:20:00,625 - mmcls - INFO - Epoch [47][2500/5005]\tlr: 1.000e-02, eta: 6:35:00, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.6726\n2022-12-31 20:20:09,197 - mmcls - INFO - Epoch [47][2600/5005]\tlr: 1.000e-02, eta: 6:34:51, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6651\n2022-12-31 20:20:17,893 - mmcls - INFO - Epoch [47][2700/5005]\tlr: 1.000e-02, eta: 6:34:42, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6534\n2022-12-31 20:20:27,090 - mmcls - INFO - Epoch [47][2800/5005]\tlr: 1.000e-02, eta: 6:34:33, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6679\n2022-12-31 20:20:35,804 - mmcls - INFO - Epoch [47][2900/5005]\tlr: 1.000e-02, eta: 6:34:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6684\n2022-12-31 20:20:44,554 - mmcls - INFO - Epoch [47][3000/5005]\tlr: 1.000e-02, eta: 6:34:15, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6544\n2022-12-31 20:20:53,483 - mmcls - INFO - Epoch [47][3100/5005]\tlr: 1.000e-02, eta: 6:34:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6396\n2022-12-31 20:21:02,158 - mmcls - INFO - Epoch [47][3200/5005]\tlr: 1.000e-02, eta: 6:33:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6511\n2022-12-31 20:21:11,227 - mmcls - INFO - Epoch [47][3300/5005]\tlr: 1.000e-02, eta: 6:33:49, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.6566\n2022-12-31 20:21:19,877 - mmcls - INFO - Epoch [47][3400/5005]\tlr: 1.000e-02, eta: 6:33:40, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6749\n2022-12-31 20:21:28,826 - mmcls - INFO - Epoch [47][3500/5005]\tlr: 1.000e-02, eta: 6:33:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6677\n2022-12-31 20:21:37,866 - mmcls - INFO - Epoch [47][3600/5005]\tlr: 1.000e-02, eta: 6:33:22, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.6708\n2022-12-31 20:21:46,365 - mmcls - INFO - Epoch [47][3700/5005]\tlr: 1.000e-02, eta: 6:33:13, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.6369\n2022-12-31 20:21:54,979 - mmcls - INFO - Epoch [47][3800/5005]\tlr: 1.000e-02, eta: 6:33:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6544\n2022-12-31 20:22:03,662 - mmcls - INFO - Epoch [47][3900/5005]\tlr: 1.000e-02, eta: 6:32:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6709\n2022-12-31 20:22:12,362 - mmcls - INFO - Epoch [47][4000/5005]\tlr: 1.000e-02, eta: 6:32:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6569\n2022-12-31 20:22:21,202 - mmcls - INFO - Epoch [47][4100/5005]\tlr: 1.000e-02, eta: 6:32:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6956\n2022-12-31 20:22:29,910 - mmcls - INFO - Epoch [47][4200/5005]\tlr: 1.000e-02, eta: 6:32:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6724\n2022-12-31 20:22:38,896 - mmcls - INFO - Epoch [47][4300/5005]\tlr: 1.000e-02, eta: 6:32:19, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6744\n2022-12-31 20:22:48,133 - mmcls - INFO - Epoch [47][4400/5005]\tlr: 1.000e-02, eta: 6:32:11, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6715\n2022-12-31 20:22:56,980 - mmcls - INFO - Epoch [47][4500/5005]\tlr: 1.000e-02, eta: 6:32:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6410\n2022-12-31 20:23:05,681 - mmcls - INFO - Epoch [47][4600/5005]\tlr: 1.000e-02, eta: 6:31:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6930\n2022-12-31 20:23:14,524 - mmcls - INFO - Epoch [47][4700/5005]\tlr: 1.000e-02, eta: 6:31:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6721\n2022-12-31 20:23:23,581 - mmcls - INFO - Epoch [47][4800/5005]\tlr: 1.000e-02, eta: 6:31:36, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6630\n2022-12-31 20:23:32,235 - mmcls - INFO - Epoch [47][4900/5005]\tlr: 1.000e-02, eta: 6:31:26, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6838\n2022-12-31 20:23:40,977 - mmcls - INFO - Epoch [47][5000/5005]\tlr: 1.000e-02, eta: 6:31:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6439\n2022-12-31 20:24:03,582 - mmcls - INFO - Epoch(val) [47][196]\taccuracy_top-1: 65.7820, accuracy_top-5: 86.8500\n2022-12-31 20:24:14,677 - mmcls - INFO - Epoch [48][100/5005]\tlr: 1.000e-02, eta: 6:31:10, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.6260\n2022-12-31 20:24:23,504 - mmcls - INFO - Epoch [48][200/5005]\tlr: 1.000e-02, eta: 6:31:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6213\n2022-12-31 20:24:32,468 - mmcls - INFO - Epoch [48][300/5005]\tlr: 1.000e-02, eta: 6:30:53, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6220\n2022-12-31 20:24:41,674 - mmcls - INFO - Epoch [48][400/5005]\tlr: 1.000e-02, eta: 6:30:44, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.6218\n2022-12-31 20:24:50,403 - mmcls - INFO - Epoch [48][500/5005]\tlr: 1.000e-02, eta: 6:30:35, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6276\n2022-12-31 20:24:59,175 - mmcls - INFO - Epoch [48][600/5005]\tlr: 1.000e-02, eta: 6:30:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6224\n2022-12-31 20:25:07,903 - mmcls - INFO - Epoch [48][700/5005]\tlr: 1.000e-02, eta: 6:30:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6211\n2022-12-31 20:25:16,661 - mmcls - INFO - Epoch [48][800/5005]\tlr: 1.000e-02, eta: 6:30:08, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6481\n2022-12-31 20:25:25,310 - mmcls - INFO - Epoch [48][900/5005]\tlr: 1.000e-02, eta: 6:29:59, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6203\n2022-12-31 20:25:34,075 - mmcls - INFO - Epoch [48][1000/5005]\tlr: 1.000e-02, eta: 6:29:50, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6417\n2022-12-31 20:25:42,742 - mmcls - INFO - Epoch [48][1100/5005]\tlr: 1.000e-02, eta: 6:29:41, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6236\n2022-12-31 20:25:51,529 - mmcls - INFO - Epoch [48][1200/5005]\tlr: 1.000e-02, eta: 6:29:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6280\n2022-12-31 20:26:00,168 - mmcls - INFO - Epoch [48][1300/5005]\tlr: 1.000e-02, eta: 6:29:23, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6508\n2022-12-31 20:26:08,831 - mmcls - INFO - Epoch [48][1400/5005]\tlr: 1.000e-02, eta: 6:29:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6234\n2022-12-31 20:26:17,522 - mmcls - INFO - Epoch [48][1500/5005]\tlr: 1.000e-02, eta: 6:29:05, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6461\n2022-12-31 20:26:26,457 - mmcls - INFO - Epoch [48][1600/5005]\tlr: 1.000e-02, eta: 6:28:56, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6476\n2022-12-31 20:26:35,095 - mmcls - INFO - Epoch [48][1700/5005]\tlr: 1.000e-02, eta: 6:28:47, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6254\n2022-12-31 20:26:43,673 - mmcls - INFO - Epoch [48][1800/5005]\tlr: 1.000e-02, eta: 6:28:38, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6626\n2022-12-31 20:26:52,715 - mmcls - INFO - Epoch [48][1900/5005]\tlr: 1.000e-02, eta: 6:28:29, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6519\n2022-12-31 20:27:01,488 - mmcls - INFO - Epoch [48][2000/5005]\tlr: 1.000e-02, eta: 6:28:20, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6461\n2022-12-31 20:27:10,375 - mmcls - INFO - Epoch [48][2100/5005]\tlr: 1.000e-02, eta: 6:28:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6652\n2022-12-31 20:27:19,241 - mmcls - INFO - Epoch [48][2200/5005]\tlr: 1.000e-02, eta: 6:28:03, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6490\n2022-12-31 20:27:28,065 - mmcls - INFO - Epoch [48][2300/5005]\tlr: 1.000e-02, eta: 6:27:54, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6721\n2022-12-31 20:27:37,142 - mmcls - INFO - Epoch [48][2400/5005]\tlr: 1.000e-02, eta: 6:27:45, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6625\n2022-12-31 20:27:45,884 - mmcls - INFO - Epoch [48][2500/5005]\tlr: 1.000e-02, eta: 6:27:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6668\n2022-12-31 20:27:54,456 - mmcls - INFO - Epoch [48][2600/5005]\tlr: 1.000e-02, eta: 6:27:27, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6588\n2022-12-31 20:28:03,236 - mmcls - INFO - Epoch [48][2700/5005]\tlr: 1.000e-02, eta: 6:27:18, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6488\n2022-12-31 20:28:12,122 - mmcls - INFO - Epoch [48][2800/5005]\tlr: 1.000e-02, eta: 6:27:09, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6581\n2022-12-31 20:28:20,658 - mmcls - INFO - Epoch [48][2900/5005]\tlr: 1.000e-02, eta: 6:27:00, time: 0.085, data_time: 0.019, memory: 1224, loss: 1.6795\n2022-12-31 20:28:29,559 - mmcls - INFO - Epoch [48][3000/5005]\tlr: 1.000e-02, eta: 6:26:51, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6712\n2022-12-31 20:28:38,640 - mmcls - INFO - Epoch [48][3100/5005]\tlr: 1.000e-02, eta: 6:26:43, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6464\n2022-12-31 20:28:47,378 - mmcls - INFO - Epoch [48][3200/5005]\tlr: 1.000e-02, eta: 6:26:34, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6504\n2022-12-31 20:28:56,505 - mmcls - INFO - Epoch [48][3300/5005]\tlr: 1.000e-02, eta: 6:26:25, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.6394\n2022-12-31 20:29:05,572 - mmcls - INFO - Epoch [48][3400/5005]\tlr: 1.000e-02, eta: 6:26:17, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6769\n2022-12-31 20:29:14,334 - mmcls - INFO - Epoch [48][3500/5005]\tlr: 1.000e-02, eta: 6:26:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6711\n2022-12-31 20:29:23,074 - mmcls - INFO - Epoch [48][3600/5005]\tlr: 1.000e-02, eta: 6:25:59, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6781\n2022-12-31 20:29:31,718 - mmcls - INFO - Epoch [48][3700/5005]\tlr: 1.000e-02, eta: 6:25:50, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6396\n2022-12-31 20:29:40,698 - mmcls - INFO - Epoch [48][3800/5005]\tlr: 1.000e-02, eta: 6:25:41, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6705\n2022-12-31 20:29:49,973 - mmcls - INFO - Epoch [48][3900/5005]\tlr: 1.000e-02, eta: 6:25:33, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6763\n2022-12-31 20:29:58,737 - mmcls - INFO - Epoch [48][4000/5005]\tlr: 1.000e-02, eta: 6:25:24, time: 0.088, data_time: 0.031, memory: 1224, loss: 1.6756\n2022-12-31 20:30:07,436 - mmcls - INFO - Epoch [48][4100/5005]\tlr: 1.000e-02, eta: 6:25:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6697\n2022-12-31 20:30:16,301 - mmcls - INFO - Epoch [48][4200/5005]\tlr: 1.000e-02, eta: 6:25:06, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6524\n2022-12-31 20:30:24,943 - mmcls - INFO - Epoch [48][4300/5005]\tlr: 1.000e-02, eta: 6:24:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6756\n2022-12-31 20:30:33,554 - mmcls - INFO - Epoch [48][4400/5005]\tlr: 1.000e-02, eta: 6:24:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6895\n2022-12-31 20:30:42,272 - mmcls - INFO - Epoch [48][4500/5005]\tlr: 1.000e-02, eta: 6:24:39, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6557\n2022-12-31 20:30:51,031 - mmcls - INFO - Epoch [48][4600/5005]\tlr: 1.000e-02, eta: 6:24:30, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6774\n2022-12-31 20:30:59,909 - mmcls - INFO - Epoch [48][4700/5005]\tlr: 1.000e-02, eta: 6:24:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6604\n2022-12-31 20:31:08,525 - mmcls - INFO - Epoch [48][4800/5005]\tlr: 1.000e-02, eta: 6:24:12, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6584\n2022-12-31 20:31:17,511 - mmcls - INFO - Epoch [48][4900/5005]\tlr: 1.000e-02, eta: 6:24:03, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6578\n2022-12-31 20:31:26,585 - mmcls - INFO - Epoch [48][5000/5005]\tlr: 1.000e-02, eta: 6:23:54, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6717\n2022-12-31 20:31:49,223 - mmcls - INFO - Epoch(val) [48][196]\taccuracy_top-1: 65.7800, accuracy_top-5: 87.0580\n2022-12-31 20:32:00,324 - mmcls - INFO - Epoch [49][100/5005]\tlr: 1.000e-02, eta: 6:23:47, time: 0.111, data_time: 0.046, memory: 1224, loss: 1.6101\n2022-12-31 20:32:09,213 - mmcls - INFO - Epoch [49][200/5005]\tlr: 1.000e-02, eta: 6:23:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6182\n2022-12-31 20:32:18,073 - mmcls - INFO - Epoch [49][300/5005]\tlr: 1.000e-02, eta: 6:23:29, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6276\n2022-12-31 20:32:27,039 - mmcls - INFO - Epoch [49][400/5005]\tlr: 1.000e-02, eta: 6:23:21, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6283\n2022-12-31 20:32:36,007 - mmcls - INFO - Epoch [49][500/5005]\tlr: 1.000e-02, eta: 6:23:12, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.6456\n2022-12-31 20:32:45,203 - mmcls - INFO - Epoch [49][600/5005]\tlr: 1.000e-02, eta: 6:23:03, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.6370\n2022-12-31 20:32:54,194 - mmcls - INFO - Epoch [49][700/5005]\tlr: 1.000e-02, eta: 6:22:55, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6425\n2022-12-31 20:33:03,101 - mmcls - INFO - Epoch [49][800/5005]\tlr: 1.000e-02, eta: 6:22:46, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.5934\n2022-12-31 20:33:11,889 - mmcls - INFO - Epoch [49][900/5005]\tlr: 1.000e-02, eta: 6:22:37, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6171\n2022-12-31 20:33:20,568 - mmcls - INFO - Epoch [49][1000/5005]\tlr: 1.000e-02, eta: 6:22:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6592\n2022-12-31 20:33:29,316 - mmcls - INFO - Epoch [49][1100/5005]\tlr: 1.000e-02, eta: 6:22:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6162\n2022-12-31 20:33:37,862 - mmcls - INFO - Epoch [49][1200/5005]\tlr: 1.000e-02, eta: 6:22:10, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6602\n2022-12-31 20:33:46,560 - mmcls - INFO - Epoch [49][1300/5005]\tlr: 1.000e-02, eta: 6:22:01, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.6392\n2022-12-31 20:33:55,339 - mmcls - INFO - Epoch [49][1400/5005]\tlr: 1.000e-02, eta: 6:21:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6641\n2022-12-31 20:34:04,320 - mmcls - INFO - Epoch [49][1500/5005]\tlr: 1.000e-02, eta: 6:21:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6377\n2022-12-31 20:34:13,041 - mmcls - INFO - Epoch [49][1600/5005]\tlr: 1.000e-02, eta: 6:21:34, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6408\n2022-12-31 20:34:21,754 - mmcls - INFO - Epoch [49][1700/5005]\tlr: 1.000e-02, eta: 6:21:25, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6552\n2022-12-31 20:34:30,770 - mmcls - INFO - Epoch [49][1800/5005]\tlr: 1.000e-02, eta: 6:21:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6612\n2022-12-31 20:34:39,549 - mmcls - INFO - Epoch [49][1900/5005]\tlr: 1.000e-02, eta: 6:21:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6530\n2022-12-31 20:34:48,353 - mmcls - INFO - Epoch [49][2000/5005]\tlr: 1.000e-02, eta: 6:20:59, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6468\n2022-12-31 20:34:57,063 - mmcls - INFO - Epoch [49][2100/5005]\tlr: 1.000e-02, eta: 6:20:50, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6507\n2022-12-31 20:35:05,795 - mmcls - INFO - Epoch [49][2200/5005]\tlr: 1.000e-02, eta: 6:20:41, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.6425\n2022-12-31 20:35:14,637 - mmcls - INFO - Epoch [49][2300/5005]\tlr: 1.000e-02, eta: 6:20:32, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6577\n2022-12-31 20:35:23,367 - mmcls - INFO - Epoch [49][2400/5005]\tlr: 1.000e-02, eta: 6:20:23, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6473\n2022-12-31 20:35:32,028 - mmcls - INFO - Epoch [49][2500/5005]\tlr: 1.000e-02, eta: 6:20:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6535\n2022-12-31 20:35:41,261 - mmcls - INFO - Epoch [49][2600/5005]\tlr: 1.000e-02, eta: 6:20:05, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6588\n2022-12-31 20:35:49,887 - mmcls - INFO - Epoch [49][2700/5005]\tlr: 1.000e-02, eta: 6:19:56, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6608\n2022-12-31 20:35:58,856 - mmcls - INFO - Epoch [49][2800/5005]\tlr: 1.000e-02, eta: 6:19:48, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6577\n2022-12-31 20:36:07,768 - mmcls - INFO - Epoch [49][2900/5005]\tlr: 1.000e-02, eta: 6:19:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6671\n2022-12-31 20:36:16,641 - mmcls - INFO - Epoch [49][3000/5005]\tlr: 1.000e-02, eta: 6:19:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6641\n2022-12-31 20:36:25,360 - mmcls - INFO - Epoch [49][3100/5005]\tlr: 1.000e-02, eta: 6:19:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6657\n2022-12-31 20:36:34,462 - mmcls - INFO - Epoch [49][3200/5005]\tlr: 1.000e-02, eta: 6:19:12, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6483\n2022-12-31 20:36:43,191 - mmcls - INFO - Epoch [49][3300/5005]\tlr: 1.000e-02, eta: 6:19:03, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6448\n2022-12-31 20:36:52,002 - mmcls - INFO - Epoch [49][3400/5005]\tlr: 1.000e-02, eta: 6:18:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6672\n2022-12-31 20:37:00,758 - mmcls - INFO - Epoch [49][3500/5005]\tlr: 1.000e-02, eta: 6:18:46, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6684\n2022-12-31 20:37:09,635 - mmcls - INFO - Epoch [49][3600/5005]\tlr: 1.000e-02, eta: 6:18:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6414\n2022-12-31 20:37:18,394 - mmcls - INFO - Epoch [49][3700/5005]\tlr: 1.000e-02, eta: 6:18:28, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6666\n2022-12-31 20:37:27,111 - mmcls - INFO - Epoch [49][3800/5005]\tlr: 1.000e-02, eta: 6:18:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6507\n2022-12-31 20:37:35,666 - mmcls - INFO - Epoch [49][3900/5005]\tlr: 1.000e-02, eta: 6:18:10, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6581\n2022-12-31 20:37:44,602 - mmcls - INFO - Epoch [49][4000/5005]\tlr: 1.000e-02, eta: 6:18:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6830\n2022-12-31 20:37:53,496 - mmcls - INFO - Epoch [49][4100/5005]\tlr: 1.000e-02, eta: 6:17:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6652\n2022-12-31 20:38:02,238 - mmcls - INFO - Epoch [49][4200/5005]\tlr: 1.000e-02, eta: 6:17:43, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6790\n2022-12-31 20:38:11,343 - mmcls - INFO - Epoch [49][4300/5005]\tlr: 1.000e-02, eta: 6:17:35, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6841\n2022-12-31 20:38:19,829 - mmcls - INFO - Epoch [49][4400/5005]\tlr: 1.000e-02, eta: 6:17:25, time: 0.085, data_time: 0.027, memory: 1224, loss: 1.6531\n2022-12-31 20:38:28,522 - mmcls - INFO - Epoch [49][4500/5005]\tlr: 1.000e-02, eta: 6:17:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6880\n2022-12-31 20:38:37,331 - mmcls - INFO - Epoch [49][4600/5005]\tlr: 1.000e-02, eta: 6:17:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6636\n2022-12-31 20:38:46,237 - mmcls - INFO - Epoch [49][4700/5005]\tlr: 1.000e-02, eta: 6:16:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7037\n2022-12-31 20:38:55,311 - mmcls - INFO - Epoch [49][4800/5005]\tlr: 1.000e-02, eta: 6:16:50, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6724\n2022-12-31 20:39:03,840 - mmcls - INFO - Epoch [49][4900/5005]\tlr: 1.000e-02, eta: 6:16:41, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6749\n2022-12-31 20:39:12,588 - mmcls - INFO - Epoch [49][5000/5005]\tlr: 1.000e-02, eta: 6:16:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6754\n2022-12-31 20:39:35,222 - mmcls - INFO - Epoch(val) [49][196]\taccuracy_top-1: 66.1600, accuracy_top-5: 86.9960\n2022-12-31 20:39:46,302 - mmcls - INFO - Epoch [50][100/5005]\tlr: 1.000e-02, eta: 6:16:24, time: 0.111, data_time: 0.051, memory: 1224, loss: 1.6133\n2022-12-31 20:39:55,211 - mmcls - INFO - Epoch [50][200/5005]\tlr: 1.000e-02, eta: 6:16:16, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6046\n2022-12-31 20:40:04,089 - mmcls - INFO - Epoch [50][300/5005]\tlr: 1.000e-02, eta: 6:16:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6392\n2022-12-31 20:40:12,771 - mmcls - INFO - Epoch [50][400/5005]\tlr: 1.000e-02, eta: 6:15:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6348\n2022-12-31 20:40:21,423 - mmcls - INFO - Epoch [50][500/5005]\tlr: 1.000e-02, eta: 6:15:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6296\n2022-12-31 20:40:30,177 - mmcls - INFO - Epoch [50][600/5005]\tlr: 1.000e-02, eta: 6:15:40, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6370\n2022-12-31 20:40:39,086 - mmcls - INFO - Epoch [50][700/5005]\tlr: 1.000e-02, eta: 6:15:31, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6393\n2022-12-31 20:40:48,301 - mmcls - INFO - Epoch [50][800/5005]\tlr: 1.000e-02, eta: 6:15:22, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6361\n2022-12-31 20:40:57,149 - mmcls - INFO - Epoch [50][900/5005]\tlr: 1.000e-02, eta: 6:15:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6345\n2022-12-31 20:41:06,092 - mmcls - INFO - Epoch [50][1000/5005]\tlr: 1.000e-02, eta: 6:15:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6629\n2022-12-31 20:41:14,953 - mmcls - INFO - Epoch [50][1100/5005]\tlr: 1.000e-02, eta: 6:14:56, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6340\n2022-12-31 20:41:23,708 - mmcls - INFO - Epoch [50][1200/5005]\tlr: 1.000e-02, eta: 6:14:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6278\n2022-12-31 20:41:32,368 - mmcls - INFO - Epoch [50][1300/5005]\tlr: 1.000e-02, eta: 6:14:38, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6498\n2022-12-31 20:41:41,268 - mmcls - INFO - Epoch [50][1400/5005]\tlr: 1.000e-02, eta: 6:14:29, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6544\n2022-12-31 20:41:49,807 - mmcls - INFO - Epoch [50][1500/5005]\tlr: 1.000e-02, eta: 6:14:20, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6523\n2022-12-31 20:41:58,596 - mmcls - INFO - Epoch [50][1600/5005]\tlr: 1.000e-02, eta: 6:14:11, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6508\n2022-12-31 20:42:07,061 - mmcls - INFO - Epoch [50][1700/5005]\tlr: 1.000e-02, eta: 6:14:02, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6420\n2022-12-31 20:42:15,605 - mmcls - INFO - Epoch [50][1800/5005]\tlr: 1.000e-02, eta: 6:13:53, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6254\n2022-12-31 20:42:24,352 - mmcls - INFO - Epoch [50][1900/5005]\tlr: 1.000e-02, eta: 6:13:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6457\n2022-12-31 20:42:33,298 - mmcls - INFO - Epoch [50][2000/5005]\tlr: 1.000e-02, eta: 6:13:35, time: 0.089, data_time: 0.044, memory: 1224, loss: 1.6248\n2022-12-31 20:42:42,079 - mmcls - INFO - Epoch [50][2100/5005]\tlr: 1.000e-02, eta: 6:13:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6264\n2022-12-31 20:42:50,809 - mmcls - INFO - Epoch [50][2200/5005]\tlr: 1.000e-02, eta: 6:13:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6649\n2022-12-31 20:42:59,744 - mmcls - INFO - Epoch [50][2300/5005]\tlr: 1.000e-02, eta: 6:13:08, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6503\n2022-12-31 20:43:08,496 - mmcls - INFO - Epoch [50][2400/5005]\tlr: 1.000e-02, eta: 6:12:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6613\n2022-12-31 20:43:17,224 - mmcls - INFO - Epoch [50][2500/5005]\tlr: 1.000e-02, eta: 6:12:50, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6721\n2022-12-31 20:43:26,201 - mmcls - INFO - Epoch [50][2600/5005]\tlr: 1.000e-02, eta: 6:12:42, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6639\n2022-12-31 20:43:34,880 - mmcls - INFO - Epoch [50][2700/5005]\tlr: 1.000e-02, eta: 6:12:33, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6454\n2022-12-31 20:43:44,003 - mmcls - INFO - Epoch [50][2800/5005]\tlr: 1.000e-02, eta: 6:12:24, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6428\n2022-12-31 20:43:52,730 - mmcls - INFO - Epoch [50][2900/5005]\tlr: 1.000e-02, eta: 6:12:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6221\n2022-12-31 20:44:01,360 - mmcls - INFO - Epoch [50][3000/5005]\tlr: 1.000e-02, eta: 6:12:06, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6534\n2022-12-31 20:44:10,243 - mmcls - INFO - Epoch [50][3100/5005]\tlr: 1.000e-02, eta: 6:11:57, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6653\n2022-12-31 20:44:19,231 - mmcls - INFO - Epoch [50][3200/5005]\tlr: 1.000e-02, eta: 6:11:48, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6524\n2022-12-31 20:44:28,129 - mmcls - INFO - Epoch [50][3300/5005]\tlr: 1.000e-02, eta: 6:11:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6355\n2022-12-31 20:44:37,080 - mmcls - INFO - Epoch [50][3400/5005]\tlr: 1.000e-02, eta: 6:11:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6666\n2022-12-31 20:44:45,918 - mmcls - INFO - Epoch [50][3500/5005]\tlr: 1.000e-02, eta: 6:11:22, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6643\n2022-12-31 20:44:55,003 - mmcls - INFO - Epoch [50][3600/5005]\tlr: 1.000e-02, eta: 6:11:13, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6675\n2022-12-31 20:45:04,147 - mmcls - INFO - Epoch [50][3700/5005]\tlr: 1.000e-02, eta: 6:11:05, time: 0.091, data_time: 0.029, memory: 1224, loss: 1.6814\n2022-12-31 20:45:12,823 - mmcls - INFO - Epoch [50][3800/5005]\tlr: 1.000e-02, eta: 6:10:56, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6527\n2022-12-31 20:45:21,800 - mmcls - INFO - Epoch [50][3900/5005]\tlr: 1.000e-02, eta: 6:10:47, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6751\n2022-12-31 20:45:30,441 - mmcls - INFO - Epoch [50][4000/5005]\tlr: 1.000e-02, eta: 6:10:38, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6494\n2022-12-31 20:45:39,164 - mmcls - INFO - Epoch [50][4100/5005]\tlr: 1.000e-02, eta: 6:10:29, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6416\n2022-12-31 20:45:47,738 - mmcls - INFO - Epoch [50][4200/5005]\tlr: 1.000e-02, eta: 6:10:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6505\n2022-12-31 20:45:56,521 - mmcls - INFO - Epoch [50][4300/5005]\tlr: 1.000e-02, eta: 6:10:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6645\n2022-12-31 20:46:05,044 - mmcls - INFO - Epoch [50][4400/5005]\tlr: 1.000e-02, eta: 6:10:02, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6621\n2022-12-31 20:46:13,924 - mmcls - INFO - Epoch [50][4500/5005]\tlr: 1.000e-02, eta: 6:09:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6895\n2022-12-31 20:46:22,902 - mmcls - INFO - Epoch [50][4600/5005]\tlr: 1.000e-02, eta: 6:09:44, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6627\n2022-12-31 20:46:32,047 - mmcls - INFO - Epoch [50][4700/5005]\tlr: 1.000e-02, eta: 6:09:36, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.6633\n2022-12-31 20:46:40,618 - mmcls - INFO - Epoch [50][4800/5005]\tlr: 1.000e-02, eta: 6:09:27, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6567\n2022-12-31 20:46:49,326 - mmcls - INFO - Epoch [50][4900/5005]\tlr: 1.000e-02, eta: 6:09:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6868\n2022-12-31 20:46:58,132 - mmcls - INFO - Epoch [50][5000/5005]\tlr: 1.000e-02, eta: 6:09:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6668\n2022-12-31 20:46:58,579 - mmcls - INFO - Saving checkpoint at 50 epochs\n2022-12-31 20:47:20,888 - mmcls - INFO - Epoch(val) [50][196]\taccuracy_top-1: 65.7480, accuracy_top-5: 87.1020\n2022-12-31 20:47:32,005 - mmcls - INFO - Epoch [51][100/5005]\tlr: 1.000e-02, eta: 6:09:01, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.6347\n2022-12-31 20:47:40,878 - mmcls - INFO - Epoch [51][200/5005]\tlr: 1.000e-02, eta: 6:08:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5984\n2022-12-31 20:47:49,685 - mmcls - INFO - Epoch [51][300/5005]\tlr: 1.000e-02, eta: 6:08:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6390\n2022-12-31 20:47:58,720 - mmcls - INFO - Epoch [51][400/5005]\tlr: 1.000e-02, eta: 6:08:35, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6097\n2022-12-31 20:48:07,626 - mmcls - INFO - Epoch [51][500/5005]\tlr: 1.000e-02, eta: 6:08:26, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5980\n2022-12-31 20:48:16,754 - mmcls - INFO - Epoch [51][600/5005]\tlr: 1.000e-02, eta: 6:08:17, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6589\n2022-12-31 20:48:25,621 - mmcls - INFO - Epoch [51][700/5005]\tlr: 1.000e-02, eta: 6:08:09, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6254\n2022-12-31 20:48:34,297 - mmcls - INFO - Epoch [51][800/5005]\tlr: 1.000e-02, eta: 6:08:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6494\n2022-12-31 20:48:43,206 - mmcls - INFO - Epoch [51][900/5005]\tlr: 1.000e-02, eta: 6:07:51, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6303\n2022-12-31 20:48:52,141 - mmcls - INFO - Epoch [51][1000/5005]\tlr: 1.000e-02, eta: 6:07:42, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6269\n2022-12-31 20:49:00,930 - mmcls - INFO - Epoch [51][1100/5005]\tlr: 1.000e-02, eta: 6:07:33, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6414\n2022-12-31 20:49:09,596 - mmcls - INFO - Epoch [51][1200/5005]\tlr: 1.000e-02, eta: 6:07:24, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6313\n2022-12-31 20:49:18,406 - mmcls - INFO - Epoch [51][1300/5005]\tlr: 1.000e-02, eta: 6:07:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6442\n2022-12-31 20:49:27,464 - mmcls - INFO - Epoch [51][1400/5005]\tlr: 1.000e-02, eta: 6:07:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6476\n2022-12-31 20:49:36,303 - mmcls - INFO - Epoch [51][1500/5005]\tlr: 1.000e-02, eta: 6:06:58, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6471\n2022-12-31 20:49:45,232 - mmcls - INFO - Epoch [51][1600/5005]\tlr: 1.000e-02, eta: 6:06:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6518\n2022-12-31 20:49:54,396 - mmcls - INFO - Epoch [51][1700/5005]\tlr: 1.000e-02, eta: 6:06:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6565\n2022-12-31 20:50:02,948 - mmcls - INFO - Epoch [51][1800/5005]\tlr: 1.000e-02, eta: 6:06:31, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6411\n2022-12-31 20:50:12,003 - mmcls - INFO - Epoch [51][1900/5005]\tlr: 1.000e-02, eta: 6:06:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6267\n2022-12-31 20:50:20,814 - mmcls - INFO - Epoch [51][2000/5005]\tlr: 1.000e-02, eta: 6:06:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6791\n2022-12-31 20:50:29,659 - mmcls - INFO - Epoch [51][2100/5005]\tlr: 1.000e-02, eta: 6:06:05, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6358\n2022-12-31 20:50:38,508 - mmcls - INFO - Epoch [51][2200/5005]\tlr: 1.000e-02, eta: 6:05:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6353\n2022-12-31 20:50:47,183 - mmcls - INFO - Epoch [51][2300/5005]\tlr: 1.000e-02, eta: 6:05:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6607\n2022-12-31 20:50:55,929 - mmcls - INFO - Epoch [51][2400/5005]\tlr: 1.000e-02, eta: 6:05:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6686\n2022-12-31 20:51:04,680 - mmcls - INFO - Epoch [51][2500/5005]\tlr: 1.000e-02, eta: 6:05:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6550\n2022-12-31 20:51:13,275 - mmcls - INFO - Epoch [51][2600/5005]\tlr: 1.000e-02, eta: 6:05:20, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6521\n2022-12-31 20:51:22,133 - mmcls - INFO - Epoch [51][2700/5005]\tlr: 1.000e-02, eta: 6:05:11, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6486\n2022-12-31 20:51:30,937 - mmcls - INFO - Epoch [51][2800/5005]\tlr: 1.000e-02, eta: 6:05:02, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6664\n2022-12-31 20:51:39,731 - mmcls - INFO - Epoch [51][2900/5005]\tlr: 1.000e-02, eta: 6:04:53, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.6375\n2022-12-31 20:51:48,744 - mmcls - INFO - Epoch [51][3000/5005]\tlr: 1.000e-02, eta: 6:04:45, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6530\n2022-12-31 20:51:57,725 - mmcls - INFO - Epoch [51][3100/5005]\tlr: 1.000e-02, eta: 6:04:36, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6426\n2022-12-31 20:52:06,492 - mmcls - INFO - Epoch [51][3200/5005]\tlr: 1.000e-02, eta: 6:04:27, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6667\n2022-12-31 20:52:15,284 - mmcls - INFO - Epoch [51][3300/5005]\tlr: 1.000e-02, eta: 6:04:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6435\n2022-12-31 20:52:24,281 - mmcls - INFO - Epoch [51][3400/5005]\tlr: 1.000e-02, eta: 6:04:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6701\n2022-12-31 20:52:33,313 - mmcls - INFO - Epoch [51][3500/5005]\tlr: 1.000e-02, eta: 6:04:01, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6752\n2022-12-31 20:52:42,180 - mmcls - INFO - Epoch [51][3600/5005]\tlr: 1.000e-02, eta: 6:03:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6818\n2022-12-31 20:52:50,932 - mmcls - INFO - Epoch [51][3700/5005]\tlr: 1.000e-02, eta: 6:03:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6583\n2022-12-31 20:52:59,625 - mmcls - INFO - Epoch [51][3800/5005]\tlr: 1.000e-02, eta: 6:03:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6504\n2022-12-31 20:53:08,681 - mmcls - INFO - Epoch [51][3900/5005]\tlr: 1.000e-02, eta: 6:03:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6613\n2022-12-31 20:53:17,687 - mmcls - INFO - Epoch [51][4000/5005]\tlr: 1.000e-02, eta: 6:03:16, time: 0.090, data_time: 0.026, memory: 1224, loss: 1.6628\n2022-12-31 20:53:26,437 - mmcls - INFO - Epoch [51][4100/5005]\tlr: 1.000e-02, eta: 6:03:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6324\n2022-12-31 20:53:35,293 - mmcls - INFO - Epoch [51][4200/5005]\tlr: 1.000e-02, eta: 6:02:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6482\n2022-12-31 20:53:44,290 - mmcls - INFO - Epoch [51][4300/5005]\tlr: 1.000e-02, eta: 6:02:50, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6737\n2022-12-31 20:53:53,262 - mmcls - INFO - Epoch [51][4400/5005]\tlr: 1.000e-02, eta: 6:02:41, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6695\n2022-12-31 20:54:01,805 - mmcls - INFO - Epoch [51][4500/5005]\tlr: 1.000e-02, eta: 6:02:32, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6592\n2022-12-31 20:54:10,725 - mmcls - INFO - Epoch [51][4600/5005]\tlr: 1.000e-02, eta: 6:02:23, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6669\n2022-12-31 20:54:19,852 - mmcls - INFO - Epoch [51][4700/5005]\tlr: 1.000e-02, eta: 6:02:15, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6666\n2022-12-31 20:54:28,528 - mmcls - INFO - Epoch [51][4800/5005]\tlr: 1.000e-02, eta: 6:02:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6617\n2022-12-31 20:54:37,069 - mmcls - INFO - Epoch [51][4900/5005]\tlr: 1.000e-02, eta: 6:01:56, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6656\n2022-12-31 20:54:46,189 - mmcls - INFO - Epoch [51][5000/5005]\tlr: 1.000e-02, eta: 6:01:48, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6727\n2022-12-31 20:55:08,896 - mmcls - INFO - Epoch(val) [51][196]\taccuracy_top-1: 65.9360, accuracy_top-5: 87.0100\n2022-12-31 20:55:19,712 - mmcls - INFO - Epoch [52][100/5005]\tlr: 1.000e-02, eta: 6:01:40, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.6296\n2022-12-31 20:55:28,455 - mmcls - INFO - Epoch [52][200/5005]\tlr: 1.000e-02, eta: 6:01:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6344\n2022-12-31 20:55:37,137 - mmcls - INFO - Epoch [52][300/5005]\tlr: 1.000e-02, eta: 6:01:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6250\n2022-12-31 20:55:46,274 - mmcls - INFO - Epoch [52][400/5005]\tlr: 1.000e-02, eta: 6:01:13, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6180\n2022-12-31 20:55:54,874 - mmcls - INFO - Epoch [52][500/5005]\tlr: 1.000e-02, eta: 6:01:04, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.6222\n2022-12-31 20:56:03,809 - mmcls - INFO - Epoch [52][600/5005]\tlr: 1.000e-02, eta: 6:00:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6310\n2022-12-31 20:56:12,546 - mmcls - INFO - Epoch [52][700/5005]\tlr: 1.000e-02, eta: 6:00:47, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.6218\n2022-12-31 20:56:21,580 - mmcls - INFO - Epoch [52][800/5005]\tlr: 1.000e-02, eta: 6:00:38, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6414\n2022-12-31 20:56:30,238 - mmcls - INFO - Epoch [52][900/5005]\tlr: 1.000e-02, eta: 6:00:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6165\n2022-12-31 20:56:39,378 - mmcls - INFO - Epoch [52][1000/5005]\tlr: 1.000e-02, eta: 6:00:20, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.6224\n2022-12-31 20:56:48,350 - mmcls - INFO - Epoch [52][1100/5005]\tlr: 1.000e-02, eta: 6:00:12, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6379\n2022-12-31 20:56:57,371 - mmcls - INFO - Epoch [52][1200/5005]\tlr: 1.000e-02, eta: 6:00:03, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6393\n2022-12-31 20:57:06,271 - mmcls - INFO - Epoch [52][1300/5005]\tlr: 1.000e-02, eta: 5:59:54, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6408\n2022-12-31 20:57:15,150 - mmcls - INFO - Epoch [52][1400/5005]\tlr: 1.000e-02, eta: 5:59:45, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6303\n2022-12-31 20:57:23,943 - mmcls - INFO - Epoch [52][1500/5005]\tlr: 1.000e-02, eta: 5:59:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6400\n2022-12-31 20:57:32,813 - mmcls - INFO - Epoch [52][1600/5005]\tlr: 1.000e-02, eta: 5:59:28, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6191\n2022-12-31 20:57:41,806 - mmcls - INFO - Epoch [52][1700/5005]\tlr: 1.000e-02, eta: 5:59:19, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6147\n2022-12-31 20:57:50,935 - mmcls - INFO - Epoch [52][1800/5005]\tlr: 1.000e-02, eta: 5:59:10, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.6333\n2022-12-31 20:58:00,029 - mmcls - INFO - Epoch [52][1900/5005]\tlr: 1.000e-02, eta: 5:59:02, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6720\n2022-12-31 20:58:08,594 - mmcls - INFO - Epoch [52][2000/5005]\tlr: 1.000e-02, eta: 5:58:52, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6618\n2022-12-31 20:58:17,352 - mmcls - INFO - Epoch [52][2100/5005]\tlr: 1.000e-02, eta: 5:58:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6493\n2022-12-31 20:58:26,244 - mmcls - INFO - Epoch [52][2200/5005]\tlr: 1.000e-02, eta: 5:58:35, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6615\n2022-12-31 20:58:35,447 - mmcls - INFO - Epoch [52][2300/5005]\tlr: 1.000e-02, eta: 5:58:26, time: 0.092, data_time: 0.021, memory: 1224, loss: 1.6742\n2022-12-31 20:58:44,048 - mmcls - INFO - Epoch [52][2400/5005]\tlr: 1.000e-02, eta: 5:58:17, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6362\n2022-12-31 20:58:52,845 - mmcls - INFO - Epoch [52][2500/5005]\tlr: 1.000e-02, eta: 5:58:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6753\n2022-12-31 20:59:01,590 - mmcls - INFO - Epoch [52][2600/5005]\tlr: 1.000e-02, eta: 5:57:59, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6594\n2022-12-31 20:59:10,328 - mmcls - INFO - Epoch [52][2700/5005]\tlr: 1.000e-02, eta: 5:57:50, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6418\n2022-12-31 20:59:19,398 - mmcls - INFO - Epoch [52][2800/5005]\tlr: 1.000e-02, eta: 5:57:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6551\n2022-12-31 20:59:28,271 - mmcls - INFO - Epoch [52][2900/5005]\tlr: 1.000e-02, eta: 5:57:33, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6692\n2022-12-31 20:59:37,036 - mmcls - INFO - Epoch [52][3000/5005]\tlr: 1.000e-02, eta: 5:57:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6498\n2022-12-31 20:59:45,693 - mmcls - INFO - Epoch [52][3100/5005]\tlr: 1.000e-02, eta: 5:57:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6686\n2022-12-31 20:59:54,468 - mmcls - INFO - Epoch [52][3200/5005]\tlr: 1.000e-02, eta: 5:57:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6457\n2022-12-31 21:00:03,347 - mmcls - INFO - Epoch [52][3300/5005]\tlr: 1.000e-02, eta: 5:56:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6683\n2022-12-31 21:00:12,108 - mmcls - INFO - Epoch [52][3400/5005]\tlr: 1.000e-02, eta: 5:56:48, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6437\n2022-12-31 21:00:20,822 - mmcls - INFO - Epoch [52][3500/5005]\tlr: 1.000e-02, eta: 5:56:39, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6661\n2022-12-31 21:00:29,558 - mmcls - INFO - Epoch [52][3600/5005]\tlr: 1.000e-02, eta: 5:56:30, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6436\n2022-12-31 21:00:38,407 - mmcls - INFO - Epoch [52][3700/5005]\tlr: 1.000e-02, eta: 5:56:21, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6548\n2022-12-31 21:00:47,226 - mmcls - INFO - Epoch [52][3800/5005]\tlr: 1.000e-02, eta: 5:56:12, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6528\n2022-12-31 21:00:55,793 - mmcls - INFO - Epoch [52][3900/5005]\tlr: 1.000e-02, eta: 5:56:03, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.6577\n2022-12-31 21:01:04,315 - mmcls - INFO - Epoch [52][4000/5005]\tlr: 1.000e-02, eta: 5:55:54, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6388\n2022-12-31 21:01:13,036 - mmcls - INFO - Epoch [52][4100/5005]\tlr: 1.000e-02, eta: 5:55:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6411\n2022-12-31 21:01:21,923 - mmcls - INFO - Epoch [52][4200/5005]\tlr: 1.000e-02, eta: 5:55:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6552\n2022-12-31 21:01:30,981 - mmcls - INFO - Epoch [52][4300/5005]\tlr: 1.000e-02, eta: 5:55:28, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6921\n2022-12-31 21:01:39,653 - mmcls - INFO - Epoch [52][4400/5005]\tlr: 1.000e-02, eta: 5:55:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6573\n2022-12-31 21:01:48,416 - mmcls - INFO - Epoch [52][4500/5005]\tlr: 1.000e-02, eta: 5:55:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6809\n2022-12-31 21:01:56,979 - mmcls - INFO - Epoch [52][4600/5005]\tlr: 1.000e-02, eta: 5:55:01, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6650\n2022-12-31 21:02:06,011 - mmcls - INFO - Epoch [52][4700/5005]\tlr: 1.000e-02, eta: 5:54:52, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6474\n2022-12-31 21:02:15,151 - mmcls - INFO - Epoch [52][4800/5005]\tlr: 1.000e-02, eta: 5:54:43, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6634\n2022-12-31 21:02:24,196 - mmcls - INFO - Epoch [52][4900/5005]\tlr: 1.000e-02, eta: 5:54:35, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6376\n2022-12-31 21:02:32,912 - mmcls - INFO - Epoch [52][5000/5005]\tlr: 1.000e-02, eta: 5:54:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6521\n2022-12-31 21:02:55,530 - mmcls - INFO - Epoch(val) [52][196]\taccuracy_top-1: 66.1820, accuracy_top-5: 87.1020\n2022-12-31 21:03:06,530 - mmcls - INFO - Epoch [53][100/5005]\tlr: 1.000e-02, eta: 5:54:18, time: 0.110, data_time: 0.054, memory: 1224, loss: 1.6410\n2022-12-31 21:03:15,220 - mmcls - INFO - Epoch [53][200/5005]\tlr: 1.000e-02, eta: 5:54:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5974\n2022-12-31 21:03:24,090 - mmcls - INFO - Epoch [53][300/5005]\tlr: 1.000e-02, eta: 5:54:00, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.6205\n2022-12-31 21:03:33,080 - mmcls - INFO - Epoch [53][400/5005]\tlr: 1.000e-02, eta: 5:53:51, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6156\n2022-12-31 21:03:42,096 - mmcls - INFO - Epoch [53][500/5005]\tlr: 1.000e-02, eta: 5:53:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6288\n2022-12-31 21:03:51,098 - mmcls - INFO - Epoch [53][600/5005]\tlr: 1.000e-02, eta: 5:53:34, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6283\n2022-12-31 21:03:59,963 - mmcls - INFO - Epoch [53][700/5005]\tlr: 1.000e-02, eta: 5:53:25, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6435\n2022-12-31 21:04:09,053 - mmcls - INFO - Epoch [53][800/5005]\tlr: 1.000e-02, eta: 5:53:16, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6461\n2022-12-31 21:04:17,818 - mmcls - INFO - Epoch [53][900/5005]\tlr: 1.000e-02, eta: 5:53:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6292\n2022-12-31 21:04:26,676 - mmcls - INFO - Epoch [53][1000/5005]\tlr: 1.000e-02, eta: 5:52:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6579\n2022-12-31 21:04:35,438 - mmcls - INFO - Epoch [53][1100/5005]\tlr: 1.000e-02, eta: 5:52:50, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6224\n2022-12-31 21:04:44,212 - mmcls - INFO - Epoch [53][1200/5005]\tlr: 1.000e-02, eta: 5:52:41, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6316\n2022-12-31 21:04:53,349 - mmcls - INFO - Epoch [53][1300/5005]\tlr: 1.000e-02, eta: 5:52:32, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6368\n2022-12-31 21:05:01,900 - mmcls - INFO - Epoch [53][1400/5005]\tlr: 1.000e-02, eta: 5:52:23, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6461\n2022-12-31 21:05:10,660 - mmcls - INFO - Epoch [53][1500/5005]\tlr: 1.000e-02, eta: 5:52:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6216\n2022-12-31 21:05:19,431 - mmcls - INFO - Epoch [53][1600/5005]\tlr: 1.000e-02, eta: 5:52:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6283\n2022-12-31 21:05:28,229 - mmcls - INFO - Epoch [53][1700/5005]\tlr: 1.000e-02, eta: 5:51:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6622\n2022-12-31 21:05:36,909 - mmcls - INFO - Epoch [53][1800/5005]\tlr: 1.000e-02, eta: 5:51:47, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6398\n2022-12-31 21:05:45,948 - mmcls - INFO - Epoch [53][1900/5005]\tlr: 1.000e-02, eta: 5:51:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6506\n2022-12-31 21:05:54,931 - mmcls - INFO - Epoch [53][2000/5005]\tlr: 1.000e-02, eta: 5:51:30, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6509\n2022-12-31 21:06:03,809 - mmcls - INFO - Epoch [53][2100/5005]\tlr: 1.000e-02, eta: 5:51:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6548\n2022-12-31 21:06:12,725 - mmcls - INFO - Epoch [53][2200/5005]\tlr: 1.000e-02, eta: 5:51:12, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6228\n2022-12-31 21:06:21,355 - mmcls - INFO - Epoch [53][2300/5005]\tlr: 1.000e-02, eta: 5:51:03, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6329\n2022-12-31 21:06:29,902 - mmcls - INFO - Epoch [53][2400/5005]\tlr: 1.000e-02, eta: 5:50:54, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.6612\n2022-12-31 21:06:38,597 - mmcls - INFO - Epoch [53][2500/5005]\tlr: 1.000e-02, eta: 5:50:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6565\n2022-12-31 21:06:47,320 - mmcls - INFO - Epoch [53][2600/5005]\tlr: 1.000e-02, eta: 5:50:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6480\n2022-12-31 21:06:56,089 - mmcls - INFO - Epoch [53][2700/5005]\tlr: 1.000e-02, eta: 5:50:27, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6490\n2022-12-31 21:07:04,936 - mmcls - INFO - Epoch [53][2800/5005]\tlr: 1.000e-02, eta: 5:50:18, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6748\n2022-12-31 21:07:13,441 - mmcls - INFO - Epoch [53][2900/5005]\tlr: 1.000e-02, eta: 5:50:09, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6553\n2022-12-31 21:07:22,236 - mmcls - INFO - Epoch [53][3000/5005]\tlr: 1.000e-02, eta: 5:50:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6531\n2022-12-31 21:07:31,012 - mmcls - INFO - Epoch [53][3100/5005]\tlr: 1.000e-02, eta: 5:49:51, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6587\n2022-12-31 21:07:39,726 - mmcls - INFO - Epoch [53][3200/5005]\tlr: 1.000e-02, eta: 5:49:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6525\n2022-12-31 21:07:48,979 - mmcls - INFO - Epoch [53][3300/5005]\tlr: 1.000e-02, eta: 5:49:34, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6518\n2022-12-31 21:07:57,860 - mmcls - INFO - Epoch [53][3400/5005]\tlr: 1.000e-02, eta: 5:49:25, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6448\n2022-12-31 21:08:06,746 - mmcls - INFO - Epoch [53][3500/5005]\tlr: 1.000e-02, eta: 5:49:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6719\n2022-12-31 21:08:15,529 - mmcls - INFO - Epoch [53][3600/5005]\tlr: 1.000e-02, eta: 5:49:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6549\n2022-12-31 21:08:24,135 - mmcls - INFO - Epoch [53][3700/5005]\tlr: 1.000e-02, eta: 5:48:58, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6499\n2022-12-31 21:08:33,166 - mmcls - INFO - Epoch [53][3800/5005]\tlr: 1.000e-02, eta: 5:48:50, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6611\n2022-12-31 21:08:41,628 - mmcls - INFO - Epoch [53][3900/5005]\tlr: 1.000e-02, eta: 5:48:40, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.6484\n2022-12-31 21:08:50,493 - mmcls - INFO - Epoch [53][4000/5005]\tlr: 1.000e-02, eta: 5:48:32, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6616\n2022-12-31 21:08:59,645 - mmcls - INFO - Epoch [53][4100/5005]\tlr: 1.000e-02, eta: 5:48:23, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6635\n2022-12-31 21:09:08,735 - mmcls - INFO - Epoch [53][4200/5005]\tlr: 1.000e-02, eta: 5:48:14, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6687\n2022-12-31 21:09:17,481 - mmcls - INFO - Epoch [53][4300/5005]\tlr: 1.000e-02, eta: 5:48:05, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6686\n2022-12-31 21:09:26,100 - mmcls - INFO - Epoch [53][4400/5005]\tlr: 1.000e-02, eta: 5:47:56, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6537\n2022-12-31 21:09:35,441 - mmcls - INFO - Epoch [53][4500/5005]\tlr: 1.000e-02, eta: 5:47:48, time: 0.093, data_time: 0.016, memory: 1224, loss: 1.6651\n2022-12-31 21:09:44,229 - mmcls - INFO - Epoch [53][4600/5005]\tlr: 1.000e-02, eta: 5:47:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6552\n2022-12-31 21:09:53,004 - mmcls - INFO - Epoch [53][4700/5005]\tlr: 1.000e-02, eta: 5:47:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6599\n2022-12-31 21:10:01,679 - mmcls - INFO - Epoch [53][4800/5005]\tlr: 1.000e-02, eta: 5:47:21, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6745\n2022-12-31 21:10:10,295 - mmcls - INFO - Epoch [53][4900/5005]\tlr: 1.000e-02, eta: 5:47:12, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6586\n2022-12-31 21:10:19,209 - mmcls - INFO - Epoch [53][5000/5005]\tlr: 1.000e-02, eta: 5:47:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6427\n2022-12-31 21:10:41,884 - mmcls - INFO - Epoch(val) [53][196]\taccuracy_top-1: 66.2100, accuracy_top-5: 87.2500\n2022-12-31 21:10:53,238 - mmcls - INFO - Epoch [54][100/5005]\tlr: 1.000e-02, eta: 5:46:56, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.6296\n2022-12-31 21:11:01,857 - mmcls - INFO - Epoch [54][200/5005]\tlr: 1.000e-02, eta: 5:46:47, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6162\n2022-12-31 21:11:10,893 - mmcls - INFO - Epoch [54][300/5005]\tlr: 1.000e-02, eta: 5:46:38, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6517\n2022-12-31 21:11:19,494 - mmcls - INFO - Epoch [54][400/5005]\tlr: 1.000e-02, eta: 5:46:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5965\n2022-12-31 21:11:28,384 - mmcls - INFO - Epoch [54][500/5005]\tlr: 1.000e-02, eta: 5:46:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6024\n2022-12-31 21:11:37,088 - mmcls - INFO - Epoch [54][600/5005]\tlr: 1.000e-02, eta: 5:46:11, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6145\n2022-12-31 21:11:46,021 - mmcls - INFO - Epoch [54][700/5005]\tlr: 1.000e-02, eta: 5:46:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6344\n2022-12-31 21:11:54,603 - mmcls - INFO - Epoch [54][800/5005]\tlr: 1.000e-02, eta: 5:45:53, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6271\n2022-12-31 21:12:03,288 - mmcls - INFO - Epoch [54][900/5005]\tlr: 1.000e-02, eta: 5:45:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6376\n2022-12-31 21:12:12,371 - mmcls - INFO - Epoch [54][1000/5005]\tlr: 1.000e-02, eta: 5:45:36, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6050\n2022-12-31 21:12:21,275 - mmcls - INFO - Epoch [54][1100/5005]\tlr: 1.000e-02, eta: 5:45:27, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6422\n2022-12-31 21:12:30,222 - mmcls - INFO - Epoch [54][1200/5005]\tlr: 1.000e-02, eta: 5:45:18, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6529\n2022-12-31 21:12:38,962 - mmcls - INFO - Epoch [54][1300/5005]\tlr: 1.000e-02, eta: 5:45:09, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6135\n2022-12-31 21:12:47,736 - mmcls - INFO - Epoch [54][1400/5005]\tlr: 1.000e-02, eta: 5:45:00, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6317\n2022-12-31 21:12:56,422 - mmcls - INFO - Epoch [54][1500/5005]\tlr: 1.000e-02, eta: 5:44:51, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6332\n2022-12-31 21:13:04,925 - mmcls - INFO - Epoch [54][1600/5005]\tlr: 1.000e-02, eta: 5:44:42, time: 0.085, data_time: 0.022, memory: 1224, loss: 1.6428\n2022-12-31 21:13:13,824 - mmcls - INFO - Epoch [54][1700/5005]\tlr: 1.000e-02, eta: 5:44:33, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.6438\n2022-12-31 21:13:22,750 - mmcls - INFO - Epoch [54][1800/5005]\tlr: 1.000e-02, eta: 5:44:24, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6276\n2022-12-31 21:13:31,705 - mmcls - INFO - Epoch [54][1900/5005]\tlr: 1.000e-02, eta: 5:44:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6634\n2022-12-31 21:13:40,457 - mmcls - INFO - Epoch [54][2000/5005]\tlr: 1.000e-02, eta: 5:44:07, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6598\n2022-12-31 21:13:49,121 - mmcls - INFO - Epoch [54][2100/5005]\tlr: 1.000e-02, eta: 5:43:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6450\n2022-12-31 21:13:57,789 - mmcls - INFO - Epoch [54][2200/5005]\tlr: 1.000e-02, eta: 5:43:49, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6455\n2022-12-31 21:14:07,183 - mmcls - INFO - Epoch [54][2300/5005]\tlr: 1.000e-02, eta: 5:43:40, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6338\n2022-12-31 21:14:15,953 - mmcls - INFO - Epoch [54][2400/5005]\tlr: 1.000e-02, eta: 5:43:31, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6598\n2022-12-31 21:14:24,721 - mmcls - INFO - Epoch [54][2500/5005]\tlr: 1.000e-02, eta: 5:43:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6432\n2022-12-31 21:14:33,567 - mmcls - INFO - Epoch [54][2600/5005]\tlr: 1.000e-02, eta: 5:43:13, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6582\n2022-12-31 21:14:42,381 - mmcls - INFO - Epoch [54][2700/5005]\tlr: 1.000e-02, eta: 5:43:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6476\n2022-12-31 21:14:51,229 - mmcls - INFO - Epoch [54][2800/5005]\tlr: 1.000e-02, eta: 5:42:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6392\n2022-12-31 21:15:00,033 - mmcls - INFO - Epoch [54][2900/5005]\tlr: 1.000e-02, eta: 5:42:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6627\n2022-12-31 21:15:08,726 - mmcls - INFO - Epoch [54][3000/5005]\tlr: 1.000e-02, eta: 5:42:38, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6743\n2022-12-31 21:15:17,489 - mmcls - INFO - Epoch [54][3100/5005]\tlr: 1.000e-02, eta: 5:42:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6488\n2022-12-31 21:15:25,995 - mmcls - INFO - Epoch [54][3200/5005]\tlr: 1.000e-02, eta: 5:42:20, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6344\n2022-12-31 21:15:34,743 - mmcls - INFO - Epoch [54][3300/5005]\tlr: 1.000e-02, eta: 5:42:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6433\n2022-12-31 21:15:43,509 - mmcls - INFO - Epoch [54][3400/5005]\tlr: 1.000e-02, eta: 5:42:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6597\n2022-12-31 21:15:52,207 - mmcls - INFO - Epoch [54][3500/5005]\tlr: 1.000e-02, eta: 5:41:53, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6717\n2022-12-31 21:16:01,210 - mmcls - INFO - Epoch [54][3600/5005]\tlr: 1.000e-02, eta: 5:41:44, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6518\n2022-12-31 21:16:10,151 - mmcls - INFO - Epoch [54][3700/5005]\tlr: 1.000e-02, eta: 5:41:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6682\n2022-12-31 21:16:19,055 - mmcls - INFO - Epoch [54][3800/5005]\tlr: 1.000e-02, eta: 5:41:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6540\n2022-12-31 21:16:27,852 - mmcls - INFO - Epoch [54][3900/5005]\tlr: 1.000e-02, eta: 5:41:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6616\n2022-12-31 21:16:36,878 - mmcls - INFO - Epoch [54][4000/5005]\tlr: 1.000e-02, eta: 5:41:09, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6532\n2022-12-31 21:16:45,335 - mmcls - INFO - Epoch [54][4100/5005]\tlr: 1.000e-02, eta: 5:41:00, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6472\n2022-12-31 21:16:54,246 - mmcls - INFO - Epoch [54][4200/5005]\tlr: 1.000e-02, eta: 5:40:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6717\n2022-12-31 21:17:03,170 - mmcls - INFO - Epoch [54][4300/5005]\tlr: 1.000e-02, eta: 5:40:42, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6486\n2022-12-31 21:17:11,875 - mmcls - INFO - Epoch [54][4400/5005]\tlr: 1.000e-02, eta: 5:40:33, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6571\n2022-12-31 21:17:20,934 - mmcls - INFO - Epoch [54][4500/5005]\tlr: 1.000e-02, eta: 5:40:25, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.6567\n2022-12-31 21:17:29,846 - mmcls - INFO - Epoch [54][4600/5005]\tlr: 1.000e-02, eta: 5:40:16, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6857\n2022-12-31 21:17:38,462 - mmcls - INFO - Epoch [54][4700/5005]\tlr: 1.000e-02, eta: 5:40:07, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6729\n2022-12-31 21:17:47,412 - mmcls - INFO - Epoch [54][4800/5005]\tlr: 1.000e-02, eta: 5:39:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6660\n2022-12-31 21:17:56,047 - mmcls - INFO - Epoch [54][4900/5005]\tlr: 1.000e-02, eta: 5:39:49, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6742\n2022-12-31 21:18:04,902 - mmcls - INFO - Epoch [54][5000/5005]\tlr: 1.000e-02, eta: 5:39:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6535\n2022-12-31 21:18:27,561 - mmcls - INFO - Epoch(val) [54][196]\taccuracy_top-1: 65.9220, accuracy_top-5: 87.0200\n2022-12-31 21:18:38,371 - mmcls - INFO - Epoch [55][100/5005]\tlr: 1.000e-02, eta: 5:39:32, time: 0.108, data_time: 0.065, memory: 1224, loss: 1.6249\n2022-12-31 21:18:47,137 - mmcls - INFO - Epoch [55][200/5005]\tlr: 1.000e-02, eta: 5:39:23, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6204\n2022-12-31 21:18:56,384 - mmcls - INFO - Epoch [55][300/5005]\tlr: 1.000e-02, eta: 5:39:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6329\n2022-12-31 21:19:05,555 - mmcls - INFO - Epoch [55][400/5005]\tlr: 1.000e-02, eta: 5:39:06, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.5984\n2022-12-31 21:19:14,329 - mmcls - INFO - Epoch [55][500/5005]\tlr: 1.000e-02, eta: 5:38:57, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6172\n2022-12-31 21:19:23,128 - mmcls - INFO - Epoch [55][600/5005]\tlr: 1.000e-02, eta: 5:38:48, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6400\n2022-12-31 21:19:32,001 - mmcls - INFO - Epoch [55][700/5005]\tlr: 1.000e-02, eta: 5:38:39, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6223\n2022-12-31 21:19:40,993 - mmcls - INFO - Epoch [55][800/5005]\tlr: 1.000e-02, eta: 5:38:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6233\n2022-12-31 21:19:49,868 - mmcls - INFO - Epoch [55][900/5005]\tlr: 1.000e-02, eta: 5:38:22, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6161\n2022-12-31 21:19:58,633 - mmcls - INFO - Epoch [55][1000/5005]\tlr: 1.000e-02, eta: 5:38:13, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6472\n2022-12-31 21:20:07,432 - mmcls - INFO - Epoch [55][1100/5005]\tlr: 1.000e-02, eta: 5:38:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6428\n2022-12-31 21:20:16,303 - mmcls - INFO - Epoch [55][1200/5005]\tlr: 1.000e-02, eta: 5:37:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6599\n2022-12-31 21:20:25,368 - mmcls - INFO - Epoch [55][1300/5005]\tlr: 1.000e-02, eta: 5:37:46, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6234\n2022-12-31 21:20:34,010 - mmcls - INFO - Epoch [55][1400/5005]\tlr: 1.000e-02, eta: 5:37:37, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6107\n2022-12-31 21:20:42,975 - mmcls - INFO - Epoch [55][1500/5005]\tlr: 1.000e-02, eta: 5:37:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6360\n2022-12-31 21:20:51,924 - mmcls - INFO - Epoch [55][1600/5005]\tlr: 1.000e-02, eta: 5:37:20, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6204\n2022-12-31 21:21:00,827 - mmcls - INFO - Epoch [55][1700/5005]\tlr: 1.000e-02, eta: 5:37:11, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6407\n2022-12-31 21:21:09,683 - mmcls - INFO - Epoch [55][1800/5005]\tlr: 1.000e-02, eta: 5:37:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6227\n2022-12-31 21:21:18,589 - mmcls - INFO - Epoch [55][1900/5005]\tlr: 1.000e-02, eta: 5:36:53, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6139\n2022-12-31 21:21:27,197 - mmcls - INFO - Epoch [55][2000/5005]\tlr: 1.000e-02, eta: 5:36:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6294\n2022-12-31 21:21:36,066 - mmcls - INFO - Epoch [55][2100/5005]\tlr: 1.000e-02, eta: 5:36:35, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6502\n2022-12-31 21:21:44,866 - mmcls - INFO - Epoch [55][2200/5005]\tlr: 1.000e-02, eta: 5:36:27, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6328\n2022-12-31 21:21:53,389 - mmcls - INFO - Epoch [55][2300/5005]\tlr: 1.000e-02, eta: 5:36:17, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6276\n2022-12-31 21:22:02,446 - mmcls - INFO - Epoch [55][2400/5005]\tlr: 1.000e-02, eta: 5:36:09, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6656\n2022-12-31 21:22:11,224 - mmcls - INFO - Epoch [55][2500/5005]\tlr: 1.000e-02, eta: 5:36:00, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6399\n2022-12-31 21:22:19,934 - mmcls - INFO - Epoch [55][2600/5005]\tlr: 1.000e-02, eta: 5:35:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6448\n2022-12-31 21:22:28,766 - mmcls - INFO - Epoch [55][2700/5005]\tlr: 1.000e-02, eta: 5:35:42, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6302\n2022-12-31 21:22:37,475 - mmcls - INFO - Epoch [55][2800/5005]\tlr: 1.000e-02, eta: 5:35:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6494\n2022-12-31 21:22:46,370 - mmcls - INFO - Epoch [55][2900/5005]\tlr: 1.000e-02, eta: 5:35:24, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6626\n2022-12-31 21:22:54,990 - mmcls - INFO - Epoch [55][3000/5005]\tlr: 1.000e-02, eta: 5:35:15, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6754\n2022-12-31 21:23:03,737 - mmcls - INFO - Epoch [55][3100/5005]\tlr: 1.000e-02, eta: 5:35:06, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6593\n2022-12-31 21:23:12,765 - mmcls - INFO - Epoch [55][3200/5005]\tlr: 1.000e-02, eta: 5:34:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6461\n2022-12-31 21:23:21,395 - mmcls - INFO - Epoch [55][3300/5005]\tlr: 1.000e-02, eta: 5:34:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6218\n2022-12-31 21:23:30,152 - mmcls - INFO - Epoch [55][3400/5005]\tlr: 1.000e-02, eta: 5:34:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6622\n2022-12-31 21:23:39,130 - mmcls - INFO - Epoch [55][3500/5005]\tlr: 1.000e-02, eta: 5:34:31, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6861\n2022-12-31 21:23:47,941 - mmcls - INFO - Epoch [55][3600/5005]\tlr: 1.000e-02, eta: 5:34:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6489\n2022-12-31 21:23:56,732 - mmcls - INFO - Epoch [55][3700/5005]\tlr: 1.000e-02, eta: 5:34:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6657\n2022-12-31 21:24:05,737 - mmcls - INFO - Epoch [55][3800/5005]\tlr: 1.000e-02, eta: 5:34:04, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6568\n2022-12-31 21:24:14,988 - mmcls - INFO - Epoch [55][3900/5005]\tlr: 1.000e-02, eta: 5:33:56, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6524\n2022-12-31 21:24:23,690 - mmcls - INFO - Epoch [55][4000/5005]\tlr: 1.000e-02, eta: 5:33:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6477\n2022-12-31 21:24:32,827 - mmcls - INFO - Epoch [55][4100/5005]\tlr: 1.000e-02, eta: 5:33:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6548\n2022-12-31 21:24:41,796 - mmcls - INFO - Epoch [55][4200/5005]\tlr: 1.000e-02, eta: 5:33:29, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6486\n2022-12-31 21:24:50,734 - mmcls - INFO - Epoch [55][4300/5005]\tlr: 1.000e-02, eta: 5:33:21, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.6815\n2022-12-31 21:24:59,318 - mmcls - INFO - Epoch [55][4400/5005]\tlr: 1.000e-02, eta: 5:33:12, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6845\n2022-12-31 21:25:08,012 - mmcls - INFO - Epoch [55][4500/5005]\tlr: 1.000e-02, eta: 5:33:03, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6559\n2022-12-31 21:25:17,138 - mmcls - INFO - Epoch [55][4600/5005]\tlr: 1.000e-02, eta: 5:32:54, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6458\n2022-12-31 21:25:25,803 - mmcls - INFO - Epoch [55][4700/5005]\tlr: 1.000e-02, eta: 5:32:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6490\n2022-12-31 21:25:34,419 - mmcls - INFO - Epoch [55][4800/5005]\tlr: 1.000e-02, eta: 5:32:36, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6726\n2022-12-31 21:25:43,466 - mmcls - INFO - Epoch [55][4900/5005]\tlr: 1.000e-02, eta: 5:32:27, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6596\n2022-12-31 21:25:52,315 - mmcls - INFO - Epoch [55][5000/5005]\tlr: 1.000e-02, eta: 5:32:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6483\n2022-12-31 21:26:14,985 - mmcls - INFO - Epoch(val) [55][196]\taccuracy_top-1: 65.4740, accuracy_top-5: 86.8940\n2022-12-31 21:26:25,781 - mmcls - INFO - Epoch [56][100/5005]\tlr: 1.000e-02, eta: 5:32:10, time: 0.108, data_time: 0.047, memory: 1224, loss: 1.5985\n2022-12-31 21:26:34,431 - mmcls - INFO - Epoch [56][200/5005]\tlr: 1.000e-02, eta: 5:32:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6143\n2022-12-31 21:26:43,120 - mmcls - INFO - Epoch [56][300/5005]\tlr: 1.000e-02, eta: 5:31:52, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6119\n2022-12-31 21:26:51,863 - mmcls - INFO - Epoch [56][400/5005]\tlr: 1.000e-02, eta: 5:31:43, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6210\n2022-12-31 21:27:00,427 - mmcls - INFO - Epoch [56][500/5005]\tlr: 1.000e-02, eta: 5:31:34, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6137\n2022-12-31 21:27:09,085 - mmcls - INFO - Epoch [56][600/5005]\tlr: 1.000e-02, eta: 5:31:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6314\n2022-12-31 21:27:17,951 - mmcls - INFO - Epoch [56][700/5005]\tlr: 1.000e-02, eta: 5:31:16, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6334\n2022-12-31 21:27:26,886 - mmcls - INFO - Epoch [56][800/5005]\tlr: 1.000e-02, eta: 5:31:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6300\n2022-12-31 21:27:35,701 - mmcls - INFO - Epoch [56][900/5005]\tlr: 1.000e-02, eta: 5:30:59, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6461\n2022-12-31 21:27:44,816 - mmcls - INFO - Epoch [56][1000/5005]\tlr: 1.000e-02, eta: 5:30:50, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6426\n2022-12-31 21:27:53,475 - mmcls - INFO - Epoch [56][1100/5005]\tlr: 1.000e-02, eta: 5:30:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6586\n2022-12-31 21:28:02,281 - mmcls - INFO - Epoch [56][1200/5005]\tlr: 1.000e-02, eta: 5:30:32, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6559\n2022-12-31 21:28:11,070 - mmcls - INFO - Epoch [56][1300/5005]\tlr: 1.000e-02, eta: 5:30:23, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6340\n2022-12-31 21:28:19,777 - mmcls - INFO - Epoch [56][1400/5005]\tlr: 1.000e-02, eta: 5:30:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6398\n2022-12-31 21:28:28,523 - mmcls - INFO - Epoch [56][1500/5005]\tlr: 1.000e-02, eta: 5:30:05, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6298\n2022-12-31 21:28:37,232 - mmcls - INFO - Epoch [56][1600/5005]\tlr: 1.000e-02, eta: 5:29:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6317\n2022-12-31 21:28:46,006 - mmcls - INFO - Epoch [56][1700/5005]\tlr: 1.000e-02, eta: 5:29:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6287\n2022-12-31 21:28:54,670 - mmcls - INFO - Epoch [56][1800/5005]\tlr: 1.000e-02, eta: 5:29:38, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6280\n2022-12-31 21:29:03,469 - mmcls - INFO - Epoch [56][1900/5005]\tlr: 1.000e-02, eta: 5:29:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6437\n2022-12-31 21:29:12,207 - mmcls - INFO - Epoch [56][2000/5005]\tlr: 1.000e-02, eta: 5:29:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6393\n2022-12-31 21:29:21,301 - mmcls - INFO - Epoch [56][2100/5005]\tlr: 1.000e-02, eta: 5:29:12, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6441\n2022-12-31 21:29:30,456 - mmcls - INFO - Epoch [56][2200/5005]\tlr: 1.000e-02, eta: 5:29:03, time: 0.092, data_time: 0.032, memory: 1224, loss: 1.6406\n2022-12-31 21:29:39,101 - mmcls - INFO - Epoch [56][2300/5005]\tlr: 1.000e-02, eta: 5:28:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6592\n2022-12-31 21:29:48,164 - mmcls - INFO - Epoch [56][2400/5005]\tlr: 1.000e-02, eta: 5:28:46, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6386\n2022-12-31 21:29:56,967 - mmcls - INFO - Epoch [56][2500/5005]\tlr: 1.000e-02, eta: 5:28:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6471\n2022-12-31 21:30:05,628 - mmcls - INFO - Epoch [56][2600/5005]\tlr: 1.000e-02, eta: 5:28:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6507\n2022-12-31 21:30:14,213 - mmcls - INFO - Epoch [56][2700/5005]\tlr: 1.000e-02, eta: 5:28:19, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6170\n2022-12-31 21:30:23,149 - mmcls - INFO - Epoch [56][2800/5005]\tlr: 1.000e-02, eta: 5:28:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6539\n2022-12-31 21:30:32,045 - mmcls - INFO - Epoch [56][2900/5005]\tlr: 1.000e-02, eta: 5:28:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6592\n2022-12-31 21:30:40,607 - mmcls - INFO - Epoch [56][3000/5005]\tlr: 1.000e-02, eta: 5:27:52, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6295\n2022-12-31 21:30:49,749 - mmcls - INFO - Epoch [56][3100/5005]\tlr: 1.000e-02, eta: 5:27:43, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6342\n2022-12-31 21:30:58,698 - mmcls - INFO - Epoch [56][3200/5005]\tlr: 1.000e-02, eta: 5:27:35, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.6501\n2022-12-31 21:31:07,754 - mmcls - INFO - Epoch [56][3300/5005]\tlr: 1.000e-02, eta: 5:27:26, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6640\n2022-12-31 21:31:16,530 - mmcls - INFO - Epoch [56][3400/5005]\tlr: 1.000e-02, eta: 5:27:17, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6626\n2022-12-31 21:31:25,365 - mmcls - INFO - Epoch [56][3500/5005]\tlr: 1.000e-02, eta: 5:27:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6140\n2022-12-31 21:31:34,156 - mmcls - INFO - Epoch [56][3600/5005]\tlr: 1.000e-02, eta: 5:26:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6460\n2022-12-31 21:31:43,059 - mmcls - INFO - Epoch [56][3700/5005]\tlr: 1.000e-02, eta: 5:26:50, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6495\n2022-12-31 21:31:52,121 - mmcls - INFO - Epoch [56][3800/5005]\tlr: 1.000e-02, eta: 5:26:42, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6643\n2022-12-31 21:32:00,616 - mmcls - INFO - Epoch [56][3900/5005]\tlr: 1.000e-02, eta: 5:26:33, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6526\n2022-12-31 21:32:09,383 - mmcls - INFO - Epoch [56][4000/5005]\tlr: 1.000e-02, eta: 5:26:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6656\n2022-12-31 21:32:18,443 - mmcls - INFO - Epoch [56][4100/5005]\tlr: 1.000e-02, eta: 5:26:15, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6542\n2022-12-31 21:32:27,223 - mmcls - INFO - Epoch [56][4200/5005]\tlr: 1.000e-02, eta: 5:26:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6443\n2022-12-31 21:32:36,464 - mmcls - INFO - Epoch [56][4300/5005]\tlr: 1.000e-02, eta: 5:25:57, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6417\n2022-12-31 21:32:45,522 - mmcls - INFO - Epoch [56][4400/5005]\tlr: 1.000e-02, eta: 5:25:49, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6566\n2022-12-31 21:32:54,289 - mmcls - INFO - Epoch [56][4500/5005]\tlr: 1.000e-02, eta: 5:25:40, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6651\n2022-12-31 21:33:02,915 - mmcls - INFO - Epoch [56][4600/5005]\tlr: 1.000e-02, eta: 5:25:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6483\n2022-12-31 21:33:11,827 - mmcls - INFO - Epoch [56][4700/5005]\tlr: 1.000e-02, eta: 5:25:22, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6507\n2022-12-31 21:33:20,825 - mmcls - INFO - Epoch [56][4800/5005]\tlr: 1.000e-02, eta: 5:25:13, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6547\n2022-12-31 21:33:29,705 - mmcls - INFO - Epoch [56][4900/5005]\tlr: 1.000e-02, eta: 5:25:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6647\n2022-12-31 21:33:38,487 - mmcls - INFO - Epoch [56][5000/5005]\tlr: 1.000e-02, eta: 5:24:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6701\n2022-12-31 21:34:01,223 - mmcls - INFO - Epoch(val) [56][196]\taccuracy_top-1: 66.2260, accuracy_top-5: 87.0920\n2022-12-31 21:34:12,415 - mmcls - INFO - Epoch [57][100/5005]\tlr: 1.000e-02, eta: 5:24:48, time: 0.112, data_time: 0.041, memory: 1224, loss: 1.6251\n2022-12-31 21:34:21,391 - mmcls - INFO - Epoch [57][200/5005]\tlr: 1.000e-02, eta: 5:24:39, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5955\n2022-12-31 21:34:30,314 - mmcls - INFO - Epoch [57][300/5005]\tlr: 1.000e-02, eta: 5:24:30, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6260\n2022-12-31 21:34:38,914 - mmcls - INFO - Epoch [57][400/5005]\tlr: 1.000e-02, eta: 5:24:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6028\n2022-12-31 21:34:47,832 - mmcls - INFO - Epoch [57][500/5005]\tlr: 1.000e-02, eta: 5:24:12, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5966\n2022-12-31 21:34:56,789 - mmcls - INFO - Epoch [57][600/5005]\tlr: 1.000e-02, eta: 5:24:04, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6043\n2022-12-31 21:35:05,869 - mmcls - INFO - Epoch [57][700/5005]\tlr: 1.000e-02, eta: 5:23:55, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6232\n2022-12-31 21:35:14,625 - mmcls - INFO - Epoch [57][800/5005]\tlr: 1.000e-02, eta: 5:23:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6223\n2022-12-31 21:35:23,312 - mmcls - INFO - Epoch [57][900/5005]\tlr: 1.000e-02, eta: 5:23:37, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6124\n2022-12-31 21:35:32,180 - mmcls - INFO - Epoch [57][1000/5005]\tlr: 1.000e-02, eta: 5:23:28, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6457\n2022-12-31 21:35:41,040 - mmcls - INFO - Epoch [57][1100/5005]\tlr: 1.000e-02, eta: 5:23:19, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6156\n2022-12-31 21:35:49,674 - mmcls - INFO - Epoch [57][1200/5005]\tlr: 1.000e-02, eta: 5:23:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6325\n2022-12-31 21:35:58,463 - mmcls - INFO - Epoch [57][1300/5005]\tlr: 1.000e-02, eta: 5:23:01, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6241\n2022-12-31 21:36:07,261 - mmcls - INFO - Epoch [57][1400/5005]\tlr: 1.000e-02, eta: 5:22:52, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6405\n2022-12-31 21:36:15,910 - mmcls - INFO - Epoch [57][1500/5005]\tlr: 1.000e-02, eta: 5:22:43, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6364\n2022-12-31 21:36:24,960 - mmcls - INFO - Epoch [57][1600/5005]\tlr: 1.000e-02, eta: 5:22:35, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6057\n2022-12-31 21:36:33,599 - mmcls - INFO - Epoch [57][1700/5005]\tlr: 1.000e-02, eta: 5:22:26, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6594\n2022-12-31 21:36:42,213 - mmcls - INFO - Epoch [57][1800/5005]\tlr: 1.000e-02, eta: 5:22:17, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6285\n2022-12-31 21:36:50,818 - mmcls - INFO - Epoch [57][1900/5005]\tlr: 1.000e-02, eta: 5:22:08, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6521\n2022-12-31 21:37:00,009 - mmcls - INFO - Epoch [57][2000/5005]\tlr: 1.000e-02, eta: 5:21:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6440\n2022-12-31 21:37:08,979 - mmcls - INFO - Epoch [57][2100/5005]\tlr: 1.000e-02, eta: 5:21:50, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6267\n2022-12-31 21:37:17,963 - mmcls - INFO - Epoch [57][2200/5005]\tlr: 1.000e-02, eta: 5:21:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6465\n2022-12-31 21:37:26,983 - mmcls - INFO - Epoch [57][2300/5005]\tlr: 1.000e-02, eta: 5:21:33, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6185\n2022-12-31 21:37:35,493 - mmcls - INFO - Epoch [57][2400/5005]\tlr: 1.000e-02, eta: 5:21:24, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6328\n2022-12-31 21:37:44,281 - mmcls - INFO - Epoch [57][2500/5005]\tlr: 1.000e-02, eta: 5:21:15, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6261\n2022-12-31 21:37:52,791 - mmcls - INFO - Epoch [57][2600/5005]\tlr: 1.000e-02, eta: 5:21:06, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6591\n2022-12-31 21:38:01,696 - mmcls - INFO - Epoch [57][2700/5005]\tlr: 1.000e-02, eta: 5:20:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6335\n2022-12-31 21:38:10,481 - mmcls - INFO - Epoch [57][2800/5005]\tlr: 1.000e-02, eta: 5:20:48, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6370\n2022-12-31 21:38:19,250 - mmcls - INFO - Epoch [57][2900/5005]\tlr: 1.000e-02, eta: 5:20:39, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.6226\n2022-12-31 21:38:28,005 - mmcls - INFO - Epoch [57][3000/5005]\tlr: 1.000e-02, eta: 5:20:30, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6495\n2022-12-31 21:38:37,109 - mmcls - INFO - Epoch [57][3100/5005]\tlr: 1.000e-02, eta: 5:20:21, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6610\n2022-12-31 21:38:46,170 - mmcls - INFO - Epoch [57][3200/5005]\tlr: 1.000e-02, eta: 5:20:13, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6643\n2022-12-31 21:38:55,012 - mmcls - INFO - Epoch [57][3300/5005]\tlr: 1.000e-02, eta: 5:20:04, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6521\n2022-12-31 21:39:04,256 - mmcls - INFO - Epoch [57][3400/5005]\tlr: 1.000e-02, eta: 5:19:55, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6561\n2022-12-31 21:39:12,793 - mmcls - INFO - Epoch [57][3500/5005]\tlr: 1.000e-02, eta: 5:19:46, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6647\n2022-12-31 21:39:21,477 - mmcls - INFO - Epoch [57][3600/5005]\tlr: 1.000e-02, eta: 5:19:37, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6261\n2022-12-31 21:39:30,203 - mmcls - INFO - Epoch [57][3700/5005]\tlr: 1.000e-02, eta: 5:19:28, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6873\n2022-12-31 21:39:38,947 - mmcls - INFO - Epoch [57][3800/5005]\tlr: 1.000e-02, eta: 5:19:19, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6465\n2022-12-31 21:39:47,590 - mmcls - INFO - Epoch [57][3900/5005]\tlr: 1.000e-02, eta: 5:19:10, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6590\n2022-12-31 21:39:56,349 - mmcls - INFO - Epoch [57][4000/5005]\tlr: 1.000e-02, eta: 5:19:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6668\n2022-12-31 21:40:04,977 - mmcls - INFO - Epoch [57][4100/5005]\tlr: 1.000e-02, eta: 5:18:52, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6600\n2022-12-31 21:40:13,896 - mmcls - INFO - Epoch [57][4200/5005]\tlr: 1.000e-02, eta: 5:18:44, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6772\n2022-12-31 21:40:22,505 - mmcls - INFO - Epoch [57][4300/5005]\tlr: 1.000e-02, eta: 5:18:35, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6449\n2022-12-31 21:40:31,545 - mmcls - INFO - Epoch [57][4400/5005]\tlr: 1.000e-02, eta: 5:18:26, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6458\n2022-12-31 21:40:40,104 - mmcls - INFO - Epoch [57][4500/5005]\tlr: 1.000e-02, eta: 5:18:17, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6531\n2022-12-31 21:40:48,907 - mmcls - INFO - Epoch [57][4600/5005]\tlr: 1.000e-02, eta: 5:18:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6728\n2022-12-31 21:40:57,462 - mmcls - INFO - Epoch [57][4700/5005]\tlr: 1.000e-02, eta: 5:17:59, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6641\n2022-12-31 21:41:06,652 - mmcls - INFO - Epoch [57][4800/5005]\tlr: 1.000e-02, eta: 5:17:50, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6583\n2022-12-31 21:41:15,433 - mmcls - INFO - Epoch [57][4900/5005]\tlr: 1.000e-02, eta: 5:17:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6420\n2022-12-31 21:41:24,117 - mmcls - INFO - Epoch [57][5000/5005]\tlr: 1.000e-02, eta: 5:17:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6486\n2022-12-31 21:41:46,707 - mmcls - INFO - Epoch(val) [57][196]\taccuracy_top-1: 66.0460, accuracy_top-5: 87.2300\n2022-12-31 21:41:57,710 - mmcls - INFO - Epoch [58][100/5005]\tlr: 1.000e-02, eta: 5:17:24, time: 0.110, data_time: 0.035, memory: 1224, loss: 1.5943\n2022-12-31 21:42:06,412 - mmcls - INFO - Epoch [58][200/5005]\tlr: 1.000e-02, eta: 5:17:15, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5771\n2022-12-31 21:42:15,253 - mmcls - INFO - Epoch [58][300/5005]\tlr: 1.000e-02, eta: 5:17:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6126\n2022-12-31 21:42:23,837 - mmcls - INFO - Epoch [58][400/5005]\tlr: 1.000e-02, eta: 5:16:57, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6286\n2022-12-31 21:42:33,187 - mmcls - INFO - Epoch [58][500/5005]\tlr: 1.000e-02, eta: 5:16:49, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.6011\n2022-12-31 21:42:41,693 - mmcls - INFO - Epoch [58][600/5005]\tlr: 1.000e-02, eta: 5:16:40, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.6316\n2022-12-31 21:42:50,348 - mmcls - INFO - Epoch [58][700/5005]\tlr: 1.000e-02, eta: 5:16:31, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5970\n2022-12-31 21:42:59,081 - mmcls - INFO - Epoch [58][800/5005]\tlr: 1.000e-02, eta: 5:16:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5954\n2022-12-31 21:43:07,828 - mmcls - INFO - Epoch [58][900/5005]\tlr: 1.000e-02, eta: 5:16:13, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6232\n2022-12-31 21:43:16,719 - mmcls - INFO - Epoch [58][1000/5005]\tlr: 1.000e-02, eta: 5:16:04, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6332\n2022-12-31 21:43:25,806 - mmcls - INFO - Epoch [58][1100/5005]\tlr: 1.000e-02, eta: 5:15:55, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6302\n2022-12-31 21:43:34,792 - mmcls - INFO - Epoch [58][1200/5005]\tlr: 1.000e-02, eta: 5:15:47, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6221\n2022-12-31 21:43:43,563 - mmcls - INFO - Epoch [58][1300/5005]\tlr: 1.000e-02, eta: 5:15:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6309\n2022-12-31 21:43:52,604 - mmcls - INFO - Epoch [58][1400/5005]\tlr: 1.000e-02, eta: 5:15:29, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6468\n2022-12-31 21:44:01,607 - mmcls - INFO - Epoch [58][1500/5005]\tlr: 1.000e-02, eta: 5:15:20, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6294\n2022-12-31 21:44:10,202 - mmcls - INFO - Epoch [58][1600/5005]\tlr: 1.000e-02, eta: 5:15:11, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6528\n2022-12-31 21:44:19,343 - mmcls - INFO - Epoch [58][1700/5005]\tlr: 1.000e-02, eta: 5:15:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6278\n2022-12-31 21:44:28,151 - mmcls - INFO - Epoch [58][1800/5005]\tlr: 1.000e-02, eta: 5:14:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6426\n2022-12-31 21:44:37,155 - mmcls - INFO - Epoch [58][1900/5005]\tlr: 1.000e-02, eta: 5:14:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6221\n2022-12-31 21:44:45,912 - mmcls - INFO - Epoch [58][2000/5005]\tlr: 1.000e-02, eta: 5:14:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6459\n2022-12-31 21:44:54,668 - mmcls - INFO - Epoch [58][2100/5005]\tlr: 1.000e-02, eta: 5:14:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6409\n2022-12-31 21:45:03,304 - mmcls - INFO - Epoch [58][2200/5005]\tlr: 1.000e-02, eta: 5:14:18, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.6423\n2022-12-31 21:45:12,072 - mmcls - INFO - Epoch [58][2300/5005]\tlr: 1.000e-02, eta: 5:14:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6165\n2022-12-31 21:45:21,111 - mmcls - INFO - Epoch [58][2400/5005]\tlr: 1.000e-02, eta: 5:14:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6362\n2022-12-31 21:45:29,912 - mmcls - INFO - Epoch [58][2500/5005]\tlr: 1.000e-02, eta: 5:13:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6465\n2022-12-31 21:45:39,353 - mmcls - INFO - Epoch [58][2600/5005]\tlr: 1.000e-02, eta: 5:13:43, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6380\n2022-12-31 21:45:48,449 - mmcls - INFO - Epoch [58][2700/5005]\tlr: 1.000e-02, eta: 5:13:34, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6301\n2022-12-31 21:45:57,222 - mmcls - INFO - Epoch [58][2800/5005]\tlr: 1.000e-02, eta: 5:13:26, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6690\n2022-12-31 21:46:06,117 - mmcls - INFO - Epoch [58][2900/5005]\tlr: 1.000e-02, eta: 5:13:17, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6152\n2022-12-31 21:46:14,771 - mmcls - INFO - Epoch [58][3000/5005]\tlr: 1.000e-02, eta: 5:13:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6495\n2022-12-31 21:46:23,902 - mmcls - INFO - Epoch [58][3100/5005]\tlr: 1.000e-02, eta: 5:12:59, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6446\n2022-12-31 21:46:32,582 - mmcls - INFO - Epoch [58][3200/5005]\tlr: 1.000e-02, eta: 5:12:50, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6417\n2022-12-31 21:46:41,408 - mmcls - INFO - Epoch [58][3300/5005]\tlr: 1.000e-02, eta: 5:12:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6825\n2022-12-31 21:46:49,924 - mmcls - INFO - Epoch [58][3400/5005]\tlr: 1.000e-02, eta: 5:12:32, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6628\n2022-12-31 21:46:58,650 - mmcls - INFO - Epoch [58][3500/5005]\tlr: 1.000e-02, eta: 5:12:23, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6352\n2022-12-31 21:47:07,390 - mmcls - INFO - Epoch [58][3600/5005]\tlr: 1.000e-02, eta: 5:12:14, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6419\n2022-12-31 21:47:16,186 - mmcls - INFO - Epoch [58][3700/5005]\tlr: 1.000e-02, eta: 5:12:05, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6818\n2022-12-31 21:47:25,168 - mmcls - INFO - Epoch [58][3800/5005]\tlr: 1.000e-02, eta: 5:11:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6450\n2022-12-31 21:47:33,726 - mmcls - INFO - Epoch [58][3900/5005]\tlr: 1.000e-02, eta: 5:11:47, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6269\n2022-12-31 21:47:42,600 - mmcls - INFO - Epoch [58][4000/5005]\tlr: 1.000e-02, eta: 5:11:39, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6592\n2022-12-31 21:47:51,251 - mmcls - INFO - Epoch [58][4100/5005]\tlr: 1.000e-02, eta: 5:11:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6671\n2022-12-31 21:48:00,216 - mmcls - INFO - Epoch [58][4200/5005]\tlr: 1.000e-02, eta: 5:11:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6521\n2022-12-31 21:48:08,723 - mmcls - INFO - Epoch [58][4300/5005]\tlr: 1.000e-02, eta: 5:11:12, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6728\n2022-12-31 21:48:17,373 - mmcls - INFO - Epoch [58][4400/5005]\tlr: 1.000e-02, eta: 5:11:03, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6359\n2022-12-31 21:48:26,474 - mmcls - INFO - Epoch [58][4500/5005]\tlr: 1.000e-02, eta: 5:10:54, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6672\n2022-12-31 21:48:35,095 - mmcls - INFO - Epoch [58][4600/5005]\tlr: 1.000e-02, eta: 5:10:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6542\n2022-12-31 21:48:44,108 - mmcls - INFO - Epoch [58][4700/5005]\tlr: 1.000e-02, eta: 5:10:36, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6439\n2022-12-31 21:48:53,146 - mmcls - INFO - Epoch [58][4800/5005]\tlr: 1.000e-02, eta: 5:10:28, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6400\n2022-12-31 21:49:01,813 - mmcls - INFO - Epoch [58][4900/5005]\tlr: 1.000e-02, eta: 5:10:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6617\n2022-12-31 21:49:10,693 - mmcls - INFO - Epoch [58][5000/5005]\tlr: 1.000e-02, eta: 5:10:10, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6672\n2022-12-31 21:49:33,400 - mmcls - INFO - Epoch(val) [58][196]\taccuracy_top-1: 65.6820, accuracy_top-5: 86.8980\n2022-12-31 21:49:44,270 - mmcls - INFO - Epoch [59][100/5005]\tlr: 1.000e-02, eta: 5:10:02, time: 0.108, data_time: 0.036, memory: 1224, loss: 1.6147\n2022-12-31 21:49:53,155 - mmcls - INFO - Epoch [59][200/5005]\tlr: 1.000e-02, eta: 5:09:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6205\n2022-12-31 21:50:01,991 - mmcls - INFO - Epoch [59][300/5005]\tlr: 1.000e-02, eta: 5:09:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6244\n2022-12-31 21:50:10,702 - mmcls - INFO - Epoch [59][400/5005]\tlr: 1.000e-02, eta: 5:09:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6370\n2022-12-31 21:50:19,220 - mmcls - INFO - Epoch [59][500/5005]\tlr: 1.000e-02, eta: 5:09:26, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6313\n2022-12-31 21:50:27,746 - mmcls - INFO - Epoch [59][600/5005]\tlr: 1.000e-02, eta: 5:09:17, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6071\n2022-12-31 21:50:36,513 - mmcls - INFO - Epoch [59][700/5005]\tlr: 1.000e-02, eta: 5:09:08, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6308\n2022-12-31 21:50:45,204 - mmcls - INFO - Epoch [59][800/5005]\tlr: 1.000e-02, eta: 5:08:59, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6055\n2022-12-31 21:50:53,977 - mmcls - INFO - Epoch [59][900/5005]\tlr: 1.000e-02, eta: 5:08:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6263\n2022-12-31 21:51:02,585 - mmcls - INFO - Epoch [59][1000/5005]\tlr: 1.000e-02, eta: 5:08:41, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6193\n2022-12-31 21:51:11,665 - mmcls - INFO - Epoch [59][1100/5005]\tlr: 1.000e-02, eta: 5:08:32, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6131\n2022-12-31 21:51:20,454 - mmcls - INFO - Epoch [59][1200/5005]\tlr: 1.000e-02, eta: 5:08:23, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6224\n2022-12-31 21:51:29,226 - mmcls - INFO - Epoch [59][1300/5005]\tlr: 1.000e-02, eta: 5:08:14, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6306\n2022-12-31 21:51:37,839 - mmcls - INFO - Epoch [59][1400/5005]\tlr: 1.000e-02, eta: 5:08:05, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6330\n2022-12-31 21:51:46,578 - mmcls - INFO - Epoch [59][1500/5005]\tlr: 1.000e-02, eta: 5:07:56, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6378\n2022-12-31 21:51:55,258 - mmcls - INFO - Epoch [59][1600/5005]\tlr: 1.000e-02, eta: 5:07:48, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6257\n2022-12-31 21:52:03,921 - mmcls - INFO - Epoch [59][1700/5005]\tlr: 1.000e-02, eta: 5:07:39, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6286\n2022-12-31 21:52:12,773 - mmcls - INFO - Epoch [59][1800/5005]\tlr: 1.000e-02, eta: 5:07:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6401\n2022-12-31 21:52:21,896 - mmcls - INFO - Epoch [59][1900/5005]\tlr: 1.000e-02, eta: 5:07:21, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6580\n2022-12-31 21:52:30,647 - mmcls - INFO - Epoch [59][2000/5005]\tlr: 1.000e-02, eta: 5:07:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6215\n2022-12-31 21:52:39,652 - mmcls - INFO - Epoch [59][2100/5005]\tlr: 1.000e-02, eta: 5:07:03, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6401\n2022-12-31 21:52:48,326 - mmcls - INFO - Epoch [59][2200/5005]\tlr: 1.000e-02, eta: 5:06:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6328\n2022-12-31 21:52:57,101 - mmcls - INFO - Epoch [59][2300/5005]\tlr: 1.000e-02, eta: 5:06:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6313\n2022-12-31 21:53:06,282 - mmcls - INFO - Epoch [59][2400/5005]\tlr: 1.000e-02, eta: 5:06:37, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6375\n2022-12-31 21:53:15,199 - mmcls - INFO - Epoch [59][2500/5005]\tlr: 1.000e-02, eta: 5:06:28, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6485\n2022-12-31 21:53:23,847 - mmcls - INFO - Epoch [59][2600/5005]\tlr: 1.000e-02, eta: 5:06:19, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6456\n2022-12-31 21:53:33,018 - mmcls - INFO - Epoch [59][2700/5005]\tlr: 1.000e-02, eta: 5:06:10, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6445\n2022-12-31 21:53:42,008 - mmcls - INFO - Epoch [59][2800/5005]\tlr: 1.000e-02, eta: 5:06:02, time: 0.090, data_time: 0.039, memory: 1224, loss: 1.6413\n2022-12-31 21:53:50,725 - mmcls - INFO - Epoch [59][2900/5005]\tlr: 1.000e-02, eta: 5:05:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6583\n2022-12-31 21:53:59,454 - mmcls - INFO - Epoch [59][3000/5005]\tlr: 1.000e-02, eta: 5:05:44, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6224\n2022-12-31 21:54:08,322 - mmcls - INFO - Epoch [59][3100/5005]\tlr: 1.000e-02, eta: 5:05:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6532\n2022-12-31 21:54:17,414 - mmcls - INFO - Epoch [59][3200/5005]\tlr: 1.000e-02, eta: 5:05:26, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6494\n2022-12-31 21:54:26,374 - mmcls - INFO - Epoch [59][3300/5005]\tlr: 1.000e-02, eta: 5:05:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6151\n2022-12-31 21:54:35,474 - mmcls - INFO - Epoch [59][3400/5005]\tlr: 1.000e-02, eta: 5:05:09, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6295\n2022-12-31 21:54:44,345 - mmcls - INFO - Epoch [59][3500/5005]\tlr: 1.000e-02, eta: 5:05:00, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6542\n2022-12-31 21:54:53,473 - mmcls - INFO - Epoch [59][3600/5005]\tlr: 1.000e-02, eta: 5:04:51, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6380\n2022-12-31 21:55:02,468 - mmcls - INFO - Epoch [59][3700/5005]\tlr: 1.000e-02, eta: 5:04:42, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6410\n2022-12-31 21:55:11,241 - mmcls - INFO - Epoch [59][3800/5005]\tlr: 1.000e-02, eta: 5:04:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6407\n2022-12-31 21:55:20,307 - mmcls - INFO - Epoch [59][3900/5005]\tlr: 1.000e-02, eta: 5:04:25, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6453\n2022-12-31 21:55:29,447 - mmcls - INFO - Epoch [59][4000/5005]\tlr: 1.000e-02, eta: 5:04:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6476\n2022-12-31 21:55:37,971 - mmcls - INFO - Epoch [59][4100/5005]\tlr: 1.000e-02, eta: 5:04:07, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6212\n2022-12-31 21:55:46,863 - mmcls - INFO - Epoch [59][4200/5005]\tlr: 1.000e-02, eta: 5:03:58, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6502\n2022-12-31 21:55:55,660 - mmcls - INFO - Epoch [59][4300/5005]\tlr: 1.000e-02, eta: 5:03:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6472\n2022-12-31 21:56:04,526 - mmcls - INFO - Epoch [59][4400/5005]\tlr: 1.000e-02, eta: 5:03:41, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6772\n2022-12-31 21:56:13,217 - mmcls - INFO - Epoch [59][4500/5005]\tlr: 1.000e-02, eta: 5:03:32, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6549\n2022-12-31 21:56:21,962 - mmcls - INFO - Epoch [59][4600/5005]\tlr: 1.000e-02, eta: 5:03:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6528\n2022-12-31 21:56:30,849 - mmcls - INFO - Epoch [59][4700/5005]\tlr: 1.000e-02, eta: 5:03:14, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6257\n2022-12-31 21:56:39,506 - mmcls - INFO - Epoch [59][4800/5005]\tlr: 1.000e-02, eta: 5:03:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6602\n2022-12-31 21:56:48,320 - mmcls - INFO - Epoch [59][4900/5005]\tlr: 1.000e-02, eta: 5:02:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6635\n2022-12-31 21:56:57,573 - mmcls - INFO - Epoch [59][5000/5005]\tlr: 1.000e-02, eta: 5:02:47, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6590\n2022-12-31 21:57:20,128 - mmcls - INFO - Epoch(val) [59][196]\taccuracy_top-1: 65.7640, accuracy_top-5: 86.6940\n2022-12-31 21:57:31,398 - mmcls - INFO - Epoch [60][100/5005]\tlr: 1.000e-02, eta: 5:02:39, time: 0.112, data_time: 0.054, memory: 1224, loss: 1.5921\n2022-12-31 21:57:40,127 - mmcls - INFO - Epoch [60][200/5005]\tlr: 1.000e-02, eta: 5:02:31, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.6077\n2022-12-31 21:57:48,942 - mmcls - INFO - Epoch [60][300/5005]\tlr: 1.000e-02, eta: 5:02:22, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6283\n2022-12-31 21:57:57,689 - mmcls - INFO - Epoch [60][400/5005]\tlr: 1.000e-02, eta: 5:02:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6163\n2022-12-31 21:58:06,392 - mmcls - INFO - Epoch [60][500/5005]\tlr: 1.000e-02, eta: 5:02:04, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6153\n2022-12-31 21:58:15,068 - mmcls - INFO - Epoch [60][600/5005]\tlr: 1.000e-02, eta: 5:01:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6229\n2022-12-31 21:58:24,314 - mmcls - INFO - Epoch [60][700/5005]\tlr: 1.000e-02, eta: 5:01:46, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6153\n2022-12-31 21:58:33,195 - mmcls - INFO - Epoch [60][800/5005]\tlr: 1.000e-02, eta: 5:01:37, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6564\n2022-12-31 21:58:42,026 - mmcls - INFO - Epoch [60][900/5005]\tlr: 1.000e-02, eta: 5:01:28, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6045\n2022-12-31 21:58:51,002 - mmcls - INFO - Epoch [60][1000/5005]\tlr: 1.000e-02, eta: 5:01:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6174\n2022-12-31 21:58:59,648 - mmcls - INFO - Epoch [60][1100/5005]\tlr: 1.000e-02, eta: 5:01:11, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6129\n2022-12-31 21:59:08,433 - mmcls - INFO - Epoch [60][1200/5005]\tlr: 1.000e-02, eta: 5:01:02, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.6126\n2022-12-31 21:59:17,457 - mmcls - INFO - Epoch [60][1300/5005]\tlr: 1.000e-02, eta: 5:00:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6327\n2022-12-31 21:59:26,262 - mmcls - INFO - Epoch [60][1400/5005]\tlr: 1.000e-02, eta: 5:00:44, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6309\n2022-12-31 21:59:34,908 - mmcls - INFO - Epoch [60][1500/5005]\tlr: 1.000e-02, eta: 5:00:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6186\n2022-12-31 21:59:43,604 - mmcls - INFO - Epoch [60][1600/5005]\tlr: 1.000e-02, eta: 5:00:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6359\n2022-12-31 21:59:52,416 - mmcls - INFO - Epoch [60][1700/5005]\tlr: 1.000e-02, eta: 5:00:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6290\n2022-12-31 22:00:01,223 - mmcls - INFO - Epoch [60][1800/5005]\tlr: 1.000e-02, eta: 5:00:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6314\n2022-12-31 22:00:09,946 - mmcls - INFO - Epoch [60][1900/5005]\tlr: 1.000e-02, eta: 5:00:00, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6264\n2022-12-31 22:00:18,642 - mmcls - INFO - Epoch [60][2000/5005]\tlr: 1.000e-02, eta: 4:59:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6383\n2022-12-31 22:00:27,310 - mmcls - INFO - Epoch [60][2100/5005]\tlr: 1.000e-02, eta: 4:59:42, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6539\n2022-12-31 22:00:35,882 - mmcls - INFO - Epoch [60][2200/5005]\tlr: 1.000e-02, eta: 4:59:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6320\n2022-12-31 22:00:44,681 - mmcls - INFO - Epoch [60][2300/5005]\tlr: 1.000e-02, eta: 4:59:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6451\n2022-12-31 22:00:53,341 - mmcls - INFO - Epoch [60][2400/5005]\tlr: 1.000e-02, eta: 4:59:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6493\n2022-12-31 22:01:02,360 - mmcls - INFO - Epoch [60][2500/5005]\tlr: 1.000e-02, eta: 4:59:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6081\n2022-12-31 22:01:11,064 - mmcls - INFO - Epoch [60][2600/5005]\tlr: 1.000e-02, eta: 4:58:57, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6499\n2022-12-31 22:01:20,007 - mmcls - INFO - Epoch [60][2700/5005]\tlr: 1.000e-02, eta: 4:58:48, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6383\n2022-12-31 22:01:28,658 - mmcls - INFO - Epoch [60][2800/5005]\tlr: 1.000e-02, eta: 4:58:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6486\n2022-12-31 22:01:37,561 - mmcls - INFO - Epoch [60][2900/5005]\tlr: 1.000e-02, eta: 4:58:30, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6194\n2022-12-31 22:01:46,640 - mmcls - INFO - Epoch [60][3000/5005]\tlr: 1.000e-02, eta: 4:58:22, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6407\n2022-12-31 22:01:55,317 - mmcls - INFO - Epoch [60][3100/5005]\tlr: 1.000e-02, eta: 4:58:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6352\n2022-12-31 22:02:04,220 - mmcls - INFO - Epoch [60][3200/5005]\tlr: 1.000e-02, eta: 4:58:04, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.6260\n2022-12-31 22:02:12,928 - mmcls - INFO - Epoch [60][3300/5005]\tlr: 1.000e-02, eta: 4:57:55, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6186\n2022-12-31 22:02:22,002 - mmcls - INFO - Epoch [60][3400/5005]\tlr: 1.000e-02, eta: 4:57:46, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6495\n2022-12-31 22:02:30,633 - mmcls - INFO - Epoch [60][3500/5005]\tlr: 1.000e-02, eta: 4:57:37, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.6499\n2022-12-31 22:02:39,472 - mmcls - INFO - Epoch [60][3600/5005]\tlr: 1.000e-02, eta: 4:57:28, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6572\n2022-12-31 22:02:48,154 - mmcls - INFO - Epoch [60][3700/5005]\tlr: 1.000e-02, eta: 4:57:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6485\n2022-12-31 22:02:57,057 - mmcls - INFO - Epoch [60][3800/5005]\tlr: 1.000e-02, eta: 4:57:11, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6396\n2022-12-31 22:03:05,809 - mmcls - INFO - Epoch [60][3900/5005]\tlr: 1.000e-02, eta: 4:57:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6452\n2022-12-31 22:03:14,642 - mmcls - INFO - Epoch [60][4000/5005]\tlr: 1.000e-02, eta: 4:56:53, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6371\n2022-12-31 22:03:23,156 - mmcls - INFO - Epoch [60][4100/5005]\tlr: 1.000e-02, eta: 4:56:44, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6505\n2022-12-31 22:03:31,873 - mmcls - INFO - Epoch [60][4200/5005]\tlr: 1.000e-02, eta: 4:56:35, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6665\n2022-12-31 22:03:40,724 - mmcls - INFO - Epoch [60][4300/5005]\tlr: 1.000e-02, eta: 4:56:26, time: 0.089, data_time: 0.029, memory: 1224, loss: 1.6340\n2022-12-31 22:03:49,197 - mmcls - INFO - Epoch [60][4400/5005]\tlr: 1.000e-02, eta: 4:56:17, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6585\n2022-12-31 22:03:57,900 - mmcls - INFO - Epoch [60][4500/5005]\tlr: 1.000e-02, eta: 4:56:08, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6544\n2022-12-31 22:04:06,777 - mmcls - INFO - Epoch [60][4600/5005]\tlr: 1.000e-02, eta: 4:55:59, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6616\n2022-12-31 22:04:15,430 - mmcls - INFO - Epoch [60][4700/5005]\tlr: 1.000e-02, eta: 4:55:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6332\n2022-12-31 22:04:24,252 - mmcls - INFO - Epoch [60][4800/5005]\tlr: 1.000e-02, eta: 4:55:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6525\n2022-12-31 22:04:32,908 - mmcls - INFO - Epoch [60][4900/5005]\tlr: 1.000e-02, eta: 4:55:32, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6772\n2022-12-31 22:04:41,519 - mmcls - INFO - Epoch [60][5000/5005]\tlr: 1.000e-02, eta: 4:55:23, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6569\n2022-12-31 22:05:04,258 - mmcls - INFO - Epoch(val) [60][196]\taccuracy_top-1: 65.8700, accuracy_top-5: 87.0500\n2022-12-31 22:05:15,086 - mmcls - INFO - Epoch [61][100/5005]\tlr: 1.000e-03, eta: 4:55:15, time: 0.108, data_time: 0.041, memory: 1224, loss: 1.5603\n2022-12-31 22:05:24,158 - mmcls - INFO - Epoch [61][200/5005]\tlr: 1.000e-03, eta: 4:55:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5258\n2022-12-31 22:05:33,221 - mmcls - INFO - Epoch [61][300/5005]\tlr: 1.000e-03, eta: 4:54:57, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.5039\n2022-12-31 22:05:41,769 - mmcls - INFO - Epoch [61][400/5005]\tlr: 1.000e-03, eta: 4:54:48, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.4802\n2022-12-31 22:05:50,972 - mmcls - INFO - Epoch [61][500/5005]\tlr: 1.000e-03, eta: 4:54:40, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4945\n2022-12-31 22:05:59,878 - mmcls - INFO - Epoch [61][600/5005]\tlr: 1.000e-03, eta: 4:54:31, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.4956\n2022-12-31 22:06:08,779 - mmcls - INFO - Epoch [61][700/5005]\tlr: 1.000e-03, eta: 4:54:22, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.4753\n2022-12-31 22:06:17,911 - mmcls - INFO - Epoch [61][800/5005]\tlr: 1.000e-03, eta: 4:54:13, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.4793\n2022-12-31 22:06:26,682 - mmcls - INFO - Epoch [61][900/5005]\tlr: 1.000e-03, eta: 4:54:04, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.4518\n2022-12-31 22:06:35,504 - mmcls - INFO - Epoch [61][1000/5005]\tlr: 1.000e-03, eta: 4:53:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4720\n2022-12-31 22:06:44,340 - mmcls - INFO - Epoch [61][1100/5005]\tlr: 1.000e-03, eta: 4:53:47, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.4676\n2022-12-31 22:06:53,182 - mmcls - INFO - Epoch [61][1200/5005]\tlr: 1.000e-03, eta: 4:53:38, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.4642\n2022-12-31 22:07:01,815 - mmcls - INFO - Epoch [61][1300/5005]\tlr: 1.000e-03, eta: 4:53:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4537\n2022-12-31 22:07:10,707 - mmcls - INFO - Epoch [61][1400/5005]\tlr: 1.000e-03, eta: 4:53:20, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.4442\n2022-12-31 22:07:19,703 - mmcls - INFO - Epoch [61][1500/5005]\tlr: 1.000e-03, eta: 4:53:11, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.4599\n2022-12-31 22:07:28,506 - mmcls - INFO - Epoch [61][1600/5005]\tlr: 1.000e-03, eta: 4:53:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.4585\n2022-12-31 22:07:37,198 - mmcls - INFO - Epoch [61][1700/5005]\tlr: 1.000e-03, eta: 4:52:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.4555\n2022-12-31 22:07:46,041 - mmcls - INFO - Epoch [61][1800/5005]\tlr: 1.000e-03, eta: 4:52:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4671\n2022-12-31 22:07:54,787 - mmcls - INFO - Epoch [61][1900/5005]\tlr: 1.000e-03, eta: 4:52:36, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.4313\n2022-12-31 22:08:03,674 - mmcls - INFO - Epoch [61][2000/5005]\tlr: 1.000e-03, eta: 4:52:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.4336\n2022-12-31 22:08:12,883 - mmcls - INFO - Epoch [61][2100/5005]\tlr: 1.000e-03, eta: 4:52:18, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.4574\n2022-12-31 22:08:21,889 - mmcls - INFO - Epoch [61][2200/5005]\tlr: 1.000e-03, eta: 4:52:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.4446\n2022-12-31 22:08:30,256 - mmcls - INFO - Epoch [61][2300/5005]\tlr: 1.000e-03, eta: 4:52:00, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.4476\n2022-12-31 22:08:38,971 - mmcls - INFO - Epoch [61][2400/5005]\tlr: 1.000e-03, eta: 4:51:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.4453\n2022-12-31 22:08:47,914 - mmcls - INFO - Epoch [61][2500/5005]\tlr: 1.000e-03, eta: 4:51:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4252\n2022-12-31 22:08:56,759 - mmcls - INFO - Epoch [61][2600/5005]\tlr: 1.000e-03, eta: 4:51:34, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.4542\n2022-12-31 22:09:05,638 - mmcls - INFO - Epoch [61][2700/5005]\tlr: 1.000e-03, eta: 4:51:25, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.4286\n2022-12-31 22:09:14,222 - mmcls - INFO - Epoch [61][2800/5005]\tlr: 1.000e-03, eta: 4:51:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.4510\n2022-12-31 22:09:22,993 - mmcls - INFO - Epoch [61][2900/5005]\tlr: 1.000e-03, eta: 4:51:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.4430\n2022-12-31 22:09:31,803 - mmcls - INFO - Epoch [61][3000/5005]\tlr: 1.000e-03, eta: 4:50:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4392\n2022-12-31 22:09:40,783 - mmcls - INFO - Epoch [61][3100/5005]\tlr: 1.000e-03, eta: 4:50:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.4463\n2022-12-31 22:09:49,509 - mmcls - INFO - Epoch [61][3200/5005]\tlr: 1.000e-03, eta: 4:50:40, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.4447\n2022-12-31 22:09:58,273 - mmcls - INFO - Epoch [61][3300/5005]\tlr: 1.000e-03, eta: 4:50:31, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.4540\n2022-12-31 22:10:07,044 - mmcls - INFO - Epoch [61][3400/5005]\tlr: 1.000e-03, eta: 4:50:22, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.4278\n2022-12-31 22:10:15,786 - mmcls - INFO - Epoch [61][3500/5005]\tlr: 1.000e-03, eta: 4:50:14, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.4510\n2022-12-31 22:10:24,669 - mmcls - INFO - Epoch [61][3600/5005]\tlr: 1.000e-03, eta: 4:50:05, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4473\n2022-12-31 22:10:33,515 - mmcls - INFO - Epoch [61][3700/5005]\tlr: 1.000e-03, eta: 4:49:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.4514\n2022-12-31 22:10:42,287 - mmcls - INFO - Epoch [61][3800/5005]\tlr: 1.000e-03, eta: 4:49:47, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.4287\n2022-12-31 22:10:50,867 - mmcls - INFO - Epoch [61][3900/5005]\tlr: 1.000e-03, eta: 4:49:38, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.4227\n2022-12-31 22:10:59,556 - mmcls - INFO - Epoch [61][4000/5005]\tlr: 1.000e-03, eta: 4:49:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4243\n2022-12-31 22:11:08,499 - mmcls - INFO - Epoch [61][4100/5005]\tlr: 1.000e-03, eta: 4:49:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4082\n2022-12-31 22:11:17,674 - mmcls - INFO - Epoch [61][4200/5005]\tlr: 1.000e-03, eta: 4:49:12, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4580\n2022-12-31 22:11:26,160 - mmcls - INFO - Epoch [61][4300/5005]\tlr: 1.000e-03, eta: 4:49:02, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.4499\n2022-12-31 22:11:35,186 - mmcls - INFO - Epoch [61][4400/5005]\tlr: 1.000e-03, eta: 4:48:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.4472\n2022-12-31 22:11:43,987 - mmcls - INFO - Epoch [61][4500/5005]\tlr: 1.000e-03, eta: 4:48:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.4386\n2022-12-31 22:11:52,891 - mmcls - INFO - Epoch [61][4600/5005]\tlr: 1.000e-03, eta: 4:48:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4268\n2022-12-31 22:12:01,652 - mmcls - INFO - Epoch [61][4700/5005]\tlr: 1.000e-03, eta: 4:48:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4223\n2022-12-31 22:12:10,697 - mmcls - INFO - Epoch [61][4800/5005]\tlr: 1.000e-03, eta: 4:48:18, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.4146\n2022-12-31 22:12:19,585 - mmcls - INFO - Epoch [61][4900/5005]\tlr: 1.000e-03, eta: 4:48:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4094\n2022-12-31 22:12:28,542 - mmcls - INFO - Epoch [61][5000/5005]\tlr: 1.000e-03, eta: 4:48:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4094\n2022-12-31 22:12:51,237 - mmcls - INFO - Epoch(val) [61][196]\taccuracy_top-1: 69.7560, accuracy_top-5: 89.1280\n2022-12-31 22:13:02,437 - mmcls - INFO - Epoch [62][100/5005]\tlr: 1.000e-03, eta: 4:47:53, time: 0.112, data_time: 0.055, memory: 1224, loss: 1.4027\n2022-12-31 22:13:11,102 - mmcls - INFO - Epoch [62][200/5005]\tlr: 1.000e-03, eta: 4:47:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.4195\n2022-12-31 22:13:19,716 - mmcls - INFO - Epoch [62][300/5005]\tlr: 1.000e-03, eta: 4:47:35, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.4232\n2022-12-31 22:13:28,674 - mmcls - INFO - Epoch [62][400/5005]\tlr: 1.000e-03, eta: 4:47:26, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3997\n2022-12-31 22:13:37,421 - mmcls - INFO - Epoch [62][500/5005]\tlr: 1.000e-03, eta: 4:47:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4216\n2022-12-31 22:13:46,340 - mmcls - INFO - Epoch [62][600/5005]\tlr: 1.000e-03, eta: 4:47:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4227\n2022-12-31 22:13:55,044 - mmcls - INFO - Epoch [62][700/5005]\tlr: 1.000e-03, eta: 4:46:59, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.4171\n2022-12-31 22:14:03,668 - mmcls - INFO - Epoch [62][800/5005]\tlr: 1.000e-03, eta: 4:46:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.4308\n2022-12-31 22:14:13,002 - mmcls - INFO - Epoch [62][900/5005]\tlr: 1.000e-03, eta: 4:46:42, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.4013\n2022-12-31 22:14:21,630 - mmcls - INFO - Epoch [62][1000/5005]\tlr: 1.000e-03, eta: 4:46:33, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.4108\n2022-12-31 22:14:30,542 - mmcls - INFO - Epoch [62][1100/5005]\tlr: 1.000e-03, eta: 4:46:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4206\n2022-12-31 22:14:39,196 - mmcls - INFO - Epoch [62][1200/5005]\tlr: 1.000e-03, eta: 4:46:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.4198\n2022-12-31 22:14:47,872 - mmcls - INFO - Epoch [62][1300/5005]\tlr: 1.000e-03, eta: 4:46:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3885\n2022-12-31 22:14:57,003 - mmcls - INFO - Epoch [62][1400/5005]\tlr: 1.000e-03, eta: 4:45:57, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.4075\n2022-12-31 22:15:05,478 - mmcls - INFO - Epoch [62][1500/5005]\tlr: 1.000e-03, eta: 4:45:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.4241\n2022-12-31 22:15:14,232 - mmcls - INFO - Epoch [62][1600/5005]\tlr: 1.000e-03, eta: 4:45:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.4104\n2022-12-31 22:15:22,593 - mmcls - INFO - Epoch [62][1700/5005]\tlr: 1.000e-03, eta: 4:45:30, time: 0.084, data_time: 0.014, memory: 1224, loss: 1.3927\n2022-12-31 22:15:31,479 - mmcls - INFO - Epoch [62][1800/5005]\tlr: 1.000e-03, eta: 4:45:21, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4145\n2022-12-31 22:15:40,375 - mmcls - INFO - Epoch [62][1900/5005]\tlr: 1.000e-03, eta: 4:45:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4115\n2022-12-31 22:15:49,303 - mmcls - INFO - Epoch [62][2000/5005]\tlr: 1.000e-03, eta: 4:45:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.3972\n2022-12-31 22:15:58,412 - mmcls - INFO - Epoch [62][2100/5005]\tlr: 1.000e-03, eta: 4:44:55, time: 0.091, data_time: 0.029, memory: 1224, loss: 1.4187\n2022-12-31 22:16:07,350 - mmcls - INFO - Epoch [62][2200/5005]\tlr: 1.000e-03, eta: 4:44:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.4186\n2022-12-31 22:16:16,213 - mmcls - INFO - Epoch [62][2300/5005]\tlr: 1.000e-03, eta: 4:44:37, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.4136\n2022-12-31 22:16:24,808 - mmcls - INFO - Epoch [62][2400/5005]\tlr: 1.000e-03, eta: 4:44:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4125\n2022-12-31 22:16:33,641 - mmcls - INFO - Epoch [62][2500/5005]\tlr: 1.000e-03, eta: 4:44:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.4256\n2022-12-31 22:16:42,323 - mmcls - INFO - Epoch [62][2600/5005]\tlr: 1.000e-03, eta: 4:44:10, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.4050\n2022-12-31 22:16:50,934 - mmcls - INFO - Epoch [62][2700/5005]\tlr: 1.000e-03, eta: 4:44:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4036\n2022-12-31 22:16:59,449 - mmcls - INFO - Epoch [62][2800/5005]\tlr: 1.000e-03, eta: 4:43:52, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.3877\n2022-12-31 22:17:08,589 - mmcls - INFO - Epoch [62][2900/5005]\tlr: 1.000e-03, eta: 4:43:44, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.4080\n2022-12-31 22:17:17,493 - mmcls - INFO - Epoch [62][3000/5005]\tlr: 1.000e-03, eta: 4:43:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4149\n2022-12-31 22:17:26,215 - mmcls - INFO - Epoch [62][3100/5005]\tlr: 1.000e-03, eta: 4:43:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3932\n2022-12-31 22:17:35,208 - mmcls - INFO - Epoch [62][3200/5005]\tlr: 1.000e-03, eta: 4:43:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.4107\n2022-12-31 22:17:43,860 - mmcls - INFO - Epoch [62][3300/5005]\tlr: 1.000e-03, eta: 4:43:08, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.4033\n2022-12-31 22:17:52,529 - mmcls - INFO - Epoch [62][3400/5005]\tlr: 1.000e-03, eta: 4:42:59, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.4236\n2022-12-31 22:18:01,438 - mmcls - INFO - Epoch [62][3500/5005]\tlr: 1.000e-03, eta: 4:42:50, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.4130\n2022-12-31 22:18:10,259 - mmcls - INFO - Epoch [62][3600/5005]\tlr: 1.000e-03, eta: 4:42:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.4063\n2022-12-31 22:18:19,139 - mmcls - INFO - Epoch [62][3700/5005]\tlr: 1.000e-03, eta: 4:42:33, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.4095\n2022-12-31 22:18:27,803 - mmcls - INFO - Epoch [62][3800/5005]\tlr: 1.000e-03, eta: 4:42:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4192\n2022-12-31 22:18:36,769 - mmcls - INFO - Epoch [62][3900/5005]\tlr: 1.000e-03, eta: 4:42:15, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.4010\n2022-12-31 22:18:45,376 - mmcls - INFO - Epoch [62][4000/5005]\tlr: 1.000e-03, eta: 4:42:06, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3891\n2022-12-31 22:18:54,413 - mmcls - INFO - Epoch [62][4100/5005]\tlr: 1.000e-03, eta: 4:41:57, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.4343\n2022-12-31 22:19:03,401 - mmcls - INFO - Epoch [62][4200/5005]\tlr: 1.000e-03, eta: 4:41:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.4075\n2022-12-31 22:19:12,170 - mmcls - INFO - Epoch [62][4300/5005]\tlr: 1.000e-03, eta: 4:41:39, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3889\n2022-12-31 22:19:20,702 - mmcls - INFO - Epoch [62][4400/5005]\tlr: 1.000e-03, eta: 4:41:30, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3858\n2022-12-31 22:19:29,491 - mmcls - INFO - Epoch [62][4500/5005]\tlr: 1.000e-03, eta: 4:41:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.4077\n2022-12-31 22:19:38,459 - mmcls - INFO - Epoch [62][4600/5005]\tlr: 1.000e-03, eta: 4:41:13, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.3908\n2022-12-31 22:19:47,147 - mmcls - INFO - Epoch [62][4700/5005]\tlr: 1.000e-03, eta: 4:41:04, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3722\n2022-12-31 22:19:55,835 - mmcls - INFO - Epoch [62][4800/5005]\tlr: 1.000e-03, eta: 4:40:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.4011\n2022-12-31 22:20:04,687 - mmcls - INFO - Epoch [62][4900/5005]\tlr: 1.000e-03, eta: 4:40:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4089\n2022-12-31 22:20:13,926 - mmcls - INFO - Epoch [62][5000/5005]\tlr: 1.000e-03, eta: 4:40:37, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4081\n2022-12-31 22:20:36,702 - mmcls - INFO - Epoch(val) [62][196]\taccuracy_top-1: 69.9340, accuracy_top-5: 89.3200\n2022-12-31 22:20:47,685 - mmcls - INFO - Epoch [63][100/5005]\tlr: 1.000e-03, eta: 4:40:29, time: 0.110, data_time: 0.034, memory: 1224, loss: 1.3962\n2022-12-31 22:20:56,257 - mmcls - INFO - Epoch [63][200/5005]\tlr: 1.000e-03, eta: 4:40:20, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3745\n2022-12-31 22:21:04,925 - mmcls - INFO - Epoch [63][300/5005]\tlr: 1.000e-03, eta: 4:40:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3873\n2022-12-31 22:21:13,918 - mmcls - INFO - Epoch [63][400/5005]\tlr: 1.000e-03, eta: 4:40:02, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3939\n2022-12-31 22:21:22,855 - mmcls - INFO - Epoch [63][500/5005]\tlr: 1.000e-03, eta: 4:39:54, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.3831\n2022-12-31 22:21:31,782 - mmcls - INFO - Epoch [63][600/5005]\tlr: 1.000e-03, eta: 4:39:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3730\n2022-12-31 22:21:40,575 - mmcls - INFO - Epoch [63][700/5005]\tlr: 1.000e-03, eta: 4:39:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3748\n2022-12-31 22:21:49,650 - mmcls - INFO - Epoch [63][800/5005]\tlr: 1.000e-03, eta: 4:39:27, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.3974\n2022-12-31 22:21:58,504 - mmcls - INFO - Epoch [63][900/5005]\tlr: 1.000e-03, eta: 4:39:18, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3876\n2022-12-31 22:22:07,171 - mmcls - INFO - Epoch [63][1000/5005]\tlr: 1.000e-03, eta: 4:39:09, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.3790\n2022-12-31 22:22:16,100 - mmcls - INFO - Epoch [63][1100/5005]\tlr: 1.000e-03, eta: 4:39:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4058\n2022-12-31 22:22:24,742 - mmcls - INFO - Epoch [63][1200/5005]\tlr: 1.000e-03, eta: 4:38:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3838\n2022-12-31 22:22:33,923 - mmcls - INFO - Epoch [63][1300/5005]\tlr: 1.000e-03, eta: 4:38:43, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.3697\n2022-12-31 22:22:42,764 - mmcls - INFO - Epoch [63][1400/5005]\tlr: 1.000e-03, eta: 4:38:34, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3817\n2022-12-31 22:22:51,665 - mmcls - INFO - Epoch [63][1500/5005]\tlr: 1.000e-03, eta: 4:38:25, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.3908\n2022-12-31 22:23:00,161 - mmcls - INFO - Epoch [63][1600/5005]\tlr: 1.000e-03, eta: 4:38:16, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3915\n2022-12-31 22:23:08,940 - mmcls - INFO - Epoch [63][1700/5005]\tlr: 1.000e-03, eta: 4:38:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3854\n2022-12-31 22:23:17,573 - mmcls - INFO - Epoch [63][1800/5005]\tlr: 1.000e-03, eta: 4:37:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3999\n2022-12-31 22:23:26,564 - mmcls - INFO - Epoch [63][1900/5005]\tlr: 1.000e-03, eta: 4:37:49, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.3759\n2022-12-31 22:23:35,731 - mmcls - INFO - Epoch [63][2000/5005]\tlr: 1.000e-03, eta: 4:37:41, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.4128\n2022-12-31 22:23:44,439 - mmcls - INFO - Epoch [63][2100/5005]\tlr: 1.000e-03, eta: 4:37:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3813\n2022-12-31 22:23:53,236 - mmcls - INFO - Epoch [63][2200/5005]\tlr: 1.000e-03, eta: 4:37:23, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3803\n2022-12-31 22:24:02,063 - mmcls - INFO - Epoch [63][2300/5005]\tlr: 1.000e-03, eta: 4:37:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3971\n2022-12-31 22:24:10,707 - mmcls - INFO - Epoch [63][2400/5005]\tlr: 1.000e-03, eta: 4:37:05, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.3854\n2022-12-31 22:24:19,436 - mmcls - INFO - Epoch [63][2500/5005]\tlr: 1.000e-03, eta: 4:36:56, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3913\n2022-12-31 22:24:28,471 - mmcls - INFO - Epoch [63][2600/5005]\tlr: 1.000e-03, eta: 4:36:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.4173\n2022-12-31 22:24:37,362 - mmcls - INFO - Epoch [63][2700/5005]\tlr: 1.000e-03, eta: 4:36:39, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3670\n2022-12-31 22:24:46,236 - mmcls - INFO - Epoch [63][2800/5005]\tlr: 1.000e-03, eta: 4:36:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4081\n2022-12-31 22:24:55,515 - mmcls - INFO - Epoch [63][2900/5005]\tlr: 1.000e-03, eta: 4:36:21, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.4055\n2022-12-31 22:25:04,042 - mmcls - INFO - Epoch [63][3000/5005]\tlr: 1.000e-03, eta: 4:36:12, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3868\n2022-12-31 22:25:13,083 - mmcls - INFO - Epoch [63][3100/5005]\tlr: 1.000e-03, eta: 4:36:03, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3896\n2022-12-31 22:25:21,985 - mmcls - INFO - Epoch [63][3200/5005]\tlr: 1.000e-03, eta: 4:35:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3752\n2022-12-31 22:25:30,746 - mmcls - INFO - Epoch [63][3300/5005]\tlr: 1.000e-03, eta: 4:35:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3853\n2022-12-31 22:25:39,425 - mmcls - INFO - Epoch [63][3400/5005]\tlr: 1.000e-03, eta: 4:35:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.4137\n2022-12-31 22:25:48,240 - mmcls - INFO - Epoch [63][3500/5005]\tlr: 1.000e-03, eta: 4:35:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3950\n2022-12-31 22:25:57,005 - mmcls - INFO - Epoch [63][3600/5005]\tlr: 1.000e-03, eta: 4:35:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3917\n2022-12-31 22:26:05,699 - mmcls - INFO - Epoch [63][3700/5005]\tlr: 1.000e-03, eta: 4:35:10, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3788\n2022-12-31 22:26:14,666 - mmcls - INFO - Epoch [63][3800/5005]\tlr: 1.000e-03, eta: 4:35:01, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3996\n2022-12-31 22:26:23,249 - mmcls - INFO - Epoch [63][3900/5005]\tlr: 1.000e-03, eta: 4:34:52, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.3834\n2022-12-31 22:26:32,017 - mmcls - INFO - Epoch [63][4000/5005]\tlr: 1.000e-03, eta: 4:34:43, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3935\n2022-12-31 22:26:40,978 - mmcls - INFO - Epoch [63][4100/5005]\tlr: 1.000e-03, eta: 4:34:34, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.4070\n2022-12-31 22:26:49,715 - mmcls - INFO - Epoch [63][4200/5005]\tlr: 1.000e-03, eta: 4:34:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3850\n2022-12-31 22:26:58,932 - mmcls - INFO - Epoch [63][4300/5005]\tlr: 1.000e-03, eta: 4:34:17, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3834\n2022-12-31 22:27:07,711 - mmcls - INFO - Epoch [63][4400/5005]\tlr: 1.000e-03, eta: 4:34:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3845\n2022-12-31 22:27:16,873 - mmcls - INFO - Epoch [63][4500/5005]\tlr: 1.000e-03, eta: 4:33:59, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.3705\n2022-12-31 22:27:25,676 - mmcls - INFO - Epoch [63][4600/5005]\tlr: 1.000e-03, eta: 4:33:50, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3854\n2022-12-31 22:27:34,508 - mmcls - INFO - Epoch [63][4700/5005]\tlr: 1.000e-03, eta: 4:33:42, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.4019\n2022-12-31 22:27:43,525 - mmcls - INFO - Epoch [63][4800/5005]\tlr: 1.000e-03, eta: 4:33:33, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.4001\n2022-12-31 22:27:52,138 - mmcls - INFO - Epoch [63][4900/5005]\tlr: 1.000e-03, eta: 4:33:24, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3843\n2022-12-31 22:28:00,904 - mmcls - INFO - Epoch [63][5000/5005]\tlr: 1.000e-03, eta: 4:33:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3693\n2022-12-31 22:28:23,380 - mmcls - INFO - Epoch(val) [63][196]\taccuracy_top-1: 70.2380, accuracy_top-5: 89.3960\n2022-12-31 22:28:34,260 - mmcls - INFO - Epoch [64][100/5005]\tlr: 1.000e-03, eta: 4:33:06, time: 0.109, data_time: 0.046, memory: 1224, loss: 1.4006\n2022-12-31 22:28:43,193 - mmcls - INFO - Epoch [64][200/5005]\tlr: 1.000e-03, eta: 4:32:58, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3802\n2022-12-31 22:28:52,129 - mmcls - INFO - Epoch [64][300/5005]\tlr: 1.000e-03, eta: 4:32:49, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3693\n2022-12-31 22:29:01,051 - mmcls - INFO - Epoch [64][400/5005]\tlr: 1.000e-03, eta: 4:32:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.3744\n2022-12-31 22:29:09,970 - mmcls - INFO - Epoch [64][500/5005]\tlr: 1.000e-03, eta: 4:32:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3582\n2022-12-31 22:29:18,820 - mmcls - INFO - Epoch [64][600/5005]\tlr: 1.000e-03, eta: 4:32:22, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3706\n2022-12-31 22:29:27,634 - mmcls - INFO - Epoch [64][700/5005]\tlr: 1.000e-03, eta: 4:32:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3790\n2022-12-31 22:29:36,354 - mmcls - INFO - Epoch [64][800/5005]\tlr: 1.000e-03, eta: 4:32:05, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3876\n2022-12-31 22:29:45,151 - mmcls - INFO - Epoch [64][900/5005]\tlr: 1.000e-03, eta: 4:31:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3794\n2022-12-31 22:29:53,648 - mmcls - INFO - Epoch [64][1000/5005]\tlr: 1.000e-03, eta: 4:31:47, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3697\n2022-12-31 22:30:02,719 - mmcls - INFO - Epoch [64][1100/5005]\tlr: 1.000e-03, eta: 4:31:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3939\n2022-12-31 22:30:11,850 - mmcls - INFO - Epoch [64][1200/5005]\tlr: 1.000e-03, eta: 4:31:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3657\n2022-12-31 22:30:20,744 - mmcls - INFO - Epoch [64][1300/5005]\tlr: 1.000e-03, eta: 4:31:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3828\n2022-12-31 22:30:29,479 - mmcls - INFO - Epoch [64][1400/5005]\tlr: 1.000e-03, eta: 4:31:11, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3976\n2022-12-31 22:30:38,333 - mmcls - INFO - Epoch [64][1500/5005]\tlr: 1.000e-03, eta: 4:31:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3741\n2022-12-31 22:30:46,819 - mmcls - INFO - Epoch [64][1600/5005]\tlr: 1.000e-03, eta: 4:30:54, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3735\n2022-12-31 22:30:55,825 - mmcls - INFO - Epoch [64][1700/5005]\tlr: 1.000e-03, eta: 4:30:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3740\n2022-12-31 22:31:04,757 - mmcls - INFO - Epoch [64][1800/5005]\tlr: 1.000e-03, eta: 4:30:36, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.3851\n2022-12-31 22:31:13,772 - mmcls - INFO - Epoch [64][1900/5005]\tlr: 1.000e-03, eta: 4:30:27, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3696\n2022-12-31 22:31:22,353 - mmcls - INFO - Epoch [64][2000/5005]\tlr: 1.000e-03, eta: 4:30:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3810\n2022-12-31 22:31:31,138 - mmcls - INFO - Epoch [64][2100/5005]\tlr: 1.000e-03, eta: 4:30:09, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3665\n2022-12-31 22:31:39,946 - mmcls - INFO - Epoch [64][2200/5005]\tlr: 1.000e-03, eta: 4:30:00, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3783\n2022-12-31 22:31:49,220 - mmcls - INFO - Epoch [64][2300/5005]\tlr: 1.000e-03, eta: 4:29:52, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.3732\n2022-12-31 22:31:57,906 - mmcls - INFO - Epoch [64][2400/5005]\tlr: 1.000e-03, eta: 4:29:43, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3813\n2022-12-31 22:32:06,709 - mmcls - INFO - Epoch [64][2500/5005]\tlr: 1.000e-03, eta: 4:29:34, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3756\n2022-12-31 22:32:15,558 - mmcls - INFO - Epoch [64][2600/5005]\tlr: 1.000e-03, eta: 4:29:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3842\n2022-12-31 22:32:24,201 - mmcls - INFO - Epoch [64][2700/5005]\tlr: 1.000e-03, eta: 4:29:16, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3744\n2022-12-31 22:32:33,056 - mmcls - INFO - Epoch [64][2800/5005]\tlr: 1.000e-03, eta: 4:29:07, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3821\n2022-12-31 22:32:41,770 - mmcls - INFO - Epoch [64][2900/5005]\tlr: 1.000e-03, eta: 4:28:58, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.3490\n2022-12-31 22:32:50,951 - mmcls - INFO - Epoch [64][3000/5005]\tlr: 1.000e-03, eta: 4:28:50, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.3647\n2022-12-31 22:33:00,221 - mmcls - INFO - Epoch [64][3100/5005]\tlr: 1.000e-03, eta: 4:28:41, time: 0.093, data_time: 0.006, memory: 1224, loss: 1.3734\n2022-12-31 22:33:09,342 - mmcls - INFO - Epoch [64][3200/5005]\tlr: 1.000e-03, eta: 4:28:32, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.3734\n2022-12-31 22:33:18,166 - mmcls - INFO - Epoch [64][3300/5005]\tlr: 1.000e-03, eta: 4:28:23, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3506\n2022-12-31 22:33:26,945 - mmcls - INFO - Epoch [64][3400/5005]\tlr: 1.000e-03, eta: 4:28:15, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3725\n2022-12-31 22:33:35,540 - mmcls - INFO - Epoch [64][3500/5005]\tlr: 1.000e-03, eta: 4:28:06, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3677\n2022-12-31 22:33:44,584 - mmcls - INFO - Epoch [64][3600/5005]\tlr: 1.000e-03, eta: 4:27:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3694\n2022-12-31 22:33:53,796 - mmcls - INFO - Epoch [64][3700/5005]\tlr: 1.000e-03, eta: 4:27:48, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3683\n2022-12-31 22:34:02,752 - mmcls - INFO - Epoch [64][3800/5005]\tlr: 1.000e-03, eta: 4:27:39, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3608\n2022-12-31 22:34:11,357 - mmcls - INFO - Epoch [64][3900/5005]\tlr: 1.000e-03, eta: 4:27:30, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3768\n2022-12-31 22:34:20,118 - mmcls - INFO - Epoch [64][4000/5005]\tlr: 1.000e-03, eta: 4:27:21, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3687\n2022-12-31 22:34:28,739 - mmcls - INFO - Epoch [64][4100/5005]\tlr: 1.000e-03, eta: 4:27:13, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3743\n2022-12-31 22:34:37,417 - mmcls - INFO - Epoch [64][4200/5005]\tlr: 1.000e-03, eta: 4:27:04, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3759\n2022-12-31 22:34:46,075 - mmcls - INFO - Epoch [64][4300/5005]\tlr: 1.000e-03, eta: 4:26:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3823\n2022-12-31 22:34:54,858 - mmcls - INFO - Epoch [64][4400/5005]\tlr: 1.000e-03, eta: 4:26:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3738\n2022-12-31 22:35:03,571 - mmcls - INFO - Epoch [64][4500/5005]\tlr: 1.000e-03, eta: 4:26:37, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3571\n2022-12-31 22:35:12,426 - mmcls - INFO - Epoch [64][4600/5005]\tlr: 1.000e-03, eta: 4:26:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3480\n2022-12-31 22:35:21,826 - mmcls - INFO - Epoch [64][4700/5005]\tlr: 1.000e-03, eta: 4:26:19, time: 0.094, data_time: 0.006, memory: 1224, loss: 1.3635\n2022-12-31 22:35:30,618 - mmcls - INFO - Epoch [64][4800/5005]\tlr: 1.000e-03, eta: 4:26:10, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3879\n2022-12-31 22:35:39,254 - mmcls - INFO - Epoch [64][4900/5005]\tlr: 1.000e-03, eta: 4:26:01, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3897\n2022-12-31 22:35:47,814 - mmcls - INFO - Epoch [64][5000/5005]\tlr: 1.000e-03, eta: 4:25:52, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3858\n2022-12-31 22:36:10,624 - mmcls - INFO - Epoch(val) [64][196]\taccuracy_top-1: 70.4280, accuracy_top-5: 89.5100\n2022-12-31 22:36:21,486 - mmcls - INFO - Epoch [65][100/5005]\tlr: 1.000e-03, eta: 4:25:44, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.3422\n2022-12-31 22:36:30,392 - mmcls - INFO - Epoch [65][200/5005]\tlr: 1.000e-03, eta: 4:25:35, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.3661\n2022-12-31 22:36:39,208 - mmcls - INFO - Epoch [65][300/5005]\tlr: 1.000e-03, eta: 4:25:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3690\n2022-12-31 22:36:48,133 - mmcls - INFO - Epoch [65][400/5005]\tlr: 1.000e-03, eta: 4:25:18, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.3624\n2022-12-31 22:36:57,116 - mmcls - INFO - Epoch [65][500/5005]\tlr: 1.000e-03, eta: 4:25:09, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3762\n2022-12-31 22:37:05,766 - mmcls - INFO - Epoch [65][600/5005]\tlr: 1.000e-03, eta: 4:25:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3752\n2022-12-31 22:37:14,423 - mmcls - INFO - Epoch [65][700/5005]\tlr: 1.000e-03, eta: 4:24:51, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3746\n2022-12-31 22:37:23,421 - mmcls - INFO - Epoch [65][800/5005]\tlr: 1.000e-03, eta: 4:24:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3833\n2022-12-31 22:37:32,368 - mmcls - INFO - Epoch [65][900/5005]\tlr: 1.000e-03, eta: 4:24:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3649\n2022-12-31 22:37:40,825 - mmcls - INFO - Epoch [65][1000/5005]\tlr: 1.000e-03, eta: 4:24:24, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.3805\n2022-12-31 22:37:49,437 - mmcls - INFO - Epoch [65][1100/5005]\tlr: 1.000e-03, eta: 4:24:15, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3555\n2022-12-31 22:37:58,125 - mmcls - INFO - Epoch [65][1200/5005]\tlr: 1.000e-03, eta: 4:24:06, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3573\n2022-12-31 22:38:06,793 - mmcls - INFO - Epoch [65][1300/5005]\tlr: 1.000e-03, eta: 4:23:57, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.3718\n2022-12-31 22:38:15,488 - mmcls - INFO - Epoch [65][1400/5005]\tlr: 1.000e-03, eta: 4:23:48, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.3680\n2022-12-31 22:38:24,253 - mmcls - INFO - Epoch [65][1500/5005]\tlr: 1.000e-03, eta: 4:23:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3645\n2022-12-31 22:38:32,824 - mmcls - INFO - Epoch [65][1600/5005]\tlr: 1.000e-03, eta: 4:23:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3335\n2022-12-31 22:38:41,766 - mmcls - INFO - Epoch [65][1700/5005]\tlr: 1.000e-03, eta: 4:23:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3579\n2022-12-31 22:38:50,530 - mmcls - INFO - Epoch [65][1800/5005]\tlr: 1.000e-03, eta: 4:23:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3798\n2022-12-31 22:38:59,175 - mmcls - INFO - Epoch [65][1900/5005]\tlr: 1.000e-03, eta: 4:23:04, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3561\n2022-12-31 22:39:08,129 - mmcls - INFO - Epoch [65][2000/5005]\tlr: 1.000e-03, eta: 4:22:55, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3658\n2022-12-31 22:39:17,024 - mmcls - INFO - Epoch [65][2100/5005]\tlr: 1.000e-03, eta: 4:22:46, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.3635\n2022-12-31 22:39:25,577 - mmcls - INFO - Epoch [65][2200/5005]\tlr: 1.000e-03, eta: 4:22:37, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3619\n2022-12-31 22:39:34,217 - mmcls - INFO - Epoch [65][2300/5005]\tlr: 1.000e-03, eta: 4:22:28, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3793\n2022-12-31 22:39:42,901 - mmcls - INFO - Epoch [65][2400/5005]\tlr: 1.000e-03, eta: 4:22:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3620\n2022-12-31 22:39:52,100 - mmcls - INFO - Epoch [65][2500/5005]\tlr: 1.000e-03, eta: 4:22:10, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3945\n2022-12-31 22:40:00,858 - mmcls - INFO - Epoch [65][2600/5005]\tlr: 1.000e-03, eta: 4:22:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3774\n2022-12-31 22:40:09,705 - mmcls - INFO - Epoch [65][2700/5005]\tlr: 1.000e-03, eta: 4:21:53, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3759\n2022-12-31 22:40:18,789 - mmcls - INFO - Epoch [65][2800/5005]\tlr: 1.000e-03, eta: 4:21:44, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.3708\n2022-12-31 22:40:27,435 - mmcls - INFO - Epoch [65][2900/5005]\tlr: 1.000e-03, eta: 4:21:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3823\n2022-12-31 22:40:36,009 - mmcls - INFO - Epoch [65][3000/5005]\tlr: 1.000e-03, eta: 4:21:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3549\n2022-12-31 22:40:44,820 - mmcls - INFO - Epoch [65][3100/5005]\tlr: 1.000e-03, eta: 4:21:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3654\n2022-12-31 22:40:53,601 - mmcls - INFO - Epoch [65][3200/5005]\tlr: 1.000e-03, eta: 4:21:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3615\n2022-12-31 22:41:02,727 - mmcls - INFO - Epoch [65][3300/5005]\tlr: 1.000e-03, eta: 4:21:00, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.3571\n2022-12-31 22:41:11,532 - mmcls - INFO - Epoch [65][3400/5005]\tlr: 1.000e-03, eta: 4:20:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3578\n2022-12-31 22:41:20,129 - mmcls - INFO - Epoch [65][3500/5005]\tlr: 1.000e-03, eta: 4:20:42, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3662\n2022-12-31 22:41:28,811 - mmcls - INFO - Epoch [65][3600/5005]\tlr: 1.000e-03, eta: 4:20:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3630\n2022-12-31 22:41:37,886 - mmcls - INFO - Epoch [65][3700/5005]\tlr: 1.000e-03, eta: 4:20:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3748\n2022-12-31 22:41:46,793 - mmcls - INFO - Epoch [65][3800/5005]\tlr: 1.000e-03, eta: 4:20:15, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3553\n2022-12-31 22:41:55,873 - mmcls - INFO - Epoch [65][3900/5005]\tlr: 1.000e-03, eta: 4:20:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3680\n2022-12-31 22:42:04,501 - mmcls - INFO - Epoch [65][4000/5005]\tlr: 1.000e-03, eta: 4:19:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3665\n2022-12-31 22:42:13,408 - mmcls - INFO - Epoch [65][4100/5005]\tlr: 1.000e-03, eta: 4:19:49, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3532\n2022-12-31 22:42:22,397 - mmcls - INFO - Epoch [65][4200/5005]\tlr: 1.000e-03, eta: 4:19:40, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.3474\n2022-12-31 22:42:31,323 - mmcls - INFO - Epoch [65][4300/5005]\tlr: 1.000e-03, eta: 4:19:31, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3547\n2022-12-31 22:42:40,190 - mmcls - INFO - Epoch [65][4400/5005]\tlr: 1.000e-03, eta: 4:19:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3454\n2022-12-31 22:42:48,797 - mmcls - INFO - Epoch [65][4500/5005]\tlr: 1.000e-03, eta: 4:19:13, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3740\n2022-12-31 22:42:57,521 - mmcls - INFO - Epoch [65][4600/5005]\tlr: 1.000e-03, eta: 4:19:04, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.3529\n2022-12-31 22:43:06,374 - mmcls - INFO - Epoch [65][4700/5005]\tlr: 1.000e-03, eta: 4:18:55, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3889\n2022-12-31 22:43:15,151 - mmcls - INFO - Epoch [65][4800/5005]\tlr: 1.000e-03, eta: 4:18:47, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3860\n2022-12-31 22:43:24,051 - mmcls - INFO - Epoch [65][4900/5005]\tlr: 1.000e-03, eta: 4:18:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3639\n2022-12-31 22:43:33,026 - mmcls - INFO - Epoch [65][5000/5005]\tlr: 1.000e-03, eta: 4:18:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3649\n2022-12-31 22:43:55,695 - mmcls - INFO - Epoch(val) [65][196]\taccuracy_top-1: 70.3720, accuracy_top-5: 89.4000\n2022-12-31 22:44:06,879 - mmcls - INFO - Epoch [66][100/5005]\tlr: 1.000e-03, eta: 4:18:21, time: 0.112, data_time: 0.035, memory: 1224, loss: 1.3452\n2022-12-31 22:44:15,787 - mmcls - INFO - Epoch [66][200/5005]\tlr: 1.000e-03, eta: 4:18:12, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3467\n2022-12-31 22:44:24,291 - mmcls - INFO - Epoch [66][300/5005]\tlr: 1.000e-03, eta: 4:18:03, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.3626\n2022-12-31 22:44:33,087 - mmcls - INFO - Epoch [66][400/5005]\tlr: 1.000e-03, eta: 4:17:54, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3334\n2022-12-31 22:44:42,180 - mmcls - INFO - Epoch [66][500/5005]\tlr: 1.000e-03, eta: 4:17:45, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3702\n2022-12-31 22:44:50,719 - mmcls - INFO - Epoch [66][600/5005]\tlr: 1.000e-03, eta: 4:17:36, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.3776\n2022-12-31 22:44:59,360 - mmcls - INFO - Epoch [66][700/5005]\tlr: 1.000e-03, eta: 4:17:27, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3502\n2022-12-31 22:45:08,066 - mmcls - INFO - Epoch [66][800/5005]\tlr: 1.000e-03, eta: 4:17:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3525\n2022-12-31 22:45:17,014 - mmcls - INFO - Epoch [66][900/5005]\tlr: 1.000e-03, eta: 4:17:09, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3598\n2022-12-31 22:45:26,039 - mmcls - INFO - Epoch [66][1000/5005]\tlr: 1.000e-03, eta: 4:17:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3777\n2022-12-31 22:45:34,754 - mmcls - INFO - Epoch [66][1100/5005]\tlr: 1.000e-03, eta: 4:16:52, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3467\n2022-12-31 22:45:43,633 - mmcls - INFO - Epoch [66][1200/5005]\tlr: 1.000e-03, eta: 4:16:43, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3449\n2022-12-31 22:45:52,542 - mmcls - INFO - Epoch [66][1300/5005]\tlr: 1.000e-03, eta: 4:16:34, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3720\n2022-12-31 22:46:01,317 - mmcls - INFO - Epoch [66][1400/5005]\tlr: 1.000e-03, eta: 4:16:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3627\n2022-12-31 22:46:10,302 - mmcls - INFO - Epoch [66][1500/5005]\tlr: 1.000e-03, eta: 4:16:16, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3662\n2022-12-31 22:46:19,151 - mmcls - INFO - Epoch [66][1600/5005]\tlr: 1.000e-03, eta: 4:16:08, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.3620\n2022-12-31 22:46:27,686 - mmcls - INFO - Epoch [66][1700/5005]\tlr: 1.000e-03, eta: 4:15:58, time: 0.085, data_time: 0.029, memory: 1224, loss: 1.3340\n2022-12-31 22:46:36,554 - mmcls - INFO - Epoch [66][1800/5005]\tlr: 1.000e-03, eta: 4:15:50, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3451\n2022-12-31 22:46:45,561 - mmcls - INFO - Epoch [66][1900/5005]\tlr: 1.000e-03, eta: 4:15:41, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3500\n2022-12-31 22:46:54,420 - mmcls - INFO - Epoch [66][2000/5005]\tlr: 1.000e-03, eta: 4:15:32, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3520\n2022-12-31 22:47:03,482 - mmcls - INFO - Epoch [66][2100/5005]\tlr: 1.000e-03, eta: 4:15:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3692\n2022-12-31 22:47:12,009 - mmcls - INFO - Epoch [66][2200/5005]\tlr: 1.000e-03, eta: 4:15:14, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3513\n2022-12-31 22:47:20,631 - mmcls - INFO - Epoch [66][2300/5005]\tlr: 1.000e-03, eta: 4:15:05, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.3799\n2022-12-31 22:47:29,286 - mmcls - INFO - Epoch [66][2400/5005]\tlr: 1.000e-03, eta: 4:14:56, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3671\n2022-12-31 22:47:38,134 - mmcls - INFO - Epoch [66][2500/5005]\tlr: 1.000e-03, eta: 4:14:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3681\n2022-12-31 22:47:46,706 - mmcls - INFO - Epoch [66][2600/5005]\tlr: 1.000e-03, eta: 4:14:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3416\n2022-12-31 22:47:55,541 - mmcls - INFO - Epoch [66][2700/5005]\tlr: 1.000e-03, eta: 4:14:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3651\n2022-12-31 22:48:04,273 - mmcls - INFO - Epoch [66][2800/5005]\tlr: 1.000e-03, eta: 4:14:21, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3497\n2022-12-31 22:48:12,853 - mmcls - INFO - Epoch [66][2900/5005]\tlr: 1.000e-03, eta: 4:14:12, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3651\n2022-12-31 22:48:21,724 - mmcls - INFO - Epoch [66][3000/5005]\tlr: 1.000e-03, eta: 4:14:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3668\n2022-12-31 22:48:30,522 - mmcls - INFO - Epoch [66][3100/5005]\tlr: 1.000e-03, eta: 4:13:54, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3594\n2022-12-31 22:48:39,080 - mmcls - INFO - Epoch [66][3200/5005]\tlr: 1.000e-03, eta: 4:13:45, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3413\n2022-12-31 22:48:47,718 - mmcls - INFO - Epoch [66][3300/5005]\tlr: 1.000e-03, eta: 4:13:36, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.3492\n2022-12-31 22:48:56,395 - mmcls - INFO - Epoch [66][3400/5005]\tlr: 1.000e-03, eta: 4:13:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3494\n2022-12-31 22:49:05,336 - mmcls - INFO - Epoch [66][3500/5005]\tlr: 1.000e-03, eta: 4:13:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3459\n2022-12-31 22:49:14,037 - mmcls - INFO - Epoch [66][3600/5005]\tlr: 1.000e-03, eta: 4:13:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3781\n2022-12-31 22:49:22,719 - mmcls - INFO - Epoch [66][3700/5005]\tlr: 1.000e-03, eta: 4:13:00, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3427\n2022-12-31 22:49:31,822 - mmcls - INFO - Epoch [66][3800/5005]\tlr: 1.000e-03, eta: 4:12:52, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.3495\n2022-12-31 22:49:40,510 - mmcls - INFO - Epoch [66][3900/5005]\tlr: 1.000e-03, eta: 4:12:43, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3696\n2022-12-31 22:49:49,442 - mmcls - INFO - Epoch [66][4000/5005]\tlr: 1.000e-03, eta: 4:12:34, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3626\n2022-12-31 22:49:57,948 - mmcls - INFO - Epoch [66][4100/5005]\tlr: 1.000e-03, eta: 4:12:25, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3630\n2022-12-31 22:50:06,747 - mmcls - INFO - Epoch [66][4200/5005]\tlr: 1.000e-03, eta: 4:12:16, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3759\n2022-12-31 22:50:15,518 - mmcls - INFO - Epoch [66][4300/5005]\tlr: 1.000e-03, eta: 4:12:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3511\n2022-12-31 22:50:24,460 - mmcls - INFO - Epoch [66][4400/5005]\tlr: 1.000e-03, eta: 4:11:58, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3482\n2022-12-31 22:50:33,189 - mmcls - INFO - Epoch [66][4500/5005]\tlr: 1.000e-03, eta: 4:11:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3592\n2022-12-31 22:50:41,856 - mmcls - INFO - Epoch [66][4600/5005]\tlr: 1.000e-03, eta: 4:11:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3274\n2022-12-31 22:50:50,645 - mmcls - INFO - Epoch [66][4700/5005]\tlr: 1.000e-03, eta: 4:11:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3437\n2022-12-31 22:50:59,316 - mmcls - INFO - Epoch [66][4800/5005]\tlr: 1.000e-03, eta: 4:11:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3518\n2022-12-31 22:51:08,381 - mmcls - INFO - Epoch [66][4900/5005]\tlr: 1.000e-03, eta: 4:11:14, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3515\n2022-12-31 22:51:17,174 - mmcls - INFO - Epoch [66][5000/5005]\tlr: 1.000e-03, eta: 4:11:05, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3675\n2022-12-31 22:51:39,875 - mmcls - INFO - Epoch(val) [66][196]\taccuracy_top-1: 70.4720, accuracy_top-5: 89.5600\n2022-12-31 22:51:51,093 - mmcls - INFO - Epoch [67][100/5005]\tlr: 1.000e-03, eta: 4:10:57, time: 0.112, data_time: 0.051, memory: 1224, loss: 1.3531\n2022-12-31 22:51:59,759 - mmcls - INFO - Epoch [67][200/5005]\tlr: 1.000e-03, eta: 4:10:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3537\n2022-12-31 22:52:08,488 - mmcls - INFO - Epoch [67][300/5005]\tlr: 1.000e-03, eta: 4:10:39, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3440\n2022-12-31 22:52:17,154 - mmcls - INFO - Epoch [67][400/5005]\tlr: 1.000e-03, eta: 4:10:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3492\n2022-12-31 22:52:25,813 - mmcls - INFO - Epoch [67][500/5005]\tlr: 1.000e-03, eta: 4:10:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3390\n2022-12-31 22:52:34,756 - mmcls - INFO - Epoch [67][600/5005]\tlr: 1.000e-03, eta: 4:10:12, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.3504\n2022-12-31 22:52:43,443 - mmcls - INFO - Epoch [67][700/5005]\tlr: 1.000e-03, eta: 4:10:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3433\n2022-12-31 22:52:52,305 - mmcls - INFO - Epoch [67][800/5005]\tlr: 1.000e-03, eta: 4:09:54, time: 0.088, data_time: 0.046, memory: 1224, loss: 1.3470\n2022-12-31 22:53:00,819 - mmcls - INFO - Epoch [67][900/5005]\tlr: 1.000e-03, eta: 4:09:45, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.3564\n2022-12-31 22:53:09,579 - mmcls - INFO - Epoch [67][1000/5005]\tlr: 1.000e-03, eta: 4:09:36, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3429\n2022-12-31 22:53:18,273 - mmcls - INFO - Epoch [67][1100/5005]\tlr: 1.000e-03, eta: 4:09:27, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3503\n2022-12-31 22:53:27,046 - mmcls - INFO - Epoch [67][1200/5005]\tlr: 1.000e-03, eta: 4:09:18, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3500\n2022-12-31 22:53:35,954 - mmcls - INFO - Epoch [67][1300/5005]\tlr: 1.000e-03, eta: 4:09:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3624\n2022-12-31 22:53:44,759 - mmcls - INFO - Epoch [67][1400/5005]\tlr: 1.000e-03, eta: 4:09:01, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3468\n2022-12-31 22:53:54,074 - mmcls - INFO - Epoch [67][1500/5005]\tlr: 1.000e-03, eta: 4:08:52, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.3320\n2022-12-31 22:54:03,051 - mmcls - INFO - Epoch [67][1600/5005]\tlr: 1.000e-03, eta: 4:08:43, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.3607\n2022-12-31 22:54:11,783 - mmcls - INFO - Epoch [67][1700/5005]\tlr: 1.000e-03, eta: 4:08:34, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3575\n2022-12-31 22:54:20,799 - mmcls - INFO - Epoch [67][1800/5005]\tlr: 1.000e-03, eta: 4:08:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3488\n2022-12-31 22:54:29,599 - mmcls - INFO - Epoch [67][1900/5005]\tlr: 1.000e-03, eta: 4:08:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3448\n2022-12-31 22:54:38,365 - mmcls - INFO - Epoch [67][2000/5005]\tlr: 1.000e-03, eta: 4:08:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3410\n2022-12-31 22:54:47,030 - mmcls - INFO - Epoch [67][2100/5005]\tlr: 1.000e-03, eta: 4:07:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3372\n2022-12-31 22:54:55,794 - mmcls - INFO - Epoch [67][2200/5005]\tlr: 1.000e-03, eta: 4:07:50, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3556\n2022-12-31 22:55:04,496 - mmcls - INFO - Epoch [67][2300/5005]\tlr: 1.000e-03, eta: 4:07:41, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3355\n2022-12-31 22:55:13,326 - mmcls - INFO - Epoch [67][2400/5005]\tlr: 1.000e-03, eta: 4:07:32, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3478\n2022-12-31 22:55:21,936 - mmcls - INFO - Epoch [67][2500/5005]\tlr: 1.000e-03, eta: 4:07:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3401\n2022-12-31 22:55:30,521 - mmcls - INFO - Epoch [67][2600/5005]\tlr: 1.000e-03, eta: 4:07:14, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3704\n2022-12-31 22:55:39,110 - mmcls - INFO - Epoch [67][2700/5005]\tlr: 1.000e-03, eta: 4:07:05, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.3523\n2022-12-31 22:55:47,966 - mmcls - INFO - Epoch [67][2800/5005]\tlr: 1.000e-03, eta: 4:06:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3411\n2022-12-31 22:55:56,793 - mmcls - INFO - Epoch [67][2900/5005]\tlr: 1.000e-03, eta: 4:06:48, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.3426\n2022-12-31 22:56:05,477 - mmcls - INFO - Epoch [67][3000/5005]\tlr: 1.000e-03, eta: 4:06:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3315\n2022-12-31 22:56:14,226 - mmcls - INFO - Epoch [67][3100/5005]\tlr: 1.000e-03, eta: 4:06:30, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3520\n2022-12-31 22:56:23,061 - mmcls - INFO - Epoch [67][3200/5005]\tlr: 1.000e-03, eta: 4:06:21, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3428\n2022-12-31 22:56:31,790 - mmcls - INFO - Epoch [67][3300/5005]\tlr: 1.000e-03, eta: 4:06:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3219\n2022-12-31 22:56:40,892 - mmcls - INFO - Epoch [67][3400/5005]\tlr: 1.000e-03, eta: 4:06:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.3635\n2022-12-31 22:56:49,362 - mmcls - INFO - Epoch [67][3500/5005]\tlr: 1.000e-03, eta: 4:05:54, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.3679\n2022-12-31 22:56:58,255 - mmcls - INFO - Epoch [67][3600/5005]\tlr: 1.000e-03, eta: 4:05:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3563\n2022-12-31 22:57:06,932 - mmcls - INFO - Epoch [67][3700/5005]\tlr: 1.000e-03, eta: 4:05:36, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3498\n2022-12-31 22:57:15,672 - mmcls - INFO - Epoch [67][3800/5005]\tlr: 1.000e-03, eta: 4:05:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3327\n2022-12-31 22:57:24,356 - mmcls - INFO - Epoch [67][3900/5005]\tlr: 1.000e-03, eta: 4:05:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3510\n2022-12-31 22:57:33,436 - mmcls - INFO - Epoch [67][4000/5005]\tlr: 1.000e-03, eta: 4:05:10, time: 0.091, data_time: 0.041, memory: 1224, loss: 1.3575\n2022-12-31 22:57:42,517 - mmcls - INFO - Epoch [67][4100/5005]\tlr: 1.000e-03, eta: 4:05:01, time: 0.091, data_time: 0.041, memory: 1224, loss: 1.3353\n2022-12-31 22:57:51,251 - mmcls - INFO - Epoch [67][4200/5005]\tlr: 1.000e-03, eta: 4:04:52, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3368\n2022-12-31 22:57:59,976 - mmcls - INFO - Epoch [67][4300/5005]\tlr: 1.000e-03, eta: 4:04:43, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3592\n2022-12-31 22:58:09,433 - mmcls - INFO - Epoch [67][4400/5005]\tlr: 1.000e-03, eta: 4:04:35, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.3691\n2022-12-31 22:58:18,076 - mmcls - INFO - Epoch [67][4500/5005]\tlr: 1.000e-03, eta: 4:04:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3395\n2022-12-31 22:58:27,059 - mmcls - INFO - Epoch [67][4600/5005]\tlr: 1.000e-03, eta: 4:04:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3608\n2022-12-31 22:58:35,777 - mmcls - INFO - Epoch [67][4700/5005]\tlr: 1.000e-03, eta: 4:04:08, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3571\n2022-12-31 22:58:44,564 - mmcls - INFO - Epoch [67][4800/5005]\tlr: 1.000e-03, eta: 4:03:59, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3703\n2022-12-31 22:58:53,133 - mmcls - INFO - Epoch [67][4900/5005]\tlr: 1.000e-03, eta: 4:03:50, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3625\n2022-12-31 22:59:01,729 - mmcls - INFO - Epoch [67][5000/5005]\tlr: 1.000e-03, eta: 4:03:41, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3446\n2022-12-31 22:59:24,328 - mmcls - INFO - Epoch(val) [67][196]\taccuracy_top-1: 70.4220, accuracy_top-5: 89.6000\n2022-12-31 22:59:35,942 - mmcls - INFO - Epoch [68][100/5005]\tlr: 1.000e-03, eta: 4:03:33, time: 0.116, data_time: 0.062, memory: 1224, loss: 1.3478\n2022-12-31 22:59:44,828 - mmcls - INFO - Epoch [68][200/5005]\tlr: 1.000e-03, eta: 4:03:24, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.3504\n2022-12-31 22:59:53,374 - mmcls - INFO - Epoch [68][300/5005]\tlr: 1.000e-03, eta: 4:03:15, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3433\n2022-12-31 23:00:02,464 - mmcls - INFO - Epoch [68][400/5005]\tlr: 1.000e-03, eta: 4:03:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3488\n2022-12-31 23:00:11,106 - mmcls - INFO - Epoch [68][500/5005]\tlr: 1.000e-03, eta: 4:02:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3439\n2022-12-31 23:00:19,839 - mmcls - INFO - Epoch [68][600/5005]\tlr: 1.000e-03, eta: 4:02:49, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3427\n2022-12-31 23:00:28,569 - mmcls - INFO - Epoch [68][700/5005]\tlr: 1.000e-03, eta: 4:02:40, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3330\n2022-12-31 23:00:37,208 - mmcls - INFO - Epoch [68][800/5005]\tlr: 1.000e-03, eta: 4:02:31, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3357\n2022-12-31 23:00:45,871 - mmcls - INFO - Epoch [68][900/5005]\tlr: 1.000e-03, eta: 4:02:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3397\n2022-12-31 23:00:54,744 - mmcls - INFO - Epoch [68][1000/5005]\tlr: 1.000e-03, eta: 4:02:13, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3509\n2022-12-31 23:01:03,296 - mmcls - INFO - Epoch [68][1100/5005]\tlr: 1.000e-03, eta: 4:02:04, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.3325\n2022-12-31 23:01:11,864 - mmcls - INFO - Epoch [68][1200/5005]\tlr: 1.000e-03, eta: 4:01:55, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3470\n2022-12-31 23:01:20,603 - mmcls - INFO - Epoch [68][1300/5005]\tlr: 1.000e-03, eta: 4:01:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3500\n2022-12-31 23:01:29,515 - mmcls - INFO - Epoch [68][1400/5005]\tlr: 1.000e-03, eta: 4:01:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3536\n2022-12-31 23:01:38,216 - mmcls - INFO - Epoch [68][1500/5005]\tlr: 1.000e-03, eta: 4:01:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3427\n2022-12-31 23:01:47,174 - mmcls - INFO - Epoch [68][1600/5005]\tlr: 1.000e-03, eta: 4:01:19, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3439\n2022-12-31 23:01:56,017 - mmcls - INFO - Epoch [68][1700/5005]\tlr: 1.000e-03, eta: 4:01:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3318\n2022-12-31 23:02:04,477 - mmcls - INFO - Epoch [68][1800/5005]\tlr: 1.000e-03, eta: 4:01:02, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3414\n2022-12-31 23:02:13,366 - mmcls - INFO - Epoch [68][1900/5005]\tlr: 1.000e-03, eta: 4:00:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3267\n2022-12-31 23:02:22,011 - mmcls - INFO - Epoch [68][2000/5005]\tlr: 1.000e-03, eta: 4:00:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3284\n2022-12-31 23:02:30,805 - mmcls - INFO - Epoch [68][2100/5005]\tlr: 1.000e-03, eta: 4:00:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3461\n2022-12-31 23:02:39,459 - mmcls - INFO - Epoch [68][2200/5005]\tlr: 1.000e-03, eta: 4:00:26, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3488\n2022-12-31 23:02:48,250 - mmcls - INFO - Epoch [68][2300/5005]\tlr: 1.000e-03, eta: 4:00:17, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3440\n2022-12-31 23:02:57,083 - mmcls - INFO - Epoch [68][2400/5005]\tlr: 1.000e-03, eta: 4:00:08, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3624\n2022-12-31 23:03:05,916 - mmcls - INFO - Epoch [68][2500/5005]\tlr: 1.000e-03, eta: 3:59:59, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3335\n2022-12-31 23:03:15,114 - mmcls - INFO - Epoch [68][2600/5005]\tlr: 1.000e-03, eta: 3:59:51, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.3619\n2022-12-31 23:03:24,089 - mmcls - INFO - Epoch [68][2700/5005]\tlr: 1.000e-03, eta: 3:59:42, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.3404\n2022-12-31 23:03:32,758 - mmcls - INFO - Epoch [68][2800/5005]\tlr: 1.000e-03, eta: 3:59:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3436\n2022-12-31 23:03:41,459 - mmcls - INFO - Epoch [68][2900/5005]\tlr: 1.000e-03, eta: 3:59:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3400\n2022-12-31 23:03:50,361 - mmcls - INFO - Epoch [68][3000/5005]\tlr: 1.000e-03, eta: 3:59:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3572\n2022-12-31 23:03:58,989 - mmcls - INFO - Epoch [68][3100/5005]\tlr: 1.000e-03, eta: 3:59:06, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3413\n2022-12-31 23:04:07,437 - mmcls - INFO - Epoch [68][3200/5005]\tlr: 1.000e-03, eta: 3:58:57, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3457\n2022-12-31 23:04:16,212 - mmcls - INFO - Epoch [68][3300/5005]\tlr: 1.000e-03, eta: 3:58:48, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3377\n2022-12-31 23:04:25,070 - mmcls - INFO - Epoch [68][3400/5005]\tlr: 1.000e-03, eta: 3:58:39, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3462\n2022-12-31 23:04:33,810 - mmcls - INFO - Epoch [68][3500/5005]\tlr: 1.000e-03, eta: 3:58:31, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.3439\n2022-12-31 23:04:42,724 - mmcls - INFO - Epoch [68][3600/5005]\tlr: 1.000e-03, eta: 3:58:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3339\n2022-12-31 23:04:51,665 - mmcls - INFO - Epoch [68][3700/5005]\tlr: 1.000e-03, eta: 3:58:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3336\n2022-12-31 23:05:00,497 - mmcls - INFO - Epoch [68][3800/5005]\tlr: 1.000e-03, eta: 3:58:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3304\n2022-12-31 23:05:09,307 - mmcls - INFO - Epoch [68][3900/5005]\tlr: 1.000e-03, eta: 3:57:55, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3452\n2022-12-31 23:05:18,218 - mmcls - INFO - Epoch [68][4000/5005]\tlr: 1.000e-03, eta: 3:57:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3382\n2022-12-31 23:05:27,242 - mmcls - INFO - Epoch [68][4100/5005]\tlr: 1.000e-03, eta: 3:57:38, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.3375\n2022-12-31 23:05:35,823 - mmcls - INFO - Epoch [68][4200/5005]\tlr: 1.000e-03, eta: 3:57:29, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.3294\n2022-12-31 23:05:44,923 - mmcls - INFO - Epoch [68][4300/5005]\tlr: 1.000e-03, eta: 3:57:20, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3447\n2022-12-31 23:05:53,578 - mmcls - INFO - Epoch [68][4400/5005]\tlr: 1.000e-03, eta: 3:57:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3336\n2022-12-31 23:06:02,275 - mmcls - INFO - Epoch [68][4500/5005]\tlr: 1.000e-03, eta: 3:57:02, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3323\n2022-12-31 23:06:11,008 - mmcls - INFO - Epoch [68][4600/5005]\tlr: 1.000e-03, eta: 3:56:53, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3412\n2022-12-31 23:06:19,765 - mmcls - INFO - Epoch [68][4700/5005]\tlr: 1.000e-03, eta: 3:56:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3308\n2022-12-31 23:06:29,205 - mmcls - INFO - Epoch [68][4800/5005]\tlr: 1.000e-03, eta: 3:56:36, time: 0.094, data_time: 0.013, memory: 1224, loss: 1.3520\n2022-12-31 23:06:37,946 - mmcls - INFO - Epoch [68][4900/5005]\tlr: 1.000e-03, eta: 3:56:27, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3507\n2022-12-31 23:06:46,630 - mmcls - INFO - Epoch [68][5000/5005]\tlr: 1.000e-03, eta: 3:56:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3455\n2022-12-31 23:07:09,494 - mmcls - INFO - Epoch(val) [68][196]\taccuracy_top-1: 70.6840, accuracy_top-5: 89.6440\n2022-12-31 23:07:20,468 - mmcls - INFO - Epoch [69][100/5005]\tlr: 1.000e-03, eta: 3:56:09, time: 0.109, data_time: 0.039, memory: 1224, loss: 1.3329\n2022-12-31 23:07:29,049 - mmcls - INFO - Epoch [69][200/5005]\tlr: 1.000e-03, eta: 3:56:00, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3281\n2022-12-31 23:07:37,720 - mmcls - INFO - Epoch [69][300/5005]\tlr: 1.000e-03, eta: 3:55:51, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3405\n2022-12-31 23:07:46,747 - mmcls - INFO - Epoch [69][400/5005]\tlr: 1.000e-03, eta: 3:55:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3292\n2022-12-31 23:07:55,725 - mmcls - INFO - Epoch [69][500/5005]\tlr: 1.000e-03, eta: 3:55:34, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3085\n2022-12-31 23:08:04,501 - mmcls - INFO - Epoch [69][600/5005]\tlr: 1.000e-03, eta: 3:55:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3458\n2022-12-31 23:08:13,096 - mmcls - INFO - Epoch [69][700/5005]\tlr: 1.000e-03, eta: 3:55:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3371\n2022-12-31 23:08:21,869 - mmcls - INFO - Epoch [69][800/5005]\tlr: 1.000e-03, eta: 3:55:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3466\n2022-12-31 23:08:30,315 - mmcls - INFO - Epoch [69][900/5005]\tlr: 1.000e-03, eta: 3:54:58, time: 0.084, data_time: 0.016, memory: 1224, loss: 1.3093\n2022-12-31 23:08:39,385 - mmcls - INFO - Epoch [69][1000/5005]\tlr: 1.000e-03, eta: 3:54:49, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.3521\n2022-12-31 23:08:48,093 - mmcls - INFO - Epoch [69][1100/5005]\tlr: 1.000e-03, eta: 3:54:40, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3360\n2022-12-31 23:08:57,097 - mmcls - INFO - Epoch [69][1200/5005]\tlr: 1.000e-03, eta: 3:54:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3378\n2022-12-31 23:09:06,222 - mmcls - INFO - Epoch [69][1300/5005]\tlr: 1.000e-03, eta: 3:54:23, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.3342\n2022-12-31 23:09:15,282 - mmcls - INFO - Epoch [69][1400/5005]\tlr: 1.000e-03, eta: 3:54:14, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3387\n2022-12-31 23:09:23,879 - mmcls - INFO - Epoch [69][1500/5005]\tlr: 1.000e-03, eta: 3:54:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3379\n2022-12-31 23:09:32,981 - mmcls - INFO - Epoch [69][1600/5005]\tlr: 1.000e-03, eta: 3:53:56, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3418\n2022-12-31 23:09:41,897 - mmcls - INFO - Epoch [69][1700/5005]\tlr: 1.000e-03, eta: 3:53:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3390\n2022-12-31 23:09:50,626 - mmcls - INFO - Epoch [69][1800/5005]\tlr: 1.000e-03, eta: 3:53:39, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3189\n2022-12-31 23:09:59,415 - mmcls - INFO - Epoch [69][1900/5005]\tlr: 1.000e-03, eta: 3:53:30, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3436\n2022-12-31 23:10:08,434 - mmcls - INFO - Epoch [69][2000/5005]\tlr: 1.000e-03, eta: 3:53:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3427\n2022-12-31 23:10:17,178 - mmcls - INFO - Epoch [69][2100/5005]\tlr: 1.000e-03, eta: 3:53:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3440\n2022-12-31 23:10:25,953 - mmcls - INFO - Epoch [69][2200/5005]\tlr: 1.000e-03, eta: 3:53:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3202\n2022-12-31 23:10:34,894 - mmcls - INFO - Epoch [69][2300/5005]\tlr: 1.000e-03, eta: 3:52:54, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3501\n2022-12-31 23:10:43,619 - mmcls - INFO - Epoch [69][2400/5005]\tlr: 1.000e-03, eta: 3:52:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3505\n2022-12-31 23:10:52,288 - mmcls - INFO - Epoch [69][2500/5005]\tlr: 1.000e-03, eta: 3:52:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3570\n2022-12-31 23:11:01,503 - mmcls - INFO - Epoch [69][2600/5005]\tlr: 1.000e-03, eta: 3:52:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.3358\n2022-12-31 23:11:10,147 - mmcls - INFO - Epoch [69][2700/5005]\tlr: 1.000e-03, eta: 3:52:19, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.3284\n2022-12-31 23:11:18,973 - mmcls - INFO - Epoch [69][2800/5005]\tlr: 1.000e-03, eta: 3:52:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3293\n2022-12-31 23:11:27,654 - mmcls - INFO - Epoch [69][2900/5005]\tlr: 1.000e-03, eta: 3:52:01, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3580\n2022-12-31 23:11:36,497 - mmcls - INFO - Epoch [69][3000/5005]\tlr: 1.000e-03, eta: 3:51:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3457\n2022-12-31 23:11:45,502 - mmcls - INFO - Epoch [69][3100/5005]\tlr: 1.000e-03, eta: 3:51:43, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3453\n2022-12-31 23:11:54,515 - mmcls - INFO - Epoch [69][3200/5005]\tlr: 1.000e-03, eta: 3:51:35, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.3171\n2022-12-31 23:12:03,476 - mmcls - INFO - Epoch [69][3300/5005]\tlr: 1.000e-03, eta: 3:51:26, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3383\n2022-12-31 23:12:12,393 - mmcls - INFO - Epoch [69][3400/5005]\tlr: 1.000e-03, eta: 3:51:17, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.3176\n2022-12-31 23:12:21,256 - mmcls - INFO - Epoch [69][3500/5005]\tlr: 1.000e-03, eta: 3:51:08, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3320\n2022-12-31 23:12:30,203 - mmcls - INFO - Epoch [69][3600/5005]\tlr: 1.000e-03, eta: 3:50:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3456\n2022-12-31 23:12:39,063 - mmcls - INFO - Epoch [69][3700/5005]\tlr: 1.000e-03, eta: 3:50:50, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3501\n2022-12-31 23:12:47,675 - mmcls - INFO - Epoch [69][3800/5005]\tlr: 1.000e-03, eta: 3:50:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3391\n2022-12-31 23:12:56,606 - mmcls - INFO - Epoch [69][3900/5005]\tlr: 1.000e-03, eta: 3:50:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3232\n2022-12-31 23:13:05,382 - mmcls - INFO - Epoch [69][4000/5005]\tlr: 1.000e-03, eta: 3:50:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3383\n2022-12-31 23:13:14,279 - mmcls - INFO - Epoch [69][4100/5005]\tlr: 1.000e-03, eta: 3:50:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3232\n2022-12-31 23:13:22,982 - mmcls - INFO - Epoch [69][4200/5005]\tlr: 1.000e-03, eta: 3:50:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3500\n2022-12-31 23:13:31,541 - mmcls - INFO - Epoch [69][4300/5005]\tlr: 1.000e-03, eta: 3:49:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3245\n2022-12-31 23:13:40,103 - mmcls - INFO - Epoch [69][4400/5005]\tlr: 1.000e-03, eta: 3:49:48, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.3571\n2022-12-31 23:13:49,134 - mmcls - INFO - Epoch [69][4500/5005]\tlr: 1.000e-03, eta: 3:49:39, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3372\n2022-12-31 23:13:57,891 - mmcls - INFO - Epoch [69][4600/5005]\tlr: 1.000e-03, eta: 3:49:30, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3347\n2022-12-31 23:14:06,611 - mmcls - INFO - Epoch [69][4700/5005]\tlr: 1.000e-03, eta: 3:49:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3432\n2022-12-31 23:14:15,203 - mmcls - INFO - Epoch [69][4800/5005]\tlr: 1.000e-03, eta: 3:49:13, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3372\n2022-12-31 23:14:24,009 - mmcls - INFO - Epoch [69][4900/5005]\tlr: 1.000e-03, eta: 3:49:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3431\n2022-12-31 23:14:32,607 - mmcls - INFO - Epoch [69][5000/5005]\tlr: 1.000e-03, eta: 3:48:55, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3465\n2022-12-31 23:14:55,341 - mmcls - INFO - Epoch(val) [69][196]\taccuracy_top-1: 70.7200, accuracy_top-5: 89.6600\n2022-12-31 23:15:06,288 - mmcls - INFO - Epoch [70][100/5005]\tlr: 1.000e-03, eta: 3:48:46, time: 0.109, data_time: 0.061, memory: 1224, loss: 1.3194\n2022-12-31 23:15:15,010 - mmcls - INFO - Epoch [70][200/5005]\tlr: 1.000e-03, eta: 3:48:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3099\n2022-12-31 23:15:23,618 - mmcls - INFO - Epoch [70][300/5005]\tlr: 1.000e-03, eta: 3:48:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3049\n2022-12-31 23:15:32,776 - mmcls - INFO - Epoch [70][400/5005]\tlr: 1.000e-03, eta: 3:48:20, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.3230\n2022-12-31 23:15:41,589 - mmcls - INFO - Epoch [70][500/5005]\tlr: 1.000e-03, eta: 3:48:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3236\n2022-12-31 23:15:50,353 - mmcls - INFO - Epoch [70][600/5005]\tlr: 1.000e-03, eta: 3:48:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.3271\n2022-12-31 23:15:59,152 - mmcls - INFO - Epoch [70][700/5005]\tlr: 1.000e-03, eta: 3:47:53, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3273\n2022-12-31 23:16:07,814 - mmcls - INFO - Epoch [70][800/5005]\tlr: 1.000e-03, eta: 3:47:44, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.3423\n2022-12-31 23:16:16,697 - mmcls - INFO - Epoch [70][900/5005]\tlr: 1.000e-03, eta: 3:47:35, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.3217\n2022-12-31 23:16:25,481 - mmcls - INFO - Epoch [70][1000/5005]\tlr: 1.000e-03, eta: 3:47:26, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3387\n2022-12-31 23:16:34,242 - mmcls - INFO - Epoch [70][1100/5005]\tlr: 1.000e-03, eta: 3:47:17, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3389\n2022-12-31 23:16:42,740 - mmcls - INFO - Epoch [70][1200/5005]\tlr: 1.000e-03, eta: 3:47:08, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3344\n2022-12-31 23:16:51,499 - mmcls - INFO - Epoch [70][1300/5005]\tlr: 1.000e-03, eta: 3:46:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3224\n2022-12-31 23:17:00,169 - mmcls - INFO - Epoch [70][1400/5005]\tlr: 1.000e-03, eta: 3:46:50, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3271\n2022-12-31 23:17:09,258 - mmcls - INFO - Epoch [70][1500/5005]\tlr: 1.000e-03, eta: 3:46:42, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.3252\n2022-12-31 23:17:17,865 - mmcls - INFO - Epoch [70][1600/5005]\tlr: 1.000e-03, eta: 3:46:33, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3271\n2022-12-31 23:17:26,789 - mmcls - INFO - Epoch [70][1700/5005]\tlr: 1.000e-03, eta: 3:46:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3256\n2022-12-31 23:17:35,413 - mmcls - INFO - Epoch [70][1800/5005]\tlr: 1.000e-03, eta: 3:46:15, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3475\n2022-12-31 23:17:44,227 - mmcls - INFO - Epoch [70][1900/5005]\tlr: 1.000e-03, eta: 3:46:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3236\n2022-12-31 23:17:53,513 - mmcls - INFO - Epoch [70][2000/5005]\tlr: 1.000e-03, eta: 3:45:57, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.3308\n2022-12-31 23:18:02,214 - mmcls - INFO - Epoch [70][2100/5005]\tlr: 1.000e-03, eta: 3:45:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3594\n2022-12-31 23:18:11,051 - mmcls - INFO - Epoch [70][2200/5005]\tlr: 1.000e-03, eta: 3:45:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3361\n2022-12-31 23:18:19,786 - mmcls - INFO - Epoch [70][2300/5005]\tlr: 1.000e-03, eta: 3:45:31, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.3306\n2022-12-31 23:18:28,574 - mmcls - INFO - Epoch [70][2400/5005]\tlr: 1.000e-03, eta: 3:45:22, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3282\n2022-12-31 23:18:37,496 - mmcls - INFO - Epoch [70][2500/5005]\tlr: 1.000e-03, eta: 3:45:13, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.3352\n2022-12-31 23:18:46,490 - mmcls - INFO - Epoch [70][2600/5005]\tlr: 1.000e-03, eta: 3:45:04, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3364\n2022-12-31 23:18:55,243 - mmcls - INFO - Epoch [70][2700/5005]\tlr: 1.000e-03, eta: 3:44:55, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3164\n2022-12-31 23:19:04,075 - mmcls - INFO - Epoch [70][2800/5005]\tlr: 1.000e-03, eta: 3:44:47, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.3379\n2022-12-31 23:19:12,877 - mmcls - INFO - Epoch [70][2900/5005]\tlr: 1.000e-03, eta: 3:44:38, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3250\n2022-12-31 23:19:21,507 - mmcls - INFO - Epoch [70][3000/5005]\tlr: 1.000e-03, eta: 3:44:29, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.3156\n2022-12-31 23:19:30,285 - mmcls - INFO - Epoch [70][3100/5005]\tlr: 1.000e-03, eta: 3:44:20, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3207\n2022-12-31 23:19:39,014 - mmcls - INFO - Epoch [70][3200/5005]\tlr: 1.000e-03, eta: 3:44:11, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.3313\n2022-12-31 23:19:47,742 - mmcls - INFO - Epoch [70][3300/5005]\tlr: 1.000e-03, eta: 3:44:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3386\n2022-12-31 23:19:56,809 - mmcls - INFO - Epoch [70][3400/5005]\tlr: 1.000e-03, eta: 3:43:53, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3435\n2022-12-31 23:20:05,544 - mmcls - INFO - Epoch [70][3500/5005]\tlr: 1.000e-03, eta: 3:43:44, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3224\n2022-12-31 23:20:13,972 - mmcls - INFO - Epoch [70][3600/5005]\tlr: 1.000e-03, eta: 3:43:35, time: 0.084, data_time: 0.009, memory: 1224, loss: 1.3333\n2022-12-31 23:20:22,802 - mmcls - INFO - Epoch [70][3700/5005]\tlr: 1.000e-03, eta: 3:43:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3663\n2022-12-31 23:20:31,739 - mmcls - INFO - Epoch [70][3800/5005]\tlr: 1.000e-03, eta: 3:43:18, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3423\n2022-12-31 23:20:40,402 - mmcls - INFO - Epoch [70][3900/5005]\tlr: 1.000e-03, eta: 3:43:09, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3220\n2022-12-31 23:20:49,195 - mmcls - INFO - Epoch [70][4000/5005]\tlr: 1.000e-03, eta: 3:43:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3353\n2022-12-31 23:20:57,907 - mmcls - INFO - Epoch [70][4100/5005]\tlr: 1.000e-03, eta: 3:42:51, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3118\n2022-12-31 23:21:06,861 - mmcls - INFO - Epoch [70][4200/5005]\tlr: 1.000e-03, eta: 3:42:42, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3717\n2022-12-31 23:21:15,721 - mmcls - INFO - Epoch [70][4300/5005]\tlr: 1.000e-03, eta: 3:42:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3174\n2022-12-31 23:21:24,431 - mmcls - INFO - Epoch [70][4400/5005]\tlr: 1.000e-03, eta: 3:42:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3416\n2022-12-31 23:21:33,206 - mmcls - INFO - Epoch [70][4500/5005]\tlr: 1.000e-03, eta: 3:42:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3437\n2022-12-31 23:21:42,167 - mmcls - INFO - Epoch [70][4600/5005]\tlr: 1.000e-03, eta: 3:42:07, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.3196\n2022-12-31 23:21:51,080 - mmcls - INFO - Epoch [70][4700/5005]\tlr: 1.000e-03, eta: 3:41:58, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3293\n2022-12-31 23:21:59,904 - mmcls - INFO - Epoch [70][4800/5005]\tlr: 1.000e-03, eta: 3:41:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3493\n2022-12-31 23:22:08,875 - mmcls - INFO - Epoch [70][4900/5005]\tlr: 1.000e-03, eta: 3:41:40, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3287\n2022-12-31 23:22:17,590 - mmcls - INFO - Epoch [70][5000/5005]\tlr: 1.000e-03, eta: 3:41:31, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.3245\n2022-12-31 23:22:40,253 - mmcls - INFO - Epoch(val) [70][196]\taccuracy_top-1: 70.8500, accuracy_top-5: 89.6880\n2022-12-31 23:22:51,311 - mmcls - INFO - Epoch [71][100/5005]\tlr: 1.000e-03, eta: 3:41:23, time: 0.110, data_time: 0.032, memory: 1224, loss: 1.3259\n2022-12-31 23:23:00,281 - mmcls - INFO - Epoch [71][200/5005]\tlr: 1.000e-03, eta: 3:41:14, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.3298\n2022-12-31 23:23:09,007 - mmcls - INFO - Epoch [71][300/5005]\tlr: 1.000e-03, eta: 3:41:05, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3195\n2022-12-31 23:23:17,707 - mmcls - INFO - Epoch [71][400/5005]\tlr: 1.000e-03, eta: 3:40:56, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3387\n2022-12-31 23:23:26,592 - mmcls - INFO - Epoch [71][500/5005]\tlr: 1.000e-03, eta: 3:40:47, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3162\n2022-12-31 23:23:35,668 - mmcls - INFO - Epoch [71][600/5005]\tlr: 1.000e-03, eta: 3:40:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3407\n2022-12-31 23:23:44,503 - mmcls - INFO - Epoch [71][700/5005]\tlr: 1.000e-03, eta: 3:40:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3168\n2022-12-31 23:23:52,962 - mmcls - INFO - Epoch [71][800/5005]\tlr: 1.000e-03, eta: 3:40:21, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.3212\n2022-12-31 23:24:01,754 - mmcls - INFO - Epoch [71][900/5005]\tlr: 1.000e-03, eta: 3:40:12, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3201\n2022-12-31 23:24:10,509 - mmcls - INFO - Epoch [71][1000/5005]\tlr: 1.000e-03, eta: 3:40:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3173\n2022-12-31 23:24:19,251 - mmcls - INFO - Epoch [71][1100/5005]\tlr: 1.000e-03, eta: 3:39:54, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2936\n2022-12-31 23:24:27,865 - mmcls - INFO - Epoch [71][1200/5005]\tlr: 1.000e-03, eta: 3:39:45, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3146\n2022-12-31 23:24:36,590 - mmcls - INFO - Epoch [71][1300/5005]\tlr: 1.000e-03, eta: 3:39:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3171\n2022-12-31 23:24:45,359 - mmcls - INFO - Epoch [71][1400/5005]\tlr: 1.000e-03, eta: 3:39:27, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3289\n2022-12-31 23:24:54,136 - mmcls - INFO - Epoch [71][1500/5005]\tlr: 1.000e-03, eta: 3:39:18, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3339\n2022-12-31 23:25:03,011 - mmcls - INFO - Epoch [71][1600/5005]\tlr: 1.000e-03, eta: 3:39:09, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3424\n2022-12-31 23:25:11,751 - mmcls - INFO - Epoch [71][1700/5005]\tlr: 1.000e-03, eta: 3:39:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3229\n2022-12-31 23:25:20,590 - mmcls - INFO - Epoch [71][1800/5005]\tlr: 1.000e-03, eta: 3:38:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3157\n2022-12-31 23:25:29,494 - mmcls - INFO - Epoch [71][1900/5005]\tlr: 1.000e-03, eta: 3:38:43, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3182\n2022-12-31 23:25:38,214 - mmcls - INFO - Epoch [71][2000/5005]\tlr: 1.000e-03, eta: 3:38:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3221\n2022-12-31 23:25:46,922 - mmcls - INFO - Epoch [71][2100/5005]\tlr: 1.000e-03, eta: 3:38:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3359\n2022-12-31 23:25:55,709 - mmcls - INFO - Epoch [71][2200/5005]\tlr: 1.000e-03, eta: 3:38:16, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.3200\n2022-12-31 23:26:04,083 - mmcls - INFO - Epoch [71][2300/5005]\tlr: 1.000e-03, eta: 3:38:07, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.3268\n2022-12-31 23:26:12,841 - mmcls - INFO - Epoch [71][2400/5005]\tlr: 1.000e-03, eta: 3:37:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3188\n2022-12-31 23:26:21,656 - mmcls - INFO - Epoch [71][2500/5005]\tlr: 1.000e-03, eta: 3:37:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3294\n2022-12-31 23:26:30,361 - mmcls - INFO - Epoch [71][2600/5005]\tlr: 1.000e-03, eta: 3:37:40, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3261\n2022-12-31 23:26:38,989 - mmcls - INFO - Epoch [71][2700/5005]\tlr: 1.000e-03, eta: 3:37:31, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.3463\n2022-12-31 23:26:47,981 - mmcls - INFO - Epoch [71][2800/5005]\tlr: 1.000e-03, eta: 3:37:23, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.3138\n2022-12-31 23:26:56,575 - mmcls - INFO - Epoch [71][2900/5005]\tlr: 1.000e-03, eta: 3:37:14, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3086\n2022-12-31 23:27:05,408 - mmcls - INFO - Epoch [71][3000/5005]\tlr: 1.000e-03, eta: 3:37:05, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3297\n2022-12-31 23:27:14,230 - mmcls - INFO - Epoch [71][3100/5005]\tlr: 1.000e-03, eta: 3:36:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3352\n2022-12-31 23:27:22,931 - mmcls - INFO - Epoch [71][3200/5005]\tlr: 1.000e-03, eta: 3:36:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3473\n2022-12-31 23:27:31,722 - mmcls - INFO - Epoch [71][3300/5005]\tlr: 1.000e-03, eta: 3:36:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2955\n2022-12-31 23:27:40,833 - mmcls - INFO - Epoch [71][3400/5005]\tlr: 1.000e-03, eta: 3:36:29, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3284\n2022-12-31 23:27:49,698 - mmcls - INFO - Epoch [71][3500/5005]\tlr: 1.000e-03, eta: 3:36:21, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.3112\n2022-12-31 23:27:58,232 - mmcls - INFO - Epoch [71][3600/5005]\tlr: 1.000e-03, eta: 3:36:12, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.3421\n2022-12-31 23:28:07,339 - mmcls - INFO - Epoch [71][3700/5005]\tlr: 1.000e-03, eta: 3:36:03, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.3161\n2022-12-31 23:28:16,077 - mmcls - INFO - Epoch [71][3800/5005]\tlr: 1.000e-03, eta: 3:35:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3206\n2022-12-31 23:28:24,731 - mmcls - INFO - Epoch [71][3900/5005]\tlr: 1.000e-03, eta: 3:35:45, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3227\n2022-12-31 23:28:33,663 - mmcls - INFO - Epoch [71][4000/5005]\tlr: 1.000e-03, eta: 3:35:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3294\n2022-12-31 23:28:42,581 - mmcls - INFO - Epoch [71][4100/5005]\tlr: 1.000e-03, eta: 3:35:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3057\n2022-12-31 23:28:51,680 - mmcls - INFO - Epoch [71][4200/5005]\tlr: 1.000e-03, eta: 3:35:19, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.3256\n2022-12-31 23:29:00,576 - mmcls - INFO - Epoch [71][4300/5005]\tlr: 1.000e-03, eta: 3:35:10, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3182\n2022-12-31 23:29:09,945 - mmcls - INFO - Epoch [71][4400/5005]\tlr: 1.000e-03, eta: 3:35:01, time: 0.094, data_time: 0.012, memory: 1224, loss: 1.3344\n2022-12-31 23:29:18,779 - mmcls - INFO - Epoch [71][4500/5005]\tlr: 1.000e-03, eta: 3:34:52, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3517\n2022-12-31 23:29:27,750 - mmcls - INFO - Epoch [71][4600/5005]\tlr: 1.000e-03, eta: 3:34:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3375\n2022-12-31 23:29:36,378 - mmcls - INFO - Epoch [71][4700/5005]\tlr: 1.000e-03, eta: 3:34:35, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3493\n2022-12-31 23:29:45,214 - mmcls - INFO - Epoch [71][4800/5005]\tlr: 1.000e-03, eta: 3:34:26, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3229\n2022-12-31 23:29:53,875 - mmcls - INFO - Epoch [71][4900/5005]\tlr: 1.000e-03, eta: 3:34:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3449\n2022-12-31 23:30:02,800 - mmcls - INFO - Epoch [71][5000/5005]\tlr: 1.000e-03, eta: 3:34:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3500\n2022-12-31 23:30:25,541 - mmcls - INFO - Epoch(val) [71][196]\taccuracy_top-1: 70.7460, accuracy_top-5: 89.7800\n2022-12-31 23:30:36,312 - mmcls - INFO - Epoch [72][100/5005]\tlr: 1.000e-03, eta: 3:33:59, time: 0.107, data_time: 0.048, memory: 1224, loss: 1.3252\n2022-12-31 23:30:44,878 - mmcls - INFO - Epoch [72][200/5005]\tlr: 1.000e-03, eta: 3:33:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3451\n2022-12-31 23:30:53,692 - mmcls - INFO - Epoch [72][300/5005]\tlr: 1.000e-03, eta: 3:33:41, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3291\n2022-12-31 23:31:02,740 - mmcls - INFO - Epoch [72][400/5005]\tlr: 1.000e-03, eta: 3:33:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3321\n2022-12-31 23:31:11,687 - mmcls - INFO - Epoch [72][500/5005]\tlr: 1.000e-03, eta: 3:33:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3227\n2022-12-31 23:31:20,326 - mmcls - INFO - Epoch [72][600/5005]\tlr: 1.000e-03, eta: 3:33:15, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3279\n2022-12-31 23:31:29,044 - mmcls - INFO - Epoch [72][700/5005]\tlr: 1.000e-03, eta: 3:33:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3161\n2022-12-31 23:31:37,981 - mmcls - INFO - Epoch [72][800/5005]\tlr: 1.000e-03, eta: 3:32:57, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.3265\n2022-12-31 23:31:46,831 - mmcls - INFO - Epoch [72][900/5005]\tlr: 1.000e-03, eta: 3:32:48, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3097\n2022-12-31 23:31:56,052 - mmcls - INFO - Epoch [72][1000/5005]\tlr: 1.000e-03, eta: 3:32:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.3357\n2022-12-31 23:32:05,238 - mmcls - INFO - Epoch [72][1100/5005]\tlr: 1.000e-03, eta: 3:32:31, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3173\n2022-12-31 23:32:14,188 - mmcls - INFO - Epoch [72][1200/5005]\tlr: 1.000e-03, eta: 3:32:22, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3140\n2022-12-31 23:32:22,819 - mmcls - INFO - Epoch [72][1300/5005]\tlr: 1.000e-03, eta: 3:32:13, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3051\n2022-12-31 23:32:31,797 - mmcls - INFO - Epoch [72][1400/5005]\tlr: 1.000e-03, eta: 3:32:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3384\n2022-12-31 23:32:40,528 - mmcls - INFO - Epoch [72][1500/5005]\tlr: 1.000e-03, eta: 3:31:55, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3129\n2022-12-31 23:32:49,145 - mmcls - INFO - Epoch [72][1600/5005]\tlr: 1.000e-03, eta: 3:31:46, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3355\n2022-12-31 23:32:57,933 - mmcls - INFO - Epoch [72][1700/5005]\tlr: 1.000e-03, eta: 3:31:38, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3248\n2022-12-31 23:33:06,626 - mmcls - INFO - Epoch [72][1800/5005]\tlr: 1.000e-03, eta: 3:31:29, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3311\n2022-12-31 23:33:15,425 - mmcls - INFO - Epoch [72][1900/5005]\tlr: 1.000e-03, eta: 3:31:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3103\n2022-12-31 23:33:24,270 - mmcls - INFO - Epoch [72][2000/5005]\tlr: 1.000e-03, eta: 3:31:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3096\n2022-12-31 23:33:33,321 - mmcls - INFO - Epoch [72][2100/5005]\tlr: 1.000e-03, eta: 3:31:02, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3252\n2022-12-31 23:33:41,961 - mmcls - INFO - Epoch [72][2200/5005]\tlr: 1.000e-03, eta: 3:30:53, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3183\n2022-12-31 23:33:50,667 - mmcls - INFO - Epoch [72][2300/5005]\tlr: 1.000e-03, eta: 3:30:44, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3202\n2022-12-31 23:33:59,432 - mmcls - INFO - Epoch [72][2400/5005]\tlr: 1.000e-03, eta: 3:30:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3344\n2022-12-31 23:34:08,164 - mmcls - INFO - Epoch [72][2500/5005]\tlr: 1.000e-03, eta: 3:30:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3035\n2022-12-31 23:34:17,108 - mmcls - INFO - Epoch [72][2600/5005]\tlr: 1.000e-03, eta: 3:30:18, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.3057\n2022-12-31 23:34:25,980 - mmcls - INFO - Epoch [72][2700/5005]\tlr: 1.000e-03, eta: 3:30:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3367\n2022-12-31 23:34:34,946 - mmcls - INFO - Epoch [72][2800/5005]\tlr: 1.000e-03, eta: 3:30:00, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3086\n2022-12-31 23:34:43,758 - mmcls - INFO - Epoch [72][2900/5005]\tlr: 1.000e-03, eta: 3:29:51, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3263\n2022-12-31 23:34:52,685 - mmcls - INFO - Epoch [72][3000/5005]\tlr: 1.000e-03, eta: 3:29:42, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3060\n2022-12-31 23:35:01,502 - mmcls - INFO - Epoch [72][3100/5005]\tlr: 1.000e-03, eta: 3:29:34, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3168\n2022-12-31 23:35:10,256 - mmcls - INFO - Epoch [72][3200/5005]\tlr: 1.000e-03, eta: 3:29:25, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3162\n2022-12-31 23:35:19,196 - mmcls - INFO - Epoch [72][3300/5005]\tlr: 1.000e-03, eta: 3:29:16, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.3113\n2022-12-31 23:35:28,244 - mmcls - INFO - Epoch [72][3400/5005]\tlr: 1.000e-03, eta: 3:29:07, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3248\n2022-12-31 23:35:37,029 - mmcls - INFO - Epoch [72][3500/5005]\tlr: 1.000e-03, eta: 3:28:58, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3188\n2022-12-31 23:35:46,009 - mmcls - INFO - Epoch [72][3600/5005]\tlr: 1.000e-03, eta: 3:28:49, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3192\n2022-12-31 23:35:54,872 - mmcls - INFO - Epoch [72][3700/5005]\tlr: 1.000e-03, eta: 3:28:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3264\n2022-12-31 23:36:03,813 - mmcls - INFO - Epoch [72][3800/5005]\tlr: 1.000e-03, eta: 3:28:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3349\n2022-12-31 23:36:12,438 - mmcls - INFO - Epoch [72][3900/5005]\tlr: 1.000e-03, eta: 3:28:23, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3342\n2022-12-31 23:36:21,176 - mmcls - INFO - Epoch [72][4000/5005]\tlr: 1.000e-03, eta: 3:28:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3168\n2022-12-31 23:36:30,188 - mmcls - INFO - Epoch [72][4100/5005]\tlr: 1.000e-03, eta: 3:28:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3380\n2022-12-31 23:36:38,973 - mmcls - INFO - Epoch [72][4200/5005]\tlr: 1.000e-03, eta: 3:27:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3178\n2022-12-31 23:36:47,641 - mmcls - INFO - Epoch [72][4300/5005]\tlr: 1.000e-03, eta: 3:27:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3418\n2022-12-31 23:36:56,455 - mmcls - INFO - Epoch [72][4400/5005]\tlr: 1.000e-03, eta: 3:27:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3338\n2022-12-31 23:37:05,018 - mmcls - INFO - Epoch [72][4500/5005]\tlr: 1.000e-03, eta: 3:27:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3241\n2022-12-31 23:37:13,888 - mmcls - INFO - Epoch [72][4600/5005]\tlr: 1.000e-03, eta: 3:27:21, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3411\n2022-12-31 23:37:22,650 - mmcls - INFO - Epoch [72][4700/5005]\tlr: 1.000e-03, eta: 3:27:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3211\n2022-12-31 23:37:31,589 - mmcls - INFO - Epoch [72][4800/5005]\tlr: 1.000e-03, eta: 3:27:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3203\n2022-12-31 23:37:40,621 - mmcls - INFO - Epoch [72][4900/5005]\tlr: 1.000e-03, eta: 3:26:54, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3196\n2022-12-31 23:37:49,094 - mmcls - INFO - Epoch [72][5000/5005]\tlr: 1.000e-03, eta: 3:26:45, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.3489\n2022-12-31 23:38:11,851 - mmcls - INFO - Epoch(val) [72][196]\taccuracy_top-1: 70.7920, accuracy_top-5: 89.7060\n2022-12-31 23:38:23,085 - mmcls - INFO - Epoch [73][100/5005]\tlr: 1.000e-03, eta: 3:26:37, time: 0.112, data_time: 0.036, memory: 1224, loss: 1.3193\n2022-12-31 23:38:32,143 - mmcls - INFO - Epoch [73][200/5005]\tlr: 1.000e-03, eta: 3:26:28, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3135\n2022-12-31 23:38:40,669 - mmcls - INFO - Epoch [73][300/5005]\tlr: 1.000e-03, eta: 3:26:19, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3077\n2022-12-31 23:38:49,605 - mmcls - INFO - Epoch [73][400/5005]\tlr: 1.000e-03, eta: 3:26:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3181\n2022-12-31 23:38:58,633 - mmcls - INFO - Epoch [73][500/5005]\tlr: 1.000e-03, eta: 3:26:01, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3058\n2022-12-31 23:39:07,400 - mmcls - INFO - Epoch [73][600/5005]\tlr: 1.000e-03, eta: 3:25:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3213\n2022-12-31 23:39:16,668 - mmcls - INFO - Epoch [73][700/5005]\tlr: 1.000e-03, eta: 3:25:44, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.3335\n2022-12-31 23:39:25,634 - mmcls - INFO - Epoch [73][800/5005]\tlr: 1.000e-03, eta: 3:25:35, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3100\n2022-12-31 23:39:34,408 - mmcls - INFO - Epoch [73][900/5005]\tlr: 1.000e-03, eta: 3:25:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3061\n2022-12-31 23:39:43,560 - mmcls - INFO - Epoch [73][1000/5005]\tlr: 1.000e-03, eta: 3:25:17, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.3145\n2022-12-31 23:39:52,576 - mmcls - INFO - Epoch [73][1100/5005]\tlr: 1.000e-03, eta: 3:25:08, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3246\n2022-12-31 23:40:01,402 - mmcls - INFO - Epoch [73][1200/5005]\tlr: 1.000e-03, eta: 3:25:00, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3299\n2022-12-31 23:40:10,106 - mmcls - INFO - Epoch [73][1300/5005]\tlr: 1.000e-03, eta: 3:24:51, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3042\n2022-12-31 23:40:18,721 - mmcls - INFO - Epoch [73][1400/5005]\tlr: 1.000e-03, eta: 3:24:42, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.3111\n2022-12-31 23:40:27,735 - mmcls - INFO - Epoch [73][1500/5005]\tlr: 1.000e-03, eta: 3:24:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3312\n2022-12-31 23:40:36,346 - mmcls - INFO - Epoch [73][1600/5005]\tlr: 1.000e-03, eta: 3:24:24, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3182\n2022-12-31 23:40:45,186 - mmcls - INFO - Epoch [73][1700/5005]\tlr: 1.000e-03, eta: 3:24:15, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3333\n2022-12-31 23:40:54,207 - mmcls - INFO - Epoch [73][1800/5005]\tlr: 1.000e-03, eta: 3:24:06, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3085\n2022-12-31 23:41:03,326 - mmcls - INFO - Epoch [73][1900/5005]\tlr: 1.000e-03, eta: 3:23:58, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3018\n2022-12-31 23:41:11,994 - mmcls - INFO - Epoch [73][2000/5005]\tlr: 1.000e-03, eta: 3:23:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3136\n2022-12-31 23:41:21,001 - mmcls - INFO - Epoch [73][2100/5005]\tlr: 1.000e-03, eta: 3:23:40, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3384\n2022-12-31 23:41:29,786 - mmcls - INFO - Epoch [73][2200/5005]\tlr: 1.000e-03, eta: 3:23:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3231\n2022-12-31 23:41:38,851 - mmcls - INFO - Epoch [73][2300/5005]\tlr: 1.000e-03, eta: 3:23:22, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3115\n2022-12-31 23:41:47,489 - mmcls - INFO - Epoch [73][2400/5005]\tlr: 1.000e-03, eta: 3:23:13, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3163\n2022-12-31 23:41:56,380 - mmcls - INFO - Epoch [73][2500/5005]\tlr: 1.000e-03, eta: 3:23:04, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3087\n2022-12-31 23:42:05,710 - mmcls - INFO - Epoch [73][2600/5005]\tlr: 1.000e-03, eta: 3:22:56, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.3040\n2022-12-31 23:42:14,536 - mmcls - INFO - Epoch [73][2700/5005]\tlr: 1.000e-03, eta: 3:22:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3059\n2022-12-31 23:42:23,346 - mmcls - INFO - Epoch [73][2800/5005]\tlr: 1.000e-03, eta: 3:22:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3102\n2022-12-31 23:42:31,933 - mmcls - INFO - Epoch [73][2900/5005]\tlr: 1.000e-03, eta: 3:22:29, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3234\n2022-12-31 23:42:40,856 - mmcls - INFO - Epoch [73][3000/5005]\tlr: 1.000e-03, eta: 3:22:20, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3034\n2022-12-31 23:42:49,710 - mmcls - INFO - Epoch [73][3100/5005]\tlr: 1.000e-03, eta: 3:22:11, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2843\n2022-12-31 23:42:58,333 - mmcls - INFO - Epoch [73][3200/5005]\tlr: 1.000e-03, eta: 3:22:02, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3054\n2022-12-31 23:43:07,176 - mmcls - INFO - Epoch [73][3300/5005]\tlr: 1.000e-03, eta: 3:21:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3155\n2022-12-31 23:43:15,907 - mmcls - INFO - Epoch [73][3400/5005]\tlr: 1.000e-03, eta: 3:21:45, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3416\n2022-12-31 23:43:24,637 - mmcls - INFO - Epoch [73][3500/5005]\tlr: 1.000e-03, eta: 3:21:36, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3368\n2022-12-31 23:43:33,302 - mmcls - INFO - Epoch [73][3600/5005]\tlr: 1.000e-03, eta: 3:21:27, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3372\n2022-12-31 23:43:42,317 - mmcls - INFO - Epoch [73][3700/5005]\tlr: 1.000e-03, eta: 3:21:18, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3483\n2022-12-31 23:43:51,106 - mmcls - INFO - Epoch [73][3800/5005]\tlr: 1.000e-03, eta: 3:21:09, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3189\n2022-12-31 23:43:59,980 - mmcls - INFO - Epoch [73][3900/5005]\tlr: 1.000e-03, eta: 3:21:00, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3106\n2022-12-31 23:44:08,737 - mmcls - INFO - Epoch [73][4000/5005]\tlr: 1.000e-03, eta: 3:20:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3249\n2022-12-31 23:44:17,654 - mmcls - INFO - Epoch [73][4100/5005]\tlr: 1.000e-03, eta: 3:20:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3305\n2022-12-31 23:44:26,532 - mmcls - INFO - Epoch [73][4200/5005]\tlr: 1.000e-03, eta: 3:20:34, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3166\n2022-12-31 23:44:34,926 - mmcls - INFO - Epoch [73][4300/5005]\tlr: 1.000e-03, eta: 3:20:25, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.3008\n2022-12-31 23:44:43,601 - mmcls - INFO - Epoch [73][4400/5005]\tlr: 1.000e-03, eta: 3:20:16, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.3272\n2022-12-31 23:44:52,283 - mmcls - INFO - Epoch [73][4500/5005]\tlr: 1.000e-03, eta: 3:20:07, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.3248\n2022-12-31 23:45:00,962 - mmcls - INFO - Epoch [73][4600/5005]\tlr: 1.000e-03, eta: 3:19:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3255\n2022-12-31 23:45:09,644 - mmcls - INFO - Epoch [73][4700/5005]\tlr: 1.000e-03, eta: 3:19:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3454\n2022-12-31 23:45:18,463 - mmcls - INFO - Epoch [73][4800/5005]\tlr: 1.000e-03, eta: 3:19:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2998\n2022-12-31 23:45:27,221 - mmcls - INFO - Epoch [73][4900/5005]\tlr: 1.000e-03, eta: 3:19:31, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.3254\n2022-12-31 23:45:36,067 - mmcls - INFO - Epoch [73][5000/5005]\tlr: 1.000e-03, eta: 3:19:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3311\n2022-12-31 23:45:58,926 - mmcls - INFO - Epoch(val) [73][196]\taccuracy_top-1: 70.7760, accuracy_top-5: 89.6520\n2022-12-31 23:46:09,850 - mmcls - INFO - Epoch [74][100/5005]\tlr: 1.000e-03, eta: 3:19:14, time: 0.109, data_time: 0.047, memory: 1224, loss: 1.3014\n2022-12-31 23:46:18,749 - mmcls - INFO - Epoch [74][200/5005]\tlr: 1.000e-03, eta: 3:19:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3119\n2022-12-31 23:46:27,607 - mmcls - INFO - Epoch [74][300/5005]\tlr: 1.000e-03, eta: 3:18:56, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3177\n2022-12-31 23:46:36,554 - mmcls - INFO - Epoch [74][400/5005]\tlr: 1.000e-03, eta: 3:18:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3055\n2022-12-31 23:46:45,024 - mmcls - INFO - Epoch [74][500/5005]\tlr: 1.000e-03, eta: 3:18:38, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.2970\n2022-12-31 23:46:53,602 - mmcls - INFO - Epoch [74][600/5005]\tlr: 1.000e-03, eta: 3:18:29, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3164\n2022-12-31 23:47:02,479 - mmcls - INFO - Epoch [74][700/5005]\tlr: 1.000e-03, eta: 3:18:20, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3008\n2022-12-31 23:47:11,046 - mmcls - INFO - Epoch [74][800/5005]\tlr: 1.000e-03, eta: 3:18:12, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3427\n2022-12-31 23:47:19,976 - mmcls - INFO - Epoch [74][900/5005]\tlr: 1.000e-03, eta: 3:18:03, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3054\n2022-12-31 23:47:28,714 - mmcls - INFO - Epoch [74][1000/5005]\tlr: 1.000e-03, eta: 3:17:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3273\n2022-12-31 23:47:37,241 - mmcls - INFO - Epoch [74][1100/5005]\tlr: 1.000e-03, eta: 3:17:45, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.3021\n2022-12-31 23:47:46,522 - mmcls - INFO - Epoch [74][1200/5005]\tlr: 1.000e-03, eta: 3:17:36, time: 0.093, data_time: 0.027, memory: 1224, loss: 1.3088\n2022-12-31 23:47:55,300 - mmcls - INFO - Epoch [74][1300/5005]\tlr: 1.000e-03, eta: 3:17:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2897\n2022-12-31 23:48:04,127 - mmcls - INFO - Epoch [74][1400/5005]\tlr: 1.000e-03, eta: 3:17:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2908\n2022-12-31 23:48:13,018 - mmcls - INFO - Epoch [74][1500/5005]\tlr: 1.000e-03, eta: 3:17:10, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3087\n2022-12-31 23:48:22,095 - mmcls - INFO - Epoch [74][1600/5005]\tlr: 1.000e-03, eta: 3:17:01, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3121\n2022-12-31 23:48:31,356 - mmcls - INFO - Epoch [74][1700/5005]\tlr: 1.000e-03, eta: 3:16:52, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.3056\n2022-12-31 23:48:40,152 - mmcls - INFO - Epoch [74][1800/5005]\tlr: 1.000e-03, eta: 3:16:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2961\n2022-12-31 23:48:48,926 - mmcls - INFO - Epoch [74][1900/5005]\tlr: 1.000e-03, eta: 3:16:34, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2947\n2022-12-31 23:48:57,551 - mmcls - INFO - Epoch [74][2000/5005]\tlr: 1.000e-03, eta: 3:16:25, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3349\n2022-12-31 23:49:06,603 - mmcls - INFO - Epoch [74][2100/5005]\tlr: 1.000e-03, eta: 3:16:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3522\n2022-12-31 23:49:15,138 - mmcls - INFO - Epoch [74][2200/5005]\tlr: 1.000e-03, eta: 3:16:08, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.3075\n2022-12-31 23:49:23,679 - mmcls - INFO - Epoch [74][2300/5005]\tlr: 1.000e-03, eta: 3:15:59, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3001\n2022-12-31 23:49:32,202 - mmcls - INFO - Epoch [74][2400/5005]\tlr: 1.000e-03, eta: 3:15:50, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3032\n2022-12-31 23:49:40,902 - mmcls - INFO - Epoch [74][2500/5005]\tlr: 1.000e-03, eta: 3:15:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3106\n2022-12-31 23:49:49,646 - mmcls - INFO - Epoch [74][2600/5005]\tlr: 1.000e-03, eta: 3:15:32, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3210\n2022-12-31 23:49:58,621 - mmcls - INFO - Epoch [74][2700/5005]\tlr: 1.000e-03, eta: 3:15:23, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3235\n2022-12-31 23:50:07,593 - mmcls - INFO - Epoch [74][2800/5005]\tlr: 1.000e-03, eta: 3:15:14, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3146\n2022-12-31 23:50:16,210 - mmcls - INFO - Epoch [74][2900/5005]\tlr: 1.000e-03, eta: 3:15:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3043\n2022-12-31 23:50:24,880 - mmcls - INFO - Epoch [74][3000/5005]\tlr: 1.000e-03, eta: 3:14:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3140\n2022-12-31 23:50:34,074 - mmcls - INFO - Epoch [74][3100/5005]\tlr: 1.000e-03, eta: 3:14:48, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3155\n2022-12-31 23:50:43,086 - mmcls - INFO - Epoch [74][3200/5005]\tlr: 1.000e-03, eta: 3:14:39, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.3071\n2022-12-31 23:50:51,926 - mmcls - INFO - Epoch [74][3300/5005]\tlr: 1.000e-03, eta: 3:14:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3160\n2022-12-31 23:51:00,628 - mmcls - INFO - Epoch [74][3400/5005]\tlr: 1.000e-03, eta: 3:14:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3165\n2022-12-31 23:51:09,719 - mmcls - INFO - Epoch [74][3500/5005]\tlr: 1.000e-03, eta: 3:14:12, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3235\n2022-12-31 23:51:18,325 - mmcls - INFO - Epoch [74][3600/5005]\tlr: 1.000e-03, eta: 3:14:03, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3391\n2022-12-31 23:51:27,134 - mmcls - INFO - Epoch [74][3700/5005]\tlr: 1.000e-03, eta: 3:13:55, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3092\n2022-12-31 23:51:35,867 - mmcls - INFO - Epoch [74][3800/5005]\tlr: 1.000e-03, eta: 3:13:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3129\n2022-12-31 23:51:44,334 - mmcls - INFO - Epoch [74][3900/5005]\tlr: 1.000e-03, eta: 3:13:37, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3230\n2022-12-31 23:51:53,421 - mmcls - INFO - Epoch [74][4000/5005]\tlr: 1.000e-03, eta: 3:13:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3227\n2022-12-31 23:52:02,599 - mmcls - INFO - Epoch [74][4100/5005]\tlr: 1.000e-03, eta: 3:13:19, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3237\n2022-12-31 23:52:11,519 - mmcls - INFO - Epoch [74][4200/5005]\tlr: 1.000e-03, eta: 3:13:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3200\n2022-12-31 23:52:20,750 - mmcls - INFO - Epoch [74][4300/5005]\tlr: 1.000e-03, eta: 3:13:02, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.3174\n2022-12-31 23:52:29,521 - mmcls - INFO - Epoch [74][4400/5005]\tlr: 1.000e-03, eta: 3:12:53, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3308\n2022-12-31 23:52:38,205 - mmcls - INFO - Epoch [74][4500/5005]\tlr: 1.000e-03, eta: 3:12:44, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.3064\n2022-12-31 23:52:47,001 - mmcls - INFO - Epoch [74][4600/5005]\tlr: 1.000e-03, eta: 3:12:35, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3234\n2022-12-31 23:52:55,733 - mmcls - INFO - Epoch [74][4700/5005]\tlr: 1.000e-03, eta: 3:12:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3080\n2022-12-31 23:53:04,516 - mmcls - INFO - Epoch [74][4800/5005]\tlr: 1.000e-03, eta: 3:12:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3241\n2022-12-31 23:53:13,422 - mmcls - INFO - Epoch [74][4900/5005]\tlr: 1.000e-03, eta: 3:12:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3190\n2022-12-31 23:53:21,980 - mmcls - INFO - Epoch [74][5000/5005]\tlr: 1.000e-03, eta: 3:11:59, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2951\n2022-12-31 23:53:44,619 - mmcls - INFO - Epoch(val) [74][196]\taccuracy_top-1: 70.9300, accuracy_top-5: 89.7420\n2022-12-31 23:53:55,705 - mmcls - INFO - Epoch [75][100/5005]\tlr: 1.000e-03, eta: 3:11:51, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.3078\n2022-12-31 23:54:04,274 - mmcls - INFO - Epoch [75][200/5005]\tlr: 1.000e-03, eta: 3:11:42, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3172\n2022-12-31 23:54:13,592 - mmcls - INFO - Epoch [75][300/5005]\tlr: 1.000e-03, eta: 3:11:33, time: 0.093, data_time: 0.020, memory: 1224, loss: 1.2919\n2022-12-31 23:54:22,502 - mmcls - INFO - Epoch [75][400/5005]\tlr: 1.000e-03, eta: 3:11:24, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2839\n2022-12-31 23:54:31,108 - mmcls - INFO - Epoch [75][500/5005]\tlr: 1.000e-03, eta: 3:11:15, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3017\n2022-12-31 23:54:39,845 - mmcls - INFO - Epoch [75][600/5005]\tlr: 1.000e-03, eta: 3:11:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3150\n2022-12-31 23:54:48,737 - mmcls - INFO - Epoch [75][700/5005]\tlr: 1.000e-03, eta: 3:10:58, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.2933\n2022-12-31 23:54:57,538 - mmcls - INFO - Epoch [75][800/5005]\tlr: 1.000e-03, eta: 3:10:49, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2944\n2022-12-31 23:55:06,288 - mmcls - INFO - Epoch [75][900/5005]\tlr: 1.000e-03, eta: 3:10:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3293\n2022-12-31 23:55:14,859 - mmcls - INFO - Epoch [75][1000/5005]\tlr: 1.000e-03, eta: 3:10:31, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2939\n2022-12-31 23:55:23,849 - mmcls - INFO - Epoch [75][1100/5005]\tlr: 1.000e-03, eta: 3:10:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3169\n2022-12-31 23:55:32,879 - mmcls - INFO - Epoch [75][1200/5005]\tlr: 1.000e-03, eta: 3:10:13, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2920\n2022-12-31 23:55:41,674 - mmcls - INFO - Epoch [75][1300/5005]\tlr: 1.000e-03, eta: 3:10:04, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3173\n2022-12-31 23:55:50,602 - mmcls - INFO - Epoch [75][1400/5005]\tlr: 1.000e-03, eta: 3:09:56, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3148\n2022-12-31 23:55:59,589 - mmcls - INFO - Epoch [75][1500/5005]\tlr: 1.000e-03, eta: 3:09:47, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3211\n2022-12-31 23:56:08,369 - mmcls - INFO - Epoch [75][1600/5005]\tlr: 1.000e-03, eta: 3:09:38, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3008\n2022-12-31 23:56:17,255 - mmcls - INFO - Epoch [75][1700/5005]\tlr: 1.000e-03, eta: 3:09:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3059\n2022-12-31 23:56:26,253 - mmcls - INFO - Epoch [75][1800/5005]\tlr: 1.000e-03, eta: 3:09:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3075\n2022-12-31 23:56:34,922 - mmcls - INFO - Epoch [75][1900/5005]\tlr: 1.000e-03, eta: 3:09:11, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3105\n2022-12-31 23:56:43,610 - mmcls - INFO - Epoch [75][2000/5005]\tlr: 1.000e-03, eta: 3:09:02, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.3049\n2022-12-31 23:56:52,582 - mmcls - INFO - Epoch [75][2100/5005]\tlr: 1.000e-03, eta: 3:08:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3198\n2022-12-31 23:57:01,300 - mmcls - INFO - Epoch [75][2200/5005]\tlr: 1.000e-03, eta: 3:08:45, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3367\n2022-12-31 23:57:09,911 - mmcls - INFO - Epoch [75][2300/5005]\tlr: 1.000e-03, eta: 3:08:36, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3189\n2022-12-31 23:57:18,983 - mmcls - INFO - Epoch [75][2400/5005]\tlr: 1.000e-03, eta: 3:08:27, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2956\n2022-12-31 23:57:27,644 - mmcls - INFO - Epoch [75][2500/5005]\tlr: 1.000e-03, eta: 3:08:18, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3171\n2022-12-31 23:57:36,229 - mmcls - INFO - Epoch [75][2600/5005]\tlr: 1.000e-03, eta: 3:08:09, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2870\n2022-12-31 23:57:45,138 - mmcls - INFO - Epoch [75][2700/5005]\tlr: 1.000e-03, eta: 3:08:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3103\n2022-12-31 23:57:53,804 - mmcls - INFO - Epoch [75][2800/5005]\tlr: 1.000e-03, eta: 3:07:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3074\n2022-12-31 23:58:02,563 - mmcls - INFO - Epoch [75][2900/5005]\tlr: 1.000e-03, eta: 3:07:42, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3031\n2022-12-31 23:58:11,585 - mmcls - INFO - Epoch [75][3000/5005]\tlr: 1.000e-03, eta: 3:07:34, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3114\n2022-12-31 23:58:20,427 - mmcls - INFO - Epoch [75][3100/5005]\tlr: 1.000e-03, eta: 3:07:25, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2911\n2022-12-31 23:58:29,062 - mmcls - INFO - Epoch [75][3200/5005]\tlr: 1.000e-03, eta: 3:07:16, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3295\n2022-12-31 23:58:38,097 - mmcls - INFO - Epoch [75][3300/5005]\tlr: 1.000e-03, eta: 3:07:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3111\n2022-12-31 23:58:46,821 - mmcls - INFO - Epoch [75][3400/5005]\tlr: 1.000e-03, eta: 3:06:58, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.3133\n2022-12-31 23:58:55,634 - mmcls - INFO - Epoch [75][3500/5005]\tlr: 1.000e-03, eta: 3:06:49, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3095\n2022-12-31 23:59:04,916 - mmcls - INFO - Epoch [75][3600/5005]\tlr: 1.000e-03, eta: 3:06:41, time: 0.093, data_time: 0.015, memory: 1224, loss: 1.3036\n2022-12-31 23:59:13,553 - mmcls - INFO - Epoch [75][3700/5005]\tlr: 1.000e-03, eta: 3:06:32, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3204\n2022-12-31 23:59:22,141 - mmcls - INFO - Epoch [75][3800/5005]\tlr: 1.000e-03, eta: 3:06:23, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.3287\n2022-12-31 23:59:30,852 - mmcls - INFO - Epoch [75][3900/5005]\tlr: 1.000e-03, eta: 3:06:14, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3078\n2022-12-31 23:59:39,460 - mmcls - INFO - Epoch [75][4000/5005]\tlr: 1.000e-03, eta: 3:06:05, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3180\n2022-12-31 23:59:48,173 - mmcls - INFO - Epoch [75][4100/5005]\tlr: 1.000e-03, eta: 3:05:56, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3031\n2022-12-31 23:59:56,840 - mmcls - INFO - Epoch [75][4200/5005]\tlr: 1.000e-03, eta: 3:05:47, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3408\n2023-01-01 00:00:05,582 - mmcls - INFO - Epoch [75][4300/5005]\tlr: 1.000e-03, eta: 3:05:38, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3209\n2023-01-01 00:00:14,465 - mmcls - INFO - Epoch [75][4400/5005]\tlr: 1.000e-03, eta: 3:05:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3175\n2023-01-01 00:00:23,371 - mmcls - INFO - Epoch [75][4500/5005]\tlr: 1.000e-03, eta: 3:05:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3061\n2023-01-01 00:00:32,434 - mmcls - INFO - Epoch [75][4600/5005]\tlr: 1.000e-03, eta: 3:05:12, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.3217\n2023-01-01 00:00:41,098 - mmcls - INFO - Epoch [75][4700/5005]\tlr: 1.000e-03, eta: 3:05:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3197\n2023-01-01 00:00:49,874 - mmcls - INFO - Epoch [75][4800/5005]\tlr: 1.000e-03, eta: 3:04:54, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3153\n2023-01-01 00:00:58,453 - mmcls - INFO - Epoch [75][4900/5005]\tlr: 1.000e-03, eta: 3:04:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3522\n2023-01-01 00:01:07,366 - mmcls - INFO - Epoch [75][5000/5005]\tlr: 1.000e-03, eta: 3:04:36, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3133\n2023-01-01 00:01:29,986 - mmcls - INFO - Epoch(val) [75][196]\taccuracy_top-1: 70.7880, accuracy_top-5: 89.8980\n2023-01-01 00:01:41,381 - mmcls - INFO - Epoch [76][100/5005]\tlr: 1.000e-03, eta: 3:04:28, time: 0.114, data_time: 0.045, memory: 1224, loss: 1.3082\n2023-01-01 00:01:50,075 - mmcls - INFO - Epoch [76][200/5005]\tlr: 1.000e-03, eta: 3:04:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3059\n2023-01-01 00:01:59,153 - mmcls - INFO - Epoch [76][300/5005]\tlr: 1.000e-03, eta: 3:04:10, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.3050\n2023-01-01 00:02:08,243 - mmcls - INFO - Epoch [76][400/5005]\tlr: 1.000e-03, eta: 3:04:01, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3209\n2023-01-01 00:02:17,286 - mmcls - INFO - Epoch [76][500/5005]\tlr: 1.000e-03, eta: 3:03:52, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3198\n2023-01-01 00:02:26,061 - mmcls - INFO - Epoch [76][600/5005]\tlr: 1.000e-03, eta: 3:03:43, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3098\n2023-01-01 00:02:34,855 - mmcls - INFO - Epoch [76][700/5005]\tlr: 1.000e-03, eta: 3:03:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2880\n2023-01-01 00:02:43,872 - mmcls - INFO - Epoch [76][800/5005]\tlr: 1.000e-03, eta: 3:03:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3116\n2023-01-01 00:02:52,713 - mmcls - INFO - Epoch [76][900/5005]\tlr: 1.000e-03, eta: 3:03:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3150\n2023-01-01 00:03:01,513 - mmcls - INFO - Epoch [76][1000/5005]\tlr: 1.000e-03, eta: 3:03:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3195\n2023-01-01 00:03:10,201 - mmcls - INFO - Epoch [76][1100/5005]\tlr: 1.000e-03, eta: 3:02:59, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3090\n2023-01-01 00:03:19,426 - mmcls - INFO - Epoch [76][1200/5005]\tlr: 1.000e-03, eta: 3:02:50, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3215\n2023-01-01 00:03:28,278 - mmcls - INFO - Epoch [76][1300/5005]\tlr: 1.000e-03, eta: 3:02:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3086\n2023-01-01 00:03:37,236 - mmcls - INFO - Epoch [76][1400/5005]\tlr: 1.000e-03, eta: 3:02:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3286\n2023-01-01 00:03:45,913 - mmcls - INFO - Epoch [76][1500/5005]\tlr: 1.000e-03, eta: 3:02:24, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2935\n2023-01-01 00:03:54,908 - mmcls - INFO - Epoch [76][1600/5005]\tlr: 1.000e-03, eta: 3:02:15, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3080\n2023-01-01 00:04:04,011 - mmcls - INFO - Epoch [76][1700/5005]\tlr: 1.000e-03, eta: 3:02:06, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.3001\n2023-01-01 00:04:13,203 - mmcls - INFO - Epoch [76][1800/5005]\tlr: 1.000e-03, eta: 3:01:58, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2970\n2023-01-01 00:04:21,792 - mmcls - INFO - Epoch [76][1900/5005]\tlr: 1.000e-03, eta: 3:01:49, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3048\n2023-01-01 00:04:30,778 - mmcls - INFO - Epoch [76][2000/5005]\tlr: 1.000e-03, eta: 3:01:40, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.3109\n2023-01-01 00:04:39,643 - mmcls - INFO - Epoch [76][2100/5005]\tlr: 1.000e-03, eta: 3:01:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2949\n2023-01-01 00:04:48,469 - mmcls - INFO - Epoch [76][2200/5005]\tlr: 1.000e-03, eta: 3:01:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2870\n2023-01-01 00:04:57,172 - mmcls - INFO - Epoch [76][2300/5005]\tlr: 1.000e-03, eta: 3:01:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3163\n2023-01-01 00:05:05,894 - mmcls - INFO - Epoch [76][2400/5005]\tlr: 1.000e-03, eta: 3:01:04, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3024\n2023-01-01 00:05:14,495 - mmcls - INFO - Epoch [76][2500/5005]\tlr: 1.000e-03, eta: 3:00:55, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3052\n2023-01-01 00:05:23,542 - mmcls - INFO - Epoch [76][2600/5005]\tlr: 1.000e-03, eta: 3:00:47, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.3047\n2023-01-01 00:05:32,503 - mmcls - INFO - Epoch [76][2700/5005]\tlr: 1.000e-03, eta: 3:00:38, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3182\n2023-01-01 00:05:41,326 - mmcls - INFO - Epoch [76][2800/5005]\tlr: 1.000e-03, eta: 3:00:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3069\n2023-01-01 00:05:50,131 - mmcls - INFO - Epoch [76][2900/5005]\tlr: 1.000e-03, eta: 3:00:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2949\n2023-01-01 00:05:58,624 - mmcls - INFO - Epoch [76][3000/5005]\tlr: 1.000e-03, eta: 3:00:11, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.2927\n2023-01-01 00:06:07,580 - mmcls - INFO - Epoch [76][3100/5005]\tlr: 1.000e-03, eta: 3:00:02, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3305\n2023-01-01 00:06:16,158 - mmcls - INFO - Epoch [76][3200/5005]\tlr: 1.000e-03, eta: 2:59:53, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3074\n2023-01-01 00:06:24,677 - mmcls - INFO - Epoch [76][3300/5005]\tlr: 1.000e-03, eta: 2:59:44, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.3127\n2023-01-01 00:06:33,442 - mmcls - INFO - Epoch [76][3400/5005]\tlr: 1.000e-03, eta: 2:59:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3178\n2023-01-01 00:06:42,428 - mmcls - INFO - Epoch [76][3500/5005]\tlr: 1.000e-03, eta: 2:59:27, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.3264\n2023-01-01 00:06:51,237 - mmcls - INFO - Epoch [76][3600/5005]\tlr: 1.000e-03, eta: 2:59:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3133\n2023-01-01 00:06:59,893 - mmcls - INFO - Epoch [76][3700/5005]\tlr: 1.000e-03, eta: 2:59:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2870\n2023-01-01 00:07:08,702 - mmcls - INFO - Epoch [76][3800/5005]\tlr: 1.000e-03, eta: 2:59:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3089\n2023-01-01 00:07:17,510 - mmcls - INFO - Epoch [76][3900/5005]\tlr: 1.000e-03, eta: 2:58:51, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3060\n2023-01-01 00:07:26,226 - mmcls - INFO - Epoch [76][4000/5005]\tlr: 1.000e-03, eta: 2:58:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3119\n2023-01-01 00:07:34,831 - mmcls - INFO - Epoch [76][4100/5005]\tlr: 1.000e-03, eta: 2:58:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3027\n2023-01-01 00:07:43,725 - mmcls - INFO - Epoch [76][4200/5005]\tlr: 1.000e-03, eta: 2:58:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3229\n2023-01-01 00:07:52,434 - mmcls - INFO - Epoch [76][4300/5005]\tlr: 1.000e-03, eta: 2:58:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3095\n2023-01-01 00:08:01,100 - mmcls - INFO - Epoch [76][4400/5005]\tlr: 1.000e-03, eta: 2:58:07, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3110\n2023-01-01 00:08:09,720 - mmcls - INFO - Epoch [76][4500/5005]\tlr: 1.000e-03, eta: 2:57:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3251\n2023-01-01 00:08:18,478 - mmcls - INFO - Epoch [76][4600/5005]\tlr: 1.000e-03, eta: 2:57:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3127\n2023-01-01 00:08:27,318 - mmcls - INFO - Epoch [76][4700/5005]\tlr: 1.000e-03, eta: 2:57:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3160\n2023-01-01 00:08:36,383 - mmcls - INFO - Epoch [76][4800/5005]\tlr: 1.000e-03, eta: 2:57:31, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3159\n2023-01-01 00:08:45,323 - mmcls - INFO - Epoch [76][4900/5005]\tlr: 1.000e-03, eta: 2:57:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3030\n2023-01-01 00:08:54,001 - mmcls - INFO - Epoch [76][5000/5005]\tlr: 1.000e-03, eta: 2:57:13, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3080\n2023-01-01 00:09:16,650 - mmcls - INFO - Epoch(val) [76][196]\taccuracy_top-1: 70.9440, accuracy_top-5: 89.8660\n2023-01-01 00:09:27,377 - mmcls - INFO - Epoch [77][100/5005]\tlr: 1.000e-03, eta: 2:57:05, time: 0.107, data_time: 0.048, memory: 1224, loss: 1.3065\n2023-01-01 00:09:36,342 - mmcls - INFO - Epoch [77][200/5005]\tlr: 1.000e-03, eta: 2:56:56, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2947\n2023-01-01 00:09:45,032 - mmcls - INFO - Epoch [77][300/5005]\tlr: 1.000e-03, eta: 2:56:47, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2938\n2023-01-01 00:09:53,892 - mmcls - INFO - Epoch [77][400/5005]\tlr: 1.000e-03, eta: 2:56:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3075\n2023-01-01 00:10:02,833 - mmcls - INFO - Epoch [77][500/5005]\tlr: 1.000e-03, eta: 2:56:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3001\n2023-01-01 00:10:11,703 - mmcls - INFO - Epoch [77][600/5005]\tlr: 1.000e-03, eta: 2:56:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2984\n2023-01-01 00:10:20,233 - mmcls - INFO - Epoch [77][700/5005]\tlr: 1.000e-03, eta: 2:56:11, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3249\n2023-01-01 00:10:28,905 - mmcls - INFO - Epoch [77][800/5005]\tlr: 1.000e-03, eta: 2:56:02, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.3162\n2023-01-01 00:10:37,882 - mmcls - INFO - Epoch [77][900/5005]\tlr: 1.000e-03, eta: 2:55:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2896\n2023-01-01 00:10:46,710 - mmcls - INFO - Epoch [77][1000/5005]\tlr: 1.000e-03, eta: 2:55:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2987\n2023-01-01 00:10:55,274 - mmcls - INFO - Epoch [77][1100/5005]\tlr: 1.000e-03, eta: 2:55:36, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2997\n2023-01-01 00:11:04,072 - mmcls - INFO - Epoch [77][1200/5005]\tlr: 1.000e-03, eta: 2:55:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2937\n2023-01-01 00:11:12,915 - mmcls - INFO - Epoch [77][1300/5005]\tlr: 1.000e-03, eta: 2:55:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2945\n2023-01-01 00:11:21,582 - mmcls - INFO - Epoch [77][1400/5005]\tlr: 1.000e-03, eta: 2:55:09, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2874\n2023-01-01 00:11:30,291 - mmcls - INFO - Epoch [77][1500/5005]\tlr: 1.000e-03, eta: 2:55:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3273\n2023-01-01 00:11:38,869 - mmcls - INFO - Epoch [77][1600/5005]\tlr: 1.000e-03, eta: 2:54:51, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3031\n2023-01-01 00:11:47,607 - mmcls - INFO - Epoch [77][1700/5005]\tlr: 1.000e-03, eta: 2:54:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.2937\n2023-01-01 00:11:56,508 - mmcls - INFO - Epoch [77][1800/5005]\tlr: 1.000e-03, eta: 2:54:34, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3020\n2023-01-01 00:12:05,247 - mmcls - INFO - Epoch [77][1900/5005]\tlr: 1.000e-03, eta: 2:54:25, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3022\n2023-01-01 00:12:13,823 - mmcls - INFO - Epoch [77][2000/5005]\tlr: 1.000e-03, eta: 2:54:16, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.2893\n2023-01-01 00:12:22,576 - mmcls - INFO - Epoch [77][2100/5005]\tlr: 1.000e-03, eta: 2:54:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2970\n2023-01-01 00:12:31,562 - mmcls - INFO - Epoch [77][2200/5005]\tlr: 1.000e-03, eta: 2:53:58, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.3163\n2023-01-01 00:12:40,656 - mmcls - INFO - Epoch [77][2300/5005]\tlr: 1.000e-03, eta: 2:53:49, time: 0.091, data_time: 0.038, memory: 1224, loss: 1.3072\n2023-01-01 00:12:49,417 - mmcls - INFO - Epoch [77][2400/5005]\tlr: 1.000e-03, eta: 2:53:40, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3011\n2023-01-01 00:12:58,192 - mmcls - INFO - Epoch [77][2500/5005]\tlr: 1.000e-03, eta: 2:53:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3109\n2023-01-01 00:13:07,314 - mmcls - INFO - Epoch [77][2600/5005]\tlr: 1.000e-03, eta: 2:53:23, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3002\n2023-01-01 00:13:15,897 - mmcls - INFO - Epoch [77][2700/5005]\tlr: 1.000e-03, eta: 2:53:14, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3037\n2023-01-01 00:13:24,695 - mmcls - INFO - Epoch [77][2800/5005]\tlr: 1.000e-03, eta: 2:53:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2876\n2023-01-01 00:13:33,338 - mmcls - INFO - Epoch [77][2900/5005]\tlr: 1.000e-03, eta: 2:52:56, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2962\n2023-01-01 00:13:42,385 - mmcls - INFO - Epoch [77][3000/5005]\tlr: 1.000e-03, eta: 2:52:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3169\n2023-01-01 00:13:51,154 - mmcls - INFO - Epoch [77][3100/5005]\tlr: 1.000e-03, eta: 2:52:38, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3070\n2023-01-01 00:14:00,177 - mmcls - INFO - Epoch [77][3200/5005]\tlr: 1.000e-03, eta: 2:52:30, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3193\n2023-01-01 00:14:08,880 - mmcls - INFO - Epoch [77][3300/5005]\tlr: 1.000e-03, eta: 2:52:21, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3168\n2023-01-01 00:14:17,866 - mmcls - INFO - Epoch [77][3400/5005]\tlr: 1.000e-03, eta: 2:52:12, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2967\n2023-01-01 00:14:26,486 - mmcls - INFO - Epoch [77][3500/5005]\tlr: 1.000e-03, eta: 2:52:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3048\n2023-01-01 00:14:34,916 - mmcls - INFO - Epoch [77][3600/5005]\tlr: 1.000e-03, eta: 2:51:54, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.3163\n2023-01-01 00:14:43,612 - mmcls - INFO - Epoch [77][3700/5005]\tlr: 1.000e-03, eta: 2:51:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3113\n2023-01-01 00:14:52,623 - mmcls - INFO - Epoch [77][3800/5005]\tlr: 1.000e-03, eta: 2:51:36, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3169\n2023-01-01 00:15:01,252 - mmcls - INFO - Epoch [77][3900/5005]\tlr: 1.000e-03, eta: 2:51:27, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2975\n2023-01-01 00:15:10,046 - mmcls - INFO - Epoch [77][4000/5005]\tlr: 1.000e-03, eta: 2:51:19, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3123\n2023-01-01 00:15:19,049 - mmcls - INFO - Epoch [77][4100/5005]\tlr: 1.000e-03, eta: 2:51:10, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3244\n2023-01-01 00:15:27,601 - mmcls - INFO - Epoch [77][4200/5005]\tlr: 1.000e-03, eta: 2:51:01, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.3140\n2023-01-01 00:15:36,877 - mmcls - INFO - Epoch [77][4300/5005]\tlr: 1.000e-03, eta: 2:50:52, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.3098\n2023-01-01 00:15:45,386 - mmcls - INFO - Epoch [77][4400/5005]\tlr: 1.000e-03, eta: 2:50:43, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.3165\n2023-01-01 00:15:54,333 - mmcls - INFO - Epoch [77][4500/5005]\tlr: 1.000e-03, eta: 2:50:34, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3086\n2023-01-01 00:16:03,614 - mmcls - INFO - Epoch [77][4600/5005]\tlr: 1.000e-03, eta: 2:50:26, time: 0.093, data_time: 0.006, memory: 1224, loss: 1.3232\n2023-01-01 00:16:12,449 - mmcls - INFO - Epoch [77][4700/5005]\tlr: 1.000e-03, eta: 2:50:17, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2943\n2023-01-01 00:16:21,160 - mmcls - INFO - Epoch [77][4800/5005]\tlr: 1.000e-03, eta: 2:50:08, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3104\n2023-01-01 00:16:29,781 - mmcls - INFO - Epoch [77][4900/5005]\tlr: 1.000e-03, eta: 2:49:59, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3113\n2023-01-01 00:16:38,721 - mmcls - INFO - Epoch [77][5000/5005]\tlr: 1.000e-03, eta: 2:49:50, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.3196\n2023-01-01 00:17:01,272 - mmcls - INFO - Epoch(val) [77][196]\taccuracy_top-1: 70.8320, accuracy_top-5: 89.9060\n2023-01-01 00:17:12,214 - mmcls - INFO - Epoch [78][100/5005]\tlr: 1.000e-03, eta: 2:49:41, time: 0.109, data_time: 0.054, memory: 1224, loss: 1.2829\n2023-01-01 00:17:20,909 - mmcls - INFO - Epoch [78][200/5005]\tlr: 1.000e-03, eta: 2:49:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2961\n2023-01-01 00:17:29,828 - mmcls - INFO - Epoch [78][300/5005]\tlr: 1.000e-03, eta: 2:49:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3014\n2023-01-01 00:17:38,660 - mmcls - INFO - Epoch [78][400/5005]\tlr: 1.000e-03, eta: 2:49:15, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2814\n2023-01-01 00:17:47,595 - mmcls - INFO - Epoch [78][500/5005]\tlr: 1.000e-03, eta: 2:49:06, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3028\n2023-01-01 00:17:56,141 - mmcls - INFO - Epoch [78][600/5005]\tlr: 1.000e-03, eta: 2:48:57, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2921\n2023-01-01 00:18:05,299 - mmcls - INFO - Epoch [78][700/5005]\tlr: 1.000e-03, eta: 2:48:48, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3095\n2023-01-01 00:18:14,078 - mmcls - INFO - Epoch [78][800/5005]\tlr: 1.000e-03, eta: 2:48:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2919\n2023-01-01 00:18:23,026 - mmcls - INFO - Epoch [78][900/5005]\tlr: 1.000e-03, eta: 2:48:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2879\n2023-01-01 00:18:31,660 - mmcls - INFO - Epoch [78][1000/5005]\tlr: 1.000e-03, eta: 2:48:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2995\n2023-01-01 00:18:40,581 - mmcls - INFO - Epoch [78][1100/5005]\tlr: 1.000e-03, eta: 2:48:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2977\n2023-01-01 00:18:49,280 - mmcls - INFO - Epoch [78][1200/5005]\tlr: 1.000e-03, eta: 2:48:04, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2964\n2023-01-01 00:18:57,913 - mmcls - INFO - Epoch [78][1300/5005]\tlr: 1.000e-03, eta: 2:47:55, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2928\n2023-01-01 00:19:06,880 - mmcls - INFO - Epoch [78][1400/5005]\tlr: 1.000e-03, eta: 2:47:46, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3078\n2023-01-01 00:19:16,143 - mmcls - INFO - Epoch [78][1500/5005]\tlr: 1.000e-03, eta: 2:47:37, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2998\n2023-01-01 00:19:25,001 - mmcls - INFO - Epoch [78][1600/5005]\tlr: 1.000e-03, eta: 2:47:28, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.3132\n2023-01-01 00:19:33,750 - mmcls - INFO - Epoch [78][1700/5005]\tlr: 1.000e-03, eta: 2:47:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3230\n2023-01-01 00:19:42,821 - mmcls - INFO - Epoch [78][1800/5005]\tlr: 1.000e-03, eta: 2:47:11, time: 0.091, data_time: 0.036, memory: 1224, loss: 1.2753\n2023-01-01 00:19:51,721 - mmcls - INFO - Epoch [78][1900/5005]\tlr: 1.000e-03, eta: 2:47:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2958\n2023-01-01 00:20:00,617 - mmcls - INFO - Epoch [78][2000/5005]\tlr: 1.000e-03, eta: 2:46:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3168\n2023-01-01 00:20:09,592 - mmcls - INFO - Epoch [78][2100/5005]\tlr: 1.000e-03, eta: 2:46:44, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3026\n2023-01-01 00:20:18,362 - mmcls - INFO - Epoch [78][2200/5005]\tlr: 1.000e-03, eta: 2:46:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2881\n2023-01-01 00:20:26,886 - mmcls - INFO - Epoch [78][2300/5005]\tlr: 1.000e-03, eta: 2:46:26, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.3093\n2023-01-01 00:20:35,616 - mmcls - INFO - Epoch [78][2400/5005]\tlr: 1.000e-03, eta: 2:46:18, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2845\n2023-01-01 00:20:44,413 - mmcls - INFO - Epoch [78][2500/5005]\tlr: 1.000e-03, eta: 2:46:09, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2930\n2023-01-01 00:20:53,234 - mmcls - INFO - Epoch [78][2600/5005]\tlr: 1.000e-03, eta: 2:46:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2912\n2023-01-01 00:21:02,040 - mmcls - INFO - Epoch [78][2700/5005]\tlr: 1.000e-03, eta: 2:45:51, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3015\n2023-01-01 00:21:10,691 - mmcls - INFO - Epoch [78][2800/5005]\tlr: 1.000e-03, eta: 2:45:42, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3126\n2023-01-01 00:21:19,803 - mmcls - INFO - Epoch [78][2900/5005]\tlr: 1.000e-03, eta: 2:45:33, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.2819\n2023-01-01 00:21:28,554 - mmcls - INFO - Epoch [78][3000/5005]\tlr: 1.000e-03, eta: 2:45:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3061\n2023-01-01 00:21:37,449 - mmcls - INFO - Epoch [78][3100/5005]\tlr: 1.000e-03, eta: 2:45:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2889\n2023-01-01 00:21:46,158 - mmcls - INFO - Epoch [78][3200/5005]\tlr: 1.000e-03, eta: 2:45:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3008\n2023-01-01 00:21:54,694 - mmcls - INFO - Epoch [78][3300/5005]\tlr: 1.000e-03, eta: 2:44:58, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.3192\n2023-01-01 00:22:03,941 - mmcls - INFO - Epoch [78][3400/5005]\tlr: 1.000e-03, eta: 2:44:49, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3005\n2023-01-01 00:22:12,619 - mmcls - INFO - Epoch [78][3500/5005]\tlr: 1.000e-03, eta: 2:44:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2997\n2023-01-01 00:22:21,844 - mmcls - INFO - Epoch [78][3600/5005]\tlr: 1.000e-03, eta: 2:44:31, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3108\n2023-01-01 00:22:30,596 - mmcls - INFO - Epoch [78][3700/5005]\tlr: 1.000e-03, eta: 2:44:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3219\n2023-01-01 00:22:39,208 - mmcls - INFO - Epoch [78][3800/5005]\tlr: 1.000e-03, eta: 2:44:14, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2987\n2023-01-01 00:22:48,034 - mmcls - INFO - Epoch [78][3900/5005]\tlr: 1.000e-03, eta: 2:44:05, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3052\n2023-01-01 00:22:56,768 - mmcls - INFO - Epoch [78][4000/5005]\tlr: 1.000e-03, eta: 2:43:56, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3028\n2023-01-01 00:23:05,822 - mmcls - INFO - Epoch [78][4100/5005]\tlr: 1.000e-03, eta: 2:43:47, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2885\n2023-01-01 00:23:14,694 - mmcls - INFO - Epoch [78][4200/5005]\tlr: 1.000e-03, eta: 2:43:38, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3164\n2023-01-01 00:23:23,615 - mmcls - INFO - Epoch [78][4300/5005]\tlr: 1.000e-03, eta: 2:43:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3057\n2023-01-01 00:23:32,403 - mmcls - INFO - Epoch [78][4400/5005]\tlr: 1.000e-03, eta: 2:43:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2902\n2023-01-01 00:23:41,625 - mmcls - INFO - Epoch [78][4500/5005]\tlr: 1.000e-03, eta: 2:43:12, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.3237\n2023-01-01 00:23:50,569 - mmcls - INFO - Epoch [78][4600/5005]\tlr: 1.000e-03, eta: 2:43:03, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2999\n2023-01-01 00:23:59,335 - mmcls - INFO - Epoch [78][4700/5005]\tlr: 1.000e-03, eta: 2:42:54, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.3047\n2023-01-01 00:24:08,162 - mmcls - INFO - Epoch [78][4800/5005]\tlr: 1.000e-03, eta: 2:42:45, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3174\n2023-01-01 00:24:16,908 - mmcls - INFO - Epoch [78][4900/5005]\tlr: 1.000e-03, eta: 2:42:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3119\n2023-01-01 00:24:25,763 - mmcls - INFO - Epoch [78][5000/5005]\tlr: 1.000e-03, eta: 2:42:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2989\n2023-01-01 00:24:48,558 - mmcls - INFO - Epoch(val) [78][196]\taccuracy_top-1: 71.0000, accuracy_top-5: 89.8480\n2023-01-01 00:24:59,815 - mmcls - INFO - Epoch [79][100/5005]\tlr: 1.000e-03, eta: 2:42:19, time: 0.112, data_time: 0.031, memory: 1224, loss: 1.2893\n2023-01-01 00:25:08,691 - mmcls - INFO - Epoch [79][200/5005]\tlr: 1.000e-03, eta: 2:42:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2951\n2023-01-01 00:25:17,559 - mmcls - INFO - Epoch [79][300/5005]\tlr: 1.000e-03, eta: 2:42:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2902\n2023-01-01 00:25:26,770 - mmcls - INFO - Epoch [79][400/5005]\tlr: 1.000e-03, eta: 2:41:52, time: 0.092, data_time: 0.025, memory: 1224, loss: 1.2893\n2023-01-01 00:25:35,674 - mmcls - INFO - Epoch [79][500/5005]\tlr: 1.000e-03, eta: 2:41:43, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3006\n2023-01-01 00:25:44,611 - mmcls - INFO - Epoch [79][600/5005]\tlr: 1.000e-03, eta: 2:41:35, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2970\n2023-01-01 00:25:53,415 - mmcls - INFO - Epoch [79][700/5005]\tlr: 1.000e-03, eta: 2:41:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2629\n2023-01-01 00:26:02,297 - mmcls - INFO - Epoch [79][800/5005]\tlr: 1.000e-03, eta: 2:41:17, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2993\n2023-01-01 00:26:10,918 - mmcls - INFO - Epoch [79][900/5005]\tlr: 1.000e-03, eta: 2:41:08, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.3081\n2023-01-01 00:26:19,825 - mmcls - INFO - Epoch [79][1000/5005]\tlr: 1.000e-03, eta: 2:40:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3022\n2023-01-01 00:26:28,528 - mmcls - INFO - Epoch [79][1100/5005]\tlr: 1.000e-03, eta: 2:40:50, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.2824\n2023-01-01 00:26:37,346 - mmcls - INFO - Epoch [79][1200/5005]\tlr: 1.000e-03, eta: 2:40:41, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2900\n2023-01-01 00:26:46,161 - mmcls - INFO - Epoch [79][1300/5005]\tlr: 1.000e-03, eta: 2:40:32, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3031\n2023-01-01 00:26:55,010 - mmcls - INFO - Epoch [79][1400/5005]\tlr: 1.000e-03, eta: 2:40:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2951\n2023-01-01 00:27:04,104 - mmcls - INFO - Epoch [79][1500/5005]\tlr: 1.000e-03, eta: 2:40:15, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2777\n2023-01-01 00:27:12,927 - mmcls - INFO - Epoch [79][1600/5005]\tlr: 1.000e-03, eta: 2:40:06, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2879\n2023-01-01 00:27:21,574 - mmcls - INFO - Epoch [79][1700/5005]\tlr: 1.000e-03, eta: 2:39:57, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3004\n2023-01-01 00:27:30,726 - mmcls - INFO - Epoch [79][1800/5005]\tlr: 1.000e-03, eta: 2:39:48, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.2778\n2023-01-01 00:27:39,784 - mmcls - INFO - Epoch [79][1900/5005]\tlr: 1.000e-03, eta: 2:39:39, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.3041\n2023-01-01 00:27:48,384 - mmcls - INFO - Epoch [79][2000/5005]\tlr: 1.000e-03, eta: 2:39:31, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3142\n2023-01-01 00:27:57,431 - mmcls - INFO - Epoch [79][2100/5005]\tlr: 1.000e-03, eta: 2:39:22, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2987\n2023-01-01 00:28:06,066 - mmcls - INFO - Epoch [79][2200/5005]\tlr: 1.000e-03, eta: 2:39:13, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3009\n2023-01-01 00:28:14,970 - mmcls - INFO - Epoch [79][2300/5005]\tlr: 1.000e-03, eta: 2:39:04, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2893\n2023-01-01 00:28:23,759 - mmcls - INFO - Epoch [79][2400/5005]\tlr: 1.000e-03, eta: 2:38:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3021\n2023-01-01 00:28:32,647 - mmcls - INFO - Epoch [79][2500/5005]\tlr: 1.000e-03, eta: 2:38:46, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3047\n2023-01-01 00:28:41,325 - mmcls - INFO - Epoch [79][2600/5005]\tlr: 1.000e-03, eta: 2:38:37, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3011\n2023-01-01 00:28:50,066 - mmcls - INFO - Epoch [79][2700/5005]\tlr: 1.000e-03, eta: 2:38:28, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2937\n2023-01-01 00:28:58,931 - mmcls - INFO - Epoch [79][2800/5005]\tlr: 1.000e-03, eta: 2:38:20, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3275\n2023-01-01 00:29:08,057 - mmcls - INFO - Epoch [79][2900/5005]\tlr: 1.000e-03, eta: 2:38:11, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2968\n2023-01-01 00:29:16,835 - mmcls - INFO - Epoch [79][3000/5005]\tlr: 1.000e-03, eta: 2:38:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2995\n2023-01-01 00:29:25,702 - mmcls - INFO - Epoch [79][3100/5005]\tlr: 1.000e-03, eta: 2:37:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2994\n2023-01-01 00:29:34,489 - mmcls - INFO - Epoch [79][3200/5005]\tlr: 1.000e-03, eta: 2:37:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3077\n2023-01-01 00:29:43,581 - mmcls - INFO - Epoch [79][3300/5005]\tlr: 1.000e-03, eta: 2:37:35, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.3142\n2023-01-01 00:29:52,403 - mmcls - INFO - Epoch [79][3400/5005]\tlr: 1.000e-03, eta: 2:37:27, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3127\n2023-01-01 00:30:01,072 - mmcls - INFO - Epoch [79][3500/5005]\tlr: 1.000e-03, eta: 2:37:18, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3232\n2023-01-01 00:30:09,937 - mmcls - INFO - Epoch [79][3600/5005]\tlr: 1.000e-03, eta: 2:37:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3163\n2023-01-01 00:30:18,682 - mmcls - INFO - Epoch [79][3700/5005]\tlr: 1.000e-03, eta: 2:37:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3136\n2023-01-01 00:30:27,275 - mmcls - INFO - Epoch [79][3800/5005]\tlr: 1.000e-03, eta: 2:36:51, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2831\n2023-01-01 00:30:36,280 - mmcls - INFO - Epoch [79][3900/5005]\tlr: 1.000e-03, eta: 2:36:42, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.2930\n2023-01-01 00:30:45,136 - mmcls - INFO - Epoch [79][4000/5005]\tlr: 1.000e-03, eta: 2:36:33, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2900\n2023-01-01 00:30:53,925 - mmcls - INFO - Epoch [79][4100/5005]\tlr: 1.000e-03, eta: 2:36:25, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3092\n2023-01-01 00:31:02,676 - mmcls - INFO - Epoch [79][4200/5005]\tlr: 1.000e-03, eta: 2:36:16, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3183\n2023-01-01 00:31:11,496 - mmcls - INFO - Epoch [79][4300/5005]\tlr: 1.000e-03, eta: 2:36:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3062\n2023-01-01 00:31:20,269 - mmcls - INFO - Epoch [79][4400/5005]\tlr: 1.000e-03, eta: 2:35:58, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3120\n2023-01-01 00:31:29,170 - mmcls - INFO - Epoch [79][4500/5005]\tlr: 1.000e-03, eta: 2:35:49, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2844\n2023-01-01 00:31:37,842 - mmcls - INFO - Epoch [79][4600/5005]\tlr: 1.000e-03, eta: 2:35:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3209\n2023-01-01 00:31:46,676 - mmcls - INFO - Epoch [79][4700/5005]\tlr: 1.000e-03, eta: 2:35:31, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2798\n2023-01-01 00:31:55,674 - mmcls - INFO - Epoch [79][4800/5005]\tlr: 1.000e-03, eta: 2:35:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3105\n2023-01-01 00:32:04,125 - mmcls - INFO - Epoch [79][4900/5005]\tlr: 1.000e-03, eta: 2:35:14, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.3036\n2023-01-01 00:32:13,130 - mmcls - INFO - Epoch [79][5000/5005]\tlr: 1.000e-03, eta: 2:35:05, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2972\n2023-01-01 00:32:35,662 - mmcls - INFO - Epoch(val) [79][196]\taccuracy_top-1: 70.8720, accuracy_top-5: 89.8440\n2023-01-01 00:32:46,835 - mmcls - INFO - Epoch [80][100/5005]\tlr: 1.000e-03, eta: 2:34:56, time: 0.111, data_time: 0.036, memory: 1224, loss: 1.3141\n2023-01-01 00:32:55,612 - mmcls - INFO - Epoch [80][200/5005]\tlr: 1.000e-03, eta: 2:34:47, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3048\n2023-01-01 00:33:04,561 - mmcls - INFO - Epoch [80][300/5005]\tlr: 1.000e-03, eta: 2:34:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2886\n2023-01-01 00:33:13,193 - mmcls - INFO - Epoch [80][400/5005]\tlr: 1.000e-03, eta: 2:34:29, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2773\n2023-01-01 00:33:22,095 - mmcls - INFO - Epoch [80][500/5005]\tlr: 1.000e-03, eta: 2:34:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2810\n2023-01-01 00:33:31,096 - mmcls - INFO - Epoch [80][600/5005]\tlr: 1.000e-03, eta: 2:34:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2821\n2023-01-01 00:33:40,193 - mmcls - INFO - Epoch [80][700/5005]\tlr: 1.000e-03, eta: 2:34:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.2897\n2023-01-01 00:33:48,908 - mmcls - INFO - Epoch [80][800/5005]\tlr: 1.000e-03, eta: 2:33:54, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3056\n2023-01-01 00:33:57,756 - mmcls - INFO - Epoch [80][900/5005]\tlr: 1.000e-03, eta: 2:33:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3023\n2023-01-01 00:34:06,237 - mmcls - INFO - Epoch [80][1000/5005]\tlr: 1.000e-03, eta: 2:33:36, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2800\n2023-01-01 00:34:15,428 - mmcls - INFO - Epoch [80][1100/5005]\tlr: 1.000e-03, eta: 2:33:27, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2942\n2023-01-01 00:34:24,006 - mmcls - INFO - Epoch [80][1200/5005]\tlr: 1.000e-03, eta: 2:33:18, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2819\n2023-01-01 00:34:32,843 - mmcls - INFO - Epoch [80][1300/5005]\tlr: 1.000e-03, eta: 2:33:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2939\n2023-01-01 00:34:41,553 - mmcls - INFO - Epoch [80][1400/5005]\tlr: 1.000e-03, eta: 2:33:01, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.2898\n2023-01-01 00:34:50,173 - mmcls - INFO - Epoch [80][1500/5005]\tlr: 1.000e-03, eta: 2:32:52, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2871\n2023-01-01 00:34:59,160 - mmcls - INFO - Epoch [80][1600/5005]\tlr: 1.000e-03, eta: 2:32:43, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.2850\n2023-01-01 00:35:07,836 - mmcls - INFO - Epoch [80][1700/5005]\tlr: 1.000e-03, eta: 2:32:34, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2795\n2023-01-01 00:35:16,905 - mmcls - INFO - Epoch [80][1800/5005]\tlr: 1.000e-03, eta: 2:32:25, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3082\n2023-01-01 00:35:25,510 - mmcls - INFO - Epoch [80][1900/5005]\tlr: 1.000e-03, eta: 2:32:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2849\n2023-01-01 00:35:34,361 - mmcls - INFO - Epoch [80][2000/5005]\tlr: 1.000e-03, eta: 2:32:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2886\n2023-01-01 00:35:43,014 - mmcls - INFO - Epoch [80][2100/5005]\tlr: 1.000e-03, eta: 2:31:59, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2865\n2023-01-01 00:35:52,056 - mmcls - INFO - Epoch [80][2200/5005]\tlr: 1.000e-03, eta: 2:31:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3099\n2023-01-01 00:36:00,784 - mmcls - INFO - Epoch [80][2300/5005]\tlr: 1.000e-03, eta: 2:31:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2879\n2023-01-01 00:36:09,373 - mmcls - INFO - Epoch [80][2400/5005]\tlr: 1.000e-03, eta: 2:31:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3045\n2023-01-01 00:36:18,370 - mmcls - INFO - Epoch [80][2500/5005]\tlr: 1.000e-03, eta: 2:31:23, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2736\n2023-01-01 00:36:26,949 - mmcls - INFO - Epoch [80][2600/5005]\tlr: 1.000e-03, eta: 2:31:14, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2990\n2023-01-01 00:36:35,554 - mmcls - INFO - Epoch [80][2700/5005]\tlr: 1.000e-03, eta: 2:31:05, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2946\n2023-01-01 00:36:44,163 - mmcls - INFO - Epoch [80][2800/5005]\tlr: 1.000e-03, eta: 2:30:56, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3148\n2023-01-01 00:36:52,836 - mmcls - INFO - Epoch [80][2900/5005]\tlr: 1.000e-03, eta: 2:30:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2886\n2023-01-01 00:37:01,609 - mmcls - INFO - Epoch [80][3000/5005]\tlr: 1.000e-03, eta: 2:30:39, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3011\n2023-01-01 00:37:10,160 - mmcls - INFO - Epoch [80][3100/5005]\tlr: 1.000e-03, eta: 2:30:30, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.3041\n2023-01-01 00:37:19,292 - mmcls - INFO - Epoch [80][3200/5005]\tlr: 1.000e-03, eta: 2:30:21, time: 0.091, data_time: 0.034, memory: 1224, loss: 1.3308\n2023-01-01 00:37:28,082 - mmcls - INFO - Epoch [80][3300/5005]\tlr: 1.000e-03, eta: 2:30:12, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2721\n2023-01-01 00:37:36,724 - mmcls - INFO - Epoch [80][3400/5005]\tlr: 1.000e-03, eta: 2:30:03, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2986\n2023-01-01 00:37:45,469 - mmcls - INFO - Epoch [80][3500/5005]\tlr: 1.000e-03, eta: 2:29:54, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3231\n2023-01-01 00:37:54,176 - mmcls - INFO - Epoch [80][3600/5005]\tlr: 1.000e-03, eta: 2:29:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3109\n2023-01-01 00:38:02,724 - mmcls - INFO - Epoch [80][3700/5005]\tlr: 1.000e-03, eta: 2:29:36, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.3084\n2023-01-01 00:38:11,401 - mmcls - INFO - Epoch [80][3800/5005]\tlr: 1.000e-03, eta: 2:29:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3036\n2023-01-01 00:38:20,134 - mmcls - INFO - Epoch [80][3900/5005]\tlr: 1.000e-03, eta: 2:29:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2956\n2023-01-01 00:38:29,504 - mmcls - INFO - Epoch [80][4000/5005]\tlr: 1.000e-03, eta: 2:29:10, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.3044\n2023-01-01 00:38:38,352 - mmcls - INFO - Epoch [80][4100/5005]\tlr: 1.000e-03, eta: 2:29:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2895\n2023-01-01 00:38:46,848 - mmcls - INFO - Epoch [80][4200/5005]\tlr: 1.000e-03, eta: 2:28:52, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3085\n2023-01-01 00:38:55,898 - mmcls - INFO - Epoch [80][4300/5005]\tlr: 1.000e-03, eta: 2:28:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3175\n2023-01-01 00:39:04,955 - mmcls - INFO - Epoch [80][4400/5005]\tlr: 1.000e-03, eta: 2:28:35, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3032\n2023-01-01 00:39:13,758 - mmcls - INFO - Epoch [80][4500/5005]\tlr: 1.000e-03, eta: 2:28:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2877\n2023-01-01 00:39:22,721 - mmcls - INFO - Epoch [80][4600/5005]\tlr: 1.000e-03, eta: 2:28:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2962\n2023-01-01 00:39:31,575 - mmcls - INFO - Epoch [80][4700/5005]\tlr: 1.000e-03, eta: 2:28:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3089\n2023-01-01 00:39:40,527 - mmcls - INFO - Epoch [80][4800/5005]\tlr: 1.000e-03, eta: 2:27:59, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3244\n2023-01-01 00:39:49,564 - mmcls - INFO - Epoch [80][4900/5005]\tlr: 1.000e-03, eta: 2:27:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2871\n2023-01-01 00:39:58,264 - mmcls - INFO - Epoch [80][5000/5005]\tlr: 1.000e-03, eta: 2:27:41, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3107\n2023-01-01 00:40:21,095 - mmcls - INFO - Epoch(val) [80][196]\taccuracy_top-1: 70.8120, accuracy_top-5: 89.8560\n2023-01-01 00:40:31,956 - mmcls - INFO - Epoch [81][100/5005]\tlr: 1.000e-03, eta: 2:27:33, time: 0.108, data_time: 0.050, memory: 1224, loss: 1.2983\n2023-01-01 00:40:41,106 - mmcls - INFO - Epoch [81][200/5005]\tlr: 1.000e-03, eta: 2:27:24, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.2828\n2023-01-01 00:40:50,266 - mmcls - INFO - Epoch [81][300/5005]\tlr: 1.000e-03, eta: 2:27:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.2888\n2023-01-01 00:40:59,180 - mmcls - INFO - Epoch [81][400/5005]\tlr: 1.000e-03, eta: 2:27:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3094\n2023-01-01 00:41:08,110 - mmcls - INFO - Epoch [81][500/5005]\tlr: 1.000e-03, eta: 2:26:57, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.2818\n2023-01-01 00:41:16,933 - mmcls - INFO - Epoch [81][600/5005]\tlr: 1.000e-03, eta: 2:26:48, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.2824\n2023-01-01 00:41:25,583 - mmcls - INFO - Epoch [81][700/5005]\tlr: 1.000e-03, eta: 2:26:40, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.2877\n2023-01-01 00:41:34,524 - mmcls - INFO - Epoch [81][800/5005]\tlr: 1.000e-03, eta: 2:26:31, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2852\n2023-01-01 00:41:43,393 - mmcls - INFO - Epoch [81][900/5005]\tlr: 1.000e-03, eta: 2:26:22, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.3032\n2023-01-01 00:41:52,158 - mmcls - INFO - Epoch [81][1000/5005]\tlr: 1.000e-03, eta: 2:26:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2860\n2023-01-01 00:42:01,046 - mmcls - INFO - Epoch [81][1100/5005]\tlr: 1.000e-03, eta: 2:26:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2938\n2023-01-01 00:42:10,104 - mmcls - INFO - Epoch [81][1200/5005]\tlr: 1.000e-03, eta: 2:25:55, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2951\n2023-01-01 00:42:18,954 - mmcls - INFO - Epoch [81][1300/5005]\tlr: 1.000e-03, eta: 2:25:47, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2849\n2023-01-01 00:42:27,734 - mmcls - INFO - Epoch [81][1400/5005]\tlr: 1.000e-03, eta: 2:25:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2818\n2023-01-01 00:42:36,584 - mmcls - INFO - Epoch [81][1500/5005]\tlr: 1.000e-03, eta: 2:25:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3066\n2023-01-01 00:42:45,426 - mmcls - INFO - Epoch [81][1600/5005]\tlr: 1.000e-03, eta: 2:25:20, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2743\n2023-01-01 00:42:54,229 - mmcls - INFO - Epoch [81][1700/5005]\tlr: 1.000e-03, eta: 2:25:11, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2878\n2023-01-01 00:43:02,900 - mmcls - INFO - Epoch [81][1800/5005]\tlr: 1.000e-03, eta: 2:25:02, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.3188\n2023-01-01 00:43:11,704 - mmcls - INFO - Epoch [81][1900/5005]\tlr: 1.000e-03, eta: 2:24:53, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2842\n2023-01-01 00:43:20,364 - mmcls - INFO - Epoch [81][2000/5005]\tlr: 1.000e-03, eta: 2:24:44, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2795\n2023-01-01 00:43:29,254 - mmcls - INFO - Epoch [81][2100/5005]\tlr: 1.000e-03, eta: 2:24:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2781\n2023-01-01 00:43:38,098 - mmcls - INFO - Epoch [81][2200/5005]\tlr: 1.000e-03, eta: 2:24:27, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3047\n2023-01-01 00:43:46,832 - mmcls - INFO - Epoch [81][2300/5005]\tlr: 1.000e-03, eta: 2:24:18, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2829\n2023-01-01 00:43:55,584 - mmcls - INFO - Epoch [81][2400/5005]\tlr: 1.000e-03, eta: 2:24:09, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3008\n2023-01-01 00:44:04,048 - mmcls - INFO - Epoch [81][2500/5005]\tlr: 1.000e-03, eta: 2:24:00, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.2849\n2023-01-01 00:44:12,760 - mmcls - INFO - Epoch [81][2600/5005]\tlr: 1.000e-03, eta: 2:23:51, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2912\n2023-01-01 00:44:21,473 - mmcls - INFO - Epoch [81][2700/5005]\tlr: 1.000e-03, eta: 2:23:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2861\n2023-01-01 00:44:30,274 - mmcls - INFO - Epoch [81][2800/5005]\tlr: 1.000e-03, eta: 2:23:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2875\n2023-01-01 00:44:39,169 - mmcls - INFO - Epoch [81][2900/5005]\tlr: 1.000e-03, eta: 2:23:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2989\n2023-01-01 00:44:47,808 - mmcls - INFO - Epoch [81][3000/5005]\tlr: 1.000e-03, eta: 2:23:16, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2879\n2023-01-01 00:44:56,503 - mmcls - INFO - Epoch [81][3100/5005]\tlr: 1.000e-03, eta: 2:23:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3019\n2023-01-01 00:45:05,408 - mmcls - INFO - Epoch [81][3200/5005]\tlr: 1.000e-03, eta: 2:22:58, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.2877\n2023-01-01 00:45:14,175 - mmcls - INFO - Epoch [81][3300/5005]\tlr: 1.000e-03, eta: 2:22:49, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2807\n2023-01-01 00:45:23,626 - mmcls - INFO - Epoch [81][3400/5005]\tlr: 1.000e-03, eta: 2:22:40, time: 0.094, data_time: 0.031, memory: 1224, loss: 1.2908\n2023-01-01 00:45:32,588 - mmcls - INFO - Epoch [81][3500/5005]\tlr: 1.000e-03, eta: 2:22:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3014\n2023-01-01 00:45:41,258 - mmcls - INFO - Epoch [81][3600/5005]\tlr: 1.000e-03, eta: 2:22:23, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2978\n2023-01-01 00:45:50,165 - mmcls - INFO - Epoch [81][3700/5005]\tlr: 1.000e-03, eta: 2:22:14, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2921\n2023-01-01 00:45:59,078 - mmcls - INFO - Epoch [81][3800/5005]\tlr: 1.000e-03, eta: 2:22:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2983\n2023-01-01 00:46:07,920 - mmcls - INFO - Epoch [81][3900/5005]\tlr: 1.000e-03, eta: 2:21:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2900\n2023-01-01 00:46:17,024 - mmcls - INFO - Epoch [81][4000/5005]\tlr: 1.000e-03, eta: 2:21:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2788\n2023-01-01 00:46:25,806 - mmcls - INFO - Epoch [81][4100/5005]\tlr: 1.000e-03, eta: 2:21:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2878\n2023-01-01 00:46:34,773 - mmcls - INFO - Epoch [81][4200/5005]\tlr: 1.000e-03, eta: 2:21:30, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3129\n2023-01-01 00:46:43,551 - mmcls - INFO - Epoch [81][4300/5005]\tlr: 1.000e-03, eta: 2:21:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2799\n2023-01-01 00:46:52,349 - mmcls - INFO - Epoch [81][4400/5005]\tlr: 1.000e-03, eta: 2:21:12, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2992\n2023-01-01 00:47:01,039 - mmcls - INFO - Epoch [81][4500/5005]\tlr: 1.000e-03, eta: 2:21:03, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2932\n2023-01-01 00:47:09,840 - mmcls - INFO - Epoch [81][4600/5005]\tlr: 1.000e-03, eta: 2:20:54, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2722\n2023-01-01 00:47:18,574 - mmcls - INFO - Epoch [81][4700/5005]\tlr: 1.000e-03, eta: 2:20:45, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2962\n2023-01-01 00:47:27,093 - mmcls - INFO - Epoch [81][4800/5005]\tlr: 1.000e-03, eta: 2:20:36, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3120\n2023-01-01 00:47:36,177 - mmcls - INFO - Epoch [81][4900/5005]\tlr: 1.000e-03, eta: 2:20:27, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3039\n2023-01-01 00:47:44,700 - mmcls - INFO - Epoch [81][5000/5005]\tlr: 1.000e-03, eta: 2:20:19, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.3071\n2023-01-01 00:48:07,370 - mmcls - INFO - Epoch(val) [81][196]\taccuracy_top-1: 70.9600, accuracy_top-5: 89.8140\n2023-01-01 00:48:18,282 - mmcls - INFO - Epoch [82][100/5005]\tlr: 1.000e-03, eta: 2:20:10, time: 0.109, data_time: 0.041, memory: 1224, loss: 1.2730\n2023-01-01 00:48:27,046 - mmcls - INFO - Epoch [82][200/5005]\tlr: 1.000e-03, eta: 2:20:01, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.3126\n2023-01-01 00:48:36,110 - mmcls - INFO - Epoch [82][300/5005]\tlr: 1.000e-03, eta: 2:19:52, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2974\n2023-01-01 00:48:44,751 - mmcls - INFO - Epoch [82][400/5005]\tlr: 1.000e-03, eta: 2:19:43, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2873\n2023-01-01 00:48:53,480 - mmcls - INFO - Epoch [82][500/5005]\tlr: 1.000e-03, eta: 2:19:34, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2987\n2023-01-01 00:49:02,564 - mmcls - INFO - Epoch [82][600/5005]\tlr: 1.000e-03, eta: 2:19:25, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3060\n2023-01-01 00:49:11,246 - mmcls - INFO - Epoch [82][700/5005]\tlr: 1.000e-03, eta: 2:19:16, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2958\n2023-01-01 00:49:20,046 - mmcls - INFO - Epoch [82][800/5005]\tlr: 1.000e-03, eta: 2:19:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3001\n2023-01-01 00:49:28,765 - mmcls - INFO - Epoch [82][900/5005]\tlr: 1.000e-03, eta: 2:18:59, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2774\n2023-01-01 00:49:37,962 - mmcls - INFO - Epoch [82][1000/5005]\tlr: 1.000e-03, eta: 2:18:50, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2871\n2023-01-01 00:49:46,634 - mmcls - INFO - Epoch [82][1100/5005]\tlr: 1.000e-03, eta: 2:18:41, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2862\n2023-01-01 00:49:55,435 - mmcls - INFO - Epoch [82][1200/5005]\tlr: 1.000e-03, eta: 2:18:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2895\n2023-01-01 00:50:04,458 - mmcls - INFO - Epoch [82][1300/5005]\tlr: 1.000e-03, eta: 2:18:23, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2945\n2023-01-01 00:50:13,415 - mmcls - INFO - Epoch [82][1400/5005]\tlr: 1.000e-03, eta: 2:18:15, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3015\n2023-01-01 00:50:21,993 - mmcls - INFO - Epoch [82][1500/5005]\tlr: 1.000e-03, eta: 2:18:06, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2938\n2023-01-01 00:50:30,621 - mmcls - INFO - Epoch [82][1600/5005]\tlr: 1.000e-03, eta: 2:17:57, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2721\n2023-01-01 00:50:39,441 - mmcls - INFO - Epoch [82][1700/5005]\tlr: 1.000e-03, eta: 2:17:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2834\n2023-01-01 00:50:48,453 - mmcls - INFO - Epoch [82][1800/5005]\tlr: 1.000e-03, eta: 2:17:39, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2878\n2023-01-01 00:50:57,169 - mmcls - INFO - Epoch [82][1900/5005]\tlr: 1.000e-03, eta: 2:17:30, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.3010\n2023-01-01 00:51:06,325 - mmcls - INFO - Epoch [82][2000/5005]\tlr: 1.000e-03, eta: 2:17:21, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2895\n2023-01-01 00:51:14,732 - mmcls - INFO - Epoch [82][2100/5005]\tlr: 1.000e-03, eta: 2:17:12, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.2899\n2023-01-01 00:51:23,709 - mmcls - INFO - Epoch [82][2200/5005]\tlr: 1.000e-03, eta: 2:17:04, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2963\n2023-01-01 00:51:32,646 - mmcls - INFO - Epoch [82][2300/5005]\tlr: 1.000e-03, eta: 2:16:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2974\n2023-01-01 00:51:41,493 - mmcls - INFO - Epoch [82][2400/5005]\tlr: 1.000e-03, eta: 2:16:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2739\n2023-01-01 00:51:50,487 - mmcls - INFO - Epoch [82][2500/5005]\tlr: 1.000e-03, eta: 2:16:37, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3045\n2023-01-01 00:51:59,052 - mmcls - INFO - Epoch [82][2600/5005]\tlr: 1.000e-03, eta: 2:16:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2940\n2023-01-01 00:52:07,664 - mmcls - INFO - Epoch [82][2700/5005]\tlr: 1.000e-03, eta: 2:16:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3043\n2023-01-01 00:52:16,546 - mmcls - INFO - Epoch [82][2800/5005]\tlr: 1.000e-03, eta: 2:16:10, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.3021\n2023-01-01 00:52:25,205 - mmcls - INFO - Epoch [82][2900/5005]\tlr: 1.000e-03, eta: 2:16:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2918\n2023-01-01 00:52:34,045 - mmcls - INFO - Epoch [82][3000/5005]\tlr: 1.000e-03, eta: 2:15:53, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2677\n2023-01-01 00:52:42,702 - mmcls - INFO - Epoch [82][3100/5005]\tlr: 1.000e-03, eta: 2:15:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2935\n2023-01-01 00:52:51,446 - mmcls - INFO - Epoch [82][3200/5005]\tlr: 1.000e-03, eta: 2:15:35, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2918\n2023-01-01 00:53:00,300 - mmcls - INFO - Epoch [82][3300/5005]\tlr: 1.000e-03, eta: 2:15:26, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2744\n2023-01-01 00:53:09,090 - mmcls - INFO - Epoch [82][3400/5005]\tlr: 1.000e-03, eta: 2:15:17, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2925\n2023-01-01 00:53:17,698 - mmcls - INFO - Epoch [82][3500/5005]\tlr: 1.000e-03, eta: 2:15:08, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.3016\n2023-01-01 00:53:26,574 - mmcls - INFO - Epoch [82][3600/5005]\tlr: 1.000e-03, eta: 2:14:59, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2831\n2023-01-01 00:53:35,156 - mmcls - INFO - Epoch [82][3700/5005]\tlr: 1.000e-03, eta: 2:14:50, time: 0.086, data_time: 0.031, memory: 1224, loss: 1.3006\n2023-01-01 00:53:44,091 - mmcls - INFO - Epoch [82][3800/5005]\tlr: 1.000e-03, eta: 2:14:42, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.2782\n2023-01-01 00:53:52,747 - mmcls - INFO - Epoch [82][3900/5005]\tlr: 1.000e-03, eta: 2:14:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2758\n2023-01-01 00:54:01,491 - mmcls - INFO - Epoch [82][4000/5005]\tlr: 1.000e-03, eta: 2:14:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2847\n2023-01-01 00:54:10,363 - mmcls - INFO - Epoch [82][4100/5005]\tlr: 1.000e-03, eta: 2:14:15, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2831\n2023-01-01 00:54:19,080 - mmcls - INFO - Epoch [82][4200/5005]\tlr: 1.000e-03, eta: 2:14:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2811\n2023-01-01 00:54:28,035 - mmcls - INFO - Epoch [82][4300/5005]\tlr: 1.000e-03, eta: 2:13:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3008\n2023-01-01 00:54:37,130 - mmcls - INFO - Epoch [82][4400/5005]\tlr: 1.000e-03, eta: 2:13:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2813\n2023-01-01 00:54:46,159 - mmcls - INFO - Epoch [82][4500/5005]\tlr: 1.000e-03, eta: 2:13:40, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3124\n2023-01-01 00:54:55,005 - mmcls - INFO - Epoch [82][4600/5005]\tlr: 1.000e-03, eta: 2:13:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2967\n2023-01-01 00:55:03,787 - mmcls - INFO - Epoch [82][4700/5005]\tlr: 1.000e-03, eta: 2:13:22, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3040\n2023-01-01 00:55:12,524 - mmcls - INFO - Epoch [82][4800/5005]\tlr: 1.000e-03, eta: 2:13:13, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3220\n2023-01-01 00:55:21,494 - mmcls - INFO - Epoch [82][4900/5005]\tlr: 1.000e-03, eta: 2:13:04, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.3056\n2023-01-01 00:55:30,468 - mmcls - INFO - Epoch [82][5000/5005]\tlr: 1.000e-03, eta: 2:12:55, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3092\n2023-01-01 00:55:53,188 - mmcls - INFO - Epoch(val) [82][196]\taccuracy_top-1: 71.0820, accuracy_top-5: 89.9260\n2023-01-01 00:56:04,320 - mmcls - INFO - Epoch [83][100/5005]\tlr: 1.000e-03, eta: 2:12:47, time: 0.111, data_time: 0.057, memory: 1224, loss: 1.2851\n2023-01-01 00:56:12,883 - mmcls - INFO - Epoch [83][200/5005]\tlr: 1.000e-03, eta: 2:12:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3035\n2023-01-01 00:56:21,822 - mmcls - INFO - Epoch [83][300/5005]\tlr: 1.000e-03, eta: 2:12:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2717\n2023-01-01 00:56:30,567 - mmcls - INFO - Epoch [83][400/5005]\tlr: 1.000e-03, eta: 2:12:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2804\n2023-01-01 00:56:39,197 - mmcls - INFO - Epoch [83][500/5005]\tlr: 1.000e-03, eta: 2:12:11, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2509\n2023-01-01 00:56:48,269 - mmcls - INFO - Epoch [83][600/5005]\tlr: 1.000e-03, eta: 2:12:02, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2801\n2023-01-01 00:56:56,860 - mmcls - INFO - Epoch [83][700/5005]\tlr: 1.000e-03, eta: 2:11:53, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2589\n2023-01-01 00:57:05,760 - mmcls - INFO - Epoch [83][800/5005]\tlr: 1.000e-03, eta: 2:11:44, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2735\n2023-01-01 00:57:14,551 - mmcls - INFO - Epoch [83][900/5005]\tlr: 1.000e-03, eta: 2:11:36, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2896\n2023-01-01 00:57:23,259 - mmcls - INFO - Epoch [83][1000/5005]\tlr: 1.000e-03, eta: 2:11:27, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2804\n2023-01-01 00:57:32,178 - mmcls - INFO - Epoch [83][1100/5005]\tlr: 1.000e-03, eta: 2:11:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2860\n2023-01-01 00:57:41,331 - mmcls - INFO - Epoch [83][1200/5005]\tlr: 1.000e-03, eta: 2:11:09, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.3012\n2023-01-01 00:57:50,041 - mmcls - INFO - Epoch [83][1300/5005]\tlr: 1.000e-03, eta: 2:11:00, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2683\n2023-01-01 00:57:59,026 - mmcls - INFO - Epoch [83][1400/5005]\tlr: 1.000e-03, eta: 2:10:51, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2939\n2023-01-01 00:58:07,798 - mmcls - INFO - Epoch [83][1500/5005]\tlr: 1.000e-03, eta: 2:10:42, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2981\n2023-01-01 00:58:16,733 - mmcls - INFO - Epoch [83][1600/5005]\tlr: 1.000e-03, eta: 2:10:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2792\n2023-01-01 00:58:25,634 - mmcls - INFO - Epoch [83][1700/5005]\tlr: 1.000e-03, eta: 2:10:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2844\n2023-01-01 00:58:34,315 - mmcls - INFO - Epoch [83][1800/5005]\tlr: 1.000e-03, eta: 2:10:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2756\n2023-01-01 00:58:43,168 - mmcls - INFO - Epoch [83][1900/5005]\tlr: 1.000e-03, eta: 2:10:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2809\n2023-01-01 00:58:52,015 - mmcls - INFO - Epoch [83][2000/5005]\tlr: 1.000e-03, eta: 2:09:58, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2690\n2023-01-01 00:59:00,843 - mmcls - INFO - Epoch [83][2100/5005]\tlr: 1.000e-03, eta: 2:09:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2972\n2023-01-01 00:59:09,379 - mmcls - INFO - Epoch [83][2200/5005]\tlr: 1.000e-03, eta: 2:09:40, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.3035\n2023-01-01 00:59:18,214 - mmcls - INFO - Epoch [83][2300/5005]\tlr: 1.000e-03, eta: 2:09:32, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.2941\n2023-01-01 00:59:27,434 - mmcls - INFO - Epoch [83][2400/5005]\tlr: 1.000e-03, eta: 2:09:23, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.2883\n2023-01-01 00:59:35,898 - mmcls - INFO - Epoch [83][2500/5005]\tlr: 1.000e-03, eta: 2:09:14, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.2925\n2023-01-01 00:59:44,623 - mmcls - INFO - Epoch [83][2600/5005]\tlr: 1.000e-03, eta: 2:09:05, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2862\n2023-01-01 00:59:53,211 - mmcls - INFO - Epoch [83][2700/5005]\tlr: 1.000e-03, eta: 2:08:56, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3044\n2023-01-01 01:00:01,698 - mmcls - INFO - Epoch [83][2800/5005]\tlr: 1.000e-03, eta: 2:08:47, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.2890\n2023-01-01 01:00:10,718 - mmcls - INFO - Epoch [83][2900/5005]\tlr: 1.000e-03, eta: 2:08:38, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2819\n2023-01-01 01:00:19,750 - mmcls - INFO - Epoch [83][3000/5005]\tlr: 1.000e-03, eta: 2:08:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3019\n2023-01-01 01:00:28,355 - mmcls - INFO - Epoch [83][3100/5005]\tlr: 1.000e-03, eta: 2:08:21, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2893\n2023-01-01 01:00:37,125 - mmcls - INFO - Epoch [83][3200/5005]\tlr: 1.000e-03, eta: 2:08:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2851\n2023-01-01 01:00:45,913 - mmcls - INFO - Epoch [83][3300/5005]\tlr: 1.000e-03, eta: 2:08:03, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3029\n2023-01-01 01:00:55,026 - mmcls - INFO - Epoch [83][3400/5005]\tlr: 1.000e-03, eta: 2:07:54, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.3108\n2023-01-01 01:01:03,700 - mmcls - INFO - Epoch [83][3500/5005]\tlr: 1.000e-03, eta: 2:07:45, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3104\n2023-01-01 01:01:12,595 - mmcls - INFO - Epoch [83][3600/5005]\tlr: 1.000e-03, eta: 2:07:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2883\n2023-01-01 01:01:21,561 - mmcls - INFO - Epoch [83][3700/5005]\tlr: 1.000e-03, eta: 2:07:27, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3123\n2023-01-01 01:01:30,172 - mmcls - INFO - Epoch [83][3800/5005]\tlr: 1.000e-03, eta: 2:07:19, time: 0.086, data_time: 0.030, memory: 1224, loss: 1.2895\n2023-01-01 01:01:38,777 - mmcls - INFO - Epoch [83][3900/5005]\tlr: 1.000e-03, eta: 2:07:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2712\n2023-01-01 01:01:47,423 - mmcls - INFO - Epoch [83][4000/5005]\tlr: 1.000e-03, eta: 2:07:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2919\n2023-01-01 01:01:56,302 - mmcls - INFO - Epoch [83][4100/5005]\tlr: 1.000e-03, eta: 2:06:52, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2910\n2023-01-01 01:02:05,056 - mmcls - INFO - Epoch [83][4200/5005]\tlr: 1.000e-03, eta: 2:06:43, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.2830\n2023-01-01 01:02:13,685 - mmcls - INFO - Epoch [83][4300/5005]\tlr: 1.000e-03, eta: 2:06:34, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3035\n2023-01-01 01:02:22,293 - mmcls - INFO - Epoch [83][4400/5005]\tlr: 1.000e-03, eta: 2:06:25, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2990\n2023-01-01 01:02:31,288 - mmcls - INFO - Epoch [83][4500/5005]\tlr: 1.000e-03, eta: 2:06:16, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.2826\n2023-01-01 01:02:40,032 - mmcls - INFO - Epoch [83][4600/5005]\tlr: 1.000e-03, eta: 2:06:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2977\n2023-01-01 01:02:48,723 - mmcls - INFO - Epoch [83][4700/5005]\tlr: 1.000e-03, eta: 2:05:59, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.2798\n2023-01-01 01:02:57,779 - mmcls - INFO - Epoch [83][4800/5005]\tlr: 1.000e-03, eta: 2:05:50, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.3106\n2023-01-01 01:03:06,546 - mmcls - INFO - Epoch [83][4900/5005]\tlr: 1.000e-03, eta: 2:05:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2874\n2023-01-01 01:03:15,471 - mmcls - INFO - Epoch [83][5000/5005]\tlr: 1.000e-03, eta: 2:05:32, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.2951\n2023-01-01 01:03:38,052 - mmcls - INFO - Epoch(val) [83][196]\taccuracy_top-1: 71.0400, accuracy_top-5: 89.8040\n2023-01-01 01:03:49,270 - mmcls - INFO - Epoch [84][100/5005]\tlr: 1.000e-03, eta: 2:05:23, time: 0.112, data_time: 0.033, memory: 1224, loss: 1.2824\n2023-01-01 01:03:58,133 - mmcls - INFO - Epoch [84][200/5005]\tlr: 1.000e-03, eta: 2:05:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2707\n2023-01-01 01:04:06,954 - mmcls - INFO - Epoch [84][300/5005]\tlr: 1.000e-03, eta: 2:05:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2750\n2023-01-01 01:04:15,532 - mmcls - INFO - Epoch [84][400/5005]\tlr: 1.000e-03, eta: 2:04:57, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2698\n2023-01-01 01:04:24,579 - mmcls - INFO - Epoch [84][500/5005]\tlr: 1.000e-03, eta: 2:04:48, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2743\n2023-01-01 01:04:33,604 - mmcls - INFO - Epoch [84][600/5005]\tlr: 1.000e-03, eta: 2:04:39, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2959\n2023-01-01 01:04:42,630 - mmcls - INFO - Epoch [84][700/5005]\tlr: 1.000e-03, eta: 2:04:30, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2790\n2023-01-01 01:04:51,133 - mmcls - INFO - Epoch [84][800/5005]\tlr: 1.000e-03, eta: 2:04:21, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2762\n2023-01-01 01:04:59,962 - mmcls - INFO - Epoch [84][900/5005]\tlr: 1.000e-03, eta: 2:04:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2684\n2023-01-01 01:05:08,513 - mmcls - INFO - Epoch [84][1000/5005]\tlr: 1.000e-03, eta: 2:04:03, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.2734\n2023-01-01 01:05:17,174 - mmcls - INFO - Epoch [84][1100/5005]\tlr: 1.000e-03, eta: 2:03:55, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2869\n2023-01-01 01:05:26,246 - mmcls - INFO - Epoch [84][1200/5005]\tlr: 1.000e-03, eta: 2:03:46, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2898\n2023-01-01 01:05:35,179 - mmcls - INFO - Epoch [84][1300/5005]\tlr: 1.000e-03, eta: 2:03:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2870\n2023-01-01 01:05:44,339 - mmcls - INFO - Epoch [84][1400/5005]\tlr: 1.000e-03, eta: 2:03:28, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.2938\n2023-01-01 01:05:53,042 - mmcls - INFO - Epoch [84][1500/5005]\tlr: 1.000e-03, eta: 2:03:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2607\n2023-01-01 01:06:01,946 - mmcls - INFO - Epoch [84][1600/5005]\tlr: 1.000e-03, eta: 2:03:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2794\n2023-01-01 01:06:10,834 - mmcls - INFO - Epoch [84][1700/5005]\tlr: 1.000e-03, eta: 2:03:02, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2906\n2023-01-01 01:06:19,796 - mmcls - INFO - Epoch [84][1800/5005]\tlr: 1.000e-03, eta: 2:02:53, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2810\n2023-01-01 01:06:28,669 - mmcls - INFO - Epoch [84][1900/5005]\tlr: 1.000e-03, eta: 2:02:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2835\n2023-01-01 01:06:37,518 - mmcls - INFO - Epoch [84][2000/5005]\tlr: 1.000e-03, eta: 2:02:35, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2598\n2023-01-01 01:06:46,312 - mmcls - INFO - Epoch [84][2100/5005]\tlr: 1.000e-03, eta: 2:02:26, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2724\n2023-01-01 01:06:55,185 - mmcls - INFO - Epoch [84][2200/5005]\tlr: 1.000e-03, eta: 2:02:17, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2808\n2023-01-01 01:07:03,868 - mmcls - INFO - Epoch [84][2300/5005]\tlr: 1.000e-03, eta: 2:02:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2836\n2023-01-01 01:07:12,517 - mmcls - INFO - Epoch [84][2400/5005]\tlr: 1.000e-03, eta: 2:02:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2846\n2023-01-01 01:07:21,242 - mmcls - INFO - Epoch [84][2500/5005]\tlr: 1.000e-03, eta: 2:01:51, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2789\n2023-01-01 01:07:30,008 - mmcls - INFO - Epoch [84][2600/5005]\tlr: 1.000e-03, eta: 2:01:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2974\n2023-01-01 01:07:38,991 - mmcls - INFO - Epoch [84][2700/5005]\tlr: 1.000e-03, eta: 2:01:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2999\n2023-01-01 01:07:47,880 - mmcls - INFO - Epoch [84][2800/5005]\tlr: 1.000e-03, eta: 2:01:24, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.2779\n2023-01-01 01:07:56,754 - mmcls - INFO - Epoch [84][2900/5005]\tlr: 1.000e-03, eta: 2:01:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2900\n2023-01-01 01:08:05,608 - mmcls - INFO - Epoch [84][3000/5005]\tlr: 1.000e-03, eta: 2:01:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2846\n2023-01-01 01:08:14,697 - mmcls - INFO - Epoch [84][3100/5005]\tlr: 1.000e-03, eta: 2:00:58, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.3015\n2023-01-01 01:08:23,368 - mmcls - INFO - Epoch [84][3200/5005]\tlr: 1.000e-03, eta: 2:00:49, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.3140\n2023-01-01 01:08:32,310 - mmcls - INFO - Epoch [84][3300/5005]\tlr: 1.000e-03, eta: 2:00:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2662\n2023-01-01 01:08:41,188 - mmcls - INFO - Epoch [84][3400/5005]\tlr: 1.000e-03, eta: 2:00:31, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.3041\n2023-01-01 01:08:50,037 - mmcls - INFO - Epoch [84][3500/5005]\tlr: 1.000e-03, eta: 2:00:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2718\n2023-01-01 01:08:58,771 - mmcls - INFO - Epoch [84][3600/5005]\tlr: 1.000e-03, eta: 2:00:13, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2778\n2023-01-01 01:09:07,450 - mmcls - INFO - Epoch [84][3700/5005]\tlr: 1.000e-03, eta: 2:00:04, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3163\n2023-01-01 01:09:16,086 - mmcls - INFO - Epoch [84][3800/5005]\tlr: 1.000e-03, eta: 1:59:56, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3188\n2023-01-01 01:09:25,067 - mmcls - INFO - Epoch [84][3900/5005]\tlr: 1.000e-03, eta: 1:59:47, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2827\n2023-01-01 01:09:34,098 - mmcls - INFO - Epoch [84][4000/5005]\tlr: 1.000e-03, eta: 1:59:38, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2985\n2023-01-01 01:09:42,716 - mmcls - INFO - Epoch [84][4100/5005]\tlr: 1.000e-03, eta: 1:59:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2982\n2023-01-01 01:09:51,665 - mmcls - INFO - Epoch [84][4200/5005]\tlr: 1.000e-03, eta: 1:59:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2778\n2023-01-01 01:10:00,437 - mmcls - INFO - Epoch [84][4300/5005]\tlr: 1.000e-03, eta: 1:59:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3051\n2023-01-01 01:10:08,971 - mmcls - INFO - Epoch [84][4400/5005]\tlr: 1.000e-03, eta: 1:59:02, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2918\n2023-01-01 01:10:17,615 - mmcls - INFO - Epoch [84][4500/5005]\tlr: 1.000e-03, eta: 1:58:53, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2959\n2023-01-01 01:10:26,233 - mmcls - INFO - Epoch [84][4600/5005]\tlr: 1.000e-03, eta: 1:58:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2955\n2023-01-01 01:10:35,010 - mmcls - INFO - Epoch [84][4700/5005]\tlr: 1.000e-03, eta: 1:58:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2938\n2023-01-01 01:10:43,818 - mmcls - INFO - Epoch [84][4800/5005]\tlr: 1.000e-03, eta: 1:58:27, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3134\n2023-01-01 01:10:52,542 - mmcls - INFO - Epoch [84][4900/5005]\tlr: 1.000e-03, eta: 1:58:18, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2823\n2023-01-01 01:11:01,259 - mmcls - INFO - Epoch [84][5000/5005]\tlr: 1.000e-03, eta: 1:58:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3082\n2023-01-01 01:11:24,002 - mmcls - INFO - Epoch(val) [84][196]\taccuracy_top-1: 70.9780, accuracy_top-5: 89.8520\n2023-01-01 01:11:35,364 - mmcls - INFO - Epoch [85][100/5005]\tlr: 1.000e-03, eta: 1:58:00, time: 0.113, data_time: 0.044, memory: 1224, loss: 1.2856\n2023-01-01 01:11:44,203 - mmcls - INFO - Epoch [85][200/5005]\tlr: 1.000e-03, eta: 1:57:51, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.2766\n2023-01-01 01:11:53,030 - mmcls - INFO - Epoch [85][300/5005]\tlr: 1.000e-03, eta: 1:57:42, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2819\n2023-01-01 01:12:01,846 - mmcls - INFO - Epoch [85][400/5005]\tlr: 1.000e-03, eta: 1:57:34, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.2694\n2023-01-01 01:12:10,509 - mmcls - INFO - Epoch [85][500/5005]\tlr: 1.000e-03, eta: 1:57:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2782\n2023-01-01 01:12:19,799 - mmcls - INFO - Epoch [85][600/5005]\tlr: 1.000e-03, eta: 1:57:16, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2691\n2023-01-01 01:12:28,396 - mmcls - INFO - Epoch [85][700/5005]\tlr: 1.000e-03, eta: 1:57:07, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3021\n2023-01-01 01:12:37,345 - mmcls - INFO - Epoch [85][800/5005]\tlr: 1.000e-03, eta: 1:56:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2866\n2023-01-01 01:12:46,102 - mmcls - INFO - Epoch [85][900/5005]\tlr: 1.000e-03, eta: 1:56:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2738\n2023-01-01 01:12:54,805 - mmcls - INFO - Epoch [85][1000/5005]\tlr: 1.000e-03, eta: 1:56:40, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2733\n2023-01-01 01:13:03,815 - mmcls - INFO - Epoch [85][1100/5005]\tlr: 1.000e-03, eta: 1:56:32, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2930\n2023-01-01 01:13:13,042 - mmcls - INFO - Epoch [85][1200/5005]\tlr: 1.000e-03, eta: 1:56:23, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.2859\n2023-01-01 01:13:21,733 - mmcls - INFO - Epoch [85][1300/5005]\tlr: 1.000e-03, eta: 1:56:14, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2638\n2023-01-01 01:13:30,534 - mmcls - INFO - Epoch [85][1400/5005]\tlr: 1.000e-03, eta: 1:56:05, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2885\n2023-01-01 01:13:39,449 - mmcls - INFO - Epoch [85][1500/5005]\tlr: 1.000e-03, eta: 1:55:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2716\n2023-01-01 01:13:48,127 - mmcls - INFO - Epoch [85][1600/5005]\tlr: 1.000e-03, eta: 1:55:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2826\n2023-01-01 01:13:57,059 - mmcls - INFO - Epoch [85][1700/5005]\tlr: 1.000e-03, eta: 1:55:39, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2855\n2023-01-01 01:14:06,106 - mmcls - INFO - Epoch [85][1800/5005]\tlr: 1.000e-03, eta: 1:55:30, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2940\n2023-01-01 01:14:14,917 - mmcls - INFO - Epoch [85][1900/5005]\tlr: 1.000e-03, eta: 1:55:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2825\n2023-01-01 01:14:23,539 - mmcls - INFO - Epoch [85][2000/5005]\tlr: 1.000e-03, eta: 1:55:12, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2754\n2023-01-01 01:14:32,402 - mmcls - INFO - Epoch [85][2100/5005]\tlr: 1.000e-03, eta: 1:55:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2850\n2023-01-01 01:14:40,945 - mmcls - INFO - Epoch [85][2200/5005]\tlr: 1.000e-03, eta: 1:54:54, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2802\n2023-01-01 01:14:49,981 - mmcls - INFO - Epoch [85][2300/5005]\tlr: 1.000e-03, eta: 1:54:45, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2769\n2023-01-01 01:14:58,959 - mmcls - INFO - Epoch [85][2400/5005]\tlr: 1.000e-03, eta: 1:54:37, time: 0.090, data_time: 0.035, memory: 1224, loss: 1.2784\n2023-01-01 01:15:07,671 - mmcls - INFO - Epoch [85][2500/5005]\tlr: 1.000e-03, eta: 1:54:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2938\n2023-01-01 01:15:16,607 - mmcls - INFO - Epoch [85][2600/5005]\tlr: 1.000e-03, eta: 1:54:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2875\n2023-01-01 01:15:25,313 - mmcls - INFO - Epoch [85][2700/5005]\tlr: 1.000e-03, eta: 1:54:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2830\n2023-01-01 01:15:34,075 - mmcls - INFO - Epoch [85][2800/5005]\tlr: 1.000e-03, eta: 1:54:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2957\n2023-01-01 01:15:42,550 - mmcls - INFO - Epoch [85][2900/5005]\tlr: 1.000e-03, eta: 1:53:52, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.2799\n2023-01-01 01:15:51,452 - mmcls - INFO - Epoch [85][3000/5005]\tlr: 1.000e-03, eta: 1:53:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3005\n2023-01-01 01:16:00,406 - mmcls - INFO - Epoch [85][3100/5005]\tlr: 1.000e-03, eta: 1:53:34, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2718\n2023-01-01 01:16:09,233 - mmcls - INFO - Epoch [85][3200/5005]\tlr: 1.000e-03, eta: 1:53:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2734\n2023-01-01 01:16:17,954 - mmcls - INFO - Epoch [85][3300/5005]\tlr: 1.000e-03, eta: 1:53:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2756\n2023-01-01 01:16:26,676 - mmcls - INFO - Epoch [85][3400/5005]\tlr: 1.000e-03, eta: 1:53:08, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2902\n2023-01-01 01:16:35,280 - mmcls - INFO - Epoch [85][3500/5005]\tlr: 1.000e-03, eta: 1:52:59, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2947\n2023-01-01 01:16:44,065 - mmcls - INFO - Epoch [85][3600/5005]\tlr: 1.000e-03, eta: 1:52:50, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3015\n2023-01-01 01:16:53,274 - mmcls - INFO - Epoch [85][3700/5005]\tlr: 1.000e-03, eta: 1:52:41, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2902\n2023-01-01 01:17:01,938 - mmcls - INFO - Epoch [85][3800/5005]\tlr: 1.000e-03, eta: 1:52:32, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2739\n2023-01-01 01:17:11,092 - mmcls - INFO - Epoch [85][3900/5005]\tlr: 1.000e-03, eta: 1:52:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.2858\n2023-01-01 01:17:19,962 - mmcls - INFO - Epoch [85][4000/5005]\tlr: 1.000e-03, eta: 1:52:15, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2845\n2023-01-01 01:17:28,658 - mmcls - INFO - Epoch [85][4100/5005]\tlr: 1.000e-03, eta: 1:52:06, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.2826\n2023-01-01 01:17:37,498 - mmcls - INFO - Epoch [85][4200/5005]\tlr: 1.000e-03, eta: 1:51:57, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2864\n2023-01-01 01:17:46,205 - mmcls - INFO - Epoch [85][4300/5005]\tlr: 1.000e-03, eta: 1:51:48, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2880\n2023-01-01 01:17:55,332 - mmcls - INFO - Epoch [85][4400/5005]\tlr: 1.000e-03, eta: 1:51:39, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2827\n2023-01-01 01:18:04,179 - mmcls - INFO - Epoch [85][4500/5005]\tlr: 1.000e-03, eta: 1:51:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2744\n2023-01-01 01:18:13,256 - mmcls - INFO - Epoch [85][4600/5005]\tlr: 1.000e-03, eta: 1:51:22, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2894\n2023-01-01 01:18:21,955 - mmcls - INFO - Epoch [85][4700/5005]\tlr: 1.000e-03, eta: 1:51:13, time: 0.087, data_time: 0.043, memory: 1224, loss: 1.3038\n2023-01-01 01:18:30,673 - mmcls - INFO - Epoch [85][4800/5005]\tlr: 1.000e-03, eta: 1:51:04, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2985\n2023-01-01 01:18:39,599 - mmcls - INFO - Epoch [85][4900/5005]\tlr: 1.000e-03, eta: 1:50:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2717\n2023-01-01 01:18:48,104 - mmcls - INFO - Epoch [85][5000/5005]\tlr: 1.000e-03, eta: 1:50:46, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.2970\n2023-01-01 01:19:10,698 - mmcls - INFO - Epoch(val) [85][196]\taccuracy_top-1: 70.9540, accuracy_top-5: 89.8720\n2023-01-01 01:19:21,518 - mmcls - INFO - Epoch [86][100/5005]\tlr: 1.000e-03, eta: 1:50:37, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.2708\n2023-01-01 01:19:30,489 - mmcls - INFO - Epoch [86][200/5005]\tlr: 1.000e-03, eta: 1:50:28, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.2815\n2023-01-01 01:19:39,140 - mmcls - INFO - Epoch [86][300/5005]\tlr: 1.000e-03, eta: 1:50:19, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2638\n2023-01-01 01:19:47,854 - mmcls - INFO - Epoch [86][400/5005]\tlr: 1.000e-03, eta: 1:50:11, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2629\n2023-01-01 01:19:56,661 - mmcls - INFO - Epoch [86][500/5005]\tlr: 1.000e-03, eta: 1:50:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2863\n2023-01-01 01:20:05,549 - mmcls - INFO - Epoch [86][600/5005]\tlr: 1.000e-03, eta: 1:49:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2655\n2023-01-01 01:20:14,470 - mmcls - INFO - Epoch [86][700/5005]\tlr: 1.000e-03, eta: 1:49:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2810\n2023-01-01 01:20:23,340 - mmcls - INFO - Epoch [86][800/5005]\tlr: 1.000e-03, eta: 1:49:35, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2767\n2023-01-01 01:20:32,114 - mmcls - INFO - Epoch [86][900/5005]\tlr: 1.000e-03, eta: 1:49:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2689\n2023-01-01 01:20:41,657 - mmcls - INFO - Epoch [86][1000/5005]\tlr: 1.000e-03, eta: 1:49:18, time: 0.095, data_time: 0.005, memory: 1224, loss: 1.3059\n2023-01-01 01:20:50,431 - mmcls - INFO - Epoch [86][1100/5005]\tlr: 1.000e-03, eta: 1:49:09, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2729\n2023-01-01 01:20:59,229 - mmcls - INFO - Epoch [86][1200/5005]\tlr: 1.000e-03, eta: 1:49:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2617\n2023-01-01 01:21:07,813 - mmcls - INFO - Epoch [86][1300/5005]\tlr: 1.000e-03, eta: 1:48:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2662\n2023-01-01 01:21:16,371 - mmcls - INFO - Epoch [86][1400/5005]\tlr: 1.000e-03, eta: 1:48:42, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2783\n2023-01-01 01:21:25,096 - mmcls - INFO - Epoch [86][1500/5005]\tlr: 1.000e-03, eta: 1:48:33, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2595\n2023-01-01 01:21:33,864 - mmcls - INFO - Epoch [86][1600/5005]\tlr: 1.000e-03, eta: 1:48:24, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2882\n2023-01-01 01:21:42,628 - mmcls - INFO - Epoch [86][1700/5005]\tlr: 1.000e-03, eta: 1:48:15, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2813\n2023-01-01 01:21:51,579 - mmcls - INFO - Epoch [86][1800/5005]\tlr: 1.000e-03, eta: 1:48:07, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2765\n2023-01-01 01:22:00,347 - mmcls - INFO - Epoch [86][1900/5005]\tlr: 1.000e-03, eta: 1:47:58, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2691\n2023-01-01 01:22:09,060 - mmcls - INFO - Epoch [86][2000/5005]\tlr: 1.000e-03, eta: 1:47:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2733\n2023-01-01 01:22:17,816 - mmcls - INFO - Epoch [86][2100/5005]\tlr: 1.000e-03, eta: 1:47:40, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.2849\n2023-01-01 01:22:26,458 - mmcls - INFO - Epoch [86][2200/5005]\tlr: 1.000e-03, eta: 1:47:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2952\n2023-01-01 01:22:35,084 - mmcls - INFO - Epoch [86][2300/5005]\tlr: 1.000e-03, eta: 1:47:22, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2911\n2023-01-01 01:22:43,856 - mmcls - INFO - Epoch [86][2400/5005]\tlr: 1.000e-03, eta: 1:47:13, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.2814\n2023-01-01 01:22:52,837 - mmcls - INFO - Epoch [86][2500/5005]\tlr: 1.000e-03, eta: 1:47:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.2935\n2023-01-01 01:23:01,642 - mmcls - INFO - Epoch [86][2600/5005]\tlr: 1.000e-03, eta: 1:46:56, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2802\n2023-01-01 01:23:10,522 - mmcls - INFO - Epoch [86][2700/5005]\tlr: 1.000e-03, eta: 1:46:47, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2898\n2023-01-01 01:23:19,091 - mmcls - INFO - Epoch [86][2800/5005]\tlr: 1.000e-03, eta: 1:46:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3070\n2023-01-01 01:23:27,773 - mmcls - INFO - Epoch [86][2900/5005]\tlr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2827\n2023-01-01 01:23:37,137 - mmcls - INFO - Epoch [86][3000/5005]\tlr: 1.000e-03, eta: 1:46:20, time: 0.094, data_time: 0.017, memory: 1224, loss: 1.2926\n2023-01-01 01:23:46,146 - mmcls - INFO - Epoch [86][3100/5005]\tlr: 1.000e-03, eta: 1:46:11, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3014\n2023-01-01 01:23:54,852 - mmcls - INFO - Epoch [86][3200/5005]\tlr: 1.000e-03, eta: 1:46:03, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2815\n2023-01-01 01:24:03,793 - mmcls - INFO - Epoch [86][3300/5005]\tlr: 1.000e-03, eta: 1:45:54, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2794\n2023-01-01 01:24:12,393 - mmcls - INFO - Epoch [86][3400/5005]\tlr: 1.000e-03, eta: 1:45:45, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2709\n2023-01-01 01:24:21,314 - mmcls - INFO - Epoch [86][3500/5005]\tlr: 1.000e-03, eta: 1:45:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3008\n2023-01-01 01:24:30,115 - mmcls - INFO - Epoch [86][3600/5005]\tlr: 1.000e-03, eta: 1:45:27, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2713\n2023-01-01 01:24:39,003 - mmcls - INFO - Epoch [86][3700/5005]\tlr: 1.000e-03, eta: 1:45:18, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2851\n2023-01-01 01:24:47,704 - mmcls - INFO - Epoch [86][3800/5005]\tlr: 1.000e-03, eta: 1:45:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2720\n2023-01-01 01:24:57,237 - mmcls - INFO - Epoch [86][3900/5005]\tlr: 1.000e-03, eta: 1:45:01, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.3062\n2023-01-01 01:25:06,341 - mmcls - INFO - Epoch [86][4000/5005]\tlr: 1.000e-03, eta: 1:44:52, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.2945\n2023-01-01 01:25:15,033 - mmcls - INFO - Epoch [86][4100/5005]\tlr: 1.000e-03, eta: 1:44:43, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2879\n2023-01-01 01:25:23,692 - mmcls - INFO - Epoch [86][4200/5005]\tlr: 1.000e-03, eta: 1:44:34, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2985\n2023-01-01 01:25:32,305 - mmcls - INFO - Epoch [86][4300/5005]\tlr: 1.000e-03, eta: 1:44:25, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2830\n2023-01-01 01:25:40,962 - mmcls - INFO - Epoch [86][4400/5005]\tlr: 1.000e-03, eta: 1:44:16, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2899\n2023-01-01 01:25:49,712 - mmcls - INFO - Epoch [86][4500/5005]\tlr: 1.000e-03, eta: 1:44:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2898\n2023-01-01 01:25:58,639 - mmcls - INFO - Epoch [86][4600/5005]\tlr: 1.000e-03, eta: 1:43:59, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2780\n2023-01-01 01:26:07,604 - mmcls - INFO - Epoch [86][4700/5005]\tlr: 1.000e-03, eta: 1:43:50, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2835\n2023-01-01 01:26:16,303 - mmcls - INFO - Epoch [86][4800/5005]\tlr: 1.000e-03, eta: 1:43:41, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2711\n2023-01-01 01:26:25,008 - mmcls - INFO - Epoch [86][4900/5005]\tlr: 1.000e-03, eta: 1:43:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2981\n2023-01-01 01:26:33,537 - mmcls - INFO - Epoch [86][5000/5005]\tlr: 1.000e-03, eta: 1:43:23, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2920\n2023-01-01 01:28:47,399 - mmcls - INFO - Epoch(val) [86][196]\taccuracy_top-1: 71.1700, accuracy_top-5: 89.8860\n2023-01-01 01:28:58,477 - mmcls - INFO - Epoch [87][100/5005]\tlr: 1.000e-03, eta: 1:43:14, time: 0.110, data_time: 0.047, memory: 1224, loss: 1.2761\n2023-01-01 01:29:07,149 - mmcls - INFO - Epoch [87][200/5005]\tlr: 1.000e-03, eta: 1:43:05, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2642\n2023-01-01 01:29:15,978 - mmcls - INFO - Epoch [87][300/5005]\tlr: 1.000e-03, eta: 1:42:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2820\n2023-01-01 01:29:24,923 - mmcls - INFO - Epoch [87][400/5005]\tlr: 1.000e-03, eta: 1:42:47, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2617\n2023-01-01 01:29:33,653 - mmcls - INFO - Epoch [87][500/5005]\tlr: 1.000e-03, eta: 1:42:39, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.2728\n2023-01-01 01:29:42,692 - mmcls - INFO - Epoch [87][600/5005]\tlr: 1.000e-03, eta: 1:42:30, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2617\n2023-01-01 01:29:51,583 - mmcls - INFO - Epoch [87][700/5005]\tlr: 1.000e-03, eta: 1:42:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2930\n2023-01-01 01:30:00,386 - mmcls - INFO - Epoch [87][800/5005]\tlr: 1.000e-03, eta: 1:42:12, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2949\n2023-01-01 01:30:09,089 - mmcls - INFO - Epoch [87][900/5005]\tlr: 1.000e-03, eta: 1:42:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2633\n2023-01-01 01:30:17,848 - mmcls - INFO - Epoch [87][1000/5005]\tlr: 1.000e-03, eta: 1:41:54, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2929\n2023-01-01 01:30:27,039 - mmcls - INFO - Epoch [87][1100/5005]\tlr: 1.000e-03, eta: 1:41:46, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2744\n2023-01-01 01:30:35,800 - mmcls - INFO - Epoch [87][1200/5005]\tlr: 1.000e-03, eta: 1:41:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2815\n2023-01-01 01:30:44,361 - mmcls - INFO - Epoch [87][1300/5005]\tlr: 1.000e-03, eta: 1:41:28, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.2768\n2023-01-01 01:30:53,226 - mmcls - INFO - Epoch [87][1400/5005]\tlr: 1.000e-03, eta: 1:41:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2875\n2023-01-01 01:31:01,999 - mmcls - INFO - Epoch [87][1500/5005]\tlr: 1.000e-03, eta: 1:41:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2617\n2023-01-01 01:31:10,867 - mmcls - INFO - Epoch [87][1600/5005]\tlr: 1.000e-03, eta: 1:41:01, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.2634\n2023-01-01 01:31:19,798 - mmcls - INFO - Epoch [87][1700/5005]\tlr: 1.000e-03, eta: 1:40:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2829\n2023-01-01 01:31:28,774 - mmcls - INFO - Epoch [87][1800/5005]\tlr: 1.000e-03, eta: 1:40:44, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.2802\n2023-01-01 01:31:37,733 - mmcls - INFO - Epoch [87][1900/5005]\tlr: 1.000e-03, eta: 1:40:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2630\n2023-01-01 01:31:46,326 - mmcls - INFO - Epoch [87][2000/5005]\tlr: 1.000e-03, eta: 1:40:26, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3037\n2023-01-01 01:31:55,161 - mmcls - INFO - Epoch [87][2100/5005]\tlr: 1.000e-03, eta: 1:40:17, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2783\n2023-01-01 01:32:04,070 - mmcls - INFO - Epoch [87][2200/5005]\tlr: 1.000e-03, eta: 1:40:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2636\n2023-01-01 01:32:13,120 - mmcls - INFO - Epoch [87][2300/5005]\tlr: 1.000e-03, eta: 1:39:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2880\n2023-01-01 01:32:22,361 - mmcls - INFO - Epoch [87][2400/5005]\tlr: 1.000e-03, eta: 1:39:50, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2767\n2023-01-01 01:32:31,331 - mmcls - INFO - Epoch [87][2500/5005]\tlr: 1.000e-03, eta: 1:39:42, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2761\n2023-01-01 01:32:40,226 - mmcls - INFO - Epoch [87][2600/5005]\tlr: 1.000e-03, eta: 1:39:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3008\n2023-01-01 01:32:49,190 - mmcls - INFO - Epoch [87][2700/5005]\tlr: 1.000e-03, eta: 1:39:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2730\n2023-01-01 01:32:57,921 - mmcls - INFO - Epoch [87][2800/5005]\tlr: 1.000e-03, eta: 1:39:15, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2806\n2023-01-01 01:33:06,631 - mmcls - INFO - Epoch [87][2900/5005]\tlr: 1.000e-03, eta: 1:39:06, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2821\n2023-01-01 01:33:15,394 - mmcls - INFO - Epoch [87][3000/5005]\tlr: 1.000e-03, eta: 1:38:57, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2996\n2023-01-01 01:33:24,179 - mmcls - INFO - Epoch [87][3100/5005]\tlr: 1.000e-03, eta: 1:38:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2612\n2023-01-01 01:33:33,278 - mmcls - INFO - Epoch [87][3200/5005]\tlr: 1.000e-03, eta: 1:38:40, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.2955\n2023-01-01 01:33:41,886 - mmcls - INFO - Epoch [87][3300/5005]\tlr: 1.000e-03, eta: 1:38:31, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2976\n2023-01-01 01:33:50,344 - mmcls - INFO - Epoch [87][3400/5005]\tlr: 1.000e-03, eta: 1:38:22, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.2673\n2023-01-01 01:33:59,051 - mmcls - INFO - Epoch [87][3500/5005]\tlr: 1.000e-03, eta: 1:38:13, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2718\n2023-01-01 01:34:07,837 - mmcls - INFO - Epoch [87][3600/5005]\tlr: 1.000e-03, eta: 1:38:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2988\n2023-01-01 01:34:16,638 - mmcls - INFO - Epoch [87][3700/5005]\tlr: 1.000e-03, eta: 1:37:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2930\n2023-01-01 01:34:25,495 - mmcls - INFO - Epoch [87][3800/5005]\tlr: 1.000e-03, eta: 1:37:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2861\n2023-01-01 01:34:34,317 - mmcls - INFO - Epoch [87][3900/5005]\tlr: 1.000e-03, eta: 1:37:38, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2840\n2023-01-01 01:34:43,118 - mmcls - INFO - Epoch [87][4000/5005]\tlr: 1.000e-03, eta: 1:37:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2952\n2023-01-01 01:34:51,803 - mmcls - INFO - Epoch [87][4100/5005]\tlr: 1.000e-03, eta: 1:37:20, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2908\n2023-01-01 01:35:00,456 - mmcls - INFO - Epoch [87][4200/5005]\tlr: 1.000e-03, eta: 1:37:11, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2728\n2023-01-01 01:35:09,109 - mmcls - INFO - Epoch [87][4300/5005]\tlr: 1.000e-03, eta: 1:37:02, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2766\n2023-01-01 01:35:18,092 - mmcls - INFO - Epoch [87][4400/5005]\tlr: 1.000e-03, eta: 1:36:53, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2922\n2023-01-01 01:35:26,661 - mmcls - INFO - Epoch [87][4500/5005]\tlr: 1.000e-03, eta: 1:36:44, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3032\n2023-01-01 01:35:35,349 - mmcls - INFO - Epoch [87][4600/5005]\tlr: 1.000e-03, eta: 1:36:35, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.2808\n2023-01-01 01:35:43,947 - mmcls - INFO - Epoch [87][4700/5005]\tlr: 1.000e-03, eta: 1:36:27, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2534\n2023-01-01 01:35:52,691 - mmcls - INFO - Epoch [87][4800/5005]\tlr: 1.000e-03, eta: 1:36:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2895\n2023-01-01 01:36:01,503 - mmcls - INFO - Epoch [87][4900/5005]\tlr: 1.000e-03, eta: 1:36:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2984\n2023-01-01 01:36:10,487 - mmcls - INFO - Epoch [87][5000/5005]\tlr: 1.000e-03, eta: 1:36:00, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.2941\n2023-01-01 01:36:33,231 - mmcls - INFO - Epoch(val) [87][196]\taccuracy_top-1: 70.9100, accuracy_top-5: 89.8600\n2023-01-01 01:36:44,473 - mmcls - INFO - Epoch [88][100/5005]\tlr: 1.000e-03, eta: 1:35:51, time: 0.112, data_time: 0.032, memory: 1224, loss: 1.2761\n2023-01-01 01:36:53,422 - mmcls - INFO - Epoch [88][200/5005]\tlr: 1.000e-03, eta: 1:35:42, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2545\n2023-01-01 01:37:02,569 - mmcls - INFO - Epoch [88][300/5005]\tlr: 1.000e-03, eta: 1:35:33, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2694\n2023-01-01 01:37:11,249 - mmcls - INFO - Epoch [88][400/5005]\tlr: 1.000e-03, eta: 1:35:24, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2663\n2023-01-01 01:37:20,034 - mmcls - INFO - Epoch [88][500/5005]\tlr: 1.000e-03, eta: 1:35:16, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2672\n2023-01-01 01:37:28,955 - mmcls - INFO - Epoch [88][600/5005]\tlr: 1.000e-03, eta: 1:35:07, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2765\n2023-01-01 01:37:37,864 - mmcls - INFO - Epoch [88][700/5005]\tlr: 1.000e-03, eta: 1:34:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2720\n2023-01-01 01:37:46,516 - mmcls - INFO - Epoch [88][800/5005]\tlr: 1.000e-03, eta: 1:34:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2557\n2023-01-01 01:37:55,264 - mmcls - INFO - Epoch [88][900/5005]\tlr: 1.000e-03, eta: 1:34:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2646\n2023-01-01 01:38:03,906 - mmcls - INFO - Epoch [88][1000/5005]\tlr: 1.000e-03, eta: 1:34:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2691\n2023-01-01 01:38:12,674 - mmcls - INFO - Epoch [88][1100/5005]\tlr: 1.000e-03, eta: 1:34:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2711\n2023-01-01 01:38:21,274 - mmcls - INFO - Epoch [88][1200/5005]\tlr: 1.000e-03, eta: 1:34:13, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2703\n2023-01-01 01:38:30,089 - mmcls - INFO - Epoch [88][1300/5005]\tlr: 1.000e-03, eta: 1:34:05, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2650\n2023-01-01 01:38:38,895 - mmcls - INFO - Epoch [88][1400/5005]\tlr: 1.000e-03, eta: 1:33:56, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2642\n2023-01-01 01:38:47,524 - mmcls - INFO - Epoch [88][1500/5005]\tlr: 1.000e-03, eta: 1:33:47, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.2896\n2023-01-01 01:38:56,410 - mmcls - INFO - Epoch [88][1600/5005]\tlr: 1.000e-03, eta: 1:33:38, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2746\n2023-01-01 01:39:05,035 - mmcls - INFO - Epoch [88][1700/5005]\tlr: 1.000e-03, eta: 1:33:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2968\n2023-01-01 01:39:14,382 - mmcls - INFO - Epoch [88][1800/5005]\tlr: 1.000e-03, eta: 1:33:20, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2855\n2023-01-01 01:39:23,081 - mmcls - INFO - Epoch [88][1900/5005]\tlr: 1.000e-03, eta: 1:33:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2809\n2023-01-01 01:39:32,045 - mmcls - INFO - Epoch [88][2000/5005]\tlr: 1.000e-03, eta: 1:33:03, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2807\n2023-01-01 01:39:40,952 - mmcls - INFO - Epoch [88][2100/5005]\tlr: 1.000e-03, eta: 1:32:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.2771\n2023-01-01 01:39:49,410 - mmcls - INFO - Epoch [88][2200/5005]\tlr: 1.000e-03, eta: 1:32:45, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2794\n2023-01-01 01:39:58,131 - mmcls - INFO - Epoch [88][2300/5005]\tlr: 1.000e-03, eta: 1:32:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2885\n2023-01-01 01:40:06,880 - mmcls - INFO - Epoch [88][2400/5005]\tlr: 1.000e-03, eta: 1:32:27, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2956\n2023-01-01 01:40:15,481 - mmcls - INFO - Epoch [88][2500/5005]\tlr: 1.000e-03, eta: 1:32:18, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2794\n2023-01-01 01:40:24,220 - mmcls - INFO - Epoch [88][2600/5005]\tlr: 1.000e-03, eta: 1:32:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2720\n2023-01-01 01:40:32,943 - mmcls - INFO - Epoch [88][2700/5005]\tlr: 1.000e-03, eta: 1:32:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2774\n2023-01-01 01:40:41,505 - mmcls - INFO - Epoch [88][2800/5005]\tlr: 1.000e-03, eta: 1:31:52, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.2761\n2023-01-01 01:40:50,326 - mmcls - INFO - Epoch [88][2900/5005]\tlr: 1.000e-03, eta: 1:31:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2753\n2023-01-01 01:40:58,994 - mmcls - INFO - Epoch [88][3000/5005]\tlr: 1.000e-03, eta: 1:31:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2841\n2023-01-01 01:41:07,718 - mmcls - INFO - Epoch [88][3100/5005]\tlr: 1.000e-03, eta: 1:31:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2762\n2023-01-01 01:41:16,648 - mmcls - INFO - Epoch [88][3200/5005]\tlr: 1.000e-03, eta: 1:31:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2946\n2023-01-01 01:41:25,618 - mmcls - INFO - Epoch [88][3300/5005]\tlr: 1.000e-03, eta: 1:31:07, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2821\n2023-01-01 01:41:34,437 - mmcls - INFO - Epoch [88][3400/5005]\tlr: 1.000e-03, eta: 1:30:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2948\n2023-01-01 01:41:43,324 - mmcls - INFO - Epoch [88][3500/5005]\tlr: 1.000e-03, eta: 1:30:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2832\n2023-01-01 01:41:52,307 - mmcls - INFO - Epoch [88][3600/5005]\tlr: 1.000e-03, eta: 1:30:41, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2776\n2023-01-01 01:42:01,199 - mmcls - INFO - Epoch [88][3700/5005]\tlr: 1.000e-03, eta: 1:30:32, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2684\n2023-01-01 01:42:10,107 - mmcls - INFO - Epoch [88][3800/5005]\tlr: 1.000e-03, eta: 1:30:23, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2485\n2023-01-01 01:42:18,735 - mmcls - INFO - Epoch [88][3900/5005]\tlr: 1.000e-03, eta: 1:30:14, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2665\n2023-01-01 01:42:27,977 - mmcls - INFO - Epoch [88][4000/5005]\tlr: 1.000e-03, eta: 1:30:05, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2732\n2023-01-01 01:42:36,835 - mmcls - INFO - Epoch [88][4100/5005]\tlr: 1.000e-03, eta: 1:29:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2880\n2023-01-01 01:42:45,437 - mmcls - INFO - Epoch [88][4200/5005]\tlr: 1.000e-03, eta: 1:29:48, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3018\n2023-01-01 01:42:54,296 - mmcls - INFO - Epoch [88][4300/5005]\tlr: 1.000e-03, eta: 1:29:39, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.2886\n2023-01-01 01:43:03,122 - mmcls - INFO - Epoch [88][4400/5005]\tlr: 1.000e-03, eta: 1:29:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2786\n2023-01-01 01:43:11,763 - mmcls - INFO - Epoch [88][4500/5005]\tlr: 1.000e-03, eta: 1:29:21, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.2774\n2023-01-01 01:43:20,906 - mmcls - INFO - Epoch [88][4600/5005]\tlr: 1.000e-03, eta: 1:29:12, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.2871\n2023-01-01 01:43:29,560 - mmcls - INFO - Epoch [88][4700/5005]\tlr: 1.000e-03, eta: 1:29:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2622\n2023-01-01 01:43:38,215 - mmcls - INFO - Epoch [88][4800/5005]\tlr: 1.000e-03, eta: 1:28:55, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3072\n2023-01-01 01:43:47,212 - mmcls - INFO - Epoch [88][4900/5005]\tlr: 1.000e-03, eta: 1:28:46, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2661\n2023-01-01 01:43:55,937 - mmcls - INFO - Epoch [88][5000/5005]\tlr: 1.000e-03, eta: 1:28:37, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2888\n2023-01-01 01:44:18,527 - mmcls - INFO - Epoch(val) [88][196]\taccuracy_top-1: 71.0680, accuracy_top-5: 89.8800\n2023-01-01 01:44:29,600 - mmcls - INFO - Epoch [89][100/5005]\tlr: 1.000e-03, eta: 1:28:28, time: 0.111, data_time: 0.062, memory: 1224, loss: 1.2769\n2023-01-01 01:44:38,219 - mmcls - INFO - Epoch [89][200/5005]\tlr: 1.000e-03, eta: 1:28:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2625\n2023-01-01 01:44:46,955 - mmcls - INFO - Epoch [89][300/5005]\tlr: 1.000e-03, eta: 1:28:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2824\n2023-01-01 01:44:55,812 - mmcls - INFO - Epoch [89][400/5005]\tlr: 1.000e-03, eta: 1:28:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2479\n2023-01-01 01:45:04,960 - mmcls - INFO - Epoch [89][500/5005]\tlr: 1.000e-03, eta: 1:27:52, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2844\n2023-01-01 01:45:13,991 - mmcls - INFO - Epoch [89][600/5005]\tlr: 1.000e-03, eta: 1:27:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2664\n2023-01-01 01:45:22,832 - mmcls - INFO - Epoch [89][700/5005]\tlr: 1.000e-03, eta: 1:27:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2622\n2023-01-01 01:45:32,043 - mmcls - INFO - Epoch [89][800/5005]\tlr: 1.000e-03, eta: 1:27:26, time: 0.092, data_time: 0.043, memory: 1224, loss: 1.2707\n2023-01-01 01:45:41,101 - mmcls - INFO - Epoch [89][900/5005]\tlr: 1.000e-03, eta: 1:27:17, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.2562\n2023-01-01 01:45:49,827 - mmcls - INFO - Epoch [89][1000/5005]\tlr: 1.000e-03, eta: 1:27:08, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2736\n2023-01-01 01:45:58,771 - mmcls - INFO - Epoch [89][1100/5005]\tlr: 1.000e-03, eta: 1:26:59, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2557\n2023-01-01 01:46:07,563 - mmcls - INFO - Epoch [89][1200/5005]\tlr: 1.000e-03, eta: 1:26:50, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2726\n2023-01-01 01:46:16,399 - mmcls - INFO - Epoch [89][1300/5005]\tlr: 1.000e-03, eta: 1:26:42, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2945\n2023-01-01 01:46:25,264 - mmcls - INFO - Epoch [89][1400/5005]\tlr: 1.000e-03, eta: 1:26:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2565\n2023-01-01 01:46:33,948 - mmcls - INFO - Epoch [89][1500/5005]\tlr: 1.000e-03, eta: 1:26:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2822\n2023-01-01 01:46:42,613 - mmcls - INFO - Epoch [89][1600/5005]\tlr: 1.000e-03, eta: 1:26:15, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2769\n2023-01-01 01:46:51,231 - mmcls - INFO - Epoch [89][1700/5005]\tlr: 1.000e-03, eta: 1:26:06, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.2812\n2023-01-01 01:47:00,089 - mmcls - INFO - Epoch [89][1800/5005]\tlr: 1.000e-03, eta: 1:25:57, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.2826\n2023-01-01 01:47:09,578 - mmcls - INFO - Epoch [89][1900/5005]\tlr: 1.000e-03, eta: 1:25:48, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.2781\n2023-01-01 01:47:18,240 - mmcls - INFO - Epoch [89][2000/5005]\tlr: 1.000e-03, eta: 1:25:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2593\n2023-01-01 01:47:27,257 - mmcls - INFO - Epoch [89][2100/5005]\tlr: 1.000e-03, eta: 1:25:31, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2818\n2023-01-01 01:47:36,313 - mmcls - INFO - Epoch [89][2200/5005]\tlr: 1.000e-03, eta: 1:25:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2567\n2023-01-01 01:47:45,234 - mmcls - INFO - Epoch [89][2300/5005]\tlr: 1.000e-03, eta: 1:25:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2823\n2023-01-01 01:47:54,091 - mmcls - INFO - Epoch [89][2400/5005]\tlr: 1.000e-03, eta: 1:25:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2920\n2023-01-01 01:48:03,155 - mmcls - INFO - Epoch [89][2500/5005]\tlr: 1.000e-03, eta: 1:24:55, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2887\n2023-01-01 01:48:11,747 - mmcls - INFO - Epoch [89][2600/5005]\tlr: 1.000e-03, eta: 1:24:47, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2739\n2023-01-01 01:48:20,489 - mmcls - INFO - Epoch [89][2700/5005]\tlr: 1.000e-03, eta: 1:24:38, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2564\n2023-01-01 01:48:29,158 - mmcls - INFO - Epoch [89][2800/5005]\tlr: 1.000e-03, eta: 1:24:29, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2779\n2023-01-01 01:48:37,810 - mmcls - INFO - Epoch [89][2900/5005]\tlr: 1.000e-03, eta: 1:24:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2828\n2023-01-01 01:48:46,506 - mmcls - INFO - Epoch [89][3000/5005]\tlr: 1.000e-03, eta: 1:24:11, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2805\n2023-01-01 01:48:55,015 - mmcls - INFO - Epoch [89][3100/5005]\tlr: 1.000e-03, eta: 1:24:02, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.2896\n2023-01-01 01:49:03,452 - mmcls - INFO - Epoch [89][3200/5005]\tlr: 1.000e-03, eta: 1:23:53, time: 0.084, data_time: 0.013, memory: 1224, loss: 1.2726\n2023-01-01 01:49:12,244 - mmcls - INFO - Epoch [89][3300/5005]\tlr: 1.000e-03, eta: 1:23:44, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2916\n2023-01-01 01:49:20,962 - mmcls - INFO - Epoch [89][3400/5005]\tlr: 1.000e-03, eta: 1:23:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2857\n2023-01-01 01:49:29,840 - mmcls - INFO - Epoch [89][3500/5005]\tlr: 1.000e-03, eta: 1:23:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2705\n2023-01-01 01:49:38,710 - mmcls - INFO - Epoch [89][3600/5005]\tlr: 1.000e-03, eta: 1:23:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2780\n2023-01-01 01:49:47,435 - mmcls - INFO - Epoch [89][3700/5005]\tlr: 1.000e-03, eta: 1:23:09, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2863\n2023-01-01 01:49:56,207 - mmcls - INFO - Epoch [89][3800/5005]\tlr: 1.000e-03, eta: 1:23:00, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2804\n2023-01-01 01:50:05,070 - mmcls - INFO - Epoch [89][3900/5005]\tlr: 1.000e-03, eta: 1:22:51, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2806\n2023-01-01 01:50:13,650 - mmcls - INFO - Epoch [89][4000/5005]\tlr: 1.000e-03, eta: 1:22:42, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.3034\n2023-01-01 01:50:22,428 - mmcls - INFO - Epoch [89][4100/5005]\tlr: 1.000e-03, eta: 1:22:33, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2929\n2023-01-01 01:50:31,125 - mmcls - INFO - Epoch [89][4200/5005]\tlr: 1.000e-03, eta: 1:22:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2803\n2023-01-01 01:50:39,562 - mmcls - INFO - Epoch [89][4300/5005]\tlr: 1.000e-03, eta: 1:22:16, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2758\n2023-01-01 01:50:48,412 - mmcls - INFO - Epoch [89][4400/5005]\tlr: 1.000e-03, eta: 1:22:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2783\n2023-01-01 01:50:57,678 - mmcls - INFO - Epoch [89][4500/5005]\tlr: 1.000e-03, eta: 1:21:58, time: 0.093, data_time: 0.022, memory: 1224, loss: 1.2896\n2023-01-01 01:51:06,508 - mmcls - INFO - Epoch [89][4600/5005]\tlr: 1.000e-03, eta: 1:21:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2810\n2023-01-01 01:51:15,301 - mmcls - INFO - Epoch [89][4700/5005]\tlr: 1.000e-03, eta: 1:21:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2766\n2023-01-01 01:51:24,307 - mmcls - INFO - Epoch [89][4800/5005]\tlr: 1.000e-03, eta: 1:21:31, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2687\n2023-01-01 01:51:32,922 - mmcls - INFO - Epoch [89][4900/5005]\tlr: 1.000e-03, eta: 1:21:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2689\n2023-01-01 01:51:41,856 - mmcls - INFO - Epoch [89][5000/5005]\tlr: 1.000e-03, eta: 1:21:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2807\n2023-01-01 01:52:04,736 - mmcls - INFO - Epoch(val) [89][196]\taccuracy_top-1: 71.0760, accuracy_top-5: 89.9500\n2023-01-01 01:52:15,864 - mmcls - INFO - Epoch [90][100/5005]\tlr: 1.000e-03, eta: 1:21:05, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.2517\n2023-01-01 01:52:25,028 - mmcls - INFO - Epoch [90][200/5005]\tlr: 1.000e-03, eta: 1:20:56, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2510\n2023-01-01 01:52:33,595 - mmcls - INFO - Epoch [90][300/5005]\tlr: 1.000e-03, eta: 1:20:47, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2792\n2023-01-01 01:52:42,270 - mmcls - INFO - Epoch [90][400/5005]\tlr: 1.000e-03, eta: 1:20:38, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.2709\n2023-01-01 01:52:51,067 - mmcls - INFO - Epoch [90][500/5005]\tlr: 1.000e-03, eta: 1:20:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2680\n2023-01-01 01:53:00,049 - mmcls - INFO - Epoch [90][600/5005]\tlr: 1.000e-03, eta: 1:20:20, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2829\n2023-01-01 01:53:08,738 - mmcls - INFO - Epoch [90][700/5005]\tlr: 1.000e-03, eta: 1:20:12, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2779\n2023-01-01 01:53:17,900 - mmcls - INFO - Epoch [90][800/5005]\tlr: 1.000e-03, eta: 1:20:03, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.2567\n2023-01-01 01:53:26,679 - mmcls - INFO - Epoch [90][900/5005]\tlr: 1.000e-03, eta: 1:19:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2775\n2023-01-01 01:53:35,187 - mmcls - INFO - Epoch [90][1000/5005]\tlr: 1.000e-03, eta: 1:19:45, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2599\n2023-01-01 01:53:43,849 - mmcls - INFO - Epoch [90][1100/5005]\tlr: 1.000e-03, eta: 1:19:36, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2426\n2023-01-01 01:53:52,441 - mmcls - INFO - Epoch [90][1200/5005]\tlr: 1.000e-03, eta: 1:19:27, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.2880\n2023-01-01 01:54:01,547 - mmcls - INFO - Epoch [90][1300/5005]\tlr: 1.000e-03, eta: 1:19:18, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2675\n2023-01-01 01:54:10,669 - mmcls - INFO - Epoch [90][1400/5005]\tlr: 1.000e-03, eta: 1:19:10, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2585\n2023-01-01 01:54:19,760 - mmcls - INFO - Epoch [90][1500/5005]\tlr: 1.000e-03, eta: 1:19:01, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2993\n2023-01-01 01:54:28,769 - mmcls - INFO - Epoch [90][1600/5005]\tlr: 1.000e-03, eta: 1:18:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2694\n2023-01-01 01:54:37,501 - mmcls - INFO - Epoch [90][1700/5005]\tlr: 1.000e-03, eta: 1:18:43, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2606\n2023-01-01 01:54:46,537 - mmcls - INFO - Epoch [90][1800/5005]\tlr: 1.000e-03, eta: 1:18:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2781\n2023-01-01 01:54:54,897 - mmcls - INFO - Epoch [90][1900/5005]\tlr: 1.000e-03, eta: 1:18:25, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.2629\n2023-01-01 01:55:03,781 - mmcls - INFO - Epoch [90][2000/5005]\tlr: 1.000e-03, eta: 1:18:16, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2944\n2023-01-01 01:55:12,532 - mmcls - INFO - Epoch [90][2100/5005]\tlr: 1.000e-03, eta: 1:18:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2641\n2023-01-01 01:55:21,598 - mmcls - INFO - Epoch [90][2200/5005]\tlr: 1.000e-03, eta: 1:17:59, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.2699\n2023-01-01 01:55:30,764 - mmcls - INFO - Epoch [90][2300/5005]\tlr: 1.000e-03, eta: 1:17:50, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.2809\n2023-01-01 01:55:39,365 - mmcls - INFO - Epoch [90][2400/5005]\tlr: 1.000e-03, eta: 1:17:41, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2815\n2023-01-01 01:55:48,362 - mmcls - INFO - Epoch [90][2500/5005]\tlr: 1.000e-03, eta: 1:17:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2828\n2023-01-01 01:55:57,412 - mmcls - INFO - Epoch [90][2600/5005]\tlr: 1.000e-03, eta: 1:17:23, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.2953\n2023-01-01 01:56:05,999 - mmcls - INFO - Epoch [90][2700/5005]\tlr: 1.000e-03, eta: 1:17:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.2620\n2023-01-01 01:56:14,726 - mmcls - INFO - Epoch [90][2800/5005]\tlr: 1.000e-03, eta: 1:17:06, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2580\n2023-01-01 01:56:23,570 - mmcls - INFO - Epoch [90][2900/5005]\tlr: 1.000e-03, eta: 1:16:57, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2724\n2023-01-01 01:56:32,886 - mmcls - INFO - Epoch [90][3000/5005]\tlr: 1.000e-03, eta: 1:16:48, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2700\n2023-01-01 01:56:41,403 - mmcls - INFO - Epoch [90][3100/5005]\tlr: 1.000e-03, eta: 1:16:39, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2732\n2023-01-01 01:56:50,203 - mmcls - INFO - Epoch [90][3200/5005]\tlr: 1.000e-03, eta: 1:16:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2733\n2023-01-01 01:56:58,890 - mmcls - INFO - Epoch [90][3300/5005]\tlr: 1.000e-03, eta: 1:16:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2491\n2023-01-01 01:57:07,437 - mmcls - INFO - Epoch [90][3400/5005]\tlr: 1.000e-03, eta: 1:16:12, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.2849\n2023-01-01 01:57:16,462 - mmcls - INFO - Epoch [90][3500/5005]\tlr: 1.000e-03, eta: 1:16:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2931\n2023-01-01 01:57:25,172 - mmcls - INFO - Epoch [90][3600/5005]\tlr: 1.000e-03, eta: 1:15:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2762\n2023-01-01 01:57:33,904 - mmcls - INFO - Epoch [90][3700/5005]\tlr: 1.000e-03, eta: 1:15:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2769\n2023-01-01 01:57:42,990 - mmcls - INFO - Epoch [90][3800/5005]\tlr: 1.000e-03, eta: 1:15:37, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2759\n2023-01-01 01:57:51,715 - mmcls - INFO - Epoch [90][3900/5005]\tlr: 1.000e-03, eta: 1:15:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2803\n2023-01-01 01:58:00,456 - mmcls - INFO - Epoch [90][4000/5005]\tlr: 1.000e-03, eta: 1:15:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2547\n2023-01-01 01:58:09,901 - mmcls - INFO - Epoch [90][4100/5005]\tlr: 1.000e-03, eta: 1:15:11, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.2809\n2023-01-01 01:58:18,550 - mmcls - INFO - Epoch [90][4200/5005]\tlr: 1.000e-03, eta: 1:15:02, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2914\n2023-01-01 01:58:27,208 - mmcls - INFO - Epoch [90][4300/5005]\tlr: 1.000e-03, eta: 1:14:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2629\n2023-01-01 01:58:35,773 - mmcls - INFO - Epoch [90][4400/5005]\tlr: 1.000e-03, eta: 1:14:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2721\n2023-01-01 01:58:44,691 - mmcls - INFO - Epoch [90][4500/5005]\tlr: 1.000e-03, eta: 1:14:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3016\n2023-01-01 01:58:53,367 - mmcls - INFO - Epoch [90][4600/5005]\tlr: 1.000e-03, eta: 1:14:26, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2897\n2023-01-01 01:59:02,072 - mmcls - INFO - Epoch [90][4700/5005]\tlr: 1.000e-03, eta: 1:14:17, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2914\n2023-01-01 01:59:11,003 - mmcls - INFO - Epoch [90][4800/5005]\tlr: 1.000e-03, eta: 1:14:08, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2819\n2023-01-01 01:59:19,843 - mmcls - INFO - Epoch [90][4900/5005]\tlr: 1.000e-03, eta: 1:14:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2689\n2023-01-01 01:59:28,873 - mmcls - INFO - Epoch [90][5000/5005]\tlr: 1.000e-03, eta: 1:13:51, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2769\n2023-01-01 01:59:51,424 - mmcls - INFO - Epoch(val) [90][196]\taccuracy_top-1: 70.9520, accuracy_top-5: 89.8720\n2023-01-01 02:00:02,404 - mmcls - INFO - Epoch [91][100/5005]\tlr: 1.000e-04, eta: 1:13:42, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.2593\n2023-01-01 02:00:11,326 - mmcls - INFO - Epoch [91][200/5005]\tlr: 1.000e-04, eta: 1:13:33, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2482\n2023-01-01 02:00:20,149 - mmcls - INFO - Epoch [91][300/5005]\tlr: 1.000e-04, eta: 1:13:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2556\n2023-01-01 02:00:29,223 - mmcls - INFO - Epoch [91][400/5005]\tlr: 1.000e-04, eta: 1:13:15, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.2425\n2023-01-01 02:00:38,083 - mmcls - INFO - Epoch [91][500/5005]\tlr: 1.000e-04, eta: 1:13:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2561\n2023-01-01 02:00:46,761 - mmcls - INFO - Epoch [91][600/5005]\tlr: 1.000e-04, eta: 1:12:57, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2513\n2023-01-01 02:00:55,398 - mmcls - INFO - Epoch [91][700/5005]\tlr: 1.000e-04, eta: 1:12:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2426\n2023-01-01 02:01:04,264 - mmcls - INFO - Epoch [91][800/5005]\tlr: 1.000e-04, eta: 1:12:40, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2318\n2023-01-01 02:01:12,922 - mmcls - INFO - Epoch [91][900/5005]\tlr: 1.000e-04, eta: 1:12:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2373\n2023-01-01 02:01:21,694 - mmcls - INFO - Epoch [91][1000/5005]\tlr: 1.000e-04, eta: 1:12:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2388\n2023-01-01 02:01:30,438 - mmcls - INFO - Epoch [91][1100/5005]\tlr: 1.000e-04, eta: 1:12:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2603\n2023-01-01 02:01:39,390 - mmcls - INFO - Epoch [91][1200/5005]\tlr: 1.000e-04, eta: 1:12:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2582\n2023-01-01 02:01:48,096 - mmcls - INFO - Epoch [91][1300/5005]\tlr: 1.000e-04, eta: 1:11:55, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2639\n2023-01-01 02:01:57,049 - mmcls - INFO - Epoch [91][1400/5005]\tlr: 1.000e-04, eta: 1:11:47, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.2435\n2023-01-01 02:02:05,666 - mmcls - INFO - Epoch [91][1500/5005]\tlr: 1.000e-04, eta: 1:11:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2423\n2023-01-01 02:02:14,511 - mmcls - INFO - Epoch [91][1600/5005]\tlr: 1.000e-04, eta: 1:11:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2513\n2023-01-01 02:02:23,406 - mmcls - INFO - Epoch [91][1700/5005]\tlr: 1.000e-04, eta: 1:11:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2490\n2023-01-01 02:02:32,177 - mmcls - INFO - Epoch [91][1800/5005]\tlr: 1.000e-04, eta: 1:11:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2375\n2023-01-01 02:02:41,089 - mmcls - INFO - Epoch [91][1900/5005]\tlr: 1.000e-04, eta: 1:11:02, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.2562\n2023-01-01 02:02:49,995 - mmcls - INFO - Epoch [91][2000/5005]\tlr: 1.000e-04, eta: 1:10:53, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.2396\n2023-01-01 02:02:58,670 - mmcls - INFO - Epoch [91][2100/5005]\tlr: 1.000e-04, eta: 1:10:45, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2397\n2023-01-01 02:03:07,225 - mmcls - INFO - Epoch [91][2200/5005]\tlr: 1.000e-04, eta: 1:10:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2591\n2023-01-01 02:03:15,973 - mmcls - INFO - Epoch [91][2300/5005]\tlr: 1.000e-04, eta: 1:10:27, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2316\n2023-01-01 02:03:24,736 - mmcls - INFO - Epoch [91][2400/5005]\tlr: 1.000e-04, eta: 1:10:18, time: 0.088, data_time: 0.044, memory: 1224, loss: 1.2210\n2023-01-01 02:03:33,694 - mmcls - INFO - Epoch [91][2500/5005]\tlr: 1.000e-04, eta: 1:10:09, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2473\n2023-01-01 02:03:42,479 - mmcls - INFO - Epoch [91][2600/5005]\tlr: 1.000e-04, eta: 1:10:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2614\n2023-01-01 02:03:51,136 - mmcls - INFO - Epoch [91][2700/5005]\tlr: 1.000e-04, eta: 1:09:51, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2454\n2023-01-01 02:03:59,971 - mmcls - INFO - Epoch [91][2800/5005]\tlr: 1.000e-04, eta: 1:09:43, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2515\n2023-01-01 02:04:08,717 - mmcls - INFO - Epoch [91][2900/5005]\tlr: 1.000e-04, eta: 1:09:34, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2383\n2023-01-01 02:04:17,532 - mmcls - INFO - Epoch [91][3000/5005]\tlr: 1.000e-04, eta: 1:09:25, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2641\n2023-01-01 02:04:26,741 - mmcls - INFO - Epoch [91][3100/5005]\tlr: 1.000e-04, eta: 1:09:16, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.2565\n2023-01-01 02:04:35,435 - mmcls - INFO - Epoch [91][3200/5005]\tlr: 1.000e-04, eta: 1:09:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2344\n2023-01-01 02:04:44,342 - mmcls - INFO - Epoch [91][3300/5005]\tlr: 1.000e-04, eta: 1:08:58, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.2363\n2023-01-01 02:04:52,938 - mmcls - INFO - Epoch [91][3400/5005]\tlr: 1.000e-04, eta: 1:08:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2328\n2023-01-01 02:05:01,370 - mmcls - INFO - Epoch [91][3500/5005]\tlr: 1.000e-04, eta: 1:08:40, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2448\n2023-01-01 02:05:10,200 - mmcls - INFO - Epoch [91][3600/5005]\tlr: 1.000e-04, eta: 1:08:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2267\n2023-01-01 02:05:18,957 - mmcls - INFO - Epoch [91][3700/5005]\tlr: 1.000e-04, eta: 1:08:23, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.2500\n2023-01-01 02:05:27,613 - mmcls - INFO - Epoch [91][3800/5005]\tlr: 1.000e-04, eta: 1:08:14, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2356\n2023-01-01 02:05:36,837 - mmcls - INFO - Epoch [91][3900/5005]\tlr: 1.000e-04, eta: 1:08:05, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.2369\n2023-01-01 02:05:46,000 - mmcls - INFO - Epoch [91][4000/5005]\tlr: 1.000e-04, eta: 1:07:56, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2350\n2023-01-01 02:05:54,710 - mmcls - INFO - Epoch [91][4100/5005]\tlr: 1.000e-04, eta: 1:07:47, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2243\n2023-01-01 02:06:03,635 - mmcls - INFO - Epoch [91][4200/5005]\tlr: 1.000e-04, eta: 1:07:39, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2559\n2023-01-01 02:06:12,478 - mmcls - INFO - Epoch [91][4300/5005]\tlr: 1.000e-04, eta: 1:07:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2381\n2023-01-01 02:06:21,449 - mmcls - INFO - Epoch [91][4400/5005]\tlr: 1.000e-04, eta: 1:07:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2501\n2023-01-01 02:06:30,404 - mmcls - INFO - Epoch [91][4500/5005]\tlr: 1.000e-04, eta: 1:07:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2383\n2023-01-01 02:06:39,341 - mmcls - INFO - Epoch [91][4600/5005]\tlr: 1.000e-04, eta: 1:07:03, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2397\n2023-01-01 02:06:48,056 - mmcls - INFO - Epoch [91][4700/5005]\tlr: 1.000e-04, eta: 1:06:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2739\n2023-01-01 02:06:56,734 - mmcls - INFO - Epoch [91][4800/5005]\tlr: 1.000e-04, eta: 1:06:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2629\n2023-01-01 02:07:05,531 - mmcls - INFO - Epoch [91][4900/5005]\tlr: 1.000e-04, eta: 1:06:37, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2391\n2023-01-01 02:07:14,426 - mmcls - INFO - Epoch [91][5000/5005]\tlr: 1.000e-04, eta: 1:06:28, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2403\n2023-01-01 02:07:36,961 - mmcls - INFO - Epoch(val) [91][196]\taccuracy_top-1: 71.3920, accuracy_top-5: 90.1500\n2023-01-01 02:07:47,861 - mmcls - INFO - Epoch [92][100/5005]\tlr: 1.000e-04, eta: 1:06:19, time: 0.109, data_time: 0.036, memory: 1224, loss: 1.2221\n2023-01-01 02:07:56,764 - mmcls - INFO - Epoch [92][200/5005]\tlr: 1.000e-04, eta: 1:06:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2454\n2023-01-01 02:08:05,544 - mmcls - INFO - Epoch [92][300/5005]\tlr: 1.000e-04, eta: 1:06:01, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2372\n2023-01-01 02:08:14,241 - mmcls - INFO - Epoch [92][400/5005]\tlr: 1.000e-04, eta: 1:05:52, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2293\n2023-01-01 02:08:22,942 - mmcls - INFO - Epoch [92][500/5005]\tlr: 1.000e-04, eta: 1:05:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2180\n2023-01-01 02:08:31,641 - mmcls - INFO - Epoch [92][600/5005]\tlr: 1.000e-04, eta: 1:05:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2193\n2023-01-01 02:08:40,408 - mmcls - INFO - Epoch [92][700/5005]\tlr: 1.000e-04, eta: 1:05:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2297\n2023-01-01 02:08:49,157 - mmcls - INFO - Epoch [92][800/5005]\tlr: 1.000e-04, eta: 1:05:17, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2375\n2023-01-01 02:08:58,152 - mmcls - INFO - Epoch [92][900/5005]\tlr: 1.000e-04, eta: 1:05:08, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2607\n2023-01-01 02:09:06,738 - mmcls - INFO - Epoch [92][1000/5005]\tlr: 1.000e-04, eta: 1:04:59, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2393\n2023-01-01 02:09:15,622 - mmcls - INFO - Epoch [92][1100/5005]\tlr: 1.000e-04, eta: 1:04:50, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2622\n2023-01-01 02:09:24,342 - mmcls - INFO - Epoch [92][1200/5005]\tlr: 1.000e-04, eta: 1:04:41, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2489\n2023-01-01 02:09:33,245 - mmcls - INFO - Epoch [92][1300/5005]\tlr: 1.000e-04, eta: 1:04:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2403\n2023-01-01 02:09:41,989 - mmcls - INFO - Epoch [92][1400/5005]\tlr: 1.000e-04, eta: 1:04:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2166\n2023-01-01 02:09:50,424 - mmcls - INFO - Epoch [92][1500/5005]\tlr: 1.000e-04, eta: 1:04:14, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2442\n2023-01-01 02:09:59,266 - mmcls - INFO - Epoch [92][1600/5005]\tlr: 1.000e-04, eta: 1:04:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2280\n2023-01-01 02:10:07,891 - mmcls - INFO - Epoch [92][1700/5005]\tlr: 1.000e-04, eta: 1:03:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2564\n2023-01-01 02:10:16,926 - mmcls - INFO - Epoch [92][1800/5005]\tlr: 1.000e-04, eta: 1:03:48, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2369\n2023-01-01 02:10:25,828 - mmcls - INFO - Epoch [92][1900/5005]\tlr: 1.000e-04, eta: 1:03:39, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.2334\n2023-01-01 02:10:34,702 - mmcls - INFO - Epoch [92][2000/5005]\tlr: 1.000e-04, eta: 1:03:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2396\n2023-01-01 02:10:43,665 - mmcls - INFO - Epoch [92][2100/5005]\tlr: 1.000e-04, eta: 1:03:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2264\n2023-01-01 02:10:52,443 - mmcls - INFO - Epoch [92][2200/5005]\tlr: 1.000e-04, eta: 1:03:13, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2573\n2023-01-01 02:11:01,709 - mmcls - INFO - Epoch [92][2300/5005]\tlr: 1.000e-04, eta: 1:03:04, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2390\n2023-01-01 02:11:10,483 - mmcls - INFO - Epoch [92][2400/5005]\tlr: 1.000e-04, eta: 1:02:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2557\n2023-01-01 02:11:18,951 - mmcls - INFO - Epoch [92][2500/5005]\tlr: 1.000e-04, eta: 1:02:46, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.2408\n2023-01-01 02:11:27,913 - mmcls - INFO - Epoch [92][2600/5005]\tlr: 1.000e-04, eta: 1:02:37, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.2391\n2023-01-01 02:11:36,425 - mmcls - INFO - Epoch [92][2700/5005]\tlr: 1.000e-04, eta: 1:02:28, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2432\n2023-01-01 02:11:45,640 - mmcls - INFO - Epoch [92][2800/5005]\tlr: 1.000e-04, eta: 1:02:19, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.2240\n2023-01-01 02:11:54,524 - mmcls - INFO - Epoch [92][2900/5005]\tlr: 1.000e-04, eta: 1:02:11, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2250\n2023-01-01 02:12:03,326 - mmcls - INFO - Epoch [92][3000/5005]\tlr: 1.000e-04, eta: 1:02:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2354\n2023-01-01 02:12:12,224 - mmcls - INFO - Epoch [92][3100/5005]\tlr: 1.000e-04, eta: 1:01:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2471\n2023-01-01 02:12:21,184 - mmcls - INFO - Epoch [92][3200/5005]\tlr: 1.000e-04, eta: 1:01:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2268\n2023-01-01 02:12:29,974 - mmcls - INFO - Epoch [92][3300/5005]\tlr: 1.000e-04, eta: 1:01:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2516\n2023-01-01 02:12:38,839 - mmcls - INFO - Epoch [92][3400/5005]\tlr: 1.000e-04, eta: 1:01:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2219\n2023-01-01 02:12:47,559 - mmcls - INFO - Epoch [92][3500/5005]\tlr: 1.000e-04, eta: 1:01:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2557\n2023-01-01 02:12:56,464 - mmcls - INFO - Epoch [92][3600/5005]\tlr: 1.000e-04, eta: 1:01:09, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2319\n2023-01-01 02:13:05,232 - mmcls - INFO - Epoch [92][3700/5005]\tlr: 1.000e-04, eta: 1:01:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2497\n2023-01-01 02:13:14,107 - mmcls - INFO - Epoch [92][3800/5005]\tlr: 1.000e-04, eta: 1:00:51, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2347\n2023-01-01 02:13:22,766 - mmcls - INFO - Epoch [92][3900/5005]\tlr: 1.000e-04, eta: 1:00:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2414\n2023-01-01 02:13:31,278 - mmcls - INFO - Epoch [92][4000/5005]\tlr: 1.000e-04, eta: 1:00:33, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2320\n2023-01-01 02:13:40,115 - mmcls - INFO - Epoch [92][4100/5005]\tlr: 1.000e-04, eta: 1:00:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2608\n2023-01-01 02:13:48,885 - mmcls - INFO - Epoch [92][4200/5005]\tlr: 1.000e-04, eta: 1:00:15, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.2207\n2023-01-01 02:13:58,056 - mmcls - INFO - Epoch [92][4300/5005]\tlr: 1.000e-04, eta: 1:00:07, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.2383\n2023-01-01 02:14:06,897 - mmcls - INFO - Epoch [92][4400/5005]\tlr: 1.000e-04, eta: 0:59:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2420\n2023-01-01 02:14:15,659 - mmcls - INFO - Epoch [92][4500/5005]\tlr: 1.000e-04, eta: 0:59:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2457\n2023-01-01 02:14:24,552 - mmcls - INFO - Epoch [92][4600/5005]\tlr: 1.000e-04, eta: 0:59:40, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2302\n2023-01-01 02:14:33,300 - mmcls - INFO - Epoch [92][4700/5005]\tlr: 1.000e-04, eta: 0:59:31, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2333\n2023-01-01 02:14:42,269 - mmcls - INFO - Epoch [92][4800/5005]\tlr: 1.000e-04, eta: 0:59:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2604\n2023-01-01 02:14:51,373 - mmcls - INFO - Epoch [92][4900/5005]\tlr: 1.000e-04, eta: 0:59:13, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.2319\n2023-01-01 02:15:00,173 - mmcls - INFO - Epoch [92][5000/5005]\tlr: 1.000e-04, eta: 0:59:05, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.2458\n2023-01-01 02:15:22,780 - mmcls - INFO - Epoch(val) [92][196]\taccuracy_top-1: 71.4800, accuracy_top-5: 90.1240\n2023-01-01 02:15:33,907 - mmcls - INFO - Epoch [93][100/5005]\tlr: 1.000e-04, eta: 0:58:55, time: 0.111, data_time: 0.069, memory: 1224, loss: 1.2322\n2023-01-01 02:15:42,555 - mmcls - INFO - Epoch [93][200/5005]\tlr: 1.000e-04, eta: 0:58:47, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2203\n2023-01-01 02:15:51,470 - mmcls - INFO - Epoch [93][300/5005]\tlr: 1.000e-04, eta: 0:58:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2550\n2023-01-01 02:16:00,397 - mmcls - INFO - Epoch [93][400/5005]\tlr: 1.000e-04, eta: 0:58:29, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.2211\n2023-01-01 02:16:09,142 - mmcls - INFO - Epoch [93][500/5005]\tlr: 1.000e-04, eta: 0:58:20, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2154\n2023-01-01 02:16:18,204 - mmcls - INFO - Epoch [93][600/5005]\tlr: 1.000e-04, eta: 0:58:11, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2191\n2023-01-01 02:16:27,126 - mmcls - INFO - Epoch [93][700/5005]\tlr: 1.000e-04, eta: 0:58:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2394\n2023-01-01 02:16:35,958 - mmcls - INFO - Epoch [93][800/5005]\tlr: 1.000e-04, eta: 0:57:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2354\n2023-01-01 02:16:44,988 - mmcls - INFO - Epoch [93][900/5005]\tlr: 1.000e-04, eta: 0:57:45, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2557\n2023-01-01 02:16:53,510 - mmcls - INFO - Epoch [93][1000/5005]\tlr: 1.000e-04, eta: 0:57:36, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.2329\n2023-01-01 02:17:02,250 - mmcls - INFO - Epoch [93][1100/5005]\tlr: 1.000e-04, eta: 0:57:27, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2210\n2023-01-01 02:17:10,887 - mmcls - INFO - Epoch [93][1200/5005]\tlr: 1.000e-04, eta: 0:57:18, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.2241\n2023-01-01 02:17:19,529 - mmcls - INFO - Epoch [93][1300/5005]\tlr: 1.000e-04, eta: 0:57:09, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2548\n2023-01-01 02:17:28,187 - mmcls - INFO - Epoch [93][1400/5005]\tlr: 1.000e-04, eta: 0:57:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2458\n2023-01-01 02:17:37,065 - mmcls - INFO - Epoch [93][1500/5005]\tlr: 1.000e-04, eta: 0:56:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2156\n2023-01-01 02:17:45,952 - mmcls - INFO - Epoch [93][1600/5005]\tlr: 1.000e-04, eta: 0:56:43, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2472\n2023-01-01 02:17:54,626 - mmcls - INFO - Epoch [93][1700/5005]\tlr: 1.000e-04, eta: 0:56:34, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2553\n2023-01-01 02:18:03,410 - mmcls - INFO - Epoch [93][1800/5005]\tlr: 1.000e-04, eta: 0:56:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2171\n2023-01-01 02:18:11,887 - mmcls - INFO - Epoch [93][1900/5005]\tlr: 1.000e-04, eta: 0:56:16, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2351\n2023-01-01 02:18:20,805 - mmcls - INFO - Epoch [93][2000/5005]\tlr: 1.000e-04, eta: 0:56:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2468\n2023-01-01 02:18:29,520 - mmcls - INFO - Epoch [93][2100/5005]\tlr: 1.000e-04, eta: 0:55:58, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2440\n2023-01-01 02:18:38,702 - mmcls - INFO - Epoch [93][2200/5005]\tlr: 1.000e-04, eta: 0:55:49, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2418\n2023-01-01 02:18:47,461 - mmcls - INFO - Epoch [93][2300/5005]\tlr: 1.000e-04, eta: 0:55:41, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.2414\n2023-01-01 02:18:56,266 - mmcls - INFO - Epoch [93][2400/5005]\tlr: 1.000e-04, eta: 0:55:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2655\n2023-01-01 02:19:05,031 - mmcls - INFO - Epoch [93][2500/5005]\tlr: 1.000e-04, eta: 0:55:23, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2619\n2023-01-01 02:19:13,834 - mmcls - INFO - Epoch [93][2600/5005]\tlr: 1.000e-04, eta: 0:55:14, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2314\n2023-01-01 02:19:22,587 - mmcls - INFO - Epoch [93][2700/5005]\tlr: 1.000e-04, eta: 0:55:05, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2297\n2023-01-01 02:19:31,699 - mmcls - INFO - Epoch [93][2800/5005]\tlr: 1.000e-04, eta: 0:54:56, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2392\n2023-01-01 02:19:40,570 - mmcls - INFO - Epoch [93][2900/5005]\tlr: 1.000e-04, eta: 0:54:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2360\n2023-01-01 02:19:49,184 - mmcls - INFO - Epoch [93][3000/5005]\tlr: 1.000e-04, eta: 0:54:39, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2317\n2023-01-01 02:19:58,253 - mmcls - INFO - Epoch [93][3100/5005]\tlr: 1.000e-04, eta: 0:54:30, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2208\n2023-01-01 02:20:07,028 - mmcls - INFO - Epoch [93][3200/5005]\tlr: 1.000e-04, eta: 0:54:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2227\n2023-01-01 02:20:15,934 - mmcls - INFO - Epoch [93][3300/5005]\tlr: 1.000e-04, eta: 0:54:12, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2399\n2023-01-01 02:20:24,961 - mmcls - INFO - Epoch [93][3400/5005]\tlr: 1.000e-04, eta: 0:54:03, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2283\n2023-01-01 02:20:33,758 - mmcls - INFO - Epoch [93][3500/5005]\tlr: 1.000e-04, eta: 0:53:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2488\n2023-01-01 02:20:42,639 - mmcls - INFO - Epoch [93][3600/5005]\tlr: 1.000e-04, eta: 0:53:46, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.2252\n2023-01-01 02:20:51,629 - mmcls - INFO - Epoch [93][3700/5005]\tlr: 1.000e-04, eta: 0:53:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2571\n2023-01-01 02:21:00,318 - mmcls - INFO - Epoch [93][3800/5005]\tlr: 1.000e-04, eta: 0:53:28, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2337\n2023-01-01 02:21:09,232 - mmcls - INFO - Epoch [93][3900/5005]\tlr: 1.000e-04, eta: 0:53:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2258\n2023-01-01 02:21:17,825 - mmcls - INFO - Epoch [93][4000/5005]\tlr: 1.000e-04, eta: 0:53:10, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2435\n2023-01-01 02:21:26,764 - mmcls - INFO - Epoch [93][4100/5005]\tlr: 1.000e-04, eta: 0:53:01, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2205\n2023-01-01 02:21:35,838 - mmcls - INFO - Epoch [93][4200/5005]\tlr: 1.000e-04, eta: 0:52:52, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2256\n2023-01-01 02:21:44,390 - mmcls - INFO - Epoch [93][4300/5005]\tlr: 1.000e-04, eta: 0:52:44, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2352\n2023-01-01 02:21:53,572 - mmcls - INFO - Epoch [93][4400/5005]\tlr: 1.000e-04, eta: 0:52:35, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.2434\n2023-01-01 02:22:02,060 - mmcls - INFO - Epoch [93][4500/5005]\tlr: 1.000e-04, eta: 0:52:26, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2436\n2023-01-01 02:22:10,562 - mmcls - INFO - Epoch [93][4600/5005]\tlr: 1.000e-04, eta: 0:52:17, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2375\n2023-01-01 02:22:19,403 - mmcls - INFO - Epoch [93][4700/5005]\tlr: 1.000e-04, eta: 0:52:08, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2653\n2023-01-01 02:22:28,086 - mmcls - INFO - Epoch [93][4800/5005]\tlr: 1.000e-04, eta: 0:51:59, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2337\n2023-01-01 02:22:36,847 - mmcls - INFO - Epoch [93][4900/5005]\tlr: 1.000e-04, eta: 0:51:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2379\n2023-01-01 02:22:45,499 - mmcls - INFO - Epoch [93][5000/5005]\tlr: 1.000e-04, eta: 0:51:42, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2175\n2023-01-01 02:23:08,155 - mmcls - INFO - Epoch(val) [93][196]\taccuracy_top-1: 71.5360, accuracy_top-5: 90.1280\n2023-01-01 02:23:19,115 - mmcls - INFO - Epoch [94][100/5005]\tlr: 1.000e-04, eta: 0:51:32, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.2231\n2023-01-01 02:23:27,988 - mmcls - INFO - Epoch [94][200/5005]\tlr: 1.000e-04, eta: 0:51:23, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2338\n2023-01-01 02:23:36,903 - mmcls - INFO - Epoch [94][300/5005]\tlr: 1.000e-04, eta: 0:51:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2309\n2023-01-01 02:23:45,406 - mmcls - INFO - Epoch [94][400/5005]\tlr: 1.000e-04, eta: 0:51:06, time: 0.085, data_time: 0.022, memory: 1224, loss: 1.2268\n2023-01-01 02:23:54,368 - mmcls - INFO - Epoch [94][500/5005]\tlr: 1.000e-04, eta: 0:50:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2188\n2023-01-01 02:24:03,463 - mmcls - INFO - Epoch [94][600/5005]\tlr: 1.000e-04, eta: 0:50:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2402\n2023-01-01 02:24:12,236 - mmcls - INFO - Epoch [94][700/5005]\tlr: 1.000e-04, eta: 0:50:39, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2232\n2023-01-01 02:24:21,071 - mmcls - INFO - Epoch [94][800/5005]\tlr: 1.000e-04, eta: 0:50:30, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2467\n2023-01-01 02:24:29,605 - mmcls - INFO - Epoch [94][900/5005]\tlr: 1.000e-04, eta: 0:50:21, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2226\n2023-01-01 02:24:38,542 - mmcls - INFO - Epoch [94][1000/5005]\tlr: 1.000e-04, eta: 0:50:13, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2185\n2023-01-01 02:24:47,374 - mmcls - INFO - Epoch [94][1100/5005]\tlr: 1.000e-04, eta: 0:50:04, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2104\n2023-01-01 02:24:56,174 - mmcls - INFO - Epoch [94][1200/5005]\tlr: 1.000e-04, eta: 0:49:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2529\n2023-01-01 02:25:04,835 - mmcls - INFO - Epoch [94][1300/5005]\tlr: 1.000e-04, eta: 0:49:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.2343\n2023-01-01 02:25:13,708 - mmcls - INFO - Epoch [94][1400/5005]\tlr: 1.000e-04, eta: 0:49:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2252\n2023-01-01 02:25:22,418 - mmcls - INFO - Epoch [94][1500/5005]\tlr: 1.000e-04, eta: 0:49:28, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2372\n2023-01-01 02:25:31,121 - mmcls - INFO - Epoch [94][1600/5005]\tlr: 1.000e-04, eta: 0:49:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2249\n2023-01-01 02:25:39,849 - mmcls - INFO - Epoch [94][1700/5005]\tlr: 1.000e-04, eta: 0:49:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2114\n2023-01-01 02:25:48,629 - mmcls - INFO - Epoch [94][1800/5005]\tlr: 1.000e-04, eta: 0:49:02, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.2313\n2023-01-01 02:25:57,179 - mmcls - INFO - Epoch [94][1900/5005]\tlr: 1.000e-04, eta: 0:48:53, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2321\n2023-01-01 02:26:05,926 - mmcls - INFO - Epoch [94][2000/5005]\tlr: 1.000e-04, eta: 0:48:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2612\n2023-01-01 02:26:15,000 - mmcls - INFO - Epoch [94][2100/5005]\tlr: 1.000e-04, eta: 0:48:35, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2405\n2023-01-01 02:26:24,288 - mmcls - INFO - Epoch [94][2200/5005]\tlr: 1.000e-04, eta: 0:48:26, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2324\n2023-01-01 02:26:33,157 - mmcls - INFO - Epoch [94][2300/5005]\tlr: 1.000e-04, eta: 0:48:18, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2392\n2023-01-01 02:26:42,067 - mmcls - INFO - Epoch [94][2400/5005]\tlr: 1.000e-04, eta: 0:48:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2265\n2023-01-01 02:26:50,780 - mmcls - INFO - Epoch [94][2500/5005]\tlr: 1.000e-04, eta: 0:48:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2153\n2023-01-01 02:26:59,483 - mmcls - INFO - Epoch [94][2600/5005]\tlr: 1.000e-04, eta: 0:47:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2220\n2023-01-01 02:27:08,075 - mmcls - INFO - Epoch [94][2700/5005]\tlr: 1.000e-04, eta: 0:47:42, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2158\n2023-01-01 02:27:17,414 - mmcls - INFO - Epoch [94][2800/5005]\tlr: 1.000e-04, eta: 0:47:33, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2244\n2023-01-01 02:27:26,259 - mmcls - INFO - Epoch [94][2900/5005]\tlr: 1.000e-04, eta: 0:47:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2105\n2023-01-01 02:27:35,162 - mmcls - INFO - Epoch [94][3000/5005]\tlr: 1.000e-04, eta: 0:47:16, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2200\n2023-01-01 02:27:43,934 - mmcls - INFO - Epoch [94][3100/5005]\tlr: 1.000e-04, eta: 0:47:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2470\n2023-01-01 02:27:52,840 - mmcls - INFO - Epoch [94][3200/5005]\tlr: 1.000e-04, eta: 0:46:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2430\n2023-01-01 02:28:01,743 - mmcls - INFO - Epoch [94][3300/5005]\tlr: 1.000e-04, eta: 0:46:49, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2281\n2023-01-01 02:28:10,925 - mmcls - INFO - Epoch [94][3400/5005]\tlr: 1.000e-04, eta: 0:46:40, time: 0.092, data_time: 0.038, memory: 1224, loss: 1.2344\n2023-01-01 02:28:19,742 - mmcls - INFO - Epoch [94][3500/5005]\tlr: 1.000e-04, eta: 0:46:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2322\n2023-01-01 02:28:28,692 - mmcls - INFO - Epoch [94][3600/5005]\tlr: 1.000e-04, eta: 0:46:22, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2526\n2023-01-01 02:28:37,428 - mmcls - INFO - Epoch [94][3700/5005]\tlr: 1.000e-04, eta: 0:46:14, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2237\n2023-01-01 02:28:46,062 - mmcls - INFO - Epoch [94][3800/5005]\tlr: 1.000e-04, eta: 0:46:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2270\n2023-01-01 02:28:54,819 - mmcls - INFO - Epoch [94][3900/5005]\tlr: 1.000e-04, eta: 0:45:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2226\n2023-01-01 02:29:03,596 - mmcls - INFO - Epoch [94][4000/5005]\tlr: 1.000e-04, eta: 0:45:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2415\n2023-01-01 02:29:12,301 - mmcls - INFO - Epoch [94][4100/5005]\tlr: 1.000e-04, eta: 0:45:38, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2141\n2023-01-01 02:29:20,967 - mmcls - INFO - Epoch [94][4200/5005]\tlr: 1.000e-04, eta: 0:45:29, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.2332\n2023-01-01 02:29:29,940 - mmcls - INFO - Epoch [94][4300/5005]\tlr: 1.000e-04, eta: 0:45:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2397\n2023-01-01 02:29:38,843 - mmcls - INFO - Epoch [94][4400/5005]\tlr: 1.000e-04, eta: 0:45:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2487\n2023-01-01 02:29:47,817 - mmcls - INFO - Epoch [94][4500/5005]\tlr: 1.000e-04, eta: 0:45:03, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2330\n2023-01-01 02:29:56,762 - mmcls - INFO - Epoch [94][4600/5005]\tlr: 1.000e-04, eta: 0:44:54, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.2448\n2023-01-01 02:30:05,577 - mmcls - INFO - Epoch [94][4700/5005]\tlr: 1.000e-04, eta: 0:44:45, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2149\n2023-01-01 02:30:14,126 - mmcls - INFO - Epoch [94][4800/5005]\tlr: 1.000e-04, eta: 0:44:36, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2510\n2023-01-01 02:30:22,941 - mmcls - INFO - Epoch [94][4900/5005]\tlr: 1.000e-04, eta: 0:44:27, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2387\n2023-01-01 02:30:31,698 - mmcls - INFO - Epoch [94][5000/5005]\tlr: 1.000e-04, eta: 0:44:18, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.2505\n2023-01-01 02:30:54,330 - mmcls - INFO - Epoch(val) [94][196]\taccuracy_top-1: 71.5540, accuracy_top-5: 90.2140\n2023-01-01 02:31:05,421 - mmcls - INFO - Epoch [95][100/5005]\tlr: 1.000e-04, eta: 0:44:09, time: 0.111, data_time: 0.061, memory: 1224, loss: 1.2457\n2023-01-01 02:31:14,275 - mmcls - INFO - Epoch [95][200/5005]\tlr: 1.000e-04, eta: 0:44:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2312\n2023-01-01 02:31:22,894 - mmcls - INFO - Epoch [95][300/5005]\tlr: 1.000e-04, eta: 0:43:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2208\n2023-01-01 02:31:31,650 - mmcls - INFO - Epoch [95][400/5005]\tlr: 1.000e-04, eta: 0:43:43, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2210\n2023-01-01 02:31:40,839 - mmcls - INFO - Epoch [95][500/5005]\tlr: 1.000e-04, eta: 0:43:34, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2192\n2023-01-01 02:31:49,660 - mmcls - INFO - Epoch [95][600/5005]\tlr: 1.000e-04, eta: 0:43:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2204\n2023-01-01 02:31:58,529 - mmcls - INFO - Epoch [95][700/5005]\tlr: 1.000e-04, eta: 0:43:16, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2115\n2023-01-01 02:32:07,202 - mmcls - INFO - Epoch [95][800/5005]\tlr: 1.000e-04, eta: 0:43:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2260\n2023-01-01 02:32:16,034 - mmcls - INFO - Epoch [95][900/5005]\tlr: 1.000e-04, eta: 0:42:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2403\n2023-01-01 02:32:24,750 - mmcls - INFO - Epoch [95][1000/5005]\tlr: 1.000e-04, eta: 0:42:50, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2350\n2023-01-01 02:32:33,772 - mmcls - INFO - Epoch [95][1100/5005]\tlr: 1.000e-04, eta: 0:42:41, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2268\n2023-01-01 02:32:42,953 - mmcls - INFO - Epoch [95][1200/5005]\tlr: 1.000e-04, eta: 0:42:32, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2448\n2023-01-01 02:32:51,664 - mmcls - INFO - Epoch [95][1300/5005]\tlr: 1.000e-04, eta: 0:42:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2320\n2023-01-01 02:33:00,833 - mmcls - INFO - Epoch [95][1400/5005]\tlr: 1.000e-04, eta: 0:42:14, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2295\n2023-01-01 02:33:09,454 - mmcls - INFO - Epoch [95][1500/5005]\tlr: 1.000e-04, eta: 0:42:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2487\n2023-01-01 02:33:18,453 - mmcls - INFO - Epoch [95][1600/5005]\tlr: 1.000e-04, eta: 0:41:56, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2379\n2023-01-01 02:33:27,163 - mmcls - INFO - Epoch [95][1700/5005]\tlr: 1.000e-04, eta: 0:41:48, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2317\n2023-01-01 02:33:36,054 - mmcls - INFO - Epoch [95][1800/5005]\tlr: 1.000e-04, eta: 0:41:39, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2035\n2023-01-01 02:33:44,809 - mmcls - INFO - Epoch [95][1900/5005]\tlr: 1.000e-04, eta: 0:41:30, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2266\n2023-01-01 02:33:53,642 - mmcls - INFO - Epoch [95][2000/5005]\tlr: 1.000e-04, eta: 0:41:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2154\n2023-01-01 02:34:02,421 - mmcls - INFO - Epoch [95][2100/5005]\tlr: 1.000e-04, eta: 0:41:12, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2251\n2023-01-01 02:34:10,804 - mmcls - INFO - Epoch [95][2200/5005]\tlr: 1.000e-04, eta: 0:41:03, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.2318\n2023-01-01 02:34:19,670 - mmcls - INFO - Epoch [95][2300/5005]\tlr: 1.000e-04, eta: 0:40:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2391\n2023-01-01 02:34:28,408 - mmcls - INFO - Epoch [95][2400/5005]\tlr: 1.000e-04, eta: 0:40:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2312\n2023-01-01 02:34:37,170 - mmcls - INFO - Epoch [95][2500/5005]\tlr: 1.000e-04, eta: 0:40:37, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.2017\n2023-01-01 02:34:46,138 - mmcls - INFO - Epoch [95][2600/5005]\tlr: 1.000e-04, eta: 0:40:28, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2482\n2023-01-01 02:34:55,148 - mmcls - INFO - Epoch [95][2700/5005]\tlr: 1.000e-04, eta: 0:40:19, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.2308\n2023-01-01 02:35:03,948 - mmcls - INFO - Epoch [95][2800/5005]\tlr: 1.000e-04, eta: 0:40:10, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2544\n2023-01-01 02:35:12,745 - mmcls - INFO - Epoch [95][2900/5005]\tlr: 1.000e-04, eta: 0:40:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2462\n2023-01-01 02:35:21,439 - mmcls - INFO - Epoch [95][3000/5005]\tlr: 1.000e-04, eta: 0:39:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2178\n2023-01-01 02:35:30,417 - mmcls - INFO - Epoch [95][3100/5005]\tlr: 1.000e-04, eta: 0:39:44, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2195\n2023-01-01 02:35:39,669 - mmcls - INFO - Epoch [95][3200/5005]\tlr: 1.000e-04, eta: 0:39:35, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2412\n2023-01-01 02:35:48,433 - mmcls - INFO - Epoch [95][3300/5005]\tlr: 1.000e-04, eta: 0:39:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2268\n2023-01-01 02:35:57,407 - mmcls - INFO - Epoch [95][3400/5005]\tlr: 1.000e-04, eta: 0:39:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2301\n2023-01-01 02:36:06,455 - mmcls - INFO - Epoch [95][3500/5005]\tlr: 1.000e-04, eta: 0:39:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2342\n2023-01-01 02:36:15,229 - mmcls - INFO - Epoch [95][3600/5005]\tlr: 1.000e-04, eta: 0:38:59, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2188\n2023-01-01 02:36:23,775 - mmcls - INFO - Epoch [95][3700/5005]\tlr: 1.000e-04, eta: 0:38:51, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.2277\n2023-01-01 02:36:32,374 - mmcls - INFO - Epoch [95][3800/5005]\tlr: 1.000e-04, eta: 0:38:42, time: 0.086, data_time: 0.035, memory: 1224, loss: 1.2262\n2023-01-01 02:36:40,970 - mmcls - INFO - Epoch [95][3900/5005]\tlr: 1.000e-04, eta: 0:38:33, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.2564\n2023-01-01 02:36:49,472 - mmcls - INFO - Epoch [95][4000/5005]\tlr: 1.000e-04, eta: 0:38:24, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2336\n2023-01-01 02:36:58,451 - mmcls - INFO - Epoch [95][4100/5005]\tlr: 1.000e-04, eta: 0:38:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2062\n2023-01-01 02:37:07,053 - mmcls - INFO - Epoch [95][4200/5005]\tlr: 1.000e-04, eta: 0:38:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2086\n2023-01-01 02:37:15,885 - mmcls - INFO - Epoch [95][4300/5005]\tlr: 1.000e-04, eta: 0:37:57, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2490\n2023-01-01 02:37:24,477 - mmcls - INFO - Epoch [95][4400/5005]\tlr: 1.000e-04, eta: 0:37:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2342\n2023-01-01 02:37:33,244 - mmcls - INFO - Epoch [95][4500/5005]\tlr: 1.000e-04, eta: 0:37:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2297\n2023-01-01 02:37:42,190 - mmcls - INFO - Epoch [95][4600/5005]\tlr: 1.000e-04, eta: 0:37:31, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2172\n2023-01-01 02:37:50,993 - mmcls - INFO - Epoch [95][4700/5005]\tlr: 1.000e-04, eta: 0:37:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2337\n2023-01-01 02:37:59,856 - mmcls - INFO - Epoch [95][4800/5005]\tlr: 1.000e-04, eta: 0:37:13, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.2354\n2023-01-01 02:38:08,681 - mmcls - INFO - Epoch [95][4900/5005]\tlr: 1.000e-04, eta: 0:37:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2431\n2023-01-01 02:38:17,495 - mmcls - INFO - Epoch [95][5000/5005]\tlr: 1.000e-04, eta: 0:36:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2244\n2023-01-01 02:38:40,214 - mmcls - INFO - Epoch(val) [95][196]\taccuracy_top-1: 71.6720, accuracy_top-5: 90.1620\n2023-01-01 02:38:51,203 - mmcls - INFO - Epoch [96][100/5005]\tlr: 1.000e-04, eta: 0:36:46, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.1938\n2023-01-01 02:39:00,084 - mmcls - INFO - Epoch [96][200/5005]\tlr: 1.000e-04, eta: 0:36:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2391\n2023-01-01 02:39:08,674 - mmcls - INFO - Epoch [96][300/5005]\tlr: 1.000e-04, eta: 0:36:28, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2236\n2023-01-01 02:39:17,542 - mmcls - INFO - Epoch [96][400/5005]\tlr: 1.000e-04, eta: 0:36:20, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2475\n2023-01-01 02:39:26,318 - mmcls - INFO - Epoch [96][500/5005]\tlr: 1.000e-04, eta: 0:36:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2362\n2023-01-01 02:39:35,093 - mmcls - INFO - Epoch [96][600/5005]\tlr: 1.000e-04, eta: 0:36:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2188\n2023-01-01 02:39:43,802 - mmcls - INFO - Epoch [96][700/5005]\tlr: 1.000e-04, eta: 0:35:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2319\n2023-01-01 02:39:52,599 - mmcls - INFO - Epoch [96][800/5005]\tlr: 1.000e-04, eta: 0:35:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2197\n2023-01-01 02:40:01,739 - mmcls - INFO - Epoch [96][900/5005]\tlr: 1.000e-04, eta: 0:35:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2361\n2023-01-01 02:40:10,495 - mmcls - INFO - Epoch [96][1000/5005]\tlr: 1.000e-04, eta: 0:35:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2394\n2023-01-01 02:40:19,276 - mmcls - INFO - Epoch [96][1100/5005]\tlr: 1.000e-04, eta: 0:35:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2415\n2023-01-01 02:40:28,116 - mmcls - INFO - Epoch [96][1200/5005]\tlr: 1.000e-04, eta: 0:35:09, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2218\n2023-01-01 02:40:36,711 - mmcls - INFO - Epoch [96][1300/5005]\tlr: 1.000e-04, eta: 0:35:00, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2445\n2023-01-01 02:40:45,341 - mmcls - INFO - Epoch [96][1400/5005]\tlr: 1.000e-04, eta: 0:34:51, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.2291\n2023-01-01 02:40:54,027 - mmcls - INFO - Epoch [96][1500/5005]\tlr: 1.000e-04, eta: 0:34:42, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2371\n2023-01-01 02:41:02,778 - mmcls - INFO - Epoch [96][1600/5005]\tlr: 1.000e-04, eta: 0:34:33, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2212\n2023-01-01 02:41:11,623 - mmcls - INFO - Epoch [96][1700/5005]\tlr: 1.000e-04, eta: 0:34:24, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2221\n2023-01-01 02:41:20,373 - mmcls - INFO - Epoch [96][1800/5005]\tlr: 1.000e-04, eta: 0:34:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2136\n2023-01-01 02:41:29,084 - mmcls - INFO - Epoch [96][1900/5005]\tlr: 1.000e-04, eta: 0:34:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2423\n2023-01-01 02:41:37,882 - mmcls - INFO - Epoch [96][2000/5005]\tlr: 1.000e-04, eta: 0:33:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2338\n2023-01-01 02:41:47,149 - mmcls - INFO - Epoch [96][2100/5005]\tlr: 1.000e-04, eta: 0:33:49, time: 0.093, data_time: 0.019, memory: 1224, loss: 1.2283\n2023-01-01 02:41:55,981 - mmcls - INFO - Epoch [96][2200/5005]\tlr: 1.000e-04, eta: 0:33:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2140\n2023-01-01 02:42:05,044 - mmcls - INFO - Epoch [96][2300/5005]\tlr: 1.000e-04, eta: 0:33:31, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2263\n2023-01-01 02:42:13,982 - mmcls - INFO - Epoch [96][2400/5005]\tlr: 1.000e-04, eta: 0:33:22, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2256\n2023-01-01 02:42:22,613 - mmcls - INFO - Epoch [96][2500/5005]\tlr: 1.000e-04, eta: 0:33:14, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2271\n2023-01-01 02:42:31,206 - mmcls - INFO - Epoch [96][2600/5005]\tlr: 1.000e-04, eta: 0:33:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2138\n2023-01-01 02:42:39,996 - mmcls - INFO - Epoch [96][2700/5005]\tlr: 1.000e-04, eta: 0:32:56, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2261\n2023-01-01 02:42:48,674 - mmcls - INFO - Epoch [96][2800/5005]\tlr: 1.000e-04, eta: 0:32:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2367\n2023-01-01 02:42:57,654 - mmcls - INFO - Epoch [96][2900/5005]\tlr: 1.000e-04, eta: 0:32:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.2223\n2023-01-01 02:43:06,253 - mmcls - INFO - Epoch [96][3000/5005]\tlr: 1.000e-04, eta: 0:32:29, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2379\n2023-01-01 02:43:15,084 - mmcls - INFO - Epoch [96][3100/5005]\tlr: 1.000e-04, eta: 0:32:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2453\n2023-01-01 02:43:23,928 - mmcls - INFO - Epoch [96][3200/5005]\tlr: 1.000e-04, eta: 0:32:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2352\n2023-01-01 02:43:32,417 - mmcls - INFO - Epoch [96][3300/5005]\tlr: 1.000e-04, eta: 0:32:03, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2399\n2023-01-01 02:43:41,478 - mmcls - INFO - Epoch [96][3400/5005]\tlr: 1.000e-04, eta: 0:31:54, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.2244\n2023-01-01 02:43:49,801 - mmcls - INFO - Epoch [96][3500/5005]\tlr: 1.000e-04, eta: 0:31:45, time: 0.083, data_time: 0.004, memory: 1224, loss: 1.2431\n2023-01-01 02:43:58,598 - mmcls - INFO - Epoch [96][3600/5005]\tlr: 1.000e-04, eta: 0:31:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2244\n2023-01-01 02:44:07,115 - mmcls - INFO - Epoch [96][3700/5005]\tlr: 1.000e-04, eta: 0:31:27, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2318\n2023-01-01 02:44:15,864 - mmcls - INFO - Epoch [96][3800/5005]\tlr: 1.000e-04, eta: 0:31:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2453\n2023-01-01 02:44:24,524 - mmcls - INFO - Epoch [96][3900/5005]\tlr: 1.000e-04, eta: 0:31:10, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2192\n2023-01-01 02:44:33,264 - mmcls - INFO - Epoch [96][4000/5005]\tlr: 1.000e-04, eta: 0:31:01, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2317\n2023-01-01 02:44:41,887 - mmcls - INFO - Epoch [96][4100/5005]\tlr: 1.000e-04, eta: 0:30:52, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2298\n2023-01-01 02:44:50,799 - mmcls - INFO - Epoch [96][4200/5005]\tlr: 1.000e-04, eta: 0:30:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2458\n2023-01-01 02:44:59,805 - mmcls - INFO - Epoch [96][4300/5005]\tlr: 1.000e-04, eta: 0:30:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2296\n2023-01-01 02:45:08,634 - mmcls - INFO - Epoch [96][4400/5005]\tlr: 1.000e-04, eta: 0:30:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2354\n2023-01-01 02:45:17,363 - mmcls - INFO - Epoch [96][4500/5005]\tlr: 1.000e-04, eta: 0:30:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2244\n2023-01-01 02:45:26,000 - mmcls - INFO - Epoch [96][4600/5005]\tlr: 1.000e-04, eta: 0:30:08, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2309\n2023-01-01 02:45:34,758 - mmcls - INFO - Epoch [96][4700/5005]\tlr: 1.000e-04, eta: 0:29:59, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2288\n2023-01-01 02:45:43,623 - mmcls - INFO - Epoch [96][4800/5005]\tlr: 1.000e-04, eta: 0:29:50, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2457\n2023-01-01 02:45:52,684 - mmcls - INFO - Epoch [96][4900/5005]\tlr: 1.000e-04, eta: 0:29:41, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.2386\n2023-01-01 02:46:01,805 - mmcls - INFO - Epoch [96][5000/5005]\tlr: 1.000e-04, eta: 0:29:32, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2523\n2023-01-01 02:46:24,377 - mmcls - INFO - Epoch(val) [96][196]\taccuracy_top-1: 71.6240, accuracy_top-5: 90.1460\n2023-01-01 02:46:35,241 - mmcls - INFO - Epoch [97][100/5005]\tlr: 1.000e-04, eta: 0:29:23, time: 0.108, data_time: 0.064, memory: 1224, loss: 1.2338\n2023-01-01 02:46:44,063 - mmcls - INFO - Epoch [97][200/5005]\tlr: 1.000e-04, eta: 0:29:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2229\n2023-01-01 02:46:53,102 - mmcls - INFO - Epoch [97][300/5005]\tlr: 1.000e-04, eta: 0:29:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2287\n2023-01-01 02:47:01,673 - mmcls - INFO - Epoch [97][400/5005]\tlr: 1.000e-04, eta: 0:28:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2285\n2023-01-01 02:47:10,575 - mmcls - INFO - Epoch [97][500/5005]\tlr: 1.000e-04, eta: 0:28:48, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2308\n2023-01-01 02:47:19,270 - mmcls - INFO - Epoch [97][600/5005]\tlr: 1.000e-04, eta: 0:28:39, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2200\n2023-01-01 02:47:27,826 - mmcls - INFO - Epoch [97][700/5005]\tlr: 1.000e-04, eta: 0:28:30, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2221\n2023-01-01 02:47:36,457 - mmcls - INFO - Epoch [97][800/5005]\tlr: 1.000e-04, eta: 0:28:21, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2344\n2023-01-01 02:47:45,519 - mmcls - INFO - Epoch [97][900/5005]\tlr: 1.000e-04, eta: 0:28:12, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.1948\n2023-01-01 02:47:54,629 - mmcls - INFO - Epoch [97][1000/5005]\tlr: 1.000e-04, eta: 0:28:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2146\n2023-01-01 02:48:03,377 - mmcls - INFO - Epoch [97][1100/5005]\tlr: 1.000e-04, eta: 0:27:54, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2324\n2023-01-01 02:48:12,318 - mmcls - INFO - Epoch [97][1200/5005]\tlr: 1.000e-04, eta: 0:27:46, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.2014\n2023-01-01 02:48:21,055 - mmcls - INFO - Epoch [97][1300/5005]\tlr: 1.000e-04, eta: 0:27:37, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2248\n2023-01-01 02:48:29,867 - mmcls - INFO - Epoch [97][1400/5005]\tlr: 1.000e-04, eta: 0:27:28, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2194\n2023-01-01 02:48:38,578 - mmcls - INFO - Epoch [97][1500/5005]\tlr: 1.000e-04, eta: 0:27:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2405\n2023-01-01 02:48:47,286 - mmcls - INFO - Epoch [97][1600/5005]\tlr: 1.000e-04, eta: 0:27:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2400\n2023-01-01 02:48:56,055 - mmcls - INFO - Epoch [97][1700/5005]\tlr: 1.000e-04, eta: 0:27:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2407\n2023-01-01 02:49:04,958 - mmcls - INFO - Epoch [97][1800/5005]\tlr: 1.000e-04, eta: 0:26:52, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2181\n2023-01-01 02:49:14,068 - mmcls - INFO - Epoch [97][1900/5005]\tlr: 1.000e-04, eta: 0:26:44, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2202\n2023-01-01 02:49:23,097 - mmcls - INFO - Epoch [97][2000/5005]\tlr: 1.000e-04, eta: 0:26:35, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2338\n2023-01-01 02:49:31,727 - mmcls - INFO - Epoch [97][2100/5005]\tlr: 1.000e-04, eta: 0:26:26, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2183\n2023-01-01 02:49:41,034 - mmcls - INFO - Epoch [97][2200/5005]\tlr: 1.000e-04, eta: 0:26:17, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2292\n2023-01-01 02:49:50,027 - mmcls - INFO - Epoch [97][2300/5005]\tlr: 1.000e-04, eta: 0:26:08, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2285\n2023-01-01 02:49:58,681 - mmcls - INFO - Epoch [97][2400/5005]\tlr: 1.000e-04, eta: 0:25:59, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2102\n2023-01-01 02:50:07,406 - mmcls - INFO - Epoch [97][2500/5005]\tlr: 1.000e-04, eta: 0:25:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2180\n2023-01-01 02:50:15,952 - mmcls - INFO - Epoch [97][2600/5005]\tlr: 1.000e-04, eta: 0:25:42, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2347\n2023-01-01 02:50:25,267 - mmcls - INFO - Epoch [97][2700/5005]\tlr: 1.000e-04, eta: 0:25:33, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2376\n2023-01-01 02:50:33,975 - mmcls - INFO - Epoch [97][2800/5005]\tlr: 1.000e-04, eta: 0:25:24, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2309\n2023-01-01 02:50:43,010 - mmcls - INFO - Epoch [97][2900/5005]\tlr: 1.000e-04, eta: 0:25:15, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2407\n2023-01-01 02:50:51,947 - mmcls - INFO - Epoch [97][3000/5005]\tlr: 1.000e-04, eta: 0:25:06, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2268\n2023-01-01 02:51:01,036 - mmcls - INFO - Epoch [97][3100/5005]\tlr: 1.000e-04, eta: 0:24:57, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2325\n2023-01-01 02:51:09,705 - mmcls - INFO - Epoch [97][3200/5005]\tlr: 1.000e-04, eta: 0:24:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2276\n2023-01-01 02:51:18,740 - mmcls - INFO - Epoch [97][3300/5005]\tlr: 1.000e-04, eta: 0:24:40, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.2317\n2023-01-01 02:51:27,448 - mmcls - INFO - Epoch [97][3400/5005]\tlr: 1.000e-04, eta: 0:24:31, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2425\n2023-01-01 02:51:36,312 - mmcls - INFO - Epoch [97][3500/5005]\tlr: 1.000e-04, eta: 0:24:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2353\n2023-01-01 02:51:45,399 - mmcls - INFO - Epoch [97][3600/5005]\tlr: 1.000e-04, eta: 0:24:13, time: 0.091, data_time: 0.000, memory: 1224, loss: 1.2308\n2023-01-01 02:51:54,260 - mmcls - INFO - Epoch [97][3700/5005]\tlr: 1.000e-04, eta: 0:24:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2311\n2023-01-01 02:52:02,937 - mmcls - INFO - Epoch [97][3800/5005]\tlr: 1.000e-04, eta: 0:23:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2239\n2023-01-01 02:52:11,658 - mmcls - INFO - Epoch [97][3900/5005]\tlr: 1.000e-04, eta: 0:23:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2220\n2023-01-01 02:52:20,233 - mmcls - INFO - Epoch [97][4000/5005]\tlr: 1.000e-04, eta: 0:23:38, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2256\n2023-01-01 02:52:28,830 - mmcls - INFO - Epoch [97][4100/5005]\tlr: 1.000e-04, eta: 0:23:29, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2438\n2023-01-01 02:52:37,404 - mmcls - INFO - Epoch [97][4200/5005]\tlr: 1.000e-04, eta: 0:23:20, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2236\n2023-01-01 02:52:46,458 - mmcls - INFO - Epoch [97][4300/5005]\tlr: 1.000e-04, eta: 0:23:11, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2398\n2023-01-01 02:52:55,366 - mmcls - INFO - Epoch [97][4400/5005]\tlr: 1.000e-04, eta: 0:23:02, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2186\n2023-01-01 02:53:04,254 - mmcls - INFO - Epoch [97][4500/5005]\tlr: 1.000e-04, eta: 0:22:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2273\n2023-01-01 02:53:13,293 - mmcls - INFO - Epoch [97][4600/5005]\tlr: 1.000e-04, eta: 0:22:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2426\n2023-01-01 02:53:22,134 - mmcls - INFO - Epoch [97][4700/5005]\tlr: 1.000e-04, eta: 0:22:36, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2328\n2023-01-01 02:53:30,915 - mmcls - INFO - Epoch [97][4800/5005]\tlr: 1.000e-04, eta: 0:22:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2497\n2023-01-01 02:53:40,011 - mmcls - INFO - Epoch [97][4900/5005]\tlr: 1.000e-04, eta: 0:22:18, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2172\n2023-01-01 02:53:49,155 - mmcls - INFO - Epoch [97][5000/5005]\tlr: 1.000e-04, eta: 0:22:09, time: 0.091, data_time: 0.018, memory: 1224, loss: 1.2549\n2023-01-01 02:54:11,786 - mmcls - INFO - Epoch(val) [97][196]\taccuracy_top-1: 71.6400, accuracy_top-5: 90.2020\n2023-01-01 02:54:22,804 - mmcls - INFO - Epoch [98][100/5005]\tlr: 1.000e-04, eta: 0:22:00, time: 0.110, data_time: 0.053, memory: 1224, loss: 1.2417\n2023-01-01 02:54:31,613 - mmcls - INFO - Epoch [98][200/5005]\tlr: 1.000e-04, eta: 0:21:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2436\n2023-01-01 02:54:40,894 - mmcls - INFO - Epoch [98][300/5005]\tlr: 1.000e-04, eta: 0:21:42, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2246\n2023-01-01 02:54:49,523 - mmcls - INFO - Epoch [98][400/5005]\tlr: 1.000e-04, eta: 0:21:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2162\n2023-01-01 02:54:58,347 - mmcls - INFO - Epoch [98][500/5005]\tlr: 1.000e-04, eta: 0:21:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2400\n2023-01-01 02:55:06,951 - mmcls - INFO - Epoch [98][600/5005]\tlr: 1.000e-04, eta: 0:21:16, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2232\n2023-01-01 02:55:15,639 - mmcls - INFO - Epoch [98][700/5005]\tlr: 1.000e-04, eta: 0:21:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2212\n2023-01-01 02:55:24,651 - mmcls - INFO - Epoch [98][800/5005]\tlr: 1.000e-04, eta: 0:20:58, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.2223\n2023-01-01 02:55:33,530 - mmcls - INFO - Epoch [98][900/5005]\tlr: 1.000e-04, eta: 0:20:49, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2084\n2023-01-01 02:55:42,150 - mmcls - INFO - Epoch [98][1000/5005]\tlr: 1.000e-04, eta: 0:20:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2321\n2023-01-01 02:55:50,961 - mmcls - INFO - Epoch [98][1100/5005]\tlr: 1.000e-04, eta: 0:20:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.1992\n2023-01-01 02:55:59,765 - mmcls - INFO - Epoch [98][1200/5005]\tlr: 1.000e-04, eta: 0:20:23, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2266\n2023-01-01 02:56:08,779 - mmcls - INFO - Epoch [98][1300/5005]\tlr: 1.000e-04, eta: 0:20:14, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2411\n2023-01-01 02:56:17,495 - mmcls - INFO - Epoch [98][1400/5005]\tlr: 1.000e-04, eta: 0:20:05, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2236\n2023-01-01 02:56:26,154 - mmcls - INFO - Epoch [98][1500/5005]\tlr: 1.000e-04, eta: 0:19:56, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2258\n2023-01-01 02:56:35,066 - mmcls - INFO - Epoch [98][1600/5005]\tlr: 1.000e-04, eta: 0:19:47, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2184\n2023-01-01 02:56:43,826 - mmcls - INFO - Epoch [98][1700/5005]\tlr: 1.000e-04, eta: 0:19:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2363\n2023-01-01 02:56:52,863 - mmcls - INFO - Epoch [98][1800/5005]\tlr: 1.000e-04, eta: 0:19:29, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2142\n2023-01-01 02:57:01,693 - mmcls - INFO - Epoch [98][1900/5005]\tlr: 1.000e-04, eta: 0:19:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2073\n2023-01-01 02:57:11,035 - mmcls - INFO - Epoch [98][2000/5005]\tlr: 1.000e-04, eta: 0:19:12, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2343\n2023-01-01 02:57:20,539 - mmcls - INFO - Epoch [98][2100/5005]\tlr: 1.000e-04, eta: 0:19:03, time: 0.095, data_time: 0.005, memory: 1224, loss: 1.2308\n2023-01-01 02:57:29,327 - mmcls - INFO - Epoch [98][2200/5005]\tlr: 1.000e-04, eta: 0:18:54, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2436\n2023-01-01 02:57:37,960 - mmcls - INFO - Epoch [98][2300/5005]\tlr: 1.000e-04, eta: 0:18:45, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2227\n2023-01-01 02:57:46,914 - mmcls - INFO - Epoch [98][2400/5005]\tlr: 1.000e-04, eta: 0:18:36, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.2464\n2023-01-01 02:57:55,612 - mmcls - INFO - Epoch [98][2500/5005]\tlr: 1.000e-04, eta: 0:18:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2323\n2023-01-01 02:58:04,311 - mmcls - INFO - Epoch [98][2600/5005]\tlr: 1.000e-04, eta: 0:18:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2349\n2023-01-01 02:58:12,737 - mmcls - INFO - Epoch [98][2700/5005]\tlr: 1.000e-04, eta: 0:18:10, time: 0.084, data_time: 0.003, memory: 1224, loss: 1.2159\n2023-01-01 02:58:21,736 - mmcls - INFO - Epoch [98][2800/5005]\tlr: 1.000e-04, eta: 0:18:01, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2330\n2023-01-01 02:58:30,589 - mmcls - INFO - Epoch [98][2900/5005]\tlr: 1.000e-04, eta: 0:17:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2358\n2023-01-01 02:58:39,266 - mmcls - INFO - Epoch [98][3000/5005]\tlr: 1.000e-04, eta: 0:17:43, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2363\n2023-01-01 02:58:48,206 - mmcls - INFO - Epoch [98][3100/5005]\tlr: 1.000e-04, eta: 0:17:34, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2124\n2023-01-01 02:58:56,996 - mmcls - INFO - Epoch [98][3200/5005]\tlr: 1.000e-04, eta: 0:17:25, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2046\n2023-01-01 02:59:05,733 - mmcls - INFO - Epoch [98][3300/5005]\tlr: 1.000e-04, eta: 0:17:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2256\n2023-01-01 02:59:14,471 - mmcls - INFO - Epoch [98][3400/5005]\tlr: 1.000e-04, eta: 0:17:08, time: 0.087, data_time: 0.041, memory: 1224, loss: 1.2313\n2023-01-01 02:59:23,445 - mmcls - INFO - Epoch [98][3500/5005]\tlr: 1.000e-04, eta: 0:16:59, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2473\n2023-01-01 02:59:32,241 - mmcls - INFO - Epoch [98][3600/5005]\tlr: 1.000e-04, eta: 0:16:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2433\n2023-01-01 02:59:41,114 - mmcls - INFO - Epoch [98][3700/5005]\tlr: 1.000e-04, eta: 0:16:41, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2314\n2023-01-01 02:59:50,070 - mmcls - INFO - Epoch [98][3800/5005]\tlr: 1.000e-04, eta: 0:16:32, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2322\n2023-01-01 02:59:58,716 - mmcls - INFO - Epoch [98][3900/5005]\tlr: 1.000e-04, eta: 0:16:24, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.2311\n2023-01-01 03:00:07,574 - mmcls - INFO - Epoch [98][4000/5005]\tlr: 1.000e-04, eta: 0:16:15, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2278\n2023-01-01 03:00:16,349 - mmcls - INFO - Epoch [98][4100/5005]\tlr: 1.000e-04, eta: 0:16:06, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2321\n2023-01-01 03:00:25,069 - mmcls - INFO - Epoch [98][4200/5005]\tlr: 1.000e-04, eta: 0:15:57, time: 0.087, data_time: 0.040, memory: 1224, loss: 1.2368\n2023-01-01 03:00:33,756 - mmcls - INFO - Epoch [98][4300/5005]\tlr: 1.000e-04, eta: 0:15:48, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2218\n2023-01-01 03:00:42,706 - mmcls - INFO - Epoch [98][4400/5005]\tlr: 1.000e-04, eta: 0:15:39, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2348\n2023-01-01 03:00:51,586 - mmcls - INFO - Epoch [98][4500/5005]\tlr: 1.000e-04, eta: 0:15:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2303\n2023-01-01 03:01:00,302 - mmcls - INFO - Epoch [98][4600/5005]\tlr: 1.000e-04, eta: 0:15:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2204\n2023-01-01 03:01:09,061 - mmcls - INFO - Epoch [98][4700/5005]\tlr: 1.000e-04, eta: 0:15:13, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2420\n2023-01-01 03:01:17,795 - mmcls - INFO - Epoch [98][4800/5005]\tlr: 1.000e-04, eta: 0:15:04, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2286\n2023-01-01 03:01:26,583 - mmcls - INFO - Epoch [98][4900/5005]\tlr: 1.000e-04, eta: 0:14:55, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2158\n2023-01-01 03:01:35,429 - mmcls - INFO - Epoch [98][5000/5005]\tlr: 1.000e-04, eta: 0:14:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2404\n2023-01-01 03:01:58,226 - mmcls - INFO - Epoch(val) [98][196]\taccuracy_top-1: 71.6960, accuracy_top-5: 90.1540\n2023-01-01 03:02:09,094 - mmcls - INFO - Epoch [99][100/5005]\tlr: 1.000e-04, eta: 0:14:37, time: 0.108, data_time: 0.058, memory: 1224, loss: 1.2313\n2023-01-01 03:02:17,979 - mmcls - INFO - Epoch [99][200/5005]\tlr: 1.000e-04, eta: 0:14:28, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2384\n2023-01-01 03:02:26,664 - mmcls - INFO - Epoch [99][300/5005]\tlr: 1.000e-04, eta: 0:14:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2412\n2023-01-01 03:02:35,766 - mmcls - INFO - Epoch [99][400/5005]\tlr: 1.000e-04, eta: 0:14:10, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.2398\n2023-01-01 03:02:44,347 - mmcls - INFO - Epoch [99][500/5005]\tlr: 1.000e-04, eta: 0:14:01, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2172\n2023-01-01 03:02:52,944 - mmcls - INFO - Epoch [99][600/5005]\tlr: 1.000e-04, eta: 0:13:53, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2151\n2023-01-01 03:03:01,959 - mmcls - INFO - Epoch [99][700/5005]\tlr: 1.000e-04, eta: 0:13:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2073\n2023-01-01 03:03:11,154 - mmcls - INFO - Epoch [99][800/5005]\tlr: 1.000e-04, eta: 0:13:35, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.2160\n2023-01-01 03:03:19,894 - mmcls - INFO - Epoch [99][900/5005]\tlr: 1.000e-04, eta: 0:13:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2329\n2023-01-01 03:03:28,530 - mmcls - INFO - Epoch [99][1000/5005]\tlr: 1.000e-04, eta: 0:13:17, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.2243\n2023-01-01 03:03:37,648 - mmcls - INFO - Epoch [99][1100/5005]\tlr: 1.000e-04, eta: 0:13:08, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2324\n2023-01-01 03:03:46,731 - mmcls - INFO - Epoch [99][1200/5005]\tlr: 1.000e-04, eta: 0:12:59, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.2252\n2023-01-01 03:03:55,513 - mmcls - INFO - Epoch [99][1300/5005]\tlr: 1.000e-04, eta: 0:12:51, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.1974\n2023-01-01 03:04:04,304 - mmcls - INFO - Epoch [99][1400/5005]\tlr: 1.000e-04, eta: 0:12:42, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2254\n2023-01-01 03:04:13,009 - mmcls - INFO - Epoch [99][1500/5005]\tlr: 1.000e-04, eta: 0:12:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2252\n2023-01-01 03:04:21,883 - mmcls - INFO - Epoch [99][1600/5005]\tlr: 1.000e-04, eta: 0:12:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2314\n2023-01-01 03:04:30,533 - mmcls - INFO - Epoch [99][1700/5005]\tlr: 1.000e-04, eta: 0:12:15, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2387\n2023-01-01 03:04:39,054 - mmcls - INFO - Epoch [99][1800/5005]\tlr: 1.000e-04, eta: 0:12:06, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.2167\n2023-01-01 03:04:47,507 - mmcls - INFO - Epoch [99][1900/5005]\tlr: 1.000e-04, eta: 0:11:57, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2335\n2023-01-01 03:04:56,177 - mmcls - INFO - Epoch [99][2000/5005]\tlr: 1.000e-04, eta: 0:11:49, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2326\n2023-01-01 03:05:04,747 - mmcls - INFO - Epoch [99][2100/5005]\tlr: 1.000e-04, eta: 0:11:40, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.2180\n2023-01-01 03:05:13,592 - mmcls - INFO - Epoch [99][2200/5005]\tlr: 1.000e-04, eta: 0:11:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2544\n2023-01-01 03:05:22,390 - mmcls - INFO - Epoch [99][2300/5005]\tlr: 1.000e-04, eta: 0:11:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2365\n2023-01-01 03:05:31,514 - mmcls - INFO - Epoch [99][2400/5005]\tlr: 1.000e-04, eta: 0:11:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2217\n2023-01-01 03:05:40,344 - mmcls - INFO - Epoch [99][2500/5005]\tlr: 1.000e-04, eta: 0:11:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2192\n2023-01-01 03:05:49,195 - mmcls - INFO - Epoch [99][2600/5005]\tlr: 1.000e-04, eta: 0:10:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2203\n2023-01-01 03:05:58,023 - mmcls - INFO - Epoch [99][2700/5005]\tlr: 1.000e-04, eta: 0:10:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2493\n2023-01-01 03:06:06,798 - mmcls - INFO - Epoch [99][2800/5005]\tlr: 1.000e-04, eta: 0:10:38, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2274\n2023-01-01 03:06:15,590 - mmcls - INFO - Epoch [99][2900/5005]\tlr: 1.000e-04, eta: 0:10:29, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2380\n2023-01-01 03:06:24,144 - mmcls - INFO - Epoch [99][3000/5005]\tlr: 1.000e-04, eta: 0:10:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2417\n2023-01-01 03:06:32,940 - mmcls - INFO - Epoch [99][3100/5005]\tlr: 1.000e-04, eta: 0:10:11, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.2068\n2023-01-01 03:06:42,077 - mmcls - INFO - Epoch [99][3200/5005]\tlr: 1.000e-04, eta: 0:10:02, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.2200\n2023-01-01 03:06:50,869 - mmcls - INFO - Epoch [99][3300/5005]\tlr: 1.000e-04, eta: 0:09:54, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.2268\n2023-01-01 03:07:00,041 - mmcls - INFO - Epoch [99][3400/5005]\tlr: 1.000e-04, eta: 0:09:45, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2254\n2023-01-01 03:07:08,687 - mmcls - INFO - Epoch [99][3500/5005]\tlr: 1.000e-04, eta: 0:09:36, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2396\n2023-01-01 03:07:17,419 - mmcls - INFO - Epoch [99][3600/5005]\tlr: 1.000e-04, eta: 0:09:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2280\n2023-01-01 03:07:26,124 - mmcls - INFO - Epoch [99][3700/5005]\tlr: 1.000e-04, eta: 0:09:18, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2300\n2023-01-01 03:07:35,007 - mmcls - INFO - Epoch [99][3800/5005]\tlr: 1.000e-04, eta: 0:09:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2300\n2023-01-01 03:07:43,868 - mmcls - INFO - Epoch [99][3900/5005]\tlr: 1.000e-04, eta: 0:09:00, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2278\n2023-01-01 03:07:52,585 - mmcls - INFO - Epoch [99][4000/5005]\tlr: 1.000e-04, eta: 0:08:52, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2112\n2023-01-01 03:08:01,663 - mmcls - INFO - Epoch [99][4100/5005]\tlr: 1.000e-04, eta: 0:08:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2278\n2023-01-01 03:08:10,519 - mmcls - INFO - Epoch [99][4200/5005]\tlr: 1.000e-04, eta: 0:08:34, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2130\n2023-01-01 03:08:19,526 - mmcls - INFO - Epoch [99][4300/5005]\tlr: 1.000e-04, eta: 0:08:25, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2146\n2023-01-01 03:08:28,176 - mmcls - INFO - Epoch [99][4400/5005]\tlr: 1.000e-04, eta: 0:08:16, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2229\n2023-01-01 03:08:37,006 - mmcls - INFO - Epoch [99][4500/5005]\tlr: 1.000e-04, eta: 0:08:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2451\n2023-01-01 03:08:45,654 - mmcls - INFO - Epoch [99][4600/5005]\tlr: 1.000e-04, eta: 0:07:58, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2240\n2023-01-01 03:08:54,752 - mmcls - INFO - Epoch [99][4700/5005]\tlr: 1.000e-04, eta: 0:07:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2335\n2023-01-01 03:09:03,320 - mmcls - INFO - Epoch [99][4800/5005]\tlr: 1.000e-04, eta: 0:07:41, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2231\n2023-01-01 03:09:12,196 - mmcls - INFO - Epoch [99][4900/5005]\tlr: 1.000e-04, eta: 0:07:32, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2261\n2023-01-01 03:09:20,957 - mmcls - INFO - Epoch [99][5000/5005]\tlr: 1.000e-04, eta: 0:07:23, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2232\n2023-01-01 03:09:43,603 - mmcls - INFO - Epoch(val) [99][196]\taccuracy_top-1: 71.5960, accuracy_top-5: 90.1140\n2023-01-01 03:09:54,559 - mmcls - INFO - Epoch [100][100/5005]\tlr: 1.000e-04, eta: 0:07:14, time: 0.109, data_time: 0.030, memory: 1224, loss: 1.2175\n2023-01-01 03:10:03,307 - mmcls - INFO - Epoch [100][200/5005]\tlr: 1.000e-04, eta: 0:07:05, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2294\n2023-01-01 03:10:11,968 - mmcls - INFO - Epoch [100][300/5005]\tlr: 1.000e-04, eta: 0:06:56, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2100\n2023-01-01 03:10:21,057 - mmcls - INFO - Epoch [100][400/5005]\tlr: 1.000e-04, eta: 0:06:47, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.2207\n2023-01-01 03:10:29,534 - mmcls - INFO - Epoch [100][500/5005]\tlr: 1.000e-04, eta: 0:06:38, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2498\n2023-01-01 03:10:38,605 - mmcls - INFO - Epoch [100][600/5005]\tlr: 1.000e-04, eta: 0:06:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2211\n2023-01-01 03:10:47,346 - mmcls - INFO - Epoch [100][700/5005]\tlr: 1.000e-04, eta: 0:06:21, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2161\n2023-01-01 03:10:56,100 - mmcls - INFO - Epoch [100][800/5005]\tlr: 1.000e-04, eta: 0:06:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2212\n2023-01-01 03:11:05,582 - mmcls - INFO - Epoch [100][900/5005]\tlr: 1.000e-04, eta: 0:06:03, time: 0.095, data_time: 0.006, memory: 1224, loss: 1.2465\n2023-01-01 03:11:14,262 - mmcls - INFO - Epoch [100][1000/5005]\tlr: 1.000e-04, eta: 0:05:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2416\n2023-01-01 03:11:23,132 - mmcls - INFO - Epoch [100][1100/5005]\tlr: 1.000e-04, eta: 0:05:45, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.1948\n2023-01-01 03:11:31,809 - mmcls - INFO - Epoch [100][1200/5005]\tlr: 1.000e-04, eta: 0:05:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2341\n2023-01-01 03:11:40,464 - mmcls - INFO - Epoch [100][1300/5005]\tlr: 1.000e-04, eta: 0:05:28, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2149\n2023-01-01 03:11:49,265 - mmcls - INFO - Epoch [100][1400/5005]\tlr: 1.000e-04, eta: 0:05:19, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2349\n2023-01-01 03:11:58,294 - mmcls - INFO - Epoch [100][1500/5005]\tlr: 1.000e-04, eta: 0:05:10, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2265\n2023-01-01 03:12:07,186 - mmcls - INFO - Epoch [100][1600/5005]\tlr: 1.000e-04, eta: 0:05:01, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.2456\n2023-01-01 03:12:16,093 - mmcls - INFO - Epoch [100][1700/5005]\tlr: 1.000e-04, eta: 0:04:52, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2285\n2023-01-01 03:12:25,032 - mmcls - INFO - Epoch [100][1800/5005]\tlr: 1.000e-04, eta: 0:04:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2184\n2023-01-01 03:12:34,190 - mmcls - INFO - Epoch [100][1900/5005]\tlr: 1.000e-04, eta: 0:04:34, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.2283\n2023-01-01 03:12:42,992 - mmcls - INFO - Epoch [100][2000/5005]\tlr: 1.000e-04, eta: 0:04:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2500\n2023-01-01 03:12:51,812 - mmcls - INFO - Epoch [100][2100/5005]\tlr: 1.000e-04, eta: 0:04:17, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.2379\n2023-01-01 03:13:00,517 - mmcls - INFO - Epoch [100][2200/5005]\tlr: 1.000e-04, eta: 0:04:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2180\n2023-01-01 03:13:09,284 - mmcls - INFO - Epoch [100][2300/5005]\tlr: 1.000e-04, eta: 0:03:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2147\n2023-01-01 03:13:18,582 - mmcls - INFO - Epoch [100][2400/5005]\tlr: 1.000e-04, eta: 0:03:50, time: 0.093, data_time: 0.031, memory: 1224, loss: 1.2010\n2023-01-01 03:13:27,488 - mmcls - INFO - Epoch [100][2500/5005]\tlr: 1.000e-04, eta: 0:03:41, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2219\n2023-01-01 03:13:36,287 - mmcls - INFO - Epoch [100][2600/5005]\tlr: 1.000e-04, eta: 0:03:32, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2233\n2023-01-01 03:13:44,875 - mmcls - INFO - Epoch [100][2700/5005]\tlr: 1.000e-04, eta: 0:03:24, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2213\n2023-01-01 03:13:53,941 - mmcls - INFO - Epoch [100][2800/5005]\tlr: 1.000e-04, eta: 0:03:15, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2028\n2023-01-01 03:14:02,844 - mmcls - INFO - Epoch [100][2900/5005]\tlr: 1.000e-04, eta: 0:03:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2282\n2023-01-01 03:14:11,478 - mmcls - INFO - Epoch [100][3000/5005]\tlr: 1.000e-04, eta: 0:02:57, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2239\n2023-01-01 03:14:20,549 - mmcls - INFO - Epoch [100][3100/5005]\tlr: 1.000e-04, eta: 0:02:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2161\n2023-01-01 03:14:29,435 - mmcls - INFO - Epoch [100][3200/5005]\tlr: 1.000e-04, eta: 0:02:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2328\n2023-01-01 03:14:38,097 - mmcls - INFO - Epoch [100][3300/5005]\tlr: 1.000e-04, eta: 0:02:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2263\n2023-01-01 03:14:46,794 - mmcls - INFO - Epoch [100][3400/5005]\tlr: 1.000e-04, eta: 0:02:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2487\n2023-01-01 03:14:55,876 - mmcls - INFO - Epoch [100][3500/5005]\tlr: 1.000e-04, eta: 0:02:13, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2514\n2023-01-01 03:15:04,671 - mmcls - INFO - Epoch [100][3600/5005]\tlr: 1.000e-04, eta: 0:02:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2297\n2023-01-01 03:15:13,961 - mmcls - INFO - Epoch [100][3700/5005]\tlr: 1.000e-04, eta: 0:01:55, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.2171\n2023-01-01 03:15:22,796 - mmcls - INFO - Epoch [100][3800/5005]\tlr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2539\n2023-01-01 03:15:31,476 - mmcls - INFO - Epoch [100][3900/5005]\tlr: 1.000e-04, eta: 0:01:37, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2212\n2023-01-01 03:15:40,229 - mmcls - INFO - Epoch [100][4000/5005]\tlr: 1.000e-04, eta: 0:01:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2156\n2023-01-01 03:15:49,078 - mmcls - INFO - Epoch [100][4100/5005]\tlr: 1.000e-04, eta: 0:01:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2416\n2023-01-01 03:15:58,031 - mmcls - INFO - Epoch [100][4200/5005]\tlr: 1.000e-04, eta: 0:01:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2284\n2023-01-01 03:16:06,916 - mmcls - INFO - Epoch [100][4300/5005]\tlr: 1.000e-04, eta: 0:01:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2206\n2023-01-01 03:16:15,744 - mmcls - INFO - Epoch [100][4400/5005]\tlr: 1.000e-04, eta: 0:00:53, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2132\n2023-01-01 03:16:24,684 - mmcls - INFO - Epoch [100][4500/5005]\tlr: 1.000e-04, eta: 0:00:44, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2234\n2023-01-01 03:16:33,245 - mmcls - INFO - Epoch [100][4600/5005]\tlr: 1.000e-04, eta: 0:00:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2242\n2023-01-01 03:16:41,944 - mmcls - INFO - Epoch [100][4700/5005]\tlr: 1.000e-04, eta: 0:00:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2359\n2023-01-01 03:16:50,822 - mmcls - INFO - Epoch [100][4800/5005]\tlr: 1.000e-04, eta: 0:00:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2263\n2023-01-01 03:16:59,554 - mmcls - INFO - Epoch [100][4900/5005]\tlr: 1.000e-04, eta: 0:00:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2047\n2023-01-01 03:17:08,062 - mmcls - INFO - Epoch [100][5000/5005]\tlr: 1.000e-04, eta: 0:00:00, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.2166\n2023-01-01 03:17:08,412 - mmcls - INFO - Saving checkpoint at 100 epochs\n2023-01-01 03:17:30,821 - mmcls - INFO - Epoch(val) [100][196]\taccuracy_top-1: 71.6440, accuracy_top-5: 90.2320\n"
  },
  {
    "path": "classification-NO-activation-function/logs/pinet_norelu.log",
    "content": "2023-01-02 13:57:26,413 - mmcls - INFO - Environment info:\n------------------------------------------------------------\nsys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 10.2, V10.2.8\nGCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 10.2\nMMClassification: 0.24.0+91b85bb\n------------------------------------------------------------\n\n2023-01-02 13:57:26,414 - mmcls - INFO - Distributed training: True\n2023-01-02 13:57:26,577 - mmcls - INFO - Config:\nmodel = dict(\n    type='ImageClassifier',\n    backbone=dict(\n        type='ResNet',\n        depth=18,\n        num_stages=4,\n        out_indices=(3, ),\n        style='pytorch'),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=1000,\n        in_channels=512,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1)),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=32,\n    workers_per_gpu=2,\n    train=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[30, 60, 90])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=50)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/resnet18_8xb32_in1k_pi'\ngpu_ids = range(0, 8)\n\n2023-01-02 13:57:32,376 - mmcls - INFO - Set random seed to 560605285, deterministic: False\n2023-01-02 13:57:32,636 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n2023-01-02 13:57:32,722 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\nName of parameter - Initialization information\n\nbackbone.conv1.weight - torch.Size([64, 3, 7, 7]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.0.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.1.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.0.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.downsample.1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.1.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.0.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.downsample.1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.1.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.0.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.downsample.1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.1.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nhead.fc.weight - torch.Size([1000, 512]): \nNormalInit: mean=0, std=0.01, bias=0 \n\nhead.fc.bias - torch.Size([1000]): \nNormalInit: mean=0, std=0.01, bias=0 \n2023-01-02 13:58:01,844 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi\n2023-01-02 13:58:01,845 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) DistOptimizerHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2023-01-02 13:58:01,845 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs\n2023-01-02 13:58:01,845 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend.\n2023-01-02 13:58:13,661 - mmcls - INFO - Epoch [1][100/5005]\tlr: 1.000e-01, eta: 16:23:29, time: 0.118, data_time: 0.032, memory: 1125, loss: 6.7216\n2023-01-02 13:58:22,752 - mmcls - INFO - Epoch [1][200/5005]\tlr: 1.000e-01, eta: 14:30:45, time: 0.091, data_time: 0.001, memory: 1125, loss: 6.4537\n2023-01-02 13:58:31,383 - mmcls - INFO - Epoch [1][300/5005]\tlr: 1.000e-01, eta: 13:40:06, time: 0.086, data_time: 0.013, memory: 1125, loss: 6.2304\n2023-01-02 13:58:40,451 - mmcls - INFO - Epoch [1][400/5005]\tlr: 1.000e-01, eta: 13:24:02, time: 0.091, data_time: 0.012, memory: 1125, loss: 6.0236\n2023-01-02 13:58:49,221 - mmcls - INFO - Epoch [1][500/5005]\tlr: 1.000e-01, eta: 13:09:08, time: 0.088, data_time: 0.000, memory: 1125, loss: 5.8518\n2023-01-02 13:58:58,027 - mmcls - INFO - Epoch [1][600/5005]\tlr: 1.000e-01, eta: 12:59:42, time: 0.088, data_time: 0.026, memory: 1125, loss: 5.7128\n2023-01-02 13:59:06,656 - mmcls - INFO - Epoch [1][700/5005]\tlr: 1.000e-01, eta: 12:50:52, time: 0.086, data_time: 0.008, memory: 1125, loss: 5.6081\n2023-01-02 13:59:15,609 - mmcls - INFO - Epoch [1][800/5005]\tlr: 1.000e-01, eta: 12:47:35, time: 0.090, data_time: 0.005, memory: 1125, loss: 5.4917\n2023-01-02 13:59:24,197 - mmcls - INFO - Epoch [1][900/5005]\tlr: 1.000e-01, eta: 12:41:37, time: 0.086, data_time: 0.003, memory: 1125, loss: 5.4012\n2023-01-02 13:59:32,980 - mmcls - INFO - Epoch [1][1000/5005]\tlr: 1.000e-01, eta: 12:38:29, time: 0.088, data_time: 0.013, memory: 1125, loss: 5.3144\n2023-01-02 13:59:42,330 - mmcls - INFO - Epoch [1][1100/5005]\tlr: 1.000e-01, eta: 12:40:08, time: 0.093, data_time: 0.024, memory: 1125, loss: 5.2574\n2023-01-02 13:59:51,017 - mmcls - INFO - Epoch [1][1200/5005]\tlr: 1.000e-01, eta: 12:36:50, time: 0.087, data_time: 0.022, memory: 1125, loss: 5.1409\n2023-01-02 14:00:00,044 - mmcls - INFO - Epoch [1][1300/5005]\tlr: 1.000e-01, eta: 12:36:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 5.0996\n2023-01-02 14:00:08,613 - mmcls - INFO - Epoch [1][1400/5005]\tlr: 1.000e-01, eta: 12:33:01, time: 0.086, data_time: 0.028, memory: 1125, loss: 5.0061\n2023-01-02 14:00:17,572 - mmcls - INFO - Epoch [1][1500/5005]\tlr: 1.000e-01, eta: 12:32:20, time: 0.090, data_time: 0.012, memory: 1125, loss: 4.9650\n2023-01-02 14:00:26,345 - mmcls - INFO - Epoch [1][1600/5005]\tlr: 1.000e-01, eta: 12:30:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 4.8796\n2023-01-02 14:00:35,367 - mmcls - INFO - Epoch [1][1700/5005]\tlr: 1.000e-01, eta: 12:30:33, time: 0.090, data_time: 0.003, memory: 1125, loss: 4.8402\n2023-01-02 14:00:44,208 - mmcls - INFO - Epoch [1][1800/5005]\tlr: 1.000e-01, eta: 12:29:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 4.7744\n2023-01-02 14:00:52,827 - mmcls - INFO - Epoch [1][1900/5005]\tlr: 1.000e-01, eta: 12:27:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 4.7170\n2023-01-02 14:01:01,377 - mmcls - INFO - Epoch [1][2000/5005]\tlr: 1.000e-01, eta: 12:25:38, time: 0.085, data_time: 0.007, memory: 1125, loss: 4.6737\n2023-01-02 14:01:09,982 - mmcls - INFO - Epoch [1][2100/5005]\tlr: 1.000e-01, eta: 12:24:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 4.6141\n2023-01-02 14:01:18,844 - mmcls - INFO - Epoch [1][2200/5005]\tlr: 1.000e-01, eta: 12:23:30, time: 0.089, data_time: 0.009, memory: 1125, loss: 4.5775\n2023-01-02 14:01:27,551 - mmcls - INFO - Epoch [1][2300/5005]\tlr: 1.000e-01, eta: 12:22:28, time: 0.087, data_time: 0.013, memory: 1125, loss: 4.5183\n2023-01-02 14:01:36,521 - mmcls - INFO - Epoch [1][2400/5005]\tlr: 1.000e-01, eta: 12:22:25, time: 0.090, data_time: 0.009, memory: 1125, loss: 4.5045\n2023-01-02 14:01:45,233 - mmcls - INFO - Epoch [1][2500/5005]\tlr: 1.000e-01, eta: 12:21:30, time: 0.087, data_time: 0.011, memory: 1125, loss: 4.4384\n2023-01-02 14:01:54,011 - mmcls - INFO - Epoch [1][2600/5005]\tlr: 1.000e-01, eta: 12:20:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 4.3905\n2023-01-02 14:02:02,589 - mmcls - INFO - Epoch [1][2700/5005]\tlr: 1.000e-01, eta: 12:19:36, time: 0.086, data_time: 0.003, memory: 1125, loss: 4.3860\n2023-01-02 14:02:11,415 - mmcls - INFO - Epoch [1][2800/5005]\tlr: 1.000e-01, eta: 12:19:12, time: 0.088, data_time: 0.027, memory: 1125, loss: 4.3300\n2023-01-02 14:02:20,399 - mmcls - INFO - Epoch [1][2900/5005]\tlr: 1.000e-01, eta: 12:19:16, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.3115\n2023-01-02 14:02:29,372 - mmcls - INFO - Epoch [1][3000/5005]\tlr: 1.000e-01, eta: 12:19:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 4.2887\n2023-01-02 14:02:38,053 - mmcls - INFO - Epoch [1][3100/5005]\tlr: 1.000e-01, eta: 12:18:30, time: 0.087, data_time: 0.004, memory: 1125, loss: 4.2473\n2023-01-02 14:02:46,800 - mmcls - INFO - Epoch [1][3200/5005]\tlr: 1.000e-01, eta: 12:17:55, time: 0.087, data_time: 0.032, memory: 1125, loss: 4.2131\n2023-01-02 14:02:55,629 - mmcls - INFO - Epoch [1][3300/5005]\tlr: 1.000e-01, eta: 12:17:35, time: 0.088, data_time: 0.008, memory: 1125, loss: 4.1839\n2023-01-02 14:03:04,240 - mmcls - INFO - Epoch [1][3400/5005]\tlr: 1.000e-01, eta: 12:16:44, time: 0.086, data_time: 0.012, memory: 1125, loss: 4.1642\n2023-01-02 14:03:12,852 - mmcls - INFO - Epoch [1][3500/5005]\tlr: 1.000e-01, eta: 12:15:54, time: 0.086, data_time: 0.006, memory: 1125, loss: 4.1469\n2023-01-02 14:03:21,618 - mmcls - INFO - Epoch [1][3600/5005]\tlr: 1.000e-01, eta: 12:15:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 4.0707\n2023-01-02 14:03:30,326 - mmcls - INFO - Epoch [1][3700/5005]\tlr: 1.000e-01, eta: 12:14:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 4.1024\n2023-01-02 14:03:38,995 - mmcls - INFO - Epoch [1][3800/5005]\tlr: 1.000e-01, eta: 12:14:21, time: 0.087, data_time: 0.028, memory: 1125, loss: 4.1037\n2023-01-02 14:03:47,723 - mmcls - INFO - Epoch [1][3900/5005]\tlr: 1.000e-01, eta: 12:13:55, time: 0.087, data_time: 0.004, memory: 1125, loss: 4.0537\n2023-01-02 14:03:56,519 - mmcls - INFO - Epoch [1][4000/5005]\tlr: 1.000e-01, eta: 12:13:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 4.0364\n2023-01-02 14:04:05,209 - mmcls - INFO - Epoch [1][4100/5005]\tlr: 1.000e-01, eta: 12:13:06, time: 0.087, data_time: 0.010, memory: 1125, loss: 4.0029\n2023-01-02 14:04:13,998 - mmcls - INFO - Epoch [1][4200/5005]\tlr: 1.000e-01, eta: 12:12:49, time: 0.088, data_time: 0.010, memory: 1125, loss: 3.9831\n2023-01-02 14:04:22,992 - mmcls - INFO - Epoch [1][4300/5005]\tlr: 1.000e-01, eta: 12:12:55, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.9957\n2023-01-02 14:04:31,727 - mmcls - INFO - Epoch [1][4400/5005]\tlr: 1.000e-01, eta: 12:12:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.9515\n2023-01-02 14:04:40,854 - mmcls - INFO - Epoch [1][4500/5005]\tlr: 1.000e-01, eta: 12:12:52, time: 0.091, data_time: 0.013, memory: 1125, loss: 3.9311\n2023-01-02 14:04:49,813 - mmcls - INFO - Epoch [1][4600/5005]\tlr: 1.000e-01, eta: 12:12:53, time: 0.090, data_time: 0.015, memory: 1125, loss: 3.9065\n2023-01-02 14:04:58,510 - mmcls - INFO - Epoch [1][4700/5005]\tlr: 1.000e-01, eta: 12:12:26, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.9106\n2023-01-02 14:05:07,002 - mmcls - INFO - Epoch [1][4800/5005]\tlr: 1.000e-01, eta: 12:11:39, time: 0.085, data_time: 0.004, memory: 1125, loss: 3.8843\n2023-01-02 14:05:15,925 - mmcls - INFO - Epoch [1][4900/5005]\tlr: 1.000e-01, eta: 12:11:37, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.8728\n2023-01-02 14:05:24,482 - mmcls - INFO - Epoch [1][5000/5005]\tlr: 1.000e-01, eta: 12:10:58, time: 0.086, data_time: 0.022, memory: 1125, loss: 3.8471\n2023-01-02 14:05:47,770 - mmcls - INFO - Epoch(val) [1][196]\taccuracy_top-1: 29.1120, accuracy_top-5: 52.7260\n2023-01-02 14:05:58,565 - mmcls - INFO - Epoch [2][100/5005]\tlr: 1.000e-01, eta: 12:13:13, time: 0.108, data_time: 0.038, memory: 1125, loss: 3.7960\n2023-01-02 14:06:07,253 - mmcls - INFO - Epoch [2][200/5005]\tlr: 1.000e-01, eta: 12:12:45, time: 0.087, data_time: 0.008, memory: 1125, loss: 3.7810\n2023-01-02 14:06:16,265 - mmcls - INFO - Epoch [2][300/5005]\tlr: 1.000e-01, eta: 12:12:49, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.7801\n2023-01-02 14:06:24,971 - mmcls - INFO - Epoch [2][400/5005]\tlr: 1.000e-01, eta: 12:12:24, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.7731\n2023-01-02 14:06:33,748 - mmcls - INFO - Epoch [2][500/5005]\tlr: 1.000e-01, eta: 12:12:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.7721\n2023-01-02 14:06:42,846 - mmcls - INFO - Epoch [2][600/5005]\tlr: 1.000e-01, eta: 12:12:17, time: 0.091, data_time: 0.016, memory: 1125, loss: 3.7409\n2023-01-02 14:06:51,836 - mmcls - INFO - Epoch [2][700/5005]\tlr: 1.000e-01, eta: 12:12:18, time: 0.090, data_time: 0.012, memory: 1125, loss: 3.7093\n2023-01-02 14:07:00,533 - mmcls - INFO - Epoch [2][800/5005]\tlr: 1.000e-01, eta: 12:11:53, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.7298\n2023-01-02 14:07:09,499 - mmcls - INFO - Epoch [2][900/5005]\tlr: 1.000e-01, eta: 12:11:52, time: 0.090, data_time: 0.024, memory: 1125, loss: 3.7059\n2023-01-02 14:07:18,236 - mmcls - INFO - Epoch [2][1000/5005]\tlr: 1.000e-01, eta: 12:11:32, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.7281\n2023-01-02 14:07:26,912 - mmcls - INFO - Epoch [2][1100/5005]\tlr: 1.000e-01, eta: 12:11:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.6853\n2023-01-02 14:07:35,761 - mmcls - INFO - Epoch [2][1200/5005]\tlr: 1.000e-01, eta: 12:10:55, time: 0.089, data_time: 0.020, memory: 1125, loss: 3.6787\n2023-01-02 14:07:44,409 - mmcls - INFO - Epoch [2][1300/5005]\tlr: 1.000e-01, eta: 12:10:28, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.6545\n2023-01-02 14:07:53,074 - mmcls - INFO - Epoch [2][1400/5005]\tlr: 1.000e-01, eta: 12:10:04, time: 0.087, data_time: 0.036, memory: 1125, loss: 3.6267\n2023-01-02 14:08:01,813 - mmcls - INFO - Epoch [2][1500/5005]\tlr: 1.000e-01, eta: 12:09:46, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.6460\n2023-01-02 14:08:10,612 - mmcls - INFO - Epoch [2][1600/5005]\tlr: 1.000e-01, eta: 12:09:31, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.6666\n2023-01-02 14:08:19,369 - mmcls - INFO - Epoch [2][1700/5005]\tlr: 1.000e-01, eta: 12:09:15, time: 0.088, data_time: 0.018, memory: 1125, loss: 3.6377\n2023-01-02 14:08:28,054 - mmcls - INFO - Epoch [2][1800/5005]\tlr: 1.000e-01, eta: 12:08:53, time: 0.087, data_time: 0.031, memory: 1125, loss: 3.6239\n2023-01-02 14:08:36,991 - mmcls - INFO - Epoch [2][1900/5005]\tlr: 1.000e-01, eta: 12:08:50, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.5787\n2023-01-02 14:08:45,414 - mmcls - INFO - Epoch [2][2000/5005]\tlr: 1.000e-01, eta: 12:08:11, time: 0.084, data_time: 0.014, memory: 1125, loss: 3.6004\n2023-01-02 14:08:54,214 - mmcls - INFO - Epoch [2][2100/5005]\tlr: 1.000e-01, eta: 12:07:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.5631\n2023-01-02 14:09:03,016 - mmcls - INFO - Epoch [2][2200/5005]\tlr: 1.000e-01, eta: 12:07:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 3.5865\n2023-01-02 14:09:11,855 - mmcls - INFO - Epoch [2][2300/5005]\tlr: 1.000e-01, eta: 12:07:35, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.5694\n2023-01-02 14:09:20,894 - mmcls - INFO - Epoch [2][2400/5005]\tlr: 1.000e-01, eta: 12:07:39, time: 0.090, data_time: 0.022, memory: 1125, loss: 3.5889\n2023-01-02 14:09:29,452 - mmcls - INFO - Epoch [2][2500/5005]\tlr: 1.000e-01, eta: 12:07:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.5807\n2023-01-02 14:09:38,015 - mmcls - INFO - Epoch [2][2600/5005]\tlr: 1.000e-01, eta: 12:06:43, time: 0.086, data_time: 0.011, memory: 1125, loss: 3.5289\n2023-01-02 14:09:46,761 - mmcls - INFO - Epoch [2][2700/5005]\tlr: 1.000e-01, eta: 12:06:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.5261\n2023-01-02 14:09:55,712 - mmcls - INFO - Epoch [2][2800/5005]\tlr: 1.000e-01, eta: 12:06:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.5424\n2023-01-02 14:10:04,506 - mmcls - INFO - Epoch [2][2900/5005]\tlr: 1.000e-01, eta: 12:06:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.5257\n2023-01-02 14:10:13,263 - mmcls - INFO - Epoch [2][3000/5005]\tlr: 1.000e-01, eta: 12:05:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.5201\n2023-01-02 14:10:21,738 - mmcls - INFO - Epoch [2][3100/5005]\tlr: 1.000e-01, eta: 12:05:28, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.5033\n2023-01-02 14:10:30,333 - mmcls - INFO - Epoch [2][3200/5005]\tlr: 1.000e-01, eta: 12:05:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.4899\n2023-01-02 14:10:39,239 - mmcls - INFO - Epoch [2][3300/5005]\tlr: 1.000e-01, eta: 12:05:00, time: 0.089, data_time: 0.046, memory: 1125, loss: 3.4838\n2023-01-02 14:10:48,194 - mmcls - INFO - Epoch [2][3400/5005]\tlr: 1.000e-01, eta: 12:04:58, time: 0.090, data_time: 0.005, memory: 1125, loss: 3.4990\n2023-01-02 14:10:56,644 - mmcls - INFO - Epoch [2][3500/5005]\tlr: 1.000e-01, eta: 12:04:26, time: 0.084, data_time: 0.001, memory: 1125, loss: 3.4438\n2023-01-02 14:11:05,505 - mmcls - INFO - Epoch [2][3600/5005]\tlr: 1.000e-01, eta: 12:04:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.4726\n2023-01-02 14:11:14,017 - mmcls - INFO - Epoch [2][3700/5005]\tlr: 1.000e-01, eta: 12:03:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.4733\n2023-01-02 14:11:22,368 - mmcls - INFO - Epoch [2][3800/5005]\tlr: 1.000e-01, eta: 12:03:16, time: 0.084, data_time: 0.001, memory: 1125, loss: 3.4658\n2023-01-02 14:11:31,211 - mmcls - INFO - Epoch [2][3900/5005]\tlr: 1.000e-01, eta: 12:03:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.4773\n2023-01-02 14:11:40,104 - mmcls - INFO - Epoch [2][4000/5005]\tlr: 1.000e-01, eta: 12:03:02, time: 0.089, data_time: 0.038, memory: 1125, loss: 3.4446\n2023-01-02 14:11:48,736 - mmcls - INFO - Epoch [2][4100/5005]\tlr: 1.000e-01, eta: 12:02:44, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.4515\n2023-01-02 14:11:57,541 - mmcls - INFO - Epoch [2][4200/5005]\tlr: 1.000e-01, eta: 12:02:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.4241\n2023-01-02 14:12:06,352 - mmcls - INFO - Epoch [2][4300/5005]\tlr: 1.000e-01, eta: 12:02:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.4420\n2023-01-02 14:12:15,033 - mmcls - INFO - Epoch [2][4400/5005]\tlr: 1.000e-01, eta: 12:02:07, time: 0.087, data_time: 0.020, memory: 1125, loss: 3.4339\n2023-01-02 14:12:24,044 - mmcls - INFO - Epoch [2][4500/5005]\tlr: 1.000e-01, eta: 12:02:08, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.4188\n2023-01-02 14:12:32,719 - mmcls - INFO - Epoch [2][4600/5005]\tlr: 1.000e-01, eta: 12:01:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.4073\n2023-01-02 14:12:41,802 - mmcls - INFO - Epoch [2][4700/5005]\tlr: 1.000e-01, eta: 12:01:56, time: 0.091, data_time: 0.007, memory: 1125, loss: 3.3928\n2023-01-02 14:12:50,572 - mmcls - INFO - Epoch [2][4800/5005]\tlr: 1.000e-01, eta: 12:01:44, time: 0.088, data_time: 0.049, memory: 1125, loss: 3.4004\n2023-01-02 14:12:58,868 - mmcls - INFO - Epoch [2][4900/5005]\tlr: 1.000e-01, eta: 12:01:10, time: 0.083, data_time: 0.017, memory: 1125, loss: 3.3770\n2023-01-02 14:13:07,709 - mmcls - INFO - Epoch [2][5000/5005]\tlr: 1.000e-01, eta: 12:01:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.4078\n2023-01-02 14:13:30,849 - mmcls - INFO - Epoch(val) [2][196]\taccuracy_top-1: 35.1040, accuracy_top-5: 60.1120\n2023-01-02 14:13:42,036 - mmcls - INFO - Epoch [3][100/5005]\tlr: 1.000e-01, eta: 12:02:24, time: 0.112, data_time: 0.062, memory: 1125, loss: 3.3333\n2023-01-02 14:13:50,667 - mmcls - INFO - Epoch [3][200/5005]\tlr: 1.000e-01, eta: 12:02:06, time: 0.086, data_time: 0.025, memory: 1125, loss: 3.3084\n2023-01-02 14:13:59,392 - mmcls - INFO - Epoch [3][300/5005]\tlr: 1.000e-01, eta: 12:01:52, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.3154\n2023-01-02 14:14:08,040 - mmcls - INFO - Epoch [3][400/5005]\tlr: 1.000e-01, eta: 12:01:34, time: 0.086, data_time: 0.012, memory: 1125, loss: 3.3643\n2023-01-02 14:14:16,748 - mmcls - INFO - Epoch [3][500/5005]\tlr: 1.000e-01, eta: 12:01:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.3228\n2023-01-02 14:14:25,662 - mmcls - INFO - Epoch [3][600/5005]\tlr: 1.000e-01, eta: 12:01:14, time: 0.089, data_time: 0.011, memory: 1125, loss: 3.3300\n2023-01-02 14:14:34,451 - mmcls - INFO - Epoch [3][700/5005]\tlr: 1.000e-01, eta: 12:01:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.3287\n2023-01-02 14:14:43,063 - mmcls - INFO - Epoch [3][800/5005]\tlr: 1.000e-01, eta: 12:00:44, time: 0.086, data_time: 0.010, memory: 1125, loss: 3.3256\n2023-01-02 14:14:51,639 - mmcls - INFO - Epoch [3][900/5005]\tlr: 1.000e-01, eta: 12:00:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.3459\n2023-01-02 14:15:00,366 - mmcls - INFO - Epoch [3][1000/5005]\tlr: 1.000e-01, eta: 12:00:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.3520\n2023-01-02 14:15:09,084 - mmcls - INFO - Epoch [3][1100/5005]\tlr: 1.000e-01, eta: 11:59:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.3345\n2023-01-02 14:15:17,565 - mmcls - INFO - Epoch [3][1200/5005]\tlr: 1.000e-01, eta: 11:59:32, time: 0.085, data_time: 0.032, memory: 1125, loss: 3.3008\n2023-01-02 14:15:26,207 - mmcls - INFO - Epoch [3][1300/5005]\tlr: 1.000e-01, eta: 11:59:16, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.3020\n2023-01-02 14:15:35,254 - mmcls - INFO - Epoch [3][1400/5005]\tlr: 1.000e-01, eta: 11:59:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 3.3133\n2023-01-02 14:15:43,896 - mmcls - INFO - Epoch [3][1500/5005]\tlr: 1.000e-01, eta: 11:59:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.3350\n2023-01-02 14:15:52,667 - mmcls - INFO - Epoch [3][1600/5005]\tlr: 1.000e-01, eta: 11:58:49, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.3185\n2023-01-02 14:16:01,282 - mmcls - INFO - Epoch [3][1700/5005]\tlr: 1.000e-01, eta: 11:58:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 3.2907\n2023-01-02 14:16:09,877 - mmcls - INFO - Epoch [3][1800/5005]\tlr: 1.000e-01, eta: 11:58:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 3.3159\n2023-01-02 14:16:18,741 - mmcls - INFO - Epoch [3][1900/5005]\tlr: 1.000e-01, eta: 11:58:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.3320\n2023-01-02 14:16:27,390 - mmcls - INFO - Epoch [3][2000/5005]\tlr: 1.000e-01, eta: 11:57:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.3032\n2023-01-02 14:16:36,396 - mmcls - INFO - Epoch [3][2100/5005]\tlr: 1.000e-01, eta: 11:57:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.2939\n2023-01-02 14:16:45,509 - mmcls - INFO - Epoch [3][2200/5005]\tlr: 1.000e-01, eta: 11:57:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 3.2853\n2023-01-02 14:16:54,424 - mmcls - INFO - Epoch [3][2300/5005]\tlr: 1.000e-01, eta: 11:57:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.2435\n2023-01-02 14:17:03,152 - mmcls - INFO - Epoch [3][2400/5005]\tlr: 1.000e-01, eta: 11:57:34, time: 0.087, data_time: 0.020, memory: 1125, loss: 3.2653\n2023-01-02 14:17:12,147 - mmcls - INFO - Epoch [3][2500/5005]\tlr: 1.000e-01, eta: 11:57:32, time: 0.090, data_time: 0.029, memory: 1125, loss: 3.2438\n2023-01-02 14:17:20,655 - mmcls - INFO - Epoch [3][2600/5005]\tlr: 1.000e-01, eta: 11:57:11, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.2889\n2023-01-02 14:17:29,368 - mmcls - INFO - Epoch [3][2700/5005]\tlr: 1.000e-01, eta: 11:56:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2767\n2023-01-02 14:17:38,297 - mmcls - INFO - Epoch [3][2800/5005]\tlr: 1.000e-01, eta: 11:56:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.2733\n2023-01-02 14:17:46,934 - mmcls - INFO - Epoch [3][2900/5005]\tlr: 1.000e-01, eta: 11:56:38, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2407\n2023-01-02 14:17:55,447 - mmcls - INFO - Epoch [3][3000/5005]\tlr: 1.000e-01, eta: 11:56:18, time: 0.085, data_time: 0.009, memory: 1125, loss: 3.2583\n2023-01-02 14:18:04,385 - mmcls - INFO - Epoch [3][3100/5005]\tlr: 1.000e-01, eta: 11:56:14, time: 0.089, data_time: 0.024, memory: 1125, loss: 3.2377\n2023-01-02 14:18:13,247 - mmcls - INFO - Epoch [3][3200/5005]\tlr: 1.000e-01, eta: 11:56:07, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.2548\n2023-01-02 14:18:22,002 - mmcls - INFO - Epoch [3][3300/5005]\tlr: 1.000e-01, eta: 11:55:55, time: 0.087, data_time: 0.018, memory: 1125, loss: 3.2715\n2023-01-02 14:18:30,720 - mmcls - INFO - Epoch [3][3400/5005]\tlr: 1.000e-01, eta: 11:55:43, time: 0.087, data_time: 0.024, memory: 1125, loss: 3.2439\n2023-01-02 14:18:39,147 - mmcls - INFO - Epoch [3][3500/5005]\tlr: 1.000e-01, eta: 11:55:20, time: 0.084, data_time: 0.006, memory: 1125, loss: 3.2191\n2023-01-02 14:18:48,091 - mmcls - INFO - Epoch [3][3600/5005]\tlr: 1.000e-01, eta: 11:55:16, time: 0.089, data_time: 0.020, memory: 1125, loss: 3.2309\n2023-01-02 14:18:56,758 - mmcls - INFO - Epoch [3][3700/5005]\tlr: 1.000e-01, eta: 11:55:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2302\n2023-01-02 14:19:05,482 - mmcls - INFO - Epoch [3][3800/5005]\tlr: 1.000e-01, eta: 11:54:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2237\n2023-01-02 14:19:14,094 - mmcls - INFO - Epoch [3][3900/5005]\tlr: 1.000e-01, eta: 11:54:34, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2400\n2023-01-02 14:19:22,819 - mmcls - INFO - Epoch [3][4000/5005]\tlr: 1.000e-01, eta: 11:54:22, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.2361\n2023-01-02 14:19:31,348 - mmcls - INFO - Epoch [3][4100/5005]\tlr: 1.000e-01, eta: 11:54:03, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.2289\n2023-01-02 14:19:40,123 - mmcls - INFO - Epoch [3][4200/5005]\tlr: 1.000e-01, eta: 11:53:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.2093\n2023-01-02 14:19:49,347 - mmcls - INFO - Epoch [3][4300/5005]\tlr: 1.000e-01, eta: 11:53:59, time: 0.092, data_time: 0.003, memory: 1125, loss: 3.2403\n2023-01-02 14:19:58,034 - mmcls - INFO - Epoch [3][4400/5005]\tlr: 1.000e-01, eta: 11:53:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.2210\n2023-01-02 14:20:06,662 - mmcls - INFO - Epoch [3][4500/5005]\tlr: 1.000e-01, eta: 11:53:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2159\n2023-01-02 14:20:15,476 - mmcls - INFO - Epoch [3][4600/5005]\tlr: 1.000e-01, eta: 11:53:22, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.2036\n2023-01-02 14:20:24,158 - mmcls - INFO - Epoch [3][4700/5005]\tlr: 1.000e-01, eta: 11:53:09, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.1943\n2023-01-02 14:20:32,922 - mmcls - INFO - Epoch [3][4800/5005]\tlr: 1.000e-01, eta: 11:52:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.2171\n2023-01-02 14:20:41,482 - mmcls - INFO - Epoch [3][4900/5005]\tlr: 1.000e-01, eta: 11:52:42, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.1994\n2023-01-02 14:20:50,302 - mmcls - INFO - Epoch [3][5000/5005]\tlr: 1.000e-01, eta: 11:52:33, time: 0.088, data_time: 0.026, memory: 1125, loss: 3.2051\n2023-01-02 14:21:12,943 - mmcls - INFO - Epoch(val) [3][196]\taccuracy_top-1: 39.9600, accuracy_top-5: 64.9560\n2023-01-02 14:21:24,136 - mmcls - INFO - Epoch [4][100/5005]\tlr: 1.000e-01, eta: 11:53:26, time: 0.112, data_time: 0.051, memory: 1125, loss: 3.1131\n2023-01-02 14:21:33,058 - mmcls - INFO - Epoch [4][200/5005]\tlr: 1.000e-01, eta: 11:53:20, time: 0.089, data_time: 0.018, memory: 1125, loss: 3.1158\n2023-01-02 14:21:41,933 - mmcls - INFO - Epoch [4][300/5005]\tlr: 1.000e-01, eta: 11:53:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.1188\n2023-01-02 14:21:50,972 - mmcls - INFO - Epoch [4][400/5005]\tlr: 1.000e-01, eta: 11:53:11, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.1672\n2023-01-02 14:21:59,826 - mmcls - INFO - Epoch [4][500/5005]\tlr: 1.000e-01, eta: 11:53:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.1210\n2023-01-02 14:22:08,511 - mmcls - INFO - Epoch [4][600/5005]\tlr: 1.000e-01, eta: 11:52:50, time: 0.087, data_time: 0.009, memory: 1125, loss: 3.1879\n2023-01-02 14:22:17,119 - mmcls - INFO - Epoch [4][700/5005]\tlr: 1.000e-01, eta: 11:52:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.1573\n2023-01-02 14:22:25,893 - mmcls - INFO - Epoch [4][800/5005]\tlr: 1.000e-01, eta: 11:52:25, time: 0.088, data_time: 0.012, memory: 1125, loss: 3.1364\n2023-01-02 14:22:34,469 - mmcls - INFO - Epoch [4][900/5005]\tlr: 1.000e-01, eta: 11:52:08, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1412\n2023-01-02 14:22:43,141 - mmcls - INFO - Epoch [4][1000/5005]\tlr: 1.000e-01, eta: 11:51:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.1448\n2023-01-02 14:22:52,136 - mmcls - INFO - Epoch [4][1100/5005]\tlr: 1.000e-01, eta: 11:51:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.1381\n2023-01-02 14:23:00,875 - mmcls - INFO - Epoch [4][1200/5005]\tlr: 1.000e-01, eta: 11:51:41, time: 0.087, data_time: 0.017, memory: 1125, loss: 3.1426\n2023-01-02 14:23:10,024 - mmcls - INFO - Epoch [4][1300/5005]\tlr: 1.000e-01, eta: 11:51:41, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.1770\n2023-01-02 14:23:18,791 - mmcls - INFO - Epoch [4][1400/5005]\tlr: 1.000e-01, eta: 11:51:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.1552\n2023-01-02 14:23:27,285 - mmcls - INFO - Epoch [4][1500/5005]\tlr: 1.000e-01, eta: 11:51:13, time: 0.085, data_time: 0.008, memory: 1125, loss: 3.1531\n2023-01-02 14:23:36,110 - mmcls - INFO - Epoch [4][1600/5005]\tlr: 1.000e-01, eta: 11:51:04, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.1557\n2023-01-02 14:23:44,715 - mmcls - INFO - Epoch [4][1700/5005]\tlr: 1.000e-01, eta: 11:50:49, time: 0.086, data_time: 0.025, memory: 1125, loss: 3.1353\n2023-01-02 14:23:53,747 - mmcls - INFO - Epoch [4][1800/5005]\tlr: 1.000e-01, eta: 11:50:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.1173\n2023-01-02 14:24:02,416 - mmcls - INFO - Epoch [4][1900/5005]\tlr: 1.000e-01, eta: 11:50:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.1344\n2023-01-02 14:24:10,999 - mmcls - INFO - Epoch [4][2000/5005]\tlr: 1.000e-01, eta: 11:50:18, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.1405\n2023-01-02 14:24:19,884 - mmcls - INFO - Epoch [4][2100/5005]\tlr: 1.000e-01, eta: 11:50:11, time: 0.089, data_time: 0.011, memory: 1125, loss: 3.1367\n2023-01-02 14:24:28,570 - mmcls - INFO - Epoch [4][2200/5005]\tlr: 1.000e-01, eta: 11:49:58, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.1424\n2023-01-02 14:24:37,188 - mmcls - INFO - Epoch [4][2300/5005]\tlr: 1.000e-01, eta: 11:49:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 3.1286\n2023-01-02 14:24:45,749 - mmcls - INFO - Epoch [4][2400/5005]\tlr: 1.000e-01, eta: 11:49:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.1214\n2023-01-02 14:24:54,626 - mmcls - INFO - Epoch [4][2500/5005]\tlr: 1.000e-01, eta: 11:49:21, time: 0.089, data_time: 0.027, memory: 1125, loss: 3.1218\n2023-01-02 14:25:03,117 - mmcls - INFO - Epoch [4][2600/5005]\tlr: 1.000e-01, eta: 11:49:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.1824\n2023-01-02 14:25:11,910 - mmcls - INFO - Epoch [4][2700/5005]\tlr: 1.000e-01, eta: 11:48:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.1256\n2023-01-02 14:25:21,118 - mmcls - INFO - Epoch [4][2800/5005]\tlr: 1.000e-01, eta: 11:48:57, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.0892\n2023-01-02 14:25:30,034 - mmcls - INFO - Epoch [4][2900/5005]\tlr: 1.000e-01, eta: 11:48:50, time: 0.089, data_time: 0.013, memory: 1125, loss: 3.1399\n2023-01-02 14:25:39,021 - mmcls - INFO - Epoch [4][3000/5005]\tlr: 1.000e-01, eta: 11:48:46, time: 0.090, data_time: 0.030, memory: 1125, loss: 3.1751\n2023-01-02 14:25:47,556 - mmcls - INFO - Epoch [4][3100/5005]\tlr: 1.000e-01, eta: 11:48:30, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.1041\n2023-01-02 14:25:56,513 - mmcls - INFO - Epoch [4][3200/5005]\tlr: 1.000e-01, eta: 11:48:25, time: 0.090, data_time: 0.016, memory: 1125, loss: 3.1026\n2023-01-02 14:26:05,507 - mmcls - INFO - Epoch [4][3300/5005]\tlr: 1.000e-01, eta: 11:48:21, time: 0.090, data_time: 0.032, memory: 1125, loss: 3.1057\n2023-01-02 14:26:14,322 - mmcls - INFO - Epoch [4][3400/5005]\tlr: 1.000e-01, eta: 11:48:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.1114\n2023-01-02 14:26:23,127 - mmcls - INFO - Epoch [4][3500/5005]\tlr: 1.000e-01, eta: 11:48:03, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.1241\n2023-01-02 14:26:31,856 - mmcls - INFO - Epoch [4][3600/5005]\tlr: 1.000e-01, eta: 11:47:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 3.1183\n2023-01-02 14:26:40,590 - mmcls - INFO - Epoch [4][3700/5005]\tlr: 1.000e-01, eta: 11:47:41, time: 0.087, data_time: 0.028, memory: 1125, loss: 3.0828\n2023-01-02 14:26:49,221 - mmcls - INFO - Epoch [4][3800/5005]\tlr: 1.000e-01, eta: 11:47:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0934\n2023-01-02 14:26:58,061 - mmcls - INFO - Epoch [4][3900/5005]\tlr: 1.000e-01, eta: 11:47:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.1240\n2023-01-02 14:27:06,768 - mmcls - INFO - Epoch [4][4000/5005]\tlr: 1.000e-01, eta: 11:47:08, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.1182\n2023-01-02 14:27:15,436 - mmcls - INFO - Epoch [4][4100/5005]\tlr: 1.000e-01, eta: 11:46:55, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.1000\n2023-01-02 14:27:24,187 - mmcls - INFO - Epoch [4][4200/5005]\tlr: 1.000e-01, eta: 11:46:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 3.0869\n2023-01-02 14:27:32,903 - mmcls - INFO - Epoch [4][4300/5005]\tlr: 1.000e-01, eta: 11:46:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.1074\n2023-01-02 14:27:41,807 - mmcls - INFO - Epoch [4][4400/5005]\tlr: 1.000e-01, eta: 11:46:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.1028\n2023-01-02 14:27:50,531 - mmcls - INFO - Epoch [4][4500/5005]\tlr: 1.000e-01, eta: 11:46:16, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.1017\n2023-01-02 14:27:59,505 - mmcls - INFO - Epoch [4][4600/5005]\tlr: 1.000e-01, eta: 11:46:11, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.1245\n2023-01-02 14:28:08,402 - mmcls - INFO - Epoch [4][4700/5005]\tlr: 1.000e-01, eta: 11:46:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.0789\n2023-01-02 14:28:16,991 - mmcls - INFO - Epoch [4][4800/5005]\tlr: 1.000e-01, eta: 11:45:50, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1092\n2023-01-02 14:28:25,910 - mmcls - INFO - Epoch [4][4900/5005]\tlr: 1.000e-01, eta: 11:45:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.1001\n2023-01-02 14:28:35,064 - mmcls - INFO - Epoch [4][5000/5005]\tlr: 1.000e-01, eta: 11:45:44, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.1240\n2023-01-02 14:28:57,792 - mmcls - INFO - Epoch(val) [4][196]\taccuracy_top-1: 42.0100, accuracy_top-5: 66.9020\n2023-01-02 14:29:08,720 - mmcls - INFO - Epoch [5][100/5005]\tlr: 1.000e-01, eta: 11:46:14, time: 0.109, data_time: 0.049, memory: 1125, loss: 3.0220\n2023-01-02 14:29:17,491 - mmcls - INFO - Epoch [5][200/5005]\tlr: 1.000e-01, eta: 11:46:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0346\n2023-01-02 14:29:26,591 - mmcls - INFO - Epoch [5][300/5005]\tlr: 1.000e-01, eta: 11:46:02, time: 0.091, data_time: 0.000, memory: 1125, loss: 3.0180\n2023-01-02 14:29:35,389 - mmcls - INFO - Epoch [5][400/5005]\tlr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0381\n2023-01-02 14:29:43,788 - mmcls - INFO - Epoch [5][500/5005]\tlr: 1.000e-01, eta: 11:45:33, time: 0.084, data_time: 0.012, memory: 1125, loss: 3.0530\n2023-01-02 14:29:52,673 - mmcls - INFO - Epoch [5][600/5005]\tlr: 1.000e-01, eta: 11:45:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0275\n2023-01-02 14:30:01,575 - mmcls - INFO - Epoch [5][700/5005]\tlr: 1.000e-01, eta: 11:45:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0422\n2023-01-02 14:30:10,162 - mmcls - INFO - Epoch [5][800/5005]\tlr: 1.000e-01, eta: 11:45:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.0433\n2023-01-02 14:30:19,126 - mmcls - INFO - Epoch [5][900/5005]\tlr: 1.000e-01, eta: 11:45:00, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.0309\n2023-01-02 14:30:27,736 - mmcls - INFO - Epoch [5][1000/5005]\tlr: 1.000e-01, eta: 11:44:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.0654\n2023-01-02 14:30:36,258 - mmcls - INFO - Epoch [5][1100/5005]\tlr: 1.000e-01, eta: 11:44:30, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.0620\n2023-01-02 14:30:45,232 - mmcls - INFO - Epoch [5][1200/5005]\tlr: 1.000e-01, eta: 11:44:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 3.0736\n2023-01-02 14:30:53,850 - mmcls - INFO - Epoch [5][1300/5005]\tlr: 1.000e-01, eta: 11:44:12, time: 0.086, data_time: 0.018, memory: 1125, loss: 3.0687\n2023-01-02 14:31:02,728 - mmcls - INFO - Epoch [5][1400/5005]\tlr: 1.000e-01, eta: 11:44:04, time: 0.089, data_time: 0.023, memory: 1125, loss: 3.0512\n2023-01-02 14:31:11,651 - mmcls - INFO - Epoch [5][1500/5005]\tlr: 1.000e-01, eta: 11:43:58, time: 0.089, data_time: 0.000, memory: 1125, loss: 3.0553\n2023-01-02 14:31:20,642 - mmcls - INFO - Epoch [5][1600/5005]\tlr: 1.000e-01, eta: 11:43:53, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.0283\n2023-01-02 14:31:29,112 - mmcls - INFO - Epoch [5][1700/5005]\tlr: 1.000e-01, eta: 11:43:36, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0577\n2023-01-02 14:31:37,559 - mmcls - INFO - Epoch [5][1800/5005]\tlr: 1.000e-01, eta: 11:43:19, time: 0.085, data_time: 0.023, memory: 1125, loss: 3.0478\n2023-01-02 14:31:46,286 - mmcls - INFO - Epoch [5][1900/5005]\tlr: 1.000e-01, eta: 11:43:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0519\n2023-01-02 14:31:54,785 - mmcls - INFO - Epoch [5][2000/5005]\tlr: 1.000e-01, eta: 11:42:53, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.0372\n2023-01-02 14:32:03,363 - mmcls - INFO - Epoch [5][2100/5005]\tlr: 1.000e-01, eta: 11:42:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0422\n2023-01-02 14:32:12,351 - mmcls - INFO - Epoch [5][2200/5005]\tlr: 1.000e-01, eta: 11:42:34, time: 0.090, data_time: 0.020, memory: 1125, loss: 3.0502\n2023-01-02 14:32:21,213 - mmcls - INFO - Epoch [5][2300/5005]\tlr: 1.000e-01, eta: 11:42:26, time: 0.089, data_time: 0.016, memory: 1125, loss: 3.0721\n2023-01-02 14:32:29,991 - mmcls - INFO - Epoch [5][2400/5005]\tlr: 1.000e-01, eta: 11:42:16, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.0511\n2023-01-02 14:32:38,582 - mmcls - INFO - Epoch [5][2500/5005]\tlr: 1.000e-01, eta: 11:42:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.0831\n2023-01-02 14:32:47,335 - mmcls - INFO - Epoch [5][2600/5005]\tlr: 1.000e-01, eta: 11:41:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.0097\n2023-01-02 14:32:56,043 - mmcls - INFO - Epoch [5][2700/5005]\tlr: 1.000e-01, eta: 11:41:41, time: 0.087, data_time: 0.018, memory: 1125, loss: 3.0608\n2023-01-02 14:33:04,633 - mmcls - INFO - Epoch [5][2800/5005]\tlr: 1.000e-01, eta: 11:41:28, time: 0.086, data_time: 0.014, memory: 1125, loss: 3.0170\n2023-01-02 14:33:13,275 - mmcls - INFO - Epoch [5][2900/5005]\tlr: 1.000e-01, eta: 11:41:16, time: 0.086, data_time: 0.024, memory: 1125, loss: 3.0466\n2023-01-02 14:33:22,222 - mmcls - INFO - Epoch [5][3000/5005]\tlr: 1.000e-01, eta: 11:41:10, time: 0.090, data_time: 0.030, memory: 1125, loss: 3.0204\n2023-01-02 14:33:30,961 - mmcls - INFO - Epoch [5][3100/5005]\tlr: 1.000e-01, eta: 11:40:59, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.0622\n2023-01-02 14:33:39,557 - mmcls - INFO - Epoch [5][3200/5005]\tlr: 1.000e-01, eta: 11:40:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 3.0448\n2023-01-02 14:33:48,025 - mmcls - INFO - Epoch [5][3300/5005]\tlr: 1.000e-01, eta: 11:40:30, time: 0.085, data_time: 0.008, memory: 1125, loss: 3.0254\n2023-01-02 14:33:56,887 - mmcls - INFO - Epoch [5][3400/5005]\tlr: 1.000e-01, eta: 11:40:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 3.0540\n2023-01-02 14:34:05,954 - mmcls - INFO - Epoch [5][3500/5005]\tlr: 1.000e-01, eta: 11:40:19, time: 0.091, data_time: 0.012, memory: 1125, loss: 3.0490\n2023-01-02 14:34:14,786 - mmcls - INFO - Epoch [5][3600/5005]\tlr: 1.000e-01, eta: 11:40:11, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.0444\n2023-01-02 14:34:23,823 - mmcls - INFO - Epoch [5][3700/5005]\tlr: 1.000e-01, eta: 11:40:06, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.0414\n2023-01-02 14:34:32,460 - mmcls - INFO - Epoch [5][3800/5005]\tlr: 1.000e-01, eta: 11:39:54, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0614\n2023-01-02 14:34:41,312 - mmcls - INFO - Epoch [5][3900/5005]\tlr: 1.000e-01, eta: 11:39:46, time: 0.089, data_time: 0.015, memory: 1125, loss: 3.0436\n2023-01-02 14:34:49,944 - mmcls - INFO - Epoch [5][4000/5005]\tlr: 1.000e-01, eta: 11:39:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0514\n2023-01-02 14:34:58,920 - mmcls - INFO - Epoch [5][4100/5005]\tlr: 1.000e-01, eta: 11:39:28, time: 0.090, data_time: 0.017, memory: 1125, loss: 3.0210\n2023-01-02 14:35:07,509 - mmcls - INFO - Epoch [5][4200/5005]\tlr: 1.000e-01, eta: 11:39:15, time: 0.086, data_time: 0.019, memory: 1125, loss: 3.0356\n2023-01-02 14:35:16,456 - mmcls - INFO - Epoch [5][4300/5005]\tlr: 1.000e-01, eta: 11:39:09, time: 0.089, data_time: 0.032, memory: 1125, loss: 3.0307\n2023-01-02 14:35:25,247 - mmcls - INFO - Epoch [5][4400/5005]\tlr: 1.000e-01, eta: 11:39:00, time: 0.088, data_time: 0.045, memory: 1125, loss: 3.0226\n2023-01-02 14:35:34,075 - mmcls - INFO - Epoch [5][4500/5005]\tlr: 1.000e-01, eta: 11:38:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0161\n2023-01-02 14:35:42,708 - mmcls - INFO - Epoch [5][4600/5005]\tlr: 1.000e-01, eta: 11:38:39, time: 0.086, data_time: 0.031, memory: 1125, loss: 3.0251\n2023-01-02 14:35:51,823 - mmcls - INFO - Epoch [5][4700/5005]\tlr: 1.000e-01, eta: 11:38:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 3.0200\n2023-01-02 14:36:00,554 - mmcls - INFO - Epoch [5][4800/5005]\tlr: 1.000e-01, eta: 11:38:26, time: 0.087, data_time: 0.007, memory: 1125, loss: 3.0380\n2023-01-02 14:36:09,254 - mmcls - INFO - Epoch [5][4900/5005]\tlr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.022, memory: 1125, loss: 3.0229\n2023-01-02 14:36:17,823 - mmcls - INFO - Epoch [5][5000/5005]\tlr: 1.000e-01, eta: 11:38:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.0262\n2023-01-02 14:36:40,518 - mmcls - INFO - Epoch(val) [5][196]\taccuracy_top-1: 43.3380, accuracy_top-5: 68.1840\n2023-01-02 14:36:51,299 - mmcls - INFO - Epoch [6][100/5005]\tlr: 1.000e-01, eta: 11:38:21, time: 0.108, data_time: 0.035, memory: 1125, loss: 2.9812\n2023-01-02 14:37:00,163 - mmcls - INFO - Epoch [6][200/5005]\tlr: 1.000e-01, eta: 11:38:13, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9813\n2023-01-02 14:37:08,769 - mmcls - INFO - Epoch [6][300/5005]\tlr: 1.000e-01, eta: 11:38:00, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.9888\n2023-01-02 14:37:17,620 - mmcls - INFO - Epoch [6][400/5005]\tlr: 1.000e-01, eta: 11:37:52, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9919\n2023-01-02 14:37:26,445 - mmcls - INFO - Epoch [6][500/5005]\tlr: 1.000e-01, eta: 11:37:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9567\n2023-01-02 14:37:35,114 - mmcls - INFO - Epoch [6][600/5005]\tlr: 1.000e-01, eta: 11:37:32, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9761\n2023-01-02 14:37:43,789 - mmcls - INFO - Epoch [6][700/5005]\tlr: 1.000e-01, eta: 11:37:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9974\n2023-01-02 14:37:52,546 - mmcls - INFO - Epoch [6][800/5005]\tlr: 1.000e-01, eta: 11:37:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9946\n2023-01-02 14:38:01,109 - mmcls - INFO - Epoch [6][900/5005]\tlr: 1.000e-01, eta: 11:36:57, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.9493\n2023-01-02 14:38:09,948 - mmcls - INFO - Epoch [6][1000/5005]\tlr: 1.000e-01, eta: 11:36:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9926\n2023-01-02 14:38:18,694 - mmcls - INFO - Epoch [6][1100/5005]\tlr: 1.000e-01, eta: 11:36:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9675\n2023-01-02 14:38:27,340 - mmcls - INFO - Epoch [6][1200/5005]\tlr: 1.000e-01, eta: 11:36:27, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9929\n2023-01-02 14:38:36,214 - mmcls - INFO - Epoch [6][1300/5005]\tlr: 1.000e-01, eta: 11:36:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9758\n2023-01-02 14:38:45,534 - mmcls - INFO - Epoch [6][1400/5005]\tlr: 1.000e-01, eta: 11:36:20, time: 0.093, data_time: 0.013, memory: 1125, loss: 3.0134\n2023-01-02 14:38:54,356 - mmcls - INFO - Epoch [6][1500/5005]\tlr: 1.000e-01, eta: 11:36:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9883\n2023-01-02 14:39:03,085 - mmcls - INFO - Epoch [6][1600/5005]\tlr: 1.000e-01, eta: 11:36:01, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.0147\n2023-01-02 14:39:11,820 - mmcls - INFO - Epoch [6][1700/5005]\tlr: 1.000e-01, eta: 11:35:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.9864\n2023-01-02 14:39:20,336 - mmcls - INFO - Epoch [6][1800/5005]\tlr: 1.000e-01, eta: 11:35:36, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.0101\n2023-01-02 14:39:29,232 - mmcls - INFO - Epoch [6][1900/5005]\tlr: 1.000e-01, eta: 11:35:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.9994\n2023-01-02 14:39:38,068 - mmcls - INFO - Epoch [6][2000/5005]\tlr: 1.000e-01, eta: 11:35:21, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.9782\n2023-01-02 14:39:46,773 - mmcls - INFO - Epoch [6][2100/5005]\tlr: 1.000e-01, eta: 11:35:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9974\n2023-01-02 14:39:55,237 - mmcls - INFO - Epoch [6][2200/5005]\tlr: 1.000e-01, eta: 11:34:55, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.0088\n2023-01-02 14:40:04,123 - mmcls - INFO - Epoch [6][2300/5005]\tlr: 1.000e-01, eta: 11:34:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9771\n2023-01-02 14:40:12,683 - mmcls - INFO - Epoch [6][2400/5005]\tlr: 1.000e-01, eta: 11:34:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.9843\n2023-01-02 14:40:21,541 - mmcls - INFO - Epoch [6][2500/5005]\tlr: 1.000e-01, eta: 11:34:27, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.0082\n2023-01-02 14:40:30,313 - mmcls - INFO - Epoch [6][2600/5005]\tlr: 1.000e-01, eta: 11:34:17, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9878\n2023-01-02 14:40:38,806 - mmcls - INFO - Epoch [6][2700/5005]\tlr: 1.000e-01, eta: 11:34:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0128\n2023-01-02 14:40:47,432 - mmcls - INFO - Epoch [6][2800/5005]\tlr: 1.000e-01, eta: 11:33:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9815\n2023-01-02 14:40:56,114 - mmcls - INFO - Epoch [6][2900/5005]\tlr: 1.000e-01, eta: 11:33:40, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.9812\n2023-01-02 14:41:04,835 - mmcls - INFO - Epoch [6][3000/5005]\tlr: 1.000e-01, eta: 11:33:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9583\n2023-01-02 14:41:13,376 - mmcls - INFO - Epoch [6][3100/5005]\tlr: 1.000e-01, eta: 11:33:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0103\n2023-01-02 14:41:22,243 - mmcls - INFO - Epoch [6][3200/5005]\tlr: 1.000e-01, eta: 11:33:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.9967\n2023-01-02 14:41:30,865 - mmcls - INFO - Epoch [6][3300/5005]\tlr: 1.000e-01, eta: 11:32:57, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9908\n2023-01-02 14:41:39,666 - mmcls - INFO - Epoch [6][3400/5005]\tlr: 1.000e-01, eta: 11:32:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.9883\n2023-01-02 14:41:48,448 - mmcls - INFO - Epoch [6][3500/5005]\tlr: 1.000e-01, eta: 11:32:39, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.0260\n2023-01-02 14:41:57,253 - mmcls - INFO - Epoch [6][3600/5005]\tlr: 1.000e-01, eta: 11:32:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.9699\n2023-01-02 14:42:06,384 - mmcls - INFO - Epoch [6][3700/5005]\tlr: 1.000e-01, eta: 11:32:26, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.9972\n2023-01-02 14:42:14,798 - mmcls - INFO - Epoch [6][3800/5005]\tlr: 1.000e-01, eta: 11:32:11, time: 0.084, data_time: 0.012, memory: 1125, loss: 2.9861\n2023-01-02 14:42:23,742 - mmcls - INFO - Epoch [6][3900/5005]\tlr: 1.000e-01, eta: 11:32:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9820\n2023-01-02 14:42:32,326 - mmcls - INFO - Epoch [6][4000/5005]\tlr: 1.000e-01, eta: 11:31:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9716\n2023-01-02 14:42:40,843 - mmcls - INFO - Epoch [6][4100/5005]\tlr: 1.000e-01, eta: 11:31:39, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.9680\n2023-01-02 14:42:49,987 - mmcls - INFO - Epoch [6][4200/5005]\tlr: 1.000e-01, eta: 11:31:35, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.9913\n2023-01-02 14:42:58,737 - mmcls - INFO - Epoch [6][4300/5005]\tlr: 1.000e-01, eta: 11:31:26, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.9889\n2023-01-02 14:43:07,313 - mmcls - INFO - Epoch [6][4400/5005]\tlr: 1.000e-01, eta: 11:31:13, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.9891\n2023-01-02 14:43:16,272 - mmcls - INFO - Epoch [6][4500/5005]\tlr: 1.000e-01, eta: 11:31:07, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.9842\n2023-01-02 14:43:25,265 - mmcls - INFO - Epoch [6][4600/5005]\tlr: 1.000e-01, eta: 11:31:01, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.0015\n2023-01-02 14:43:33,826 - mmcls - INFO - Epoch [6][4700/5005]\tlr: 1.000e-01, eta: 11:30:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.9929\n2023-01-02 14:43:43,085 - mmcls - INFO - Epoch [6][4800/5005]\tlr: 1.000e-01, eta: 11:30:47, time: 0.093, data_time: 0.007, memory: 1125, loss: 2.9976\n2023-01-02 14:43:51,745 - mmcls - INFO - Epoch [6][4900/5005]\tlr: 1.000e-01, eta: 11:30:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0232\n2023-01-02 14:44:00,331 - mmcls - INFO - Epoch [6][5000/5005]\tlr: 1.000e-01, eta: 11:30:23, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.9889\n2023-01-02 14:44:23,192 - mmcls - INFO - Epoch(val) [6][196]\taccuracy_top-1: 43.4060, accuracy_top-5: 68.6280\n2023-01-02 14:44:34,192 - mmcls - INFO - Epoch [7][100/5005]\tlr: 1.000e-01, eta: 11:30:41, time: 0.110, data_time: 0.040, memory: 1125, loss: 2.8982\n2023-01-02 14:44:43,084 - mmcls - INFO - Epoch [7][200/5005]\tlr: 1.000e-01, eta: 11:30:34, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9316\n2023-01-02 14:44:51,681 - mmcls - INFO - Epoch [7][300/5005]\tlr: 1.000e-01, eta: 11:30:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9112\n2023-01-02 14:45:00,949 - mmcls - INFO - Epoch [7][400/5005]\tlr: 1.000e-01, eta: 11:30:20, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.9378\n2023-01-02 14:45:09,466 - mmcls - INFO - Epoch [7][500/5005]\tlr: 1.000e-01, eta: 11:30:06, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.9281\n2023-01-02 14:45:18,025 - mmcls - INFO - Epoch [7][600/5005]\tlr: 1.000e-01, eta: 11:29:54, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.9406\n2023-01-02 14:45:26,799 - mmcls - INFO - Epoch [7][700/5005]\tlr: 1.000e-01, eta: 11:29:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9263\n2023-01-02 14:45:35,429 - mmcls - INFO - Epoch [7][800/5005]\tlr: 1.000e-01, eta: 11:29:33, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.9317\n2023-01-02 14:45:44,809 - mmcls - INFO - Epoch [7][900/5005]\tlr: 1.000e-01, eta: 11:29:32, time: 0.094, data_time: 0.022, memory: 1125, loss: 2.9477\n2023-01-02 14:45:53,401 - mmcls - INFO - Epoch [7][1000/5005]\tlr: 1.000e-01, eta: 11:29:20, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.9676\n2023-01-02 14:46:02,158 - mmcls - INFO - Epoch [7][1100/5005]\tlr: 1.000e-01, eta: 11:29:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9621\n2023-01-02 14:46:10,796 - mmcls - INFO - Epoch [7][1200/5005]\tlr: 1.000e-01, eta: 11:28:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9785\n2023-01-02 14:46:19,517 - mmcls - INFO - Epoch [7][1300/5005]\tlr: 1.000e-01, eta: 11:28:49, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9845\n2023-01-02 14:46:28,232 - mmcls - INFO - Epoch [7][1400/5005]\tlr: 1.000e-01, eta: 11:28:39, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.9535\n2023-01-02 14:46:36,804 - mmcls - INFO - Epoch [7][1500/5005]\tlr: 1.000e-01, eta: 11:28:27, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9542\n2023-01-02 14:46:45,791 - mmcls - INFO - Epoch [7][1600/5005]\tlr: 1.000e-01, eta: 11:28:20, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.9665\n2023-01-02 14:46:54,355 - mmcls - INFO - Epoch [7][1700/5005]\tlr: 1.000e-01, eta: 11:28:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9690\n2023-01-02 14:47:03,022 - mmcls - INFO - Epoch [7][1800/5005]\tlr: 1.000e-01, eta: 11:27:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.9735\n2023-01-02 14:47:11,617 - mmcls - INFO - Epoch [7][1900/5005]\tlr: 1.000e-01, eta: 11:27:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9356\n2023-01-02 14:47:20,389 - mmcls - INFO - Epoch [7][2000/5005]\tlr: 1.000e-01, eta: 11:27:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9586\n2023-01-02 14:47:29,160 - mmcls - INFO - Epoch [7][2100/5005]\tlr: 1.000e-01, eta: 11:27:27, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.9668\n2023-01-02 14:47:38,120 - mmcls - INFO - Epoch [7][2200/5005]\tlr: 1.000e-01, eta: 11:27:20, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9429\n2023-01-02 14:47:46,687 - mmcls - INFO - Epoch [7][2300/5005]\tlr: 1.000e-01, eta: 11:27:08, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.9575\n2023-01-02 14:47:55,281 - mmcls - INFO - Epoch [7][2400/5005]\tlr: 1.000e-01, eta: 11:26:56, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.9677\n2023-01-02 14:48:04,214 - mmcls - INFO - Epoch [7][2500/5005]\tlr: 1.000e-01, eta: 11:26:49, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9668\n2023-01-02 14:48:12,908 - mmcls - INFO - Epoch [7][2600/5005]\tlr: 1.000e-01, eta: 11:26:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9425\n2023-01-02 14:48:21,666 - mmcls - INFO - Epoch [7][2700/5005]\tlr: 1.000e-01, eta: 11:26:29, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.9252\n2023-01-02 14:48:30,600 - mmcls - INFO - Epoch [7][2800/5005]\tlr: 1.000e-01, eta: 11:26:22, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.9272\n2023-01-02 14:48:38,896 - mmcls - INFO - Epoch [7][2900/5005]\tlr: 1.000e-01, eta: 11:26:06, time: 0.083, data_time: 0.034, memory: 1125, loss: 2.9466\n2023-01-02 14:48:47,740 - mmcls - INFO - Epoch [7][3000/5005]\tlr: 1.000e-01, eta: 11:25:57, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.9503\n2023-01-02 14:48:56,433 - mmcls - INFO - Epoch [7][3100/5005]\tlr: 1.000e-01, eta: 11:25:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9654\n2023-01-02 14:49:05,060 - mmcls - INFO - Epoch [7][3200/5005]\tlr: 1.000e-01, eta: 11:25:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9160\n2023-01-02 14:49:13,845 - mmcls - INFO - Epoch [7][3300/5005]\tlr: 1.000e-01, eta: 11:25:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9491\n2023-01-02 14:49:22,741 - mmcls - INFO - Epoch [7][3400/5005]\tlr: 1.000e-01, eta: 11:25:19, time: 0.089, data_time: 0.037, memory: 1125, loss: 2.9285\n2023-01-02 14:49:31,409 - mmcls - INFO - Epoch [7][3500/5005]\tlr: 1.000e-01, eta: 11:25:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9407\n2023-01-02 14:49:40,112 - mmcls - INFO - Epoch [7][3600/5005]\tlr: 1.000e-01, eta: 11:24:58, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.9414\n2023-01-02 14:49:49,009 - mmcls - INFO - Epoch [7][3700/5005]\tlr: 1.000e-01, eta: 11:24:51, time: 0.089, data_time: 0.036, memory: 1125, loss: 2.9300\n2023-01-02 14:49:57,705 - mmcls - INFO - Epoch [7][3800/5005]\tlr: 1.000e-01, eta: 11:24:41, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9396\n2023-01-02 14:50:06,389 - mmcls - INFO - Epoch [7][3900/5005]\tlr: 1.000e-01, eta: 11:24:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.9478\n2023-01-02 14:50:14,943 - mmcls - INFO - Epoch [7][4000/5005]\tlr: 1.000e-01, eta: 11:24:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9620\n2023-01-02 14:50:23,668 - mmcls - INFO - Epoch [7][4100/5005]\tlr: 1.000e-01, eta: 11:24:08, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9389\n2023-01-02 14:50:32,818 - mmcls - INFO - Epoch [7][4200/5005]\tlr: 1.000e-01, eta: 11:24:04, time: 0.092, data_time: 0.014, memory: 1125, loss: 2.9474\n2023-01-02 14:50:41,351 - mmcls - INFO - Epoch [7][4300/5005]\tlr: 1.000e-01, eta: 11:23:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.9531\n2023-01-02 14:50:50,224 - mmcls - INFO - Epoch [7][4400/5005]\tlr: 1.000e-01, eta: 11:23:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9252\n2023-01-02 14:50:58,831 - mmcls - INFO - Epoch [7][4500/5005]\tlr: 1.000e-01, eta: 11:23:32, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9607\n2023-01-02 14:51:07,428 - mmcls - INFO - Epoch [7][4600/5005]\tlr: 1.000e-01, eta: 11:23:21, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.9620\n2023-01-02 14:51:16,083 - mmcls - INFO - Epoch [7][4700/5005]\tlr: 1.000e-01, eta: 11:23:10, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.9383\n2023-01-02 14:51:25,077 - mmcls - INFO - Epoch [7][4800/5005]\tlr: 1.000e-01, eta: 11:23:04, time: 0.090, data_time: 0.032, memory: 1125, loss: 2.9515\n2023-01-02 14:51:34,040 - mmcls - INFO - Epoch [7][4900/5005]\tlr: 1.000e-01, eta: 11:22:57, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.9258\n2023-01-02 14:51:42,714 - mmcls - INFO - Epoch [7][5000/5005]\tlr: 1.000e-01, eta: 11:22:46, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.9416\n2023-01-02 14:52:05,523 - mmcls - INFO - Epoch(val) [7][196]\taccuracy_top-1: 44.4660, accuracy_top-5: 69.6020\n2023-01-02 14:52:16,662 - mmcls - INFO - Epoch [8][100/5005]\tlr: 1.000e-01, eta: 11:23:02, time: 0.111, data_time: 0.051, memory: 1125, loss: 2.8917\n2023-01-02 14:52:25,346 - mmcls - INFO - Epoch [8][200/5005]\tlr: 1.000e-01, eta: 11:22:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.8463\n2023-01-02 14:52:33,993 - mmcls - INFO - Epoch [8][300/5005]\tlr: 1.000e-01, eta: 11:22:41, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8774\n2023-01-02 14:52:43,117 - mmcls - INFO - Epoch [8][400/5005]\tlr: 1.000e-01, eta: 11:22:36, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.8875\n2023-01-02 14:52:51,900 - mmcls - INFO - Epoch [8][500/5005]\tlr: 1.000e-01, eta: 11:22:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9092\n2023-01-02 14:53:00,809 - mmcls - INFO - Epoch [8][600/5005]\tlr: 1.000e-01, eta: 11:22:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9100\n2023-01-02 14:53:09,316 - mmcls - INFO - Epoch [8][700/5005]\tlr: 1.000e-01, eta: 11:22:07, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.9134\n2023-01-02 14:53:17,967 - mmcls - INFO - Epoch [8][800/5005]\tlr: 1.000e-01, eta: 11:21:56, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.9127\n2023-01-02 14:53:26,750 - mmcls - INFO - Epoch [8][900/5005]\tlr: 1.000e-01, eta: 11:21:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9212\n2023-01-02 14:53:35,393 - mmcls - INFO - Epoch [8][1000/5005]\tlr: 1.000e-01, eta: 11:21:36, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.9212\n2023-01-02 14:53:44,106 - mmcls - INFO - Epoch [8][1100/5005]\tlr: 1.000e-01, eta: 11:21:26, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.9003\n2023-01-02 14:53:52,961 - mmcls - INFO - Epoch [8][1200/5005]\tlr: 1.000e-01, eta: 11:21:18, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.9114\n2023-01-02 14:54:02,026 - mmcls - INFO - Epoch [8][1300/5005]\tlr: 1.000e-01, eta: 11:21:12, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.9166\n2023-01-02 14:54:10,681 - mmcls - INFO - Epoch [8][1400/5005]\tlr: 1.000e-01, eta: 11:21:01, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9165\n2023-01-02 14:54:19,444 - mmcls - INFO - Epoch [8][1500/5005]\tlr: 1.000e-01, eta: 11:20:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.9322\n2023-01-02 14:54:28,079 - mmcls - INFO - Epoch [8][1600/5005]\tlr: 1.000e-01, eta: 11:20:41, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.9327\n2023-01-02 14:54:36,990 - mmcls - INFO - Epoch [8][1700/5005]\tlr: 1.000e-01, eta: 11:20:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.9079\n2023-01-02 14:54:45,617 - mmcls - INFO - Epoch [8][1800/5005]\tlr: 1.000e-01, eta: 11:20:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9249\n2023-01-02 14:54:54,446 - mmcls - INFO - Epoch [8][1900/5005]\tlr: 1.000e-01, eta: 11:20:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9142\n2023-01-02 14:55:03,171 - mmcls - INFO - Epoch [8][2000/5005]\tlr: 1.000e-01, eta: 11:20:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9108\n2023-01-02 14:55:11,889 - mmcls - INFO - Epoch [8][2100/5005]\tlr: 1.000e-01, eta: 11:19:54, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.9250\n2023-01-02 14:55:20,711 - mmcls - INFO - Epoch [8][2200/5005]\tlr: 1.000e-01, eta: 11:19:46, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9275\n2023-01-02 14:55:29,306 - mmcls - INFO - Epoch [8][2300/5005]\tlr: 1.000e-01, eta: 11:19:34, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9232\n2023-01-02 14:55:38,160 - mmcls - INFO - Epoch [8][2400/5005]\tlr: 1.000e-01, eta: 11:19:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9106\n2023-01-02 14:55:46,737 - mmcls - INFO - Epoch [8][2500/5005]\tlr: 1.000e-01, eta: 11:19:15, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.9214\n2023-01-02 14:55:55,300 - mmcls - INFO - Epoch [8][2600/5005]\tlr: 1.000e-01, eta: 11:19:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9100\n2023-01-02 14:56:04,125 - mmcls - INFO - Epoch [8][2700/5005]\tlr: 1.000e-01, eta: 11:18:54, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9653\n2023-01-02 14:56:12,975 - mmcls - INFO - Epoch [8][2800/5005]\tlr: 1.000e-01, eta: 11:18:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9345\n2023-01-02 14:56:21,880 - mmcls - INFO - Epoch [8][2900/5005]\tlr: 1.000e-01, eta: 11:18:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9417\n2023-01-02 14:56:30,520 - mmcls - INFO - Epoch [8][3000/5005]\tlr: 1.000e-01, eta: 11:18:28, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9047\n2023-01-02 14:56:39,379 - mmcls - INFO - Epoch [8][3100/5005]\tlr: 1.000e-01, eta: 11:18:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9306\n2023-01-02 14:56:48,271 - mmcls - INFO - Epoch [8][3200/5005]\tlr: 1.000e-01, eta: 11:18:12, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.9522\n2023-01-02 14:56:57,144 - mmcls - INFO - Epoch [8][3300/5005]\tlr: 1.000e-01, eta: 11:18:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.9391\n2023-01-02 14:57:05,685 - mmcls - INFO - Epoch [8][3400/5005]\tlr: 1.000e-01, eta: 11:17:52, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.9543\n2023-01-02 14:57:14,522 - mmcls - INFO - Epoch [8][3500/5005]\tlr: 1.000e-01, eta: 11:17:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8975\n2023-01-02 14:57:23,149 - mmcls - INFO - Epoch [8][3600/5005]\tlr: 1.000e-01, eta: 11:17:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9194\n2023-01-02 14:57:31,978 - mmcls - INFO - Epoch [8][3700/5005]\tlr: 1.000e-01, eta: 11:17:24, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9088\n2023-01-02 14:57:40,841 - mmcls - INFO - Epoch [8][3800/5005]\tlr: 1.000e-01, eta: 11:17:16, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9259\n2023-01-02 14:57:49,580 - mmcls - INFO - Epoch [8][3900/5005]\tlr: 1.000e-01, eta: 11:17:06, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.9184\n2023-01-02 14:57:58,559 - mmcls - INFO - Epoch [8][4000/5005]\tlr: 1.000e-01, eta: 11:17:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8786\n2023-01-02 14:58:07,611 - mmcls - INFO - Epoch [8][4100/5005]\tlr: 1.000e-01, eta: 11:16:54, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.9180\n2023-01-02 14:58:16,169 - mmcls - INFO - Epoch [8][4200/5005]\tlr: 1.000e-01, eta: 11:16:42, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.9164\n2023-01-02 14:58:24,786 - mmcls - INFO - Epoch [8][4300/5005]\tlr: 1.000e-01, eta: 11:16:31, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.9439\n2023-01-02 14:58:33,593 - mmcls - INFO - Epoch [8][4400/5005]\tlr: 1.000e-01, eta: 11:16:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.9145\n2023-01-02 14:58:41,983 - mmcls - INFO - Epoch [8][4500/5005]\tlr: 1.000e-01, eta: 11:16:09, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.9169\n2023-01-02 14:58:50,663 - mmcls - INFO - Epoch [8][4600/5005]\tlr: 1.000e-01, eta: 11:15:59, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.9205\n2023-01-02 14:58:59,629 - mmcls - INFO - Epoch [8][4700/5005]\tlr: 1.000e-01, eta: 11:15:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9356\n2023-01-02 14:59:08,543 - mmcls - INFO - Epoch [8][4800/5005]\tlr: 1.000e-01, eta: 11:15:44, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.9031\n2023-01-02 14:59:17,381 - mmcls - INFO - Epoch [8][4900/5005]\tlr: 1.000e-01, eta: 11:15:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9374\n2023-01-02 14:59:26,237 - mmcls - INFO - Epoch [8][5000/5005]\tlr: 1.000e-01, eta: 11:15:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9000\n2023-01-02 14:59:48,926 - mmcls - INFO - Epoch(val) [8][196]\taccuracy_top-1: 44.0820, accuracy_top-5: 68.5000\n2023-01-02 14:59:59,667 - mmcls - INFO - Epoch [9][100/5005]\tlr: 1.000e-01, eta: 11:15:35, time: 0.107, data_time: 0.044, memory: 1125, loss: 2.8681\n2023-01-02 15:00:08,351 - mmcls - INFO - Epoch [9][200/5005]\tlr: 1.000e-01, eta: 11:15:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8638\n2023-01-02 15:00:17,061 - mmcls - INFO - Epoch [9][300/5005]\tlr: 1.000e-01, eta: 11:15:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8543\n2023-01-02 15:00:25,967 - mmcls - INFO - Epoch [9][400/5005]\tlr: 1.000e-01, eta: 11:15:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8557\n2023-01-02 15:00:34,510 - mmcls - INFO - Epoch [9][500/5005]\tlr: 1.000e-01, eta: 11:14:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8779\n2023-01-02 15:00:43,091 - mmcls - INFO - Epoch [9][600/5005]\tlr: 1.000e-01, eta: 11:14:44, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8857\n2023-01-02 15:00:51,631 - mmcls - INFO - Epoch [9][700/5005]\tlr: 1.000e-01, eta: 11:14:33, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.8659\n2023-01-02 15:01:00,329 - mmcls - INFO - Epoch [9][800/5005]\tlr: 1.000e-01, eta: 11:14:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8702\n2023-01-02 15:01:09,219 - mmcls - INFO - Epoch [9][900/5005]\tlr: 1.000e-01, eta: 11:14:15, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.8643\n2023-01-02 15:01:17,727 - mmcls - INFO - Epoch [9][1000/5005]\tlr: 1.000e-01, eta: 11:14:03, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.8700\n2023-01-02 15:01:26,378 - mmcls - INFO - Epoch [9][1100/5005]\tlr: 1.000e-01, eta: 11:13:52, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8889\n2023-01-02 15:01:34,981 - mmcls - INFO - Epoch [9][1200/5005]\tlr: 1.000e-01, eta: 11:13:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9077\n2023-01-02 15:01:43,768 - mmcls - INFO - Epoch [9][1300/5005]\tlr: 1.000e-01, eta: 11:13:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8749\n2023-01-02 15:01:52,893 - mmcls - INFO - Epoch [9][1400/5005]\tlr: 1.000e-01, eta: 11:13:27, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.9098\n2023-01-02 15:02:01,638 - mmcls - INFO - Epoch [9][1500/5005]\tlr: 1.000e-01, eta: 11:13:17, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8912\n2023-01-02 15:02:10,360 - mmcls - INFO - Epoch [9][1600/5005]\tlr: 1.000e-01, eta: 11:13:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9100\n2023-01-02 15:02:19,007 - mmcls - INFO - Epoch [9][1700/5005]\tlr: 1.000e-01, eta: 11:12:57, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8978\n2023-01-02 15:02:28,096 - mmcls - INFO - Epoch [9][1800/5005]\tlr: 1.000e-01, eta: 11:12:52, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.9194\n2023-01-02 15:02:36,938 - mmcls - INFO - Epoch [9][1900/5005]\tlr: 1.000e-01, eta: 11:12:43, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8765\n2023-01-02 15:02:45,803 - mmcls - INFO - Epoch [9][2000/5005]\tlr: 1.000e-01, eta: 11:12:35, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.9110\n2023-01-02 15:02:54,377 - mmcls - INFO - Epoch [9][2100/5005]\tlr: 1.000e-01, eta: 11:12:24, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8996\n2023-01-02 15:03:03,050 - mmcls - INFO - Epoch [9][2200/5005]\tlr: 1.000e-01, eta: 11:12:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8832\n2023-01-02 15:03:11,803 - mmcls - INFO - Epoch [9][2300/5005]\tlr: 1.000e-01, eta: 11:12:04, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9103\n2023-01-02 15:03:20,509 - mmcls - INFO - Epoch [9][2400/5005]\tlr: 1.000e-01, eta: 11:11:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8904\n2023-01-02 15:03:29,006 - mmcls - INFO - Epoch [9][2500/5005]\tlr: 1.000e-01, eta: 11:11:42, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.9118\n2023-01-02 15:03:37,679 - mmcls - INFO - Epoch [9][2600/5005]\tlr: 1.000e-01, eta: 11:11:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9047\n2023-01-02 15:03:46,680 - mmcls - INFO - Epoch [9][2700/5005]\tlr: 1.000e-01, eta: 11:11:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8788\n2023-01-02 15:03:55,559 - mmcls - INFO - Epoch [9][2800/5005]\tlr: 1.000e-01, eta: 11:11:17, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.9125\n2023-01-02 15:04:04,279 - mmcls - INFO - Epoch [9][2900/5005]\tlr: 1.000e-01, eta: 11:11:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9118\n2023-01-02 15:04:13,080 - mmcls - INFO - Epoch [9][3000/5005]\tlr: 1.000e-01, eta: 11:10:59, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.9252\n2023-01-02 15:04:22,098 - mmcls - INFO - Epoch [9][3100/5005]\tlr: 1.000e-01, eta: 11:10:52, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8873\n2023-01-02 15:04:30,772 - mmcls - INFO - Epoch [9][3200/5005]\tlr: 1.000e-01, eta: 11:10:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8843\n2023-01-02 15:04:39,580 - mmcls - INFO - Epoch [9][3300/5005]\tlr: 1.000e-01, eta: 11:10:33, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.9042\n2023-01-02 15:04:48,411 - mmcls - INFO - Epoch [9][3400/5005]\tlr: 1.000e-01, eta: 11:10:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9070\n2023-01-02 15:04:56,978 - mmcls - INFO - Epoch [9][3500/5005]\tlr: 1.000e-01, eta: 11:10:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9071\n2023-01-02 15:05:05,616 - mmcls - INFO - Epoch [9][3600/5005]\tlr: 1.000e-01, eta: 11:10:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9190\n2023-01-02 15:05:14,395 - mmcls - INFO - Epoch [9][3700/5005]\tlr: 1.000e-01, eta: 11:09:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9216\n2023-01-02 15:05:23,294 - mmcls - INFO - Epoch [9][3800/5005]\tlr: 1.000e-01, eta: 11:09:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8925\n2023-01-02 15:05:32,319 - mmcls - INFO - Epoch [9][3900/5005]\tlr: 1.000e-01, eta: 11:09:40, time: 0.090, data_time: 0.000, memory: 1125, loss: 2.8981\n2023-01-02 15:05:40,956 - mmcls - INFO - Epoch [9][4000/5005]\tlr: 1.000e-01, eta: 11:09:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9000\n2023-01-02 15:05:49,595 - mmcls - INFO - Epoch [9][4100/5005]\tlr: 1.000e-01, eta: 11:09:19, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.9096\n2023-01-02 15:05:58,227 - mmcls - INFO - Epoch [9][4200/5005]\tlr: 1.000e-01, eta: 11:09:08, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.9059\n2023-01-02 15:06:07,174 - mmcls - INFO - Epoch [9][4300/5005]\tlr: 1.000e-01, eta: 11:09:01, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8552\n2023-01-02 15:06:15,645 - mmcls - INFO - Epoch [9][4400/5005]\tlr: 1.000e-01, eta: 11:08:49, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8857\n2023-01-02 15:06:24,452 - mmcls - INFO - Epoch [9][4500/5005]\tlr: 1.000e-01, eta: 11:08:40, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.8717\n2023-01-02 15:06:33,432 - mmcls - INFO - Epoch [9][4600/5005]\tlr: 1.000e-01, eta: 11:08:33, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8903\n2023-01-02 15:06:42,421 - mmcls - INFO - Epoch [9][4700/5005]\tlr: 1.000e-01, eta: 11:08:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8963\n2023-01-02 15:06:51,712 - mmcls - INFO - Epoch [9][4800/5005]\tlr: 1.000e-01, eta: 11:08:23, time: 0.093, data_time: 0.002, memory: 1125, loss: 2.9031\n2023-01-02 15:07:00,495 - mmcls - INFO - Epoch [9][4900/5005]\tlr: 1.000e-01, eta: 11:08:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.9023\n2023-01-02 15:07:09,118 - mmcls - INFO - Epoch [9][5000/5005]\tlr: 1.000e-01, eta: 11:08:03, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.9074\n2023-01-02 15:07:31,928 - mmcls - INFO - Epoch(val) [9][196]\taccuracy_top-1: 44.4040, accuracy_top-5: 69.1900\n2023-01-02 15:07:42,662 - mmcls - INFO - Epoch [10][100/5005]\tlr: 1.000e-01, eta: 11:08:09, time: 0.107, data_time: 0.042, memory: 1125, loss: 2.8080\n2023-01-02 15:07:51,454 - mmcls - INFO - Epoch [10][200/5005]\tlr: 1.000e-01, eta: 11:08:00, time: 0.088, data_time: 0.042, memory: 1125, loss: 2.8723\n2023-01-02 15:08:00,312 - mmcls - INFO - Epoch [10][300/5005]\tlr: 1.000e-01, eta: 11:07:51, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.8538\n2023-01-02 15:08:08,926 - mmcls - INFO - Epoch [10][400/5005]\tlr: 1.000e-01, eta: 11:07:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8423\n2023-01-02 15:08:17,703 - mmcls - INFO - Epoch [10][500/5005]\tlr: 1.000e-01, eta: 11:07:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8541\n2023-01-02 15:08:26,429 - mmcls - INFO - Epoch [10][600/5005]\tlr: 1.000e-01, eta: 11:07:22, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8341\n2023-01-02 15:08:35,063 - mmcls - INFO - Epoch [10][700/5005]\tlr: 1.000e-01, eta: 11:07:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8561\n2023-01-02 15:08:44,166 - mmcls - INFO - Epoch [10][800/5005]\tlr: 1.000e-01, eta: 11:07:06, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.8593\n2023-01-02 15:08:52,808 - mmcls - INFO - Epoch [10][900/5005]\tlr: 1.000e-01, eta: 11:06:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8786\n2023-01-02 15:09:01,494 - mmcls - INFO - Epoch [10][1000/5005]\tlr: 1.000e-01, eta: 11:06:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8873\n2023-01-02 15:09:10,260 - mmcls - INFO - Epoch [10][1100/5005]\tlr: 1.000e-01, eta: 11:06:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8601\n2023-01-02 15:09:18,790 - mmcls - INFO - Epoch [10][1200/5005]\tlr: 1.000e-01, eta: 11:06:25, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.8584\n2023-01-02 15:09:27,772 - mmcls - INFO - Epoch [10][1300/5005]\tlr: 1.000e-01, eta: 11:06:18, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8934\n2023-01-02 15:09:36,557 - mmcls - INFO - Epoch [10][1400/5005]\tlr: 1.000e-01, eta: 11:06:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8705\n2023-01-02 15:09:45,413 - mmcls - INFO - Epoch [10][1500/5005]\tlr: 1.000e-01, eta: 11:06:00, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8633\n2023-01-02 15:09:54,364 - mmcls - INFO - Epoch [10][1600/5005]\tlr: 1.000e-01, eta: 11:05:53, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8748\n2023-01-02 15:10:03,246 - mmcls - INFO - Epoch [10][1700/5005]\tlr: 1.000e-01, eta: 11:05:45, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8849\n2023-01-02 15:10:12,302 - mmcls - INFO - Epoch [10][1800/5005]\tlr: 1.000e-01, eta: 11:05:39, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.8873\n2023-01-02 15:10:21,033 - mmcls - INFO - Epoch [10][1900/5005]\tlr: 1.000e-01, eta: 11:05:29, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8961\n2023-01-02 15:10:29,741 - mmcls - INFO - Epoch [10][2000/5005]\tlr: 1.000e-01, eta: 11:05:19, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.9094\n2023-01-02 15:10:38,735 - mmcls - INFO - Epoch [10][2100/5005]\tlr: 1.000e-01, eta: 11:05:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8846\n2023-01-02 15:10:47,373 - mmcls - INFO - Epoch [10][2200/5005]\tlr: 1.000e-01, eta: 11:05:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8684\n2023-01-02 15:10:56,009 - mmcls - INFO - Epoch [10][2300/5005]\tlr: 1.000e-01, eta: 11:04:51, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.9021\n2023-01-02 15:11:04,641 - mmcls - INFO - Epoch [10][2400/5005]\tlr: 1.000e-01, eta: 11:04:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8475\n2023-01-02 15:11:13,332 - mmcls - INFO - Epoch [10][2500/5005]\tlr: 1.000e-01, eta: 11:04:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9088\n2023-01-02 15:11:22,341 - mmcls - INFO - Epoch [10][2600/5005]\tlr: 1.000e-01, eta: 11:04:24, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8661\n2023-01-02 15:11:31,072 - mmcls - INFO - Epoch [10][2700/5005]\tlr: 1.000e-01, eta: 11:04:15, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8996\n2023-01-02 15:11:39,914 - mmcls - INFO - Epoch [10][2800/5005]\tlr: 1.000e-01, eta: 11:04:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8829\n2023-01-02 15:11:48,460 - mmcls - INFO - Epoch [10][2900/5005]\tlr: 1.000e-01, eta: 11:03:55, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9022\n2023-01-02 15:11:56,975 - mmcls - INFO - Epoch [10][3000/5005]\tlr: 1.000e-01, eta: 11:03:44, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8918\n2023-01-02 15:12:06,135 - mmcls - INFO - Epoch [10][3100/5005]\tlr: 1.000e-01, eta: 11:03:38, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.8734\n2023-01-02 15:12:14,830 - mmcls - INFO - Epoch [10][3200/5005]\tlr: 1.000e-01, eta: 11:03:28, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8930\n2023-01-02 15:12:23,684 - mmcls - INFO - Epoch [10][3300/5005]\tlr: 1.000e-01, eta: 11:03:20, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.8959\n2023-01-02 15:12:32,276 - mmcls - INFO - Epoch [10][3400/5005]\tlr: 1.000e-01, eta: 11:03:09, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9245\n2023-01-02 15:12:40,788 - mmcls - INFO - Epoch [10][3500/5005]\tlr: 1.000e-01, eta: 11:02:58, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8817\n2023-01-02 15:12:49,495 - mmcls - INFO - Epoch [10][3600/5005]\tlr: 1.000e-01, eta: 11:02:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8829\n2023-01-02 15:12:58,239 - mmcls - INFO - Epoch [10][3700/5005]\tlr: 1.000e-01, eta: 11:02:39, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8802\n2023-01-02 15:13:06,847 - mmcls - INFO - Epoch [10][3800/5005]\tlr: 1.000e-01, eta: 11:02:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8921\n2023-01-02 15:13:15,454 - mmcls - INFO - Epoch [10][3900/5005]\tlr: 1.000e-01, eta: 11:02:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8959\n2023-01-02 15:13:24,172 - mmcls - INFO - Epoch [10][4000/5005]\tlr: 1.000e-01, eta: 11:02:08, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8567\n2023-01-02 15:13:32,865 - mmcls - INFO - Epoch [10][4100/5005]\tlr: 1.000e-01, eta: 11:01:58, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.8804\n2023-01-02 15:13:41,530 - mmcls - INFO - Epoch [10][4200/5005]\tlr: 1.000e-01, eta: 11:01:48, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8793\n2023-01-02 15:13:50,270 - mmcls - INFO - Epoch [10][4300/5005]\tlr: 1.000e-01, eta: 11:01:39, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8873\n2023-01-02 15:13:58,956 - mmcls - INFO - Epoch [10][4400/5005]\tlr: 1.000e-01, eta: 11:01:29, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.8777\n2023-01-02 15:14:07,605 - mmcls - INFO - Epoch [10][4500/5005]\tlr: 1.000e-01, eta: 11:01:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8554\n2023-01-02 15:14:16,631 - mmcls - INFO - Epoch [10][4600/5005]\tlr: 1.000e-01, eta: 11:01:12, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8894\n2023-01-02 15:14:25,241 - mmcls - INFO - Epoch [10][4700/5005]\tlr: 1.000e-01, eta: 11:01:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8480\n2023-01-02 15:14:33,862 - mmcls - INFO - Epoch [10][4800/5005]\tlr: 1.000e-01, eta: 11:00:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8714\n2023-01-02 15:14:42,897 - mmcls - INFO - Epoch [10][4900/5005]\tlr: 1.000e-01, eta: 11:00:44, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8829\n2023-01-02 15:14:51,843 - mmcls - INFO - Epoch [10][5000/5005]\tlr: 1.000e-01, eta: 11:00:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8873\n2023-01-02 15:15:14,621 - mmcls - INFO - Epoch(val) [10][196]\taccuracy_top-1: 44.3000, accuracy_top-5: 68.5680\n2023-01-02 15:15:25,431 - mmcls - INFO - Epoch [11][100/5005]\tlr: 1.000e-01, eta: 11:00:42, time: 0.108, data_time: 0.043, memory: 1125, loss: 2.8049\n2023-01-02 15:15:33,864 - mmcls - INFO - Epoch [11][200/5005]\tlr: 1.000e-01, eta: 11:00:29, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.8014\n2023-01-02 15:15:42,437 - mmcls - INFO - Epoch [11][300/5005]\tlr: 1.000e-01, eta: 11:00:19, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7949\n2023-01-02 15:15:51,029 - mmcls - INFO - Epoch [11][400/5005]\tlr: 1.000e-01, eta: 11:00:08, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.8783\n2023-01-02 15:16:00,101 - mmcls - INFO - Epoch [11][500/5005]\tlr: 1.000e-01, eta: 11:00:02, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.8452\n2023-01-02 15:16:08,707 - mmcls - INFO - Epoch [11][600/5005]\tlr: 1.000e-01, eta: 10:59:51, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.8330\n2023-01-02 15:16:17,595 - mmcls - INFO - Epoch [11][700/5005]\tlr: 1.000e-01, eta: 10:59:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8399\n2023-01-02 15:16:26,323 - mmcls - INFO - Epoch [11][800/5005]\tlr: 1.000e-01, eta: 10:59:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8346\n2023-01-02 15:16:35,111 - mmcls - INFO - Epoch [11][900/5005]\tlr: 1.000e-01, eta: 10:59:25, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.8341\n2023-01-02 15:16:43,631 - mmcls - INFO - Epoch [11][1000/5005]\tlr: 1.000e-01, eta: 10:59:13, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8394\n2023-01-02 15:16:52,267 - mmcls - INFO - Epoch [11][1100/5005]\tlr: 1.000e-01, eta: 10:59:03, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8384\n2023-01-02 15:17:00,974 - mmcls - INFO - Epoch [11][1200/5005]\tlr: 1.000e-01, eta: 10:58:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8608\n2023-01-02 15:17:10,007 - mmcls - INFO - Epoch [11][1300/5005]\tlr: 1.000e-01, eta: 10:58:47, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8850\n2023-01-02 15:17:18,655 - mmcls - INFO - Epoch [11][1400/5005]\tlr: 1.000e-01, eta: 10:58:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8755\n2023-01-02 15:17:27,383 - mmcls - INFO - Epoch [11][1500/5005]\tlr: 1.000e-01, eta: 10:58:27, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.8608\n2023-01-02 15:17:36,265 - mmcls - INFO - Epoch [11][1600/5005]\tlr: 1.000e-01, eta: 10:58:19, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.8735\n2023-01-02 15:17:45,007 - mmcls - INFO - Epoch [11][1700/5005]\tlr: 1.000e-01, eta: 10:58:10, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8749\n2023-01-02 15:17:53,883 - mmcls - INFO - Epoch [11][1800/5005]\tlr: 1.000e-01, eta: 10:58:01, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.8345\n2023-01-02 15:18:02,650 - mmcls - INFO - Epoch [11][1900/5005]\tlr: 1.000e-01, eta: 10:57:52, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8835\n2023-01-02 15:18:11,289 - mmcls - INFO - Epoch [11][2000/5005]\tlr: 1.000e-01, eta: 10:57:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8670\n2023-01-02 15:18:19,832 - mmcls - INFO - Epoch [11][2100/5005]\tlr: 1.000e-01, eta: 10:57:31, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.8625\n2023-01-02 15:18:28,562 - mmcls - INFO - Epoch [11][2200/5005]\tlr: 1.000e-01, eta: 10:57:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8745\n2023-01-02 15:18:37,403 - mmcls - INFO - Epoch [11][2300/5005]\tlr: 1.000e-01, eta: 10:57:13, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8973\n2023-01-02 15:18:46,133 - mmcls - INFO - Epoch [11][2400/5005]\tlr: 1.000e-01, eta: 10:57:04, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.8745\n2023-01-02 15:18:54,772 - mmcls - INFO - Epoch [11][2500/5005]\tlr: 1.000e-01, eta: 10:56:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8773\n2023-01-02 15:19:03,947 - mmcls - INFO - Epoch [11][2600/5005]\tlr: 1.000e-01, eta: 10:56:48, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.8614\n2023-01-02 15:19:12,420 - mmcls - INFO - Epoch [11][2700/5005]\tlr: 1.000e-01, eta: 10:56:37, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.8640\n2023-01-02 15:19:21,084 - mmcls - INFO - Epoch [11][2800/5005]\tlr: 1.000e-01, eta: 10:56:27, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8487\n2023-01-02 15:19:30,073 - mmcls - INFO - Epoch [11][2900/5005]\tlr: 1.000e-01, eta: 10:56:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8559\n2023-01-02 15:19:38,610 - mmcls - INFO - Epoch [11][3000/5005]\tlr: 1.000e-01, eta: 10:56:08, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.8854\n2023-01-02 15:19:47,519 - mmcls - INFO - Epoch [11][3100/5005]\tlr: 1.000e-01, eta: 10:56:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8459\n2023-01-02 15:19:56,279 - mmcls - INFO - Epoch [11][3200/5005]\tlr: 1.000e-01, eta: 10:55:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.8410\n2023-01-02 15:20:04,939 - mmcls - INFO - Epoch [11][3300/5005]\tlr: 1.000e-01, eta: 10:55:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8938\n2023-01-02 15:20:13,897 - mmcls - INFO - Epoch [11][3400/5005]\tlr: 1.000e-01, eta: 10:55:34, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.8543\n2023-01-02 15:20:22,464 - mmcls - INFO - Epoch [11][3500/5005]\tlr: 1.000e-01, eta: 10:55:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8942\n2023-01-02 15:20:31,275 - mmcls - INFO - Epoch [11][3600/5005]\tlr: 1.000e-01, eta: 10:55:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8859\n2023-01-02 15:20:39,972 - mmcls - INFO - Epoch [11][3700/5005]\tlr: 1.000e-01, eta: 10:55:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8594\n2023-01-02 15:20:48,699 - mmcls - INFO - Epoch [11][3800/5005]\tlr: 1.000e-01, eta: 10:54:55, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.8558\n2023-01-02 15:20:57,348 - mmcls - INFO - Epoch [11][3900/5005]\tlr: 1.000e-01, eta: 10:54:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8733\n2023-01-02 15:21:05,911 - mmcls - INFO - Epoch [11][4000/5005]\tlr: 1.000e-01, eta: 10:54:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.8723\n2023-01-02 15:21:14,864 - mmcls - INFO - Epoch [11][4100/5005]\tlr: 1.000e-01, eta: 10:54:27, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.8729\n2023-01-02 15:21:23,443 - mmcls - INFO - Epoch [11][4200/5005]\tlr: 1.000e-01, eta: 10:54:17, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8508\n2023-01-02 15:21:32,318 - mmcls - INFO - Epoch [11][4300/5005]\tlr: 1.000e-01, eta: 10:54:08, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.8776\n2023-01-02 15:21:41,557 - mmcls - INFO - Epoch [11][4400/5005]\tlr: 1.000e-01, eta: 10:54:03, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.8977\n2023-01-02 15:21:50,364 - mmcls - INFO - Epoch [11][4500/5005]\tlr: 1.000e-01, eta: 10:53:55, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8407\n2023-01-02 15:21:58,980 - mmcls - INFO - Epoch [11][4600/5005]\tlr: 1.000e-01, eta: 10:53:44, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8652\n2023-01-02 15:22:07,782 - mmcls - INFO - Epoch [11][4700/5005]\tlr: 1.000e-01, eta: 10:53:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8974\n2023-01-02 15:22:16,470 - mmcls - INFO - Epoch [11][4800/5005]\tlr: 1.000e-01, eta: 10:53:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8437\n2023-01-02 15:22:25,268 - mmcls - INFO - Epoch [11][4900/5005]\tlr: 1.000e-01, eta: 10:53:17, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8457\n2023-01-02 15:22:33,706 - mmcls - INFO - Epoch [11][5000/5005]\tlr: 1.000e-01, eta: 10:53:05, time: 0.084, data_time: 0.021, memory: 1125, loss: 2.8968\n2023-01-02 15:22:56,610 - mmcls - INFO - Epoch(val) [11][196]\taccuracy_top-1: 43.8000, accuracy_top-5: 68.8280\n2023-01-02 15:23:07,490 - mmcls - INFO - Epoch [12][100/5005]\tlr: 1.000e-01, eta: 10:53:09, time: 0.109, data_time: 0.030, memory: 1125, loss: 2.8319\n2023-01-02 15:23:16,064 - mmcls - INFO - Epoch [12][200/5005]\tlr: 1.000e-01, eta: 10:52:58, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8342\n2023-01-02 15:23:25,428 - mmcls - INFO - Epoch [12][300/5005]\tlr: 1.000e-01, eta: 10:52:54, time: 0.094, data_time: 0.029, memory: 1125, loss: 2.8235\n2023-01-02 15:23:34,026 - mmcls - INFO - Epoch [12][400/5005]\tlr: 1.000e-01, eta: 10:52:44, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8387\n2023-01-02 15:23:42,525 - mmcls - INFO - Epoch [12][500/5005]\tlr: 1.000e-01, eta: 10:52:33, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8199\n2023-01-02 15:23:50,960 - mmcls - INFO - Epoch [12][600/5005]\tlr: 1.000e-01, eta: 10:52:21, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.8213\n2023-01-02 15:23:59,953 - mmcls - INFO - Epoch [12][700/5005]\tlr: 1.000e-01, eta: 10:52:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8082\n2023-01-02 15:24:08,435 - mmcls - INFO - Epoch [12][800/5005]\tlr: 1.000e-01, eta: 10:52:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8230\n2023-01-02 15:24:17,301 - mmcls - INFO - Epoch [12][900/5005]\tlr: 1.000e-01, eta: 10:51:54, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.8144\n2023-01-02 15:24:25,922 - mmcls - INFO - Epoch [12][1000/5005]\tlr: 1.000e-01, eta: 10:51:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8632\n2023-01-02 15:24:34,643 - mmcls - INFO - Epoch [12][1100/5005]\tlr: 1.000e-01, eta: 10:51:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8528\n2023-01-02 15:24:43,160 - mmcls - INFO - Epoch [12][1200/5005]\tlr: 1.000e-01, eta: 10:51:23, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8230\n2023-01-02 15:24:51,890 - mmcls - INFO - Epoch [12][1300/5005]\tlr: 1.000e-01, eta: 10:51:14, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.8256\n2023-01-02 15:25:00,821 - mmcls - INFO - Epoch [12][1400/5005]\tlr: 1.000e-01, eta: 10:51:06, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8362\n2023-01-02 15:25:10,170 - mmcls - INFO - Epoch [12][1500/5005]\tlr: 1.000e-01, eta: 10:51:02, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8218\n2023-01-02 15:25:19,127 - mmcls - INFO - Epoch [12][1600/5005]\tlr: 1.000e-01, eta: 10:50:54, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8465\n2023-01-02 15:25:27,749 - mmcls - INFO - Epoch [12][1700/5005]\tlr: 1.000e-01, eta: 10:50:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8378\n2023-01-02 15:25:36,732 - mmcls - INFO - Epoch [12][1800/5005]\tlr: 1.000e-01, eta: 10:50:37, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8408\n2023-01-02 15:25:45,239 - mmcls - INFO - Epoch [12][1900/5005]\tlr: 1.000e-01, eta: 10:50:26, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8299\n2023-01-02 15:25:54,268 - mmcls - INFO - Epoch [12][2000/5005]\tlr: 1.000e-01, eta: 10:50:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8127\n2023-01-02 15:26:03,026 - mmcls - INFO - Epoch [12][2100/5005]\tlr: 1.000e-01, eta: 10:50:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8597\n2023-01-02 15:26:11,766 - mmcls - INFO - Epoch [12][2200/5005]\tlr: 1.000e-01, eta: 10:50:00, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8575\n2023-01-02 15:26:20,688 - mmcls - INFO - Epoch [12][2300/5005]\tlr: 1.000e-01, eta: 10:49:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.8431\n2023-01-02 15:26:29,252 - mmcls - INFO - Epoch [12][2400/5005]\tlr: 1.000e-01, eta: 10:49:42, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8687\n2023-01-02 15:26:37,897 - mmcls - INFO - Epoch [12][2500/5005]\tlr: 1.000e-01, eta: 10:49:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8393\n2023-01-02 15:26:46,464 - mmcls - INFO - Epoch [12][2600/5005]\tlr: 1.000e-01, eta: 10:49:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8500\n2023-01-02 15:26:54,968 - mmcls - INFO - Epoch [12][2700/5005]\tlr: 1.000e-01, eta: 10:49:10, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.8715\n2023-01-02 15:27:03,907 - mmcls - INFO - Epoch [12][2800/5005]\tlr: 1.000e-01, eta: 10:49:02, time: 0.089, data_time: 0.033, memory: 1125, loss: 2.8389\n2023-01-02 15:27:12,540 - mmcls - INFO - Epoch [12][2900/5005]\tlr: 1.000e-01, eta: 10:48:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8558\n2023-01-02 15:27:21,584 - mmcls - INFO - Epoch [12][3000/5005]\tlr: 1.000e-01, eta: 10:48:45, time: 0.090, data_time: 0.000, memory: 1125, loss: 2.8518\n2023-01-02 15:27:30,126 - mmcls - INFO - Epoch [12][3100/5005]\tlr: 1.000e-01, eta: 10:48:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8681\n2023-01-02 15:27:38,791 - mmcls - INFO - Epoch [12][3200/5005]\tlr: 1.000e-01, eta: 10:48:25, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.8621\n2023-01-02 15:27:47,626 - mmcls - INFO - Epoch [12][3300/5005]\tlr: 1.000e-01, eta: 10:48:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8559\n2023-01-02 15:27:56,924 - mmcls - INFO - Epoch [12][3400/5005]\tlr: 1.000e-01, eta: 10:48:11, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8396\n2023-01-02 15:28:05,528 - mmcls - INFO - Epoch [12][3500/5005]\tlr: 1.000e-01, eta: 10:48:01, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8228\n2023-01-02 15:28:14,175 - mmcls - INFO - Epoch [12][3600/5005]\tlr: 1.000e-01, eta: 10:47:51, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.8173\n2023-01-02 15:28:23,127 - mmcls - INFO - Epoch [12][3700/5005]\tlr: 1.000e-01, eta: 10:47:43, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8347\n2023-01-02 15:28:31,938 - mmcls - INFO - Epoch [12][3800/5005]\tlr: 1.000e-01, eta: 10:47:35, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.8309\n2023-01-02 15:28:40,727 - mmcls - INFO - Epoch [12][3900/5005]\tlr: 1.000e-01, eta: 10:47:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8404\n2023-01-02 15:28:49,831 - mmcls - INFO - Epoch [12][4000/5005]\tlr: 1.000e-01, eta: 10:47:19, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.8249\n2023-01-02 15:28:58,684 - mmcls - INFO - Epoch [12][4100/5005]\tlr: 1.000e-01, eta: 10:47:11, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8550\n2023-01-02 15:29:07,201 - mmcls - INFO - Epoch [12][4200/5005]\tlr: 1.000e-01, eta: 10:47:00, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8375\n2023-01-02 15:29:16,031 - mmcls - INFO - Epoch [12][4300/5005]\tlr: 1.000e-01, eta: 10:46:51, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8127\n2023-01-02 15:29:24,914 - mmcls - INFO - Epoch [12][4400/5005]\tlr: 1.000e-01, eta: 10:46:43, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.8561\n2023-01-02 15:29:33,920 - mmcls - INFO - Epoch [12][4500/5005]\tlr: 1.000e-01, eta: 10:46:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8537\n2023-01-02 15:29:42,844 - mmcls - INFO - Epoch [12][4600/5005]\tlr: 1.000e-01, eta: 10:46:28, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8550\n2023-01-02 15:29:51,461 - mmcls - INFO - Epoch [12][4700/5005]\tlr: 1.000e-01, eta: 10:46:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8433\n2023-01-02 15:30:00,010 - mmcls - INFO - Epoch [12][4800/5005]\tlr: 1.000e-01, eta: 10:46:07, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8520\n2023-01-02 15:30:08,502 - mmcls - INFO - Epoch [12][4900/5005]\tlr: 1.000e-01, eta: 10:45:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8440\n2023-01-02 15:30:17,230 - mmcls - INFO - Epoch [12][5000/5005]\tlr: 1.000e-01, eta: 10:45:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8278\n2023-01-02 15:30:39,913 - mmcls - INFO - Epoch(val) [12][196]\taccuracy_top-1: 45.5140, accuracy_top-5: 70.3220\n2023-01-02 15:30:50,926 - mmcls - INFO - Epoch [13][100/5005]\tlr: 1.000e-01, eta: 10:45:51, time: 0.110, data_time: 0.052, memory: 1125, loss: 2.7919\n2023-01-02 15:30:59,822 - mmcls - INFO - Epoch [13][200/5005]\tlr: 1.000e-01, eta: 10:45:42, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.7866\n2023-01-02 15:31:08,685 - mmcls - INFO - Epoch [13][300/5005]\tlr: 1.000e-01, eta: 10:45:34, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7919\n2023-01-02 15:31:17,372 - mmcls - INFO - Epoch [13][400/5005]\tlr: 1.000e-01, eta: 10:45:25, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7930\n2023-01-02 15:31:26,115 - mmcls - INFO - Epoch [13][500/5005]\tlr: 1.000e-01, eta: 10:45:15, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8024\n2023-01-02 15:31:34,771 - mmcls - INFO - Epoch [13][600/5005]\tlr: 1.000e-01, eta: 10:45:05, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7903\n2023-01-02 15:31:43,585 - mmcls - INFO - Epoch [13][700/5005]\tlr: 1.000e-01, eta: 10:44:57, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8082\n2023-01-02 15:31:52,249 - mmcls - INFO - Epoch [13][800/5005]\tlr: 1.000e-01, eta: 10:44:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8311\n2023-01-02 15:32:00,845 - mmcls - INFO - Epoch [13][900/5005]\tlr: 1.000e-01, eta: 10:44:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7889\n2023-01-02 15:32:09,967 - mmcls - INFO - Epoch [13][1000/5005]\tlr: 1.000e-01, eta: 10:44:30, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.8203\n2023-01-02 15:32:18,576 - mmcls - INFO - Epoch [13][1100/5005]\tlr: 1.000e-01, eta: 10:44:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8076\n2023-01-02 15:32:27,407 - mmcls - INFO - Epoch [13][1200/5005]\tlr: 1.000e-01, eta: 10:44:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8021\n2023-01-02 15:32:35,851 - mmcls - INFO - Epoch [13][1300/5005]\tlr: 1.000e-01, eta: 10:44:00, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.8230\n2023-01-02 15:32:44,496 - mmcls - INFO - Epoch [13][1400/5005]\tlr: 1.000e-01, eta: 10:43:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8188\n2023-01-02 15:32:53,424 - mmcls - INFO - Epoch [13][1500/5005]\tlr: 1.000e-01, eta: 10:43:42, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8038\n2023-01-02 15:33:02,061 - mmcls - INFO - Epoch [13][1600/5005]\tlr: 1.000e-01, eta: 10:43:32, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8457\n2023-01-02 15:33:11,035 - mmcls - INFO - Epoch [13][1700/5005]\tlr: 1.000e-01, eta: 10:43:25, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8238\n2023-01-02 15:33:19,908 - mmcls - INFO - Epoch [13][1800/5005]\tlr: 1.000e-01, eta: 10:43:16, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.8380\n2023-01-02 15:33:29,239 - mmcls - INFO - Epoch [13][1900/5005]\tlr: 1.000e-01, eta: 10:43:11, time: 0.093, data_time: 0.036, memory: 1125, loss: 2.8231\n2023-01-02 15:33:38,215 - mmcls - INFO - Epoch [13][2000/5005]\tlr: 1.000e-01, eta: 10:43:04, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8445\n2023-01-02 15:33:46,923 - mmcls - INFO - Epoch [13][2100/5005]\tlr: 1.000e-01, eta: 10:42:54, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.8248\n2023-01-02 15:33:55,685 - mmcls - INFO - Epoch [13][2200/5005]\tlr: 1.000e-01, eta: 10:42:45, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.8107\n2023-01-02 15:34:04,288 - mmcls - INFO - Epoch [13][2300/5005]\tlr: 1.000e-01, eta: 10:42:35, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8320\n2023-01-02 15:34:12,906 - mmcls - INFO - Epoch [13][2400/5005]\tlr: 1.000e-01, eta: 10:42:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8427\n2023-01-02 15:34:21,614 - mmcls - INFO - Epoch [13][2500/5005]\tlr: 1.000e-01, eta: 10:42:16, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8519\n2023-01-02 15:34:30,359 - mmcls - INFO - Epoch [13][2600/5005]\tlr: 1.000e-01, eta: 10:42:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8345\n2023-01-02 15:34:39,172 - mmcls - INFO - Epoch [13][2700/5005]\tlr: 1.000e-01, eta: 10:41:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8199\n2023-01-02 15:34:47,990 - mmcls - INFO - Epoch [13][2800/5005]\tlr: 1.000e-01, eta: 10:41:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8481\n2023-01-02 15:34:56,758 - mmcls - INFO - Epoch [13][2900/5005]\tlr: 1.000e-01, eta: 10:41:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8379\n2023-01-02 15:35:05,603 - mmcls - INFO - Epoch [13][3000/5005]\tlr: 1.000e-01, eta: 10:41:32, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.8616\n2023-01-02 15:35:14,372 - mmcls - INFO - Epoch [13][3100/5005]\tlr: 1.000e-01, eta: 10:41:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.8474\n2023-01-02 15:35:22,954 - mmcls - INFO - Epoch [13][3200/5005]\tlr: 1.000e-01, eta: 10:41:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8446\n2023-01-02 15:35:31,571 - mmcls - INFO - Epoch [13][3300/5005]\tlr: 1.000e-01, eta: 10:41:02, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8285\n2023-01-02 15:35:40,226 - mmcls - INFO - Epoch [13][3400/5005]\tlr: 1.000e-01, eta: 10:40:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.8883\n2023-01-02 15:35:48,766 - mmcls - INFO - Epoch [13][3500/5005]\tlr: 1.000e-01, eta: 10:40:42, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8557\n2023-01-02 15:35:57,256 - mmcls - INFO - Epoch [13][3600/5005]\tlr: 1.000e-01, eta: 10:40:31, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8227\n2023-01-02 15:36:06,410 - mmcls - INFO - Epoch [13][3700/5005]\tlr: 1.000e-01, eta: 10:40:24, time: 0.092, data_time: 0.014, memory: 1125, loss: 2.8310\n2023-01-02 15:36:15,276 - mmcls - INFO - Epoch [13][3800/5005]\tlr: 1.000e-01, eta: 10:40:16, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8282\n2023-01-02 15:36:24,256 - mmcls - INFO - Epoch [13][3900/5005]\tlr: 1.000e-01, eta: 10:40:09, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8449\n2023-01-02 15:36:32,753 - mmcls - INFO - Epoch [13][4000/5005]\tlr: 1.000e-01, eta: 10:39:58, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8511\n2023-01-02 15:36:41,349 - mmcls - INFO - Epoch [13][4100/5005]\tlr: 1.000e-01, eta: 10:39:47, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8300\n2023-01-02 15:36:50,237 - mmcls - INFO - Epoch [13][4200/5005]\tlr: 1.000e-01, eta: 10:39:39, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.8113\n2023-01-02 15:36:58,812 - mmcls - INFO - Epoch [13][4300/5005]\tlr: 1.000e-01, eta: 10:39:29, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8256\n2023-01-02 15:37:07,624 - mmcls - INFO - Epoch [13][4400/5005]\tlr: 1.000e-01, eta: 10:39:20, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.8323\n2023-01-02 15:37:16,467 - mmcls - INFO - Epoch [13][4500/5005]\tlr: 1.000e-01, eta: 10:39:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8583\n2023-01-02 15:37:25,233 - mmcls - INFO - Epoch [13][4600/5005]\tlr: 1.000e-01, eta: 10:39:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8182\n2023-01-02 15:37:33,830 - mmcls - INFO - Epoch [13][4700/5005]\tlr: 1.000e-01, eta: 10:38:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8311\n2023-01-02 15:37:42,479 - mmcls - INFO - Epoch [13][4800/5005]\tlr: 1.000e-01, eta: 10:38:43, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.8253\n2023-01-02 15:37:51,378 - mmcls - INFO - Epoch [13][4900/5005]\tlr: 1.000e-01, eta: 10:38:35, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.8438\n2023-01-02 15:38:00,445 - mmcls - INFO - Epoch [13][5000/5005]\tlr: 1.000e-01, eta: 10:38:28, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.8601\n2023-01-02 15:38:23,241 - mmcls - INFO - Epoch(val) [13][196]\taccuracy_top-1: 45.5280, accuracy_top-5: 70.2580\n2023-01-02 15:38:34,290 - mmcls - INFO - Epoch [14][100/5005]\tlr: 1.000e-01, eta: 10:38:30, time: 0.110, data_time: 0.055, memory: 1125, loss: 2.7851\n2023-01-02 15:38:42,954 - mmcls - INFO - Epoch [14][200/5005]\tlr: 1.000e-01, eta: 10:38:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7928\n2023-01-02 15:38:51,784 - mmcls - INFO - Epoch [14][300/5005]\tlr: 1.000e-01, eta: 10:38:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7973\n2023-01-02 15:39:00,637 - mmcls - INFO - Epoch [14][400/5005]\tlr: 1.000e-01, eta: 10:38:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7954\n2023-01-02 15:39:09,333 - mmcls - INFO - Epoch [14][500/5005]\tlr: 1.000e-01, eta: 10:37:54, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7836\n2023-01-02 15:39:18,092 - mmcls - INFO - Epoch [14][600/5005]\tlr: 1.000e-01, eta: 10:37:45, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.8192\n2023-01-02 15:39:26,976 - mmcls - INFO - Epoch [14][700/5005]\tlr: 1.000e-01, eta: 10:37:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7702\n2023-01-02 15:39:35,789 - mmcls - INFO - Epoch [14][800/5005]\tlr: 1.000e-01, eta: 10:37:28, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8164\n2023-01-02 15:39:44,291 - mmcls - INFO - Epoch [14][900/5005]\tlr: 1.000e-01, eta: 10:37:17, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.8162\n2023-01-02 15:39:53,039 - mmcls - INFO - Epoch [14][1000/5005]\tlr: 1.000e-01, eta: 10:37:08, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8091\n2023-01-02 15:40:01,705 - mmcls - INFO - Epoch [14][1100/5005]\tlr: 1.000e-01, eta: 10:36:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8093\n2023-01-02 15:40:10,550 - mmcls - INFO - Epoch [14][1200/5005]\tlr: 1.000e-01, eta: 10:36:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.8269\n2023-01-02 15:40:19,294 - mmcls - INFO - Epoch [14][1300/5005]\tlr: 1.000e-01, eta: 10:36:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8027\n2023-01-02 15:40:27,971 - mmcls - INFO - Epoch [14][1400/5005]\tlr: 1.000e-01, eta: 10:36:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8114\n2023-01-02 15:40:36,710 - mmcls - INFO - Epoch [14][1500/5005]\tlr: 1.000e-01, eta: 10:36:22, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.8077\n2023-01-02 15:40:45,310 - mmcls - INFO - Epoch [14][1600/5005]\tlr: 1.000e-01, eta: 10:36:12, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.8291\n2023-01-02 15:40:54,367 - mmcls - INFO - Epoch [14][1700/5005]\tlr: 1.000e-01, eta: 10:36:05, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8201\n2023-01-02 15:41:02,930 - mmcls - INFO - Epoch [14][1800/5005]\tlr: 1.000e-01, eta: 10:35:54, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.8354\n2023-01-02 15:41:11,749 - mmcls - INFO - Epoch [14][1900/5005]\tlr: 1.000e-01, eta: 10:35:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8334\n2023-01-02 15:41:20,416 - mmcls - INFO - Epoch [14][2000/5005]\tlr: 1.000e-01, eta: 10:35:36, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7979\n2023-01-02 15:41:29,144 - mmcls - INFO - Epoch [14][2100/5005]\tlr: 1.000e-01, eta: 10:35:27, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8297\n2023-01-02 15:41:37,706 - mmcls - INFO - Epoch [14][2200/5005]\tlr: 1.000e-01, eta: 10:35:17, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8299\n2023-01-02 15:41:46,645 - mmcls - INFO - Epoch [14][2300/5005]\tlr: 1.000e-01, eta: 10:35:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8110\n2023-01-02 15:41:55,242 - mmcls - INFO - Epoch [14][2400/5005]\tlr: 1.000e-01, eta: 10:34:59, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8316\n2023-01-02 15:42:04,071 - mmcls - INFO - Epoch [14][2500/5005]\tlr: 1.000e-01, eta: 10:34:50, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.8370\n2023-01-02 15:42:12,793 - mmcls - INFO - Epoch [14][2600/5005]\tlr: 1.000e-01, eta: 10:34:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8126\n2023-01-02 15:42:21,386 - mmcls - INFO - Epoch [14][2700/5005]\tlr: 1.000e-01, eta: 10:34:31, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8056\n2023-01-02 15:42:30,486 - mmcls - INFO - Epoch [14][2800/5005]\tlr: 1.000e-01, eta: 10:34:24, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.8510\n2023-01-02 15:42:39,046 - mmcls - INFO - Epoch [14][2900/5005]\tlr: 1.000e-01, eta: 10:34:13, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.8607\n2023-01-02 15:42:47,833 - mmcls - INFO - Epoch [14][3000/5005]\tlr: 1.000e-01, eta: 10:34:04, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8353\n2023-01-02 15:42:56,843 - mmcls - INFO - Epoch [14][3100/5005]\tlr: 1.000e-01, eta: 10:33:57, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.8037\n2023-01-02 15:43:05,408 - mmcls - INFO - Epoch [14][3200/5005]\tlr: 1.000e-01, eta: 10:33:47, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.8073\n2023-01-02 15:43:14,577 - mmcls - INFO - Epoch [14][3300/5005]\tlr: 1.000e-01, eta: 10:33:40, time: 0.092, data_time: 0.016, memory: 1125, loss: 2.8480\n2023-01-02 15:43:23,548 - mmcls - INFO - Epoch [14][3400/5005]\tlr: 1.000e-01, eta: 10:33:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8372\n2023-01-02 15:43:32,476 - mmcls - INFO - Epoch [14][3500/5005]\tlr: 1.000e-01, eta: 10:33:25, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.8387\n2023-01-02 15:43:41,110 - mmcls - INFO - Epoch [14][3600/5005]\tlr: 1.000e-01, eta: 10:33:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8335\n2023-01-02 15:43:49,636 - mmcls - INFO - Epoch [14][3700/5005]\tlr: 1.000e-01, eta: 10:33:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8394\n2023-01-02 15:43:58,593 - mmcls - INFO - Epoch [14][3800/5005]\tlr: 1.000e-01, eta: 10:32:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8074\n2023-01-02 15:44:07,324 - mmcls - INFO - Epoch [14][3900/5005]\tlr: 1.000e-01, eta: 10:32:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.8394\n2023-01-02 15:44:15,967 - mmcls - INFO - Epoch [14][4000/5005]\tlr: 1.000e-01, eta: 10:32:37, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8444\n2023-01-02 15:44:24,558 - mmcls - INFO - Epoch [14][4100/5005]\tlr: 1.000e-01, eta: 10:32:27, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8276\n2023-01-02 15:44:33,510 - mmcls - INFO - Epoch [14][4200/5005]\tlr: 1.000e-01, eta: 10:32:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8333\n2023-01-02 15:44:42,464 - mmcls - INFO - Epoch [14][4300/5005]\tlr: 1.000e-01, eta: 10:32:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8289\n2023-01-02 15:44:51,447 - mmcls - INFO - Epoch [14][4400/5005]\tlr: 1.000e-01, eta: 10:32:04, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7879\n2023-01-02 15:45:00,387 - mmcls - INFO - Epoch [14][4500/5005]\tlr: 1.000e-01, eta: 10:31:56, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8038\n2023-01-02 15:45:09,365 - mmcls - INFO - Epoch [14][4600/5005]\tlr: 1.000e-01, eta: 10:31:48, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8335\n2023-01-02 15:45:18,051 - mmcls - INFO - Epoch [14][4700/5005]\tlr: 1.000e-01, eta: 10:31:39, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8420\n2023-01-02 15:45:26,747 - mmcls - INFO - Epoch [14][4800/5005]\tlr: 1.000e-01, eta: 10:31:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7850\n2023-01-02 15:45:35,574 - mmcls - INFO - Epoch [14][4900/5005]\tlr: 1.000e-01, eta: 10:31:21, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8421\n2023-01-02 15:45:44,084 - mmcls - INFO - Epoch [14][5000/5005]\tlr: 1.000e-01, eta: 10:31:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.8244\n2023-01-02 15:46:06,878 - mmcls - INFO - Epoch(val) [14][196]\taccuracy_top-1: 44.0180, accuracy_top-5: 68.6460\n2023-01-02 15:46:17,772 - mmcls - INFO - Epoch [15][100/5005]\tlr: 1.000e-01, eta: 10:31:11, time: 0.109, data_time: 0.043, memory: 1125, loss: 2.7601\n2023-01-02 15:46:26,407 - mmcls - INFO - Epoch [15][200/5005]\tlr: 1.000e-01, eta: 10:31:01, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7402\n2023-01-02 15:46:35,085 - mmcls - INFO - Epoch [15][300/5005]\tlr: 1.000e-01, eta: 10:30:52, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7804\n2023-01-02 15:46:43,827 - mmcls - INFO - Epoch [15][400/5005]\tlr: 1.000e-01, eta: 10:30:42, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8031\n2023-01-02 15:46:52,854 - mmcls - INFO - Epoch [15][500/5005]\tlr: 1.000e-01, eta: 10:30:35, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7692\n2023-01-02 15:47:01,559 - mmcls - INFO - Epoch [15][600/5005]\tlr: 1.000e-01, eta: 10:30:26, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7888\n2023-01-02 15:47:10,477 - mmcls - INFO - Epoch [15][700/5005]\tlr: 1.000e-01, eta: 10:30:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7781\n2023-01-02 15:47:19,117 - mmcls - INFO - Epoch [15][800/5005]\tlr: 1.000e-01, eta: 10:30:08, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7776\n2023-01-02 15:47:27,808 - mmcls - INFO - Epoch [15][900/5005]\tlr: 1.000e-01, eta: 10:29:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7948\n2023-01-02 15:47:36,371 - mmcls - INFO - Epoch [15][1000/5005]\tlr: 1.000e-01, eta: 10:29:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8284\n2023-01-02 15:47:45,313 - mmcls - INFO - Epoch [15][1100/5005]\tlr: 1.000e-01, eta: 10:29:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8063\n2023-01-02 15:47:53,886 - mmcls - INFO - Epoch [15][1200/5005]\tlr: 1.000e-01, eta: 10:29:30, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.7908\n2023-01-02 15:48:03,093 - mmcls - INFO - Epoch [15][1300/5005]\tlr: 1.000e-01, eta: 10:29:24, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.8253\n2023-01-02 15:48:11,810 - mmcls - INFO - Epoch [15][1400/5005]\tlr: 1.000e-01, eta: 10:29:14, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8231\n2023-01-02 15:48:20,840 - mmcls - INFO - Epoch [15][1500/5005]\tlr: 1.000e-01, eta: 10:29:07, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7962\n2023-01-02 15:48:29,484 - mmcls - INFO - Epoch [15][1600/5005]\tlr: 1.000e-01, eta: 10:28:57, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7879\n2023-01-02 15:48:38,212 - mmcls - INFO - Epoch [15][1700/5005]\tlr: 1.000e-01, eta: 10:28:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7886\n2023-01-02 15:48:46,805 - mmcls - INFO - Epoch [15][1800/5005]\tlr: 1.000e-01, eta: 10:28:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8105\n2023-01-02 15:48:55,440 - mmcls - INFO - Epoch [15][1900/5005]\tlr: 1.000e-01, eta: 10:28:28, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.8194\n2023-01-02 15:49:03,897 - mmcls - INFO - Epoch [15][2000/5005]\tlr: 1.000e-01, eta: 10:28:17, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.8245\n2023-01-02 15:49:12,346 - mmcls - INFO - Epoch [15][2100/5005]\tlr: 1.000e-01, eta: 10:28:06, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.7962\n2023-01-02 15:49:21,604 - mmcls - INFO - Epoch [15][2200/5005]\tlr: 1.000e-01, eta: 10:28:00, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8053\n2023-01-02 15:49:30,464 - mmcls - INFO - Epoch [15][2300/5005]\tlr: 1.000e-01, eta: 10:27:52, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.7935\n2023-01-02 15:49:39,657 - mmcls - INFO - Epoch [15][2400/5005]\tlr: 1.000e-01, eta: 10:27:45, time: 0.092, data_time: 0.021, memory: 1125, loss: 2.8622\n2023-01-02 15:49:48,220 - mmcls - INFO - Epoch [15][2500/5005]\tlr: 1.000e-01, eta: 10:27:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.8578\n2023-01-02 15:49:56,824 - mmcls - INFO - Epoch [15][2600/5005]\tlr: 1.000e-01, eta: 10:27:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8465\n2023-01-02 15:50:05,511 - mmcls - INFO - Epoch [15][2700/5005]\tlr: 1.000e-01, eta: 10:27:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8097\n2023-01-02 15:50:14,445 - mmcls - INFO - Epoch [15][2800/5005]\tlr: 1.000e-01, eta: 10:27:08, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.8356\n2023-01-02 15:50:23,215 - mmcls - INFO - Epoch [15][2900/5005]\tlr: 1.000e-01, eta: 10:26:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8263\n2023-01-02 15:50:31,794 - mmcls - INFO - Epoch [15][3000/5005]\tlr: 1.000e-01, eta: 10:26:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8068\n2023-01-02 15:50:40,686 - mmcls - INFO - Epoch [15][3100/5005]\tlr: 1.000e-01, eta: 10:26:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.8365\n2023-01-02 15:50:49,420 - mmcls - INFO - Epoch [15][3200/5005]\tlr: 1.000e-01, eta: 10:26:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8419\n2023-01-02 15:50:58,190 - mmcls - INFO - Epoch [15][3300/5005]\tlr: 1.000e-01, eta: 10:26:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7967\n2023-01-02 15:51:06,873 - mmcls - INFO - Epoch [15][3400/5005]\tlr: 1.000e-01, eta: 10:26:13, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.8437\n2023-01-02 15:51:15,543 - mmcls - INFO - Epoch [15][3500/5005]\tlr: 1.000e-01, eta: 10:26:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8124\n2023-01-02 15:51:24,314 - mmcls - INFO - Epoch [15][3600/5005]\tlr: 1.000e-01, eta: 10:25:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8236\n2023-01-02 15:51:33,230 - mmcls - INFO - Epoch [15][3700/5005]\tlr: 1.000e-01, eta: 10:25:46, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8173\n2023-01-02 15:51:42,252 - mmcls - INFO - Epoch [15][3800/5005]\tlr: 1.000e-01, eta: 10:25:39, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8150\n2023-01-02 15:51:51,014 - mmcls - INFO - Epoch [15][3900/5005]\tlr: 1.000e-01, eta: 10:25:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8306\n2023-01-02 15:51:59,653 - mmcls - INFO - Epoch [15][4000/5005]\tlr: 1.000e-01, eta: 10:25:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8235\n2023-01-02 15:52:08,450 - mmcls - INFO - Epoch [15][4100/5005]\tlr: 1.000e-01, eta: 10:25:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7919\n2023-01-02 15:52:17,193 - mmcls - INFO - Epoch [15][4200/5005]\tlr: 1.000e-01, eta: 10:25:02, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8015\n2023-01-02 15:52:26,030 - mmcls - INFO - Epoch [15][4300/5005]\tlr: 1.000e-01, eta: 10:24:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.8177\n2023-01-02 15:52:34,771 - mmcls - INFO - Epoch [15][4400/5005]\tlr: 1.000e-01, eta: 10:24:44, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8342\n2023-01-02 15:52:43,464 - mmcls - INFO - Epoch [15][4500/5005]\tlr: 1.000e-01, eta: 10:24:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8008\n2023-01-02 15:52:52,592 - mmcls - INFO - Epoch [15][4600/5005]\tlr: 1.000e-01, eta: 10:24:28, time: 0.091, data_time: 0.037, memory: 1125, loss: 2.8188\n2023-01-02 15:53:01,479 - mmcls - INFO - Epoch [15][4700/5005]\tlr: 1.000e-01, eta: 10:24:20, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8348\n2023-01-02 15:53:10,272 - mmcls - INFO - Epoch [15][4800/5005]\tlr: 1.000e-01, eta: 10:24:11, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.8186\n2023-01-02 15:53:19,066 - mmcls - INFO - Epoch [15][4900/5005]\tlr: 1.000e-01, eta: 10:24:02, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7787\n2023-01-02 15:53:27,740 - mmcls - INFO - Epoch [15][5000/5005]\tlr: 1.000e-01, eta: 10:23:52, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8110\n2023-01-02 15:53:50,549 - mmcls - INFO - Epoch(val) [15][196]\taccuracy_top-1: 46.5100, accuracy_top-5: 71.0960\n2023-01-02 15:54:01,288 - mmcls - INFO - Epoch [16][100/5005]\tlr: 1.000e-01, eta: 10:23:52, time: 0.107, data_time: 0.044, memory: 1125, loss: 2.7535\n2023-01-02 15:54:09,998 - mmcls - INFO - Epoch [16][200/5005]\tlr: 1.000e-01, eta: 10:23:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7987\n2023-01-02 15:54:18,651 - mmcls - INFO - Epoch [16][300/5005]\tlr: 1.000e-01, eta: 10:23:33, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7810\n2023-01-02 15:54:27,500 - mmcls - INFO - Epoch [16][400/5005]\tlr: 1.000e-01, eta: 10:23:24, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.7654\n2023-01-02 15:54:36,121 - mmcls - INFO - Epoch [16][500/5005]\tlr: 1.000e-01, eta: 10:23:14, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.7680\n2023-01-02 15:54:44,969 - mmcls - INFO - Epoch [16][600/5005]\tlr: 1.000e-01, eta: 10:23:06, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.7982\n2023-01-02 15:54:53,694 - mmcls - INFO - Epoch [16][700/5005]\tlr: 1.000e-01, eta: 10:22:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7752\n2023-01-02 15:55:02,342 - mmcls - INFO - Epoch [16][800/5005]\tlr: 1.000e-01, eta: 10:22:47, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7816\n2023-01-02 15:55:11,309 - mmcls - INFO - Epoch [16][900/5005]\tlr: 1.000e-01, eta: 10:22:39, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7903\n2023-01-02 15:55:20,295 - mmcls - INFO - Epoch [16][1000/5005]\tlr: 1.000e-01, eta: 10:22:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7754\n2023-01-02 15:55:28,798 - mmcls - INFO - Epoch [16][1100/5005]\tlr: 1.000e-01, eta: 10:22:21, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7716\n2023-01-02 15:55:37,396 - mmcls - INFO - Epoch [16][1200/5005]\tlr: 1.000e-01, eta: 10:22:11, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7807\n2023-01-02 15:55:46,199 - mmcls - INFO - Epoch [16][1300/5005]\tlr: 1.000e-01, eta: 10:22:02, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7993\n2023-01-02 15:55:54,849 - mmcls - INFO - Epoch [16][1400/5005]\tlr: 1.000e-01, eta: 10:21:52, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7783\n2023-01-02 15:56:03,562 - mmcls - INFO - Epoch [16][1500/5005]\tlr: 1.000e-01, eta: 10:21:43, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7939\n2023-01-02 15:56:12,326 - mmcls - INFO - Epoch [16][1600/5005]\tlr: 1.000e-01, eta: 10:21:34, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.7879\n2023-01-02 15:56:20,796 - mmcls - INFO - Epoch [16][1700/5005]\tlr: 1.000e-01, eta: 10:21:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.7913\n2023-01-02 15:56:29,625 - mmcls - INFO - Epoch [16][1800/5005]\tlr: 1.000e-01, eta: 10:21:15, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.7944\n2023-01-02 15:56:38,405 - mmcls - INFO - Epoch [16][1900/5005]\tlr: 1.000e-01, eta: 10:21:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7747\n2023-01-02 15:56:47,161 - mmcls - INFO - Epoch [16][2000/5005]\tlr: 1.000e-01, eta: 10:20:57, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7947\n2023-01-02 15:56:55,832 - mmcls - INFO - Epoch [16][2100/5005]\tlr: 1.000e-01, eta: 10:20:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7971\n2023-01-02 15:57:04,382 - mmcls - INFO - Epoch [16][2200/5005]\tlr: 1.000e-01, eta: 10:20:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.7921\n2023-01-02 15:57:12,975 - mmcls - INFO - Epoch [16][2300/5005]\tlr: 1.000e-01, eta: 10:20:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8254\n2023-01-02 15:57:21,660 - mmcls - INFO - Epoch [16][2400/5005]\tlr: 1.000e-01, eta: 10:20:18, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8223\n2023-01-02 15:57:30,691 - mmcls - INFO - Epoch [16][2500/5005]\tlr: 1.000e-01, eta: 10:20:10, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8299\n2023-01-02 15:57:39,446 - mmcls - INFO - Epoch [16][2600/5005]\tlr: 1.000e-01, eta: 10:20:01, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8037\n2023-01-02 15:57:47,970 - mmcls - INFO - Epoch [16][2700/5005]\tlr: 1.000e-01, eta: 10:19:51, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7635\n2023-01-02 15:57:56,933 - mmcls - INFO - Epoch [16][2800/5005]\tlr: 1.000e-01, eta: 10:19:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8261\n2023-01-02 15:58:05,479 - mmcls - INFO - Epoch [16][2900/5005]\tlr: 1.000e-01, eta: 10:19:33, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8039\n2023-01-02 15:58:14,098 - mmcls - INFO - Epoch [16][3000/5005]\tlr: 1.000e-01, eta: 10:19:23, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7998\n2023-01-02 15:58:22,758 - mmcls - INFO - Epoch [16][3100/5005]\tlr: 1.000e-01, eta: 10:19:13, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8152\n2023-01-02 15:58:31,518 - mmcls - INFO - Epoch [16][3200/5005]\tlr: 1.000e-01, eta: 10:19:05, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7807\n2023-01-02 15:58:40,437 - mmcls - INFO - Epoch [16][3300/5005]\tlr: 1.000e-01, eta: 10:18:56, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.8538\n2023-01-02 15:58:49,421 - mmcls - INFO - Epoch [16][3400/5005]\tlr: 1.000e-01, eta: 10:18:49, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8157\n2023-01-02 15:58:58,367 - mmcls - INFO - Epoch [16][3500/5005]\tlr: 1.000e-01, eta: 10:18:41, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.8170\n2023-01-02 15:59:07,020 - mmcls - INFO - Epoch [16][3600/5005]\tlr: 1.000e-01, eta: 10:18:31, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8215\n2023-01-02 15:59:15,788 - mmcls - INFO - Epoch [16][3700/5005]\tlr: 1.000e-01, eta: 10:18:22, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.8072\n2023-01-02 15:59:24,496 - mmcls - INFO - Epoch [16][3800/5005]\tlr: 1.000e-01, eta: 10:18:13, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8132\n2023-01-02 15:59:33,098 - mmcls - INFO - Epoch [16][3900/5005]\tlr: 1.000e-01, eta: 10:18:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8216\n2023-01-02 15:59:42,284 - mmcls - INFO - Epoch [16][4000/5005]\tlr: 1.000e-01, eta: 10:17:56, time: 0.092, data_time: 0.000, memory: 1125, loss: 2.8169\n2023-01-02 15:59:50,751 - mmcls - INFO - Epoch [16][4100/5005]\tlr: 1.000e-01, eta: 10:17:46, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.8069\n2023-01-02 15:59:59,582 - mmcls - INFO - Epoch [16][4200/5005]\tlr: 1.000e-01, eta: 10:17:37, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.8187\n2023-01-02 16:00:08,383 - mmcls - INFO - Epoch [16][4300/5005]\tlr: 1.000e-01, eta: 10:17:28, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8338\n2023-01-02 16:00:16,947 - mmcls - INFO - Epoch [16][4400/5005]\tlr: 1.000e-01, eta: 10:17:18, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8240\n2023-01-02 16:00:25,632 - mmcls - INFO - Epoch [16][4500/5005]\tlr: 1.000e-01, eta: 10:17:09, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8542\n2023-01-02 16:00:34,571 - mmcls - INFO - Epoch [16][4600/5005]\tlr: 1.000e-01, eta: 10:17:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8106\n2023-01-02 16:00:43,378 - mmcls - INFO - Epoch [16][4700/5005]\tlr: 1.000e-01, eta: 10:16:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7778\n2023-01-02 16:00:52,285 - mmcls - INFO - Epoch [16][4800/5005]\tlr: 1.000e-01, eta: 10:16:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8106\n2023-01-02 16:01:00,978 - mmcls - INFO - Epoch [16][4900/5005]\tlr: 1.000e-01, eta: 10:16:34, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8030\n2023-01-02 16:01:09,875 - mmcls - INFO - Epoch [16][5000/5005]\tlr: 1.000e-01, eta: 10:16:26, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.8186\n2023-01-02 16:01:32,657 - mmcls - INFO - Epoch(val) [16][196]\taccuracy_top-1: 46.7060, accuracy_top-5: 71.5340\n2023-01-02 16:01:44,032 - mmcls - INFO - Epoch [17][100/5005]\tlr: 1.000e-01, eta: 10:16:28, time: 0.114, data_time: 0.052, memory: 1125, loss: 2.7266\n2023-01-02 16:01:52,684 - mmcls - INFO - Epoch [17][200/5005]\tlr: 1.000e-01, eta: 10:16:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7334\n2023-01-02 16:02:01,317 - mmcls - INFO - Epoch [17][300/5005]\tlr: 1.000e-01, eta: 10:16:09, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7498\n2023-01-02 16:02:10,053 - mmcls - INFO - Epoch [17][400/5005]\tlr: 1.000e-01, eta: 10:16:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7684\n2023-01-02 16:02:19,003 - mmcls - INFO - Epoch [17][500/5005]\tlr: 1.000e-01, eta: 10:15:52, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7475\n2023-01-02 16:02:27,781 - mmcls - INFO - Epoch [17][600/5005]\tlr: 1.000e-01, eta: 10:15:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7497\n2023-01-02 16:02:36,776 - mmcls - INFO - Epoch [17][700/5005]\tlr: 1.000e-01, eta: 10:15:35, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7900\n2023-01-02 16:02:45,476 - mmcls - INFO - Epoch [17][800/5005]\tlr: 1.000e-01, eta: 10:15:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8031\n2023-01-02 16:02:54,066 - mmcls - INFO - Epoch [17][900/5005]\tlr: 1.000e-01, eta: 10:15:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7547\n2023-01-02 16:03:02,794 - mmcls - INFO - Epoch [17][1000/5005]\tlr: 1.000e-01, eta: 10:15:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7858\n2023-01-02 16:03:11,401 - mmcls - INFO - Epoch [17][1100/5005]\tlr: 1.000e-01, eta: 10:14:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7902\n2023-01-02 16:03:20,260 - mmcls - INFO - Epoch [17][1200/5005]\tlr: 1.000e-01, eta: 10:14:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7698\n2023-01-02 16:03:29,537 - mmcls - INFO - Epoch [17][1300/5005]\tlr: 1.000e-01, eta: 10:14:42, time: 0.093, data_time: 0.003, memory: 1125, loss: 2.8103\n2023-01-02 16:03:38,131 - mmcls - INFO - Epoch [17][1400/5005]\tlr: 1.000e-01, eta: 10:14:32, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8049\n2023-01-02 16:03:46,911 - mmcls - INFO - Epoch [17][1500/5005]\tlr: 1.000e-01, eta: 10:14:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7882\n2023-01-02 16:03:55,656 - mmcls - INFO - Epoch [17][1600/5005]\tlr: 1.000e-01, eta: 10:14:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8091\n2023-01-02 16:04:04,511 - mmcls - INFO - Epoch [17][1700/5005]\tlr: 1.000e-01, eta: 10:14:05, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7598\n2023-01-02 16:04:13,216 - mmcls - INFO - Epoch [17][1800/5005]\tlr: 1.000e-01, eta: 10:13:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.7738\n2023-01-02 16:04:21,920 - mmcls - INFO - Epoch [17][1900/5005]\tlr: 1.000e-01, eta: 10:13:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7924\n2023-01-02 16:04:30,764 - mmcls - INFO - Epoch [17][2000/5005]\tlr: 1.000e-01, eta: 10:13:38, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7674\n2023-01-02 16:04:39,560 - mmcls - INFO - Epoch [17][2100/5005]\tlr: 1.000e-01, eta: 10:13:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8211\n2023-01-02 16:04:48,071 - mmcls - INFO - Epoch [17][2200/5005]\tlr: 1.000e-01, eta: 10:13:19, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7878\n2023-01-02 16:04:57,114 - mmcls - INFO - Epoch [17][2300/5005]\tlr: 1.000e-01, eta: 10:13:12, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.7657\n2023-01-02 16:05:05,887 - mmcls - INFO - Epoch [17][2400/5005]\tlr: 1.000e-01, eta: 10:13:03, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.7794\n2023-01-02 16:05:14,652 - mmcls - INFO - Epoch [17][2500/5005]\tlr: 1.000e-01, eta: 10:12:54, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8314\n2023-01-02 16:05:23,509 - mmcls - INFO - Epoch [17][2600/5005]\tlr: 1.000e-01, eta: 10:12:45, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8234\n2023-01-02 16:05:32,258 - mmcls - INFO - Epoch [17][2700/5005]\tlr: 1.000e-01, eta: 10:12:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7898\n2023-01-02 16:05:40,831 - mmcls - INFO - Epoch [17][2800/5005]\tlr: 1.000e-01, eta: 10:12:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7911\n2023-01-02 16:05:49,590 - mmcls - INFO - Epoch [17][2900/5005]\tlr: 1.000e-01, eta: 10:12:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7865\n2023-01-02 16:05:58,406 - mmcls - INFO - Epoch [17][3000/5005]\tlr: 1.000e-01, eta: 10:12:08, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8145\n2023-01-02 16:06:07,347 - mmcls - INFO - Epoch [17][3100/5005]\tlr: 1.000e-01, eta: 10:12:00, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7865\n2023-01-02 16:06:16,171 - mmcls - INFO - Epoch [17][3200/5005]\tlr: 1.000e-01, eta: 10:11:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8018\n2023-01-02 16:06:24,873 - mmcls - INFO - Epoch [17][3300/5005]\tlr: 1.000e-01, eta: 10:11:42, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8026\n2023-01-02 16:06:33,647 - mmcls - INFO - Epoch [17][3400/5005]\tlr: 1.000e-01, eta: 10:11:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8199\n2023-01-02 16:06:42,320 - mmcls - INFO - Epoch [17][3500/5005]\tlr: 1.000e-01, eta: 10:11:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7994\n2023-01-02 16:06:51,098 - mmcls - INFO - Epoch [17][3600/5005]\tlr: 1.000e-01, eta: 10:11:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8069\n2023-01-02 16:06:59,615 - mmcls - INFO - Epoch [17][3700/5005]\tlr: 1.000e-01, eta: 10:11:05, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8155\n2023-01-02 16:07:08,789 - mmcls - INFO - Epoch [17][3800/5005]\tlr: 1.000e-01, eta: 10:10:58, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7992\n2023-01-02 16:07:17,497 - mmcls - INFO - Epoch [17][3900/5005]\tlr: 1.000e-01, eta: 10:10:49, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8417\n2023-01-02 16:07:26,394 - mmcls - INFO - Epoch [17][4000/5005]\tlr: 1.000e-01, eta: 10:10:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8048\n2023-01-02 16:07:35,094 - mmcls - INFO - Epoch [17][4100/5005]\tlr: 1.000e-01, eta: 10:10:31, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7980\n2023-01-02 16:07:43,856 - mmcls - INFO - Epoch [17][4200/5005]\tlr: 1.000e-01, eta: 10:10:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8072\n2023-01-02 16:07:52,973 - mmcls - INFO - Epoch [17][4300/5005]\tlr: 1.000e-01, eta: 10:10:15, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.7836\n2023-01-02 16:08:01,686 - mmcls - INFO - Epoch [17][4400/5005]\tlr: 1.000e-01, eta: 10:10:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7852\n2023-01-02 16:08:10,133 - mmcls - INFO - Epoch [17][4500/5005]\tlr: 1.000e-01, eta: 10:09:55, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.8473\n2023-01-02 16:08:19,189 - mmcls - INFO - Epoch [17][4600/5005]\tlr: 1.000e-01, eta: 10:09:48, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7892\n2023-01-02 16:08:28,086 - mmcls - INFO - Epoch [17][4700/5005]\tlr: 1.000e-01, eta: 10:09:39, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8265\n2023-01-02 16:08:36,814 - mmcls - INFO - Epoch [17][4800/5005]\tlr: 1.000e-01, eta: 10:09:30, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7952\n2023-01-02 16:08:45,552 - mmcls - INFO - Epoch [17][4900/5005]\tlr: 1.000e-01, eta: 10:09:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7943\n2023-01-02 16:08:54,151 - mmcls - INFO - Epoch [17][5000/5005]\tlr: 1.000e-01, eta: 10:09:11, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.8049\n2023-01-02 16:09:16,680 - mmcls - INFO - Epoch(val) [17][196]\taccuracy_top-1: 45.1320, accuracy_top-5: 70.1660\n2023-01-02 16:09:27,427 - mmcls - INFO - Epoch [18][100/5005]\tlr: 1.000e-01, eta: 10:09:09, time: 0.107, data_time: 0.041, memory: 1125, loss: 2.7560\n2023-01-02 16:09:36,437 - mmcls - INFO - Epoch [18][200/5005]\tlr: 1.000e-01, eta: 10:09:01, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.7118\n2023-01-02 16:09:45,298 - mmcls - INFO - Epoch [18][300/5005]\tlr: 1.000e-01, eta: 10:08:53, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7448\n2023-01-02 16:09:54,160 - mmcls - INFO - Epoch [18][400/5005]\tlr: 1.000e-01, eta: 10:08:44, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.7430\n2023-01-02 16:10:02,914 - mmcls - INFO - Epoch [18][500/5005]\tlr: 1.000e-01, eta: 10:08:35, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7601\n2023-01-02 16:10:11,384 - mmcls - INFO - Epoch [18][600/5005]\tlr: 1.000e-01, eta: 10:08:25, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.7810\n2023-01-02 16:10:20,087 - mmcls - INFO - Epoch [18][700/5005]\tlr: 1.000e-01, eta: 10:08:16, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7384\n2023-01-02 16:10:28,784 - mmcls - INFO - Epoch [18][800/5005]\tlr: 1.000e-01, eta: 10:08:06, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.7582\n2023-01-02 16:10:37,396 - mmcls - INFO - Epoch [18][900/5005]\tlr: 1.000e-01, eta: 10:07:57, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.7512\n2023-01-02 16:10:45,981 - mmcls - INFO - Epoch [18][1000/5005]\tlr: 1.000e-01, eta: 10:07:47, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7906\n2023-01-02 16:10:54,674 - mmcls - INFO - Epoch [18][1100/5005]\tlr: 1.000e-01, eta: 10:07:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7748\n2023-01-02 16:11:03,302 - mmcls - INFO - Epoch [18][1200/5005]\tlr: 1.000e-01, eta: 10:07:28, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7663\n2023-01-02 16:11:11,838 - mmcls - INFO - Epoch [18][1300/5005]\tlr: 1.000e-01, eta: 10:07:18, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.7653\n2023-01-02 16:11:20,718 - mmcls - INFO - Epoch [18][1400/5005]\tlr: 1.000e-01, eta: 10:07:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7735\n2023-01-02 16:11:29,629 - mmcls - INFO - Epoch [18][1500/5005]\tlr: 1.000e-01, eta: 10:07:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8341\n2023-01-02 16:11:38,497 - mmcls - INFO - Epoch [18][1600/5005]\tlr: 1.000e-01, eta: 10:06:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.8104\n2023-01-02 16:11:47,203 - mmcls - INFO - Epoch [18][1700/5005]\tlr: 1.000e-01, eta: 10:06:43, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8084\n2023-01-02 16:11:55,723 - mmcls - INFO - Epoch [18][1800/5005]\tlr: 1.000e-01, eta: 10:06:33, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.8056\n2023-01-02 16:12:04,480 - mmcls - INFO - Epoch [18][1900/5005]\tlr: 1.000e-01, eta: 10:06:24, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8080\n2023-01-02 16:12:13,095 - mmcls - INFO - Epoch [18][2000/5005]\tlr: 1.000e-01, eta: 10:06:15, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7865\n2023-01-02 16:12:21,725 - mmcls - INFO - Epoch [18][2100/5005]\tlr: 1.000e-01, eta: 10:06:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8241\n2023-01-02 16:12:30,665 - mmcls - INFO - Epoch [18][2200/5005]\tlr: 1.000e-01, eta: 10:05:57, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.7630\n2023-01-02 16:12:39,187 - mmcls - INFO - Epoch [18][2300/5005]\tlr: 1.000e-01, eta: 10:05:47, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7914\n2023-01-02 16:12:48,050 - mmcls - INFO - Epoch [18][2400/5005]\tlr: 1.000e-01, eta: 10:05:38, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.7917\n2023-01-02 16:12:56,510 - mmcls - INFO - Epoch [18][2500/5005]\tlr: 1.000e-01, eta: 10:05:28, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7829\n2023-01-02 16:13:05,249 - mmcls - INFO - Epoch [18][2600/5005]\tlr: 1.000e-01, eta: 10:05:19, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7610\n2023-01-02 16:13:14,319 - mmcls - INFO - Epoch [18][2700/5005]\tlr: 1.000e-01, eta: 10:05:11, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.8070\n2023-01-02 16:13:23,060 - mmcls - INFO - Epoch [18][2800/5005]\tlr: 1.000e-01, eta: 10:05:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8036\n2023-01-02 16:13:31,713 - mmcls - INFO - Epoch [18][2900/5005]\tlr: 1.000e-01, eta: 10:04:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7975\n2023-01-02 16:13:40,318 - mmcls - INFO - Epoch [18][3000/5005]\tlr: 1.000e-01, eta: 10:04:43, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7852\n2023-01-02 16:13:49,203 - mmcls - INFO - Epoch [18][3100/5005]\tlr: 1.000e-01, eta: 10:04:35, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7771\n2023-01-02 16:13:58,046 - mmcls - INFO - Epoch [18][3200/5005]\tlr: 1.000e-01, eta: 10:04:26, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.7710\n2023-01-02 16:14:06,666 - mmcls - INFO - Epoch [18][3300/5005]\tlr: 1.000e-01, eta: 10:04:16, time: 0.086, data_time: 0.034, memory: 1125, loss: 2.8006\n2023-01-02 16:14:15,223 - mmcls - INFO - Epoch [18][3400/5005]\tlr: 1.000e-01, eta: 10:04:06, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.8391\n2023-01-02 16:14:23,817 - mmcls - INFO - Epoch [18][3500/5005]\tlr: 1.000e-01, eta: 10:03:57, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8042\n2023-01-02 16:14:32,691 - mmcls - INFO - Epoch [18][3600/5005]\tlr: 1.000e-01, eta: 10:03:48, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.7870\n2023-01-02 16:14:41,395 - mmcls - INFO - Epoch [18][3700/5005]\tlr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.8071\n2023-01-02 16:14:50,196 - mmcls - INFO - Epoch [18][3800/5005]\tlr: 1.000e-01, eta: 10:03:30, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8203\n2023-01-02 16:14:59,053 - mmcls - INFO - Epoch [18][3900/5005]\tlr: 1.000e-01, eta: 10:03:22, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.7658\n2023-01-02 16:15:07,955 - mmcls - INFO - Epoch [18][4000/5005]\tlr: 1.000e-01, eta: 10:03:13, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7816\n2023-01-02 16:15:16,624 - mmcls - INFO - Epoch [18][4100/5005]\tlr: 1.000e-01, eta: 10:03:04, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7720\n2023-01-02 16:15:25,653 - mmcls - INFO - Epoch [18][4200/5005]\tlr: 1.000e-01, eta: 10:02:56, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8065\n2023-01-02 16:15:34,443 - mmcls - INFO - Epoch [18][4300/5005]\tlr: 1.000e-01, eta: 10:02:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7763\n2023-01-02 16:15:42,800 - mmcls - INFO - Epoch [18][4400/5005]\tlr: 1.000e-01, eta: 10:02:37, time: 0.084, data_time: 0.017, memory: 1125, loss: 2.7919\n2023-01-02 16:15:51,822 - mmcls - INFO - Epoch [18][4500/5005]\tlr: 1.000e-01, eta: 10:02:29, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8191\n2023-01-02 16:16:00,630 - mmcls - INFO - Epoch [18][4600/5005]\tlr: 1.000e-01, eta: 10:02:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8064\n2023-01-02 16:16:09,504 - mmcls - INFO - Epoch [18][4700/5005]\tlr: 1.000e-01, eta: 10:02:12, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8100\n2023-01-02 16:16:18,351 - mmcls - INFO - Epoch [18][4800/5005]\tlr: 1.000e-01, eta: 10:02:03, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.8051\n2023-01-02 16:16:27,284 - mmcls - INFO - Epoch [18][4900/5005]\tlr: 1.000e-01, eta: 10:01:55, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8049\n2023-01-02 16:16:35,851 - mmcls - INFO - Epoch [18][5000/5005]\tlr: 1.000e-01, eta: 10:01:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7751\n2023-01-02 16:16:58,419 - mmcls - INFO - Epoch(val) [18][196]\taccuracy_top-1: 46.1460, accuracy_top-5: 70.9880\n2023-01-02 16:17:09,439 - mmcls - INFO - Epoch [19][100/5005]\tlr: 1.000e-01, eta: 10:01:44, time: 0.110, data_time: 0.060, memory: 1125, loss: 2.7016\n2023-01-02 16:17:18,084 - mmcls - INFO - Epoch [19][200/5005]\tlr: 1.000e-01, eta: 10:01:34, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7263\n2023-01-02 16:17:26,723 - mmcls - INFO - Epoch [19][300/5005]\tlr: 1.000e-01, eta: 10:01:25, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7233\n2023-01-02 16:17:35,679 - mmcls - INFO - Epoch [19][400/5005]\tlr: 1.000e-01, eta: 10:01:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7556\n2023-01-02 16:17:44,254 - mmcls - INFO - Epoch [19][500/5005]\tlr: 1.000e-01, eta: 10:01:07, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7231\n2023-01-02 16:17:52,694 - mmcls - INFO - Epoch [19][600/5005]\tlr: 1.000e-01, eta: 10:00:56, time: 0.084, data_time: 0.022, memory: 1125, loss: 2.7724\n2023-01-02 16:18:01,350 - mmcls - INFO - Epoch [19][700/5005]\tlr: 1.000e-01, eta: 10:00:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7384\n2023-01-02 16:18:10,144 - mmcls - INFO - Epoch [19][800/5005]\tlr: 1.000e-01, eta: 10:00:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7471\n2023-01-02 16:18:18,887 - mmcls - INFO - Epoch [19][900/5005]\tlr: 1.000e-01, eta: 10:00:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7589\n2023-01-02 16:18:27,637 - mmcls - INFO - Epoch [19][1000/5005]\tlr: 1.000e-01, eta: 10:00:20, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7482\n2023-01-02 16:18:36,731 - mmcls - INFO - Epoch [19][1100/5005]\tlr: 1.000e-01, eta: 10:00:13, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.7771\n2023-01-02 16:18:45,330 - mmcls - INFO - Epoch [19][1200/5005]\tlr: 1.000e-01, eta: 10:00:03, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7295\n2023-01-02 16:18:54,236 - mmcls - INFO - Epoch [19][1300/5005]\tlr: 1.000e-01, eta: 9:59:55, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7648\n2023-01-02 16:19:03,315 - mmcls - INFO - Epoch [19][1400/5005]\tlr: 1.000e-01, eta: 9:59:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7788\n2023-01-02 16:19:11,856 - mmcls - INFO - Epoch [19][1500/5005]\tlr: 1.000e-01, eta: 9:59:37, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7618\n2023-01-02 16:19:20,721 - mmcls - INFO - Epoch [19][1600/5005]\tlr: 1.000e-01, eta: 9:59:29, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7830\n2023-01-02 16:19:29,421 - mmcls - INFO - Epoch [19][1700/5005]\tlr: 1.000e-01, eta: 9:59:19, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7678\n2023-01-02 16:19:38,109 - mmcls - INFO - Epoch [19][1800/5005]\tlr: 1.000e-01, eta: 9:59:10, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7869\n2023-01-02 16:19:46,820 - mmcls - INFO - Epoch [19][1900/5005]\tlr: 1.000e-01, eta: 9:59:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7610\n2023-01-02 16:19:55,363 - mmcls - INFO - Epoch [19][2000/5005]\tlr: 1.000e-01, eta: 9:58:51, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.8016\n2023-01-02 16:20:03,906 - mmcls - INFO - Epoch [19][2100/5005]\tlr: 1.000e-01, eta: 9:58:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7849\n2023-01-02 16:20:12,825 - mmcls - INFO - Epoch [19][2200/5005]\tlr: 1.000e-01, eta: 9:58:33, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.7876\n2023-01-02 16:20:21,565 - mmcls - INFO - Epoch [19][2300/5005]\tlr: 1.000e-01, eta: 9:58:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7840\n2023-01-02 16:20:30,473 - mmcls - INFO - Epoch [19][2400/5005]\tlr: 1.000e-01, eta: 9:58:15, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7932\n2023-01-02 16:20:39,300 - mmcls - INFO - Epoch [19][2500/5005]\tlr: 1.000e-01, eta: 9:58:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7783\n2023-01-02 16:20:48,002 - mmcls - INFO - Epoch [19][2600/5005]\tlr: 1.000e-01, eta: 9:57:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7832\n2023-01-02 16:20:56,703 - mmcls - INFO - Epoch [19][2700/5005]\tlr: 1.000e-01, eta: 9:57:48, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.7829\n2023-01-02 16:21:05,177 - mmcls - INFO - Epoch [19][2800/5005]\tlr: 1.000e-01, eta: 9:57:38, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.7919\n2023-01-02 16:21:14,332 - mmcls - INFO - Epoch [19][2900/5005]\tlr: 1.000e-01, eta: 9:57:31, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.7757\n2023-01-02 16:21:23,187 - mmcls - INFO - Epoch [19][3000/5005]\tlr: 1.000e-01, eta: 9:57:22, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7631\n2023-01-02 16:21:31,922 - mmcls - INFO - Epoch [19][3100/5005]\tlr: 1.000e-01, eta: 9:57:13, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7979\n2023-01-02 16:21:40,823 - mmcls - INFO - Epoch [19][3200/5005]\tlr: 1.000e-01, eta: 9:57:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7806\n2023-01-02 16:21:49,428 - mmcls - INFO - Epoch [19][3300/5005]\tlr: 1.000e-01, eta: 9:56:55, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7982\n2023-01-02 16:21:58,059 - mmcls - INFO - Epoch [19][3400/5005]\tlr: 1.000e-01, eta: 9:56:46, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7901\n2023-01-02 16:22:06,759 - mmcls - INFO - Epoch [19][3500/5005]\tlr: 1.000e-01, eta: 9:56:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7914\n2023-01-02 16:22:15,659 - mmcls - INFO - Epoch [19][3600/5005]\tlr: 1.000e-01, eta: 9:56:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7920\n2023-01-02 16:22:24,394 - mmcls - INFO - Epoch [19][3700/5005]\tlr: 1.000e-01, eta: 9:56:19, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7701\n2023-01-02 16:22:33,051 - mmcls - INFO - Epoch [19][3800/5005]\tlr: 1.000e-01, eta: 9:56:10, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7778\n2023-01-02 16:22:41,885 - mmcls - INFO - Epoch [19][3900/5005]\tlr: 1.000e-01, eta: 9:56:01, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7833\n2023-01-02 16:22:50,734 - mmcls - INFO - Epoch [19][4000/5005]\tlr: 1.000e-01, eta: 9:55:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7763\n2023-01-02 16:22:59,644 - mmcls - INFO - Epoch [19][4100/5005]\tlr: 1.000e-01, eta: 9:55:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7999\n2023-01-02 16:23:08,728 - mmcls - INFO - Epoch [19][4200/5005]\tlr: 1.000e-01, eta: 9:55:37, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.7961\n2023-01-02 16:23:17,259 - mmcls - INFO - Epoch [19][4300/5005]\tlr: 1.000e-01, eta: 9:55:27, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.7900\n2023-01-02 16:23:26,218 - mmcls - INFO - Epoch [19][4400/5005]\tlr: 1.000e-01, eta: 9:55:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7876\n2023-01-02 16:23:34,715 - mmcls - INFO - Epoch [19][4500/5005]\tlr: 1.000e-01, eta: 9:55:08, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8017\n2023-01-02 16:23:43,445 - mmcls - INFO - Epoch [19][4600/5005]\tlr: 1.000e-01, eta: 9:54:59, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8049\n2023-01-02 16:23:52,111 - mmcls - INFO - Epoch [19][4700/5005]\tlr: 1.000e-01, eta: 9:54:50, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7942\n2023-01-02 16:24:00,829 - mmcls - INFO - Epoch [19][4800/5005]\tlr: 1.000e-01, eta: 9:54:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7905\n2023-01-02 16:24:10,086 - mmcls - INFO - Epoch [19][4900/5005]\tlr: 1.000e-01, eta: 9:54:34, time: 0.093, data_time: 0.009, memory: 1125, loss: 2.8235\n2023-01-02 16:24:18,780 - mmcls - INFO - Epoch [19][5000/5005]\tlr: 1.000e-01, eta: 9:54:25, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7934\n2023-01-02 16:24:41,404 - mmcls - INFO - Epoch(val) [19][196]\taccuracy_top-1: 45.8820, accuracy_top-5: 70.6780\n2023-01-02 16:24:52,416 - mmcls - INFO - Epoch [20][100/5005]\tlr: 1.000e-01, eta: 9:54:23, time: 0.110, data_time: 0.042, memory: 1125, loss: 2.7377\n2023-01-02 16:25:01,121 - mmcls - INFO - Epoch [20][200/5005]\tlr: 1.000e-01, eta: 9:54:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7249\n2023-01-02 16:25:09,878 - mmcls - INFO - Epoch [20][300/5005]\tlr: 1.000e-01, eta: 9:54:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7322\n2023-01-02 16:25:18,945 - mmcls - INFO - Epoch [20][400/5005]\tlr: 1.000e-01, eta: 9:53:57, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.7664\n2023-01-02 16:25:27,603 - mmcls - INFO - Epoch [20][500/5005]\tlr: 1.000e-01, eta: 9:53:48, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7499\n2023-01-02 16:25:36,264 - mmcls - INFO - Epoch [20][600/5005]\tlr: 1.000e-01, eta: 9:53:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7580\n2023-01-02 16:25:44,912 - mmcls - INFO - Epoch [20][700/5005]\tlr: 1.000e-01, eta: 9:53:29, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7387\n2023-01-02 16:25:53,701 - mmcls - INFO - Epoch [20][800/5005]\tlr: 1.000e-01, eta: 9:53:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7511\n2023-01-02 16:26:02,547 - mmcls - INFO - Epoch [20][900/5005]\tlr: 1.000e-01, eta: 9:53:11, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7449\n2023-01-02 16:26:11,388 - mmcls - INFO - Epoch [20][1000/5005]\tlr: 1.000e-01, eta: 9:53:03, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7484\n2023-01-02 16:26:20,379 - mmcls - INFO - Epoch [20][1100/5005]\tlr: 1.000e-01, eta: 9:52:55, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7461\n2023-01-02 16:26:29,078 - mmcls - INFO - Epoch [20][1200/5005]\tlr: 1.000e-01, eta: 9:52:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7734\n2023-01-02 16:26:37,659 - mmcls - INFO - Epoch [20][1300/5005]\tlr: 1.000e-01, eta: 9:52:36, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8020\n2023-01-02 16:26:46,485 - mmcls - INFO - Epoch [20][1400/5005]\tlr: 1.000e-01, eta: 9:52:27, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8181\n2023-01-02 16:26:55,505 - mmcls - INFO - Epoch [20][1500/5005]\tlr: 1.000e-01, eta: 9:52:19, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.7784\n2023-01-02 16:27:04,330 - mmcls - INFO - Epoch [20][1600/5005]\tlr: 1.000e-01, eta: 9:52:11, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.8000\n2023-01-02 16:27:13,094 - mmcls - INFO - Epoch [20][1700/5005]\tlr: 1.000e-01, eta: 9:52:02, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7577\n2023-01-02 16:27:21,852 - mmcls - INFO - Epoch [20][1800/5005]\tlr: 1.000e-01, eta: 9:51:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7956\n2023-01-02 16:27:30,667 - mmcls - INFO - Epoch [20][1900/5005]\tlr: 1.000e-01, eta: 9:51:44, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7665\n2023-01-02 16:27:39,366 - mmcls - INFO - Epoch [20][2000/5005]\tlr: 1.000e-01, eta: 9:51:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7718\n2023-01-02 16:27:48,030 - mmcls - INFO - Epoch [20][2100/5005]\tlr: 1.000e-01, eta: 9:51:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7967\n2023-01-02 16:27:56,822 - mmcls - INFO - Epoch [20][2200/5005]\tlr: 1.000e-01, eta: 9:51:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7578\n2023-01-02 16:28:05,545 - mmcls - INFO - Epoch [20][2300/5005]\tlr: 1.000e-01, eta: 9:51:07, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7671\n2023-01-02 16:28:14,087 - mmcls - INFO - Epoch [20][2400/5005]\tlr: 1.000e-01, eta: 9:50:58, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7670\n2023-01-02 16:28:22,978 - mmcls - INFO - Epoch [20][2500/5005]\tlr: 1.000e-01, eta: 9:50:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7825\n2023-01-02 16:28:31,849 - mmcls - INFO - Epoch [20][2600/5005]\tlr: 1.000e-01, eta: 9:50:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7743\n2023-01-02 16:28:40,606 - mmcls - INFO - Epoch [20][2700/5005]\tlr: 1.000e-01, eta: 9:50:32, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7753\n2023-01-02 16:28:49,591 - mmcls - INFO - Epoch [20][2800/5005]\tlr: 1.000e-01, eta: 9:50:24, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7780\n2023-01-02 16:28:57,995 - mmcls - INFO - Epoch [20][2900/5005]\tlr: 1.000e-01, eta: 9:50:13, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.7856\n2023-01-02 16:29:06,818 - mmcls - INFO - Epoch [20][3000/5005]\tlr: 1.000e-01, eta: 9:50:04, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7867\n2023-01-02 16:29:15,386 - mmcls - INFO - Epoch [20][3100/5005]\tlr: 1.000e-01, eta: 9:49:55, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.7677\n2023-01-02 16:29:24,154 - mmcls - INFO - Epoch [20][3200/5005]\tlr: 1.000e-01, eta: 9:49:46, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.8104\n2023-01-02 16:29:32,797 - mmcls - INFO - Epoch [20][3300/5005]\tlr: 1.000e-01, eta: 9:49:36, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7921\n2023-01-02 16:29:41,535 - mmcls - INFO - Epoch [20][3400/5005]\tlr: 1.000e-01, eta: 9:49:27, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7876\n2023-01-02 16:29:50,537 - mmcls - INFO - Epoch [20][3500/5005]\tlr: 1.000e-01, eta: 9:49:19, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7976\n2023-01-02 16:29:59,427 - mmcls - INFO - Epoch [20][3600/5005]\tlr: 1.000e-01, eta: 9:49:11, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.8053\n2023-01-02 16:30:08,017 - mmcls - INFO - Epoch [20][3700/5005]\tlr: 1.000e-01, eta: 9:49:01, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7672\n2023-01-02 16:30:16,899 - mmcls - INFO - Epoch [20][3800/5005]\tlr: 1.000e-01, eta: 9:48:53, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7651\n2023-01-02 16:30:25,778 - mmcls - INFO - Epoch [20][3900/5005]\tlr: 1.000e-01, eta: 9:48:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7959\n2023-01-02 16:30:34,433 - mmcls - INFO - Epoch [20][4000/5005]\tlr: 1.000e-01, eta: 9:48:35, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8087\n2023-01-02 16:30:42,907 - mmcls - INFO - Epoch [20][4100/5005]\tlr: 1.000e-01, eta: 9:48:25, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7782\n2023-01-02 16:30:51,701 - mmcls - INFO - Epoch [20][4200/5005]\tlr: 1.000e-01, eta: 9:48:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7764\n2023-01-02 16:31:00,670 - mmcls - INFO - Epoch [20][4300/5005]\tlr: 1.000e-01, eta: 9:48:08, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7874\n2023-01-02 16:31:09,177 - mmcls - INFO - Epoch [20][4400/5005]\tlr: 1.000e-01, eta: 9:47:58, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7531\n2023-01-02 16:31:17,941 - mmcls - INFO - Epoch [20][4500/5005]\tlr: 1.000e-01, eta: 9:47:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.8207\n2023-01-02 16:31:26,675 - mmcls - INFO - Epoch [20][4600/5005]\tlr: 1.000e-01, eta: 9:47:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7703\n2023-01-02 16:31:35,392 - mmcls - INFO - Epoch [20][4700/5005]\tlr: 1.000e-01, eta: 9:47:31, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7905\n2023-01-02 16:31:44,295 - mmcls - INFO - Epoch [20][4800/5005]\tlr: 1.000e-01, eta: 9:47:22, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7810\n2023-01-02 16:31:53,156 - mmcls - INFO - Epoch [20][4900/5005]\tlr: 1.000e-01, eta: 9:47:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7717\n2023-01-02 16:32:01,691 - mmcls - INFO - Epoch [20][5000/5005]\tlr: 1.000e-01, eta: 9:47:04, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.8088\n2023-01-02 16:32:24,482 - mmcls - INFO - Epoch(val) [20][196]\taccuracy_top-1: 47.1480, accuracy_top-5: 71.9200\n2023-01-02 16:32:35,524 - mmcls - INFO - Epoch [21][100/5005]\tlr: 1.000e-01, eta: 9:47:02, time: 0.110, data_time: 0.042, memory: 1125, loss: 2.7202\n2023-01-02 16:32:44,422 - mmcls - INFO - Epoch [21][200/5005]\tlr: 1.000e-01, eta: 9:46:54, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7283\n2023-01-02 16:32:53,236 - mmcls - INFO - Epoch [21][300/5005]\tlr: 1.000e-01, eta: 9:46:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7296\n2023-01-02 16:33:01,905 - mmcls - INFO - Epoch [21][400/5005]\tlr: 1.000e-01, eta: 9:46:36, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7511\n2023-01-02 16:33:10,479 - mmcls - INFO - Epoch [21][500/5005]\tlr: 1.000e-01, eta: 9:46:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7615\n2023-01-02 16:33:19,467 - mmcls - INFO - Epoch [21][600/5005]\tlr: 1.000e-01, eta: 9:46:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7538\n2023-01-02 16:33:28,157 - mmcls - INFO - Epoch [21][700/5005]\tlr: 1.000e-01, eta: 9:46:09, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7420\n2023-01-02 16:33:36,828 - mmcls - INFO - Epoch [21][800/5005]\tlr: 1.000e-01, eta: 9:45:59, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.7315\n2023-01-02 16:33:45,760 - mmcls - INFO - Epoch [21][900/5005]\tlr: 1.000e-01, eta: 9:45:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7455\n2023-01-02 16:33:54,490 - mmcls - INFO - Epoch [21][1000/5005]\tlr: 1.000e-01, eta: 9:45:42, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.7705\n2023-01-02 16:34:03,153 - mmcls - INFO - Epoch [21][1100/5005]\tlr: 1.000e-01, eta: 9:45:33, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7757\n2023-01-02 16:34:11,772 - mmcls - INFO - Epoch [21][1200/5005]\tlr: 1.000e-01, eta: 9:45:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7664\n2023-01-02 16:34:20,697 - mmcls - INFO - Epoch [21][1300/5005]\tlr: 1.000e-01, eta: 9:45:15, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7494\n2023-01-02 16:34:29,231 - mmcls - INFO - Epoch [21][1400/5005]\tlr: 1.000e-01, eta: 9:45:05, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7545\n2023-01-02 16:34:37,939 - mmcls - INFO - Epoch [21][1500/5005]\tlr: 1.000e-01, eta: 9:44:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7713\n2023-01-02 16:34:46,352 - mmcls - INFO - Epoch [21][1600/5005]\tlr: 1.000e-01, eta: 9:44:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.7684\n2023-01-02 16:34:55,238 - mmcls - INFO - Epoch [21][1700/5005]\tlr: 1.000e-01, eta: 9:44:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7853\n2023-01-02 16:35:04,270 - mmcls - INFO - Epoch [21][1800/5005]\tlr: 1.000e-01, eta: 9:44:29, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7778\n2023-01-02 16:35:13,387 - mmcls - INFO - Epoch [21][1900/5005]\tlr: 1.000e-01, eta: 9:44:22, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7820\n2023-01-02 16:35:22,383 - mmcls - INFO - Epoch [21][2000/5005]\tlr: 1.000e-01, eta: 9:44:13, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7899\n2023-01-02 16:35:31,204 - mmcls - INFO - Epoch [21][2100/5005]\tlr: 1.000e-01, eta: 9:44:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7618\n2023-01-02 16:35:39,858 - mmcls - INFO - Epoch [21][2200/5005]\tlr: 1.000e-01, eta: 9:43:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7865\n2023-01-02 16:35:48,539 - mmcls - INFO - Epoch [21][2300/5005]\tlr: 1.000e-01, eta: 9:43:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7749\n2023-01-02 16:35:57,484 - mmcls - INFO - Epoch [21][2400/5005]\tlr: 1.000e-01, eta: 9:43:38, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7774\n2023-01-02 16:36:06,415 - mmcls - INFO - Epoch [21][2500/5005]\tlr: 1.000e-01, eta: 9:43:30, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.7835\n2023-01-02 16:36:15,161 - mmcls - INFO - Epoch [21][2600/5005]\tlr: 1.000e-01, eta: 9:43:21, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.7764\n2023-01-02 16:36:23,905 - mmcls - INFO - Epoch [21][2700/5005]\tlr: 1.000e-01, eta: 9:43:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7840\n2023-01-02 16:36:32,887 - mmcls - INFO - Epoch [21][2800/5005]\tlr: 1.000e-01, eta: 9:43:03, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7621\n2023-01-02 16:36:41,631 - mmcls - INFO - Epoch [21][2900/5005]\tlr: 1.000e-01, eta: 9:42:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7648\n2023-01-02 16:36:50,371 - mmcls - INFO - Epoch [21][3000/5005]\tlr: 1.000e-01, eta: 9:42:45, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.8159\n2023-01-02 16:36:59,322 - mmcls - INFO - Epoch [21][3100/5005]\tlr: 1.000e-01, eta: 9:42:37, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.7705\n2023-01-02 16:37:08,153 - mmcls - INFO - Epoch [21][3200/5005]\tlr: 1.000e-01, eta: 9:42:29, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7846\n2023-01-02 16:37:17,076 - mmcls - INFO - Epoch [21][3300/5005]\tlr: 1.000e-01, eta: 9:42:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7640\n2023-01-02 16:37:25,743 - mmcls - INFO - Epoch [21][3400/5005]\tlr: 1.000e-01, eta: 9:42:11, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.8216\n2023-01-02 16:37:34,564 - mmcls - INFO - Epoch [21][3500/5005]\tlr: 1.000e-01, eta: 9:42:02, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7816\n2023-01-02 16:37:43,634 - mmcls - INFO - Epoch [21][3600/5005]\tlr: 1.000e-01, eta: 9:41:54, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.7871\n2023-01-02 16:37:52,311 - mmcls - INFO - Epoch [21][3700/5005]\tlr: 1.000e-01, eta: 9:41:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7757\n2023-01-02 16:38:00,966 - mmcls - INFO - Epoch [21][3800/5005]\tlr: 1.000e-01, eta: 9:41:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7715\n2023-01-02 16:38:09,745 - mmcls - INFO - Epoch [21][3900/5005]\tlr: 1.000e-01, eta: 9:41:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7872\n2023-01-02 16:38:18,507 - mmcls - INFO - Epoch [21][4000/5005]\tlr: 1.000e-01, eta: 9:41:18, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7662\n2023-01-02 16:38:27,380 - mmcls - INFO - Epoch [21][4100/5005]\tlr: 1.000e-01, eta: 9:41:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7885\n2023-01-02 16:38:36,264 - mmcls - INFO - Epoch [21][4200/5005]\tlr: 1.000e-01, eta: 9:41:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7742\n2023-01-02 16:38:45,058 - mmcls - INFO - Epoch [21][4300/5005]\tlr: 1.000e-01, eta: 9:40:52, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7760\n2023-01-02 16:38:53,905 - mmcls - INFO - Epoch [21][4400/5005]\tlr: 1.000e-01, eta: 9:40:44, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7757\n2023-01-02 16:39:02,581 - mmcls - INFO - Epoch [21][4500/5005]\tlr: 1.000e-01, eta: 9:40:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7804\n2023-01-02 16:39:11,393 - mmcls - INFO - Epoch [21][4600/5005]\tlr: 1.000e-01, eta: 9:40:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7535\n2023-01-02 16:39:20,091 - mmcls - INFO - Epoch [21][4700/5005]\tlr: 1.000e-01, eta: 9:40:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7920\n2023-01-02 16:39:29,006 - mmcls - INFO - Epoch [21][4800/5005]\tlr: 1.000e-01, eta: 9:40:08, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7869\n2023-01-02 16:39:37,951 - mmcls - INFO - Epoch [21][4900/5005]\tlr: 1.000e-01, eta: 9:40:00, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7950\n2023-01-02 16:39:46,651 - mmcls - INFO - Epoch [21][5000/5005]\tlr: 1.000e-01, eta: 9:39:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7603\n2023-01-02 16:40:10,152 - mmcls - INFO - Epoch(val) [21][196]\taccuracy_top-1: 45.5680, accuracy_top-5: 70.2900\n2023-01-02 16:40:21,284 - mmcls - INFO - Epoch [22][100/5005]\tlr: 1.000e-01, eta: 9:39:48, time: 0.111, data_time: 0.033, memory: 1125, loss: 2.7087\n2023-01-02 16:40:30,131 - mmcls - INFO - Epoch [22][200/5005]\tlr: 1.000e-01, eta: 9:39:40, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7337\n2023-01-02 16:40:38,881 - mmcls - INFO - Epoch [22][300/5005]\tlr: 1.000e-01, eta: 9:39:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7114\n2023-01-02 16:40:47,731 - mmcls - INFO - Epoch [22][400/5005]\tlr: 1.000e-01, eta: 9:39:22, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7192\n2023-01-02 16:40:56,420 - mmcls - INFO - Epoch [22][500/5005]\tlr: 1.000e-01, eta: 9:39:13, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7290\n2023-01-02 16:41:05,195 - mmcls - INFO - Epoch [22][600/5005]\tlr: 1.000e-01, eta: 9:39:04, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7307\n2023-01-02 16:41:13,922 - mmcls - INFO - Epoch [22][700/5005]\tlr: 1.000e-01, eta: 9:38:55, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7515\n2023-01-02 16:41:22,910 - mmcls - INFO - Epoch [22][800/5005]\tlr: 1.000e-01, eta: 9:38:47, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.7344\n2023-01-02 16:41:31,710 - mmcls - INFO - Epoch [22][900/5005]\tlr: 1.000e-01, eta: 9:38:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7479\n2023-01-02 16:41:40,754 - mmcls - INFO - Epoch [22][1000/5005]\tlr: 1.000e-01, eta: 9:38:30, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7269\n2023-01-02 16:41:49,616 - mmcls - INFO - Epoch [22][1100/5005]\tlr: 1.000e-01, eta: 9:38:22, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.7594\n2023-01-02 16:41:58,192 - mmcls - INFO - Epoch [22][1200/5005]\tlr: 1.000e-01, eta: 9:38:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7550\n2023-01-02 16:42:06,896 - mmcls - INFO - Epoch [22][1300/5005]\tlr: 1.000e-01, eta: 9:38:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7714\n2023-01-02 16:42:16,348 - mmcls - INFO - Epoch [22][1400/5005]\tlr: 1.000e-01, eta: 9:37:56, time: 0.095, data_time: 0.012, memory: 1125, loss: 2.7659\n2023-01-02 16:42:25,072 - mmcls - INFO - Epoch [22][1500/5005]\tlr: 1.000e-01, eta: 9:37:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7423\n2023-01-02 16:42:34,014 - mmcls - INFO - Epoch [22][1600/5005]\tlr: 1.000e-01, eta: 9:37:39, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7309\n2023-01-02 16:42:43,092 - mmcls - INFO - Epoch [22][1700/5005]\tlr: 1.000e-01, eta: 9:37:31, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.7757\n2023-01-02 16:42:51,759 - mmcls - INFO - Epoch [22][1800/5005]\tlr: 1.000e-01, eta: 9:37:22, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7625\n2023-01-02 16:43:00,668 - mmcls - INFO - Epoch [22][1900/5005]\tlr: 1.000e-01, eta: 9:37:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7598\n2023-01-02 16:43:09,586 - mmcls - INFO - Epoch [22][2000/5005]\tlr: 1.000e-01, eta: 9:37:05, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7675\n2023-01-02 16:43:18,501 - mmcls - INFO - Epoch [22][2100/5005]\tlr: 1.000e-01, eta: 9:36:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7878\n2023-01-02 16:43:27,353 - mmcls - INFO - Epoch [22][2200/5005]\tlr: 1.000e-01, eta: 9:36:48, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7511\n2023-01-02 16:43:36,865 - mmcls - INFO - Epoch [22][2300/5005]\tlr: 1.000e-01, eta: 9:36:42, time: 0.095, data_time: 0.014, memory: 1125, loss: 2.7509\n2023-01-02 16:43:45,592 - mmcls - INFO - Epoch [22][2400/5005]\tlr: 1.000e-01, eta: 9:36:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7728\n2023-01-02 16:43:54,812 - mmcls - INFO - Epoch [22][2500/5005]\tlr: 1.000e-01, eta: 9:36:25, time: 0.092, data_time: 0.011, memory: 1125, loss: 2.7645\n2023-01-02 16:44:03,469 - mmcls - INFO - Epoch [22][2600/5005]\tlr: 1.000e-01, eta: 9:36:16, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.8062\n2023-01-02 16:44:12,207 - mmcls - INFO - Epoch [22][2700/5005]\tlr: 1.000e-01, eta: 9:36:07, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7324\n2023-01-02 16:44:20,957 - mmcls - INFO - Epoch [22][2800/5005]\tlr: 1.000e-01, eta: 9:35:58, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7621\n2023-01-02 16:44:29,571 - mmcls - INFO - Epoch [22][2900/5005]\tlr: 1.000e-01, eta: 9:35:49, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7573\n2023-01-02 16:44:38,247 - mmcls - INFO - Epoch [22][3000/5005]\tlr: 1.000e-01, eta: 9:35:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7625\n2023-01-02 16:44:47,361 - mmcls - INFO - Epoch [22][3100/5005]\tlr: 1.000e-01, eta: 9:35:32, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.7559\n2023-01-02 16:44:56,088 - mmcls - INFO - Epoch [22][3200/5005]\tlr: 1.000e-01, eta: 9:35:23, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7817\n2023-01-02 16:45:04,938 - mmcls - INFO - Epoch [22][3300/5005]\tlr: 1.000e-01, eta: 9:35:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7811\n2023-01-02 16:45:13,820 - mmcls - INFO - Epoch [22][3400/5005]\tlr: 1.000e-01, eta: 9:35:05, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.7592\n2023-01-02 16:45:22,860 - mmcls - INFO - Epoch [22][3500/5005]\tlr: 1.000e-01, eta: 9:34:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7334\n2023-01-02 16:45:31,297 - mmcls - INFO - Epoch [22][3600/5005]\tlr: 1.000e-01, eta: 9:34:47, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.7626\n2023-01-02 16:45:39,987 - mmcls - INFO - Epoch [22][3700/5005]\tlr: 1.000e-01, eta: 9:34:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7581\n2023-01-02 16:45:48,616 - mmcls - INFO - Epoch [22][3800/5005]\tlr: 1.000e-01, eta: 9:34:29, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8084\n2023-01-02 16:45:57,189 - mmcls - INFO - Epoch [22][3900/5005]\tlr: 1.000e-01, eta: 9:34:19, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7778\n2023-01-02 16:46:06,059 - mmcls - INFO - Epoch [22][4000/5005]\tlr: 1.000e-01, eta: 9:34:11, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7777\n2023-01-02 16:46:15,440 - mmcls - INFO - Epoch [22][4100/5005]\tlr: 1.000e-01, eta: 9:34:04, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.7576\n2023-01-02 16:46:24,528 - mmcls - INFO - Epoch [22][4200/5005]\tlr: 1.000e-01, eta: 9:33:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.7504\n2023-01-02 16:46:33,206 - mmcls - INFO - Epoch [22][4300/5005]\tlr: 1.000e-01, eta: 9:33:47, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7693\n2023-01-02 16:46:41,875 - mmcls - INFO - Epoch [22][4400/5005]\tlr: 1.000e-01, eta: 9:33:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7637\n2023-01-02 16:46:50,792 - mmcls - INFO - Epoch [22][4500/5005]\tlr: 1.000e-01, eta: 9:33:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7646\n2023-01-02 16:46:59,422 - mmcls - INFO - Epoch [22][4600/5005]\tlr: 1.000e-01, eta: 9:33:20, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7849\n2023-01-02 16:47:08,370 - mmcls - INFO - Epoch [22][4700/5005]\tlr: 1.000e-01, eta: 9:33:11, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7837\n2023-01-02 16:47:17,086 - mmcls - INFO - Epoch [22][4800/5005]\tlr: 1.000e-01, eta: 9:33:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8141\n2023-01-02 16:47:25,969 - mmcls - INFO - Epoch [22][4900/5005]\tlr: 1.000e-01, eta: 9:32:54, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7754\n2023-01-02 16:47:34,677 - mmcls - INFO - Epoch [22][5000/5005]\tlr: 1.000e-01, eta: 9:32:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8034\n2023-01-02 16:47:57,170 - mmcls - INFO - Epoch(val) [22][196]\taccuracy_top-1: 46.9400, accuracy_top-5: 71.9000\n2023-01-02 16:48:08,165 - mmcls - INFO - Epoch [23][100/5005]\tlr: 1.000e-01, eta: 9:32:42, time: 0.110, data_time: 0.039, memory: 1125, loss: 2.6823\n2023-01-02 16:48:16,888 - mmcls - INFO - Epoch [23][200/5005]\tlr: 1.000e-01, eta: 9:32:32, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7295\n2023-01-02 16:48:25,898 - mmcls - INFO - Epoch [23][300/5005]\tlr: 1.000e-01, eta: 9:32:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7057\n2023-01-02 16:48:34,408 - mmcls - INFO - Epoch [23][400/5005]\tlr: 1.000e-01, eta: 9:32:14, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7217\n2023-01-02 16:48:42,973 - mmcls - INFO - Epoch [23][500/5005]\tlr: 1.000e-01, eta: 9:32:05, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7200\n2023-01-02 16:48:51,804 - mmcls - INFO - Epoch [23][600/5005]\tlr: 1.000e-01, eta: 9:31:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7282\n2023-01-02 16:49:00,315 - mmcls - INFO - Epoch [23][700/5005]\tlr: 1.000e-01, eta: 9:31:46, time: 0.085, data_time: 0.025, memory: 1125, loss: 2.7217\n2023-01-02 16:49:09,486 - mmcls - INFO - Epoch [23][800/5005]\tlr: 1.000e-01, eta: 9:31:39, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7366\n2023-01-02 16:49:18,217 - mmcls - INFO - Epoch [23][900/5005]\tlr: 1.000e-01, eta: 9:31:30, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.7625\n2023-01-02 16:49:27,020 - mmcls - INFO - Epoch [23][1000/5005]\tlr: 1.000e-01, eta: 9:31:21, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7514\n2023-01-02 16:49:35,723 - mmcls - INFO - Epoch [23][1100/5005]\tlr: 1.000e-01, eta: 9:31:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7424\n2023-01-02 16:49:44,714 - mmcls - INFO - Epoch [23][1200/5005]\tlr: 1.000e-01, eta: 9:31:04, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7591\n2023-01-02 16:49:53,278 - mmcls - INFO - Epoch [23][1300/5005]\tlr: 1.000e-01, eta: 9:30:54, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7528\n2023-01-02 16:50:02,214 - mmcls - INFO - Epoch [23][1400/5005]\tlr: 1.000e-01, eta: 9:30:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7473\n2023-01-02 16:50:11,007 - mmcls - INFO - Epoch [23][1500/5005]\tlr: 1.000e-01, eta: 9:30:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7565\n2023-01-02 16:50:20,024 - mmcls - INFO - Epoch [23][1600/5005]\tlr: 1.000e-01, eta: 9:30:29, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.7190\n2023-01-02 16:50:28,859 - mmcls - INFO - Epoch [23][1700/5005]\tlr: 1.000e-01, eta: 9:30:20, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7704\n2023-01-02 16:50:37,764 - mmcls - INFO - Epoch [23][1800/5005]\tlr: 1.000e-01, eta: 9:30:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7545\n2023-01-02 16:50:46,239 - mmcls - INFO - Epoch [23][1900/5005]\tlr: 1.000e-01, eta: 9:30:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7761\n2023-01-02 16:50:55,001 - mmcls - INFO - Epoch [23][2000/5005]\tlr: 1.000e-01, eta: 9:29:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7869\n2023-01-02 16:51:03,976 - mmcls - INFO - Epoch [23][2100/5005]\tlr: 1.000e-01, eta: 9:29:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7418\n2023-01-02 16:51:12,868 - mmcls - INFO - Epoch [23][2200/5005]\tlr: 1.000e-01, eta: 9:29:36, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7564\n2023-01-02 16:51:21,408 - mmcls - INFO - Epoch [23][2300/5005]\tlr: 1.000e-01, eta: 9:29:26, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7503\n2023-01-02 16:51:29,953 - mmcls - INFO - Epoch [23][2400/5005]\tlr: 1.000e-01, eta: 9:29:16, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7633\n2023-01-02 16:51:38,546 - mmcls - INFO - Epoch [23][2500/5005]\tlr: 1.000e-01, eta: 9:29:07, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7809\n2023-01-02 16:51:47,270 - mmcls - INFO - Epoch [23][2600/5005]\tlr: 1.000e-01, eta: 9:28:58, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7603\n2023-01-02 16:51:55,867 - mmcls - INFO - Epoch [23][2700/5005]\tlr: 1.000e-01, eta: 9:28:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7621\n2023-01-02 16:52:04,790 - mmcls - INFO - Epoch [23][2800/5005]\tlr: 1.000e-01, eta: 9:28:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7735\n2023-01-02 16:52:13,646 - mmcls - INFO - Epoch [23][2900/5005]\tlr: 1.000e-01, eta: 9:28:31, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7672\n2023-01-02 16:52:22,503 - mmcls - INFO - Epoch [23][3000/5005]\tlr: 1.000e-01, eta: 9:28:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7382\n2023-01-02 16:52:31,133 - mmcls - INFO - Epoch [23][3100/5005]\tlr: 1.000e-01, eta: 9:28:13, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7717\n2023-01-02 16:52:40,086 - mmcls - INFO - Epoch [23][3200/5005]\tlr: 1.000e-01, eta: 9:28:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7494\n2023-01-02 16:52:49,542 - mmcls - INFO - Epoch [23][3300/5005]\tlr: 1.000e-01, eta: 9:27:58, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.7590\n2023-01-02 16:52:58,248 - mmcls - INFO - Epoch [23][3400/5005]\tlr: 1.000e-01, eta: 9:27:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7751\n2023-01-02 16:53:06,788 - mmcls - INFO - Epoch [23][3500/5005]\tlr: 1.000e-01, eta: 9:27:40, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7795\n2023-01-02 16:53:15,706 - mmcls - INFO - Epoch [23][3600/5005]\tlr: 1.000e-01, eta: 9:27:31, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7856\n2023-01-02 16:53:24,339 - mmcls - INFO - Epoch [23][3700/5005]\tlr: 1.000e-01, eta: 9:27:22, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.7907\n2023-01-02 16:53:32,685 - mmcls - INFO - Epoch [23][3800/5005]\tlr: 1.000e-01, eta: 9:27:11, time: 0.083, data_time: 0.001, memory: 1125, loss: 2.7658\n2023-01-02 16:53:41,524 - mmcls - INFO - Epoch [23][3900/5005]\tlr: 1.000e-01, eta: 9:27:03, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7824\n2023-01-02 16:53:50,213 - mmcls - INFO - Epoch [23][4000/5005]\tlr: 1.000e-01, eta: 9:26:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7700\n2023-01-02 16:53:59,168 - mmcls - INFO - Epoch [23][4100/5005]\tlr: 1.000e-01, eta: 9:26:45, time: 0.090, data_time: 0.042, memory: 1125, loss: 2.7720\n2023-01-02 16:54:07,586 - mmcls - INFO - Epoch [23][4200/5005]\tlr: 1.000e-01, eta: 9:26:35, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.7795\n2023-01-02 16:54:16,441 - mmcls - INFO - Epoch [23][4300/5005]\tlr: 1.000e-01, eta: 9:26:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7695\n2023-01-02 16:54:25,203 - mmcls - INFO - Epoch [23][4400/5005]\tlr: 1.000e-01, eta: 9:26:18, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7625\n2023-01-02 16:54:33,590 - mmcls - INFO - Epoch [23][4500/5005]\tlr: 1.000e-01, eta: 9:26:07, time: 0.084, data_time: 0.029, memory: 1125, loss: 2.7719\n2023-01-02 16:54:42,401 - mmcls - INFO - Epoch [23][4600/5005]\tlr: 1.000e-01, eta: 9:25:59, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7878\n2023-01-02 16:54:51,592 - mmcls - INFO - Epoch [23][4700/5005]\tlr: 1.000e-01, eta: 9:25:51, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7616\n2023-01-02 16:55:00,255 - mmcls - INFO - Epoch [23][4800/5005]\tlr: 1.000e-01, eta: 9:25:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7821\n2023-01-02 16:55:09,041 - mmcls - INFO - Epoch [23][4900/5005]\tlr: 1.000e-01, eta: 9:25:33, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7434\n2023-01-02 16:55:17,652 - mmcls - INFO - Epoch [23][5000/5005]\tlr: 1.000e-01, eta: 9:25:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7795\n2023-01-02 16:55:40,229 - mmcls - INFO - Epoch(val) [23][196]\taccuracy_top-1: 38.4400, accuracy_top-5: 61.7120\n2023-01-02 16:55:51,162 - mmcls - INFO - Epoch [24][100/5005]\tlr: 1.000e-01, eta: 9:25:20, time: 0.109, data_time: 0.029, memory: 1125, loss: 2.7405\n2023-01-02 16:55:59,931 - mmcls - INFO - Epoch [24][200/5005]\tlr: 1.000e-01, eta: 9:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7084\n2023-01-02 16:56:09,001 - mmcls - INFO - Epoch [24][300/5005]\tlr: 1.000e-01, eta: 9:25:03, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7281\n2023-01-02 16:56:17,777 - mmcls - INFO - Epoch [24][400/5005]\tlr: 1.000e-01, eta: 9:24:54, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7185\n2023-01-02 16:56:26,547 - mmcls - INFO - Epoch [24][500/5005]\tlr: 1.000e-01, eta: 9:24:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7236\n2023-01-02 16:56:35,651 - mmcls - INFO - Epoch [24][600/5005]\tlr: 1.000e-01, eta: 9:24:37, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.7302\n2023-01-02 16:56:44,269 - mmcls - INFO - Epoch [24][700/5005]\tlr: 1.000e-01, eta: 9:24:28, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.7839\n2023-01-02 16:56:52,750 - mmcls - INFO - Epoch [24][800/5005]\tlr: 1.000e-01, eta: 9:24:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7702\n2023-01-02 16:57:01,245 - mmcls - INFO - Epoch [24][900/5005]\tlr: 1.000e-01, eta: 9:24:08, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7672\n2023-01-02 16:57:10,021 - mmcls - INFO - Epoch [24][1000/5005]\tlr: 1.000e-01, eta: 9:23:59, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7634\n2023-01-02 16:57:18,868 - mmcls - INFO - Epoch [24][1100/5005]\tlr: 1.000e-01, eta: 9:23:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7622\n2023-01-02 16:57:27,814 - mmcls - INFO - Epoch [24][1200/5005]\tlr: 1.000e-01, eta: 9:23:42, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7479\n2023-01-02 16:57:36,737 - mmcls - INFO - Epoch [24][1300/5005]\tlr: 1.000e-01, eta: 9:23:34, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.7305\n2023-01-02 16:57:45,487 - mmcls - INFO - Epoch [24][1400/5005]\tlr: 1.000e-01, eta: 9:23:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7446\n2023-01-02 16:57:54,092 - mmcls - INFO - Epoch [24][1500/5005]\tlr: 1.000e-01, eta: 9:23:16, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.7467\n2023-01-02 16:58:02,967 - mmcls - INFO - Epoch [24][1600/5005]\tlr: 1.000e-01, eta: 9:23:07, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7460\n2023-01-02 16:58:11,750 - mmcls - INFO - Epoch [24][1700/5005]\tlr: 1.000e-01, eta: 9:22:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7933\n2023-01-02 16:58:20,448 - mmcls - INFO - Epoch [24][1800/5005]\tlr: 1.000e-01, eta: 9:22:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7300\n2023-01-02 16:58:29,364 - mmcls - INFO - Epoch [24][1900/5005]\tlr: 1.000e-01, eta: 9:22:40, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.7983\n2023-01-02 16:58:38,058 - mmcls - INFO - Epoch [24][2000/5005]\tlr: 1.000e-01, eta: 9:22:31, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8262\n2023-01-02 16:58:46,689 - mmcls - INFO - Epoch [24][2100/5005]\tlr: 1.000e-01, eta: 9:22:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7750\n2023-01-02 16:58:55,643 - mmcls - INFO - Epoch [24][2200/5005]\tlr: 1.000e-01, eta: 9:22:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7659\n2023-01-02 16:59:04,426 - mmcls - INFO - Epoch [24][2300/5005]\tlr: 1.000e-01, eta: 9:22:05, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7483\n2023-01-02 16:59:13,300 - mmcls - INFO - Epoch [24][2400/5005]\tlr: 1.000e-01, eta: 9:21:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7722\n2023-01-02 16:59:22,019 - mmcls - INFO - Epoch [24][2500/5005]\tlr: 1.000e-01, eta: 9:21:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7575\n2023-01-02 16:59:30,714 - mmcls - INFO - Epoch [24][2600/5005]\tlr: 1.000e-01, eta: 9:21:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7821\n2023-01-02 16:59:39,587 - mmcls - INFO - Epoch [24][2700/5005]\tlr: 1.000e-01, eta: 9:21:29, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7649\n2023-01-02 16:59:48,285 - mmcls - INFO - Epoch [24][2800/5005]\tlr: 1.000e-01, eta: 9:21:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7773\n2023-01-02 16:59:56,931 - mmcls - INFO - Epoch [24][2900/5005]\tlr: 1.000e-01, eta: 9:21:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7679\n2023-01-02 17:00:05,437 - mmcls - INFO - Epoch [24][3000/5005]\tlr: 1.000e-01, eta: 9:21:01, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7436\n2023-01-02 17:00:14,276 - mmcls - INFO - Epoch [24][3100/5005]\tlr: 1.000e-01, eta: 9:20:52, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7863\n2023-01-02 17:00:22,819 - mmcls - INFO - Epoch [24][3200/5005]\tlr: 1.000e-01, eta: 9:20:43, time: 0.085, data_time: 0.033, memory: 1125, loss: 2.7452\n2023-01-02 17:00:31,620 - mmcls - INFO - Epoch [24][3300/5005]\tlr: 1.000e-01, eta: 9:20:34, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7565\n2023-01-02 17:00:40,333 - mmcls - INFO - Epoch [24][3400/5005]\tlr: 1.000e-01, eta: 9:20:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7446\n2023-01-02 17:00:49,004 - mmcls - INFO - Epoch [24][3500/5005]\tlr: 1.000e-01, eta: 9:20:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7769\n2023-01-02 17:00:58,106 - mmcls - INFO - Epoch [24][3600/5005]\tlr: 1.000e-01, eta: 9:20:08, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.7617\n2023-01-02 17:01:06,823 - mmcls - INFO - Epoch [24][3700/5005]\tlr: 1.000e-01, eta: 9:19:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7589\n2023-01-02 17:01:15,924 - mmcls - INFO - Epoch [24][3800/5005]\tlr: 1.000e-01, eta: 9:19:51, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7520\n2023-01-02 17:01:24,659 - mmcls - INFO - Epoch [24][3900/5005]\tlr: 1.000e-01, eta: 9:19:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7666\n2023-01-02 17:01:32,976 - mmcls - INFO - Epoch [24][4000/5005]\tlr: 1.000e-01, eta: 9:19:31, time: 0.083, data_time: 0.017, memory: 1125, loss: 2.7404\n2023-01-02 17:01:42,187 - mmcls - INFO - Epoch [24][4100/5005]\tlr: 1.000e-01, eta: 9:19:24, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7800\n2023-01-02 17:01:51,179 - mmcls - INFO - Epoch [24][4200/5005]\tlr: 1.000e-01, eta: 9:19:16, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7701\n2023-01-02 17:01:59,891 - mmcls - INFO - Epoch [24][4300/5005]\tlr: 1.000e-01, eta: 9:19:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7763\n2023-01-02 17:02:08,490 - mmcls - INFO - Epoch [24][4400/5005]\tlr: 1.000e-01, eta: 9:18:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7453\n2023-01-02 17:02:17,505 - mmcls - INFO - Epoch [24][4500/5005]\tlr: 1.000e-01, eta: 9:18:49, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.7831\n2023-01-02 17:02:25,884 - mmcls - INFO - Epoch [24][4600/5005]\tlr: 1.000e-01, eta: 9:18:39, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.7473\n2023-01-02 17:02:34,390 - mmcls - INFO - Epoch [24][4700/5005]\tlr: 1.000e-01, eta: 9:18:29, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7456\n2023-01-02 17:02:43,176 - mmcls - INFO - Epoch [24][4800/5005]\tlr: 1.000e-01, eta: 9:18:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7461\n2023-01-02 17:02:51,894 - mmcls - INFO - Epoch [24][4900/5005]\tlr: 1.000e-01, eta: 9:18:11, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7560\n2023-01-02 17:03:00,438 - mmcls - INFO - Epoch [24][5000/5005]\tlr: 1.000e-01, eta: 9:18:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7659\n2023-01-02 17:03:23,030 - mmcls - INFO - Epoch(val) [24][196]\taccuracy_top-1: 44.6100, accuracy_top-5: 69.5100\n2023-01-02 17:03:33,842 - mmcls - INFO - Epoch [25][100/5005]\tlr: 1.000e-01, eta: 9:17:57, time: 0.108, data_time: 0.053, memory: 1125, loss: 2.6890\n2023-01-02 17:03:42,676 - mmcls - INFO - Epoch [25][200/5005]\tlr: 1.000e-01, eta: 9:17:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6972\n2023-01-02 17:03:51,255 - mmcls - INFO - Epoch [25][300/5005]\tlr: 1.000e-01, eta: 9:17:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7029\n2023-01-02 17:03:59,836 - mmcls - INFO - Epoch [25][400/5005]\tlr: 1.000e-01, eta: 9:17:30, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7300\n2023-01-02 17:04:08,434 - mmcls - INFO - Epoch [25][500/5005]\tlr: 1.000e-01, eta: 9:17:20, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7200\n2023-01-02 17:04:17,080 - mmcls - INFO - Epoch [25][600/5005]\tlr: 1.000e-01, eta: 9:17:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7134\n2023-01-02 17:04:25,727 - mmcls - INFO - Epoch [25][700/5005]\tlr: 1.000e-01, eta: 9:17:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7469\n2023-01-02 17:04:34,436 - mmcls - INFO - Epoch [25][800/5005]\tlr: 1.000e-01, eta: 9:16:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7378\n2023-01-02 17:04:43,462 - mmcls - INFO - Epoch [25][900/5005]\tlr: 1.000e-01, eta: 9:16:44, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.7148\n2023-01-02 17:04:52,165 - mmcls - INFO - Epoch [25][1000/5005]\tlr: 1.000e-01, eta: 9:16:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7670\n2023-01-02 17:05:01,138 - mmcls - INFO - Epoch [25][1100/5005]\tlr: 1.000e-01, eta: 9:16:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7486\n2023-01-02 17:05:10,017 - mmcls - INFO - Epoch [25][1200/5005]\tlr: 1.000e-01, eta: 9:16:18, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7490\n2023-01-02 17:05:18,655 - mmcls - INFO - Epoch [25][1300/5005]\tlr: 1.000e-01, eta: 9:16:09, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.7382\n2023-01-02 17:05:27,347 - mmcls - INFO - Epoch [25][1400/5005]\tlr: 1.000e-01, eta: 9:16:00, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7492\n2023-01-02 17:05:36,064 - mmcls - INFO - Epoch [25][1500/5005]\tlr: 1.000e-01, eta: 9:15:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7475\n2023-01-02 17:05:44,838 - mmcls - INFO - Epoch [25][1600/5005]\tlr: 1.000e-01, eta: 9:15:42, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7207\n2023-01-02 17:05:53,403 - mmcls - INFO - Epoch [25][1700/5005]\tlr: 1.000e-01, eta: 9:15:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7547\n2023-01-02 17:06:02,182 - mmcls - INFO - Epoch [25][1800/5005]\tlr: 1.000e-01, eta: 9:15:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7615\n2023-01-02 17:06:10,797 - mmcls - INFO - Epoch [25][1900/5005]\tlr: 1.000e-01, eta: 9:15:14, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7529\n2023-01-02 17:06:19,623 - mmcls - INFO - Epoch [25][2000/5005]\tlr: 1.000e-01, eta: 9:15:05, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.7852\n2023-01-02 17:06:28,460 - mmcls - INFO - Epoch [25][2100/5005]\tlr: 1.000e-01, eta: 9:14:57, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.7766\n2023-01-02 17:06:37,422 - mmcls - INFO - Epoch [25][2200/5005]\tlr: 1.000e-01, eta: 9:14:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7956\n2023-01-02 17:06:46,099 - mmcls - INFO - Epoch [25][2300/5005]\tlr: 1.000e-01, eta: 9:14:39, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.7900\n2023-01-02 17:06:54,971 - mmcls - INFO - Epoch [25][2400/5005]\tlr: 1.000e-01, eta: 9:14:31, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7651\n2023-01-02 17:07:03,996 - mmcls - INFO - Epoch [25][2500/5005]\tlr: 1.000e-01, eta: 9:14:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7417\n2023-01-02 17:07:12,760 - mmcls - INFO - Epoch [25][2600/5005]\tlr: 1.000e-01, eta: 9:14:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7426\n2023-01-02 17:07:21,696 - mmcls - INFO - Epoch [25][2700/5005]\tlr: 1.000e-01, eta: 9:14:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7448\n2023-01-02 17:07:30,442 - mmcls - INFO - Epoch [25][2800/5005]\tlr: 1.000e-01, eta: 9:13:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7657\n2023-01-02 17:07:39,000 - mmcls - INFO - Epoch [25][2900/5005]\tlr: 1.000e-01, eta: 9:13:47, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7641\n2023-01-02 17:07:47,587 - mmcls - INFO - Epoch [25][3000/5005]\tlr: 1.000e-01, eta: 9:13:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.7847\n2023-01-02 17:07:56,296 - mmcls - INFO - Epoch [25][3100/5005]\tlr: 1.000e-01, eta: 9:13:28, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.7492\n2023-01-02 17:08:05,250 - mmcls - INFO - Epoch [25][3200/5005]\tlr: 1.000e-01, eta: 9:13:20, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.7494\n2023-01-02 17:08:13,850 - mmcls - INFO - Epoch [25][3300/5005]\tlr: 1.000e-01, eta: 9:13:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7394\n2023-01-02 17:08:22,477 - mmcls - INFO - Epoch [25][3400/5005]\tlr: 1.000e-01, eta: 9:13:01, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7637\n2023-01-02 17:08:31,019 - mmcls - INFO - Epoch [25][3500/5005]\tlr: 1.000e-01, eta: 9:12:51, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7408\n2023-01-02 17:08:39,693 - mmcls - INFO - Epoch [25][3600/5005]\tlr: 1.000e-01, eta: 9:12:42, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7334\n2023-01-02 17:08:48,573 - mmcls - INFO - Epoch [25][3700/5005]\tlr: 1.000e-01, eta: 9:12:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7633\n2023-01-02 17:08:57,271 - mmcls - INFO - Epoch [25][3800/5005]\tlr: 1.000e-01, eta: 9:12:24, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7848\n2023-01-02 17:09:06,185 - mmcls - INFO - Epoch [25][3900/5005]\tlr: 1.000e-01, eta: 9:12:16, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7429\n2023-01-02 17:09:14,898 - mmcls - INFO - Epoch [25][4000/5005]\tlr: 1.000e-01, eta: 9:12:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7833\n2023-01-02 17:09:23,787 - mmcls - INFO - Epoch [25][4100/5005]\tlr: 1.000e-01, eta: 9:11:58, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7964\n2023-01-02 17:09:32,460 - mmcls - INFO - Epoch [25][4200/5005]\tlr: 1.000e-01, eta: 9:11:49, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7809\n2023-01-02 17:09:41,367 - mmcls - INFO - Epoch [25][4300/5005]\tlr: 1.000e-01, eta: 9:11:41, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7529\n2023-01-02 17:09:50,253 - mmcls - INFO - Epoch [25][4400/5005]\tlr: 1.000e-01, eta: 9:11:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7554\n2023-01-02 17:09:59,105 - mmcls - INFO - Epoch [25][4500/5005]\tlr: 1.000e-01, eta: 9:11:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7613\n2023-01-02 17:10:08,084 - mmcls - INFO - Epoch [25][4600/5005]\tlr: 1.000e-01, eta: 9:11:15, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7520\n2023-01-02 17:10:16,701 - mmcls - INFO - Epoch [25][4700/5005]\tlr: 1.000e-01, eta: 9:11:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7774\n2023-01-02 17:10:25,180 - mmcls - INFO - Epoch [25][4800/5005]\tlr: 1.000e-01, eta: 9:10:56, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7808\n2023-01-02 17:10:34,177 - mmcls - INFO - Epoch [25][4900/5005]\tlr: 1.000e-01, eta: 9:10:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7444\n2023-01-02 17:10:42,704 - mmcls - INFO - Epoch [25][5000/5005]\tlr: 1.000e-01, eta: 9:10:38, time: 0.085, data_time: 0.022, memory: 1125, loss: 2.7700\n2023-01-02 17:11:05,612 - mmcls - INFO - Epoch(val) [25][196]\taccuracy_top-1: 46.8820, accuracy_top-5: 71.5980\n2023-01-02 17:11:16,682 - mmcls - INFO - Epoch [26][100/5005]\tlr: 1.000e-01, eta: 9:10:34, time: 0.111, data_time: 0.036, memory: 1125, loss: 2.7039\n2023-01-02 17:11:25,394 - mmcls - INFO - Epoch [26][200/5005]\tlr: 1.000e-01, eta: 9:10:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7221\n2023-01-02 17:11:34,368 - mmcls - INFO - Epoch [26][300/5005]\tlr: 1.000e-01, eta: 9:10:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7090\n2023-01-02 17:11:43,155 - mmcls - INFO - Epoch [26][400/5005]\tlr: 1.000e-01, eta: 9:10:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7192\n2023-01-02 17:11:51,813 - mmcls - INFO - Epoch [26][500/5005]\tlr: 1.000e-01, eta: 9:09:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7009\n2023-01-02 17:12:00,533 - mmcls - INFO - Epoch [26][600/5005]\tlr: 1.000e-01, eta: 9:09:50, time: 0.087, data_time: 0.041, memory: 1125, loss: 2.7057\n2023-01-02 17:12:09,601 - mmcls - INFO - Epoch [26][700/5005]\tlr: 1.000e-01, eta: 9:09:42, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7428\n2023-01-02 17:12:18,206 - mmcls - INFO - Epoch [26][800/5005]\tlr: 1.000e-01, eta: 9:09:32, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7324\n2023-01-02 17:12:27,048 - mmcls - INFO - Epoch [26][900/5005]\tlr: 1.000e-01, eta: 9:09:24, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7224\n2023-01-02 17:12:35,854 - mmcls - INFO - Epoch [26][1000/5005]\tlr: 1.000e-01, eta: 9:09:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7656\n2023-01-02 17:12:44,365 - mmcls - INFO - Epoch [26][1100/5005]\tlr: 1.000e-01, eta: 9:09:05, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7757\n2023-01-02 17:12:53,166 - mmcls - INFO - Epoch [26][1200/5005]\tlr: 1.000e-01, eta: 9:08:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7314\n2023-01-02 17:13:01,719 - mmcls - INFO - Epoch [26][1300/5005]\tlr: 1.000e-01, eta: 9:08:47, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7461\n2023-01-02 17:13:10,358 - mmcls - INFO - Epoch [26][1400/5005]\tlr: 1.000e-01, eta: 9:08:38, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7227\n2023-01-02 17:13:19,162 - mmcls - INFO - Epoch [26][1500/5005]\tlr: 1.000e-01, eta: 9:08:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7536\n2023-01-02 17:13:28,077 - mmcls - INFO - Epoch [26][1600/5005]\tlr: 1.000e-01, eta: 9:08:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7508\n2023-01-02 17:13:36,680 - mmcls - INFO - Epoch [26][1700/5005]\tlr: 1.000e-01, eta: 9:08:11, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7450\n2023-01-02 17:13:45,582 - mmcls - INFO - Epoch [26][1800/5005]\tlr: 1.000e-01, eta: 9:08:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7017\n2023-01-02 17:13:54,208 - mmcls - INFO - Epoch [26][1900/5005]\tlr: 1.000e-01, eta: 9:07:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7210\n2023-01-02 17:14:02,887 - mmcls - INFO - Epoch [26][2000/5005]\tlr: 1.000e-01, eta: 9:07:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7324\n2023-01-02 17:14:11,665 - mmcls - INFO - Epoch [26][2100/5005]\tlr: 1.000e-01, eta: 9:07:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7330\n2023-01-02 17:14:20,185 - mmcls - INFO - Epoch [26][2200/5005]\tlr: 1.000e-01, eta: 9:07:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7539\n2023-01-02 17:14:29,136 - mmcls - INFO - Epoch [26][2300/5005]\tlr: 1.000e-01, eta: 9:07:17, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.7489\n2023-01-02 17:14:37,506 - mmcls - INFO - Epoch [26][2400/5005]\tlr: 1.000e-01, eta: 9:07:07, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.7745\n2023-01-02 17:14:46,311 - mmcls - INFO - Epoch [26][2500/5005]\tlr: 1.000e-01, eta: 9:06:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7411\n2023-01-02 17:14:55,105 - mmcls - INFO - Epoch [26][2600/5005]\tlr: 1.000e-01, eta: 9:06:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7634\n2023-01-02 17:15:03,803 - mmcls - INFO - Epoch [26][2700/5005]\tlr: 1.000e-01, eta: 9:06:40, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7524\n2023-01-02 17:15:12,534 - mmcls - INFO - Epoch [26][2800/5005]\tlr: 1.000e-01, eta: 9:06:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7714\n2023-01-02 17:15:21,116 - mmcls - INFO - Epoch [26][2900/5005]\tlr: 1.000e-01, eta: 9:06:22, time: 0.086, data_time: 0.031, memory: 1125, loss: 2.7548\n2023-01-02 17:15:30,115 - mmcls - INFO - Epoch [26][3000/5005]\tlr: 1.000e-01, eta: 9:06:14, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.7475\n2023-01-02 17:15:38,869 - mmcls - INFO - Epoch [26][3100/5005]\tlr: 1.000e-01, eta: 9:06:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7752\n2023-01-02 17:15:47,445 - mmcls - INFO - Epoch [26][3200/5005]\tlr: 1.000e-01, eta: 9:05:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7656\n2023-01-02 17:15:56,320 - mmcls - INFO - Epoch [26][3300/5005]\tlr: 1.000e-01, eta: 9:05:47, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7713\n2023-01-02 17:16:04,896 - mmcls - INFO - Epoch [26][3400/5005]\tlr: 1.000e-01, eta: 9:05:37, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7271\n2023-01-02 17:16:13,397 - mmcls - INFO - Epoch [26][3500/5005]\tlr: 1.000e-01, eta: 9:05:28, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.7338\n2023-01-02 17:16:22,249 - mmcls - INFO - Epoch [26][3600/5005]\tlr: 1.000e-01, eta: 9:05:19, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7590\n2023-01-02 17:16:31,027 - mmcls - INFO - Epoch [26][3700/5005]\tlr: 1.000e-01, eta: 9:05:10, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7817\n2023-01-02 17:16:39,908 - mmcls - INFO - Epoch [26][3800/5005]\tlr: 1.000e-01, eta: 9:05:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7806\n2023-01-02 17:16:48,655 - mmcls - INFO - Epoch [26][3900/5005]\tlr: 1.000e-01, eta: 9:04:53, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7492\n2023-01-02 17:16:57,418 - mmcls - INFO - Epoch [26][4000/5005]\tlr: 1.000e-01, eta: 9:04:44, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.7744\n2023-01-02 17:17:05,861 - mmcls - INFO - Epoch [26][4100/5005]\tlr: 1.000e-01, eta: 9:04:34, time: 0.084, data_time: 0.012, memory: 1125, loss: 2.7942\n2023-01-02 17:17:14,697 - mmcls - INFO - Epoch [26][4200/5005]\tlr: 1.000e-01, eta: 9:04:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7880\n2023-01-02 17:17:23,202 - mmcls - INFO - Epoch [26][4300/5005]\tlr: 1.000e-01, eta: 9:04:15, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7588\n2023-01-02 17:17:32,016 - mmcls - INFO - Epoch [26][4400/5005]\tlr: 1.000e-01, eta: 9:04:07, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7864\n2023-01-02 17:17:40,862 - mmcls - INFO - Epoch [26][4500/5005]\tlr: 1.000e-01, eta: 9:03:58, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.7655\n2023-01-02 17:17:49,795 - mmcls - INFO - Epoch [26][4600/5005]\tlr: 1.000e-01, eta: 9:03:50, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7460\n2023-01-02 17:17:58,723 - mmcls - INFO - Epoch [26][4700/5005]\tlr: 1.000e-01, eta: 9:03:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7828\n2023-01-02 17:18:07,609 - mmcls - INFO - Epoch [26][4800/5005]\tlr: 1.000e-01, eta: 9:03:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7800\n2023-01-02 17:18:16,286 - mmcls - INFO - Epoch [26][4900/5005]\tlr: 1.000e-01, eta: 9:03:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7294\n2023-01-02 17:18:25,133 - mmcls - INFO - Epoch [26][5000/5005]\tlr: 1.000e-01, eta: 9:03:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7590\n2023-01-02 17:18:47,854 - mmcls - INFO - Epoch(val) [26][196]\taccuracy_top-1: 47.6060, accuracy_top-5: 72.2680\n2023-01-02 17:18:59,039 - mmcls - INFO - Epoch [27][100/5005]\tlr: 1.000e-01, eta: 9:03:11, time: 0.112, data_time: 0.035, memory: 1125, loss: 2.6789\n2023-01-02 17:19:07,607 - mmcls - INFO - Epoch [27][200/5005]\tlr: 1.000e-01, eta: 9:03:01, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.6783\n2023-01-02 17:19:16,432 - mmcls - INFO - Epoch [27][300/5005]\tlr: 1.000e-01, eta: 9:02:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7165\n2023-01-02 17:19:25,353 - mmcls - INFO - Epoch [27][400/5005]\tlr: 1.000e-01, eta: 9:02:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7196\n2023-01-02 17:19:33,987 - mmcls - INFO - Epoch [27][500/5005]\tlr: 1.000e-01, eta: 9:02:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.6999\n2023-01-02 17:19:43,140 - mmcls - INFO - Epoch [27][600/5005]\tlr: 1.000e-01, eta: 9:02:27, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.6883\n2023-01-02 17:19:51,769 - mmcls - INFO - Epoch [27][700/5005]\tlr: 1.000e-01, eta: 9:02:18, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7368\n2023-01-02 17:20:00,650 - mmcls - INFO - Epoch [27][800/5005]\tlr: 1.000e-01, eta: 9:02:09, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.6997\n2023-01-02 17:20:09,821 - mmcls - INFO - Epoch [27][900/5005]\tlr: 1.000e-01, eta: 9:02:02, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7469\n2023-01-02 17:20:18,477 - mmcls - INFO - Epoch [27][1000/5005]\tlr: 1.000e-01, eta: 9:01:52, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7228\n2023-01-02 17:20:27,305 - mmcls - INFO - Epoch [27][1100/5005]\tlr: 1.000e-01, eta: 9:01:44, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.7277\n2023-01-02 17:20:35,920 - mmcls - INFO - Epoch [27][1200/5005]\tlr: 1.000e-01, eta: 9:01:34, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7226\n2023-01-02 17:20:44,708 - mmcls - INFO - Epoch [27][1300/5005]\tlr: 1.000e-01, eta: 9:01:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7017\n2023-01-02 17:20:53,333 - mmcls - INFO - Epoch [27][1400/5005]\tlr: 1.000e-01, eta: 9:01:16, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7282\n2023-01-02 17:21:02,038 - mmcls - INFO - Epoch [27][1500/5005]\tlr: 1.000e-01, eta: 9:01:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7536\n2023-01-02 17:21:11,020 - mmcls - INFO - Epoch [27][1600/5005]\tlr: 1.000e-01, eta: 9:00:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7609\n2023-01-02 17:21:19,761 - mmcls - INFO - Epoch [27][1700/5005]\tlr: 1.000e-01, eta: 9:00:50, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7746\n2023-01-02 17:21:28,322 - mmcls - INFO - Epoch [27][1800/5005]\tlr: 1.000e-01, eta: 9:00:40, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7629\n2023-01-02 17:21:36,855 - mmcls - INFO - Epoch [27][1900/5005]\tlr: 1.000e-01, eta: 9:00:31, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.7557\n2023-01-02 17:21:45,479 - mmcls - INFO - Epoch [27][2000/5005]\tlr: 1.000e-01, eta: 9:00:21, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7755\n2023-01-02 17:21:54,120 - mmcls - INFO - Epoch [27][2100/5005]\tlr: 1.000e-01, eta: 9:00:12, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7660\n2023-01-02 17:22:03,187 - mmcls - INFO - Epoch [27][2200/5005]\tlr: 1.000e-01, eta: 9:00:04, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.7295\n2023-01-02 17:22:12,204 - mmcls - INFO - Epoch [27][2300/5005]\tlr: 1.000e-01, eta: 8:59:56, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7710\n2023-01-02 17:22:20,826 - mmcls - INFO - Epoch [27][2400/5005]\tlr: 1.000e-01, eta: 8:59:47, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7556\n2023-01-02 17:22:29,583 - mmcls - INFO - Epoch [27][2500/5005]\tlr: 1.000e-01, eta: 8:59:38, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7576\n2023-01-02 17:22:38,393 - mmcls - INFO - Epoch [27][2600/5005]\tlr: 1.000e-01, eta: 8:59:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7386\n2023-01-02 17:22:47,145 - mmcls - INFO - Epoch [27][2700/5005]\tlr: 1.000e-01, eta: 8:59:20, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7638\n2023-01-02 17:22:55,989 - mmcls - INFO - Epoch [27][2800/5005]\tlr: 1.000e-01, eta: 8:59:11, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7459\n2023-01-02 17:23:04,762 - mmcls - INFO - Epoch [27][2900/5005]\tlr: 1.000e-01, eta: 8:59:02, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7402\n2023-01-02 17:23:13,832 - mmcls - INFO - Epoch [27][3000/5005]\tlr: 1.000e-01, eta: 8:58:54, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7439\n2023-01-02 17:23:22,340 - mmcls - INFO - Epoch [27][3100/5005]\tlr: 1.000e-01, eta: 8:58:45, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7318\n2023-01-02 17:23:30,945 - mmcls - INFO - Epoch [27][3200/5005]\tlr: 1.000e-01, eta: 8:58:35, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7613\n2023-01-02 17:23:39,650 - mmcls - INFO - Epoch [27][3300/5005]\tlr: 1.000e-01, eta: 8:58:26, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7503\n2023-01-02 17:23:48,405 - mmcls - INFO - Epoch [27][3400/5005]\tlr: 1.000e-01, eta: 8:58:17, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7451\n2023-01-02 17:23:57,020 - mmcls - INFO - Epoch [27][3500/5005]\tlr: 1.000e-01, eta: 8:58:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7799\n2023-01-02 17:24:05,574 - mmcls - INFO - Epoch [27][3600/5005]\tlr: 1.000e-01, eta: 8:57:59, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7804\n2023-01-02 17:24:14,403 - mmcls - INFO - Epoch [27][3700/5005]\tlr: 1.000e-01, eta: 8:57:50, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7396\n2023-01-02 17:24:23,166 - mmcls - INFO - Epoch [27][3800/5005]\tlr: 1.000e-01, eta: 8:57:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7596\n2023-01-02 17:24:31,966 - mmcls - INFO - Epoch [27][3900/5005]\tlr: 1.000e-01, eta: 8:57:32, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7467\n2023-01-02 17:24:40,821 - mmcls - INFO - Epoch [27][4000/5005]\tlr: 1.000e-01, eta: 8:57:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7502\n2023-01-02 17:24:49,494 - mmcls - INFO - Epoch [27][4100/5005]\tlr: 1.000e-01, eta: 8:57:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7663\n2023-01-02 17:24:58,238 - mmcls - INFO - Epoch [27][4200/5005]\tlr: 1.000e-01, eta: 8:57:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7602\n2023-01-02 17:25:06,950 - mmcls - INFO - Epoch [27][4300/5005]\tlr: 1.000e-01, eta: 8:56:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7490\n2023-01-02 17:25:15,739 - mmcls - INFO - Epoch [27][4400/5005]\tlr: 1.000e-01, eta: 8:56:47, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7623\n2023-01-02 17:25:24,526 - mmcls - INFO - Epoch [27][4500/5005]\tlr: 1.000e-01, eta: 8:56:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7656\n2023-01-02 17:25:33,328 - mmcls - INFO - Epoch [27][4600/5005]\tlr: 1.000e-01, eta: 8:56:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7590\n2023-01-02 17:25:42,254 - mmcls - INFO - Epoch [27][4700/5005]\tlr: 1.000e-01, eta: 8:56:21, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.7486\n2023-01-02 17:25:50,898 - mmcls - INFO - Epoch [27][4800/5005]\tlr: 1.000e-01, eta: 8:56:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7750\n2023-01-02 17:25:59,758 - mmcls - INFO - Epoch [27][4900/5005]\tlr: 1.000e-01, eta: 8:56:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7674\n2023-01-02 17:26:08,913 - mmcls - INFO - Epoch [27][5000/5005]\tlr: 1.000e-01, eta: 8:55:56, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7601\n2023-01-02 17:26:31,670 - mmcls - INFO - Epoch(val) [27][196]\taccuracy_top-1: 46.7160, accuracy_top-5: 71.7220\n2023-01-02 17:26:42,585 - mmcls - INFO - Epoch [28][100/5005]\tlr: 1.000e-01, eta: 8:55:51, time: 0.109, data_time: 0.058, memory: 1125, loss: 2.6768\n2023-01-02 17:26:51,173 - mmcls - INFO - Epoch [28][200/5005]\tlr: 1.000e-01, eta: 8:55:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.6970\n2023-01-02 17:26:59,655 - mmcls - INFO - Epoch [28][300/5005]\tlr: 1.000e-01, eta: 8:55:32, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.7182\n2023-01-02 17:27:08,336 - mmcls - INFO - Epoch [28][400/5005]\tlr: 1.000e-01, eta: 8:55:23, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.7071\n2023-01-02 17:27:17,037 - mmcls - INFO - Epoch [28][500/5005]\tlr: 1.000e-01, eta: 8:55:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7312\n2023-01-02 17:27:25,923 - mmcls - INFO - Epoch [28][600/5005]\tlr: 1.000e-01, eta: 8:55:05, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7054\n2023-01-02 17:27:34,738 - mmcls - INFO - Epoch [28][700/5005]\tlr: 1.000e-01, eta: 8:54:56, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7404\n2023-01-02 17:27:43,535 - mmcls - INFO - Epoch [28][800/5005]\tlr: 1.000e-01, eta: 8:54:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7538\n2023-01-02 17:27:51,933 - mmcls - INFO - Epoch [28][900/5005]\tlr: 1.000e-01, eta: 8:54:38, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.7431\n2023-01-02 17:28:00,803 - mmcls - INFO - Epoch [28][1000/5005]\tlr: 1.000e-01, eta: 8:54:29, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.7410\n2023-01-02 17:28:09,833 - mmcls - INFO - Epoch [28][1100/5005]\tlr: 1.000e-01, eta: 8:54:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7576\n2023-01-02 17:28:18,815 - mmcls - INFO - Epoch [28][1200/5005]\tlr: 1.000e-01, eta: 8:54:12, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.7419\n2023-01-02 17:28:27,876 - mmcls - INFO - Epoch [28][1300/5005]\tlr: 1.000e-01, eta: 8:54:04, time: 0.091, data_time: 0.015, memory: 1125, loss: 2.7500\n2023-01-02 17:28:36,520 - mmcls - INFO - Epoch [28][1400/5005]\tlr: 1.000e-01, eta: 8:53:55, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.7465\n2023-01-02 17:28:45,136 - mmcls - INFO - Epoch [28][1500/5005]\tlr: 1.000e-01, eta: 8:53:46, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.7270\n2023-01-02 17:28:54,380 - mmcls - INFO - Epoch [28][1600/5005]\tlr: 1.000e-01, eta: 8:53:38, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.7400\n2023-01-02 17:29:03,274 - mmcls - INFO - Epoch [28][1700/5005]\tlr: 1.000e-01, eta: 8:53:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7522\n2023-01-02 17:29:12,056 - mmcls - INFO - Epoch [28][1800/5005]\tlr: 1.000e-01, eta: 8:53:21, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7326\n2023-01-02 17:29:21,159 - mmcls - INFO - Epoch [28][1900/5005]\tlr: 1.000e-01, eta: 8:53:13, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7471\n2023-01-02 17:29:30,016 - mmcls - INFO - Epoch [28][2000/5005]\tlr: 1.000e-01, eta: 8:53:04, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7591\n2023-01-02 17:29:38,789 - mmcls - INFO - Epoch [28][2100/5005]\tlr: 1.000e-01, eta: 8:52:55, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.7524\n2023-01-02 17:29:47,425 - mmcls - INFO - Epoch [28][2200/5005]\tlr: 1.000e-01, eta: 8:52:46, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7494\n2023-01-02 17:29:56,247 - mmcls - INFO - Epoch [28][2300/5005]\tlr: 1.000e-01, eta: 8:52:37, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7241\n2023-01-02 17:30:05,187 - mmcls - INFO - Epoch [28][2400/5005]\tlr: 1.000e-01, eta: 8:52:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7607\n2023-01-02 17:30:14,057 - mmcls - INFO - Epoch [28][2500/5005]\tlr: 1.000e-01, eta: 8:52:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7283\n2023-01-02 17:30:22,691 - mmcls - INFO - Epoch [28][2600/5005]\tlr: 1.000e-01, eta: 8:52:11, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7226\n2023-01-02 17:30:32,050 - mmcls - INFO - Epoch [28][2700/5005]\tlr: 1.000e-01, eta: 8:52:04, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.7384\n2023-01-02 17:30:40,640 - mmcls - INFO - Epoch [28][2800/5005]\tlr: 1.000e-01, eta: 8:51:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7495\n2023-01-02 17:30:49,659 - mmcls - INFO - Epoch [28][2900/5005]\tlr: 1.000e-01, eta: 8:51:46, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.7258\n2023-01-02 17:30:58,256 - mmcls - INFO - Epoch [28][3000/5005]\tlr: 1.000e-01, eta: 8:51:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7826\n2023-01-02 17:31:06,806 - mmcls - INFO - Epoch [28][3100/5005]\tlr: 1.000e-01, eta: 8:51:27, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7716\n2023-01-02 17:31:15,518 - mmcls - INFO - Epoch [28][3200/5005]\tlr: 1.000e-01, eta: 8:51:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7560\n2023-01-02 17:31:24,046 - mmcls - INFO - Epoch [28][3300/5005]\tlr: 1.000e-01, eta: 8:51:09, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7356\n2023-01-02 17:31:32,629 - mmcls - INFO - Epoch [28][3400/5005]\tlr: 1.000e-01, eta: 8:50:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7275\n2023-01-02 17:31:41,249 - mmcls - INFO - Epoch [28][3500/5005]\tlr: 1.000e-01, eta: 8:50:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7596\n2023-01-02 17:31:49,986 - mmcls - INFO - Epoch [28][3600/5005]\tlr: 1.000e-01, eta: 8:50:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7689\n2023-01-02 17:31:58,822 - mmcls - INFO - Epoch [28][3700/5005]\tlr: 1.000e-01, eta: 8:50:32, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.7575\n2023-01-02 17:32:07,641 - mmcls - INFO - Epoch [28][3800/5005]\tlr: 1.000e-01, eta: 8:50:24, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7607\n2023-01-02 17:32:16,299 - mmcls - INFO - Epoch [28][3900/5005]\tlr: 1.000e-01, eta: 8:50:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7408\n2023-01-02 17:32:25,312 - mmcls - INFO - Epoch [28][4000/5005]\tlr: 1.000e-01, eta: 8:50:06, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7570\n2023-01-02 17:32:33,696 - mmcls - INFO - Epoch [28][4100/5005]\tlr: 1.000e-01, eta: 8:49:56, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.7453\n2023-01-02 17:32:42,374 - mmcls - INFO - Epoch [28][4200/5005]\tlr: 1.000e-01, eta: 8:49:47, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7780\n2023-01-02 17:32:51,019 - mmcls - INFO - Epoch [28][4300/5005]\tlr: 1.000e-01, eta: 8:49:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7489\n2023-01-02 17:32:59,717 - mmcls - INFO - Epoch [28][4400/5005]\tlr: 1.000e-01, eta: 8:49:29, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7445\n2023-01-02 17:33:08,222 - mmcls - INFO - Epoch [28][4500/5005]\tlr: 1.000e-01, eta: 8:49:19, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7774\n2023-01-02 17:33:16,654 - mmcls - INFO - Epoch [28][4600/5005]\tlr: 1.000e-01, eta: 8:49:09, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.7723\n2023-01-02 17:33:25,483 - mmcls - INFO - Epoch [28][4700/5005]\tlr: 1.000e-01, eta: 8:49:01, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7714\n2023-01-02 17:33:34,155 - mmcls - INFO - Epoch [28][4800/5005]\tlr: 1.000e-01, eta: 8:48:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7557\n2023-01-02 17:33:42,813 - mmcls - INFO - Epoch [28][4900/5005]\tlr: 1.000e-01, eta: 8:48:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7771\n2023-01-02 17:33:51,722 - mmcls - INFO - Epoch [28][5000/5005]\tlr: 1.000e-01, eta: 8:48:34, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7566\n2023-01-02 17:34:14,322 - mmcls - INFO - Epoch(val) [28][196]\taccuracy_top-1: 46.8700, accuracy_top-5: 71.5360\n2023-01-02 17:34:25,416 - mmcls - INFO - Epoch [29][100/5005]\tlr: 1.000e-01, eta: 8:48:29, time: 0.111, data_time: 0.060, memory: 1125, loss: 2.6934\n2023-01-02 17:34:34,223 - mmcls - INFO - Epoch [29][200/5005]\tlr: 1.000e-01, eta: 8:48:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7018\n2023-01-02 17:34:43,089 - mmcls - INFO - Epoch [29][300/5005]\tlr: 1.000e-01, eta: 8:48:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7138\n2023-01-02 17:34:51,899 - mmcls - INFO - Epoch [29][400/5005]\tlr: 1.000e-01, eta: 8:48:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7241\n2023-01-02 17:35:00,612 - mmcls - INFO - Epoch [29][500/5005]\tlr: 1.000e-01, eta: 8:47:54, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.7043\n2023-01-02 17:35:09,256 - mmcls - INFO - Epoch [29][600/5005]\tlr: 1.000e-01, eta: 8:47:45, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6991\n2023-01-02 17:35:17,899 - mmcls - INFO - Epoch [29][700/5005]\tlr: 1.000e-01, eta: 8:47:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7458\n2023-01-02 17:35:26,702 - mmcls - INFO - Epoch [29][800/5005]\tlr: 1.000e-01, eta: 8:47:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7183\n2023-01-02 17:35:35,525 - mmcls - INFO - Epoch [29][900/5005]\tlr: 1.000e-01, eta: 8:47:18, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.7089\n2023-01-02 17:35:44,255 - mmcls - INFO - Epoch [29][1000/5005]\tlr: 1.000e-01, eta: 8:47:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7213\n2023-01-02 17:35:52,983 - mmcls - INFO - Epoch [29][1100/5005]\tlr: 1.000e-01, eta: 8:47:00, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7538\n2023-01-02 17:36:01,719 - mmcls - INFO - Epoch [29][1200/5005]\tlr: 1.000e-01, eta: 8:46:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7252\n2023-01-02 17:36:10,573 - mmcls - INFO - Epoch [29][1300/5005]\tlr: 1.000e-01, eta: 8:46:43, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7415\n2023-01-02 17:36:19,296 - mmcls - INFO - Epoch [29][1400/5005]\tlr: 1.000e-01, eta: 8:46:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7019\n2023-01-02 17:36:28,348 - mmcls - INFO - Epoch [29][1500/5005]\tlr: 1.000e-01, eta: 8:46:25, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.7214\n2023-01-02 17:36:37,132 - mmcls - INFO - Epoch [29][1600/5005]\tlr: 1.000e-01, eta: 8:46:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7333\n2023-01-02 17:36:46,044 - mmcls - INFO - Epoch [29][1700/5005]\tlr: 1.000e-01, eta: 8:46:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7382\n2023-01-02 17:36:54,596 - mmcls - INFO - Epoch [29][1800/5005]\tlr: 1.000e-01, eta: 8:45:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7244\n2023-01-02 17:37:03,150 - mmcls - INFO - Epoch [29][1900/5005]\tlr: 1.000e-01, eta: 8:45:49, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.7241\n2023-01-02 17:37:12,067 - mmcls - INFO - Epoch [29][2000/5005]\tlr: 1.000e-01, eta: 8:45:41, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7238\n2023-01-02 17:37:20,947 - mmcls - INFO - Epoch [29][2100/5005]\tlr: 1.000e-01, eta: 8:45:32, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.7298\n2023-01-02 17:37:29,721 - mmcls - INFO - Epoch [29][2200/5005]\tlr: 1.000e-01, eta: 8:45:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7351\n2023-01-02 17:37:38,543 - mmcls - INFO - Epoch [29][2300/5005]\tlr: 1.000e-01, eta: 8:45:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7379\n2023-01-02 17:37:47,203 - mmcls - INFO - Epoch [29][2400/5005]\tlr: 1.000e-01, eta: 8:45:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7632\n2023-01-02 17:37:55,995 - mmcls - INFO - Epoch [29][2500/5005]\tlr: 1.000e-01, eta: 8:44:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7415\n2023-01-02 17:38:04,638 - mmcls - INFO - Epoch [29][2600/5005]\tlr: 1.000e-01, eta: 8:44:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7327\n2023-01-02 17:38:13,091 - mmcls - INFO - Epoch [29][2700/5005]\tlr: 1.000e-01, eta: 8:44:38, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7457\n2023-01-02 17:38:21,766 - mmcls - INFO - Epoch [29][2800/5005]\tlr: 1.000e-01, eta: 8:44:28, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7503\n2023-01-02 17:38:30,513 - mmcls - INFO - Epoch [29][2900/5005]\tlr: 1.000e-01, eta: 8:44:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7442\n2023-01-02 17:38:39,107 - mmcls - INFO - Epoch [29][3000/5005]\tlr: 1.000e-01, eta: 8:44:10, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7464\n2023-01-02 17:38:47,835 - mmcls - INFO - Epoch [29][3100/5005]\tlr: 1.000e-01, eta: 8:44:01, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7507\n2023-01-02 17:38:56,555 - mmcls - INFO - Epoch [29][3200/5005]\tlr: 1.000e-01, eta: 8:43:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7945\n2023-01-02 17:39:05,116 - mmcls - INFO - Epoch [29][3300/5005]\tlr: 1.000e-01, eta: 8:43:43, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7176\n2023-01-02 17:39:13,849 - mmcls - INFO - Epoch [29][3400/5005]\tlr: 1.000e-01, eta: 8:43:34, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7623\n2023-01-02 17:39:22,380 - mmcls - INFO - Epoch [29][3500/5005]\tlr: 1.000e-01, eta: 8:43:24, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.7663\n2023-01-02 17:39:31,018 - mmcls - INFO - Epoch [29][3600/5005]\tlr: 1.000e-01, eta: 8:43:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7172\n2023-01-02 17:39:40,263 - mmcls - INFO - Epoch [29][3700/5005]\tlr: 1.000e-01, eta: 8:43:07, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7354\n2023-01-02 17:39:48,642 - mmcls - INFO - Epoch [29][3800/5005]\tlr: 1.000e-01, eta: 8:42:58, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.7483\n2023-01-02 17:39:57,436 - mmcls - INFO - Epoch [29][3900/5005]\tlr: 1.000e-01, eta: 8:42:49, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7464\n2023-01-02 17:40:06,169 - mmcls - INFO - Epoch [29][4000/5005]\tlr: 1.000e-01, eta: 8:42:40, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7859\n2023-01-02 17:40:14,836 - mmcls - INFO - Epoch [29][4100/5005]\tlr: 1.000e-01, eta: 8:42:31, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7396\n2023-01-02 17:40:23,570 - mmcls - INFO - Epoch [29][4200/5005]\tlr: 1.000e-01, eta: 8:42:22, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7610\n2023-01-02 17:40:32,017 - mmcls - INFO - Epoch [29][4300/5005]\tlr: 1.000e-01, eta: 8:42:12, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.7586\n2023-01-02 17:40:40,955 - mmcls - INFO - Epoch [29][4400/5005]\tlr: 1.000e-01, eta: 8:42:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7628\n2023-01-02 17:40:49,964 - mmcls - INFO - Epoch [29][4500/5005]\tlr: 1.000e-01, eta: 8:41:55, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7093\n2023-01-02 17:40:58,423 - mmcls - INFO - Epoch [29][4600/5005]\tlr: 1.000e-01, eta: 8:41:46, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.7423\n2023-01-02 17:41:07,259 - mmcls - INFO - Epoch [29][4700/5005]\tlr: 1.000e-01, eta: 8:41:37, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.7344\n2023-01-02 17:41:16,490 - mmcls - INFO - Epoch [29][4800/5005]\tlr: 1.000e-01, eta: 8:41:29, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7441\n2023-01-02 17:41:25,238 - mmcls - INFO - Epoch [29][4900/5005]\tlr: 1.000e-01, eta: 8:41:20, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.7309\n2023-01-02 17:41:33,767 - mmcls - INFO - Epoch [29][5000/5005]\tlr: 1.000e-01, eta: 8:41:11, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7415\n2023-01-02 17:41:56,650 - mmcls - INFO - Epoch(val) [29][196]\taccuracy_top-1: 46.5840, accuracy_top-5: 71.5840\n2023-01-02 17:42:07,584 - mmcls - INFO - Epoch [30][100/5005]\tlr: 1.000e-01, eta: 8:41:06, time: 0.109, data_time: 0.043, memory: 1125, loss: 2.6688\n2023-01-02 17:42:16,117 - mmcls - INFO - Epoch [30][200/5005]\tlr: 1.000e-01, eta: 8:40:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.6735\n2023-01-02 17:42:24,996 - mmcls - INFO - Epoch [30][300/5005]\tlr: 1.000e-01, eta: 8:40:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.6809\n2023-01-02 17:42:33,581 - mmcls - INFO - Epoch [30][400/5005]\tlr: 1.000e-01, eta: 8:40:38, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6810\n2023-01-02 17:42:42,588 - mmcls - INFO - Epoch [30][500/5005]\tlr: 1.000e-01, eta: 8:40:30, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7232\n2023-01-02 17:42:51,457 - mmcls - INFO - Epoch [30][600/5005]\tlr: 1.000e-01, eta: 8:40:21, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7288\n2023-01-02 17:43:00,062 - mmcls - INFO - Epoch [30][700/5005]\tlr: 1.000e-01, eta: 8:40:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6969\n2023-01-02 17:43:08,826 - mmcls - INFO - Epoch [30][800/5005]\tlr: 1.000e-01, eta: 8:40:03, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7253\n2023-01-02 17:43:17,681 - mmcls - INFO - Epoch [30][900/5005]\tlr: 1.000e-01, eta: 8:39:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7297\n2023-01-02 17:43:26,428 - mmcls - INFO - Epoch [30][1000/5005]\tlr: 1.000e-01, eta: 8:39:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7405\n2023-01-02 17:43:34,962 - mmcls - INFO - Epoch [30][1100/5005]\tlr: 1.000e-01, eta: 8:39:36, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7503\n2023-01-02 17:43:43,998 - mmcls - INFO - Epoch [30][1200/5005]\tlr: 1.000e-01, eta: 8:39:28, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7273\n2023-01-02 17:43:52,680 - mmcls - INFO - Epoch [30][1300/5005]\tlr: 1.000e-01, eta: 8:39:19, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7409\n2023-01-02 17:44:01,498 - mmcls - INFO - Epoch [30][1400/5005]\tlr: 1.000e-01, eta: 8:39:10, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7325\n2023-01-02 17:44:10,338 - mmcls - INFO - Epoch [30][1500/5005]\tlr: 1.000e-01, eta: 8:39:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7325\n2023-01-02 17:44:19,062 - mmcls - INFO - Epoch [30][1600/5005]\tlr: 1.000e-01, eta: 8:38:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7440\n2023-01-02 17:44:27,718 - mmcls - INFO - Epoch [30][1700/5005]\tlr: 1.000e-01, eta: 8:38:43, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7221\n2023-01-02 17:44:36,351 - mmcls - INFO - Epoch [30][1800/5005]\tlr: 1.000e-01, eta: 8:38:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7422\n2023-01-02 17:44:44,944 - mmcls - INFO - Epoch [30][1900/5005]\tlr: 1.000e-01, eta: 8:38:25, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7530\n2023-01-02 17:44:53,631 - mmcls - INFO - Epoch [30][2000/5005]\tlr: 1.000e-01, eta: 8:38:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7329\n2023-01-02 17:45:02,241 - mmcls - INFO - Epoch [30][2100/5005]\tlr: 1.000e-01, eta: 8:38:06, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7285\n2023-01-02 17:45:10,818 - mmcls - INFO - Epoch [30][2200/5005]\tlr: 1.000e-01, eta: 8:37:57, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7356\n2023-01-02 17:45:19,841 - mmcls - INFO - Epoch [30][2300/5005]\tlr: 1.000e-01, eta: 8:37:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7488\n2023-01-02 17:45:28,435 - mmcls - INFO - Epoch [30][2400/5005]\tlr: 1.000e-01, eta: 8:37:39, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7392\n2023-01-02 17:45:36,952 - mmcls - INFO - Epoch [30][2500/5005]\tlr: 1.000e-01, eta: 8:37:30, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7307\n2023-01-02 17:45:45,740 - mmcls - INFO - Epoch [30][2600/5005]\tlr: 1.000e-01, eta: 8:37:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7282\n2023-01-02 17:45:54,290 - mmcls - INFO - Epoch [30][2700/5005]\tlr: 1.000e-01, eta: 8:37:12, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7124\n2023-01-02 17:46:02,962 - mmcls - INFO - Epoch [30][2800/5005]\tlr: 1.000e-01, eta: 8:37:03, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.7612\n2023-01-02 17:46:11,676 - mmcls - INFO - Epoch [30][2900/5005]\tlr: 1.000e-01, eta: 8:36:54, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7756\n2023-01-02 17:46:20,688 - mmcls - INFO - Epoch [30][3000/5005]\tlr: 1.000e-01, eta: 8:36:45, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7655\n2023-01-02 17:46:29,297 - mmcls - INFO - Epoch [30][3100/5005]\tlr: 1.000e-01, eta: 8:36:36, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7438\n2023-01-02 17:46:38,346 - mmcls - INFO - Epoch [30][3200/5005]\tlr: 1.000e-01, eta: 8:36:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7240\n2023-01-02 17:46:47,132 - mmcls - INFO - Epoch [30][3300/5005]\tlr: 1.000e-01, eta: 8:36:19, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7358\n2023-01-02 17:46:56,021 - mmcls - INFO - Epoch [30][3400/5005]\tlr: 1.000e-01, eta: 8:36:10, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7255\n2023-01-02 17:47:04,789 - mmcls - INFO - Epoch [30][3500/5005]\tlr: 1.000e-01, eta: 8:36:02, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7347\n2023-01-02 17:47:13,531 - mmcls - INFO - Epoch [30][3600/5005]\tlr: 1.000e-01, eta: 8:35:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7208\n2023-01-02 17:47:22,211 - mmcls - INFO - Epoch [30][3700/5005]\tlr: 1.000e-01, eta: 8:35:43, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7401\n2023-01-02 17:47:30,781 - mmcls - INFO - Epoch [30][3800/5005]\tlr: 1.000e-01, eta: 8:35:34, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7512\n2023-01-02 17:47:39,488 - mmcls - INFO - Epoch [30][3900/5005]\tlr: 1.000e-01, eta: 8:35:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7480\n2023-01-02 17:47:48,225 - mmcls - INFO - Epoch [30][4000/5005]\tlr: 1.000e-01, eta: 8:35:16, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7583\n2023-01-02 17:47:57,075 - mmcls - INFO - Epoch [30][4100/5005]\tlr: 1.000e-01, eta: 8:35:07, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7553\n2023-01-02 17:48:06,034 - mmcls - INFO - Epoch [30][4200/5005]\tlr: 1.000e-01, eta: 8:34:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7895\n2023-01-02 17:48:14,655 - mmcls - INFO - Epoch [30][4300/5005]\tlr: 1.000e-01, eta: 8:34:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7481\n2023-01-02 17:48:23,558 - mmcls - INFO - Epoch [30][4400/5005]\tlr: 1.000e-01, eta: 8:34:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7295\n2023-01-02 17:48:32,101 - mmcls - INFO - Epoch [30][4500/5005]\tlr: 1.000e-01, eta: 8:34:32, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7428\n2023-01-02 17:48:40,795 - mmcls - INFO - Epoch [30][4600/5005]\tlr: 1.000e-01, eta: 8:34:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7354\n2023-01-02 17:48:49,525 - mmcls - INFO - Epoch [30][4700/5005]\tlr: 1.000e-01, eta: 8:34:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7695\n2023-01-02 17:48:58,363 - mmcls - INFO - Epoch [30][4800/5005]\tlr: 1.000e-01, eta: 8:34:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7839\n2023-01-02 17:49:07,001 - mmcls - INFO - Epoch [30][4900/5005]\tlr: 1.000e-01, eta: 8:33:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7489\n2023-01-02 17:49:15,461 - mmcls - INFO - Epoch [30][5000/5005]\tlr: 1.000e-01, eta: 8:33:46, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7697\n2023-01-02 17:49:38,023 - mmcls - INFO - Epoch(val) [30][196]\taccuracy_top-1: 47.3160, accuracy_top-5: 72.0300\n2023-01-02 17:49:49,123 - mmcls - INFO - Epoch [31][100/5005]\tlr: 1.000e-02, eta: 8:33:41, time: 0.111, data_time: 0.035, memory: 1125, loss: 2.5818\n2023-01-02 17:49:57,720 - mmcls - INFO - Epoch [31][200/5005]\tlr: 1.000e-02, eta: 8:33:32, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.4888\n2023-01-02 17:50:06,360 - mmcls - INFO - Epoch [31][300/5005]\tlr: 1.000e-02, eta: 8:33:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4139\n2023-01-02 17:50:15,292 - mmcls - INFO - Epoch [31][400/5005]\tlr: 1.000e-02, eta: 8:33:14, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4022\n2023-01-02 17:50:23,904 - mmcls - INFO - Epoch [31][500/5005]\tlr: 1.000e-02, eta: 8:33:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4123\n2023-01-02 17:50:32,679 - mmcls - INFO - Epoch [31][600/5005]\tlr: 1.000e-02, eta: 8:32:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4339\n2023-01-02 17:50:41,290 - mmcls - INFO - Epoch [31][700/5005]\tlr: 1.000e-02, eta: 8:32:47, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.3671\n2023-01-02 17:50:50,186 - mmcls - INFO - Epoch [31][800/5005]\tlr: 1.000e-02, eta: 8:32:39, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.3817\n2023-01-02 17:50:59,065 - mmcls - INFO - Epoch [31][900/5005]\tlr: 1.000e-02, eta: 8:32:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.3710\n2023-01-02 17:51:08,076 - mmcls - INFO - Epoch [31][1000/5005]\tlr: 1.000e-02, eta: 8:32:22, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.3543\n2023-01-02 17:51:16,887 - mmcls - INFO - Epoch [31][1100/5005]\tlr: 1.000e-02, eta: 8:32:13, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.3347\n2023-01-02 17:51:25,713 - mmcls - INFO - Epoch [31][1200/5005]\tlr: 1.000e-02, eta: 8:32:04, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.3386\n2023-01-02 17:51:34,836 - mmcls - INFO - Epoch [31][1300/5005]\tlr: 1.000e-02, eta: 8:31:56, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.3301\n2023-01-02 17:51:43,799 - mmcls - INFO - Epoch [31][1400/5005]\tlr: 1.000e-02, eta: 8:31:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.3304\n2023-01-02 17:51:52,627 - mmcls - INFO - Epoch [31][1500/5005]\tlr: 1.000e-02, eta: 8:31:39, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.3149\n2023-01-02 17:52:01,453 - mmcls - INFO - Epoch [31][1600/5005]\tlr: 1.000e-02, eta: 8:31:30, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.3279\n2023-01-02 17:52:10,080 - mmcls - INFO - Epoch [31][1700/5005]\tlr: 1.000e-02, eta: 8:31:21, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.3223\n2023-01-02 17:52:19,157 - mmcls - INFO - Epoch [31][1800/5005]\tlr: 1.000e-02, eta: 8:31:13, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.3137\n2023-01-02 17:52:27,884 - mmcls - INFO - Epoch [31][1900/5005]\tlr: 1.000e-02, eta: 8:31:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.3230\n2023-01-02 17:52:36,552 - mmcls - INFO - Epoch [31][2000/5005]\tlr: 1.000e-02, eta: 8:30:55, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.3035\n2023-01-02 17:52:45,061 - mmcls - INFO - Epoch [31][2100/5005]\tlr: 1.000e-02, eta: 8:30:45, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.3211\n2023-01-02 17:52:53,620 - mmcls - INFO - Epoch [31][2200/5005]\tlr: 1.000e-02, eta: 8:30:36, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.2944\n2023-01-02 17:53:02,522 - mmcls - INFO - Epoch [31][2300/5005]\tlr: 1.000e-02, eta: 8:30:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.3048\n2023-01-02 17:53:11,495 - mmcls - INFO - Epoch [31][2400/5005]\tlr: 1.000e-02, eta: 8:30:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.2837\n2023-01-02 17:53:20,334 - mmcls - INFO - Epoch [31][2500/5005]\tlr: 1.000e-02, eta: 8:30:10, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.2895\n2023-01-02 17:53:28,942 - mmcls - INFO - Epoch [31][2600/5005]\tlr: 1.000e-02, eta: 8:30:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.2585\n2023-01-02 17:53:37,561 - mmcls - INFO - Epoch [31][2700/5005]\tlr: 1.000e-02, eta: 8:29:52, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.2543\n2023-01-02 17:53:46,267 - mmcls - INFO - Epoch [31][2800/5005]\tlr: 1.000e-02, eta: 8:29:43, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.2756\n2023-01-02 17:53:54,836 - mmcls - INFO - Epoch [31][2900/5005]\tlr: 1.000e-02, eta: 8:29:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.2734\n2023-01-02 17:54:03,733 - mmcls - INFO - Epoch [31][3000/5005]\tlr: 1.000e-02, eta: 8:29:25, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.2619\n2023-01-02 17:54:12,292 - mmcls - INFO - Epoch [31][3100/5005]\tlr: 1.000e-02, eta: 8:29:15, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.2780\n2023-01-02 17:54:20,824 - mmcls - INFO - Epoch [31][3200/5005]\tlr: 1.000e-02, eta: 8:29:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.2480\n2023-01-02 17:54:29,544 - mmcls - INFO - Epoch [31][3300/5005]\tlr: 1.000e-02, eta: 8:28:57, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2660\n2023-01-02 17:54:38,773 - mmcls - INFO - Epoch [31][3400/5005]\tlr: 1.000e-02, eta: 8:28:49, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.2643\n2023-01-02 17:54:47,447 - mmcls - INFO - Epoch [31][3500/5005]\tlr: 1.000e-02, eta: 8:28:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2765\n2023-01-02 17:54:56,176 - mmcls - INFO - Epoch [31][3600/5005]\tlr: 1.000e-02, eta: 8:28:31, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.2523\n2023-01-02 17:55:04,856 - mmcls - INFO - Epoch [31][3700/5005]\tlr: 1.000e-02, eta: 8:28:22, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.2758\n2023-01-02 17:55:13,706 - mmcls - INFO - Epoch [31][3800/5005]\tlr: 1.000e-02, eta: 8:28:13, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.2534\n2023-01-02 17:55:22,575 - mmcls - INFO - Epoch [31][3900/5005]\tlr: 1.000e-02, eta: 8:28:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.2445\n2023-01-02 17:55:31,097 - mmcls - INFO - Epoch [31][4000/5005]\tlr: 1.000e-02, eta: 8:27:55, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.2589\n2023-01-02 17:55:40,195 - mmcls - INFO - Epoch [31][4100/5005]\tlr: 1.000e-02, eta: 8:27:47, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.2367\n2023-01-02 17:55:48,858 - mmcls - INFO - Epoch [31][4200/5005]\tlr: 1.000e-02, eta: 8:27:38, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.2451\n2023-01-02 17:55:57,680 - mmcls - INFO - Epoch [31][4300/5005]\tlr: 1.000e-02, eta: 8:27:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2508\n2023-01-02 17:56:06,529 - mmcls - INFO - Epoch [31][4400/5005]\tlr: 1.000e-02, eta: 8:27:21, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.2257\n2023-01-02 17:56:15,317 - mmcls - INFO - Epoch [31][4500/5005]\tlr: 1.000e-02, eta: 8:27:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.2705\n2023-01-02 17:56:24,129 - mmcls - INFO - Epoch [31][4600/5005]\tlr: 1.000e-02, eta: 8:27:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2471\n2023-01-02 17:56:32,897 - mmcls - INFO - Epoch [31][4700/5005]\tlr: 1.000e-02, eta: 8:26:54, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2241\n2023-01-02 17:56:41,774 - mmcls - INFO - Epoch [31][4800/5005]\tlr: 1.000e-02, eta: 8:26:46, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.2195\n2023-01-02 17:56:50,443 - mmcls - INFO - Epoch [31][4900/5005]\tlr: 1.000e-02, eta: 8:26:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2416\n2023-01-02 17:56:59,194 - mmcls - INFO - Epoch [31][5000/5005]\tlr: 1.000e-02, eta: 8:26:28, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.2529\n2023-01-02 17:57:21,931 - mmcls - INFO - Epoch(val) [31][196]\taccuracy_top-1: 56.4000, accuracy_top-5: 79.0200\n2023-01-02 17:57:33,437 - mmcls - INFO - Epoch [32][100/5005]\tlr: 1.000e-02, eta: 8:26:23, time: 0.115, data_time: 0.035, memory: 1125, loss: 2.2348\n2023-01-02 17:57:42,194 - mmcls - INFO - Epoch [32][200/5005]\tlr: 1.000e-02, eta: 8:26:14, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.2084\n2023-01-02 17:57:50,617 - mmcls - INFO - Epoch [32][300/5005]\tlr: 1.000e-02, eta: 8:26:05, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.1896\n2023-01-02 17:57:59,561 - mmcls - INFO - Epoch [32][400/5005]\tlr: 1.000e-02, eta: 8:25:56, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.2139\n2023-01-02 17:58:08,311 - mmcls - INFO - Epoch [32][500/5005]\tlr: 1.000e-02, eta: 8:25:47, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.2086\n2023-01-02 17:58:17,110 - mmcls - INFO - Epoch [32][600/5005]\tlr: 1.000e-02, eta: 8:25:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.2002\n2023-01-02 17:58:25,612 - mmcls - INFO - Epoch [32][700/5005]\tlr: 1.000e-02, eta: 8:25:29, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.2029\n2023-01-02 17:58:34,142 - mmcls - INFO - Epoch [32][800/5005]\tlr: 1.000e-02, eta: 8:25:20, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.2074\n2023-01-02 17:58:42,754 - mmcls - INFO - Epoch [32][900/5005]\tlr: 1.000e-02, eta: 8:25:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1826\n2023-01-02 17:58:51,575 - mmcls - INFO - Epoch [32][1000/5005]\tlr: 1.000e-02, eta: 8:25:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.1793\n2023-01-02 17:59:00,144 - mmcls - INFO - Epoch [32][1100/5005]\tlr: 1.000e-02, eta: 8:24:52, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.1860\n2023-01-02 17:59:09,137 - mmcls - INFO - Epoch [32][1200/5005]\tlr: 1.000e-02, eta: 8:24:44, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.2236\n2023-01-02 17:59:17,993 - mmcls - INFO - Epoch [32][1300/5005]\tlr: 1.000e-02, eta: 8:24:35, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1923\n2023-01-02 17:59:27,064 - mmcls - INFO - Epoch [32][1400/5005]\tlr: 1.000e-02, eta: 8:24:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.1909\n2023-01-02 17:59:35,980 - mmcls - INFO - Epoch [32][1500/5005]\tlr: 1.000e-02, eta: 8:24:19, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.1936\n2023-01-02 17:59:44,708 - mmcls - INFO - Epoch [32][1600/5005]\tlr: 1.000e-02, eta: 8:24:10, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.1822\n2023-01-02 17:59:53,549 - mmcls - INFO - Epoch [32][1700/5005]\tlr: 1.000e-02, eta: 8:24:01, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.2039\n2023-01-02 18:00:02,402 - mmcls - INFO - Epoch [32][1800/5005]\tlr: 1.000e-02, eta: 8:23:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.1784\n2023-01-02 18:00:10,948 - mmcls - INFO - Epoch [32][1900/5005]\tlr: 1.000e-02, eta: 8:23:43, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1895\n2023-01-02 18:00:19,614 - mmcls - INFO - Epoch [32][2000/5005]\tlr: 1.000e-02, eta: 8:23:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1872\n2023-01-02 18:00:28,505 - mmcls - INFO - Epoch [32][2100/5005]\tlr: 1.000e-02, eta: 8:23:25, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.1619\n2023-01-02 18:00:36,985 - mmcls - INFO - Epoch [32][2200/5005]\tlr: 1.000e-02, eta: 8:23:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.2023\n2023-01-02 18:00:45,910 - mmcls - INFO - Epoch [32][2300/5005]\tlr: 1.000e-02, eta: 8:23:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1882\n2023-01-02 18:00:54,844 - mmcls - INFO - Epoch [32][2400/5005]\tlr: 1.000e-02, eta: 8:22:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1801\n2023-01-02 18:01:03,878 - mmcls - INFO - Epoch [32][2500/5005]\tlr: 1.000e-02, eta: 8:22:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.1952\n2023-01-02 18:01:12,450 - mmcls - INFO - Epoch [32][2600/5005]\tlr: 1.000e-02, eta: 8:22:41, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.1713\n2023-01-02 18:01:21,499 - mmcls - INFO - Epoch [32][2700/5005]\tlr: 1.000e-02, eta: 8:22:33, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.1727\n2023-01-02 18:01:30,264 - mmcls - INFO - Epoch [32][2800/5005]\tlr: 1.000e-02, eta: 8:22:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1728\n2023-01-02 18:01:38,959 - mmcls - INFO - Epoch [32][2900/5005]\tlr: 1.000e-02, eta: 8:22:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.1931\n2023-01-02 18:01:47,516 - mmcls - INFO - Epoch [32][3000/5005]\tlr: 1.000e-02, eta: 8:22:06, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1662\n2023-01-02 18:01:56,515 - mmcls - INFO - Epoch [32][3100/5005]\tlr: 1.000e-02, eta: 8:21:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1935\n2023-01-02 18:02:05,091 - mmcls - INFO - Epoch [32][3200/5005]\tlr: 1.000e-02, eta: 8:21:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.2100\n2023-01-02 18:02:13,737 - mmcls - INFO - Epoch [32][3300/5005]\tlr: 1.000e-02, eta: 8:21:39, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1774\n2023-01-02 18:02:22,297 - mmcls - INFO - Epoch [32][3400/5005]\tlr: 1.000e-02, eta: 8:21:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.2123\n2023-01-02 18:02:30,957 - mmcls - INFO - Epoch [32][3500/5005]\tlr: 1.000e-02, eta: 8:21:20, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.1760\n2023-01-02 18:02:39,545 - mmcls - INFO - Epoch [32][3600/5005]\tlr: 1.000e-02, eta: 8:21:11, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.1779\n2023-01-02 18:02:48,479 - mmcls - INFO - Epoch [32][3700/5005]\tlr: 1.000e-02, eta: 8:21:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.1647\n2023-01-02 18:02:57,048 - mmcls - INFO - Epoch [32][3800/5005]\tlr: 1.000e-02, eta: 8:20:53, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1975\n2023-01-02 18:03:05,567 - mmcls - INFO - Epoch [32][3900/5005]\tlr: 1.000e-02, eta: 8:20:44, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.1678\n2023-01-02 18:03:14,208 - mmcls - INFO - Epoch [32][4000/5005]\tlr: 1.000e-02, eta: 8:20:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.1720\n2023-01-02 18:03:22,975 - mmcls - INFO - Epoch [32][4100/5005]\tlr: 1.000e-02, eta: 8:20:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1803\n2023-01-02 18:03:31,540 - mmcls - INFO - Epoch [32][4200/5005]\tlr: 1.000e-02, eta: 8:20:17, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.1688\n2023-01-02 18:03:40,173 - mmcls - INFO - Epoch [32][4300/5005]\tlr: 1.000e-02, eta: 8:20:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1697\n2023-01-02 18:03:48,732 - mmcls - INFO - Epoch [32][4400/5005]\tlr: 1.000e-02, eta: 8:19:58, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1938\n2023-01-02 18:03:57,798 - mmcls - INFO - Epoch [32][4500/5005]\tlr: 1.000e-02, eta: 8:19:50, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.1671\n2023-01-02 18:04:06,373 - mmcls - INFO - Epoch [32][4600/5005]\tlr: 1.000e-02, eta: 8:19:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1847\n2023-01-02 18:04:15,174 - mmcls - INFO - Epoch [32][4700/5005]\tlr: 1.000e-02, eta: 8:19:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1696\n2023-01-02 18:04:23,847 - mmcls - INFO - Epoch [32][4800/5005]\tlr: 1.000e-02, eta: 8:19:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1672\n2023-01-02 18:04:32,691 - mmcls - INFO - Epoch [32][4900/5005]\tlr: 1.000e-02, eta: 8:19:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1670\n2023-01-02 18:04:41,308 - mmcls - INFO - Epoch [32][5000/5005]\tlr: 1.000e-02, eta: 8:19:05, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1764\n2023-01-02 18:05:04,160 - mmcls - INFO - Epoch(val) [32][196]\taccuracy_top-1: 57.8620, accuracy_top-5: 80.3340\n2023-01-02 18:05:15,213 - mmcls - INFO - Epoch [33][100/5005]\tlr: 1.000e-02, eta: 8:18:59, time: 0.110, data_time: 0.044, memory: 1125, loss: 2.1562\n2023-01-02 18:05:23,963 - mmcls - INFO - Epoch [33][200/5005]\tlr: 1.000e-02, eta: 8:18:51, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1644\n2023-01-02 18:05:33,090 - mmcls - INFO - Epoch [33][300/5005]\tlr: 1.000e-02, eta: 8:18:42, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.1324\n2023-01-02 18:05:41,747 - mmcls - INFO - Epoch [33][400/5005]\tlr: 1.000e-02, eta: 8:18:33, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.1398\n2023-01-02 18:05:50,171 - mmcls - INFO - Epoch [33][500/5005]\tlr: 1.000e-02, eta: 8:18:24, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.1404\n2023-01-02 18:05:58,783 - mmcls - INFO - Epoch [33][600/5005]\tlr: 1.000e-02, eta: 8:18:15, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.1005\n2023-01-02 18:06:07,712 - mmcls - INFO - Epoch [33][700/5005]\tlr: 1.000e-02, eta: 8:18:06, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.1157\n2023-01-02 18:06:16,338 - mmcls - INFO - Epoch [33][800/5005]\tlr: 1.000e-02, eta: 8:17:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.1578\n2023-01-02 18:06:25,060 - mmcls - INFO - Epoch [33][900/5005]\tlr: 1.000e-02, eta: 8:17:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.1505\n2023-01-02 18:06:34,281 - mmcls - INFO - Epoch [33][1000/5005]\tlr: 1.000e-02, eta: 8:17:40, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.1319\n2023-01-02 18:06:42,717 - mmcls - INFO - Epoch [33][1100/5005]\tlr: 1.000e-02, eta: 8:17:30, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.1228\n2023-01-02 18:06:51,533 - mmcls - INFO - Epoch [33][1200/5005]\tlr: 1.000e-02, eta: 8:17:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1454\n2023-01-02 18:07:00,038 - mmcls - INFO - Epoch [33][1300/5005]\tlr: 1.000e-02, eta: 8:17:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1237\n2023-01-02 18:07:08,576 - mmcls - INFO - Epoch [33][1400/5005]\tlr: 1.000e-02, eta: 8:17:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.1492\n2023-01-02 18:07:17,376 - mmcls - INFO - Epoch [33][1500/5005]\tlr: 1.000e-02, eta: 8:16:54, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.1134\n2023-01-02 18:07:26,244 - mmcls - INFO - Epoch [33][1600/5005]\tlr: 1.000e-02, eta: 8:16:45, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.1457\n2023-01-02 18:07:35,028 - mmcls - INFO - Epoch [33][1700/5005]\tlr: 1.000e-02, eta: 8:16:37, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.1331\n2023-01-02 18:07:43,656 - mmcls - INFO - Epoch [33][1800/5005]\tlr: 1.000e-02, eta: 8:16:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.1490\n2023-01-02 18:07:52,278 - mmcls - INFO - Epoch [33][1900/5005]\tlr: 1.000e-02, eta: 8:16:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.1543\n2023-01-02 18:08:01,058 - mmcls - INFO - Epoch [33][2000/5005]\tlr: 1.000e-02, eta: 8:16:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1363\n2023-01-02 18:08:10,076 - mmcls - INFO - Epoch [33][2100/5005]\tlr: 1.000e-02, eta: 8:16:01, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.1449\n2023-01-02 18:08:18,917 - mmcls - INFO - Epoch [33][2200/5005]\tlr: 1.000e-02, eta: 8:15:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1240\n2023-01-02 18:08:27,658 - mmcls - INFO - Epoch [33][2300/5005]\tlr: 1.000e-02, eta: 8:15:43, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.1386\n2023-01-02 18:08:36,399 - mmcls - INFO - Epoch [33][2400/5005]\tlr: 1.000e-02, eta: 8:15:35, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.1224\n2023-01-02 18:08:45,199 - mmcls - INFO - Epoch [33][2500/5005]\tlr: 1.000e-02, eta: 8:15:26, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.1552\n2023-01-02 18:08:54,084 - mmcls - INFO - Epoch [33][2600/5005]\tlr: 1.000e-02, eta: 8:15:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1403\n2023-01-02 18:09:02,833 - mmcls - INFO - Epoch [33][2700/5005]\tlr: 1.000e-02, eta: 8:15:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.1393\n2023-01-02 18:09:11,413 - mmcls - INFO - Epoch [33][2800/5005]\tlr: 1.000e-02, eta: 8:14:59, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.1371\n2023-01-02 18:09:20,223 - mmcls - INFO - Epoch [33][2900/5005]\tlr: 1.000e-02, eta: 8:14:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1243\n2023-01-02 18:09:28,976 - mmcls - INFO - Epoch [33][3000/5005]\tlr: 1.000e-02, eta: 8:14:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1432\n2023-01-02 18:09:37,944 - mmcls - INFO - Epoch [33][3100/5005]\tlr: 1.000e-02, eta: 8:14:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1219\n2023-01-02 18:09:46,492 - mmcls - INFO - Epoch [33][3200/5005]\tlr: 1.000e-02, eta: 8:14:23, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.1307\n2023-01-02 18:09:55,123 - mmcls - INFO - Epoch [33][3300/5005]\tlr: 1.000e-02, eta: 8:14:14, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1223\n2023-01-02 18:10:03,747 - mmcls - INFO - Epoch [33][3400/5005]\tlr: 1.000e-02, eta: 8:14:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.1413\n2023-01-02 18:10:12,328 - mmcls - INFO - Epoch [33][3500/5005]\tlr: 1.000e-02, eta: 8:13:56, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.1219\n2023-01-02 18:10:21,021 - mmcls - INFO - Epoch [33][3600/5005]\tlr: 1.000e-02, eta: 8:13:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1397\n2023-01-02 18:10:29,730 - mmcls - INFO - Epoch [33][3700/5005]\tlr: 1.000e-02, eta: 8:13:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.1571\n2023-01-02 18:10:38,452 - mmcls - INFO - Epoch [33][3800/5005]\tlr: 1.000e-02, eta: 8:13:29, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.1479\n2023-01-02 18:10:47,223 - mmcls - INFO - Epoch [33][3900/5005]\tlr: 1.000e-02, eta: 8:13:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1485\n2023-01-02 18:10:56,449 - mmcls - INFO - Epoch [33][4000/5005]\tlr: 1.000e-02, eta: 8:13:12, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.1363\n2023-01-02 18:11:04,900 - mmcls - INFO - Epoch [33][4100/5005]\tlr: 1.000e-02, eta: 8:13:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1260\n2023-01-02 18:11:13,549 - mmcls - INFO - Epoch [33][4200/5005]\tlr: 1.000e-02, eta: 8:12:54, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1298\n2023-01-02 18:11:22,293 - mmcls - INFO - Epoch [33][4300/5005]\tlr: 1.000e-02, eta: 8:12:45, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.1277\n2023-01-02 18:11:30,792 - mmcls - INFO - Epoch [33][4400/5005]\tlr: 1.000e-02, eta: 8:12:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1444\n2023-01-02 18:11:39,432 - mmcls - INFO - Epoch [33][4500/5005]\tlr: 1.000e-02, eta: 8:12:26, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.1711\n2023-01-02 18:11:47,970 - mmcls - INFO - Epoch [33][4600/5005]\tlr: 1.000e-02, eta: 8:12:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1482\n2023-01-02 18:11:56,684 - mmcls - INFO - Epoch [33][4700/5005]\tlr: 1.000e-02, eta: 8:12:08, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.1467\n2023-01-02 18:12:05,218 - mmcls - INFO - Epoch [33][4800/5005]\tlr: 1.000e-02, eta: 8:11:58, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.1412\n2023-01-02 18:12:13,772 - mmcls - INFO - Epoch [33][4900/5005]\tlr: 1.000e-02, eta: 8:11:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.1504\n2023-01-02 18:12:22,361 - mmcls - INFO - Epoch [33][5000/5005]\tlr: 1.000e-02, eta: 8:11:40, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1544\n2023-01-02 18:12:45,239 - mmcls - INFO - Epoch(val) [33][196]\taccuracy_top-1: 58.0160, accuracy_top-5: 80.4460\n2023-01-02 18:12:56,155 - mmcls - INFO - Epoch [34][100/5005]\tlr: 1.000e-02, eta: 8:11:34, time: 0.109, data_time: 0.056, memory: 1125, loss: 2.0925\n2023-01-02 18:13:04,863 - mmcls - INFO - Epoch [34][200/5005]\tlr: 1.000e-02, eta: 8:11:25, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.1087\n2023-01-02 18:13:13,539 - mmcls - INFO - Epoch [34][300/5005]\tlr: 1.000e-02, eta: 8:11:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0778\n2023-01-02 18:13:22,474 - mmcls - INFO - Epoch [34][400/5005]\tlr: 1.000e-02, eta: 8:11:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0873\n2023-01-02 18:13:31,328 - mmcls - INFO - Epoch [34][500/5005]\tlr: 1.000e-02, eta: 8:10:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1122\n2023-01-02 18:13:40,129 - mmcls - INFO - Epoch [34][600/5005]\tlr: 1.000e-02, eta: 8:10:50, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.1211\n2023-01-02 18:13:48,902 - mmcls - INFO - Epoch [34][700/5005]\tlr: 1.000e-02, eta: 8:10:41, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.1079\n2023-01-02 18:13:57,772 - mmcls - INFO - Epoch [34][800/5005]\tlr: 1.000e-02, eta: 8:10:33, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.1178\n2023-01-02 18:14:06,311 - mmcls - INFO - Epoch [34][900/5005]\tlr: 1.000e-02, eta: 8:10:23, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1305\n2023-01-02 18:14:15,073 - mmcls - INFO - Epoch [34][1000/5005]\tlr: 1.000e-02, eta: 8:10:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1128\n2023-01-02 18:14:23,733 - mmcls - INFO - Epoch [34][1100/5005]\tlr: 1.000e-02, eta: 8:10:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0866\n2023-01-02 18:14:32,459 - mmcls - INFO - Epoch [34][1200/5005]\tlr: 1.000e-02, eta: 8:09:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1124\n2023-01-02 18:14:41,162 - mmcls - INFO - Epoch [34][1300/5005]\tlr: 1.000e-02, eta: 8:09:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1085\n2023-01-02 18:14:49,677 - mmcls - INFO - Epoch [34][1400/5005]\tlr: 1.000e-02, eta: 8:09:38, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0785\n2023-01-02 18:14:58,380 - mmcls - INFO - Epoch [34][1500/5005]\tlr: 1.000e-02, eta: 8:09:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0933\n2023-01-02 18:15:06,951 - mmcls - INFO - Epoch [34][1600/5005]\tlr: 1.000e-02, eta: 8:09:20, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.1120\n2023-01-02 18:15:15,611 - mmcls - INFO - Epoch [34][1700/5005]\tlr: 1.000e-02, eta: 8:09:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1190\n2023-01-02 18:15:24,318 - mmcls - INFO - Epoch [34][1800/5005]\tlr: 1.000e-02, eta: 8:09:02, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.1080\n2023-01-02 18:15:33,183 - mmcls - INFO - Epoch [34][1900/5005]\tlr: 1.000e-02, eta: 8:08:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.1173\n2023-01-02 18:15:41,828 - mmcls - INFO - Epoch [34][2000/5005]\tlr: 1.000e-02, eta: 8:08:44, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.1299\n2023-01-02 18:15:50,215 - mmcls - INFO - Epoch [34][2100/5005]\tlr: 1.000e-02, eta: 8:08:34, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0976\n2023-01-02 18:15:58,903 - mmcls - INFO - Epoch [34][2200/5005]\tlr: 1.000e-02, eta: 8:08:25, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1339\n2023-01-02 18:16:07,878 - mmcls - INFO - Epoch [34][2300/5005]\tlr: 1.000e-02, eta: 8:08:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0977\n2023-01-02 18:16:16,946 - mmcls - INFO - Epoch [34][2400/5005]\tlr: 1.000e-02, eta: 8:08:09, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.1125\n2023-01-02 18:16:25,510 - mmcls - INFO - Epoch [34][2500/5005]\tlr: 1.000e-02, eta: 8:07:59, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.1289\n2023-01-02 18:16:34,324 - mmcls - INFO - Epoch [34][2600/5005]\tlr: 1.000e-02, eta: 8:07:51, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.1009\n2023-01-02 18:16:43,272 - mmcls - INFO - Epoch [34][2700/5005]\tlr: 1.000e-02, eta: 8:07:42, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0993\n2023-01-02 18:16:51,932 - mmcls - INFO - Epoch [34][2800/5005]\tlr: 1.000e-02, eta: 8:07:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.1046\n2023-01-02 18:17:00,442 - mmcls - INFO - Epoch [34][2900/5005]\tlr: 1.000e-02, eta: 8:07:24, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1241\n2023-01-02 18:17:09,338 - mmcls - INFO - Epoch [34][3000/5005]\tlr: 1.000e-02, eta: 8:07:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1173\n2023-01-02 18:17:18,242 - mmcls - INFO - Epoch [34][3100/5005]\tlr: 1.000e-02, eta: 8:07:06, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.1357\n2023-01-02 18:17:26,847 - mmcls - INFO - Epoch [34][3200/5005]\tlr: 1.000e-02, eta: 8:06:57, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1328\n2023-01-02 18:17:35,350 - mmcls - INFO - Epoch [34][3300/5005]\tlr: 1.000e-02, eta: 8:06:48, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.1099\n2023-01-02 18:17:44,034 - mmcls - INFO - Epoch [34][3400/5005]\tlr: 1.000e-02, eta: 8:06:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1022\n2023-01-02 18:17:52,839 - mmcls - INFO - Epoch [34][3500/5005]\tlr: 1.000e-02, eta: 8:06:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1066\n2023-01-02 18:18:01,557 - mmcls - INFO - Epoch [34][3600/5005]\tlr: 1.000e-02, eta: 8:06:21, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1318\n2023-01-02 18:18:10,401 - mmcls - INFO - Epoch [34][3700/5005]\tlr: 1.000e-02, eta: 8:06:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.1070\n2023-01-02 18:18:19,216 - mmcls - INFO - Epoch [34][3800/5005]\tlr: 1.000e-02, eta: 8:06:04, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.1105\n2023-01-02 18:18:28,011 - mmcls - INFO - Epoch [34][3900/5005]\tlr: 1.000e-02, eta: 8:05:55, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.1047\n2023-01-02 18:18:36,712 - mmcls - INFO - Epoch [34][4000/5005]\tlr: 1.000e-02, eta: 8:05:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1036\n2023-01-02 18:18:45,446 - mmcls - INFO - Epoch [34][4100/5005]\tlr: 1.000e-02, eta: 8:05:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1059\n2023-01-02 18:18:53,896 - mmcls - INFO - Epoch [34][4200/5005]\tlr: 1.000e-02, eta: 8:05:27, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1126\n2023-01-02 18:19:02,584 - mmcls - INFO - Epoch [34][4300/5005]\tlr: 1.000e-02, eta: 8:05:18, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.1049\n2023-01-02 18:19:11,342 - mmcls - INFO - Epoch [34][4400/5005]\tlr: 1.000e-02, eta: 8:05:10, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.1157\n2023-01-02 18:19:20,354 - mmcls - INFO - Epoch [34][4500/5005]\tlr: 1.000e-02, eta: 8:05:01, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.1143\n2023-01-02 18:19:29,708 - mmcls - INFO - Epoch [34][4600/5005]\tlr: 1.000e-02, eta: 8:04:54, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.1231\n2023-01-02 18:19:38,611 - mmcls - INFO - Epoch [34][4700/5005]\tlr: 1.000e-02, eta: 8:04:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.1119\n2023-01-02 18:19:47,502 - mmcls - INFO - Epoch [34][4800/5005]\tlr: 1.000e-02, eta: 8:04:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1119\n2023-01-02 18:19:56,405 - mmcls - INFO - Epoch [34][4900/5005]\tlr: 1.000e-02, eta: 8:04:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1232\n2023-01-02 18:20:05,182 - mmcls - INFO - Epoch [34][5000/5005]\tlr: 1.000e-02, eta: 8:04:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.1078\n2023-01-02 18:20:27,986 - mmcls - INFO - Epoch(val) [34][196]\taccuracy_top-1: 58.6180, accuracy_top-5: 80.8720\n2023-01-02 18:20:39,018 - mmcls - INFO - Epoch [35][100/5005]\tlr: 1.000e-02, eta: 8:04:13, time: 0.110, data_time: 0.045, memory: 1125, loss: 2.0632\n2023-01-02 18:20:47,757 - mmcls - INFO - Epoch [35][200/5005]\tlr: 1.000e-02, eta: 8:04:04, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0763\n2023-01-02 18:20:56,210 - mmcls - INFO - Epoch [35][300/5005]\tlr: 1.000e-02, eta: 8:03:55, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.0768\n2023-01-02 18:21:04,937 - mmcls - INFO - Epoch [35][400/5005]\tlr: 1.000e-02, eta: 8:03:46, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0889\n2023-01-02 18:21:13,628 - mmcls - INFO - Epoch [35][500/5005]\tlr: 1.000e-02, eta: 8:03:37, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0954\n2023-01-02 18:21:22,471 - mmcls - INFO - Epoch [35][600/5005]\tlr: 1.000e-02, eta: 8:03:28, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0911\n2023-01-02 18:21:31,121 - mmcls - INFO - Epoch [35][700/5005]\tlr: 1.000e-02, eta: 8:03:19, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0646\n2023-01-02 18:21:39,707 - mmcls - INFO - Epoch [35][800/5005]\tlr: 1.000e-02, eta: 8:03:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0751\n2023-01-02 18:21:48,473 - mmcls - INFO - Epoch [35][900/5005]\tlr: 1.000e-02, eta: 8:03:01, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0861\n2023-01-02 18:21:57,302 - mmcls - INFO - Epoch [35][1000/5005]\tlr: 1.000e-02, eta: 8:02:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0692\n2023-01-02 18:22:06,127 - mmcls - INFO - Epoch [35][1100/5005]\tlr: 1.000e-02, eta: 8:02:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0840\n2023-01-02 18:22:14,689 - mmcls - INFO - Epoch [35][1200/5005]\tlr: 1.000e-02, eta: 8:02:34, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1125\n2023-01-02 18:22:23,350 - mmcls - INFO - Epoch [35][1300/5005]\tlr: 1.000e-02, eta: 8:02:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0746\n2023-01-02 18:22:31,797 - mmcls - INFO - Epoch [35][1400/5005]\tlr: 1.000e-02, eta: 8:02:16, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0808\n2023-01-02 18:22:40,754 - mmcls - INFO - Epoch [35][1500/5005]\tlr: 1.000e-02, eta: 8:02:07, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0915\n2023-01-02 18:22:49,476 - mmcls - INFO - Epoch [35][1600/5005]\tlr: 1.000e-02, eta: 8:01:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0870\n2023-01-02 18:22:57,989 - mmcls - INFO - Epoch [35][1700/5005]\tlr: 1.000e-02, eta: 8:01:49, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.0973\n2023-01-02 18:23:06,730 - mmcls - INFO - Epoch [35][1800/5005]\tlr: 1.000e-02, eta: 8:01:40, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1016\n2023-01-02 18:23:15,689 - mmcls - INFO - Epoch [35][1900/5005]\tlr: 1.000e-02, eta: 8:01:31, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.1011\n2023-01-02 18:23:24,103 - mmcls - INFO - Epoch [35][2000/5005]\tlr: 1.000e-02, eta: 8:01:22, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0790\n2023-01-02 18:23:33,010 - mmcls - INFO - Epoch [35][2100/5005]\tlr: 1.000e-02, eta: 8:01:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0945\n2023-01-02 18:23:41,911 - mmcls - INFO - Epoch [35][2200/5005]\tlr: 1.000e-02, eta: 8:01:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1031\n2023-01-02 18:23:50,372 - mmcls - INFO - Epoch [35][2300/5005]\tlr: 1.000e-02, eta: 8:00:55, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0716\n2023-01-02 18:23:59,024 - mmcls - INFO - Epoch [35][2400/5005]\tlr: 1.000e-02, eta: 8:00:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0852\n2023-01-02 18:24:08,011 - mmcls - INFO - Epoch [35][2500/5005]\tlr: 1.000e-02, eta: 8:00:38, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0978\n2023-01-02 18:24:16,782 - mmcls - INFO - Epoch [35][2600/5005]\tlr: 1.000e-02, eta: 8:00:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1069\n2023-01-02 18:24:25,628 - mmcls - INFO - Epoch [35][2700/5005]\tlr: 1.000e-02, eta: 8:00:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1047\n2023-01-02 18:24:34,404 - mmcls - INFO - Epoch [35][2800/5005]\tlr: 1.000e-02, eta: 8:00:11, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.1118\n2023-01-02 18:24:43,168 - mmcls - INFO - Epoch [35][2900/5005]\tlr: 1.000e-02, eta: 8:00:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1153\n2023-01-02 18:24:51,837 - mmcls - INFO - Epoch [35][3000/5005]\tlr: 1.000e-02, eta: 7:59:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0989\n2023-01-02 18:25:00,340 - mmcls - INFO - Epoch [35][3100/5005]\tlr: 1.000e-02, eta: 7:59:44, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1038\n2023-01-02 18:25:09,582 - mmcls - INFO - Epoch [35][3200/5005]\tlr: 1.000e-02, eta: 7:59:36, time: 0.092, data_time: 0.012, memory: 1125, loss: 2.0990\n2023-01-02 18:25:18,047 - mmcls - INFO - Epoch [35][3300/5005]\tlr: 1.000e-02, eta: 7:59:27, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1118\n2023-01-02 18:25:27,095 - mmcls - INFO - Epoch [35][3400/5005]\tlr: 1.000e-02, eta: 7:59:18, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.1203\n2023-01-02 18:25:35,758 - mmcls - INFO - Epoch [35][3500/5005]\tlr: 1.000e-02, eta: 7:59:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0851\n2023-01-02 18:25:44,464 - mmcls - INFO - Epoch [35][3600/5005]\tlr: 1.000e-02, eta: 7:59:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1016\n2023-01-02 18:25:53,184 - mmcls - INFO - Epoch [35][3700/5005]\tlr: 1.000e-02, eta: 7:58:51, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.1010\n2023-01-02 18:26:01,713 - mmcls - INFO - Epoch [35][3800/5005]\tlr: 1.000e-02, eta: 7:58:42, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.0774\n2023-01-02 18:26:10,439 - mmcls - INFO - Epoch [35][3900/5005]\tlr: 1.000e-02, eta: 7:58:33, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0888\n2023-01-02 18:26:19,011 - mmcls - INFO - Epoch [35][4000/5005]\tlr: 1.000e-02, eta: 7:58:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1025\n2023-01-02 18:26:27,668 - mmcls - INFO - Epoch [35][4100/5005]\tlr: 1.000e-02, eta: 7:58:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0900\n2023-01-02 18:26:36,646 - mmcls - INFO - Epoch [35][4200/5005]\tlr: 1.000e-02, eta: 7:58:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0958\n2023-01-02 18:26:45,543 - mmcls - INFO - Epoch [35][4300/5005]\tlr: 1.000e-02, eta: 7:57:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0941\n2023-01-02 18:26:54,186 - mmcls - INFO - Epoch [35][4400/5005]\tlr: 1.000e-02, eta: 7:57:49, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.1008\n2023-01-02 18:27:03,146 - mmcls - INFO - Epoch [35][4500/5005]\tlr: 1.000e-02, eta: 7:57:40, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.1075\n2023-01-02 18:27:11,734 - mmcls - INFO - Epoch [35][4600/5005]\tlr: 1.000e-02, eta: 7:57:31, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0893\n2023-01-02 18:27:20,484 - mmcls - INFO - Epoch [35][4700/5005]\tlr: 1.000e-02, eta: 7:57:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0938\n2023-01-02 18:27:29,005 - mmcls - INFO - Epoch [35][4800/5005]\tlr: 1.000e-02, eta: 7:57:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0890\n2023-01-02 18:27:37,897 - mmcls - INFO - Epoch [35][4900/5005]\tlr: 1.000e-02, eta: 7:57:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.1118\n2023-01-02 18:27:46,897 - mmcls - INFO - Epoch [35][5000/5005]\tlr: 1.000e-02, eta: 7:56:56, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.1089\n2023-01-02 18:28:09,634 - mmcls - INFO - Epoch(val) [35][196]\taccuracy_top-1: 58.4460, accuracy_top-5: 80.6280\n2023-01-02 18:28:20,695 - mmcls - INFO - Epoch [36][100/5005]\tlr: 1.000e-02, eta: 7:56:50, time: 0.110, data_time: 0.048, memory: 1125, loss: 2.0296\n2023-01-02 18:28:29,199 - mmcls - INFO - Epoch [36][200/5005]\tlr: 1.000e-02, eta: 7:56:41, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0817\n2023-01-02 18:28:38,181 - mmcls - INFO - Epoch [36][300/5005]\tlr: 1.000e-02, eta: 7:56:32, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0515\n2023-01-02 18:28:46,750 - mmcls - INFO - Epoch [36][400/5005]\tlr: 1.000e-02, eta: 7:56:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0496\n2023-01-02 18:28:55,436 - mmcls - INFO - Epoch [36][500/5005]\tlr: 1.000e-02, eta: 7:56:14, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0721\n2023-01-02 18:29:04,204 - mmcls - INFO - Epoch [36][600/5005]\tlr: 1.000e-02, eta: 7:56:05, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0567\n2023-01-02 18:29:12,761 - mmcls - INFO - Epoch [36][700/5005]\tlr: 1.000e-02, eta: 7:55:56, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0598\n2023-01-02 18:29:21,826 - mmcls - INFO - Epoch [36][800/5005]\tlr: 1.000e-02, eta: 7:55:48, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0459\n2023-01-02 18:29:30,082 - mmcls - INFO - Epoch [36][900/5005]\tlr: 1.000e-02, eta: 7:55:38, time: 0.082, data_time: 0.003, memory: 1125, loss: 2.0744\n2023-01-02 18:29:38,723 - mmcls - INFO - Epoch [36][1000/5005]\tlr: 1.000e-02, eta: 7:55:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0461\n2023-01-02 18:29:47,532 - mmcls - INFO - Epoch [36][1100/5005]\tlr: 1.000e-02, eta: 7:55:20, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0916\n2023-01-02 18:29:55,970 - mmcls - INFO - Epoch [36][1200/5005]\tlr: 1.000e-02, eta: 7:55:10, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.0380\n2023-01-02 18:30:04,752 - mmcls - INFO - Epoch [36][1300/5005]\tlr: 1.000e-02, eta: 7:55:02, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0807\n2023-01-02 18:30:13,530 - mmcls - INFO - Epoch [36][1400/5005]\tlr: 1.000e-02, eta: 7:54:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0577\n2023-01-02 18:30:22,315 - mmcls - INFO - Epoch [36][1500/5005]\tlr: 1.000e-02, eta: 7:54:44, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0879\n2023-01-02 18:30:31,358 - mmcls - INFO - Epoch [36][1600/5005]\tlr: 1.000e-02, eta: 7:54:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.0812\n2023-01-02 18:30:39,931 - mmcls - INFO - Epoch [36][1700/5005]\tlr: 1.000e-02, eta: 7:54:26, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0736\n2023-01-02 18:30:48,713 - mmcls - INFO - Epoch [36][1800/5005]\tlr: 1.000e-02, eta: 7:54:18, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0870\n2023-01-02 18:30:57,501 - mmcls - INFO - Epoch [36][1900/5005]\tlr: 1.000e-02, eta: 7:54:09, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0910\n2023-01-02 18:31:06,175 - mmcls - INFO - Epoch [36][2000/5005]\tlr: 1.000e-02, eta: 7:54:00, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0878\n2023-01-02 18:31:14,839 - mmcls - INFO - Epoch [36][2100/5005]\tlr: 1.000e-02, eta: 7:53:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0696\n2023-01-02 18:31:23,455 - mmcls - INFO - Epoch [36][2200/5005]\tlr: 1.000e-02, eta: 7:53:42, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.1052\n2023-01-02 18:31:32,047 - mmcls - INFO - Epoch [36][2300/5005]\tlr: 1.000e-02, eta: 7:53:32, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0608\n2023-01-02 18:31:40,588 - mmcls - INFO - Epoch [36][2400/5005]\tlr: 1.000e-02, eta: 7:53:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0756\n2023-01-02 18:31:49,573 - mmcls - INFO - Epoch [36][2500/5005]\tlr: 1.000e-02, eta: 7:53:15, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.0625\n2023-01-02 18:31:58,312 - mmcls - INFO - Epoch [36][2600/5005]\tlr: 1.000e-02, eta: 7:53:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0382\n2023-01-02 18:32:06,798 - mmcls - INFO - Epoch [36][2700/5005]\tlr: 1.000e-02, eta: 7:52:56, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0794\n2023-01-02 18:32:15,515 - mmcls - INFO - Epoch [36][2800/5005]\tlr: 1.000e-02, eta: 7:52:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0958\n2023-01-02 18:32:24,187 - mmcls - INFO - Epoch [36][2900/5005]\tlr: 1.000e-02, eta: 7:52:39, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0667\n2023-01-02 18:32:32,827 - mmcls - INFO - Epoch [36][3000/5005]\tlr: 1.000e-02, eta: 7:52:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0963\n2023-01-02 18:32:41,341 - mmcls - INFO - Epoch [36][3100/5005]\tlr: 1.000e-02, eta: 7:52:20, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0632\n2023-01-02 18:32:50,107 - mmcls - INFO - Epoch [36][3200/5005]\tlr: 1.000e-02, eta: 7:52:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0850\n2023-01-02 18:32:58,805 - mmcls - INFO - Epoch [36][3300/5005]\tlr: 1.000e-02, eta: 7:52:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0718\n2023-01-02 18:33:07,587 - mmcls - INFO - Epoch [36][3400/5005]\tlr: 1.000e-02, eta: 7:51:53, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0895\n2023-01-02 18:33:16,581 - mmcls - INFO - Epoch [36][3500/5005]\tlr: 1.000e-02, eta: 7:51:45, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0881\n2023-01-02 18:33:25,205 - mmcls - INFO - Epoch [36][3600/5005]\tlr: 1.000e-02, eta: 7:51:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.1207\n2023-01-02 18:33:34,050 - mmcls - INFO - Epoch [36][3700/5005]\tlr: 1.000e-02, eta: 7:51:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0753\n2023-01-02 18:33:43,280 - mmcls - INFO - Epoch [36][3800/5005]\tlr: 1.000e-02, eta: 7:51:19, time: 0.092, data_time: 0.037, memory: 1125, loss: 2.0886\n2023-01-02 18:33:52,240 - mmcls - INFO - Epoch [36][3900/5005]\tlr: 1.000e-02, eta: 7:51:11, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0774\n2023-01-02 18:34:00,897 - mmcls - INFO - Epoch [36][4000/5005]\tlr: 1.000e-02, eta: 7:51:02, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.0767\n2023-01-02 18:34:10,293 - mmcls - INFO - Epoch [36][4100/5005]\tlr: 1.000e-02, eta: 7:50:54, time: 0.094, data_time: 0.025, memory: 1125, loss: 2.1083\n2023-01-02 18:34:18,928 - mmcls - INFO - Epoch [36][4200/5005]\tlr: 1.000e-02, eta: 7:50:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0796\n2023-01-02 18:34:27,744 - mmcls - INFO - Epoch [36][4300/5005]\tlr: 1.000e-02, eta: 7:50:36, time: 0.088, data_time: 0.032, memory: 1125, loss: 2.0784\n2023-01-02 18:34:36,742 - mmcls - INFO - Epoch [36][4400/5005]\tlr: 1.000e-02, eta: 7:50:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0542\n2023-01-02 18:34:45,715 - mmcls - INFO - Epoch [36][4500/5005]\tlr: 1.000e-02, eta: 7:50:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1211\n2023-01-02 18:34:54,830 - mmcls - INFO - Epoch [36][4600/5005]\tlr: 1.000e-02, eta: 7:50:11, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.0615\n2023-01-02 18:35:03,692 - mmcls - INFO - Epoch [36][4700/5005]\tlr: 1.000e-02, eta: 7:50:02, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.1128\n2023-01-02 18:35:12,453 - mmcls - INFO - Epoch [36][4800/5005]\tlr: 1.000e-02, eta: 7:49:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0801\n2023-01-02 18:35:21,576 - mmcls - INFO - Epoch [36][4900/5005]\tlr: 1.000e-02, eta: 7:49:45, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.0590\n2023-01-02 18:35:30,458 - mmcls - INFO - Epoch [36][5000/5005]\tlr: 1.000e-02, eta: 7:49:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0927\n2023-01-02 18:35:53,199 - mmcls - INFO - Epoch(val) [36][196]\taccuracy_top-1: 58.8720, accuracy_top-5: 81.0720\n2023-01-02 18:36:04,322 - mmcls - INFO - Epoch [37][100/5005]\tlr: 1.000e-02, eta: 7:49:31, time: 0.111, data_time: 0.044, memory: 1125, loss: 2.0431\n2023-01-02 18:36:13,130 - mmcls - INFO - Epoch [37][200/5005]\tlr: 1.000e-02, eta: 7:49:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0586\n2023-01-02 18:36:21,983 - mmcls - INFO - Epoch [37][300/5005]\tlr: 1.000e-02, eta: 7:49:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0457\n2023-01-02 18:36:31,323 - mmcls - INFO - Epoch [37][400/5005]\tlr: 1.000e-02, eta: 7:49:05, time: 0.093, data_time: 0.008, memory: 1125, loss: 2.0416\n2023-01-02 18:36:39,917 - mmcls - INFO - Epoch [37][500/5005]\tlr: 1.000e-02, eta: 7:48:56, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0411\n2023-01-02 18:36:49,366 - mmcls - INFO - Epoch [37][600/5005]\tlr: 1.000e-02, eta: 7:48:49, time: 0.095, data_time: 0.009, memory: 1125, loss: 2.0542\n2023-01-02 18:36:58,182 - mmcls - INFO - Epoch [37][700/5005]\tlr: 1.000e-02, eta: 7:48:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0681\n2023-01-02 18:37:06,788 - mmcls - INFO - Epoch [37][800/5005]\tlr: 1.000e-02, eta: 7:48:31, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0343\n2023-01-02 18:37:16,080 - mmcls - INFO - Epoch [37][900/5005]\tlr: 1.000e-02, eta: 7:48:23, time: 0.093, data_time: 0.004, memory: 1125, loss: 2.0396\n2023-01-02 18:37:24,471 - mmcls - INFO - Epoch [37][1000/5005]\tlr: 1.000e-02, eta: 7:48:13, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.0746\n2023-01-02 18:37:33,200 - mmcls - INFO - Epoch [37][1100/5005]\tlr: 1.000e-02, eta: 7:48:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0758\n2023-01-02 18:37:41,835 - mmcls - INFO - Epoch [37][1200/5005]\tlr: 1.000e-02, eta: 7:47:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0717\n2023-01-02 18:37:50,377 - mmcls - INFO - Epoch [37][1300/5005]\tlr: 1.000e-02, eta: 7:47:46, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0605\n2023-01-02 18:37:59,091 - mmcls - INFO - Epoch [37][1400/5005]\tlr: 1.000e-02, eta: 7:47:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0767\n2023-01-02 18:38:07,766 - mmcls - INFO - Epoch [37][1500/5005]\tlr: 1.000e-02, eta: 7:47:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0655\n2023-01-02 18:38:16,395 - mmcls - INFO - Epoch [37][1600/5005]\tlr: 1.000e-02, eta: 7:47:19, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0648\n2023-01-02 18:38:25,185 - mmcls - INFO - Epoch [37][1700/5005]\tlr: 1.000e-02, eta: 7:47:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0526\n2023-01-02 18:38:33,697 - mmcls - INFO - Epoch [37][1800/5005]\tlr: 1.000e-02, eta: 7:47:01, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0584\n2023-01-02 18:38:42,367 - mmcls - INFO - Epoch [37][1900/5005]\tlr: 1.000e-02, eta: 7:46:52, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0684\n2023-01-02 18:38:51,423 - mmcls - INFO - Epoch [37][2000/5005]\tlr: 1.000e-02, eta: 7:46:43, time: 0.091, data_time: 0.039, memory: 1125, loss: 2.0791\n2023-01-02 18:39:00,177 - mmcls - INFO - Epoch [37][2100/5005]\tlr: 1.000e-02, eta: 7:46:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0638\n2023-01-02 18:39:08,932 - mmcls - INFO - Epoch [37][2200/5005]\tlr: 1.000e-02, eta: 7:46:26, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0745\n2023-01-02 18:39:17,831 - mmcls - INFO - Epoch [37][2300/5005]\tlr: 1.000e-02, eta: 7:46:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0728\n2023-01-02 18:39:27,047 - mmcls - INFO - Epoch [37][2400/5005]\tlr: 1.000e-02, eta: 7:46:09, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.0670\n2023-01-02 18:39:35,708 - mmcls - INFO - Epoch [37][2500/5005]\tlr: 1.000e-02, eta: 7:46:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0581\n2023-01-02 18:39:44,328 - mmcls - INFO - Epoch [37][2600/5005]\tlr: 1.000e-02, eta: 7:45:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0817\n2023-01-02 18:39:53,202 - mmcls - INFO - Epoch [37][2700/5005]\tlr: 1.000e-02, eta: 7:45:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0739\n2023-01-02 18:40:01,933 - mmcls - INFO - Epoch [37][2800/5005]\tlr: 1.000e-02, eta: 7:45:33, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0544\n2023-01-02 18:40:10,587 - mmcls - INFO - Epoch [37][2900/5005]\tlr: 1.000e-02, eta: 7:45:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.0697\n2023-01-02 18:40:18,864 - mmcls - INFO - Epoch [37][3000/5005]\tlr: 1.000e-02, eta: 7:45:14, time: 0.083, data_time: 0.001, memory: 1125, loss: 2.0796\n2023-01-02 18:40:28,000 - mmcls - INFO - Epoch [37][3100/5005]\tlr: 1.000e-02, eta: 7:45:06, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0749\n2023-01-02 18:40:36,824 - mmcls - INFO - Epoch [37][3200/5005]\tlr: 1.000e-02, eta: 7:44:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0766\n2023-01-02 18:40:45,662 - mmcls - INFO - Epoch [37][3300/5005]\tlr: 1.000e-02, eta: 7:44:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0901\n2023-01-02 18:40:54,470 - mmcls - INFO - Epoch [37][3400/5005]\tlr: 1.000e-02, eta: 7:44:40, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.0648\n2023-01-02 18:41:02,900 - mmcls - INFO - Epoch [37][3500/5005]\tlr: 1.000e-02, eta: 7:44:31, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.0630\n2023-01-02 18:41:11,553 - mmcls - INFO - Epoch [37][3600/5005]\tlr: 1.000e-02, eta: 7:44:22, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0854\n2023-01-02 18:41:20,697 - mmcls - INFO - Epoch [37][3700/5005]\tlr: 1.000e-02, eta: 7:44:13, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.0832\n2023-01-02 18:41:29,482 - mmcls - INFO - Epoch [37][3800/5005]\tlr: 1.000e-02, eta: 7:44:04, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0734\n2023-01-02 18:41:38,495 - mmcls - INFO - Epoch [37][3900/5005]\tlr: 1.000e-02, eta: 7:43:56, time: 0.090, data_time: 0.019, memory: 1125, loss: 2.0790\n2023-01-02 18:41:47,071 - mmcls - INFO - Epoch [37][4000/5005]\tlr: 1.000e-02, eta: 7:43:47, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0606\n2023-01-02 18:41:55,840 - mmcls - INFO - Epoch [37][4100/5005]\tlr: 1.000e-02, eta: 7:43:38, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0746\n2023-01-02 18:42:04,882 - mmcls - INFO - Epoch [37][4200/5005]\tlr: 1.000e-02, eta: 7:43:30, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.0817\n2023-01-02 18:42:13,487 - mmcls - INFO - Epoch [37][4300/5005]\tlr: 1.000e-02, eta: 7:43:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0635\n2023-01-02 18:42:22,279 - mmcls - INFO - Epoch [37][4400/5005]\tlr: 1.000e-02, eta: 7:43:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0770\n2023-01-02 18:42:31,091 - mmcls - INFO - Epoch [37][4500/5005]\tlr: 1.000e-02, eta: 7:43:03, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0693\n2023-01-02 18:42:39,512 - mmcls - INFO - Epoch [37][4600/5005]\tlr: 1.000e-02, eta: 7:42:54, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0751\n2023-01-02 18:42:48,300 - mmcls - INFO - Epoch [37][4700/5005]\tlr: 1.000e-02, eta: 7:42:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0923\n2023-01-02 18:42:57,006 - mmcls - INFO - Epoch [37][4800/5005]\tlr: 1.000e-02, eta: 7:42:36, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0539\n2023-01-02 18:43:05,517 - mmcls - INFO - Epoch [37][4900/5005]\tlr: 1.000e-02, eta: 7:42:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1091\n2023-01-02 18:43:14,660 - mmcls - INFO - Epoch [37][5000/5005]\tlr: 1.000e-02, eta: 7:42:18, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0655\n2023-01-02 18:43:37,473 - mmcls - INFO - Epoch(val) [37][196]\taccuracy_top-1: 58.9380, accuracy_top-5: 81.2060\n2023-01-02 18:43:48,265 - mmcls - INFO - Epoch [38][100/5005]\tlr: 1.000e-02, eta: 7:42:12, time: 0.108, data_time: 0.031, memory: 1125, loss: 2.0445\n2023-01-02 18:43:56,992 - mmcls - INFO - Epoch [38][200/5005]\tlr: 1.000e-02, eta: 7:42:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0324\n2023-01-02 18:44:05,487 - mmcls - INFO - Epoch [38][300/5005]\tlr: 1.000e-02, eta: 7:41:53, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0464\n2023-01-02 18:44:14,132 - mmcls - INFO - Epoch [38][400/5005]\tlr: 1.000e-02, eta: 7:41:44, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0149\n2023-01-02 18:44:22,722 - mmcls - INFO - Epoch [38][500/5005]\tlr: 1.000e-02, eta: 7:41:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0627\n2023-01-02 18:44:31,479 - mmcls - INFO - Epoch [38][600/5005]\tlr: 1.000e-02, eta: 7:41:26, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0490\n2023-01-02 18:44:40,304 - mmcls - INFO - Epoch [38][700/5005]\tlr: 1.000e-02, eta: 7:41:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0401\n2023-01-02 18:44:49,102 - mmcls - INFO - Epoch [38][800/5005]\tlr: 1.000e-02, eta: 7:41:09, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0555\n2023-01-02 18:44:57,727 - mmcls - INFO - Epoch [38][900/5005]\tlr: 1.000e-02, eta: 7:41:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0340\n2023-01-02 18:45:06,592 - mmcls - INFO - Epoch [38][1000/5005]\tlr: 1.000e-02, eta: 7:40:51, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0522\n2023-01-02 18:45:15,288 - mmcls - INFO - Epoch [38][1100/5005]\tlr: 1.000e-02, eta: 7:40:42, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0360\n2023-01-02 18:45:24,027 - mmcls - INFO - Epoch [38][1200/5005]\tlr: 1.000e-02, eta: 7:40:33, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0383\n2023-01-02 18:45:32,659 - mmcls - INFO - Epoch [38][1300/5005]\tlr: 1.000e-02, eta: 7:40:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0591\n2023-01-02 18:45:41,541 - mmcls - INFO - Epoch [38][1400/5005]\tlr: 1.000e-02, eta: 7:40:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0596\n2023-01-02 18:45:50,480 - mmcls - INFO - Epoch [38][1500/5005]\tlr: 1.000e-02, eta: 7:40:07, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0487\n2023-01-02 18:45:59,206 - mmcls - INFO - Epoch [38][1600/5005]\tlr: 1.000e-02, eta: 7:39:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0550\n2023-01-02 18:46:07,937 - mmcls - INFO - Epoch [38][1700/5005]\tlr: 1.000e-02, eta: 7:39:49, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0637\n2023-01-02 18:46:16,568 - mmcls - INFO - Epoch [38][1800/5005]\tlr: 1.000e-02, eta: 7:39:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0509\n2023-01-02 18:46:25,355 - mmcls - INFO - Epoch [38][1900/5005]\tlr: 1.000e-02, eta: 7:39:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0618\n2023-01-02 18:46:34,329 - mmcls - INFO - Epoch [38][2000/5005]\tlr: 1.000e-02, eta: 7:39:23, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0724\n2023-01-02 18:46:43,114 - mmcls - INFO - Epoch [38][2100/5005]\tlr: 1.000e-02, eta: 7:39:14, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0654\n2023-01-02 18:46:51,845 - mmcls - INFO - Epoch [38][2200/5005]\tlr: 1.000e-02, eta: 7:39:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0551\n2023-01-02 18:47:00,573 - mmcls - INFO - Epoch [38][2300/5005]\tlr: 1.000e-02, eta: 7:38:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0687\n2023-01-02 18:47:09,156 - mmcls - INFO - Epoch [38][2400/5005]\tlr: 1.000e-02, eta: 7:38:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0673\n2023-01-02 18:47:17,865 - mmcls - INFO - Epoch [38][2500/5005]\tlr: 1.000e-02, eta: 7:38:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0775\n2023-01-02 18:47:26,974 - mmcls - INFO - Epoch [38][2600/5005]\tlr: 1.000e-02, eta: 7:38:30, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0749\n2023-01-02 18:47:35,641 - mmcls - INFO - Epoch [38][2700/5005]\tlr: 1.000e-02, eta: 7:38:21, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0426\n2023-01-02 18:47:44,259 - mmcls - INFO - Epoch [38][2800/5005]\tlr: 1.000e-02, eta: 7:38:11, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0634\n2023-01-02 18:47:53,455 - mmcls - INFO - Epoch [38][2900/5005]\tlr: 1.000e-02, eta: 7:38:03, time: 0.092, data_time: 0.005, memory: 1125, loss: 2.0609\n2023-01-02 18:48:02,323 - mmcls - INFO - Epoch [38][3000/5005]\tlr: 1.000e-02, eta: 7:37:55, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0569\n2023-01-02 18:48:11,260 - mmcls - INFO - Epoch [38][3100/5005]\tlr: 1.000e-02, eta: 7:37:46, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0747\n2023-01-02 18:48:20,447 - mmcls - INFO - Epoch [38][3200/5005]\tlr: 1.000e-02, eta: 7:37:38, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.0481\n2023-01-02 18:48:29,016 - mmcls - INFO - Epoch [38][3300/5005]\tlr: 1.000e-02, eta: 7:37:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0742\n2023-01-02 18:48:38,326 - mmcls - INFO - Epoch [38][3400/5005]\tlr: 1.000e-02, eta: 7:37:21, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.0835\n2023-01-02 18:48:46,940 - mmcls - INFO - Epoch [38][3500/5005]\tlr: 1.000e-02, eta: 7:37:12, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0706\n2023-01-02 18:48:55,547 - mmcls - INFO - Epoch [38][3600/5005]\tlr: 1.000e-02, eta: 7:37:03, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0529\n2023-01-02 18:49:04,317 - mmcls - INFO - Epoch [38][3700/5005]\tlr: 1.000e-02, eta: 7:36:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0796\n2023-01-02 18:49:13,184 - mmcls - INFO - Epoch [38][3800/5005]\tlr: 1.000e-02, eta: 7:36:45, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0756\n2023-01-02 18:49:21,973 - mmcls - INFO - Epoch [38][3900/5005]\tlr: 1.000e-02, eta: 7:36:36, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0753\n2023-01-02 18:49:30,580 - mmcls - INFO - Epoch [38][4000/5005]\tlr: 1.000e-02, eta: 7:36:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0753\n2023-01-02 18:49:39,136 - mmcls - INFO - Epoch [38][4100/5005]\tlr: 1.000e-02, eta: 7:36:18, time: 0.086, data_time: 0.036, memory: 1125, loss: 2.0526\n2023-01-02 18:49:47,855 - mmcls - INFO - Epoch [38][4200/5005]\tlr: 1.000e-02, eta: 7:36:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0540\n2023-01-02 18:49:56,168 - mmcls - INFO - Epoch [38][4300/5005]\tlr: 1.000e-02, eta: 7:35:59, time: 0.083, data_time: 0.011, memory: 1125, loss: 2.0617\n2023-01-02 18:50:04,760 - mmcls - INFO - Epoch [38][4400/5005]\tlr: 1.000e-02, eta: 7:35:50, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0671\n2023-01-02 18:50:13,401 - mmcls - INFO - Epoch [38][4500/5005]\tlr: 1.000e-02, eta: 7:35:41, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0820\n2023-01-02 18:50:22,109 - mmcls - INFO - Epoch [38][4600/5005]\tlr: 1.000e-02, eta: 7:35:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0755\n2023-01-02 18:50:31,063 - mmcls - INFO - Epoch [38][4700/5005]\tlr: 1.000e-02, eta: 7:35:24, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0664\n2023-01-02 18:50:39,565 - mmcls - INFO - Epoch [38][4800/5005]\tlr: 1.000e-02, eta: 7:35:14, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0736\n2023-01-02 18:50:48,441 - mmcls - INFO - Epoch [38][4900/5005]\tlr: 1.000e-02, eta: 7:35:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0619\n2023-01-02 18:50:57,137 - mmcls - INFO - Epoch [38][5000/5005]\tlr: 1.000e-02, eta: 7:34:57, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0705\n2023-01-02 18:51:19,842 - mmcls - INFO - Epoch(val) [38][196]\taccuracy_top-1: 58.7960, accuracy_top-5: 81.2400\n2023-01-02 18:51:30,589 - mmcls - INFO - Epoch [39][100/5005]\tlr: 1.000e-02, eta: 7:34:50, time: 0.107, data_time: 0.037, memory: 1125, loss: 2.0226\n2023-01-02 18:51:39,634 - mmcls - INFO - Epoch [39][200/5005]\tlr: 1.000e-02, eta: 7:34:42, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0322\n2023-01-02 18:51:48,268 - mmcls - INFO - Epoch [39][300/5005]\tlr: 1.000e-02, eta: 7:34:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0261\n2023-01-02 18:51:57,299 - mmcls - INFO - Epoch [39][400/5005]\tlr: 1.000e-02, eta: 7:34:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0504\n2023-01-02 18:52:06,473 - mmcls - INFO - Epoch [39][500/5005]\tlr: 1.000e-02, eta: 7:34:16, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0315\n2023-01-02 18:52:15,216 - mmcls - INFO - Epoch [39][600/5005]\tlr: 1.000e-02, eta: 7:34:07, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0239\n2023-01-02 18:52:23,805 - mmcls - INFO - Epoch [39][700/5005]\tlr: 1.000e-02, eta: 7:33:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0334\n2023-01-02 18:52:32,566 - mmcls - INFO - Epoch [39][800/5005]\tlr: 1.000e-02, eta: 7:33:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0381\n2023-01-02 18:52:41,118 - mmcls - INFO - Epoch [39][900/5005]\tlr: 1.000e-02, eta: 7:33:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0391\n2023-01-02 18:52:49,656 - mmcls - INFO - Epoch [39][1000/5005]\tlr: 1.000e-02, eta: 7:33:31, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0427\n2023-01-02 18:52:58,396 - mmcls - INFO - Epoch [39][1100/5005]\tlr: 1.000e-02, eta: 7:33:22, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0389\n2023-01-02 18:53:07,273 - mmcls - INFO - Epoch [39][1200/5005]\tlr: 1.000e-02, eta: 7:33:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0575\n2023-01-02 18:53:15,872 - mmcls - INFO - Epoch [39][1300/5005]\tlr: 1.000e-02, eta: 7:33:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0435\n2023-01-02 18:53:24,698 - mmcls - INFO - Epoch [39][1400/5005]\tlr: 1.000e-02, eta: 7:32:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0758\n2023-01-02 18:53:33,671 - mmcls - INFO - Epoch [39][1500/5005]\tlr: 1.000e-02, eta: 7:32:47, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0444\n2023-01-02 18:53:42,520 - mmcls - INFO - Epoch [39][1600/5005]\tlr: 1.000e-02, eta: 7:32:38, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0735\n2023-01-02 18:53:51,054 - mmcls - INFO - Epoch [39][1700/5005]\tlr: 1.000e-02, eta: 7:32:29, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0431\n2023-01-02 18:53:59,828 - mmcls - INFO - Epoch [39][1800/5005]\tlr: 1.000e-02, eta: 7:32:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0319\n2023-01-02 18:54:08,477 - mmcls - INFO - Epoch [39][1900/5005]\tlr: 1.000e-02, eta: 7:32:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0413\n2023-01-02 18:54:17,289 - mmcls - INFO - Epoch [39][2000/5005]\tlr: 1.000e-02, eta: 7:32:02, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0585\n2023-01-02 18:54:26,147 - mmcls - INFO - Epoch [39][2100/5005]\tlr: 1.000e-02, eta: 7:31:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0829\n2023-01-02 18:54:34,981 - mmcls - INFO - Epoch [39][2200/5005]\tlr: 1.000e-02, eta: 7:31:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0595\n2023-01-02 18:54:43,816 - mmcls - INFO - Epoch [39][2300/5005]\tlr: 1.000e-02, eta: 7:31:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0686\n2023-01-02 18:54:52,582 - mmcls - INFO - Epoch [39][2400/5005]\tlr: 1.000e-02, eta: 7:31:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0754\n2023-01-02 18:55:01,172 - mmcls - INFO - Epoch [39][2500/5005]\tlr: 1.000e-02, eta: 7:31:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0815\n2023-01-02 18:55:09,811 - mmcls - INFO - Epoch [39][2600/5005]\tlr: 1.000e-02, eta: 7:31:09, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0710\n2023-01-02 18:55:18,516 - mmcls - INFO - Epoch [39][2700/5005]\tlr: 1.000e-02, eta: 7:31:00, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0511\n2023-01-02 18:55:27,052 - mmcls - INFO - Epoch [39][2800/5005]\tlr: 1.000e-02, eta: 7:30:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0423\n2023-01-02 18:55:35,895 - mmcls - INFO - Epoch [39][2900/5005]\tlr: 1.000e-02, eta: 7:30:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0803\n2023-01-02 18:55:45,021 - mmcls - INFO - Epoch [39][3000/5005]\tlr: 1.000e-02, eta: 7:30:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0542\n2023-01-02 18:55:53,897 - mmcls - INFO - Epoch [39][3100/5005]\tlr: 1.000e-02, eta: 7:30:25, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0474\n2023-01-02 18:56:02,829 - mmcls - INFO - Epoch [39][3200/5005]\tlr: 1.000e-02, eta: 7:30:16, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0503\n2023-01-02 18:56:11,292 - mmcls - INFO - Epoch [39][3300/5005]\tlr: 1.000e-02, eta: 7:30:07, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0767\n2023-01-02 18:56:19,966 - mmcls - INFO - Epoch [39][3400/5005]\tlr: 1.000e-02, eta: 7:29:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0792\n2023-01-02 18:56:28,824 - mmcls - INFO - Epoch [39][3500/5005]\tlr: 1.000e-02, eta: 7:29:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0661\n2023-01-02 18:56:37,635 - mmcls - INFO - Epoch [39][3600/5005]\tlr: 1.000e-02, eta: 7:29:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0889\n2023-01-02 18:56:46,477 - mmcls - INFO - Epoch [39][3700/5005]\tlr: 1.000e-02, eta: 7:29:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0711\n2023-01-02 18:56:55,185 - mmcls - INFO - Epoch [39][3800/5005]\tlr: 1.000e-02, eta: 7:29:23, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0520\n2023-01-02 18:57:03,642 - mmcls - INFO - Epoch [39][3900/5005]\tlr: 1.000e-02, eta: 7:29:14, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.0539\n2023-01-02 18:57:12,546 - mmcls - INFO - Epoch [39][4000/5005]\tlr: 1.000e-02, eta: 7:29:05, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.0568\n2023-01-02 18:57:21,351 - mmcls - INFO - Epoch [39][4100/5005]\tlr: 1.000e-02, eta: 7:28:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0594\n2023-01-02 18:57:30,221 - mmcls - INFO - Epoch [39][4200/5005]\tlr: 1.000e-02, eta: 7:28:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0751\n2023-01-02 18:57:39,170 - mmcls - INFO - Epoch [39][4300/5005]\tlr: 1.000e-02, eta: 7:28:39, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0411\n2023-01-02 18:57:48,304 - mmcls - INFO - Epoch [39][4400/5005]\tlr: 1.000e-02, eta: 7:28:31, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0461\n2023-01-02 18:57:57,185 - mmcls - INFO - Epoch [39][4500/5005]\tlr: 1.000e-02, eta: 7:28:22, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0931\n2023-01-02 18:58:05,839 - mmcls - INFO - Epoch [39][4600/5005]\tlr: 1.000e-02, eta: 7:28:13, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0650\n2023-01-02 18:58:14,701 - mmcls - INFO - Epoch [39][4700/5005]\tlr: 1.000e-02, eta: 7:28:04, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0638\n2023-01-02 18:58:23,214 - mmcls - INFO - Epoch [39][4800/5005]\tlr: 1.000e-02, eta: 7:27:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0911\n2023-01-02 18:58:31,784 - mmcls - INFO - Epoch [39][4900/5005]\tlr: 1.000e-02, eta: 7:27:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0683\n2023-01-02 18:58:40,653 - mmcls - INFO - Epoch [39][5000/5005]\tlr: 1.000e-02, eta: 7:27:37, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0486\n2023-01-02 18:59:03,432 - mmcls - INFO - Epoch(val) [39][196]\taccuracy_top-1: 59.1960, accuracy_top-5: 80.9420\n2023-01-02 18:59:14,307 - mmcls - INFO - Epoch [40][100/5005]\tlr: 1.000e-02, eta: 7:27:31, time: 0.109, data_time: 0.044, memory: 1125, loss: 2.0355\n2023-01-02 18:59:23,132 - mmcls - INFO - Epoch [40][200/5005]\tlr: 1.000e-02, eta: 7:27:22, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0295\n2023-01-02 18:59:31,844 - mmcls - INFO - Epoch [40][300/5005]\tlr: 1.000e-02, eta: 7:27:13, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0420\n2023-01-02 18:59:40,661 - mmcls - INFO - Epoch [40][400/5005]\tlr: 1.000e-02, eta: 7:27:04, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0599\n2023-01-02 18:59:49,307 - mmcls - INFO - Epoch [40][500/5005]\tlr: 1.000e-02, eta: 7:26:55, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.0488\n2023-01-02 18:59:58,063 - mmcls - INFO - Epoch [40][600/5005]\tlr: 1.000e-02, eta: 7:26:46, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0273\n2023-01-02 19:00:07,008 - mmcls - INFO - Epoch [40][700/5005]\tlr: 1.000e-02, eta: 7:26:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0478\n2023-01-02 19:00:15,565 - mmcls - INFO - Epoch [40][800/5005]\tlr: 1.000e-02, eta: 7:26:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0160\n2023-01-02 19:00:24,193 - mmcls - INFO - Epoch [40][900/5005]\tlr: 1.000e-02, eta: 7:26:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0689\n2023-01-02 19:00:32,860 - mmcls - INFO - Epoch [40][1000/5005]\tlr: 1.000e-02, eta: 7:26:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0518\n2023-01-02 19:00:41,218 - mmcls - INFO - Epoch [40][1100/5005]\tlr: 1.000e-02, eta: 7:26:01, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0487\n2023-01-02 19:00:49,874 - mmcls - INFO - Epoch [40][1200/5005]\tlr: 1.000e-02, eta: 7:25:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0548\n2023-01-02 19:00:58,577 - mmcls - INFO - Epoch [40][1300/5005]\tlr: 1.000e-02, eta: 7:25:43, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0291\n2023-01-02 19:01:07,439 - mmcls - INFO - Epoch [40][1400/5005]\tlr: 1.000e-02, eta: 7:25:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0580\n2023-01-02 19:01:16,345 - mmcls - INFO - Epoch [40][1500/5005]\tlr: 1.000e-02, eta: 7:25:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0444\n2023-01-02 19:01:24,746 - mmcls - INFO - Epoch [40][1600/5005]\tlr: 1.000e-02, eta: 7:25:16, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0535\n2023-01-02 19:01:34,208 - mmcls - INFO - Epoch [40][1700/5005]\tlr: 1.000e-02, eta: 7:25:08, time: 0.095, data_time: 0.010, memory: 1125, loss: 2.0217\n2023-01-02 19:01:42,803 - mmcls - INFO - Epoch [40][1800/5005]\tlr: 1.000e-02, eta: 7:24:59, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0707\n2023-01-02 19:01:51,433 - mmcls - INFO - Epoch [40][1900/5005]\tlr: 1.000e-02, eta: 7:24:50, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0558\n2023-01-02 19:02:00,160 - mmcls - INFO - Epoch [40][2000/5005]\tlr: 1.000e-02, eta: 7:24:41, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0537\n2023-01-02 19:02:08,855 - mmcls - INFO - Epoch [40][2100/5005]\tlr: 1.000e-02, eta: 7:24:32, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0492\n2023-01-02 19:02:17,521 - mmcls - INFO - Epoch [40][2200/5005]\tlr: 1.000e-02, eta: 7:24:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0513\n2023-01-02 19:02:26,102 - mmcls - INFO - Epoch [40][2300/5005]\tlr: 1.000e-02, eta: 7:24:14, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0686\n2023-01-02 19:02:34,527 - mmcls - INFO - Epoch [40][2400/5005]\tlr: 1.000e-02, eta: 7:24:05, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0691\n2023-01-02 19:02:43,276 - mmcls - INFO - Epoch [40][2500/5005]\tlr: 1.000e-02, eta: 7:23:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0537\n2023-01-02 19:02:52,350 - mmcls - INFO - Epoch [40][2600/5005]\tlr: 1.000e-02, eta: 7:23:48, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.0470\n2023-01-02 19:03:01,053 - mmcls - INFO - Epoch [40][2700/5005]\tlr: 1.000e-02, eta: 7:23:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0394\n2023-01-02 19:03:09,584 - mmcls - INFO - Epoch [40][2800/5005]\tlr: 1.000e-02, eta: 7:23:30, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.0484\n2023-01-02 19:03:18,320 - mmcls - INFO - Epoch [40][2900/5005]\tlr: 1.000e-02, eta: 7:23:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0699\n2023-01-02 19:03:26,904 - mmcls - INFO - Epoch [40][3000/5005]\tlr: 1.000e-02, eta: 7:23:12, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0958\n2023-01-02 19:03:35,631 - mmcls - INFO - Epoch [40][3100/5005]\tlr: 1.000e-02, eta: 7:23:03, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0650\n2023-01-02 19:03:44,323 - mmcls - INFO - Epoch [40][3200/5005]\tlr: 1.000e-02, eta: 7:22:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0698\n2023-01-02 19:03:53,026 - mmcls - INFO - Epoch [40][3300/5005]\tlr: 1.000e-02, eta: 7:22:45, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0492\n2023-01-02 19:04:01,793 - mmcls - INFO - Epoch [40][3400/5005]\tlr: 1.000e-02, eta: 7:22:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0664\n2023-01-02 19:04:10,274 - mmcls - INFO - Epoch [40][3500/5005]\tlr: 1.000e-02, eta: 7:22:27, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0584\n2023-01-02 19:04:18,936 - mmcls - INFO - Epoch [40][3600/5005]\tlr: 1.000e-02, eta: 7:22:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0411\n2023-01-02 19:04:27,578 - mmcls - INFO - Epoch [40][3700/5005]\tlr: 1.000e-02, eta: 7:22:09, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0729\n2023-01-02 19:04:36,163 - mmcls - INFO - Epoch [40][3800/5005]\tlr: 1.000e-02, eta: 7:21:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0601\n2023-01-02 19:04:45,130 - mmcls - INFO - Epoch [40][3900/5005]\tlr: 1.000e-02, eta: 7:21:51, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0609\n2023-01-02 19:04:53,824 - mmcls - INFO - Epoch [40][4000/5005]\tlr: 1.000e-02, eta: 7:21:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0927\n2023-01-02 19:05:02,239 - mmcls - INFO - Epoch [40][4100/5005]\tlr: 1.000e-02, eta: 7:21:33, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0721\n2023-01-02 19:05:10,851 - mmcls - INFO - Epoch [40][4200/5005]\tlr: 1.000e-02, eta: 7:21:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0539\n2023-01-02 19:05:19,315 - mmcls - INFO - Epoch [40][4300/5005]\tlr: 1.000e-02, eta: 7:21:14, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0572\n2023-01-02 19:05:28,762 - mmcls - INFO - Epoch [40][4400/5005]\tlr: 1.000e-02, eta: 7:21:06, time: 0.094, data_time: 0.013, memory: 1125, loss: 2.0562\n2023-01-02 19:05:37,913 - mmcls - INFO - Epoch [40][4500/5005]\tlr: 1.000e-02, eta: 7:20:58, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0483\n2023-01-02 19:05:46,671 - mmcls - INFO - Epoch [40][4600/5005]\tlr: 1.000e-02, eta: 7:20:49, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0915\n2023-01-02 19:05:55,361 - mmcls - INFO - Epoch [40][4700/5005]\tlr: 1.000e-02, eta: 7:20:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0636\n2023-01-02 19:06:04,219 - mmcls - INFO - Epoch [40][4800/5005]\tlr: 1.000e-02, eta: 7:20:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0723\n2023-01-02 19:06:13,150 - mmcls - INFO - Epoch [40][4900/5005]\tlr: 1.000e-02, eta: 7:20:23, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0658\n2023-01-02 19:06:21,559 - mmcls - INFO - Epoch [40][5000/5005]\tlr: 1.000e-02, eta: 7:20:14, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0304\n2023-01-02 19:06:44,091 - mmcls - INFO - Epoch(val) [40][196]\taccuracy_top-1: 59.2180, accuracy_top-5: 81.6340\n2023-01-02 19:06:55,035 - mmcls - INFO - Epoch [41][100/5005]\tlr: 1.000e-02, eta: 7:20:07, time: 0.109, data_time: 0.037, memory: 1125, loss: 2.0206\n2023-01-02 19:07:03,855 - mmcls - INFO - Epoch [41][200/5005]\tlr: 1.000e-02, eta: 7:19:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0410\n2023-01-02 19:07:12,423 - mmcls - INFO - Epoch [41][300/5005]\tlr: 1.000e-02, eta: 7:19:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0550\n2023-01-02 19:07:21,312 - mmcls - INFO - Epoch [41][400/5005]\tlr: 1.000e-02, eta: 7:19:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0275\n2023-01-02 19:07:30,049 - mmcls - INFO - Epoch [41][500/5005]\tlr: 1.000e-02, eta: 7:19:32, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0277\n2023-01-02 19:07:38,766 - mmcls - INFO - Epoch [41][600/5005]\tlr: 1.000e-02, eta: 7:19:23, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0472\n2023-01-02 19:07:47,421 - mmcls - INFO - Epoch [41][700/5005]\tlr: 1.000e-02, eta: 7:19:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0393\n2023-01-02 19:07:55,973 - mmcls - INFO - Epoch [41][800/5005]\tlr: 1.000e-02, eta: 7:19:04, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0328\n2023-01-02 19:08:04,777 - mmcls - INFO - Epoch [41][900/5005]\tlr: 1.000e-02, eta: 7:18:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0388\n2023-01-02 19:08:13,583 - mmcls - INFO - Epoch [41][1000/5005]\tlr: 1.000e-02, eta: 7:18:47, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.0324\n2023-01-02 19:08:22,277 - mmcls - INFO - Epoch [41][1100/5005]\tlr: 1.000e-02, eta: 7:18:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0051\n2023-01-02 19:08:30,940 - mmcls - INFO - Epoch [41][1200/5005]\tlr: 1.000e-02, eta: 7:18:29, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0474\n2023-01-02 19:08:39,458 - mmcls - INFO - Epoch [41][1300/5005]\tlr: 1.000e-02, eta: 7:18:20, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0622\n2023-01-02 19:08:48,050 - mmcls - INFO - Epoch [41][1400/5005]\tlr: 1.000e-02, eta: 7:18:11, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0499\n2023-01-02 19:08:56,769 - mmcls - INFO - Epoch [41][1500/5005]\tlr: 1.000e-02, eta: 7:18:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0262\n2023-01-02 19:09:05,547 - mmcls - INFO - Epoch [41][1600/5005]\tlr: 1.000e-02, eta: 7:17:53, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0568\n2023-01-02 19:09:14,140 - mmcls - INFO - Epoch [41][1700/5005]\tlr: 1.000e-02, eta: 7:17:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0637\n2023-01-02 19:09:22,967 - mmcls - INFO - Epoch [41][1800/5005]\tlr: 1.000e-02, eta: 7:17:35, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0690\n2023-01-02 19:09:31,753 - mmcls - INFO - Epoch [41][1900/5005]\tlr: 1.000e-02, eta: 7:17:26, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0488\n2023-01-02 19:09:40,408 - mmcls - INFO - Epoch [41][2000/5005]\tlr: 1.000e-02, eta: 7:17:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0353\n2023-01-02 19:09:48,978 - mmcls - INFO - Epoch [41][2100/5005]\tlr: 1.000e-02, eta: 7:17:08, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0460\n2023-01-02 19:09:57,471 - mmcls - INFO - Epoch [41][2200/5005]\tlr: 1.000e-02, eta: 7:16:59, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0665\n2023-01-02 19:10:05,994 - mmcls - INFO - Epoch [41][2300/5005]\tlr: 1.000e-02, eta: 7:16:50, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0430\n2023-01-02 19:10:14,603 - mmcls - INFO - Epoch [41][2400/5005]\tlr: 1.000e-02, eta: 7:16:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0228\n2023-01-02 19:10:23,295 - mmcls - INFO - Epoch [41][2500/5005]\tlr: 1.000e-02, eta: 7:16:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0593\n2023-01-02 19:10:32,408 - mmcls - INFO - Epoch [41][2600/5005]\tlr: 1.000e-02, eta: 7:16:23, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0586\n2023-01-02 19:10:41,011 - mmcls - INFO - Epoch [41][2700/5005]\tlr: 1.000e-02, eta: 7:16:14, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0804\n2023-01-02 19:10:49,610 - mmcls - INFO - Epoch [41][2800/5005]\tlr: 1.000e-02, eta: 7:16:05, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.0679\n2023-01-02 19:10:58,339 - mmcls - INFO - Epoch [41][2900/5005]\tlr: 1.000e-02, eta: 7:15:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0679\n2023-01-02 19:11:07,106 - mmcls - INFO - Epoch [41][3000/5005]\tlr: 1.000e-02, eta: 7:15:47, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0683\n2023-01-02 19:11:15,596 - mmcls - INFO - Epoch [41][3100/5005]\tlr: 1.000e-02, eta: 7:15:38, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0547\n2023-01-02 19:11:24,896 - mmcls - INFO - Epoch [41][3200/5005]\tlr: 1.000e-02, eta: 7:15:30, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.0576\n2023-01-02 19:11:33,446 - mmcls - INFO - Epoch [41][3300/5005]\tlr: 1.000e-02, eta: 7:15:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0601\n2023-01-02 19:11:42,218 - mmcls - INFO - Epoch [41][3400/5005]\tlr: 1.000e-02, eta: 7:15:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0559\n2023-01-02 19:11:50,881 - mmcls - INFO - Epoch [41][3500/5005]\tlr: 1.000e-02, eta: 7:15:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0569\n2023-01-02 19:11:59,729 - mmcls - INFO - Epoch [41][3600/5005]\tlr: 1.000e-02, eta: 7:14:54, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.0590\n2023-01-02 19:12:08,532 - mmcls - INFO - Epoch [41][3700/5005]\tlr: 1.000e-02, eta: 7:14:46, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.0708\n2023-01-02 19:12:17,217 - mmcls - INFO - Epoch [41][3800/5005]\tlr: 1.000e-02, eta: 7:14:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0531\n2023-01-02 19:12:25,921 - mmcls - INFO - Epoch [41][3900/5005]\tlr: 1.000e-02, eta: 7:14:28, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0418\n2023-01-02 19:12:34,725 - mmcls - INFO - Epoch [41][4000/5005]\tlr: 1.000e-02, eta: 7:14:19, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.0732\n2023-01-02 19:12:43,566 - mmcls - INFO - Epoch [41][4100/5005]\tlr: 1.000e-02, eta: 7:14:10, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0670\n2023-01-02 19:12:52,422 - mmcls - INFO - Epoch [41][4200/5005]\tlr: 1.000e-02, eta: 7:14:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0552\n2023-01-02 19:13:01,531 - mmcls - INFO - Epoch [41][4300/5005]\tlr: 1.000e-02, eta: 7:13:53, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0599\n2023-01-02 19:13:10,107 - mmcls - INFO - Epoch [41][4400/5005]\tlr: 1.000e-02, eta: 7:13:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0668\n2023-01-02 19:13:18,870 - mmcls - INFO - Epoch [41][4500/5005]\tlr: 1.000e-02, eta: 7:13:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0704\n2023-01-02 19:13:28,022 - mmcls - INFO - Epoch [41][4600/5005]\tlr: 1.000e-02, eta: 7:13:27, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0771\n2023-01-02 19:13:36,925 - mmcls - INFO - Epoch [41][4700/5005]\tlr: 1.000e-02, eta: 7:13:18, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0380\n2023-01-02 19:13:45,726 - mmcls - INFO - Epoch [41][4800/5005]\tlr: 1.000e-02, eta: 7:13:09, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0707\n2023-01-02 19:13:54,644 - mmcls - INFO - Epoch [41][4900/5005]\tlr: 1.000e-02, eta: 7:13:01, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0827\n2023-01-02 19:14:03,528 - mmcls - INFO - Epoch [41][5000/5005]\tlr: 1.000e-02, eta: 7:12:52, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0528\n2023-01-02 19:14:26,336 - mmcls - INFO - Epoch(val) [41][196]\taccuracy_top-1: 59.2860, accuracy_top-5: 81.3060\n2023-01-02 19:14:37,263 - mmcls - INFO - Epoch [42][100/5005]\tlr: 1.000e-02, eta: 7:12:45, time: 0.109, data_time: 0.039, memory: 1125, loss: 2.0201\n2023-01-02 19:14:46,120 - mmcls - INFO - Epoch [42][200/5005]\tlr: 1.000e-02, eta: 7:12:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0359\n2023-01-02 19:14:55,169 - mmcls - INFO - Epoch [42][300/5005]\tlr: 1.000e-02, eta: 7:12:28, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0276\n2023-01-02 19:15:03,891 - mmcls - INFO - Epoch [42][400/5005]\tlr: 1.000e-02, eta: 7:12:19, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0330\n2023-01-02 19:15:12,520 - mmcls - INFO - Epoch [42][500/5005]\tlr: 1.000e-02, eta: 7:12:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0245\n2023-01-02 19:15:21,345 - mmcls - INFO - Epoch [42][600/5005]\tlr: 1.000e-02, eta: 7:12:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0241\n2023-01-02 19:15:29,953 - mmcls - INFO - Epoch [42][700/5005]\tlr: 1.000e-02, eta: 7:11:52, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0413\n2023-01-02 19:15:38,627 - mmcls - INFO - Epoch [42][800/5005]\tlr: 1.000e-02, eta: 7:11:43, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0398\n2023-01-02 19:15:47,203 - mmcls - INFO - Epoch [42][900/5005]\tlr: 1.000e-02, eta: 7:11:34, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0349\n2023-01-02 19:15:56,049 - mmcls - INFO - Epoch [42][1000/5005]\tlr: 1.000e-02, eta: 7:11:26, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0093\n2023-01-02 19:16:04,752 - mmcls - INFO - Epoch [42][1100/5005]\tlr: 1.000e-02, eta: 7:11:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0230\n2023-01-02 19:16:13,841 - mmcls - INFO - Epoch [42][1200/5005]\tlr: 1.000e-02, eta: 7:11:08, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.0399\n2023-01-02 19:16:22,728 - mmcls - INFO - Epoch [42][1300/5005]\tlr: 1.000e-02, eta: 7:11:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0180\n2023-01-02 19:16:31,671 - mmcls - INFO - Epoch [42][1400/5005]\tlr: 1.000e-02, eta: 7:10:51, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.0505\n2023-01-02 19:16:40,217 - mmcls - INFO - Epoch [42][1500/5005]\tlr: 1.000e-02, eta: 7:10:42, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.0487\n2023-01-02 19:16:48,802 - mmcls - INFO - Epoch [42][1600/5005]\tlr: 1.000e-02, eta: 7:10:33, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0430\n2023-01-02 19:16:57,523 - mmcls - INFO - Epoch [42][1700/5005]\tlr: 1.000e-02, eta: 7:10:24, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0591\n2023-01-02 19:17:06,396 - mmcls - INFO - Epoch [42][1800/5005]\tlr: 1.000e-02, eta: 7:10:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0412\n2023-01-02 19:17:14,871 - mmcls - INFO - Epoch [42][1900/5005]\tlr: 1.000e-02, eta: 7:10:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0590\n2023-01-02 19:17:23,740 - mmcls - INFO - Epoch [42][2000/5005]\tlr: 1.000e-02, eta: 7:09:57, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0734\n2023-01-02 19:17:32,502 - mmcls - INFO - Epoch [42][2100/5005]\tlr: 1.000e-02, eta: 7:09:48, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0546\n2023-01-02 19:17:41,195 - mmcls - INFO - Epoch [42][2200/5005]\tlr: 1.000e-02, eta: 7:09:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0467\n2023-01-02 19:17:50,057 - mmcls - INFO - Epoch [42][2300/5005]\tlr: 1.000e-02, eta: 7:09:31, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0598\n2023-01-02 19:17:59,144 - mmcls - INFO - Epoch [42][2400/5005]\tlr: 1.000e-02, eta: 7:09:22, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0711\n2023-01-02 19:18:08,347 - mmcls - INFO - Epoch [42][2500/5005]\tlr: 1.000e-02, eta: 7:09:14, time: 0.092, data_time: 0.029, memory: 1125, loss: 2.0438\n2023-01-02 19:18:17,270 - mmcls - INFO - Epoch [42][2600/5005]\tlr: 1.000e-02, eta: 7:09:06, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.0659\n2023-01-02 19:18:25,984 - mmcls - INFO - Epoch [42][2700/5005]\tlr: 1.000e-02, eta: 7:08:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0652\n2023-01-02 19:18:34,670 - mmcls - INFO - Epoch [42][2800/5005]\tlr: 1.000e-02, eta: 7:08:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0534\n2023-01-02 19:18:43,472 - mmcls - INFO - Epoch [42][2900/5005]\tlr: 1.000e-02, eta: 7:08:39, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.0411\n2023-01-02 19:18:52,311 - mmcls - INFO - Epoch [42][3000/5005]\tlr: 1.000e-02, eta: 7:08:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0561\n2023-01-02 19:19:00,715 - mmcls - INFO - Epoch [42][3100/5005]\tlr: 1.000e-02, eta: 7:08:21, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0561\n2023-01-02 19:19:09,666 - mmcls - INFO - Epoch [42][3200/5005]\tlr: 1.000e-02, eta: 7:08:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0707\n2023-01-02 19:19:18,550 - mmcls - INFO - Epoch [42][3300/5005]\tlr: 1.000e-02, eta: 7:08:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0470\n2023-01-02 19:19:27,144 - mmcls - INFO - Epoch [42][3400/5005]\tlr: 1.000e-02, eta: 7:07:55, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0452\n2023-01-02 19:19:35,905 - mmcls - INFO - Epoch [42][3500/5005]\tlr: 1.000e-02, eta: 7:07:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0331\n2023-01-02 19:19:44,472 - mmcls - INFO - Epoch [42][3600/5005]\tlr: 1.000e-02, eta: 7:07:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0612\n2023-01-02 19:19:53,138 - mmcls - INFO - Epoch [42][3700/5005]\tlr: 1.000e-02, eta: 7:07:28, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0736\n2023-01-02 19:20:01,727 - mmcls - INFO - Epoch [42][3800/5005]\tlr: 1.000e-02, eta: 7:07:18, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0680\n2023-01-02 19:20:10,766 - mmcls - INFO - Epoch [42][3900/5005]\tlr: 1.000e-02, eta: 7:07:10, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0908\n2023-01-02 19:20:19,687 - mmcls - INFO - Epoch [42][4000/5005]\tlr: 1.000e-02, eta: 7:07:01, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0831\n2023-01-02 19:20:28,749 - mmcls - INFO - Epoch [42][4100/5005]\tlr: 1.000e-02, eta: 7:06:53, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0994\n2023-01-02 19:20:37,653 - mmcls - INFO - Epoch [42][4200/5005]\tlr: 1.000e-02, eta: 7:06:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0420\n2023-01-02 19:20:46,234 - mmcls - INFO - Epoch [42][4300/5005]\tlr: 1.000e-02, eta: 7:06:35, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0897\n2023-01-02 19:20:55,018 - mmcls - INFO - Epoch [42][4400/5005]\tlr: 1.000e-02, eta: 7:06:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0532\n2023-01-02 19:21:03,666 - mmcls - INFO - Epoch [42][4500/5005]\tlr: 1.000e-02, eta: 7:06:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0636\n2023-01-02 19:21:12,559 - mmcls - INFO - Epoch [42][4600/5005]\tlr: 1.000e-02, eta: 7:06:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0605\n2023-01-02 19:21:21,089 - mmcls - INFO - Epoch [42][4700/5005]\tlr: 1.000e-02, eta: 7:06:00, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0729\n2023-01-02 19:21:29,954 - mmcls - INFO - Epoch [42][4800/5005]\tlr: 1.000e-02, eta: 7:05:51, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0899\n2023-01-02 19:21:38,660 - mmcls - INFO - Epoch [42][4900/5005]\tlr: 1.000e-02, eta: 7:05:42, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0623\n2023-01-02 19:21:47,373 - mmcls - INFO - Epoch [42][5000/5005]\tlr: 1.000e-02, eta: 7:05:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0848\n2023-01-02 19:22:09,975 - mmcls - INFO - Epoch(val) [42][196]\taccuracy_top-1: 58.8560, accuracy_top-5: 81.1120\n2023-01-02 19:22:20,966 - mmcls - INFO - Epoch [43][100/5005]\tlr: 1.000e-02, eta: 7:05:26, time: 0.110, data_time: 0.035, memory: 1125, loss: 2.0031\n2023-01-02 19:22:29,648 - mmcls - INFO - Epoch [43][200/5005]\tlr: 1.000e-02, eta: 7:05:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0094\n2023-01-02 19:22:38,284 - mmcls - INFO - Epoch [43][300/5005]\tlr: 1.000e-02, eta: 7:05:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0286\n2023-01-02 19:22:46,923 - mmcls - INFO - Epoch [43][400/5005]\tlr: 1.000e-02, eta: 7:04:59, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.0053\n2023-01-02 19:22:55,828 - mmcls - INFO - Epoch [43][500/5005]\tlr: 1.000e-02, eta: 7:04:51, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.0237\n2023-01-02 19:23:04,530 - mmcls - INFO - Epoch [43][600/5005]\tlr: 1.000e-02, eta: 7:04:42, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0413\n2023-01-02 19:23:14,361 - mmcls - INFO - Epoch [43][700/5005]\tlr: 1.000e-02, eta: 7:04:34, time: 0.098, data_time: 0.007, memory: 1125, loss: 2.0514\n2023-01-02 19:23:23,055 - mmcls - INFO - Epoch [43][800/5005]\tlr: 1.000e-02, eta: 7:04:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0255\n2023-01-02 19:23:32,005 - mmcls - INFO - Epoch [43][900/5005]\tlr: 1.000e-02, eta: 7:04:17, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.0356\n2023-01-02 19:23:40,981 - mmcls - INFO - Epoch [43][1000/5005]\tlr: 1.000e-02, eta: 7:04:08, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.0292\n2023-01-02 19:23:49,965 - mmcls - INFO - Epoch [43][1100/5005]\tlr: 1.000e-02, eta: 7:04:00, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.0171\n2023-01-02 19:23:58,724 - mmcls - INFO - Epoch [43][1200/5005]\tlr: 1.000e-02, eta: 7:03:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0468\n2023-01-02 19:24:07,554 - mmcls - INFO - Epoch [43][1300/5005]\tlr: 1.000e-02, eta: 7:03:42, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0623\n2023-01-02 19:24:17,236 - mmcls - INFO - Epoch [43][1400/5005]\tlr: 1.000e-02, eta: 7:03:34, time: 0.097, data_time: 0.001, memory: 1125, loss: 2.0479\n2023-01-02 19:24:25,942 - mmcls - INFO - Epoch [43][1500/5005]\tlr: 1.000e-02, eta: 7:03:26, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0295\n2023-01-02 19:24:34,943 - mmcls - INFO - Epoch [43][1600/5005]\tlr: 1.000e-02, eta: 7:03:17, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0380\n2023-01-02 19:24:44,027 - mmcls - INFO - Epoch [43][1700/5005]\tlr: 1.000e-02, eta: 7:03:09, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0525\n2023-01-02 19:24:53,018 - mmcls - INFO - Epoch [43][1800/5005]\tlr: 1.000e-02, eta: 7:03:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0218\n2023-01-02 19:25:01,683 - mmcls - INFO - Epoch [43][1900/5005]\tlr: 1.000e-02, eta: 7:02:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0575\n2023-01-02 19:25:10,416 - mmcls - INFO - Epoch [43][2000/5005]\tlr: 1.000e-02, eta: 7:02:42, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0311\n2023-01-02 19:25:19,121 - mmcls - INFO - Epoch [43][2100/5005]\tlr: 1.000e-02, eta: 7:02:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0610\n2023-01-02 19:25:27,997 - mmcls - INFO - Epoch [43][2200/5005]\tlr: 1.000e-02, eta: 7:02:25, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.0495\n2023-01-02 19:25:36,604 - mmcls - INFO - Epoch [43][2300/5005]\tlr: 1.000e-02, eta: 7:02:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0472\n2023-01-02 19:25:45,434 - mmcls - INFO - Epoch [43][2400/5005]\tlr: 1.000e-02, eta: 7:02:07, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0713\n2023-01-02 19:25:54,138 - mmcls - INFO - Epoch [43][2500/5005]\tlr: 1.000e-02, eta: 7:01:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0467\n2023-01-02 19:26:02,925 - mmcls - INFO - Epoch [43][2600/5005]\tlr: 1.000e-02, eta: 7:01:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0601\n2023-01-02 19:26:11,576 - mmcls - INFO - Epoch [43][2700/5005]\tlr: 1.000e-02, eta: 7:01:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0469\n2023-01-02 19:26:20,298 - mmcls - INFO - Epoch [43][2800/5005]\tlr: 1.000e-02, eta: 7:01:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0539\n2023-01-02 19:26:28,912 - mmcls - INFO - Epoch [43][2900/5005]\tlr: 1.000e-02, eta: 7:01:22, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0857\n2023-01-02 19:26:37,719 - mmcls - INFO - Epoch [43][3000/5005]\tlr: 1.000e-02, eta: 7:01:13, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.0759\n2023-01-02 19:26:46,506 - mmcls - INFO - Epoch [43][3100/5005]\tlr: 1.000e-02, eta: 7:01:04, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0348\n2023-01-02 19:26:54,988 - mmcls - INFO - Epoch [43][3200/5005]\tlr: 1.000e-02, eta: 7:00:55, time: 0.085, data_time: 0.022, memory: 1125, loss: 2.0667\n2023-01-02 19:27:03,563 - mmcls - INFO - Epoch [43][3300/5005]\tlr: 1.000e-02, eta: 7:00:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0591\n2023-01-02 19:27:12,367 - mmcls - INFO - Epoch [43][3400/5005]\tlr: 1.000e-02, eta: 7:00:37, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0729\n2023-01-02 19:27:20,977 - mmcls - INFO - Epoch [43][3500/5005]\tlr: 1.000e-02, eta: 7:00:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0449\n2023-01-02 19:27:29,758 - mmcls - INFO - Epoch [43][3600/5005]\tlr: 1.000e-02, eta: 7:00:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0702\n2023-01-02 19:27:38,660 - mmcls - INFO - Epoch [43][3700/5005]\tlr: 1.000e-02, eta: 7:00:11, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0670\n2023-01-02 19:27:47,216 - mmcls - INFO - Epoch [43][3800/5005]\tlr: 1.000e-02, eta: 7:00:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0778\n2023-01-02 19:27:55,879 - mmcls - INFO - Epoch [43][3900/5005]\tlr: 1.000e-02, eta: 6:59:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0840\n2023-01-02 19:28:04,848 - mmcls - INFO - Epoch [43][4000/5005]\tlr: 1.000e-02, eta: 6:59:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0543\n2023-01-02 19:28:13,651 - mmcls - INFO - Epoch [43][4100/5005]\tlr: 1.000e-02, eta: 6:59:35, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0592\n2023-01-02 19:28:22,332 - mmcls - INFO - Epoch [43][4200/5005]\tlr: 1.000e-02, eta: 6:59:26, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0636\n2023-01-02 19:28:30,809 - mmcls - INFO - Epoch [43][4300/5005]\tlr: 1.000e-02, eta: 6:59:17, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0740\n2023-01-02 19:28:39,330 - mmcls - INFO - Epoch [43][4400/5005]\tlr: 1.000e-02, eta: 6:59:08, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.0485\n2023-01-02 19:28:47,911 - mmcls - INFO - Epoch [43][4500/5005]\tlr: 1.000e-02, eta: 6:58:59, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0637\n2023-01-02 19:28:57,288 - mmcls - INFO - Epoch [43][4600/5005]\tlr: 1.000e-02, eta: 6:58:51, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.0945\n2023-01-02 19:29:06,209 - mmcls - INFO - Epoch [43][4700/5005]\tlr: 1.000e-02, eta: 6:58:42, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.0635\n2023-01-02 19:29:15,128 - mmcls - INFO - Epoch [43][4800/5005]\tlr: 1.000e-02, eta: 6:58:34, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0715\n2023-01-02 19:29:24,009 - mmcls - INFO - Epoch [43][4900/5005]\tlr: 1.000e-02, eta: 6:58:25, time: 0.089, data_time: 0.031, memory: 1125, loss: 2.0785\n2023-01-02 19:29:32,502 - mmcls - INFO - Epoch [43][5000/5005]\tlr: 1.000e-02, eta: 6:58:16, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0666\n2023-01-02 19:29:55,163 - mmcls - INFO - Epoch(val) [43][196]\taccuracy_top-1: 58.8480, accuracy_top-5: 81.3240\n2023-01-02 19:30:06,658 - mmcls - INFO - Epoch [44][100/5005]\tlr: 1.000e-02, eta: 6:58:10, time: 0.115, data_time: 0.050, memory: 1125, loss: 2.0243\n2023-01-02 19:30:15,277 - mmcls - INFO - Epoch [44][200/5005]\tlr: 1.000e-02, eta: 6:58:00, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9881\n2023-01-02 19:30:24,236 - mmcls - INFO - Epoch [44][300/5005]\tlr: 1.000e-02, eta: 6:57:52, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0104\n2023-01-02 19:30:33,060 - mmcls - INFO - Epoch [44][400/5005]\tlr: 1.000e-02, eta: 6:57:43, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.0223\n2023-01-02 19:30:41,680 - mmcls - INFO - Epoch [44][500/5005]\tlr: 1.000e-02, eta: 6:57:34, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0276\n2023-01-02 19:30:50,541 - mmcls - INFO - Epoch [44][600/5005]\tlr: 1.000e-02, eta: 6:57:25, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0231\n2023-01-02 19:30:59,255 - mmcls - INFO - Epoch [44][700/5005]\tlr: 1.000e-02, eta: 6:57:16, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0363\n2023-01-02 19:31:08,014 - mmcls - INFO - Epoch [44][800/5005]\tlr: 1.000e-02, eta: 6:57:08, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0514\n2023-01-02 19:31:16,413 - mmcls - INFO - Epoch [44][900/5005]\tlr: 1.000e-02, eta: 6:56:58, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0130\n2023-01-02 19:31:25,217 - mmcls - INFO - Epoch [44][1000/5005]\tlr: 1.000e-02, eta: 6:56:50, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0413\n2023-01-02 19:31:33,767 - mmcls - INFO - Epoch [44][1100/5005]\tlr: 1.000e-02, eta: 6:56:40, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0430\n2023-01-02 19:31:42,563 - mmcls - INFO - Epoch [44][1200/5005]\tlr: 1.000e-02, eta: 6:56:32, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.0730\n2023-01-02 19:31:51,211 - mmcls - INFO - Epoch [44][1300/5005]\tlr: 1.000e-02, eta: 6:56:23, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0586\n2023-01-02 19:32:00,298 - mmcls - INFO - Epoch [44][1400/5005]\tlr: 1.000e-02, eta: 6:56:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0206\n2023-01-02 19:32:09,513 - mmcls - INFO - Epoch [44][1500/5005]\tlr: 1.000e-02, eta: 6:56:06, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.0475\n2023-01-02 19:32:18,030 - mmcls - INFO - Epoch [44][1600/5005]\tlr: 1.000e-02, eta: 6:55:57, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.0439\n2023-01-02 19:32:26,684 - mmcls - INFO - Epoch [44][1700/5005]\tlr: 1.000e-02, eta: 6:55:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0348\n2023-01-02 19:32:35,175 - mmcls - INFO - Epoch [44][1800/5005]\tlr: 1.000e-02, eta: 6:55:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0526\n2023-01-02 19:32:43,883 - mmcls - INFO - Epoch [44][1900/5005]\tlr: 1.000e-02, eta: 6:55:30, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0498\n2023-01-02 19:32:52,702 - mmcls - INFO - Epoch [44][2000/5005]\tlr: 1.000e-02, eta: 6:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0560\n2023-01-02 19:33:01,367 - mmcls - INFO - Epoch [44][2100/5005]\tlr: 1.000e-02, eta: 6:55:12, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0760\n2023-01-02 19:33:10,210 - mmcls - INFO - Epoch [44][2200/5005]\tlr: 1.000e-02, eta: 6:55:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0520\n2023-01-02 19:33:18,882 - mmcls - INFO - Epoch [44][2300/5005]\tlr: 1.000e-02, eta: 6:54:54, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0826\n2023-01-02 19:33:27,462 - mmcls - INFO - Epoch [44][2400/5005]\tlr: 1.000e-02, eta: 6:54:45, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.0505\n2023-01-02 19:33:36,412 - mmcls - INFO - Epoch [44][2500/5005]\tlr: 1.000e-02, eta: 6:54:37, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0472\n2023-01-02 19:33:45,053 - mmcls - INFO - Epoch [44][2600/5005]\tlr: 1.000e-02, eta: 6:54:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0342\n2023-01-02 19:33:54,092 - mmcls - INFO - Epoch [44][2700/5005]\tlr: 1.000e-02, eta: 6:54:19, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0515\n2023-01-02 19:34:02,664 - mmcls - INFO - Epoch [44][2800/5005]\tlr: 1.000e-02, eta: 6:54:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0493\n2023-01-02 19:34:11,652 - mmcls - INFO - Epoch [44][2900/5005]\tlr: 1.000e-02, eta: 6:54:01, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.0526\n2023-01-02 19:34:20,401 - mmcls - INFO - Epoch [44][3000/5005]\tlr: 1.000e-02, eta: 6:53:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0714\n2023-01-02 19:34:29,408 - mmcls - INFO - Epoch [44][3100/5005]\tlr: 1.000e-02, eta: 6:53:44, time: 0.090, data_time: 0.038, memory: 1125, loss: 2.0642\n2023-01-02 19:34:38,179 - mmcls - INFO - Epoch [44][3200/5005]\tlr: 1.000e-02, eta: 6:53:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0664\n2023-01-02 19:34:46,775 - mmcls - INFO - Epoch [44][3300/5005]\tlr: 1.000e-02, eta: 6:53:26, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0311\n2023-01-02 19:34:55,765 - mmcls - INFO - Epoch [44][3400/5005]\tlr: 1.000e-02, eta: 6:53:18, time: 0.090, data_time: 0.018, memory: 1125, loss: 2.0469\n2023-01-02 19:35:04,418 - mmcls - INFO - Epoch [44][3500/5005]\tlr: 1.000e-02, eta: 6:53:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0471\n2023-01-02 19:35:13,194 - mmcls - INFO - Epoch [44][3600/5005]\tlr: 1.000e-02, eta: 6:53:00, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0577\n2023-01-02 19:35:21,971 - mmcls - INFO - Epoch [44][3700/5005]\tlr: 1.000e-02, eta: 6:52:51, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0416\n2023-01-02 19:35:30,859 - mmcls - INFO - Epoch [44][3800/5005]\tlr: 1.000e-02, eta: 6:52:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0387\n2023-01-02 19:35:39,525 - mmcls - INFO - Epoch [44][3900/5005]\tlr: 1.000e-02, eta: 6:52:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0577\n2023-01-02 19:35:48,032 - mmcls - INFO - Epoch [44][4000/5005]\tlr: 1.000e-02, eta: 6:52:24, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0653\n2023-01-02 19:35:56,702 - mmcls - INFO - Epoch [44][4100/5005]\tlr: 1.000e-02, eta: 6:52:15, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0706\n2023-01-02 19:36:05,488 - mmcls - INFO - Epoch [44][4200/5005]\tlr: 1.000e-02, eta: 6:52:06, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0574\n2023-01-02 19:36:13,986 - mmcls - INFO - Epoch [44][4300/5005]\tlr: 1.000e-02, eta: 6:51:57, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0704\n2023-01-02 19:36:22,595 - mmcls - INFO - Epoch [44][4400/5005]\tlr: 1.000e-02, eta: 6:51:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0868\n2023-01-02 19:36:31,189 - mmcls - INFO - Epoch [44][4500/5005]\tlr: 1.000e-02, eta: 6:51:39, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0511\n2023-01-02 19:36:40,109 - mmcls - INFO - Epoch [44][4600/5005]\tlr: 1.000e-02, eta: 6:51:30, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0741\n2023-01-02 19:36:48,988 - mmcls - INFO - Epoch [44][4700/5005]\tlr: 1.000e-02, eta: 6:51:22, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0772\n2023-01-02 19:36:57,756 - mmcls - INFO - Epoch [44][4800/5005]\tlr: 1.000e-02, eta: 6:51:13, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0461\n2023-01-02 19:37:06,704 - mmcls - INFO - Epoch [44][4900/5005]\tlr: 1.000e-02, eta: 6:51:04, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.0539\n2023-01-02 19:37:15,578 - mmcls - INFO - Epoch [44][5000/5005]\tlr: 1.000e-02, eta: 6:50:56, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0761\n2023-01-02 19:37:38,180 - mmcls - INFO - Epoch(val) [44][196]\taccuracy_top-1: 58.5340, accuracy_top-5: 81.0160\n2023-01-02 19:37:49,354 - mmcls - INFO - Epoch [45][100/5005]\tlr: 1.000e-02, eta: 6:50:49, time: 0.112, data_time: 0.036, memory: 1125, loss: 2.0059\n2023-01-02 19:37:58,097 - mmcls - INFO - Epoch [45][200/5005]\tlr: 1.000e-02, eta: 6:50:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0260\n2023-01-02 19:38:06,673 - mmcls - INFO - Epoch [45][300/5005]\tlr: 1.000e-02, eta: 6:50:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0154\n2023-01-02 19:38:15,559 - mmcls - INFO - Epoch [45][400/5005]\tlr: 1.000e-02, eta: 6:50:22, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.0222\n2023-01-02 19:38:24,368 - mmcls - INFO - Epoch [45][500/5005]\tlr: 1.000e-02, eta: 6:50:13, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0237\n2023-01-02 19:38:33,178 - mmcls - INFO - Epoch [45][600/5005]\tlr: 1.000e-02, eta: 6:50:05, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0233\n2023-01-02 19:38:41,996 - mmcls - INFO - Epoch [45][700/5005]\tlr: 1.000e-02, eta: 6:49:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0005\n2023-01-02 19:38:50,485 - mmcls - INFO - Epoch [45][800/5005]\tlr: 1.000e-02, eta: 6:49:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0364\n2023-01-02 19:38:59,256 - mmcls - INFO - Epoch [45][900/5005]\tlr: 1.000e-02, eta: 6:49:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0112\n2023-01-02 19:39:08,052 - mmcls - INFO - Epoch [45][1000/5005]\tlr: 1.000e-02, eta: 6:49:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0315\n2023-01-02 19:39:16,738 - mmcls - INFO - Epoch [45][1100/5005]\tlr: 1.000e-02, eta: 6:49:20, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.0500\n2023-01-02 19:39:25,430 - mmcls - INFO - Epoch [45][1200/5005]\tlr: 1.000e-02, eta: 6:49:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0523\n2023-01-02 19:39:34,149 - mmcls - INFO - Epoch [45][1300/5005]\tlr: 1.000e-02, eta: 6:49:02, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0378\n2023-01-02 19:39:42,921 - mmcls - INFO - Epoch [45][1400/5005]\tlr: 1.000e-02, eta: 6:48:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0336\n2023-01-02 19:39:51,618 - mmcls - INFO - Epoch [45][1500/5005]\tlr: 1.000e-02, eta: 6:48:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0515\n2023-01-02 19:40:00,466 - mmcls - INFO - Epoch [45][1600/5005]\tlr: 1.000e-02, eta: 6:48:36, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0550\n2023-01-02 19:40:09,136 - mmcls - INFO - Epoch [45][1700/5005]\tlr: 1.000e-02, eta: 6:48:27, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0569\n2023-01-02 19:40:17,789 - mmcls - INFO - Epoch [45][1800/5005]\tlr: 1.000e-02, eta: 6:48:18, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0555\n2023-01-02 19:40:26,567 - mmcls - INFO - Epoch [45][1900/5005]\tlr: 1.000e-02, eta: 6:48:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0521\n2023-01-02 19:40:35,275 - mmcls - INFO - Epoch [45][2000/5005]\tlr: 1.000e-02, eta: 6:48:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0436\n2023-01-02 19:40:44,071 - mmcls - INFO - Epoch [45][2100/5005]\tlr: 1.000e-02, eta: 6:47:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0468\n2023-01-02 19:40:52,571 - mmcls - INFO - Epoch [45][2200/5005]\tlr: 1.000e-02, eta: 6:47:42, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0588\n2023-01-02 19:41:01,381 - mmcls - INFO - Epoch [45][2300/5005]\tlr: 1.000e-02, eta: 6:47:33, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0342\n2023-01-02 19:41:10,213 - mmcls - INFO - Epoch [45][2400/5005]\tlr: 1.000e-02, eta: 6:47:25, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0482\n2023-01-02 19:41:18,964 - mmcls - INFO - Epoch [45][2500/5005]\tlr: 1.000e-02, eta: 6:47:16, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0369\n2023-01-02 19:41:27,831 - mmcls - INFO - Epoch [45][2600/5005]\tlr: 1.000e-02, eta: 6:47:07, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0470\n2023-01-02 19:41:36,643 - mmcls - INFO - Epoch [45][2700/5005]\tlr: 1.000e-02, eta: 6:46:58, time: 0.088, data_time: 0.040, memory: 1125, loss: 2.0759\n2023-01-02 19:41:45,112 - mmcls - INFO - Epoch [45][2800/5005]\tlr: 1.000e-02, eta: 6:46:49, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0339\n2023-01-02 19:41:54,425 - mmcls - INFO - Epoch [45][2900/5005]\tlr: 1.000e-02, eta: 6:46:41, time: 0.093, data_time: 0.011, memory: 1125, loss: 2.0249\n2023-01-02 19:42:03,381 - mmcls - INFO - Epoch [45][3000/5005]\tlr: 1.000e-02, eta: 6:46:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0596\n2023-01-02 19:42:12,046 - mmcls - INFO - Epoch [45][3100/5005]\tlr: 1.000e-02, eta: 6:46:23, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0521\n2023-01-02 19:42:20,643 - mmcls - INFO - Epoch [45][3200/5005]\tlr: 1.000e-02, eta: 6:46:14, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0582\n2023-01-02 19:42:29,338 - mmcls - INFO - Epoch [45][3300/5005]\tlr: 1.000e-02, eta: 6:46:05, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0574\n2023-01-02 19:42:37,775 - mmcls - INFO - Epoch [45][3400/5005]\tlr: 1.000e-02, eta: 6:45:56, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.0601\n2023-01-02 19:42:46,241 - mmcls - INFO - Epoch [45][3500/5005]\tlr: 1.000e-02, eta: 6:45:47, time: 0.085, data_time: 0.045, memory: 1125, loss: 2.0870\n2023-01-02 19:42:54,913 - mmcls - INFO - Epoch [45][3600/5005]\tlr: 1.000e-02, eta: 6:45:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0561\n2023-01-02 19:43:03,754 - mmcls - INFO - Epoch [45][3700/5005]\tlr: 1.000e-02, eta: 6:45:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0808\n2023-01-02 19:43:12,579 - mmcls - INFO - Epoch [45][3800/5005]\tlr: 1.000e-02, eta: 6:45:20, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0732\n2023-01-02 19:43:21,339 - mmcls - INFO - Epoch [45][3900/5005]\tlr: 1.000e-02, eta: 6:45:12, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0520\n2023-01-02 19:43:29,923 - mmcls - INFO - Epoch [45][4000/5005]\tlr: 1.000e-02, eta: 6:45:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0327\n2023-01-02 19:43:38,650 - mmcls - INFO - Epoch [45][4100/5005]\tlr: 1.000e-02, eta: 6:44:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0783\n2023-01-02 19:43:47,439 - mmcls - INFO - Epoch [45][4200/5005]\tlr: 1.000e-02, eta: 6:44:45, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0665\n2023-01-02 19:43:56,042 - mmcls - INFO - Epoch [45][4300/5005]\tlr: 1.000e-02, eta: 6:44:36, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.0671\n2023-01-02 19:44:04,920 - mmcls - INFO - Epoch [45][4400/5005]\tlr: 1.000e-02, eta: 6:44:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0739\n2023-01-02 19:44:13,921 - mmcls - INFO - Epoch [45][4500/5005]\tlr: 1.000e-02, eta: 6:44:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0542\n2023-01-02 19:44:22,748 - mmcls - INFO - Epoch [45][4600/5005]\tlr: 1.000e-02, eta: 6:44:10, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0818\n2023-01-02 19:44:31,365 - mmcls - INFO - Epoch [45][4700/5005]\tlr: 1.000e-02, eta: 6:44:01, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0550\n2023-01-02 19:44:39,936 - mmcls - INFO - Epoch [45][4800/5005]\tlr: 1.000e-02, eta: 6:43:52, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0506\n2023-01-02 19:44:48,809 - mmcls - INFO - Epoch [45][4900/5005]\tlr: 1.000e-02, eta: 6:43:43, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0842\n2023-01-02 19:44:57,569 - mmcls - INFO - Epoch [45][5000/5005]\tlr: 1.000e-02, eta: 6:43:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0895\n2023-01-02 19:45:20,091 - mmcls - INFO - Epoch(val) [45][196]\taccuracy_top-1: 58.7160, accuracy_top-5: 80.8700\n2023-01-02 19:45:31,087 - mmcls - INFO - Epoch [46][100/5005]\tlr: 1.000e-02, eta: 6:43:27, time: 0.110, data_time: 0.038, memory: 1125, loss: 2.0291\n2023-01-02 19:45:39,673 - mmcls - INFO - Epoch [46][200/5005]\tlr: 1.000e-02, eta: 6:43:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0355\n2023-01-02 19:45:48,541 - mmcls - INFO - Epoch [46][300/5005]\tlr: 1.000e-02, eta: 6:43:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0070\n2023-01-02 19:45:57,148 - mmcls - INFO - Epoch [46][400/5005]\tlr: 1.000e-02, eta: 6:43:00, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.9885\n2023-01-02 19:46:06,015 - mmcls - INFO - Epoch [46][500/5005]\tlr: 1.000e-02, eta: 6:42:52, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0161\n2023-01-02 19:46:14,653 - mmcls - INFO - Epoch [46][600/5005]\tlr: 1.000e-02, eta: 6:42:43, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.0257\n2023-01-02 19:46:23,630 - mmcls - INFO - Epoch [46][700/5005]\tlr: 1.000e-02, eta: 6:42:34, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0297\n2023-01-02 19:46:32,667 - mmcls - INFO - Epoch [46][800/5005]\tlr: 1.000e-02, eta: 6:42:25, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0248\n2023-01-02 19:46:41,527 - mmcls - INFO - Epoch [46][900/5005]\tlr: 1.000e-02, eta: 6:42:17, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0451\n2023-01-02 19:46:50,253 - mmcls - INFO - Epoch [46][1000/5005]\tlr: 1.000e-02, eta: 6:42:08, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.9996\n2023-01-02 19:46:59,030 - mmcls - INFO - Epoch [46][1100/5005]\tlr: 1.000e-02, eta: 6:41:59, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0339\n2023-01-02 19:47:07,622 - mmcls - INFO - Epoch [46][1200/5005]\tlr: 1.000e-02, eta: 6:41:50, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0334\n2023-01-02 19:47:16,844 - mmcls - INFO - Epoch [46][1300/5005]\tlr: 1.000e-02, eta: 6:41:42, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0331\n2023-01-02 19:47:25,523 - mmcls - INFO - Epoch [46][1400/5005]\tlr: 1.000e-02, eta: 6:41:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0402\n2023-01-02 19:47:34,273 - mmcls - INFO - Epoch [46][1500/5005]\tlr: 1.000e-02, eta: 6:41:24, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0767\n2023-01-02 19:47:43,604 - mmcls - INFO - Epoch [46][1600/5005]\tlr: 1.000e-02, eta: 6:41:16, time: 0.093, data_time: 0.000, memory: 1125, loss: 2.0344\n2023-01-02 19:47:52,127 - mmcls - INFO - Epoch [46][1700/5005]\tlr: 1.000e-02, eta: 6:41:07, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0641\n2023-01-02 19:48:01,041 - mmcls - INFO - Epoch [46][1800/5005]\tlr: 1.000e-02, eta: 6:40:58, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0558\n2023-01-02 19:48:09,914 - mmcls - INFO - Epoch [46][1900/5005]\tlr: 1.000e-02, eta: 6:40:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0484\n2023-01-02 19:48:18,392 - mmcls - INFO - Epoch [46][2000/5005]\tlr: 1.000e-02, eta: 6:40:40, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0642\n2023-01-02 19:48:27,230 - mmcls - INFO - Epoch [46][2100/5005]\tlr: 1.000e-02, eta: 6:40:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0484\n2023-01-02 19:48:36,105 - mmcls - INFO - Epoch [46][2200/5005]\tlr: 1.000e-02, eta: 6:40:23, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.0560\n2023-01-02 19:48:44,664 - mmcls - INFO - Epoch [46][2300/5005]\tlr: 1.000e-02, eta: 6:40:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0552\n2023-01-02 19:48:53,513 - mmcls - INFO - Epoch [46][2400/5005]\tlr: 1.000e-02, eta: 6:40:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0382\n2023-01-02 19:49:02,452 - mmcls - INFO - Epoch [46][2500/5005]\tlr: 1.000e-02, eta: 6:39:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0472\n2023-01-02 19:49:11,187 - mmcls - INFO - Epoch [46][2600/5005]\tlr: 1.000e-02, eta: 6:39:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0441\n2023-01-02 19:49:19,632 - mmcls - INFO - Epoch [46][2700/5005]\tlr: 1.000e-02, eta: 6:39:38, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0585\n2023-01-02 19:49:28,382 - mmcls - INFO - Epoch [46][2800/5005]\tlr: 1.000e-02, eta: 6:39:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0681\n2023-01-02 19:49:36,888 - mmcls - INFO - Epoch [46][2900/5005]\tlr: 1.000e-02, eta: 6:39:20, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0407\n2023-01-02 19:49:45,501 - mmcls - INFO - Epoch [46][3000/5005]\tlr: 1.000e-02, eta: 6:39:11, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0454\n2023-01-02 19:49:54,264 - mmcls - INFO - Epoch [46][3100/5005]\tlr: 1.000e-02, eta: 6:39:02, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0620\n2023-01-02 19:50:02,942 - mmcls - INFO - Epoch [46][3200/5005]\tlr: 1.000e-02, eta: 6:38:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0608\n2023-01-02 19:50:11,678 - mmcls - INFO - Epoch [46][3300/5005]\tlr: 1.000e-02, eta: 6:38:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0462\n2023-01-02 19:50:20,661 - mmcls - INFO - Epoch [46][3400/5005]\tlr: 1.000e-02, eta: 6:38:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0721\n2023-01-02 19:50:29,625 - mmcls - INFO - Epoch [46][3500/5005]\tlr: 1.000e-02, eta: 6:38:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0735\n2023-01-02 19:50:38,488 - mmcls - INFO - Epoch [46][3600/5005]\tlr: 1.000e-02, eta: 6:38:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0662\n2023-01-02 19:50:47,297 - mmcls - INFO - Epoch [46][3700/5005]\tlr: 1.000e-02, eta: 6:38:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0656\n2023-01-02 19:50:55,898 - mmcls - INFO - Epoch [46][3800/5005]\tlr: 1.000e-02, eta: 6:38:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0804\n2023-01-02 19:51:04,449 - mmcls - INFO - Epoch [46][3900/5005]\tlr: 1.000e-02, eta: 6:37:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0656\n2023-01-02 19:51:13,226 - mmcls - INFO - Epoch [46][4000/5005]\tlr: 1.000e-02, eta: 6:37:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0655\n2023-01-02 19:51:21,756 - mmcls - INFO - Epoch [46][4100/5005]\tlr: 1.000e-02, eta: 6:37:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0651\n2023-01-02 19:51:30,647 - mmcls - INFO - Epoch [46][4200/5005]\tlr: 1.000e-02, eta: 6:37:25, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.0813\n2023-01-02 19:51:39,644 - mmcls - INFO - Epoch [46][4300/5005]\tlr: 1.000e-02, eta: 6:37:16, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0629\n2023-01-02 19:51:48,557 - mmcls - INFO - Epoch [46][4400/5005]\tlr: 1.000e-02, eta: 6:37:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0983\n2023-01-02 19:51:57,323 - mmcls - INFO - Epoch [46][4500/5005]\tlr: 1.000e-02, eta: 6:36:59, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0692\n2023-01-02 19:52:06,034 - mmcls - INFO - Epoch [46][4600/5005]\tlr: 1.000e-02, eta: 6:36:50, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.0624\n2023-01-02 19:52:14,993 - mmcls - INFO - Epoch [46][4700/5005]\tlr: 1.000e-02, eta: 6:36:41, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0610\n2023-01-02 19:52:24,224 - mmcls - INFO - Epoch [46][4800/5005]\tlr: 1.000e-02, eta: 6:36:33, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.0754\n2023-01-02 19:52:33,311 - mmcls - INFO - Epoch [46][4900/5005]\tlr: 1.000e-02, eta: 6:36:25, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0488\n2023-01-02 19:52:42,039 - mmcls - INFO - Epoch [46][5000/5005]\tlr: 1.000e-02, eta: 6:36:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0762\n2023-01-02 19:53:04,739 - mmcls - INFO - Epoch(val) [46][196]\taccuracy_top-1: 58.7600, accuracy_top-5: 80.9620\n2023-01-02 19:53:15,862 - mmcls - INFO - Epoch [47][100/5005]\tlr: 1.000e-02, eta: 6:36:09, time: 0.111, data_time: 0.043, memory: 1125, loss: 2.0173\n2023-01-02 19:53:24,677 - mmcls - INFO - Epoch [47][200/5005]\tlr: 1.000e-02, eta: 6:36:00, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0309\n2023-01-02 19:53:33,220 - mmcls - INFO - Epoch [47][300/5005]\tlr: 1.000e-02, eta: 6:35:51, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0221\n2023-01-02 19:53:42,306 - mmcls - INFO - Epoch [47][400/5005]\tlr: 1.000e-02, eta: 6:35:42, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0371\n2023-01-02 19:53:50,877 - mmcls - INFO - Epoch [47][500/5005]\tlr: 1.000e-02, eta: 6:35:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0050\n2023-01-02 19:53:59,462 - mmcls - INFO - Epoch [47][600/5005]\tlr: 1.000e-02, eta: 6:35:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0260\n2023-01-02 19:54:08,142 - mmcls - INFO - Epoch [47][700/5005]\tlr: 1.000e-02, eta: 6:35:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0326\n2023-01-02 19:54:16,901 - mmcls - INFO - Epoch [47][800/5005]\tlr: 1.000e-02, eta: 6:35:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0219\n2023-01-02 19:54:25,831 - mmcls - INFO - Epoch [47][900/5005]\tlr: 1.000e-02, eta: 6:34:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0107\n2023-01-02 19:54:34,626 - mmcls - INFO - Epoch [47][1000/5005]\tlr: 1.000e-02, eta: 6:34:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0292\n2023-01-02 19:54:43,375 - mmcls - INFO - Epoch [47][1100/5005]\tlr: 1.000e-02, eta: 6:34:40, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0235\n2023-01-02 19:54:52,583 - mmcls - INFO - Epoch [47][1200/5005]\tlr: 1.000e-02, eta: 6:34:32, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.0373\n2023-01-02 19:55:01,521 - mmcls - INFO - Epoch [47][1300/5005]\tlr: 1.000e-02, eta: 6:34:23, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0507\n2023-01-02 19:55:10,114 - mmcls - INFO - Epoch [47][1400/5005]\tlr: 1.000e-02, eta: 6:34:14, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.0215\n2023-01-02 19:55:18,750 - mmcls - INFO - Epoch [47][1500/5005]\tlr: 1.000e-02, eta: 6:34:05, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0447\n2023-01-02 19:55:27,738 - mmcls - INFO - Epoch [47][1600/5005]\tlr: 1.000e-02, eta: 6:33:57, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0272\n2023-01-02 19:55:36,638 - mmcls - INFO - Epoch [47][1700/5005]\tlr: 1.000e-02, eta: 6:33:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0437\n2023-01-02 19:55:45,894 - mmcls - INFO - Epoch [47][1800/5005]\tlr: 1.000e-02, eta: 6:33:40, time: 0.093, data_time: 0.025, memory: 1125, loss: 2.0520\n2023-01-02 19:55:54,840 - mmcls - INFO - Epoch [47][1900/5005]\tlr: 1.000e-02, eta: 6:33:31, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0531\n2023-01-02 19:56:03,610 - mmcls - INFO - Epoch [47][2000/5005]\tlr: 1.000e-02, eta: 6:33:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0276\n2023-01-02 19:56:12,109 - mmcls - INFO - Epoch [47][2100/5005]\tlr: 1.000e-02, eta: 6:33:13, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0344\n2023-01-02 19:56:20,705 - mmcls - INFO - Epoch [47][2200/5005]\tlr: 1.000e-02, eta: 6:33:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0568\n2023-01-02 19:56:29,461 - mmcls - INFO - Epoch [47][2300/5005]\tlr: 1.000e-02, eta: 6:32:55, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0333\n2023-01-02 19:56:38,180 - mmcls - INFO - Epoch [47][2400/5005]\tlr: 1.000e-02, eta: 6:32:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0439\n2023-01-02 19:56:46,991 - mmcls - INFO - Epoch [47][2500/5005]\tlr: 1.000e-02, eta: 6:32:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0483\n2023-01-02 19:56:55,713 - mmcls - INFO - Epoch [47][2600/5005]\tlr: 1.000e-02, eta: 6:32:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0328\n2023-01-02 19:57:04,151 - mmcls - INFO - Epoch [47][2700/5005]\tlr: 1.000e-02, eta: 6:32:19, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0637\n2023-01-02 19:57:13,078 - mmcls - INFO - Epoch [47][2800/5005]\tlr: 1.000e-02, eta: 6:32:11, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0466\n2023-01-02 19:57:21,640 - mmcls - INFO - Epoch [47][2900/5005]\tlr: 1.000e-02, eta: 6:32:02, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.0506\n2023-01-02 19:57:30,618 - mmcls - INFO - Epoch [47][3000/5005]\tlr: 1.000e-02, eta: 6:31:53, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.0671\n2023-01-02 19:57:39,499 - mmcls - INFO - Epoch [47][3100/5005]\tlr: 1.000e-02, eta: 6:31:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0524\n2023-01-02 19:57:48,085 - mmcls - INFO - Epoch [47][3200/5005]\tlr: 1.000e-02, eta: 6:31:35, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0480\n2023-01-02 19:57:56,589 - mmcls - INFO - Epoch [47][3300/5005]\tlr: 1.000e-02, eta: 6:31:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0775\n2023-01-02 19:58:05,378 - mmcls - INFO - Epoch [47][3400/5005]\tlr: 1.000e-02, eta: 6:31:17, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0447\n2023-01-02 19:58:13,971 - mmcls - INFO - Epoch [47][3500/5005]\tlr: 1.000e-02, eta: 6:31:08, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.0516\n2023-01-02 19:58:22,849 - mmcls - INFO - Epoch [47][3600/5005]\tlr: 1.000e-02, eta: 6:31:00, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0464\n2023-01-02 19:58:31,401 - mmcls - INFO - Epoch [47][3700/5005]\tlr: 1.000e-02, eta: 6:30:51, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0702\n2023-01-02 19:58:40,311 - mmcls - INFO - Epoch [47][3800/5005]\tlr: 1.000e-02, eta: 6:30:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0524\n2023-01-02 19:58:49,055 - mmcls - INFO - Epoch [47][3900/5005]\tlr: 1.000e-02, eta: 6:30:33, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0440\n2023-01-02 19:58:57,910 - mmcls - INFO - Epoch [47][4000/5005]\tlr: 1.000e-02, eta: 6:30:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0596\n2023-01-02 19:59:06,494 - mmcls - INFO - Epoch [47][4100/5005]\tlr: 1.000e-02, eta: 6:30:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0423\n2023-01-02 19:59:15,201 - mmcls - INFO - Epoch [47][4200/5005]\tlr: 1.000e-02, eta: 6:30:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0723\n2023-01-02 19:59:23,990 - mmcls - INFO - Epoch [47][4300/5005]\tlr: 1.000e-02, eta: 6:29:58, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0652\n2023-01-02 19:59:32,817 - mmcls - INFO - Epoch [47][4400/5005]\tlr: 1.000e-02, eta: 6:29:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0801\n2023-01-02 19:59:41,777 - mmcls - INFO - Epoch [47][4500/5005]\tlr: 1.000e-02, eta: 6:29:40, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.0764\n2023-01-02 19:59:50,254 - mmcls - INFO - Epoch [47][4600/5005]\tlr: 1.000e-02, eta: 6:29:31, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0596\n2023-01-02 19:59:59,018 - mmcls - INFO - Epoch [47][4700/5005]\tlr: 1.000e-02, eta: 6:29:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0587\n2023-01-02 20:00:07,650 - mmcls - INFO - Epoch [47][4800/5005]\tlr: 1.000e-02, eta: 6:29:13, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0608\n2023-01-02 20:00:16,314 - mmcls - INFO - Epoch [47][4900/5005]\tlr: 1.000e-02, eta: 6:29:04, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0825\n2023-01-02 20:00:24,991 - mmcls - INFO - Epoch [47][5000/5005]\tlr: 1.000e-02, eta: 6:28:55, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0843\n2023-01-02 20:00:47,628 - mmcls - INFO - Epoch(val) [47][196]\taccuracy_top-1: 58.9060, accuracy_top-5: 81.0780\n2023-01-02 20:00:58,718 - mmcls - INFO - Epoch [48][100/5005]\tlr: 1.000e-02, eta: 6:28:48, time: 0.111, data_time: 0.038, memory: 1125, loss: 2.0280\n2023-01-02 20:01:07,781 - mmcls - INFO - Epoch [48][200/5005]\tlr: 1.000e-02, eta: 6:28:40, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.0136\n2023-01-02 20:01:16,947 - mmcls - INFO - Epoch [48][300/5005]\tlr: 1.000e-02, eta: 6:28:31, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0228\n2023-01-02 20:01:25,442 - mmcls - INFO - Epoch [48][400/5005]\tlr: 1.000e-02, eta: 6:28:22, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0383\n2023-01-02 20:01:34,343 - mmcls - INFO - Epoch [48][500/5005]\tlr: 1.000e-02, eta: 6:28:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0386\n2023-01-02 20:01:42,813 - mmcls - INFO - Epoch [48][600/5005]\tlr: 1.000e-02, eta: 6:28:04, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0259\n2023-01-02 20:01:51,708 - mmcls - INFO - Epoch [48][700/5005]\tlr: 1.000e-02, eta: 6:27:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0248\n2023-01-02 20:02:00,387 - mmcls - INFO - Epoch [48][800/5005]\tlr: 1.000e-02, eta: 6:27:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0566\n2023-01-02 20:02:09,220 - mmcls - INFO - Epoch [48][900/5005]\tlr: 1.000e-02, eta: 6:27:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0264\n2023-01-02 20:02:17,938 - mmcls - INFO - Epoch [48][1000/5005]\tlr: 1.000e-02, eta: 6:27:29, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0447\n2023-01-02 20:02:26,766 - mmcls - INFO - Epoch [48][1100/5005]\tlr: 1.000e-02, eta: 6:27:20, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0287\n2023-01-02 20:02:35,459 - mmcls - INFO - Epoch [48][1200/5005]\tlr: 1.000e-02, eta: 6:27:11, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.0424\n2023-01-02 20:02:44,179 - mmcls - INFO - Epoch [48][1300/5005]\tlr: 1.000e-02, eta: 6:27:03, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0400\n2023-01-02 20:02:52,541 - mmcls - INFO - Epoch [48][1400/5005]\tlr: 1.000e-02, eta: 6:26:53, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.0323\n2023-01-02 20:03:01,290 - mmcls - INFO - Epoch [48][1500/5005]\tlr: 1.000e-02, eta: 6:26:44, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0742\n2023-01-02 20:03:10,392 - mmcls - INFO - Epoch [48][1600/5005]\tlr: 1.000e-02, eta: 6:26:36, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.0388\n2023-01-02 20:03:18,888 - mmcls - INFO - Epoch [48][1700/5005]\tlr: 1.000e-02, eta: 6:26:27, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0322\n2023-01-02 20:03:27,420 - mmcls - INFO - Epoch [48][1800/5005]\tlr: 1.000e-02, eta: 6:26:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0491\n2023-01-02 20:03:36,312 - mmcls - INFO - Epoch [48][1900/5005]\tlr: 1.000e-02, eta: 6:26:09, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.0612\n2023-01-02 20:03:44,775 - mmcls - INFO - Epoch [48][2000/5005]\tlr: 1.000e-02, eta: 6:26:00, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0574\n2023-01-02 20:03:53,511 - mmcls - INFO - Epoch [48][2100/5005]\tlr: 1.000e-02, eta: 6:25:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0468\n2023-01-02 20:04:02,228 - mmcls - INFO - Epoch [48][2200/5005]\tlr: 1.000e-02, eta: 6:25:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0521\n2023-01-02 20:04:10,703 - mmcls - INFO - Epoch [48][2300/5005]\tlr: 1.000e-02, eta: 6:25:33, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0513\n2023-01-02 20:04:19,545 - mmcls - INFO - Epoch [48][2400/5005]\tlr: 1.000e-02, eta: 6:25:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0610\n2023-01-02 20:04:28,396 - mmcls - INFO - Epoch [48][2500/5005]\tlr: 1.000e-02, eta: 6:25:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0479\n2023-01-02 20:04:36,922 - mmcls - INFO - Epoch [48][2600/5005]\tlr: 1.000e-02, eta: 6:25:06, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0430\n2023-01-02 20:04:45,967 - mmcls - INFO - Epoch [48][2700/5005]\tlr: 1.000e-02, eta: 6:24:58, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.0304\n2023-01-02 20:04:54,890 - mmcls - INFO - Epoch [48][2800/5005]\tlr: 1.000e-02, eta: 6:24:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0479\n2023-01-02 20:05:04,109 - mmcls - INFO - Epoch [48][2900/5005]\tlr: 1.000e-02, eta: 6:24:41, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0643\n2023-01-02 20:05:12,961 - mmcls - INFO - Epoch [48][3000/5005]\tlr: 1.000e-02, eta: 6:24:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0285\n2023-01-02 20:05:21,655 - mmcls - INFO - Epoch [48][3100/5005]\tlr: 1.000e-02, eta: 6:24:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0716\n2023-01-02 20:05:30,431 - mmcls - INFO - Epoch [48][3200/5005]\tlr: 1.000e-02, eta: 6:24:14, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.0585\n2023-01-02 20:05:38,980 - mmcls - INFO - Epoch [48][3300/5005]\tlr: 1.000e-02, eta: 6:24:05, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.0467\n2023-01-02 20:05:47,859 - mmcls - INFO - Epoch [48][3400/5005]\tlr: 1.000e-02, eta: 6:23:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0615\n2023-01-02 20:05:56,543 - mmcls - INFO - Epoch [48][3500/5005]\tlr: 1.000e-02, eta: 6:23:48, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0729\n2023-01-02 20:06:05,212 - mmcls - INFO - Epoch [48][3600/5005]\tlr: 1.000e-02, eta: 6:23:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0368\n2023-01-02 20:06:13,615 - mmcls - INFO - Epoch [48][3700/5005]\tlr: 1.000e-02, eta: 6:23:29, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0650\n2023-01-02 20:06:22,176 - mmcls - INFO - Epoch [48][3800/5005]\tlr: 1.000e-02, eta: 6:23:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0474\n2023-01-02 20:06:30,919 - mmcls - INFO - Epoch [48][3900/5005]\tlr: 1.000e-02, eta: 6:23:12, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0278\n2023-01-02 20:06:39,462 - mmcls - INFO - Epoch [48][4000/5005]\tlr: 1.000e-02, eta: 6:23:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0561\n2023-01-02 20:06:47,954 - mmcls - INFO - Epoch [48][4100/5005]\tlr: 1.000e-02, eta: 6:22:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0526\n2023-01-02 20:06:56,866 - mmcls - INFO - Epoch [48][4200/5005]\tlr: 1.000e-02, eta: 6:22:45, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0495\n2023-01-02 20:07:05,615 - mmcls - INFO - Epoch [48][4300/5005]\tlr: 1.000e-02, eta: 6:22:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0771\n2023-01-02 20:07:14,202 - mmcls - INFO - Epoch [48][4400/5005]\tlr: 1.000e-02, eta: 6:22:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0555\n2023-01-02 20:07:22,868 - mmcls - INFO - Epoch [48][4500/5005]\tlr: 1.000e-02, eta: 6:22:18, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0882\n2023-01-02 20:07:31,772 - mmcls - INFO - Epoch [48][4600/5005]\tlr: 1.000e-02, eta: 6:22:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0521\n2023-01-02 20:07:40,812 - mmcls - INFO - Epoch [48][4700/5005]\tlr: 1.000e-02, eta: 6:22:01, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0575\n2023-01-02 20:07:49,384 - mmcls - INFO - Epoch [48][4800/5005]\tlr: 1.000e-02, eta: 6:21:52, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0488\n2023-01-02 20:07:57,892 - mmcls - INFO - Epoch [48][4900/5005]\tlr: 1.000e-02, eta: 6:21:43, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0620\n2023-01-02 20:08:06,711 - mmcls - INFO - Epoch [48][5000/5005]\tlr: 1.000e-02, eta: 6:21:34, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0390\n2023-01-02 20:08:29,373 - mmcls - INFO - Epoch(val) [48][196]\taccuracy_top-1: 58.8160, accuracy_top-5: 81.1040\n2023-01-02 20:08:40,440 - mmcls - INFO - Epoch [49][100/5005]\tlr: 1.000e-02, eta: 6:21:26, time: 0.110, data_time: 0.058, memory: 1125, loss: 1.9938\n2023-01-02 20:08:49,497 - mmcls - INFO - Epoch [49][200/5005]\tlr: 1.000e-02, eta: 6:21:18, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.0216\n2023-01-02 20:08:57,939 - mmcls - INFO - Epoch [49][300/5005]\tlr: 1.000e-02, eta: 6:21:09, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0060\n2023-01-02 20:09:06,847 - mmcls - INFO - Epoch [49][400/5005]\tlr: 1.000e-02, eta: 6:21:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0042\n2023-01-02 20:09:15,679 - mmcls - INFO - Epoch [49][500/5005]\tlr: 1.000e-02, eta: 6:20:51, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.9961\n2023-01-02 20:09:24,300 - mmcls - INFO - Epoch [49][600/5005]\tlr: 1.000e-02, eta: 6:20:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0174\n2023-01-02 20:09:32,996 - mmcls - INFO - Epoch [49][700/5005]\tlr: 1.000e-02, eta: 6:20:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0193\n2023-01-02 20:09:41,726 - mmcls - INFO - Epoch [49][800/5005]\tlr: 1.000e-02, eta: 6:20:25, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0332\n2023-01-02 20:09:50,293 - mmcls - INFO - Epoch [49][900/5005]\tlr: 1.000e-02, eta: 6:20:15, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0350\n2023-01-02 20:09:59,293 - mmcls - INFO - Epoch [49][1000/5005]\tlr: 1.000e-02, eta: 6:20:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0615\n2023-01-02 20:10:07,831 - mmcls - INFO - Epoch [49][1100/5005]\tlr: 1.000e-02, eta: 6:19:58, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0221\n2023-01-02 20:10:16,598 - mmcls - INFO - Epoch [49][1200/5005]\tlr: 1.000e-02, eta: 6:19:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0400\n2023-01-02 20:10:25,494 - mmcls - INFO - Epoch [49][1300/5005]\tlr: 1.000e-02, eta: 6:19:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0477\n2023-01-02 20:10:34,192 - mmcls - INFO - Epoch [49][1400/5005]\tlr: 1.000e-02, eta: 6:19:31, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0385\n2023-01-02 20:10:42,950 - mmcls - INFO - Epoch [49][1500/5005]\tlr: 1.000e-02, eta: 6:19:23, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0476\n2023-01-02 20:10:51,635 - mmcls - INFO - Epoch [49][1600/5005]\tlr: 1.000e-02, eta: 6:19:14, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0417\n2023-01-02 20:11:00,649 - mmcls - INFO - Epoch [49][1700/5005]\tlr: 1.000e-02, eta: 6:19:05, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.0610\n2023-01-02 20:11:09,170 - mmcls - INFO - Epoch [49][1800/5005]\tlr: 1.000e-02, eta: 6:18:56, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0329\n2023-01-02 20:11:18,036 - mmcls - INFO - Epoch [49][1900/5005]\tlr: 1.000e-02, eta: 6:18:47, time: 0.089, data_time: 0.034, memory: 1125, loss: 2.0647\n2023-01-02 20:11:26,724 - mmcls - INFO - Epoch [49][2000/5005]\tlr: 1.000e-02, eta: 6:18:38, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0547\n2023-01-02 20:11:35,495 - mmcls - INFO - Epoch [49][2100/5005]\tlr: 1.000e-02, eta: 6:18:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0473\n2023-01-02 20:11:44,112 - mmcls - INFO - Epoch [49][2200/5005]\tlr: 1.000e-02, eta: 6:18:21, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0629\n2023-01-02 20:11:52,758 - mmcls - INFO - Epoch [49][2300/5005]\tlr: 1.000e-02, eta: 6:18:12, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0414\n2023-01-02 20:12:01,402 - mmcls - INFO - Epoch [49][2400/5005]\tlr: 1.000e-02, eta: 6:18:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0598\n2023-01-02 20:12:10,208 - mmcls - INFO - Epoch [49][2500/5005]\tlr: 1.000e-02, eta: 6:17:54, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0357\n2023-01-02 20:12:18,887 - mmcls - INFO - Epoch [49][2600/5005]\tlr: 1.000e-02, eta: 6:17:45, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0617\n2023-01-02 20:12:27,668 - mmcls - INFO - Epoch [49][2700/5005]\tlr: 1.000e-02, eta: 6:17:36, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0452\n2023-01-02 20:12:36,727 - mmcls - INFO - Epoch [49][2800/5005]\tlr: 1.000e-02, eta: 6:17:28, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0728\n2023-01-02 20:12:45,790 - mmcls - INFO - Epoch [49][2900/5005]\tlr: 1.000e-02, eta: 6:17:19, time: 0.091, data_time: 0.018, memory: 1125, loss: 2.0668\n2023-01-02 20:12:54,791 - mmcls - INFO - Epoch [49][3000/5005]\tlr: 1.000e-02, eta: 6:17:10, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0530\n2023-01-02 20:13:03,736 - mmcls - INFO - Epoch [49][3100/5005]\tlr: 1.000e-02, eta: 6:17:02, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0481\n2023-01-02 20:13:12,442 - mmcls - INFO - Epoch [49][3200/5005]\tlr: 1.000e-02, eta: 6:16:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0466\n2023-01-02 20:13:21,146 - mmcls - INFO - Epoch [49][3300/5005]\tlr: 1.000e-02, eta: 6:16:44, time: 0.087, data_time: 0.038, memory: 1125, loss: 2.0582\n2023-01-02 20:13:30,311 - mmcls - INFO - Epoch [49][3400/5005]\tlr: 1.000e-02, eta: 6:16:36, time: 0.092, data_time: 0.035, memory: 1125, loss: 2.0464\n2023-01-02 20:13:39,188 - mmcls - INFO - Epoch [49][3500/5005]\tlr: 1.000e-02, eta: 6:16:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0548\n2023-01-02 20:13:47,733 - mmcls - INFO - Epoch [49][3600/5005]\tlr: 1.000e-02, eta: 6:16:18, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0743\n2023-01-02 20:13:56,136 - mmcls - INFO - Epoch [49][3700/5005]\tlr: 1.000e-02, eta: 6:16:09, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0599\n2023-01-02 20:14:05,072 - mmcls - INFO - Epoch [49][3800/5005]\tlr: 1.000e-02, eta: 6:16:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0683\n2023-01-02 20:14:13,770 - mmcls - INFO - Epoch [49][3900/5005]\tlr: 1.000e-02, eta: 6:15:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0561\n2023-01-02 20:14:22,694 - mmcls - INFO - Epoch [49][4000/5005]\tlr: 1.000e-02, eta: 6:15:42, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0504\n2023-01-02 20:14:31,454 - mmcls - INFO - Epoch [49][4100/5005]\tlr: 1.000e-02, eta: 6:15:34, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0428\n2023-01-02 20:14:40,173 - mmcls - INFO - Epoch [49][4200/5005]\tlr: 1.000e-02, eta: 6:15:25, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0613\n2023-01-02 20:14:48,817 - mmcls - INFO - Epoch [49][4300/5005]\tlr: 1.000e-02, eta: 6:15:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0548\n2023-01-02 20:14:57,762 - mmcls - INFO - Epoch [49][4400/5005]\tlr: 1.000e-02, eta: 6:15:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0530\n2023-01-02 20:15:06,401 - mmcls - INFO - Epoch [49][4500/5005]\tlr: 1.000e-02, eta: 6:14:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0454\n2023-01-02 20:15:15,155 - mmcls - INFO - Epoch [49][4600/5005]\tlr: 1.000e-02, eta: 6:14:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0620\n2023-01-02 20:15:23,872 - mmcls - INFO - Epoch [49][4700/5005]\tlr: 1.000e-02, eta: 6:14:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0588\n2023-01-02 20:15:32,743 - mmcls - INFO - Epoch [49][4800/5005]\tlr: 1.000e-02, eta: 6:14:32, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.0708\n2023-01-02 20:15:41,443 - mmcls - INFO - Epoch [49][4900/5005]\tlr: 1.000e-02, eta: 6:14:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0434\n2023-01-02 20:15:50,252 - mmcls - INFO - Epoch [49][5000/5005]\tlr: 1.000e-02, eta: 6:14:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0576\n2023-01-02 20:17:12,193 - mmcls - INFO - Epoch(val) [49][196]\taccuracy_top-1: 58.5780, accuracy_top-5: 80.8760\n2023-01-02 20:17:23,264 - mmcls - INFO - Epoch [50][100/5005]\tlr: 1.000e-02, eta: 6:14:07, time: 0.110, data_time: 0.036, memory: 1125, loss: 2.0171\n2023-01-02 20:17:32,194 - mmcls - INFO - Epoch [50][200/5005]\tlr: 1.000e-02, eta: 6:13:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.9811\n2023-01-02 20:17:40,696 - mmcls - INFO - Epoch [50][300/5005]\tlr: 1.000e-02, eta: 6:13:49, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0240\n2023-01-02 20:17:49,224 - mmcls - INFO - Epoch [50][400/5005]\tlr: 1.000e-02, eta: 6:13:40, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0109\n2023-01-02 20:17:58,339 - mmcls - INFO - Epoch [50][500/5005]\tlr: 1.000e-02, eta: 6:13:31, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.0040\n2023-01-02 20:18:07,093 - mmcls - INFO - Epoch [50][600/5005]\tlr: 1.000e-02, eta: 6:13:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.0313\n2023-01-02 20:18:15,912 - mmcls - INFO - Epoch [50][700/5005]\tlr: 1.000e-02, eta: 6:13:14, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.9965\n2023-01-02 20:18:24,270 - mmcls - INFO - Epoch [50][800/5005]\tlr: 1.000e-02, eta: 6:13:04, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0051\n2023-01-02 20:18:33,098 - mmcls - INFO - Epoch [50][900/5005]\tlr: 1.000e-02, eta: 6:12:56, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0237\n2023-01-02 20:18:42,067 - mmcls - INFO - Epoch [50][1000/5005]\tlr: 1.000e-02, eta: 6:12:47, time: 0.090, data_time: 0.031, memory: 1125, loss: 2.0250\n2023-01-02 20:18:50,735 - mmcls - INFO - Epoch [50][1100/5005]\tlr: 1.000e-02, eta: 6:12:38, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0120\n2023-01-02 20:18:59,707 - mmcls - INFO - Epoch [50][1200/5005]\tlr: 1.000e-02, eta: 6:12:30, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0564\n2023-01-02 20:19:08,595 - mmcls - INFO - Epoch [50][1300/5005]\tlr: 1.000e-02, eta: 6:12:21, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0251\n2023-01-02 20:19:17,302 - mmcls - INFO - Epoch [50][1400/5005]\tlr: 1.000e-02, eta: 6:12:12, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0415\n2023-01-02 20:19:26,057 - mmcls - INFO - Epoch [50][1500/5005]\tlr: 1.000e-02, eta: 6:12:03, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0378\n2023-01-02 20:19:34,495 - mmcls - INFO - Epoch [50][1600/5005]\tlr: 1.000e-02, eta: 6:11:54, time: 0.084, data_time: 0.016, memory: 1125, loss: 2.0351\n2023-01-02 20:19:43,232 - mmcls - INFO - Epoch [50][1700/5005]\tlr: 1.000e-02, eta: 6:11:45, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.0331\n2023-01-02 20:19:51,989 - mmcls - INFO - Epoch [50][1800/5005]\tlr: 1.000e-02, eta: 6:11:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0354\n2023-01-02 20:20:01,139 - mmcls - INFO - Epoch [50][1900/5005]\tlr: 1.000e-02, eta: 6:11:28, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0500\n2023-01-02 20:20:09,670 - mmcls - INFO - Epoch [50][2000/5005]\tlr: 1.000e-02, eta: 6:11:19, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0447\n2023-01-02 20:20:18,340 - mmcls - INFO - Epoch [50][2100/5005]\tlr: 1.000e-02, eta: 6:11:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0557\n2023-01-02 20:20:26,862 - mmcls - INFO - Epoch [50][2200/5005]\tlr: 1.000e-02, eta: 6:11:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0658\n2023-01-02 20:20:35,501 - mmcls - INFO - Epoch [50][2300/5005]\tlr: 1.000e-02, eta: 6:10:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0626\n2023-01-02 20:20:44,428 - mmcls - INFO - Epoch [50][2400/5005]\tlr: 1.000e-02, eta: 6:10:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0711\n2023-01-02 20:20:53,183 - mmcls - INFO - Epoch [50][2500/5005]\tlr: 1.000e-02, eta: 6:10:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0465\n2023-01-02 20:21:01,790 - mmcls - INFO - Epoch [50][2600/5005]\tlr: 1.000e-02, eta: 6:10:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0397\n2023-01-02 20:21:10,459 - mmcls - INFO - Epoch [50][2700/5005]\tlr: 1.000e-02, eta: 6:10:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0380\n2023-01-02 20:21:19,035 - mmcls - INFO - Epoch [50][2800/5005]\tlr: 1.000e-02, eta: 6:10:07, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0460\n2023-01-02 20:21:27,835 - mmcls - INFO - Epoch [50][2900/5005]\tlr: 1.000e-02, eta: 6:09:59, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0430\n2023-01-02 20:21:36,703 - mmcls - INFO - Epoch [50][3000/5005]\tlr: 1.000e-02, eta: 6:09:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0425\n2023-01-02 20:21:45,637 - mmcls - INFO - Epoch [50][3100/5005]\tlr: 1.000e-02, eta: 6:09:41, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0573\n2023-01-02 20:21:54,331 - mmcls - INFO - Epoch [50][3200/5005]\tlr: 1.000e-02, eta: 6:09:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0674\n2023-01-02 20:22:03,101 - mmcls - INFO - Epoch [50][3300/5005]\tlr: 1.000e-02, eta: 6:09:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0250\n2023-01-02 20:22:12,063 - mmcls - INFO - Epoch [50][3400/5005]\tlr: 1.000e-02, eta: 6:09:15, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0748\n2023-01-02 20:22:20,748 - mmcls - INFO - Epoch [50][3500/5005]\tlr: 1.000e-02, eta: 6:09:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0726\n2023-01-02 20:22:29,599 - mmcls - INFO - Epoch [50][3600/5005]\tlr: 1.000e-02, eta: 6:08:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0609\n2023-01-02 20:22:38,221 - mmcls - INFO - Epoch [50][3700/5005]\tlr: 1.000e-02, eta: 6:08:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0826\n2023-01-02 20:22:47,244 - mmcls - INFO - Epoch [50][3800/5005]\tlr: 1.000e-02, eta: 6:08:40, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.0673\n2023-01-02 20:22:56,063 - mmcls - INFO - Epoch [50][3900/5005]\tlr: 1.000e-02, eta: 6:08:31, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.0670\n2023-01-02 20:23:04,491 - mmcls - INFO - Epoch [50][4000/5005]\tlr: 1.000e-02, eta: 6:08:22, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0532\n2023-01-02 20:23:13,251 - mmcls - INFO - Epoch [50][4100/5005]\tlr: 1.000e-02, eta: 6:08:13, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0460\n2023-01-02 20:23:21,858 - mmcls - INFO - Epoch [50][4200/5005]\tlr: 1.000e-02, eta: 6:08:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0324\n2023-01-02 20:23:30,673 - mmcls - INFO - Epoch [50][4300/5005]\tlr: 1.000e-02, eta: 6:07:55, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0579\n2023-01-02 20:23:39,448 - mmcls - INFO - Epoch [50][4400/5005]\tlr: 1.000e-02, eta: 6:07:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0630\n2023-01-02 20:23:48,453 - mmcls - INFO - Epoch [50][4500/5005]\tlr: 1.000e-02, eta: 6:07:38, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.0781\n2023-01-02 20:23:57,075 - mmcls - INFO - Epoch [50][4600/5005]\tlr: 1.000e-02, eta: 6:07:29, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0718\n2023-01-02 20:24:06,119 - mmcls - INFO - Epoch [50][4700/5005]\tlr: 1.000e-02, eta: 6:07:20, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0584\n2023-01-02 20:24:14,818 - mmcls - INFO - Epoch [50][4800/5005]\tlr: 1.000e-02, eta: 6:07:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0305\n2023-01-02 20:24:23,728 - mmcls - INFO - Epoch [50][4900/5005]\tlr: 1.000e-02, eta: 6:07:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0790\n2023-01-02 20:24:32,133 - mmcls - INFO - Epoch [50][5000/5005]\tlr: 1.000e-02, eta: 6:06:53, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.0692\n2023-01-02 20:24:32,492 - mmcls - INFO - Saving checkpoint at 50 epochs\n2023-01-02 20:24:54,818 - mmcls - INFO - Epoch(val) [50][196]\taccuracy_top-1: 58.2560, accuracy_top-5: 80.6980\n2023-01-02 20:25:06,238 - mmcls - INFO - Epoch [51][100/5005]\tlr: 1.000e-02, eta: 6:06:46, time: 0.114, data_time: 0.043, memory: 1125, loss: 2.0007\n2023-01-02 20:25:14,889 - mmcls - INFO - Epoch [51][200/5005]\tlr: 1.000e-02, eta: 6:06:37, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0108\n2023-01-02 20:25:23,402 - mmcls - INFO - Epoch [51][300/5005]\tlr: 1.000e-02, eta: 6:06:28, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0070\n2023-01-02 20:25:31,958 - mmcls - INFO - Epoch [51][400/5005]\tlr: 1.000e-02, eta: 6:06:19, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0263\n2023-01-02 20:25:40,645 - mmcls - INFO - Epoch [51][500/5005]\tlr: 1.000e-02, eta: 6:06:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9903\n2023-01-02 20:25:49,449 - mmcls - INFO - Epoch [51][600/5005]\tlr: 1.000e-02, eta: 6:06:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0080\n2023-01-02 20:25:57,933 - mmcls - INFO - Epoch [51][700/5005]\tlr: 1.000e-02, eta: 6:05:52, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0458\n2023-01-02 20:26:07,063 - mmcls - INFO - Epoch [51][800/5005]\tlr: 1.000e-02, eta: 6:05:44, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0176\n2023-01-02 20:26:15,820 - mmcls - INFO - Epoch [51][900/5005]\tlr: 1.000e-02, eta: 6:05:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.0134\n2023-01-02 20:26:24,813 - mmcls - INFO - Epoch [51][1000/5005]\tlr: 1.000e-02, eta: 6:05:26, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0402\n2023-01-02 20:26:33,256 - mmcls - INFO - Epoch [51][1100/5005]\tlr: 1.000e-02, eta: 6:05:17, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.9989\n2023-01-02 20:26:41,910 - mmcls - INFO - Epoch [51][1200/5005]\tlr: 1.000e-02, eta: 6:05:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0248\n2023-01-02 20:26:50,577 - mmcls - INFO - Epoch [51][1300/5005]\tlr: 1.000e-02, eta: 6:04:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0253\n2023-01-02 20:26:59,847 - mmcls - INFO - Epoch [51][1400/5005]\tlr: 1.000e-02, eta: 6:04:51, time: 0.093, data_time: 0.002, memory: 1125, loss: 2.0162\n2023-01-02 20:27:08,579 - mmcls - INFO - Epoch [51][1500/5005]\tlr: 1.000e-02, eta: 6:04:42, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0566\n2023-01-02 20:27:17,038 - mmcls - INFO - Epoch [51][1600/5005]\tlr: 1.000e-02, eta: 6:04:33, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0506\n2023-01-02 20:27:25,671 - mmcls - INFO - Epoch [51][1700/5005]\tlr: 1.000e-02, eta: 6:04:24, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0709\n2023-01-02 20:27:34,275 - mmcls - INFO - Epoch [51][1800/5005]\tlr: 1.000e-02, eta: 6:04:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0165\n2023-01-02 20:27:42,995 - mmcls - INFO - Epoch [51][1900/5005]\tlr: 1.000e-02, eta: 6:04:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0495\n2023-01-02 20:27:51,794 - mmcls - INFO - Epoch [51][2000/5005]\tlr: 1.000e-02, eta: 6:03:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0470\n2023-01-02 20:28:00,520 - mmcls - INFO - Epoch [51][2100/5005]\tlr: 1.000e-02, eta: 6:03:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0384\n2023-01-02 20:28:09,401 - mmcls - INFO - Epoch [51][2200/5005]\tlr: 1.000e-02, eta: 6:03:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0498\n2023-01-02 20:28:18,210 - mmcls - INFO - Epoch [51][2300/5005]\tlr: 1.000e-02, eta: 6:03:31, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0412\n2023-01-02 20:28:27,161 - mmcls - INFO - Epoch [51][2400/5005]\tlr: 1.000e-02, eta: 6:03:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0397\n2023-01-02 20:28:35,803 - mmcls - INFO - Epoch [51][2500/5005]\tlr: 1.000e-02, eta: 6:03:14, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0211\n2023-01-02 20:28:44,544 - mmcls - INFO - Epoch [51][2600/5005]\tlr: 1.000e-02, eta: 6:03:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0407\n2023-01-02 20:28:53,213 - mmcls - INFO - Epoch [51][2700/5005]\tlr: 1.000e-02, eta: 6:02:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0419\n2023-01-02 20:29:02,132 - mmcls - INFO - Epoch [51][2800/5005]\tlr: 1.000e-02, eta: 6:02:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0470\n2023-01-02 20:29:11,484 - mmcls - INFO - Epoch [51][2900/5005]\tlr: 1.000e-02, eta: 6:02:39, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.0455\n2023-01-02 20:29:20,842 - mmcls - INFO - Epoch [51][3000/5005]\tlr: 1.000e-02, eta: 6:02:31, time: 0.094, data_time: 0.003, memory: 1125, loss: 2.0586\n2023-01-02 20:29:29,572 - mmcls - INFO - Epoch [51][3100/5005]\tlr: 1.000e-02, eta: 6:02:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0528\n2023-01-02 20:29:38,331 - mmcls - INFO - Epoch [51][3200/5005]\tlr: 1.000e-02, eta: 6:02:13, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0762\n2023-01-02 20:29:47,280 - mmcls - INFO - Epoch [51][3300/5005]\tlr: 1.000e-02, eta: 6:02:04, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.0642\n2023-01-02 20:29:55,909 - mmcls - INFO - Epoch [51][3400/5005]\tlr: 1.000e-02, eta: 6:01:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0838\n2023-01-02 20:30:04,799 - mmcls - INFO - Epoch [51][3500/5005]\tlr: 1.000e-02, eta: 6:01:47, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.0525\n2023-01-02 20:30:13,760 - mmcls - INFO - Epoch [51][3600/5005]\tlr: 1.000e-02, eta: 6:01:38, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.0439\n2023-01-02 20:30:22,605 - mmcls - INFO - Epoch [51][3700/5005]\tlr: 1.000e-02, eta: 6:01:29, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0621\n2023-01-02 20:30:31,579 - mmcls - INFO - Epoch [51][3800/5005]\tlr: 1.000e-02, eta: 6:01:21, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0749\n2023-01-02 20:30:40,065 - mmcls - INFO - Epoch [51][3900/5005]\tlr: 1.000e-02, eta: 6:01:11, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0303\n2023-01-02 20:30:48,664 - mmcls - INFO - Epoch [51][4000/5005]\tlr: 1.000e-02, eta: 6:01:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0462\n2023-01-02 20:30:57,863 - mmcls - INFO - Epoch [51][4100/5005]\tlr: 1.000e-02, eta: 6:00:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0575\n2023-01-02 20:31:06,542 - mmcls - INFO - Epoch [51][4200/5005]\tlr: 1.000e-02, eta: 6:00:45, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0307\n2023-01-02 20:31:15,336 - mmcls - INFO - Epoch [51][4300/5005]\tlr: 1.000e-02, eta: 6:00:36, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0550\n2023-01-02 20:31:23,954 - mmcls - INFO - Epoch [51][4400/5005]\tlr: 1.000e-02, eta: 6:00:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0502\n2023-01-02 20:31:32,863 - mmcls - INFO - Epoch [51][4500/5005]\tlr: 1.000e-02, eta: 6:00:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0715\n2023-01-02 20:31:41,829 - mmcls - INFO - Epoch [51][4600/5005]\tlr: 1.000e-02, eta: 6:00:10, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0381\n2023-01-02 20:31:50,273 - mmcls - INFO - Epoch [51][4700/5005]\tlr: 1.000e-02, eta: 6:00:01, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0549\n2023-01-02 20:31:59,049 - mmcls - INFO - Epoch [51][4800/5005]\tlr: 1.000e-02, eta: 5:59:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0674\n2023-01-02 20:32:07,780 - mmcls - INFO - Epoch [51][4900/5005]\tlr: 1.000e-02, eta: 5:59:43, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0712\n2023-01-02 20:32:16,409 - mmcls - INFO - Epoch [51][5000/5005]\tlr: 1.000e-02, eta: 5:59:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0679\n2023-01-02 20:32:38,922 - mmcls - INFO - Epoch(val) [51][196]\taccuracy_top-1: 58.9520, accuracy_top-5: 81.1940\n2023-01-02 20:32:50,241 - mmcls - INFO - Epoch [52][100/5005]\tlr: 1.000e-02, eta: 5:59:27, time: 0.113, data_time: 0.044, memory: 1125, loss: 1.9965\n2023-01-02 20:32:59,307 - mmcls - INFO - Epoch [52][200/5005]\tlr: 1.000e-02, eta: 5:59:18, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.9934\n2023-01-02 20:33:07,958 - mmcls - INFO - Epoch [52][300/5005]\tlr: 1.000e-02, eta: 5:59:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0050\n2023-01-02 20:33:16,895 - mmcls - INFO - Epoch [52][400/5005]\tlr: 1.000e-02, eta: 5:59:01, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.9964\n2023-01-02 20:33:25,659 - mmcls - INFO - Epoch [52][500/5005]\tlr: 1.000e-02, eta: 5:58:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.9885\n2023-01-02 20:33:34,508 - mmcls - INFO - Epoch [52][600/5005]\tlr: 1.000e-02, eta: 5:58:43, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0242\n2023-01-02 20:33:43,246 - mmcls - INFO - Epoch [52][700/5005]\tlr: 1.000e-02, eta: 5:58:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0388\n2023-01-02 20:33:51,870 - mmcls - INFO - Epoch [52][800/5005]\tlr: 1.000e-02, eta: 5:58:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0205\n2023-01-02 20:34:00,428 - mmcls - INFO - Epoch [52][900/5005]\tlr: 1.000e-02, eta: 5:58:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0318\n2023-01-02 20:34:09,319 - mmcls - INFO - Epoch [52][1000/5005]\tlr: 1.000e-02, eta: 5:58:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0435\n2023-01-02 20:34:17,734 - mmcls - INFO - Epoch [52][1100/5005]\tlr: 1.000e-02, eta: 5:57:59, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.0015\n2023-01-02 20:34:26,778 - mmcls - INFO - Epoch [52][1200/5005]\tlr: 1.000e-02, eta: 5:57:50, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0271\n2023-01-02 20:34:35,574 - mmcls - INFO - Epoch [52][1300/5005]\tlr: 1.000e-02, eta: 5:57:41, time: 0.088, data_time: 0.044, memory: 1125, loss: 2.0330\n2023-01-02 20:34:44,130 - mmcls - INFO - Epoch [52][1400/5005]\tlr: 1.000e-02, eta: 5:57:32, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.0266\n2023-01-02 20:34:52,936 - mmcls - INFO - Epoch [52][1500/5005]\tlr: 1.000e-02, eta: 5:57:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0197\n2023-01-02 20:35:01,715 - mmcls - INFO - Epoch [52][1600/5005]\tlr: 1.000e-02, eta: 5:57:15, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0489\n2023-01-02 20:35:10,844 - mmcls - INFO - Epoch [52][1700/5005]\tlr: 1.000e-02, eta: 5:57:06, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0452\n2023-01-02 20:35:19,852 - mmcls - INFO - Epoch [52][1800/5005]\tlr: 1.000e-02, eta: 5:56:58, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0359\n2023-01-02 20:35:28,496 - mmcls - INFO - Epoch [52][1900/5005]\tlr: 1.000e-02, eta: 5:56:49, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0417\n2023-01-02 20:35:37,049 - mmcls - INFO - Epoch [52][2000/5005]\tlr: 1.000e-02, eta: 5:56:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0220\n2023-01-02 20:35:46,194 - mmcls - INFO - Epoch [52][2100/5005]\tlr: 1.000e-02, eta: 5:56:31, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0536\n2023-01-02 20:35:54,916 - mmcls - INFO - Epoch [52][2200/5005]\tlr: 1.000e-02, eta: 5:56:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0585\n2023-01-02 20:36:04,022 - mmcls - INFO - Epoch [52][2300/5005]\tlr: 1.000e-02, eta: 5:56:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0500\n2023-01-02 20:36:12,643 - mmcls - INFO - Epoch [52][2400/5005]\tlr: 1.000e-02, eta: 5:56:05, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0419\n2023-01-02 20:36:21,700 - mmcls - INFO - Epoch [52][2500/5005]\tlr: 1.000e-02, eta: 5:55:56, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.0607\n2023-01-02 20:36:30,365 - mmcls - INFO - Epoch [52][2600/5005]\tlr: 1.000e-02, eta: 5:55:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0692\n2023-01-02 20:36:39,021 - mmcls - INFO - Epoch [52][2700/5005]\tlr: 1.000e-02, eta: 5:55:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0521\n2023-01-02 20:36:47,851 - mmcls - INFO - Epoch [52][2800/5005]\tlr: 1.000e-02, eta: 5:55:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0588\n2023-01-02 20:36:56,683 - mmcls - INFO - Epoch [52][2900/5005]\tlr: 1.000e-02, eta: 5:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0579\n2023-01-02 20:37:05,705 - mmcls - INFO - Epoch [52][3000/5005]\tlr: 1.000e-02, eta: 5:55:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0449\n2023-01-02 20:37:14,308 - mmcls - INFO - Epoch [52][3100/5005]\tlr: 1.000e-02, eta: 5:55:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0342\n2023-01-02 20:37:22,770 - mmcls - INFO - Epoch [52][3200/5005]\tlr: 1.000e-02, eta: 5:54:54, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0430\n2023-01-02 20:37:31,416 - mmcls - INFO - Epoch [52][3300/5005]\tlr: 1.000e-02, eta: 5:54:45, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0509\n2023-01-02 20:37:40,190 - mmcls - INFO - Epoch [52][3400/5005]\tlr: 1.000e-02, eta: 5:54:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0421\n2023-01-02 20:37:48,738 - mmcls - INFO - Epoch [52][3500/5005]\tlr: 1.000e-02, eta: 5:54:27, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0401\n2023-01-02 20:37:57,258 - mmcls - INFO - Epoch [52][3600/5005]\tlr: 1.000e-02, eta: 5:54:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0629\n2023-01-02 20:38:06,022 - mmcls - INFO - Epoch [52][3700/5005]\tlr: 1.000e-02, eta: 5:54:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0671\n2023-01-02 20:38:14,772 - mmcls - INFO - Epoch [52][3800/5005]\tlr: 1.000e-02, eta: 5:54:01, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0742\n2023-01-02 20:38:23,994 - mmcls - INFO - Epoch [52][3900/5005]\tlr: 1.000e-02, eta: 5:53:52, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0736\n2023-01-02 20:38:32,805 - mmcls - INFO - Epoch [52][4000/5005]\tlr: 1.000e-02, eta: 5:53:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0439\n2023-01-02 20:38:41,335 - mmcls - INFO - Epoch [52][4100/5005]\tlr: 1.000e-02, eta: 5:53:34, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.0494\n2023-01-02 20:38:50,237 - mmcls - INFO - Epoch [52][4200/5005]\tlr: 1.000e-02, eta: 5:53:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0483\n2023-01-02 20:38:59,389 - mmcls - INFO - Epoch [52][4300/5005]\tlr: 1.000e-02, eta: 5:53:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0591\n2023-01-02 20:39:08,160 - mmcls - INFO - Epoch [52][4400/5005]\tlr: 1.000e-02, eta: 5:53:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0596\n2023-01-02 20:39:16,737 - mmcls - INFO - Epoch [52][4500/5005]\tlr: 1.000e-02, eta: 5:52:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0435\n2023-01-02 20:39:25,650 - mmcls - INFO - Epoch [52][4600/5005]\tlr: 1.000e-02, eta: 5:52:51, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0624\n2023-01-02 20:39:34,258 - mmcls - INFO - Epoch [52][4700/5005]\tlr: 1.000e-02, eta: 5:52:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0650\n2023-01-02 20:39:42,910 - mmcls - INFO - Epoch [52][4800/5005]\tlr: 1.000e-02, eta: 5:52:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0582\n2023-01-02 20:39:51,767 - mmcls - INFO - Epoch [52][4900/5005]\tlr: 1.000e-02, eta: 5:52:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0704\n2023-01-02 20:40:00,322 - mmcls - INFO - Epoch [52][5000/5005]\tlr: 1.000e-02, eta: 5:52:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0712\n2023-01-02 20:40:23,120 - mmcls - INFO - Epoch(val) [52][196]\taccuracy_top-1: 59.2600, accuracy_top-5: 81.5180\n2023-01-02 20:40:34,252 - mmcls - INFO - Epoch [53][100/5005]\tlr: 1.000e-02, eta: 5:52:07, time: 0.111, data_time: 0.070, memory: 1125, loss: 1.9983\n2023-01-02 20:40:42,599 - mmcls - INFO - Epoch [53][200/5005]\tlr: 1.000e-02, eta: 5:51:58, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0188\n2023-01-02 20:40:51,839 - mmcls - INFO - Epoch [53][300/5005]\tlr: 1.000e-02, eta: 5:51:50, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.0199\n2023-01-02 20:41:00,441 - mmcls - INFO - Epoch [53][400/5005]\tlr: 1.000e-02, eta: 5:51:41, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0195\n2023-01-02 20:41:09,000 - mmcls - INFO - Epoch [53][500/5005]\tlr: 1.000e-02, eta: 5:51:32, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0084\n2023-01-02 20:41:17,956 - mmcls - INFO - Epoch [53][600/5005]\tlr: 1.000e-02, eta: 5:51:23, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0291\n2023-01-02 20:41:27,030 - mmcls - INFO - Epoch [53][700/5005]\tlr: 1.000e-02, eta: 5:51:15, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0123\n2023-01-02 20:41:35,749 - mmcls - INFO - Epoch [53][800/5005]\tlr: 1.000e-02, eta: 5:51:06, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0324\n2023-01-02 20:41:44,669 - mmcls - INFO - Epoch [53][900/5005]\tlr: 1.000e-02, eta: 5:50:57, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0214\n2023-01-02 20:41:53,355 - mmcls - INFO - Epoch [53][1000/5005]\tlr: 1.000e-02, eta: 5:50:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0274\n2023-01-02 20:42:02,060 - mmcls - INFO - Epoch [53][1100/5005]\tlr: 1.000e-02, eta: 5:50:39, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0192\n2023-01-02 20:42:10,590 - mmcls - INFO - Epoch [53][1200/5005]\tlr: 1.000e-02, eta: 5:50:30, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0304\n2023-01-02 20:42:19,750 - mmcls - INFO - Epoch [53][1300/5005]\tlr: 1.000e-02, eta: 5:50:22, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0245\n2023-01-02 20:42:28,443 - mmcls - INFO - Epoch [53][1400/5005]\tlr: 1.000e-02, eta: 5:50:13, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0342\n2023-01-02 20:42:37,013 - mmcls - INFO - Epoch [53][1500/5005]\tlr: 1.000e-02, eta: 5:50:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0098\n2023-01-02 20:42:45,938 - mmcls - INFO - Epoch [53][1600/5005]\tlr: 1.000e-02, eta: 5:49:55, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0235\n2023-01-02 20:42:54,554 - mmcls - INFO - Epoch [53][1700/5005]\tlr: 1.000e-02, eta: 5:49:46, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0677\n2023-01-02 20:43:03,314 - mmcls - INFO - Epoch [53][1800/5005]\tlr: 1.000e-02, eta: 5:49:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0160\n2023-01-02 20:43:12,001 - mmcls - INFO - Epoch [53][1900/5005]\tlr: 1.000e-02, eta: 5:49:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0170\n2023-01-02 20:43:20,585 - mmcls - INFO - Epoch [53][2000/5005]\tlr: 1.000e-02, eta: 5:49:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0225\n2023-01-02 20:43:29,593 - mmcls - INFO - Epoch [53][2100/5005]\tlr: 1.000e-02, eta: 5:49:11, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0327\n2023-01-02 20:43:38,202 - mmcls - INFO - Epoch [53][2200/5005]\tlr: 1.000e-02, eta: 5:49:02, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0244\n2023-01-02 20:43:46,925 - mmcls - INFO - Epoch [53][2300/5005]\tlr: 1.000e-02, eta: 5:48:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0239\n2023-01-02 20:43:55,653 - mmcls - INFO - Epoch [53][2400/5005]\tlr: 1.000e-02, eta: 5:48:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0495\n2023-01-02 20:44:04,365 - mmcls - INFO - Epoch [53][2500/5005]\tlr: 1.000e-02, eta: 5:48:35, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0622\n2023-01-02 20:44:13,117 - mmcls - INFO - Epoch [53][2600/5005]\tlr: 1.000e-02, eta: 5:48:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0505\n2023-01-02 20:44:22,041 - mmcls - INFO - Epoch [53][2700/5005]\tlr: 1.000e-02, eta: 5:48:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0325\n2023-01-02 20:44:30,861 - mmcls - INFO - Epoch [53][2800/5005]\tlr: 1.000e-02, eta: 5:48:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0454\n2023-01-02 20:44:39,811 - mmcls - INFO - Epoch [53][2900/5005]\tlr: 1.000e-02, eta: 5:48:00, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0747\n2023-01-02 20:44:48,739 - mmcls - INFO - Epoch [53][3000/5005]\tlr: 1.000e-02, eta: 5:47:52, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0514\n2023-01-02 20:44:57,712 - mmcls - INFO - Epoch [53][3100/5005]\tlr: 1.000e-02, eta: 5:47:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0557\n2023-01-02 20:45:06,383 - mmcls - INFO - Epoch [53][3200/5005]\tlr: 1.000e-02, eta: 5:47:34, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0512\n2023-01-02 20:45:15,168 - mmcls - INFO - Epoch [53][3300/5005]\tlr: 1.000e-02, eta: 5:47:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0368\n2023-01-02 20:45:23,656 - mmcls - INFO - Epoch [53][3400/5005]\tlr: 1.000e-02, eta: 5:47:16, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0504\n2023-01-02 20:45:32,348 - mmcls - INFO - Epoch [53][3500/5005]\tlr: 1.000e-02, eta: 5:47:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0129\n2023-01-02 20:45:41,116 - mmcls - INFO - Epoch [53][3600/5005]\tlr: 1.000e-02, eta: 5:46:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0591\n2023-01-02 20:45:49,877 - mmcls - INFO - Epoch [53][3700/5005]\tlr: 1.000e-02, eta: 5:46:50, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0446\n2023-01-02 20:45:58,570 - mmcls - INFO - Epoch [53][3800/5005]\tlr: 1.000e-02, eta: 5:46:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0608\n2023-01-02 20:46:07,207 - mmcls - INFO - Epoch [53][3900/5005]\tlr: 1.000e-02, eta: 5:46:32, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.0431\n2023-01-02 20:46:15,761 - mmcls - INFO - Epoch [53][4000/5005]\tlr: 1.000e-02, eta: 5:46:23, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0560\n2023-01-02 20:46:24,445 - mmcls - INFO - Epoch [53][4100/5005]\tlr: 1.000e-02, eta: 5:46:14, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0544\n2023-01-02 20:46:32,954 - mmcls - INFO - Epoch [53][4200/5005]\tlr: 1.000e-02, eta: 5:46:05, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0426\n2023-01-02 20:46:41,878 - mmcls - INFO - Epoch [53][4300/5005]\tlr: 1.000e-02, eta: 5:45:56, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.0635\n2023-01-02 20:46:50,725 - mmcls - INFO - Epoch [53][4400/5005]\tlr: 1.000e-02, eta: 5:45:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0377\n2023-01-02 20:46:59,314 - mmcls - INFO - Epoch [53][4500/5005]\tlr: 1.000e-02, eta: 5:45:38, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0614\n2023-01-02 20:47:08,161 - mmcls - INFO - Epoch [53][4600/5005]\tlr: 1.000e-02, eta: 5:45:30, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0600\n2023-01-02 20:47:17,309 - mmcls - INFO - Epoch [53][4700/5005]\tlr: 1.000e-02, eta: 5:45:21, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0831\n2023-01-02 20:47:26,057 - mmcls - INFO - Epoch [53][4800/5005]\tlr: 1.000e-02, eta: 5:45:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0694\n2023-01-02 20:47:34,738 - mmcls - INFO - Epoch [53][4900/5005]\tlr: 1.000e-02, eta: 5:45:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0316\n2023-01-02 20:47:43,444 - mmcls - INFO - Epoch [53][5000/5005]\tlr: 1.000e-02, eta: 5:44:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0641\n2023-01-02 20:48:06,005 - mmcls - INFO - Epoch(val) [53][196]\taccuracy_top-1: 59.2100, accuracy_top-5: 81.3380\n2023-01-02 20:48:17,099 - mmcls - INFO - Epoch [54][100/5005]\tlr: 1.000e-02, eta: 5:44:47, time: 0.111, data_time: 0.053, memory: 1125, loss: 2.0110\n2023-01-02 20:48:25,741 - mmcls - INFO - Epoch [54][200/5005]\tlr: 1.000e-02, eta: 5:44:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9945\n2023-01-02 20:48:34,141 - mmcls - INFO - Epoch [54][300/5005]\tlr: 1.000e-02, eta: 5:44:29, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0133\n2023-01-02 20:48:42,850 - mmcls - INFO - Epoch [54][400/5005]\tlr: 1.000e-02, eta: 5:44:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0300\n2023-01-02 20:48:51,782 - mmcls - INFO - Epoch [54][500/5005]\tlr: 1.000e-02, eta: 5:44:11, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0187\n2023-01-02 20:49:00,391 - mmcls - INFO - Epoch [54][600/5005]\tlr: 1.000e-02, eta: 5:44:02, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.0015\n2023-01-02 20:49:09,146 - mmcls - INFO - Epoch [54][700/5005]\tlr: 1.000e-02, eta: 5:43:54, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.0122\n2023-01-02 20:49:17,592 - mmcls - INFO - Epoch [54][800/5005]\tlr: 1.000e-02, eta: 5:43:44, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0248\n2023-01-02 20:49:26,261 - mmcls - INFO - Epoch [54][900/5005]\tlr: 1.000e-02, eta: 5:43:36, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0306\n2023-01-02 20:49:35,051 - mmcls - INFO - Epoch [54][1000/5005]\tlr: 1.000e-02, eta: 5:43:27, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0347\n2023-01-02 20:49:43,662 - mmcls - INFO - Epoch [54][1100/5005]\tlr: 1.000e-02, eta: 5:43:18, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0469\n2023-01-02 20:49:52,259 - mmcls - INFO - Epoch [54][1200/5005]\tlr: 1.000e-02, eta: 5:43:09, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0182\n2023-01-02 20:50:01,000 - mmcls - INFO - Epoch [54][1300/5005]\tlr: 1.000e-02, eta: 5:43:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0477\n2023-01-02 20:50:09,839 - mmcls - INFO - Epoch [54][1400/5005]\tlr: 1.000e-02, eta: 5:42:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0396\n2023-01-02 20:50:18,529 - mmcls - INFO - Epoch [54][1500/5005]\tlr: 1.000e-02, eta: 5:42:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0193\n2023-01-02 20:50:27,727 - mmcls - INFO - Epoch [54][1600/5005]\tlr: 1.000e-02, eta: 5:42:34, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0190\n2023-01-02 20:50:36,480 - mmcls - INFO - Epoch [54][1700/5005]\tlr: 1.000e-02, eta: 5:42:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0183\n2023-01-02 20:50:45,241 - mmcls - INFO - Epoch [54][1800/5005]\tlr: 1.000e-02, eta: 5:42:16, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0212\n2023-01-02 20:50:54,562 - mmcls - INFO - Epoch [54][1900/5005]\tlr: 1.000e-02, eta: 5:42:08, time: 0.093, data_time: 0.029, memory: 1125, loss: 2.0389\n2023-01-02 20:51:02,995 - mmcls - INFO - Epoch [54][2000/5005]\tlr: 1.000e-02, eta: 5:41:59, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0238\n2023-01-02 20:51:11,972 - mmcls - INFO - Epoch [54][2100/5005]\tlr: 1.000e-02, eta: 5:41:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0153\n2023-01-02 20:51:20,858 - mmcls - INFO - Epoch [54][2200/5005]\tlr: 1.000e-02, eta: 5:41:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0478\n2023-01-02 20:51:29,727 - mmcls - INFO - Epoch [54][2300/5005]\tlr: 1.000e-02, eta: 5:41:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0310\n2023-01-02 20:51:38,364 - mmcls - INFO - Epoch [54][2400/5005]\tlr: 1.000e-02, eta: 5:41:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0371\n2023-01-02 20:51:47,317 - mmcls - INFO - Epoch [54][2500/5005]\tlr: 1.000e-02, eta: 5:41:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0524\n2023-01-02 20:51:56,118 - mmcls - INFO - Epoch [54][2600/5005]\tlr: 1.000e-02, eta: 5:41:06, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0188\n2023-01-02 20:52:04,667 - mmcls - INFO - Epoch [54][2700/5005]\tlr: 1.000e-02, eta: 5:40:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0307\n2023-01-02 20:52:13,174 - mmcls - INFO - Epoch [54][2800/5005]\tlr: 1.000e-02, eta: 5:40:48, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0318\n2023-01-02 20:52:21,723 - mmcls - INFO - Epoch [54][2900/5005]\tlr: 1.000e-02, eta: 5:40:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0272\n2023-01-02 20:52:30,405 - mmcls - INFO - Epoch [54][3000/5005]\tlr: 1.000e-02, eta: 5:40:30, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0463\n2023-01-02 20:52:39,231 - mmcls - INFO - Epoch [54][3100/5005]\tlr: 1.000e-02, eta: 5:40:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0425\n2023-01-02 20:52:48,203 - mmcls - INFO - Epoch [54][3200/5005]\tlr: 1.000e-02, eta: 5:40:13, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0262\n2023-01-02 20:52:57,066 - mmcls - INFO - Epoch [54][3300/5005]\tlr: 1.000e-02, eta: 5:40:04, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0550\n2023-01-02 20:53:05,734 - mmcls - INFO - Epoch [54][3400/5005]\tlr: 1.000e-02, eta: 5:39:55, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0690\n2023-01-02 20:53:14,338 - mmcls - INFO - Epoch [54][3500/5005]\tlr: 1.000e-02, eta: 5:39:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0526\n2023-01-02 20:53:22,994 - mmcls - INFO - Epoch [54][3600/5005]\tlr: 1.000e-02, eta: 5:39:37, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0766\n2023-01-02 20:53:31,872 - mmcls - INFO - Epoch [54][3700/5005]\tlr: 1.000e-02, eta: 5:39:29, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0575\n2023-01-02 20:53:40,806 - mmcls - INFO - Epoch [54][3800/5005]\tlr: 1.000e-02, eta: 5:39:20, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0680\n2023-01-02 20:53:49,563 - mmcls - INFO - Epoch [54][3900/5005]\tlr: 1.000e-02, eta: 5:39:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0677\n2023-01-02 20:53:58,250 - mmcls - INFO - Epoch [54][4000/5005]\tlr: 1.000e-02, eta: 5:39:02, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0440\n2023-01-02 20:54:06,770 - mmcls - INFO - Epoch [54][4100/5005]\tlr: 1.000e-02, eta: 5:38:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0522\n2023-01-02 20:54:15,425 - mmcls - INFO - Epoch [54][4200/5005]\tlr: 1.000e-02, eta: 5:38:44, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0466\n2023-01-02 20:54:24,247 - mmcls - INFO - Epoch [54][4300/5005]\tlr: 1.000e-02, eta: 5:38:35, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0718\n2023-01-02 20:54:32,999 - mmcls - INFO - Epoch [54][4400/5005]\tlr: 1.000e-02, eta: 5:38:27, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0497\n2023-01-02 20:54:41,536 - mmcls - INFO - Epoch [54][4500/5005]\tlr: 1.000e-02, eta: 5:38:18, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0492\n2023-01-02 20:54:50,312 - mmcls - INFO - Epoch [54][4600/5005]\tlr: 1.000e-02, eta: 5:38:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0327\n2023-01-02 20:54:59,083 - mmcls - INFO - Epoch [54][4700/5005]\tlr: 1.000e-02, eta: 5:38:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0540\n2023-01-02 20:55:07,854 - mmcls - INFO - Epoch [54][4800/5005]\tlr: 1.000e-02, eta: 5:37:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0719\n2023-01-02 20:55:16,984 - mmcls - INFO - Epoch [54][4900/5005]\tlr: 1.000e-02, eta: 5:37:43, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.0558\n2023-01-02 20:55:25,734 - mmcls - INFO - Epoch [54][5000/5005]\tlr: 1.000e-02, eta: 5:37:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0452\n2023-01-02 20:55:48,293 - mmcls - INFO - Epoch(val) [54][196]\taccuracy_top-1: 59.4420, accuracy_top-5: 81.6360\n2023-01-02 20:55:59,264 - mmcls - INFO - Epoch [55][100/5005]\tlr: 1.000e-02, eta: 5:37:26, time: 0.109, data_time: 0.039, memory: 1125, loss: 2.0058\n2023-01-02 20:56:08,124 - mmcls - INFO - Epoch [55][200/5005]\tlr: 1.000e-02, eta: 5:37:17, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0088\n2023-01-02 20:56:16,664 - mmcls - INFO - Epoch [55][300/5005]\tlr: 1.000e-02, eta: 5:37:08, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0151\n2023-01-02 20:56:25,650 - mmcls - INFO - Epoch [55][400/5005]\tlr: 1.000e-02, eta: 5:37:00, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.0168\n2023-01-02 20:56:34,243 - mmcls - INFO - Epoch [55][500/5005]\tlr: 1.000e-02, eta: 5:36:51, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0146\n2023-01-02 20:56:42,921 - mmcls - INFO - Epoch [55][600/5005]\tlr: 1.000e-02, eta: 5:36:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0195\n2023-01-02 20:56:51,805 - mmcls - INFO - Epoch [55][700/5005]\tlr: 1.000e-02, eta: 5:36:33, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0142\n2023-01-02 20:57:00,739 - mmcls - INFO - Epoch [55][800/5005]\tlr: 1.000e-02, eta: 5:36:24, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0322\n2023-01-02 20:57:09,261 - mmcls - INFO - Epoch [55][900/5005]\tlr: 1.000e-02, eta: 5:36:15, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0202\n2023-01-02 20:57:18,225 - mmcls - INFO - Epoch [55][1000/5005]\tlr: 1.000e-02, eta: 5:36:07, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.0298\n2023-01-02 20:57:27,029 - mmcls - INFO - Epoch [55][1100/5005]\tlr: 1.000e-02, eta: 5:35:58, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0107\n2023-01-02 20:57:35,831 - mmcls - INFO - Epoch [55][1200/5005]\tlr: 1.000e-02, eta: 5:35:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0335\n2023-01-02 20:57:44,483 - mmcls - INFO - Epoch [55][1300/5005]\tlr: 1.000e-02, eta: 5:35:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0334\n2023-01-02 20:57:53,377 - mmcls - INFO - Epoch [55][1400/5005]\tlr: 1.000e-02, eta: 5:35:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0305\n2023-01-02 20:58:02,127 - mmcls - INFO - Epoch [55][1500/5005]\tlr: 1.000e-02, eta: 5:35:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0509\n2023-01-02 20:58:10,735 - mmcls - INFO - Epoch [55][1600/5005]\tlr: 1.000e-02, eta: 5:35:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0176\n2023-01-02 20:58:19,640 - mmcls - INFO - Epoch [55][1700/5005]\tlr: 1.000e-02, eta: 5:35:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0239\n2023-01-02 20:58:28,324 - mmcls - INFO - Epoch [55][1800/5005]\tlr: 1.000e-02, eta: 5:34:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0051\n2023-01-02 20:58:37,062 - mmcls - INFO - Epoch [55][1900/5005]\tlr: 1.000e-02, eta: 5:34:47, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0043\n2023-01-02 20:58:45,824 - mmcls - INFO - Epoch [55][2000/5005]\tlr: 1.000e-02, eta: 5:34:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0374\n2023-01-02 20:58:54,872 - mmcls - INFO - Epoch [55][2100/5005]\tlr: 1.000e-02, eta: 5:34:30, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0074\n2023-01-02 20:59:03,802 - mmcls - INFO - Epoch [55][2200/5005]\tlr: 1.000e-02, eta: 5:34:21, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.0409\n2023-01-02 20:59:12,683 - mmcls - INFO - Epoch [55][2300/5005]\tlr: 1.000e-02, eta: 5:34:12, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0517\n2023-01-02 20:59:21,148 - mmcls - INFO - Epoch [55][2400/5005]\tlr: 1.000e-02, eta: 5:34:03, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0020\n2023-01-02 20:59:29,873 - mmcls - INFO - Epoch [55][2500/5005]\tlr: 1.000e-02, eta: 5:33:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0411\n2023-01-02 20:59:38,884 - mmcls - INFO - Epoch [55][2600/5005]\tlr: 1.000e-02, eta: 5:33:46, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0302\n2023-01-02 20:59:47,530 - mmcls - INFO - Epoch [55][2700/5005]\tlr: 1.000e-02, eta: 5:33:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0420\n2023-01-02 20:59:56,574 - mmcls - INFO - Epoch [55][2800/5005]\tlr: 1.000e-02, eta: 5:33:28, time: 0.090, data_time: 0.053, memory: 1125, loss: 2.0400\n2023-01-02 21:00:05,264 - mmcls - INFO - Epoch [55][2900/5005]\tlr: 1.000e-02, eta: 5:33:19, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.0586\n2023-01-02 21:00:14,277 - mmcls - INFO - Epoch [55][3000/5005]\tlr: 1.000e-02, eta: 5:33:11, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0351\n2023-01-02 21:00:23,060 - mmcls - INFO - Epoch [55][3100/5005]\tlr: 1.000e-02, eta: 5:33:02, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0709\n2023-01-02 21:00:31,809 - mmcls - INFO - Epoch [55][3200/5005]\tlr: 1.000e-02, eta: 5:32:53, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0467\n2023-01-02 21:00:40,360 - mmcls - INFO - Epoch [55][3300/5005]\tlr: 1.000e-02, eta: 5:32:44, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.0310\n2023-01-02 21:00:48,871 - mmcls - INFO - Epoch [55][3400/5005]\tlr: 1.000e-02, eta: 5:32:35, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.0634\n2023-01-02 21:00:57,749 - mmcls - INFO - Epoch [55][3500/5005]\tlr: 1.000e-02, eta: 5:32:26, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.0536\n2023-01-02 21:01:06,573 - mmcls - INFO - Epoch [55][3600/5005]\tlr: 1.000e-02, eta: 5:32:18, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0407\n2023-01-02 21:01:15,550 - mmcls - INFO - Epoch [55][3700/5005]\tlr: 1.000e-02, eta: 5:32:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0469\n2023-01-02 21:01:24,065 - mmcls - INFO - Epoch [55][3800/5005]\tlr: 1.000e-02, eta: 5:32:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0473\n2023-01-02 21:01:32,848 - mmcls - INFO - Epoch [55][3900/5005]\tlr: 1.000e-02, eta: 5:31:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0248\n2023-01-02 21:01:41,422 - mmcls - INFO - Epoch [55][4000/5005]\tlr: 1.000e-02, eta: 5:31:42, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0545\n2023-01-02 21:01:50,273 - mmcls - INFO - Epoch [55][4100/5005]\tlr: 1.000e-02, eta: 5:31:33, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.0381\n2023-01-02 21:01:58,718 - mmcls - INFO - Epoch [55][4200/5005]\tlr: 1.000e-02, eta: 5:31:24, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0555\n2023-01-02 21:02:07,563 - mmcls - INFO - Epoch [55][4300/5005]\tlr: 1.000e-02, eta: 5:31:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0396\n2023-01-02 21:02:16,322 - mmcls - INFO - Epoch [55][4400/5005]\tlr: 1.000e-02, eta: 5:31:07, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0727\n2023-01-02 21:02:25,017 - mmcls - INFO - Epoch [55][4500/5005]\tlr: 1.000e-02, eta: 5:30:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0430\n2023-01-02 21:02:33,493 - mmcls - INFO - Epoch [55][4600/5005]\tlr: 1.000e-02, eta: 5:30:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0565\n2023-01-02 21:02:42,051 - mmcls - INFO - Epoch [55][4700/5005]\tlr: 1.000e-02, eta: 5:30:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0481\n2023-01-02 21:02:50,662 - mmcls - INFO - Epoch [55][4800/5005]\tlr: 1.000e-02, eta: 5:30:31, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0715\n2023-01-02 21:02:59,444 - mmcls - INFO - Epoch [55][4900/5005]\tlr: 1.000e-02, eta: 5:30:22, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0709\n2023-01-02 21:03:08,600 - mmcls - INFO - Epoch [55][5000/5005]\tlr: 1.000e-02, eta: 5:30:13, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.0496\n2023-01-02 21:03:31,362 - mmcls - INFO - Epoch(val) [55][196]\taccuracy_top-1: 58.8380, accuracy_top-5: 81.3180\n2023-01-02 21:03:42,336 - mmcls - INFO - Epoch [56][100/5005]\tlr: 1.000e-02, eta: 5:30:06, time: 0.109, data_time: 0.038, memory: 1125, loss: 1.9866\n2023-01-02 21:03:51,240 - mmcls - INFO - Epoch [56][200/5005]\tlr: 1.000e-02, eta: 5:29:57, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0061\n2023-01-02 21:03:59,933 - mmcls - INFO - Epoch [56][300/5005]\tlr: 1.000e-02, eta: 5:29:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0117\n2023-01-02 21:04:08,662 - mmcls - INFO - Epoch [56][400/5005]\tlr: 1.000e-02, eta: 5:29:39, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.9922\n2023-01-02 21:04:17,537 - mmcls - INFO - Epoch [56][500/5005]\tlr: 1.000e-02, eta: 5:29:30, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0020\n2023-01-02 21:04:26,178 - mmcls - INFO - Epoch [56][600/5005]\tlr: 1.000e-02, eta: 5:29:21, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.9990\n2023-01-02 21:04:35,099 - mmcls - INFO - Epoch [56][700/5005]\tlr: 1.000e-02, eta: 5:29:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0085\n2023-01-02 21:04:43,829 - mmcls - INFO - Epoch [56][800/5005]\tlr: 1.000e-02, eta: 5:29:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0311\n2023-01-02 21:04:52,661 - mmcls - INFO - Epoch [56][900/5005]\tlr: 1.000e-02, eta: 5:28:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0102\n2023-01-02 21:05:01,331 - mmcls - INFO - Epoch [56][1000/5005]\tlr: 1.000e-02, eta: 5:28:46, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0206\n2023-01-02 21:05:10,086 - mmcls - INFO - Epoch [56][1100/5005]\tlr: 1.000e-02, eta: 5:28:37, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0134\n2023-01-02 21:05:18,778 - mmcls - INFO - Epoch [56][1200/5005]\tlr: 1.000e-02, eta: 5:28:29, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0370\n2023-01-02 21:05:27,656 - mmcls - INFO - Epoch [56][1300/5005]\tlr: 1.000e-02, eta: 5:28:20, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0199\n2023-01-02 21:05:36,464 - mmcls - INFO - Epoch [56][1400/5005]\tlr: 1.000e-02, eta: 5:28:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0205\n2023-01-02 21:05:44,921 - mmcls - INFO - Epoch [56][1500/5005]\tlr: 1.000e-02, eta: 5:28:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0243\n2023-01-02 21:05:54,024 - mmcls - INFO - Epoch [56][1600/5005]\tlr: 1.000e-02, eta: 5:27:53, time: 0.091, data_time: 0.017, memory: 1125, loss: 2.0266\n2023-01-02 21:06:02,617 - mmcls - INFO - Epoch [56][1700/5005]\tlr: 1.000e-02, eta: 5:27:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.0167\n2023-01-02 21:06:11,152 - mmcls - INFO - Epoch [56][1800/5005]\tlr: 1.000e-02, eta: 5:27:35, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0329\n2023-01-02 21:06:20,001 - mmcls - INFO - Epoch [56][1900/5005]\tlr: 1.000e-02, eta: 5:27:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0269\n2023-01-02 21:06:28,548 - mmcls - INFO - Epoch [56][2000/5005]\tlr: 1.000e-02, eta: 5:27:18, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.0355\n2023-01-02 21:06:37,408 - mmcls - INFO - Epoch [56][2100/5005]\tlr: 1.000e-02, eta: 5:27:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0250\n2023-01-02 21:06:46,130 - mmcls - INFO - Epoch [56][2200/5005]\tlr: 1.000e-02, eta: 5:27:00, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0197\n2023-01-02 21:06:54,920 - mmcls - INFO - Epoch [56][2300/5005]\tlr: 1.000e-02, eta: 5:26:51, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0107\n2023-01-02 21:07:03,690 - mmcls - INFO - Epoch [56][2400/5005]\tlr: 1.000e-02, eta: 5:26:42, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0185\n2023-01-02 21:07:12,734 - mmcls - INFO - Epoch [56][2500/5005]\tlr: 1.000e-02, eta: 5:26:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0453\n2023-01-02 21:07:21,229 - mmcls - INFO - Epoch [56][2600/5005]\tlr: 1.000e-02, eta: 5:26:25, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0346\n2023-01-02 21:07:29,789 - mmcls - INFO - Epoch [56][2700/5005]\tlr: 1.000e-02, eta: 5:26:16, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0408\n2023-01-02 21:07:38,563 - mmcls - INFO - Epoch [56][2800/5005]\tlr: 1.000e-02, eta: 5:26:07, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.0282\n2023-01-02 21:07:47,178 - mmcls - INFO - Epoch [56][2900/5005]\tlr: 1.000e-02, eta: 5:25:58, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0564\n2023-01-02 21:07:55,884 - mmcls - INFO - Epoch [56][3000/5005]\tlr: 1.000e-02, eta: 5:25:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0475\n2023-01-02 21:08:04,652 - mmcls - INFO - Epoch [56][3100/5005]\tlr: 1.000e-02, eta: 5:25:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0268\n2023-01-02 21:08:13,321 - mmcls - INFO - Epoch [56][3200/5005]\tlr: 1.000e-02, eta: 5:25:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0361\n2023-01-02 21:08:22,285 - mmcls - INFO - Epoch [56][3300/5005]\tlr: 1.000e-02, eta: 5:25:23, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.0400\n2023-01-02 21:08:30,956 - mmcls - INFO - Epoch [56][3400/5005]\tlr: 1.000e-02, eta: 5:25:14, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0634\n2023-01-02 21:08:39,342 - mmcls - INFO - Epoch [56][3500/5005]\tlr: 1.000e-02, eta: 5:25:05, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0280\n2023-01-02 21:08:47,912 - mmcls - INFO - Epoch [56][3600/5005]\tlr: 1.000e-02, eta: 5:24:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0491\n2023-01-02 21:08:56,467 - mmcls - INFO - Epoch [56][3700/5005]\tlr: 1.000e-02, eta: 5:24:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0567\n2023-01-02 21:09:05,120 - mmcls - INFO - Epoch [56][3800/5005]\tlr: 1.000e-02, eta: 5:24:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0573\n2023-01-02 21:09:13,702 - mmcls - INFO - Epoch [56][3900/5005]\tlr: 1.000e-02, eta: 5:24:29, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0516\n2023-01-02 21:09:22,686 - mmcls - INFO - Epoch [56][4000/5005]\tlr: 1.000e-02, eta: 5:24:20, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0506\n2023-01-02 21:09:31,506 - mmcls - INFO - Epoch [56][4100/5005]\tlr: 1.000e-02, eta: 5:24:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0283\n2023-01-02 21:09:40,209 - mmcls - INFO - Epoch [56][4200/5005]\tlr: 1.000e-02, eta: 5:24:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0416\n2023-01-02 21:09:48,708 - mmcls - INFO - Epoch [56][4300/5005]\tlr: 1.000e-02, eta: 5:23:54, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0622\n2023-01-02 21:09:57,509 - mmcls - INFO - Epoch [56][4400/5005]\tlr: 1.000e-02, eta: 5:23:45, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0730\n2023-01-02 21:10:06,359 - mmcls - INFO - Epoch [56][4500/5005]\tlr: 1.000e-02, eta: 5:23:36, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0591\n2023-01-02 21:10:15,130 - mmcls - INFO - Epoch [56][4600/5005]\tlr: 1.000e-02, eta: 5:23:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0330\n2023-01-02 21:10:24,121 - mmcls - INFO - Epoch [56][4700/5005]\tlr: 1.000e-02, eta: 5:23:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0448\n2023-01-02 21:10:32,755 - mmcls - INFO - Epoch [56][4800/5005]\tlr: 1.000e-02, eta: 5:23:10, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0425\n2023-01-02 21:10:41,712 - mmcls - INFO - Epoch [56][4900/5005]\tlr: 1.000e-02, eta: 5:23:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0422\n2023-01-02 21:10:50,093 - mmcls - INFO - Epoch [56][5000/5005]\tlr: 1.000e-02, eta: 5:22:52, time: 0.084, data_time: 0.027, memory: 1125, loss: 2.0565\n2023-01-02 21:11:12,853 - mmcls - INFO - Epoch(val) [56][196]\taccuracy_top-1: 58.8200, accuracy_top-5: 81.2680\n2023-01-02 21:11:23,883 - mmcls - INFO - Epoch [57][100/5005]\tlr: 1.000e-02, eta: 5:22:44, time: 0.110, data_time: 0.056, memory: 1125, loss: 1.9954\n2023-01-02 21:11:32,628 - mmcls - INFO - Epoch [57][200/5005]\tlr: 1.000e-02, eta: 5:22:35, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.9724\n2023-01-02 21:11:41,179 - mmcls - INFO - Epoch [57][300/5005]\tlr: 1.000e-02, eta: 5:22:26, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0083\n2023-01-02 21:11:50,112 - mmcls - INFO - Epoch [57][400/5005]\tlr: 1.000e-02, eta: 5:22:17, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0198\n2023-01-02 21:11:59,038 - mmcls - INFO - Epoch [57][500/5005]\tlr: 1.000e-02, eta: 5:22:09, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0064\n2023-01-02 21:12:07,951 - mmcls - INFO - Epoch [57][600/5005]\tlr: 1.000e-02, eta: 5:22:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0162\n2023-01-02 21:12:17,130 - mmcls - INFO - Epoch [57][700/5005]\tlr: 1.000e-02, eta: 5:21:52, time: 0.092, data_time: 0.009, memory: 1125, loss: 2.0224\n2023-01-02 21:12:25,667 - mmcls - INFO - Epoch [57][800/5005]\tlr: 1.000e-02, eta: 5:21:43, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0059\n2023-01-02 21:12:34,398 - mmcls - INFO - Epoch [57][900/5005]\tlr: 1.000e-02, eta: 5:21:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0128\n2023-01-02 21:12:43,058 - mmcls - INFO - Epoch [57][1000/5005]\tlr: 1.000e-02, eta: 5:21:25, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0387\n2023-01-02 21:12:52,151 - mmcls - INFO - Epoch [57][1100/5005]\tlr: 1.000e-02, eta: 5:21:16, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.0282\n2023-01-02 21:13:00,897 - mmcls - INFO - Epoch [57][1200/5005]\tlr: 1.000e-02, eta: 5:21:07, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0435\n2023-01-02 21:13:09,707 - mmcls - INFO - Epoch [57][1300/5005]\tlr: 1.000e-02, eta: 5:20:59, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0173\n2023-01-02 21:13:18,513 - mmcls - INFO - Epoch [57][1400/5005]\tlr: 1.000e-02, eta: 5:20:50, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0360\n2023-01-02 21:13:27,208 - mmcls - INFO - Epoch [57][1500/5005]\tlr: 1.000e-02, eta: 5:20:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0393\n2023-01-02 21:13:35,995 - mmcls - INFO - Epoch [57][1600/5005]\tlr: 1.000e-02, eta: 5:20:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0129\n2023-01-02 21:13:44,730 - mmcls - INFO - Epoch [57][1700/5005]\tlr: 1.000e-02, eta: 5:20:23, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0180\n2023-01-02 21:13:53,495 - mmcls - INFO - Epoch [57][1800/5005]\tlr: 1.000e-02, eta: 5:20:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0501\n2023-01-02 21:14:02,382 - mmcls - INFO - Epoch [57][1900/5005]\tlr: 1.000e-02, eta: 5:20:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0305\n2023-01-02 21:14:11,067 - mmcls - INFO - Epoch [57][2000/5005]\tlr: 1.000e-02, eta: 5:19:57, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0421\n2023-01-02 21:14:19,691 - mmcls - INFO - Epoch [57][2100/5005]\tlr: 1.000e-02, eta: 5:19:48, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0183\n2023-01-02 21:14:28,956 - mmcls - INFO - Epoch [57][2200/5005]\tlr: 1.000e-02, eta: 5:19:39, time: 0.093, data_time: 0.022, memory: 1125, loss: 2.0265\n2023-01-02 21:14:37,699 - mmcls - INFO - Epoch [57][2300/5005]\tlr: 1.000e-02, eta: 5:19:31, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0334\n2023-01-02 21:14:46,617 - mmcls - INFO - Epoch [57][2400/5005]\tlr: 1.000e-02, eta: 5:19:22, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.0376\n2023-01-02 21:14:55,243 - mmcls - INFO - Epoch [57][2500/5005]\tlr: 1.000e-02, eta: 5:19:13, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0017\n2023-01-02 21:15:04,304 - mmcls - INFO - Epoch [57][2600/5005]\tlr: 1.000e-02, eta: 5:19:04, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0305\n2023-01-02 21:15:13,341 - mmcls - INFO - Epoch [57][2700/5005]\tlr: 1.000e-02, eta: 5:18:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0173\n2023-01-02 21:15:21,932 - mmcls - INFO - Epoch [57][2800/5005]\tlr: 1.000e-02, eta: 5:18:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0416\n2023-01-02 21:15:30,654 - mmcls - INFO - Epoch [57][2900/5005]\tlr: 1.000e-02, eta: 5:18:38, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.0427\n2023-01-02 21:15:39,641 - mmcls - INFO - Epoch [57][3000/5005]\tlr: 1.000e-02, eta: 5:18:29, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0354\n2023-01-02 21:15:48,288 - mmcls - INFO - Epoch [57][3100/5005]\tlr: 1.000e-02, eta: 5:18:20, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0066\n2023-01-02 21:15:57,049 - mmcls - INFO - Epoch [57][3200/5005]\tlr: 1.000e-02, eta: 5:18:12, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0541\n2023-01-02 21:16:05,864 - mmcls - INFO - Epoch [57][3300/5005]\tlr: 1.000e-02, eta: 5:18:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0631\n2023-01-02 21:16:14,611 - mmcls - INFO - Epoch [57][3400/5005]\tlr: 1.000e-02, eta: 5:17:54, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0459\n2023-01-02 21:16:23,290 - mmcls - INFO - Epoch [57][3500/5005]\tlr: 1.000e-02, eta: 5:17:45, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0481\n2023-01-02 21:16:31,827 - mmcls - INFO - Epoch [57][3600/5005]\tlr: 1.000e-02, eta: 5:17:36, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0476\n2023-01-02 21:16:40,642 - mmcls - INFO - Epoch [57][3700/5005]\tlr: 1.000e-02, eta: 5:17:27, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0393\n2023-01-02 21:16:49,080 - mmcls - INFO - Epoch [57][3800/5005]\tlr: 1.000e-02, eta: 5:17:18, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.0305\n2023-01-02 21:16:58,058 - mmcls - INFO - Epoch [57][3900/5005]\tlr: 1.000e-02, eta: 5:17:10, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.0327\n2023-01-02 21:17:06,625 - mmcls - INFO - Epoch [57][4000/5005]\tlr: 1.000e-02, eta: 5:17:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0445\n2023-01-02 21:17:15,694 - mmcls - INFO - Epoch [57][4100/5005]\tlr: 1.000e-02, eta: 5:16:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0510\n2023-01-02 21:17:24,649 - mmcls - INFO - Epoch [57][4200/5005]\tlr: 1.000e-02, eta: 5:16:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0421\n2023-01-02 21:17:33,611 - mmcls - INFO - Epoch [57][4300/5005]\tlr: 1.000e-02, eta: 5:16:35, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0448\n2023-01-02 21:17:42,445 - mmcls - INFO - Epoch [57][4400/5005]\tlr: 1.000e-02, eta: 5:16:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0540\n2023-01-02 21:17:50,961 - mmcls - INFO - Epoch [57][4500/5005]\tlr: 1.000e-02, eta: 5:16:17, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0398\n2023-01-02 21:17:59,359 - mmcls - INFO - Epoch [57][4600/5005]\tlr: 1.000e-02, eta: 5:16:08, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0704\n2023-01-02 21:18:08,214 - mmcls - INFO - Epoch [57][4700/5005]\tlr: 1.000e-02, eta: 5:15:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0564\n2023-01-02 21:18:16,949 - mmcls - INFO - Epoch [57][4800/5005]\tlr: 1.000e-02, eta: 5:15:50, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0506\n2023-01-02 21:18:25,788 - mmcls - INFO - Epoch [57][4900/5005]\tlr: 1.000e-02, eta: 5:15:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0213\n2023-01-02 21:18:34,431 - mmcls - INFO - Epoch [57][5000/5005]\tlr: 1.000e-02, eta: 5:15:32, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0334\n2023-01-02 21:18:57,100 - mmcls - INFO - Epoch(val) [57][196]\taccuracy_top-1: 58.9540, accuracy_top-5: 81.7580\n2023-01-02 21:19:07,952 - mmcls - INFO - Epoch [58][100/5005]\tlr: 1.000e-02, eta: 5:15:24, time: 0.108, data_time: 0.044, memory: 1125, loss: 2.0079\n2023-01-02 21:19:16,589 - mmcls - INFO - Epoch [58][200/5005]\tlr: 1.000e-02, eta: 5:15:16, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.9998\n2023-01-02 21:19:25,496 - mmcls - INFO - Epoch [58][300/5005]\tlr: 1.000e-02, eta: 5:15:07, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.9916\n2023-01-02 21:19:34,014 - mmcls - INFO - Epoch [58][400/5005]\tlr: 1.000e-02, eta: 5:14:58, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0089\n2023-01-02 21:19:42,920 - mmcls - INFO - Epoch [58][500/5005]\tlr: 1.000e-02, eta: 5:14:49, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0026\n2023-01-02 21:19:51,678 - mmcls - INFO - Epoch [58][600/5005]\tlr: 1.000e-02, eta: 5:14:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0027\n2023-01-02 21:20:00,258 - mmcls - INFO - Epoch [58][700/5005]\tlr: 1.000e-02, eta: 5:14:31, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.0261\n2023-01-02 21:20:08,898 - mmcls - INFO - Epoch [58][800/5005]\tlr: 1.000e-02, eta: 5:14:22, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0172\n2023-01-02 21:20:17,612 - mmcls - INFO - Epoch [58][900/5005]\tlr: 1.000e-02, eta: 5:14:14, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.0143\n2023-01-02 21:20:26,528 - mmcls - INFO - Epoch [58][1000/5005]\tlr: 1.000e-02, eta: 5:14:05, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0158\n2023-01-02 21:20:34,926 - mmcls - INFO - Epoch [58][1100/5005]\tlr: 1.000e-02, eta: 5:13:56, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.0338\n2023-01-02 21:20:43,851 - mmcls - INFO - Epoch [58][1200/5005]\tlr: 1.000e-02, eta: 5:13:47, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0531\n2023-01-02 21:20:52,372 - mmcls - INFO - Epoch [58][1300/5005]\tlr: 1.000e-02, eta: 5:13:38, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0444\n2023-01-02 21:21:01,600 - mmcls - INFO - Epoch [58][1400/5005]\tlr: 1.000e-02, eta: 5:13:30, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0003\n2023-01-02 21:21:10,163 - mmcls - INFO - Epoch [58][1500/5005]\tlr: 1.000e-02, eta: 5:13:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0323\n2023-01-02 21:21:19,039 - mmcls - INFO - Epoch [58][1600/5005]\tlr: 1.000e-02, eta: 5:13:12, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.0388\n2023-01-02 21:21:27,819 - mmcls - INFO - Epoch [58][1700/5005]\tlr: 1.000e-02, eta: 5:13:03, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0501\n2023-01-02 21:21:36,604 - mmcls - INFO - Epoch [58][1800/5005]\tlr: 1.000e-02, eta: 5:12:54, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0144\n2023-01-02 21:21:45,327 - mmcls - INFO - Epoch [58][1900/5005]\tlr: 1.000e-02, eta: 5:12:45, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0330\n2023-01-02 21:21:54,439 - mmcls - INFO - Epoch [58][2000/5005]\tlr: 1.000e-02, eta: 5:12:37, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0511\n2023-01-02 21:22:03,392 - mmcls - INFO - Epoch [58][2100/5005]\tlr: 1.000e-02, eta: 5:12:28, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.0115\n2023-01-02 21:22:12,127 - mmcls - INFO - Epoch [58][2200/5005]\tlr: 1.000e-02, eta: 5:12:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0345\n2023-01-02 21:22:21,043 - mmcls - INFO - Epoch [58][2300/5005]\tlr: 1.000e-02, eta: 5:12:11, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0006\n2023-01-02 21:22:29,950 - mmcls - INFO - Epoch [58][2400/5005]\tlr: 1.000e-02, eta: 5:12:02, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0373\n2023-01-02 21:22:38,638 - mmcls - INFO - Epoch [58][2500/5005]\tlr: 1.000e-02, eta: 5:11:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0332\n2023-01-02 21:22:47,547 - mmcls - INFO - Epoch [58][2600/5005]\tlr: 1.000e-02, eta: 5:11:44, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0228\n2023-01-02 21:22:56,182 - mmcls - INFO - Epoch [58][2700/5005]\tlr: 1.000e-02, eta: 5:11:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0259\n2023-01-02 21:23:04,788 - mmcls - INFO - Epoch [58][2800/5005]\tlr: 1.000e-02, eta: 5:11:26, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0153\n2023-01-02 21:23:13,452 - mmcls - INFO - Epoch [58][2900/5005]\tlr: 1.000e-02, eta: 5:11:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0494\n2023-01-02 21:23:22,319 - mmcls - INFO - Epoch [58][3000/5005]\tlr: 1.000e-02, eta: 5:11:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0274\n2023-01-02 21:23:31,319 - mmcls - INFO - Epoch [58][3100/5005]\tlr: 1.000e-02, eta: 5:11:00, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.0469\n2023-01-02 21:23:39,889 - mmcls - INFO - Epoch [58][3200/5005]\tlr: 1.000e-02, eta: 5:10:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0241\n2023-01-02 21:23:48,830 - mmcls - INFO - Epoch [58][3300/5005]\tlr: 1.000e-02, eta: 5:10:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0442\n2023-01-02 21:23:57,632 - mmcls - INFO - Epoch [58][3400/5005]\tlr: 1.000e-02, eta: 5:10:34, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0456\n2023-01-02 21:24:06,666 - mmcls - INFO - Epoch [58][3500/5005]\tlr: 1.000e-02, eta: 5:10:25, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0422\n2023-01-02 21:24:15,096 - mmcls - INFO - Epoch [58][3600/5005]\tlr: 1.000e-02, eta: 5:10:16, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.0363\n2023-01-02 21:24:23,809 - mmcls - INFO - Epoch [58][3700/5005]\tlr: 1.000e-02, eta: 5:10:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0509\n2023-01-02 21:24:32,465 - mmcls - INFO - Epoch [58][3800/5005]\tlr: 1.000e-02, eta: 5:09:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0270\n2023-01-02 21:24:41,228 - mmcls - INFO - Epoch [58][3900/5005]\tlr: 1.000e-02, eta: 5:09:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0318\n2023-01-02 21:24:49,925 - mmcls - INFO - Epoch [58][4000/5005]\tlr: 1.000e-02, eta: 5:09:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0569\n2023-01-02 21:24:58,771 - mmcls - INFO - Epoch [58][4100/5005]\tlr: 1.000e-02, eta: 5:09:32, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0440\n2023-01-02 21:25:07,274 - mmcls - INFO - Epoch [58][4200/5005]\tlr: 1.000e-02, eta: 5:09:23, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0113\n2023-01-02 21:25:16,169 - mmcls - INFO - Epoch [58][4300/5005]\tlr: 1.000e-02, eta: 5:09:14, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0700\n2023-01-02 21:25:24,868 - mmcls - INFO - Epoch [58][4400/5005]\tlr: 1.000e-02, eta: 5:09:05, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0502\n2023-01-02 21:25:33,631 - mmcls - INFO - Epoch [58][4500/5005]\tlr: 1.000e-02, eta: 5:08:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0626\n2023-01-02 21:25:42,471 - mmcls - INFO - Epoch [58][4600/5005]\tlr: 1.000e-02, eta: 5:08:48, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0460\n2023-01-02 21:25:51,290 - mmcls - INFO - Epoch [58][4700/5005]\tlr: 1.000e-02, eta: 5:08:39, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.0342\n2023-01-02 21:25:59,894 - mmcls - INFO - Epoch [58][4800/5005]\tlr: 1.000e-02, eta: 5:08:30, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0518\n2023-01-02 21:26:08,647 - mmcls - INFO - Epoch [58][4900/5005]\tlr: 1.000e-02, eta: 5:08:21, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0420\n2023-01-02 21:26:17,476 - mmcls - INFO - Epoch [58][5000/5005]\tlr: 1.000e-02, eta: 5:08:12, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0465\n2023-01-02 21:26:40,180 - mmcls - INFO - Epoch(val) [58][196]\taccuracy_top-1: 58.9340, accuracy_top-5: 81.2680\n2023-01-02 21:26:51,111 - mmcls - INFO - Epoch [59][100/5005]\tlr: 1.000e-02, eta: 5:08:04, time: 0.109, data_time: 0.060, memory: 1125, loss: 1.9875\n2023-01-02 21:26:59,773 - mmcls - INFO - Epoch [59][200/5005]\tlr: 1.000e-02, eta: 5:07:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9994\n2023-01-02 21:27:08,498 - mmcls - INFO - Epoch [59][300/5005]\tlr: 1.000e-02, eta: 5:07:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.9894\n2023-01-02 21:27:17,235 - mmcls - INFO - Epoch [59][400/5005]\tlr: 1.000e-02, eta: 5:07:38, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.9993\n2023-01-02 21:27:25,894 - mmcls - INFO - Epoch [59][500/5005]\tlr: 1.000e-02, eta: 5:07:29, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0186\n2023-01-02 21:27:34,680 - mmcls - INFO - Epoch [59][600/5005]\tlr: 1.000e-02, eta: 5:07:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0069\n2023-01-02 21:27:43,486 - mmcls - INFO - Epoch [59][700/5005]\tlr: 1.000e-02, eta: 5:07:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0178\n2023-01-02 21:27:52,512 - mmcls - INFO - Epoch [59][800/5005]\tlr: 1.000e-02, eta: 5:07:02, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0080\n2023-01-02 21:28:01,232 - mmcls - INFO - Epoch [59][900/5005]\tlr: 1.000e-02, eta: 5:06:54, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0058\n2023-01-02 21:28:10,245 - mmcls - INFO - Epoch [59][1000/5005]\tlr: 1.000e-02, eta: 5:06:45, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0206\n2023-01-02 21:28:18,927 - mmcls - INFO - Epoch [59][1100/5005]\tlr: 1.000e-02, eta: 5:06:36, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0054\n2023-01-02 21:28:27,527 - mmcls - INFO - Epoch [59][1200/5005]\tlr: 1.000e-02, eta: 5:06:27, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0041\n2023-01-02 21:28:36,531 - mmcls - INFO - Epoch [59][1300/5005]\tlr: 1.000e-02, eta: 5:06:18, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.0198\n2023-01-02 21:28:45,249 - mmcls - INFO - Epoch [59][1400/5005]\tlr: 1.000e-02, eta: 5:06:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0001\n2023-01-02 21:28:53,743 - mmcls - INFO - Epoch [59][1500/5005]\tlr: 1.000e-02, eta: 5:06:01, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0192\n2023-01-02 21:29:02,944 - mmcls - INFO - Epoch [59][1600/5005]\tlr: 1.000e-02, eta: 5:05:52, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0229\n2023-01-02 21:29:11,742 - mmcls - INFO - Epoch [59][1700/5005]\tlr: 1.000e-02, eta: 5:05:43, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.0254\n2023-01-02 21:29:20,755 - mmcls - INFO - Epoch [59][1800/5005]\tlr: 1.000e-02, eta: 5:05:35, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0143\n2023-01-02 21:29:29,301 - mmcls - INFO - Epoch [59][1900/5005]\tlr: 1.000e-02, eta: 5:05:26, time: 0.085, data_time: 0.034, memory: 1125, loss: 2.0206\n2023-01-02 21:29:38,031 - mmcls - INFO - Epoch [59][2000/5005]\tlr: 1.000e-02, eta: 5:05:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0220\n2023-01-02 21:29:47,173 - mmcls - INFO - Epoch [59][2100/5005]\tlr: 1.000e-02, eta: 5:05:08, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.0378\n2023-01-02 21:29:56,029 - mmcls - INFO - Epoch [59][2200/5005]\tlr: 1.000e-02, eta: 5:05:00, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0278\n2023-01-02 21:30:04,956 - mmcls - INFO - Epoch [59][2300/5005]\tlr: 1.000e-02, eta: 5:04:51, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.9976\n2023-01-02 21:30:13,686 - mmcls - INFO - Epoch [59][2400/5005]\tlr: 1.000e-02, eta: 5:04:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.9974\n2023-01-02 21:30:22,816 - mmcls - INFO - Epoch [59][2500/5005]\tlr: 1.000e-02, eta: 5:04:33, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.0246\n2023-01-02 21:30:31,336 - mmcls - INFO - Epoch [59][2600/5005]\tlr: 1.000e-02, eta: 5:04:24, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0323\n2023-01-02 21:30:40,317 - mmcls - INFO - Epoch [59][2700/5005]\tlr: 1.000e-02, eta: 5:04:16, time: 0.090, data_time: 0.045, memory: 1125, loss: 2.0425\n2023-01-02 21:30:49,034 - mmcls - INFO - Epoch [59][2800/5005]\tlr: 1.000e-02, eta: 5:04:07, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0214\n2023-01-02 21:30:57,778 - mmcls - INFO - Epoch [59][2900/5005]\tlr: 1.000e-02, eta: 5:03:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0354\n2023-01-02 21:31:06,763 - mmcls - INFO - Epoch [59][3000/5005]\tlr: 1.000e-02, eta: 5:03:49, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0254\n2023-01-02 21:31:15,696 - mmcls - INFO - Epoch [59][3100/5005]\tlr: 1.000e-02, eta: 5:03:41, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0248\n2023-01-02 21:31:24,366 - mmcls - INFO - Epoch [59][3200/5005]\tlr: 1.000e-02, eta: 5:03:32, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0487\n2023-01-02 21:31:33,305 - mmcls - INFO - Epoch [59][3300/5005]\tlr: 1.000e-02, eta: 5:03:23, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0274\n2023-01-02 21:31:42,221 - mmcls - INFO - Epoch [59][3400/5005]\tlr: 1.000e-02, eta: 5:03:14, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0365\n2023-01-02 21:31:50,916 - mmcls - INFO - Epoch [59][3500/5005]\tlr: 1.000e-02, eta: 5:03:06, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0742\n2023-01-02 21:31:59,441 - mmcls - INFO - Epoch [59][3600/5005]\tlr: 1.000e-02, eta: 5:02:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0550\n2023-01-02 21:32:08,065 - mmcls - INFO - Epoch [59][3700/5005]\tlr: 1.000e-02, eta: 5:02:48, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0455\n2023-01-02 21:32:16,647 - mmcls - INFO - Epoch [59][3800/5005]\tlr: 1.000e-02, eta: 5:02:39, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0476\n2023-01-02 21:32:25,213 - mmcls - INFO - Epoch [59][3900/5005]\tlr: 1.000e-02, eta: 5:02:30, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.0558\n2023-01-02 21:32:33,742 - mmcls - INFO - Epoch [59][4000/5005]\tlr: 1.000e-02, eta: 5:02:21, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0284\n2023-01-02 21:32:42,457 - mmcls - INFO - Epoch [59][4100/5005]\tlr: 1.000e-02, eta: 5:02:12, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0474\n2023-01-02 21:32:51,328 - mmcls - INFO - Epoch [59][4200/5005]\tlr: 1.000e-02, eta: 5:02:03, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0423\n2023-01-02 21:33:00,135 - mmcls - INFO - Epoch [59][4300/5005]\tlr: 1.000e-02, eta: 5:01:54, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0679\n2023-01-02 21:33:08,592 - mmcls - INFO - Epoch [59][4400/5005]\tlr: 1.000e-02, eta: 5:01:45, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0390\n2023-01-02 21:33:17,517 - mmcls - INFO - Epoch [59][4500/5005]\tlr: 1.000e-02, eta: 5:01:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0407\n2023-01-02 21:33:26,591 - mmcls - INFO - Epoch [59][4600/5005]\tlr: 1.000e-02, eta: 5:01:28, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0485\n2023-01-02 21:33:35,591 - mmcls - INFO - Epoch [59][4700/5005]\tlr: 1.000e-02, eta: 5:01:19, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.0655\n2023-01-02 21:33:44,700 - mmcls - INFO - Epoch [59][4800/5005]\tlr: 1.000e-02, eta: 5:01:11, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0599\n2023-01-02 21:33:53,844 - mmcls - INFO - Epoch [59][4900/5005]\tlr: 1.000e-02, eta: 5:01:02, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.0476\n2023-01-02 21:34:02,745 - mmcls - INFO - Epoch [59][5000/5005]\tlr: 1.000e-02, eta: 5:00:53, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0559\n2023-01-02 21:34:25,385 - mmcls - INFO - Epoch(val) [59][196]\taccuracy_top-1: 58.9060, accuracy_top-5: 81.2580\n2023-01-02 21:34:36,054 - mmcls - INFO - Epoch [60][100/5005]\tlr: 1.000e-02, eta: 5:00:45, time: 0.107, data_time: 0.062, memory: 1125, loss: 2.0107\n2023-01-02 21:34:44,849 - mmcls - INFO - Epoch [60][200/5005]\tlr: 1.000e-02, eta: 5:00:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0069\n2023-01-02 21:34:53,486 - mmcls - INFO - Epoch [60][300/5005]\tlr: 1.000e-02, eta: 5:00:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9866\n2023-01-02 21:35:02,289 - mmcls - INFO - Epoch [60][400/5005]\tlr: 1.000e-02, eta: 5:00:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0177\n2023-01-02 21:35:10,985 - mmcls - INFO - Epoch [60][500/5005]\tlr: 1.000e-02, eta: 5:00:10, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0122\n2023-01-02 21:35:20,108 - mmcls - INFO - Epoch [60][600/5005]\tlr: 1.000e-02, eta: 5:00:01, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.9937\n2023-01-02 21:35:29,137 - mmcls - INFO - Epoch [60][700/5005]\tlr: 1.000e-02, eta: 4:59:53, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0133\n2023-01-02 21:35:37,907 - mmcls - INFO - Epoch [60][800/5005]\tlr: 1.000e-02, eta: 4:59:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0261\n2023-01-02 21:35:46,518 - mmcls - INFO - Epoch [60][900/5005]\tlr: 1.000e-02, eta: 4:59:35, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.9930\n2023-01-02 21:35:55,478 - mmcls - INFO - Epoch [60][1000/5005]\tlr: 1.000e-02, eta: 4:59:26, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.9928\n2023-01-02 21:36:04,135 - mmcls - INFO - Epoch [60][1100/5005]\tlr: 1.000e-02, eta: 4:59:17, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.9818\n2023-01-02 21:36:13,031 - mmcls - INFO - Epoch [60][1200/5005]\tlr: 1.000e-02, eta: 4:59:09, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.0457\n2023-01-02 21:36:21,977 - mmcls - INFO - Epoch [60][1300/5005]\tlr: 1.000e-02, eta: 4:59:00, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.0179\n2023-01-02 21:36:30,835 - mmcls - INFO - Epoch [60][1400/5005]\tlr: 1.000e-02, eta: 4:58:51, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.0220\n2023-01-02 21:36:39,670 - mmcls - INFO - Epoch [60][1500/5005]\tlr: 1.000e-02, eta: 4:58:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0144\n2023-01-02 21:36:48,272 - mmcls - INFO - Epoch [60][1600/5005]\tlr: 1.000e-02, eta: 4:58:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0036\n2023-01-02 21:36:57,056 - mmcls - INFO - Epoch [60][1700/5005]\tlr: 1.000e-02, eta: 4:58:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0279\n2023-01-02 21:37:06,107 - mmcls - INFO - Epoch [60][1800/5005]\tlr: 1.000e-02, eta: 4:58:16, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0076\n2023-01-02 21:37:14,705 - mmcls - INFO - Epoch [60][1900/5005]\tlr: 1.000e-02, eta: 4:58:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0249\n2023-01-02 21:37:23,772 - mmcls - INFO - Epoch [60][2000/5005]\tlr: 1.000e-02, eta: 4:57:58, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0023\n2023-01-02 21:37:32,490 - mmcls - INFO - Epoch [60][2100/5005]\tlr: 1.000e-02, eta: 4:57:50, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0553\n2023-01-02 21:37:41,093 - mmcls - INFO - Epoch [60][2200/5005]\tlr: 1.000e-02, eta: 4:57:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0543\n2023-01-02 21:37:49,705 - mmcls - INFO - Epoch [60][2300/5005]\tlr: 1.000e-02, eta: 4:57:32, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0275\n2023-01-02 21:37:58,105 - mmcls - INFO - Epoch [60][2400/5005]\tlr: 1.000e-02, eta: 4:57:23, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0325\n2023-01-02 21:38:07,003 - mmcls - INFO - Epoch [60][2500/5005]\tlr: 1.000e-02, eta: 4:57:14, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0034\n2023-01-02 21:38:15,988 - mmcls - INFO - Epoch [60][2600/5005]\tlr: 1.000e-02, eta: 4:57:05, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0468\n2023-01-02 21:38:24,626 - mmcls - INFO - Epoch [60][2700/5005]\tlr: 1.000e-02, eta: 4:56:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0342\n2023-01-02 21:38:33,313 - mmcls - INFO - Epoch [60][2800/5005]\tlr: 1.000e-02, eta: 4:56:47, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0279\n2023-01-02 21:38:41,996 - mmcls - INFO - Epoch [60][2900/5005]\tlr: 1.000e-02, eta: 4:56:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0305\n2023-01-02 21:38:51,049 - mmcls - INFO - Epoch [60][3000/5005]\tlr: 1.000e-02, eta: 4:56:30, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0364\n2023-01-02 21:38:59,829 - mmcls - INFO - Epoch [60][3100/5005]\tlr: 1.000e-02, eta: 4:56:21, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0049\n2023-01-02 21:39:08,588 - mmcls - INFO - Epoch [60][3200/5005]\tlr: 1.000e-02, eta: 4:56:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0395\n2023-01-02 21:39:17,155 - mmcls - INFO - Epoch [60][3300/5005]\tlr: 1.000e-02, eta: 4:56:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0200\n2023-01-02 21:39:25,931 - mmcls - INFO - Epoch [60][3400/5005]\tlr: 1.000e-02, eta: 4:55:55, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.0385\n2023-01-02 21:39:34,586 - mmcls - INFO - Epoch [60][3500/5005]\tlr: 1.000e-02, eta: 4:55:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0365\n2023-01-02 21:39:43,256 - mmcls - INFO - Epoch [60][3600/5005]\tlr: 1.000e-02, eta: 4:55:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0380\n2023-01-02 21:39:52,006 - mmcls - INFO - Epoch [60][3700/5005]\tlr: 1.000e-02, eta: 4:55:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0353\n2023-01-02 21:40:00,798 - mmcls - INFO - Epoch [60][3800/5005]\tlr: 1.000e-02, eta: 4:55:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0774\n2023-01-02 21:40:09,603 - mmcls - INFO - Epoch [60][3900/5005]\tlr: 1.000e-02, eta: 4:55:10, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.0621\n2023-01-02 21:40:18,153 - mmcls - INFO - Epoch [60][4000/5005]\tlr: 1.000e-02, eta: 4:55:01, time: 0.085, data_time: 0.036, memory: 1125, loss: 2.0318\n2023-01-02 21:40:26,689 - mmcls - INFO - Epoch [60][4100/5005]\tlr: 1.000e-02, eta: 4:54:52, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0251\n2023-01-02 21:40:35,856 - mmcls - INFO - Epoch [60][4200/5005]\tlr: 1.000e-02, eta: 4:54:44, time: 0.092, data_time: 0.016, memory: 1125, loss: 2.0324\n2023-01-02 21:40:44,535 - mmcls - INFO - Epoch [60][4300/5005]\tlr: 1.000e-02, eta: 4:54:35, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0320\n2023-01-02 21:40:53,152 - mmcls - INFO - Epoch [60][4400/5005]\tlr: 1.000e-02, eta: 4:54:26, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0453\n2023-01-02 21:41:01,711 - mmcls - INFO - Epoch [60][4500/5005]\tlr: 1.000e-02, eta: 4:54:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0048\n2023-01-02 21:41:10,372 - mmcls - INFO - Epoch [60][4600/5005]\tlr: 1.000e-02, eta: 4:54:08, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0292\n2023-01-02 21:41:19,131 - mmcls - INFO - Epoch [60][4700/5005]\tlr: 1.000e-02, eta: 4:53:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0691\n2023-01-02 21:41:27,775 - mmcls - INFO - Epoch [60][4800/5005]\tlr: 1.000e-02, eta: 4:53:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0611\n2023-01-02 21:41:36,594 - mmcls - INFO - Epoch [60][4900/5005]\tlr: 1.000e-02, eta: 4:53:42, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0419\n2023-01-02 21:41:45,479 - mmcls - INFO - Epoch [60][5000/5005]\tlr: 1.000e-02, eta: 4:53:33, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0372\n2023-01-02 21:42:08,419 - mmcls - INFO - Epoch(val) [60][196]\taccuracy_top-1: 58.5800, accuracy_top-5: 80.9500\n2023-01-02 21:42:18,981 - mmcls - INFO - Epoch [61][100/5005]\tlr: 1.000e-03, eta: 4:53:25, time: 0.105, data_time: 0.039, memory: 1125, loss: 1.9652\n2023-01-02 21:42:28,051 - mmcls - INFO - Epoch [61][200/5005]\tlr: 1.000e-03, eta: 4:53:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.9211\n2023-01-02 21:42:36,756 - mmcls - INFO - Epoch [61][300/5005]\tlr: 1.000e-03, eta: 4:53:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8979\n2023-01-02 21:42:45,406 - mmcls - INFO - Epoch [61][400/5005]\tlr: 1.000e-03, eta: 4:52:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9082\n2023-01-02 21:42:54,111 - mmcls - INFO - Epoch [61][500/5005]\tlr: 1.000e-03, eta: 4:52:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9108\n2023-01-02 21:43:02,740 - mmcls - INFO - Epoch [61][600/5005]\tlr: 1.000e-03, eta: 4:52:40, time: 0.086, data_time: 0.038, memory: 1125, loss: 1.9084\n2023-01-02 21:43:11,519 - mmcls - INFO - Epoch [61][700/5005]\tlr: 1.000e-03, eta: 4:52:32, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8764\n2023-01-02 21:43:20,218 - mmcls - INFO - Epoch [61][800/5005]\tlr: 1.000e-03, eta: 4:52:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9080\n2023-01-02 21:43:28,835 - mmcls - INFO - Epoch [61][900/5005]\tlr: 1.000e-03, eta: 4:52:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8828\n2023-01-02 21:43:37,724 - mmcls - INFO - Epoch [61][1000/5005]\tlr: 1.000e-03, eta: 4:52:05, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.8972\n2023-01-02 21:43:46,517 - mmcls - INFO - Epoch [61][1100/5005]\tlr: 1.000e-03, eta: 4:51:56, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.8813\n2023-01-02 21:43:55,264 - mmcls - INFO - Epoch [61][1200/5005]\tlr: 1.000e-03, eta: 4:51:47, time: 0.088, data_time: 0.045, memory: 1125, loss: 1.8929\n2023-01-02 21:44:04,063 - mmcls - INFO - Epoch [61][1300/5005]\tlr: 1.000e-03, eta: 4:51:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8833\n2023-01-02 21:44:12,985 - mmcls - INFO - Epoch [61][1400/5005]\tlr: 1.000e-03, eta: 4:51:30, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8705\n2023-01-02 21:44:21,571 - mmcls - INFO - Epoch [61][1500/5005]\tlr: 1.000e-03, eta: 4:51:21, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8890\n2023-01-02 21:44:30,118 - mmcls - INFO - Epoch [61][1600/5005]\tlr: 1.000e-03, eta: 4:51:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.8620\n2023-01-02 21:44:38,706 - mmcls - INFO - Epoch [61][1700/5005]\tlr: 1.000e-03, eta: 4:51:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8852\n2023-01-02 21:44:47,330 - mmcls - INFO - Epoch [61][1800/5005]\tlr: 1.000e-03, eta: 4:50:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8556\n2023-01-02 21:44:56,032 - mmcls - INFO - Epoch [61][1900/5005]\tlr: 1.000e-03, eta: 4:50:45, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.8808\n2023-01-02 21:45:04,656 - mmcls - INFO - Epoch [61][2000/5005]\tlr: 1.000e-03, eta: 4:50:36, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8755\n2023-01-02 21:45:13,452 - mmcls - INFO - Epoch [61][2100/5005]\tlr: 1.000e-03, eta: 4:50:28, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.8727\n2023-01-02 21:45:22,553 - mmcls - INFO - Epoch [61][2200/5005]\tlr: 1.000e-03, eta: 4:50:19, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8767\n2023-01-02 21:45:31,154 - mmcls - INFO - Epoch [61][2300/5005]\tlr: 1.000e-03, eta: 4:50:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8652\n2023-01-02 21:45:39,817 - mmcls - INFO - Epoch [61][2400/5005]\tlr: 1.000e-03, eta: 4:50:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.8627\n2023-01-02 21:45:48,659 - mmcls - INFO - Epoch [61][2500/5005]\tlr: 1.000e-03, eta: 4:49:52, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.8795\n2023-01-02 21:45:57,341 - mmcls - INFO - Epoch [61][2600/5005]\tlr: 1.000e-03, eta: 4:49:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8467\n2023-01-02 21:46:05,973 - mmcls - INFO - Epoch [61][2700/5005]\tlr: 1.000e-03, eta: 4:49:35, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8587\n2023-01-02 21:46:14,474 - mmcls - INFO - Epoch [61][2800/5005]\tlr: 1.000e-03, eta: 4:49:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.8786\n2023-01-02 21:46:23,168 - mmcls - INFO - Epoch [61][2900/5005]\tlr: 1.000e-03, eta: 4:49:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8607\n2023-01-02 21:46:31,879 - mmcls - INFO - Epoch [61][3000/5005]\tlr: 1.000e-03, eta: 4:49:08, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.8601\n2023-01-02 21:46:40,739 - mmcls - INFO - Epoch [61][3100/5005]\tlr: 1.000e-03, eta: 4:48:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8737\n2023-01-02 21:46:49,234 - mmcls - INFO - Epoch [61][3200/5005]\tlr: 1.000e-03, eta: 4:48:50, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.8727\n2023-01-02 21:46:58,017 - mmcls - INFO - Epoch [61][3300/5005]\tlr: 1.000e-03, eta: 4:48:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8458\n2023-01-02 21:47:06,817 - mmcls - INFO - Epoch [61][3400/5005]\tlr: 1.000e-03, eta: 4:48:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8573\n2023-01-02 21:47:15,394 - mmcls - INFO - Epoch [61][3500/5005]\tlr: 1.000e-03, eta: 4:48:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.8593\n2023-01-02 21:47:24,260 - mmcls - INFO - Epoch [61][3600/5005]\tlr: 1.000e-03, eta: 4:48:15, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8848\n2023-01-02 21:47:33,100 - mmcls - INFO - Epoch [61][3700/5005]\tlr: 1.000e-03, eta: 4:48:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8811\n2023-01-02 21:47:42,023 - mmcls - INFO - Epoch [61][3800/5005]\tlr: 1.000e-03, eta: 4:47:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8560\n2023-01-02 21:47:50,713 - mmcls - INFO - Epoch [61][3900/5005]\tlr: 1.000e-03, eta: 4:47:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8565\n2023-01-02 21:47:59,425 - mmcls - INFO - Epoch [61][4000/5005]\tlr: 1.000e-03, eta: 4:47:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8362\n2023-01-02 21:48:08,011 - mmcls - INFO - Epoch [61][4100/5005]\tlr: 1.000e-03, eta: 4:47:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8646\n2023-01-02 21:48:16,623 - mmcls - INFO - Epoch [61][4200/5005]\tlr: 1.000e-03, eta: 4:47:22, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8273\n2023-01-02 21:48:25,559 - mmcls - INFO - Epoch [61][4300/5005]\tlr: 1.000e-03, eta: 4:47:13, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8344\n2023-01-02 21:48:34,776 - mmcls - INFO - Epoch [61][4400/5005]\tlr: 1.000e-03, eta: 4:47:05, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.8491\n2023-01-02 21:48:43,465 - mmcls - INFO - Epoch [61][4500/5005]\tlr: 1.000e-03, eta: 4:46:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.8562\n2023-01-02 21:48:52,213 - mmcls - INFO - Epoch [61][4600/5005]\tlr: 1.000e-03, eta: 4:46:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8508\n2023-01-02 21:49:00,969 - mmcls - INFO - Epoch [61][4700/5005]\tlr: 1.000e-03, eta: 4:46:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8635\n2023-01-02 21:49:09,415 - mmcls - INFO - Epoch [61][4800/5005]\tlr: 1.000e-03, eta: 4:46:29, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.8310\n2023-01-02 21:49:18,595 - mmcls - INFO - Epoch [61][4900/5005]\tlr: 1.000e-03, eta: 4:46:20, time: 0.092, data_time: 0.011, memory: 1125, loss: 1.8452\n2023-01-02 21:49:27,167 - mmcls - INFO - Epoch [61][5000/5005]\tlr: 1.000e-03, eta: 4:46:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8552\n2023-01-02 21:49:50,085 - mmcls - INFO - Epoch(val) [61][196]\taccuracy_top-1: 61.8940, accuracy_top-5: 83.3100\n2023-01-02 21:50:01,191 - mmcls - INFO - Epoch [62][100/5005]\tlr: 1.000e-03, eta: 4:46:03, time: 0.111, data_time: 0.029, memory: 1125, loss: 1.8341\n2023-01-02 21:50:10,077 - mmcls - INFO - Epoch [62][200/5005]\tlr: 1.000e-03, eta: 4:45:55, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.8329\n2023-01-02 21:50:19,076 - mmcls - INFO - Epoch [62][300/5005]\tlr: 1.000e-03, eta: 4:45:46, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.8621\n2023-01-02 21:50:27,780 - mmcls - INFO - Epoch [62][400/5005]\tlr: 1.000e-03, eta: 4:45:37, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.8362\n2023-01-02 21:50:36,117 - mmcls - INFO - Epoch [62][500/5005]\tlr: 1.000e-03, eta: 4:45:28, time: 0.083, data_time: 0.013, memory: 1125, loss: 1.8261\n2023-01-02 21:50:44,780 - mmcls - INFO - Epoch [62][600/5005]\tlr: 1.000e-03, eta: 4:45:19, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.8056\n2023-01-02 21:50:53,463 - mmcls - INFO - Epoch [62][700/5005]\tlr: 1.000e-03, eta: 4:45:10, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.8544\n2023-01-02 21:51:02,157 - mmcls - INFO - Epoch [62][800/5005]\tlr: 1.000e-03, eta: 4:45:01, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8563\n2023-01-02 21:51:10,909 - mmcls - INFO - Epoch [62][900/5005]\tlr: 1.000e-03, eta: 4:44:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8360\n2023-01-02 21:51:19,768 - mmcls - INFO - Epoch [62][1000/5005]\tlr: 1.000e-03, eta: 4:44:44, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.8471\n2023-01-02 21:51:28,582 - mmcls - INFO - Epoch [62][1100/5005]\tlr: 1.000e-03, eta: 4:44:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8098\n2023-01-02 21:51:37,340 - mmcls - INFO - Epoch [62][1200/5005]\tlr: 1.000e-03, eta: 4:44:26, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8417\n2023-01-02 21:51:45,911 - mmcls - INFO - Epoch [62][1300/5005]\tlr: 1.000e-03, eta: 4:44:17, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.8396\n2023-01-02 21:51:54,598 - mmcls - INFO - Epoch [62][1400/5005]\tlr: 1.000e-03, eta: 4:44:08, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8321\n2023-01-02 21:52:03,784 - mmcls - INFO - Epoch [62][1500/5005]\tlr: 1.000e-03, eta: 4:44:00, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.8242\n2023-01-02 21:52:12,234 - mmcls - INFO - Epoch [62][1600/5005]\tlr: 1.000e-03, eta: 4:43:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.8384\n2023-01-02 21:52:21,067 - mmcls - INFO - Epoch [62][1700/5005]\tlr: 1.000e-03, eta: 4:43:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8250\n2023-01-02 21:52:29,955 - mmcls - INFO - Epoch [62][1800/5005]\tlr: 1.000e-03, eta: 4:43:33, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8491\n2023-01-02 21:52:38,578 - mmcls - INFO - Epoch [62][1900/5005]\tlr: 1.000e-03, eta: 4:43:24, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8450\n2023-01-02 21:52:47,237 - mmcls - INFO - Epoch [62][2000/5005]\tlr: 1.000e-03, eta: 4:43:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8369\n2023-01-02 21:52:56,396 - mmcls - INFO - Epoch [62][2100/5005]\tlr: 1.000e-03, eta: 4:43:07, time: 0.092, data_time: 0.003, memory: 1125, loss: 1.8453\n2023-01-02 21:53:05,177 - mmcls - INFO - Epoch [62][2200/5005]\tlr: 1.000e-03, eta: 4:42:58, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.8369\n2023-01-02 21:53:13,709 - mmcls - INFO - Epoch [62][2300/5005]\tlr: 1.000e-03, eta: 4:42:49, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8259\n2023-01-02 21:53:22,802 - mmcls - INFO - Epoch [62][2400/5005]\tlr: 1.000e-03, eta: 4:42:41, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.8330\n2023-01-02 21:53:31,993 - mmcls - INFO - Epoch [62][2500/5005]\tlr: 1.000e-03, eta: 4:42:32, time: 0.092, data_time: 0.017, memory: 1125, loss: 1.8475\n2023-01-02 21:53:40,942 - mmcls - INFO - Epoch [62][2600/5005]\tlr: 1.000e-03, eta: 4:42:23, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.8353\n2023-01-02 21:53:49,504 - mmcls - INFO - Epoch [62][2700/5005]\tlr: 1.000e-03, eta: 4:42:14, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8403\n2023-01-02 21:53:58,358 - mmcls - INFO - Epoch [62][2800/5005]\tlr: 1.000e-03, eta: 4:42:06, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.8222\n2023-01-02 21:54:07,320 - mmcls - INFO - Epoch [62][2900/5005]\tlr: 1.000e-03, eta: 4:41:57, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.8219\n2023-01-02 21:54:16,016 - mmcls - INFO - Epoch [62][3000/5005]\tlr: 1.000e-03, eta: 4:41:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8374\n2023-01-02 21:54:24,629 - mmcls - INFO - Epoch [62][3100/5005]\tlr: 1.000e-03, eta: 4:41:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7985\n2023-01-02 21:54:33,346 - mmcls - INFO - Epoch [62][3200/5005]\tlr: 1.000e-03, eta: 4:41:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8470\n2023-01-02 21:54:42,168 - mmcls - INFO - Epoch [62][3300/5005]\tlr: 1.000e-03, eta: 4:41:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8387\n2023-01-02 21:54:51,056 - mmcls - INFO - Epoch [62][3400/5005]\tlr: 1.000e-03, eta: 4:41:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8465\n2023-01-02 21:54:59,618 - mmcls - INFO - Epoch [62][3500/5005]\tlr: 1.000e-03, eta: 4:41:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8312\n2023-01-02 21:55:08,420 - mmcls - INFO - Epoch [62][3600/5005]\tlr: 1.000e-03, eta: 4:40:55, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.8282\n2023-01-02 21:55:17,011 - mmcls - INFO - Epoch [62][3700/5005]\tlr: 1.000e-03, eta: 4:40:46, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.8284\n2023-01-02 21:55:25,840 - mmcls - INFO - Epoch [62][3800/5005]\tlr: 1.000e-03, eta: 4:40:37, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8274\n2023-01-02 21:55:34,577 - mmcls - INFO - Epoch [62][3900/5005]\tlr: 1.000e-03, eta: 4:40:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8478\n2023-01-02 21:55:43,118 - mmcls - INFO - Epoch [62][4000/5005]\tlr: 1.000e-03, eta: 4:40:20, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.8279\n2023-01-02 21:55:51,856 - mmcls - INFO - Epoch [62][4100/5005]\tlr: 1.000e-03, eta: 4:40:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8363\n2023-01-02 21:56:00,445 - mmcls - INFO - Epoch [62][4200/5005]\tlr: 1.000e-03, eta: 4:40:02, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.8346\n2023-01-02 21:56:09,192 - mmcls - INFO - Epoch [62][4300/5005]\tlr: 1.000e-03, eta: 4:39:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8280\n2023-01-02 21:56:17,997 - mmcls - INFO - Epoch [62][4400/5005]\tlr: 1.000e-03, eta: 4:39:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8297\n2023-01-02 21:56:26,446 - mmcls - INFO - Epoch [62][4500/5005]\tlr: 1.000e-03, eta: 4:39:35, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.8657\n2023-01-02 21:56:35,121 - mmcls - INFO - Epoch [62][4600/5005]\tlr: 1.000e-03, eta: 4:39:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8375\n2023-01-02 21:56:43,629 - mmcls - INFO - Epoch [62][4700/5005]\tlr: 1.000e-03, eta: 4:39:17, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8380\n2023-01-02 21:56:52,456 - mmcls - INFO - Epoch [62][4800/5005]\tlr: 1.000e-03, eta: 4:39:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8272\n2023-01-02 21:57:01,238 - mmcls - INFO - Epoch [62][4900/5005]\tlr: 1.000e-03, eta: 4:39:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8293\n2023-01-02 21:57:09,756 - mmcls - INFO - Epoch [62][5000/5005]\tlr: 1.000e-03, eta: 4:38:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.8253\n2023-01-02 21:57:32,482 - mmcls - INFO - Epoch(val) [62][196]\taccuracy_top-1: 62.2640, accuracy_top-5: 83.5400\n2023-01-02 21:57:43,346 - mmcls - INFO - Epoch [63][100/5005]\tlr: 1.000e-03, eta: 4:38:43, time: 0.108, data_time: 0.045, memory: 1125, loss: 1.8143\n2023-01-02 21:57:52,054 - mmcls - INFO - Epoch [63][200/5005]\tlr: 1.000e-03, eta: 4:38:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8152\n2023-01-02 21:58:00,550 - mmcls - INFO - Epoch [63][300/5005]\tlr: 1.000e-03, eta: 4:38:25, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.8172\n2023-01-02 21:58:09,111 - mmcls - INFO - Epoch [63][400/5005]\tlr: 1.000e-03, eta: 4:38:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8233\n2023-01-02 21:58:17,942 - mmcls - INFO - Epoch [63][500/5005]\tlr: 1.000e-03, eta: 4:38:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8160\n2023-01-02 21:58:26,664 - mmcls - INFO - Epoch [63][600/5005]\tlr: 1.000e-03, eta: 4:37:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7919\n2023-01-02 21:58:35,790 - mmcls - INFO - Epoch [63][700/5005]\tlr: 1.000e-03, eta: 4:37:50, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8211\n2023-01-02 21:58:44,180 - mmcls - INFO - Epoch [63][800/5005]\tlr: 1.000e-03, eta: 4:37:41, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.8173\n2023-01-02 21:58:53,074 - mmcls - INFO - Epoch [63][900/5005]\tlr: 1.000e-03, eta: 4:37:32, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8325\n2023-01-02 21:59:01,602 - mmcls - INFO - Epoch [63][1000/5005]\tlr: 1.000e-03, eta: 4:37:23, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.8195\n2023-01-02 21:59:10,106 - mmcls - INFO - Epoch [63][1100/5005]\tlr: 1.000e-03, eta: 4:37:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.8342\n2023-01-02 21:59:18,954 - mmcls - INFO - Epoch [63][1200/5005]\tlr: 1.000e-03, eta: 4:37:05, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8399\n2023-01-02 21:59:28,293 - mmcls - INFO - Epoch [63][1300/5005]\tlr: 1.000e-03, eta: 4:36:57, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.8180\n2023-01-02 21:59:36,929 - mmcls - INFO - Epoch [63][1400/5005]\tlr: 1.000e-03, eta: 4:36:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.8308\n2023-01-02 21:59:45,608 - mmcls - INFO - Epoch [63][1500/5005]\tlr: 1.000e-03, eta: 4:36:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8310\n2023-01-02 21:59:54,516 - mmcls - INFO - Epoch [63][1600/5005]\tlr: 1.000e-03, eta: 4:36:30, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.8217\n2023-01-02 22:00:03,294 - mmcls - INFO - Epoch [63][1700/5005]\tlr: 1.000e-03, eta: 4:36:21, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.8122\n2023-01-02 22:00:12,352 - mmcls - INFO - Epoch [63][1800/5005]\tlr: 1.000e-03, eta: 4:36:13, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.7951\n2023-01-02 22:00:21,054 - mmcls - INFO - Epoch [63][1900/5005]\tlr: 1.000e-03, eta: 4:36:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8327\n2023-01-02 22:00:29,613 - mmcls - INFO - Epoch [63][2000/5005]\tlr: 1.000e-03, eta: 4:35:55, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8094\n2023-01-02 22:00:38,734 - mmcls - INFO - Epoch [63][2100/5005]\tlr: 1.000e-03, eta: 4:35:46, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7998\n2023-01-02 22:00:47,361 - mmcls - INFO - Epoch [63][2200/5005]\tlr: 1.000e-03, eta: 4:35:37, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8085\n2023-01-02 22:00:56,089 - mmcls - INFO - Epoch [63][2300/5005]\tlr: 1.000e-03, eta: 4:35:29, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8040\n2023-01-02 22:01:05,085 - mmcls - INFO - Epoch [63][2400/5005]\tlr: 1.000e-03, eta: 4:35:20, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8541\n2023-01-02 22:01:13,719 - mmcls - INFO - Epoch [63][2500/5005]\tlr: 1.000e-03, eta: 4:35:11, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.8154\n2023-01-02 22:01:22,282 - mmcls - INFO - Epoch [63][2600/5005]\tlr: 1.000e-03, eta: 4:35:02, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.8057\n2023-01-02 22:01:31,108 - mmcls - INFO - Epoch [63][2700/5005]\tlr: 1.000e-03, eta: 4:34:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.8231\n2023-01-02 22:01:40,232 - mmcls - INFO - Epoch [63][2800/5005]\tlr: 1.000e-03, eta: 4:34:45, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8262\n2023-01-02 22:01:48,687 - mmcls - INFO - Epoch [63][2900/5005]\tlr: 1.000e-03, eta: 4:34:36, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.8242\n2023-01-02 22:01:57,763 - mmcls - INFO - Epoch [63][3000/5005]\tlr: 1.000e-03, eta: 4:34:27, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.8163\n2023-01-02 22:02:06,552 - mmcls - INFO - Epoch [63][3100/5005]\tlr: 1.000e-03, eta: 4:34:18, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.8040\n2023-01-02 22:02:15,312 - mmcls - INFO - Epoch [63][3200/5005]\tlr: 1.000e-03, eta: 4:34:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8274\n2023-01-02 22:02:24,053 - mmcls - INFO - Epoch [63][3300/5005]\tlr: 1.000e-03, eta: 4:34:01, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.8188\n2023-01-02 22:02:32,768 - mmcls - INFO - Epoch [63][3400/5005]\tlr: 1.000e-03, eta: 4:33:52, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8001\n2023-01-02 22:02:41,577 - mmcls - INFO - Epoch [63][3500/5005]\tlr: 1.000e-03, eta: 4:33:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8226\n2023-01-02 22:02:50,384 - mmcls - INFO - Epoch [63][3600/5005]\tlr: 1.000e-03, eta: 4:33:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8216\n2023-01-02 22:02:59,174 - mmcls - INFO - Epoch [63][3700/5005]\tlr: 1.000e-03, eta: 4:33:25, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.8072\n2023-01-02 22:03:07,800 - mmcls - INFO - Epoch [63][3800/5005]\tlr: 1.000e-03, eta: 4:33:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8219\n2023-01-02 22:03:16,730 - mmcls - INFO - Epoch [63][3900/5005]\tlr: 1.000e-03, eta: 4:33:08, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.8109\n2023-01-02 22:03:25,346 - mmcls - INFO - Epoch [63][4000/5005]\tlr: 1.000e-03, eta: 4:32:59, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.8031\n2023-01-02 22:03:34,150 - mmcls - INFO - Epoch [63][4100/5005]\tlr: 1.000e-03, eta: 4:32:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8297\n2023-01-02 22:03:43,244 - mmcls - INFO - Epoch [63][4200/5005]\tlr: 1.000e-03, eta: 4:32:41, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.8062\n2023-01-02 22:03:51,857 - mmcls - INFO - Epoch [63][4300/5005]\tlr: 1.000e-03, eta: 4:32:32, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.8196\n2023-01-02 22:04:00,586 - mmcls - INFO - Epoch [63][4400/5005]\tlr: 1.000e-03, eta: 4:32:24, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8092\n2023-01-02 22:04:09,213 - mmcls - INFO - Epoch [63][4500/5005]\tlr: 1.000e-03, eta: 4:32:15, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.8172\n2023-01-02 22:04:17,870 - mmcls - INFO - Epoch [63][4600/5005]\tlr: 1.000e-03, eta: 4:32:06, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.8171\n2023-01-02 22:04:27,126 - mmcls - INFO - Epoch [63][4700/5005]\tlr: 1.000e-03, eta: 4:31:57, time: 0.092, data_time: 0.038, memory: 1125, loss: 1.8033\n2023-01-02 22:04:35,933 - mmcls - INFO - Epoch [63][4800/5005]\tlr: 1.000e-03, eta: 4:31:49, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8151\n2023-01-02 22:04:45,036 - mmcls - INFO - Epoch [63][4900/5005]\tlr: 1.000e-03, eta: 4:31:40, time: 0.091, data_time: 0.000, memory: 1125, loss: 1.8303\n2023-01-02 22:04:53,877 - mmcls - INFO - Epoch [63][5000/5005]\tlr: 1.000e-03, eta: 4:31:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8312\n2023-01-02 22:05:16,548 - mmcls - INFO - Epoch(val) [63][196]\taccuracy_top-1: 62.1700, accuracy_top-5: 83.5240\n2023-01-02 22:05:27,627 - mmcls - INFO - Epoch [64][100/5005]\tlr: 1.000e-03, eta: 4:31:23, time: 0.111, data_time: 0.035, memory: 1125, loss: 1.7874\n2023-01-02 22:05:36,477 - mmcls - INFO - Epoch [64][200/5005]\tlr: 1.000e-03, eta: 4:31:14, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.8018\n2023-01-02 22:05:45,205 - mmcls - INFO - Epoch [64][300/5005]\tlr: 1.000e-03, eta: 4:31:05, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8119\n2023-01-02 22:05:53,878 - mmcls - INFO - Epoch [64][400/5005]\tlr: 1.000e-03, eta: 4:30:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8253\n2023-01-02 22:06:02,552 - mmcls - INFO - Epoch [64][500/5005]\tlr: 1.000e-03, eta: 4:30:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8248\n2023-01-02 22:06:11,247 - mmcls - INFO - Epoch [64][600/5005]\tlr: 1.000e-03, eta: 4:30:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8129\n2023-01-02 22:06:20,088 - mmcls - INFO - Epoch [64][700/5005]\tlr: 1.000e-03, eta: 4:30:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8143\n2023-01-02 22:06:29,100 - mmcls - INFO - Epoch [64][800/5005]\tlr: 1.000e-03, eta: 4:30:21, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7928\n2023-01-02 22:06:37,818 - mmcls - INFO - Epoch [64][900/5005]\tlr: 1.000e-03, eta: 4:30:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8071\n2023-01-02 22:06:46,468 - mmcls - INFO - Epoch [64][1000/5005]\tlr: 1.000e-03, eta: 4:30:04, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7954\n2023-01-02 22:06:55,232 - mmcls - INFO - Epoch [64][1100/5005]\tlr: 1.000e-03, eta: 4:29:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8111\n2023-01-02 22:07:03,841 - mmcls - INFO - Epoch [64][1200/5005]\tlr: 1.000e-03, eta: 4:29:46, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8028\n2023-01-02 22:07:12,595 - mmcls - INFO - Epoch [64][1300/5005]\tlr: 1.000e-03, eta: 4:29:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8233\n2023-01-02 22:07:21,323 - mmcls - INFO - Epoch [64][1400/5005]\tlr: 1.000e-03, eta: 4:29:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8172\n2023-01-02 22:07:29,978 - mmcls - INFO - Epoch [64][1500/5005]\tlr: 1.000e-03, eta: 4:29:19, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7940\n2023-01-02 22:07:38,813 - mmcls - INFO - Epoch [64][1600/5005]\tlr: 1.000e-03, eta: 4:29:11, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7975\n2023-01-02 22:07:47,369 - mmcls - INFO - Epoch [64][1700/5005]\tlr: 1.000e-03, eta: 4:29:02, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.8092\n2023-01-02 22:07:56,068 - mmcls - INFO - Epoch [64][1800/5005]\tlr: 1.000e-03, eta: 4:28:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.8088\n2023-01-02 22:08:04,995 - mmcls - INFO - Epoch [64][1900/5005]\tlr: 1.000e-03, eta: 4:28:44, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.7875\n2023-01-02 22:08:13,521 - mmcls - INFO - Epoch [64][2000/5005]\tlr: 1.000e-03, eta: 4:28:35, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7978\n2023-01-02 22:08:22,230 - mmcls - INFO - Epoch [64][2100/5005]\tlr: 1.000e-03, eta: 4:28:26, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.8198\n2023-01-02 22:08:31,053 - mmcls - INFO - Epoch [64][2200/5005]\tlr: 1.000e-03, eta: 4:28:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8126\n2023-01-02 22:08:39,850 - mmcls - INFO - Epoch [64][2300/5005]\tlr: 1.000e-03, eta: 4:28:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7789\n2023-01-02 22:08:48,416 - mmcls - INFO - Epoch [64][2400/5005]\tlr: 1.000e-03, eta: 4:28:00, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.8229\n2023-01-02 22:08:56,972 - mmcls - INFO - Epoch [64][2500/5005]\tlr: 1.000e-03, eta: 4:27:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8055\n2023-01-02 22:09:05,731 - mmcls - INFO - Epoch [64][2600/5005]\tlr: 1.000e-03, eta: 4:27:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8137\n2023-01-02 22:09:14,620 - mmcls - INFO - Epoch [64][2700/5005]\tlr: 1.000e-03, eta: 4:27:33, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.8243\n2023-01-02 22:09:23,060 - mmcls - INFO - Epoch [64][2800/5005]\tlr: 1.000e-03, eta: 4:27:24, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.8246\n2023-01-02 22:09:31,529 - mmcls - INFO - Epoch [64][2900/5005]\tlr: 1.000e-03, eta: 4:27:15, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.7989\n2023-01-02 22:09:40,189 - mmcls - INFO - Epoch [64][3000/5005]\tlr: 1.000e-03, eta: 4:27:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8013\n2023-01-02 22:09:48,947 - mmcls - INFO - Epoch [64][3100/5005]\tlr: 1.000e-03, eta: 4:26:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8045\n2023-01-02 22:09:57,344 - mmcls - INFO - Epoch [64][3200/5005]\tlr: 1.000e-03, eta: 4:26:49, time: 0.084, data_time: 0.021, memory: 1125, loss: 1.7843\n2023-01-02 22:10:06,171 - mmcls - INFO - Epoch [64][3300/5005]\tlr: 1.000e-03, eta: 4:26:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8047\n2023-01-02 22:10:14,916 - mmcls - INFO - Epoch [64][3400/5005]\tlr: 1.000e-03, eta: 4:26:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8150\n2023-01-02 22:10:23,664 - mmcls - INFO - Epoch [64][3500/5005]\tlr: 1.000e-03, eta: 4:26:22, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7964\n2023-01-02 22:10:32,693 - mmcls - INFO - Epoch [64][3600/5005]\tlr: 1.000e-03, eta: 4:26:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7984\n2023-01-02 22:10:41,147 - mmcls - INFO - Epoch [64][3700/5005]\tlr: 1.000e-03, eta: 4:26:04, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.8228\n2023-01-02 22:10:50,246 - mmcls - INFO - Epoch [64][3800/5005]\tlr: 1.000e-03, eta: 4:25:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.8270\n2023-01-02 22:10:58,953 - mmcls - INFO - Epoch [64][3900/5005]\tlr: 1.000e-03, eta: 4:25:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7950\n2023-01-02 22:11:07,549 - mmcls - INFO - Epoch [64][4000/5005]\tlr: 1.000e-03, eta: 4:25:38, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.8182\n2023-01-02 22:11:16,381 - mmcls - INFO - Epoch [64][4100/5005]\tlr: 1.000e-03, eta: 4:25:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8024\n2023-01-02 22:11:24,998 - mmcls - INFO - Epoch [64][4200/5005]\tlr: 1.000e-03, eta: 4:25:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7892\n2023-01-02 22:11:33,874 - mmcls - INFO - Epoch [64][4300/5005]\tlr: 1.000e-03, eta: 4:25:12, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8183\n2023-01-02 22:11:43,083 - mmcls - INFO - Epoch [64][4400/5005]\tlr: 1.000e-03, eta: 4:25:03, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.8066\n2023-01-02 22:11:51,742 - mmcls - INFO - Epoch [64][4500/5005]\tlr: 1.000e-03, eta: 4:24:54, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8072\n2023-01-02 22:12:00,522 - mmcls - INFO - Epoch [64][4600/5005]\tlr: 1.000e-03, eta: 4:24:45, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7924\n2023-01-02 22:12:09,110 - mmcls - INFO - Epoch [64][4700/5005]\tlr: 1.000e-03, eta: 4:24:36, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8174\n2023-01-02 22:12:17,828 - mmcls - INFO - Epoch [64][4800/5005]\tlr: 1.000e-03, eta: 4:24:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.8275\n2023-01-02 22:12:27,004 - mmcls - INFO - Epoch [64][4900/5005]\tlr: 1.000e-03, eta: 4:24:19, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.8071\n2023-01-02 22:12:35,612 - mmcls - INFO - Epoch [64][5000/5005]\tlr: 1.000e-03, eta: 4:24:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8250\n2023-01-02 22:12:58,435 - mmcls - INFO - Epoch(val) [64][196]\taccuracy_top-1: 62.5880, accuracy_top-5: 83.6740\n2023-01-02 22:13:09,155 - mmcls - INFO - Epoch [65][100/5005]\tlr: 1.000e-03, eta: 4:24:02, time: 0.107, data_time: 0.039, memory: 1125, loss: 1.7930\n2023-01-02 22:13:18,043 - mmcls - INFO - Epoch [65][200/5005]\tlr: 1.000e-03, eta: 4:23:53, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7752\n2023-01-02 22:13:26,989 - mmcls - INFO - Epoch [65][300/5005]\tlr: 1.000e-03, eta: 4:23:44, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7861\n2023-01-02 22:13:35,726 - mmcls - INFO - Epoch [65][400/5005]\tlr: 1.000e-03, eta: 4:23:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8075\n2023-01-02 22:13:44,299 - mmcls - INFO - Epoch [65][500/5005]\tlr: 1.000e-03, eta: 4:23:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7760\n2023-01-02 22:13:53,226 - mmcls - INFO - Epoch [65][600/5005]\tlr: 1.000e-03, eta: 4:23:18, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7985\n2023-01-02 22:14:01,871 - mmcls - INFO - Epoch [65][700/5005]\tlr: 1.000e-03, eta: 4:23:09, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7938\n2023-01-02 22:14:10,497 - mmcls - INFO - Epoch [65][800/5005]\tlr: 1.000e-03, eta: 4:23:00, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7914\n2023-01-02 22:14:19,410 - mmcls - INFO - Epoch [65][900/5005]\tlr: 1.000e-03, eta: 4:22:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7970\n2023-01-02 22:14:28,013 - mmcls - INFO - Epoch [65][1000/5005]\tlr: 1.000e-03, eta: 4:22:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7994\n2023-01-02 22:14:36,927 - mmcls - INFO - Epoch [65][1100/5005]\tlr: 1.000e-03, eta: 4:22:34, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.8219\n2023-01-02 22:14:46,026 - mmcls - INFO - Epoch [65][1200/5005]\tlr: 1.000e-03, eta: 4:22:25, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7978\n2023-01-02 22:14:54,878 - mmcls - INFO - Epoch [65][1300/5005]\tlr: 1.000e-03, eta: 4:22:16, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8048\n2023-01-02 22:15:03,731 - mmcls - INFO - Epoch [65][1400/5005]\tlr: 1.000e-03, eta: 4:22:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8074\n2023-01-02 22:15:12,745 - mmcls - INFO - Epoch [65][1500/5005]\tlr: 1.000e-03, eta: 4:21:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7850\n2023-01-02 22:15:21,622 - mmcls - INFO - Epoch [65][1600/5005]\tlr: 1.000e-03, eta: 4:21:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7975\n2023-01-02 22:15:30,301 - mmcls - INFO - Epoch [65][1700/5005]\tlr: 1.000e-03, eta: 4:21:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8093\n2023-01-02 22:15:39,362 - mmcls - INFO - Epoch [65][1800/5005]\tlr: 1.000e-03, eta: 4:21:32, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7929\n2023-01-02 22:15:48,272 - mmcls - INFO - Epoch [65][1900/5005]\tlr: 1.000e-03, eta: 4:21:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8227\n2023-01-02 22:15:56,817 - mmcls - INFO - Epoch [65][2000/5005]\tlr: 1.000e-03, eta: 4:21:15, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7949\n2023-01-02 22:16:05,615 - mmcls - INFO - Epoch [65][2100/5005]\tlr: 1.000e-03, eta: 4:21:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8032\n2023-01-02 22:16:14,187 - mmcls - INFO - Epoch [65][2200/5005]\tlr: 1.000e-03, eta: 4:20:57, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.8033\n2023-01-02 22:16:22,980 - mmcls - INFO - Epoch [65][2300/5005]\tlr: 1.000e-03, eta: 4:20:48, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8218\n2023-01-02 22:16:31,900 - mmcls - INFO - Epoch [65][2400/5005]\tlr: 1.000e-03, eta: 4:20:40, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.8032\n2023-01-02 22:16:40,668 - mmcls - INFO - Epoch [65][2500/5005]\tlr: 1.000e-03, eta: 4:20:31, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7926\n2023-01-02 22:16:49,245 - mmcls - INFO - Epoch [65][2600/5005]\tlr: 1.000e-03, eta: 4:20:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7900\n2023-01-02 22:16:58,292 - mmcls - INFO - Epoch [65][2700/5005]\tlr: 1.000e-03, eta: 4:20:13, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7912\n2023-01-02 22:17:06,919 - mmcls - INFO - Epoch [65][2800/5005]\tlr: 1.000e-03, eta: 4:20:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7982\n2023-01-02 22:17:15,852 - mmcls - INFO - Epoch [65][2900/5005]\tlr: 1.000e-03, eta: 4:19:56, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8014\n2023-01-02 22:17:24,496 - mmcls - INFO - Epoch [65][3000/5005]\tlr: 1.000e-03, eta: 4:19:47, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.8020\n2023-01-02 22:17:33,311 - mmcls - INFO - Epoch [65][3100/5005]\tlr: 1.000e-03, eta: 4:19:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8249\n2023-01-02 22:17:42,244 - mmcls - INFO - Epoch [65][3200/5005]\tlr: 1.000e-03, eta: 4:19:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.8001\n2023-01-02 22:17:51,284 - mmcls - INFO - Epoch [65][3300/5005]\tlr: 1.000e-03, eta: 4:19:21, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.8089\n2023-01-02 22:17:59,862 - mmcls - INFO - Epoch [65][3400/5005]\tlr: 1.000e-03, eta: 4:19:12, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7910\n2023-01-02 22:18:08,510 - mmcls - INFO - Epoch [65][3500/5005]\tlr: 1.000e-03, eta: 4:19:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8082\n2023-01-02 22:18:17,614 - mmcls - INFO - Epoch [65][3600/5005]\tlr: 1.000e-03, eta: 4:18:54, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.8011\n2023-01-02 22:18:26,517 - mmcls - INFO - Epoch [65][3700/5005]\tlr: 1.000e-03, eta: 4:18:45, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8130\n2023-01-02 22:18:35,551 - mmcls - INFO - Epoch [65][3800/5005]\tlr: 1.000e-03, eta: 4:18:37, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.8175\n2023-01-02 22:18:44,424 - mmcls - INFO - Epoch [65][3900/5005]\tlr: 1.000e-03, eta: 4:18:28, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8097\n2023-01-02 22:18:53,388 - mmcls - INFO - Epoch [65][4000/5005]\tlr: 1.000e-03, eta: 4:18:19, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.8081\n2023-01-02 22:19:01,866 - mmcls - INFO - Epoch [65][4100/5005]\tlr: 1.000e-03, eta: 4:18:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7829\n2023-01-02 22:19:10,588 - mmcls - INFO - Epoch [65][4200/5005]\tlr: 1.000e-03, eta: 4:18:01, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7939\n2023-01-02 22:19:18,973 - mmcls - INFO - Epoch [65][4300/5005]\tlr: 1.000e-03, eta: 4:17:52, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.7972\n2023-01-02 22:19:27,970 - mmcls - INFO - Epoch [65][4400/5005]\tlr: 1.000e-03, eta: 4:17:44, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.8000\n2023-01-02 22:19:36,958 - mmcls - INFO - Epoch [65][4500/5005]\tlr: 1.000e-03, eta: 4:17:35, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7767\n2023-01-02 22:19:45,576 - mmcls - INFO - Epoch [65][4600/5005]\tlr: 1.000e-03, eta: 4:17:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7862\n2023-01-02 22:19:54,402 - mmcls - INFO - Epoch [65][4700/5005]\tlr: 1.000e-03, eta: 4:17:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7839\n2023-01-02 22:20:03,006 - mmcls - INFO - Epoch [65][4800/5005]\tlr: 1.000e-03, eta: 4:17:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7920\n2023-01-02 22:20:11,994 - mmcls - INFO - Epoch [65][4900/5005]\tlr: 1.000e-03, eta: 4:17:00, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7852\n2023-01-02 22:20:20,941 - mmcls - INFO - Epoch [65][5000/5005]\tlr: 1.000e-03, eta: 4:16:51, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.8156\n2023-01-02 22:20:43,601 - mmcls - INFO - Epoch(val) [65][196]\taccuracy_top-1: 62.6320, accuracy_top-5: 83.7080\n2023-01-02 22:20:54,503 - mmcls - INFO - Epoch [66][100/5005]\tlr: 1.000e-03, eta: 4:16:43, time: 0.109, data_time: 0.041, memory: 1125, loss: 1.7912\n2023-01-02 22:21:03,889 - mmcls - INFO - Epoch [66][200/5005]\tlr: 1.000e-03, eta: 4:16:34, time: 0.094, data_time: 0.012, memory: 1125, loss: 1.8160\n2023-01-02 22:21:12,344 - mmcls - INFO - Epoch [66][300/5005]\tlr: 1.000e-03, eta: 4:16:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7760\n2023-01-02 22:21:21,768 - mmcls - INFO - Epoch [66][400/5005]\tlr: 1.000e-03, eta: 4:16:17, time: 0.094, data_time: 0.004, memory: 1125, loss: 1.7805\n2023-01-02 22:21:30,582 - mmcls - INFO - Epoch [66][500/5005]\tlr: 1.000e-03, eta: 4:16:08, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7738\n2023-01-02 22:21:39,517 - mmcls - INFO - Epoch [66][600/5005]\tlr: 1.000e-03, eta: 4:15:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7803\n2023-01-02 22:21:48,411 - mmcls - INFO - Epoch [66][700/5005]\tlr: 1.000e-03, eta: 4:15:50, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7723\n2023-01-02 22:21:56,891 - mmcls - INFO - Epoch [66][800/5005]\tlr: 1.000e-03, eta: 4:15:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7863\n2023-01-02 22:22:05,827 - mmcls - INFO - Epoch [66][900/5005]\tlr: 1.000e-03, eta: 4:15:33, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8146\n2023-01-02 22:22:14,562 - mmcls - INFO - Epoch [66][1000/5005]\tlr: 1.000e-03, eta: 4:15:24, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7901\n2023-01-02 22:22:23,302 - mmcls - INFO - Epoch [66][1100/5005]\tlr: 1.000e-03, eta: 4:15:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7841\n2023-01-02 22:22:32,406 - mmcls - INFO - Epoch [66][1200/5005]\tlr: 1.000e-03, eta: 4:15:06, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.8000\n2023-01-02 22:22:40,998 - mmcls - INFO - Epoch [66][1300/5005]\tlr: 1.000e-03, eta: 4:14:58, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7669\n2023-01-02 22:22:49,645 - mmcls - INFO - Epoch [66][1400/5005]\tlr: 1.000e-03, eta: 4:14:49, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7669\n2023-01-02 22:22:58,246 - mmcls - INFO - Epoch [66][1500/5005]\tlr: 1.000e-03, eta: 4:14:40, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7880\n2023-01-02 22:23:06,967 - mmcls - INFO - Epoch [66][1600/5005]\tlr: 1.000e-03, eta: 4:14:31, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7752\n2023-01-02 22:23:15,628 - mmcls - INFO - Epoch [66][1700/5005]\tlr: 1.000e-03, eta: 4:14:22, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7887\n2023-01-02 22:23:24,510 - mmcls - INFO - Epoch [66][1800/5005]\tlr: 1.000e-03, eta: 4:14:13, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7767\n2023-01-02 22:23:33,042 - mmcls - INFO - Epoch [66][1900/5005]\tlr: 1.000e-03, eta: 4:14:04, time: 0.085, data_time: 0.047, memory: 1125, loss: 1.7948\n2023-01-02 22:23:41,755 - mmcls - INFO - Epoch [66][2000/5005]\tlr: 1.000e-03, eta: 4:13:56, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7963\n2023-01-02 22:23:50,290 - mmcls - INFO - Epoch [66][2100/5005]\tlr: 1.000e-03, eta: 4:13:47, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7995\n2023-01-02 22:23:58,997 - mmcls - INFO - Epoch [66][2200/5005]\tlr: 1.000e-03, eta: 4:13:38, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8123\n2023-01-02 22:24:07,898 - mmcls - INFO - Epoch [66][2300/5005]\tlr: 1.000e-03, eta: 4:13:29, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7972\n2023-01-02 22:24:16,512 - mmcls - INFO - Epoch [66][2400/5005]\tlr: 1.000e-03, eta: 4:13:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7566\n2023-01-02 22:24:25,084 - mmcls - INFO - Epoch [66][2500/5005]\tlr: 1.000e-03, eta: 4:13:11, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7912\n2023-01-02 22:24:33,891 - mmcls - INFO - Epoch [66][2600/5005]\tlr: 1.000e-03, eta: 4:13:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8072\n2023-01-02 22:24:42,506 - mmcls - INFO - Epoch [66][2700/5005]\tlr: 1.000e-03, eta: 4:12:53, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7881\n2023-01-02 22:24:51,184 - mmcls - INFO - Epoch [66][2800/5005]\tlr: 1.000e-03, eta: 4:12:45, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7906\n2023-01-02 22:25:00,133 - mmcls - INFO - Epoch [66][2900/5005]\tlr: 1.000e-03, eta: 4:12:36, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.8072\n2023-01-02 22:25:08,846 - mmcls - INFO - Epoch [66][3000/5005]\tlr: 1.000e-03, eta: 4:12:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8021\n2023-01-02 22:25:17,540 - mmcls - INFO - Epoch [66][3100/5005]\tlr: 1.000e-03, eta: 4:12:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8029\n2023-01-02 22:25:26,158 - mmcls - INFO - Epoch [66][3200/5005]\tlr: 1.000e-03, eta: 4:12:09, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7690\n2023-01-02 22:25:34,853 - mmcls - INFO - Epoch [66][3300/5005]\tlr: 1.000e-03, eta: 4:12:00, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7924\n2023-01-02 22:25:43,521 - mmcls - INFO - Epoch [66][3400/5005]\tlr: 1.000e-03, eta: 4:11:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7825\n2023-01-02 22:25:52,505 - mmcls - INFO - Epoch [66][3500/5005]\tlr: 1.000e-03, eta: 4:11:43, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7679\n2023-01-02 22:26:01,504 - mmcls - INFO - Epoch [66][3600/5005]\tlr: 1.000e-03, eta: 4:11:34, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.8048\n2023-01-02 22:26:10,308 - mmcls - INFO - Epoch [66][3700/5005]\tlr: 1.000e-03, eta: 4:11:25, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7868\n2023-01-02 22:26:19,027 - mmcls - INFO - Epoch [66][3800/5005]\tlr: 1.000e-03, eta: 4:11:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7853\n2023-01-02 22:26:27,846 - mmcls - INFO - Epoch [66][3900/5005]\tlr: 1.000e-03, eta: 4:11:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7979\n2023-01-02 22:26:36,740 - mmcls - INFO - Epoch [66][4000/5005]\tlr: 1.000e-03, eta: 4:10:59, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7921\n2023-01-02 22:26:45,370 - mmcls - INFO - Epoch [66][4100/5005]\tlr: 1.000e-03, eta: 4:10:50, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.8058\n2023-01-02 22:26:53,732 - mmcls - INFO - Epoch [66][4200/5005]\tlr: 1.000e-03, eta: 4:10:41, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.8000\n2023-01-02 22:27:02,682 - mmcls - INFO - Epoch [66][4300/5005]\tlr: 1.000e-03, eta: 4:10:32, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.8140\n2023-01-02 22:27:11,294 - mmcls - INFO - Epoch [66][4400/5005]\tlr: 1.000e-03, eta: 4:10:24, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7791\n2023-01-02 22:27:19,902 - mmcls - INFO - Epoch [66][4500/5005]\tlr: 1.000e-03, eta: 4:10:15, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7746\n2023-01-02 22:27:28,506 - mmcls - INFO - Epoch [66][4600/5005]\tlr: 1.000e-03, eta: 4:10:06, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8259\n2023-01-02 22:27:37,550 - mmcls - INFO - Epoch [66][4700/5005]\tlr: 1.000e-03, eta: 4:09:57, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7891\n2023-01-02 22:27:46,185 - mmcls - INFO - Epoch [66][4800/5005]\tlr: 1.000e-03, eta: 4:09:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8120\n2023-01-02 22:27:55,035 - mmcls - INFO - Epoch [66][4900/5005]\tlr: 1.000e-03, eta: 4:09:39, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.8052\n2023-01-02 22:28:04,138 - mmcls - INFO - Epoch [66][5000/5005]\tlr: 1.000e-03, eta: 4:09:31, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.8014\n2023-01-02 22:28:26,821 - mmcls - INFO - Epoch(val) [66][196]\taccuracy_top-1: 62.5700, accuracy_top-5: 83.5600\n2023-01-02 22:28:37,913 - mmcls - INFO - Epoch [67][100/5005]\tlr: 1.000e-03, eta: 4:09:22, time: 0.111, data_time: 0.031, memory: 1125, loss: 1.7807\n2023-01-02 22:28:46,425 - mmcls - INFO - Epoch [67][200/5005]\tlr: 1.000e-03, eta: 4:09:14, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7920\n2023-01-02 22:28:55,133 - mmcls - INFO - Epoch [67][300/5005]\tlr: 1.000e-03, eta: 4:09:05, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.8262\n2023-01-02 22:29:03,584 - mmcls - INFO - Epoch [67][400/5005]\tlr: 1.000e-03, eta: 4:08:56, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7777\n2023-01-02 22:29:12,632 - mmcls - INFO - Epoch [67][500/5005]\tlr: 1.000e-03, eta: 4:08:47, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7687\n2023-01-02 22:29:21,357 - mmcls - INFO - Epoch [67][600/5005]\tlr: 1.000e-03, eta: 4:08:38, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7743\n2023-01-02 22:29:30,522 - mmcls - INFO - Epoch [67][700/5005]\tlr: 1.000e-03, eta: 4:08:30, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7759\n2023-01-02 22:29:39,220 - mmcls - INFO - Epoch [67][800/5005]\tlr: 1.000e-03, eta: 4:08:21, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7892\n2023-01-02 22:29:47,927 - mmcls - INFO - Epoch [67][900/5005]\tlr: 1.000e-03, eta: 4:08:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7807\n2023-01-02 22:29:56,765 - mmcls - INFO - Epoch [67][1000/5005]\tlr: 1.000e-03, eta: 4:08:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8116\n2023-01-02 22:30:05,627 - mmcls - INFO - Epoch [67][1100/5005]\tlr: 1.000e-03, eta: 4:07:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7926\n2023-01-02 22:30:14,287 - mmcls - INFO - Epoch [67][1200/5005]\tlr: 1.000e-03, eta: 4:07:45, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.8031\n2023-01-02 22:30:22,937 - mmcls - INFO - Epoch [67][1300/5005]\tlr: 1.000e-03, eta: 4:07:37, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7795\n2023-01-02 22:30:31,554 - mmcls - INFO - Epoch [67][1400/5005]\tlr: 1.000e-03, eta: 4:07:28, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7691\n2023-01-02 22:30:40,577 - mmcls - INFO - Epoch [67][1500/5005]\tlr: 1.000e-03, eta: 4:07:19, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7867\n2023-01-02 22:30:49,153 - mmcls - INFO - Epoch [67][1600/5005]\tlr: 1.000e-03, eta: 4:07:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7923\n2023-01-02 22:30:58,173 - mmcls - INFO - Epoch [67][1700/5005]\tlr: 1.000e-03, eta: 4:07:01, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7753\n2023-01-02 22:31:06,963 - mmcls - INFO - Epoch [67][1800/5005]\tlr: 1.000e-03, eta: 4:06:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7847\n2023-01-02 22:31:15,926 - mmcls - INFO - Epoch [67][1900/5005]\tlr: 1.000e-03, eta: 4:06:44, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7612\n2023-01-02 22:31:24,679 - mmcls - INFO - Epoch [67][2000/5005]\tlr: 1.000e-03, eta: 4:06:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7965\n2023-01-02 22:31:33,402 - mmcls - INFO - Epoch [67][2100/5005]\tlr: 1.000e-03, eta: 4:06:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7882\n2023-01-02 22:31:42,099 - mmcls - INFO - Epoch [67][2200/5005]\tlr: 1.000e-03, eta: 4:06:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7912\n2023-01-02 22:31:50,965 - mmcls - INFO - Epoch [67][2300/5005]\tlr: 1.000e-03, eta: 4:06:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7994\n2023-01-02 22:31:59,522 - mmcls - INFO - Epoch [67][2400/5005]\tlr: 1.000e-03, eta: 4:06:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7921\n2023-01-02 22:32:08,466 - mmcls - INFO - Epoch [67][2500/5005]\tlr: 1.000e-03, eta: 4:05:51, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7818\n2023-01-02 22:32:17,158 - mmcls - INFO - Epoch [67][2600/5005]\tlr: 1.000e-03, eta: 4:05:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8064\n2023-01-02 22:32:25,804 - mmcls - INFO - Epoch [67][2700/5005]\tlr: 1.000e-03, eta: 4:05:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.8094\n2023-01-02 22:32:34,279 - mmcls - INFO - Epoch [67][2800/5005]\tlr: 1.000e-03, eta: 4:05:24, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7949\n2023-01-02 22:32:43,258 - mmcls - INFO - Epoch [67][2900/5005]\tlr: 1.000e-03, eta: 4:05:16, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.8009\n2023-01-02 22:32:51,849 - mmcls - INFO - Epoch [67][3000/5005]\tlr: 1.000e-03, eta: 4:05:07, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7687\n2023-01-02 22:33:00,586 - mmcls - INFO - Epoch [67][3100/5005]\tlr: 1.000e-03, eta: 4:04:58, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7819\n2023-01-02 22:33:09,460 - mmcls - INFO - Epoch [67][3200/5005]\tlr: 1.000e-03, eta: 4:04:49, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7726\n2023-01-02 22:33:18,358 - mmcls - INFO - Epoch [67][3300/5005]\tlr: 1.000e-03, eta: 4:04:40, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7817\n2023-01-02 22:33:27,054 - mmcls - INFO - Epoch [67][3400/5005]\tlr: 1.000e-03, eta: 4:04:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7803\n2023-01-02 22:33:35,959 - mmcls - INFO - Epoch [67][3500/5005]\tlr: 1.000e-03, eta: 4:04:23, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7690\n2023-01-02 22:33:44,994 - mmcls - INFO - Epoch [67][3600/5005]\tlr: 1.000e-03, eta: 4:04:14, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7872\n2023-01-02 22:33:53,758 - mmcls - INFO - Epoch [67][3700/5005]\tlr: 1.000e-03, eta: 4:04:05, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7903\n2023-01-02 22:34:02,434 - mmcls - INFO - Epoch [67][3800/5005]\tlr: 1.000e-03, eta: 4:03:56, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7882\n2023-01-02 22:34:11,088 - mmcls - INFO - Epoch [67][3900/5005]\tlr: 1.000e-03, eta: 4:03:48, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7828\n2023-01-02 22:34:19,861 - mmcls - INFO - Epoch [67][4000/5005]\tlr: 1.000e-03, eta: 4:03:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8140\n2023-01-02 22:34:28,609 - mmcls - INFO - Epoch [67][4100/5005]\tlr: 1.000e-03, eta: 4:03:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7938\n2023-01-02 22:34:37,325 - mmcls - INFO - Epoch [67][4200/5005]\tlr: 1.000e-03, eta: 4:03:21, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8141\n2023-01-02 22:34:45,921 - mmcls - INFO - Epoch [67][4300/5005]\tlr: 1.000e-03, eta: 4:03:12, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7934\n2023-01-02 22:34:54,626 - mmcls - INFO - Epoch [67][4400/5005]\tlr: 1.000e-03, eta: 4:03:03, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7897\n2023-01-02 22:35:03,031 - mmcls - INFO - Epoch [67][4500/5005]\tlr: 1.000e-03, eta: 4:02:54, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7899\n2023-01-02 22:35:11,540 - mmcls - INFO - Epoch [67][4600/5005]\tlr: 1.000e-03, eta: 4:02:45, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7903\n2023-01-02 22:35:20,057 - mmcls - INFO - Epoch [67][4700/5005]\tlr: 1.000e-03, eta: 4:02:36, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7794\n2023-01-02 22:35:28,784 - mmcls - INFO - Epoch [67][4800/5005]\tlr: 1.000e-03, eta: 4:02:28, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7852\n2023-01-02 22:35:37,568 - mmcls - INFO - Epoch [67][4900/5005]\tlr: 1.000e-03, eta: 4:02:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7788\n2023-01-02 22:35:46,161 - mmcls - INFO - Epoch [67][5000/5005]\tlr: 1.000e-03, eta: 4:02:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7931\n2023-01-02 22:36:08,896 - mmcls - INFO - Epoch(val) [67][196]\taccuracy_top-1: 62.7560, accuracy_top-5: 83.7000\n2023-01-02 22:36:19,793 - mmcls - INFO - Epoch [68][100/5005]\tlr: 1.000e-03, eta: 4:02:01, time: 0.109, data_time: 0.040, memory: 1125, loss: 1.7695\n2023-01-02 22:36:28,445 - mmcls - INFO - Epoch [68][200/5005]\tlr: 1.000e-03, eta: 4:01:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7762\n2023-01-02 22:36:36,893 - mmcls - INFO - Epoch [68][300/5005]\tlr: 1.000e-03, eta: 4:01:44, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7678\n2023-01-02 22:36:45,556 - mmcls - INFO - Epoch [68][400/5005]\tlr: 1.000e-03, eta: 4:01:35, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7854\n2023-01-02 22:36:54,465 - mmcls - INFO - Epoch [68][500/5005]\tlr: 1.000e-03, eta: 4:01:26, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7620\n2023-01-02 22:37:03,185 - mmcls - INFO - Epoch [68][600/5005]\tlr: 1.000e-03, eta: 4:01:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7990\n2023-01-02 22:37:11,814 - mmcls - INFO - Epoch [68][700/5005]\tlr: 1.000e-03, eta: 4:01:08, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7924\n2023-01-02 22:37:20,720 - mmcls - INFO - Epoch [68][800/5005]\tlr: 1.000e-03, eta: 4:01:00, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7847\n2023-01-02 22:37:29,461 - mmcls - INFO - Epoch [68][900/5005]\tlr: 1.000e-03, eta: 4:00:51, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7724\n2023-01-02 22:37:38,196 - mmcls - INFO - Epoch [68][1000/5005]\tlr: 1.000e-03, eta: 4:00:42, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7867\n2023-01-02 22:37:46,982 - mmcls - INFO - Epoch [68][1100/5005]\tlr: 1.000e-03, eta: 4:00:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7769\n2023-01-02 22:37:55,531 - mmcls - INFO - Epoch [68][1200/5005]\tlr: 1.000e-03, eta: 4:00:24, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7503\n2023-01-02 22:38:04,589 - mmcls - INFO - Epoch [68][1300/5005]\tlr: 1.000e-03, eta: 4:00:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7724\n2023-01-02 22:38:13,401 - mmcls - INFO - Epoch [68][1400/5005]\tlr: 1.000e-03, eta: 4:00:07, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7670\n2023-01-02 22:38:21,883 - mmcls - INFO - Epoch [68][1500/5005]\tlr: 1.000e-03, eta: 3:59:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7923\n2023-01-02 22:38:30,558 - mmcls - INFO - Epoch [68][1600/5005]\tlr: 1.000e-03, eta: 3:59:49, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7892\n2023-01-02 22:38:39,770 - mmcls - INFO - Epoch [68][1700/5005]\tlr: 1.000e-03, eta: 3:59:40, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7817\n2023-01-02 22:38:48,793 - mmcls - INFO - Epoch [68][1800/5005]\tlr: 1.000e-03, eta: 3:59:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7874\n2023-01-02 22:38:57,391 - mmcls - INFO - Epoch [68][1900/5005]\tlr: 1.000e-03, eta: 3:59:23, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7915\n2023-01-02 22:39:06,188 - mmcls - INFO - Epoch [68][2000/5005]\tlr: 1.000e-03, eta: 3:59:14, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7769\n2023-01-02 22:39:15,080 - mmcls - INFO - Epoch [68][2100/5005]\tlr: 1.000e-03, eta: 3:59:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7930\n2023-01-02 22:39:24,069 - mmcls - INFO - Epoch [68][2200/5005]\tlr: 1.000e-03, eta: 3:58:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7912\n2023-01-02 22:39:32,699 - mmcls - INFO - Epoch [68][2300/5005]\tlr: 1.000e-03, eta: 3:58:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7754\n2023-01-02 22:39:41,473 - mmcls - INFO - Epoch [68][2400/5005]\tlr: 1.000e-03, eta: 3:58:39, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7725\n2023-01-02 22:39:50,218 - mmcls - INFO - Epoch [68][2500/5005]\tlr: 1.000e-03, eta: 3:58:30, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7839\n2023-01-02 22:39:58,884 - mmcls - INFO - Epoch [68][2600/5005]\tlr: 1.000e-03, eta: 3:58:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7794\n2023-01-02 22:40:07,595 - mmcls - INFO - Epoch [68][2700/5005]\tlr: 1.000e-03, eta: 3:58:12, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7689\n2023-01-02 22:40:16,550 - mmcls - INFO - Epoch [68][2800/5005]\tlr: 1.000e-03, eta: 3:58:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7820\n2023-01-02 22:40:25,131 - mmcls - INFO - Epoch [68][2900/5005]\tlr: 1.000e-03, eta: 3:57:55, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7608\n2023-01-02 22:40:33,956 - mmcls - INFO - Epoch [68][3000/5005]\tlr: 1.000e-03, eta: 3:57:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7718\n2023-01-02 22:40:42,769 - mmcls - INFO - Epoch [68][3100/5005]\tlr: 1.000e-03, eta: 3:57:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7583\n2023-01-02 22:40:51,815 - mmcls - INFO - Epoch [68][3200/5005]\tlr: 1.000e-03, eta: 3:57:28, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.8122\n2023-01-02 22:41:00,462 - mmcls - INFO - Epoch [68][3300/5005]\tlr: 1.000e-03, eta: 3:57:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7680\n2023-01-02 22:41:08,919 - mmcls - INFO - Epoch [68][3400/5005]\tlr: 1.000e-03, eta: 3:57:11, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7833\n2023-01-02 22:41:17,765 - mmcls - INFO - Epoch [68][3500/5005]\tlr: 1.000e-03, eta: 3:57:02, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7737\n2023-01-02 22:41:26,414 - mmcls - INFO - Epoch [68][3600/5005]\tlr: 1.000e-03, eta: 3:56:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7859\n2023-01-02 22:41:35,539 - mmcls - INFO - Epoch [68][3700/5005]\tlr: 1.000e-03, eta: 3:56:44, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8051\n2023-01-02 22:41:43,976 - mmcls - INFO - Epoch [68][3800/5005]\tlr: 1.000e-03, eta: 3:56:35, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7783\n2023-01-02 22:41:52,756 - mmcls - INFO - Epoch [68][3900/5005]\tlr: 1.000e-03, eta: 3:56:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7794\n2023-01-02 22:42:01,623 - mmcls - INFO - Epoch [68][4000/5005]\tlr: 1.000e-03, eta: 3:56:18, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7912\n2023-01-02 22:42:10,185 - mmcls - INFO - Epoch [68][4100/5005]\tlr: 1.000e-03, eta: 3:56:09, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7939\n2023-01-02 22:42:18,761 - mmcls - INFO - Epoch [68][4200/5005]\tlr: 1.000e-03, eta: 3:56:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7960\n2023-01-02 22:42:27,842 - mmcls - INFO - Epoch [68][4300/5005]\tlr: 1.000e-03, eta: 3:55:51, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7652\n2023-01-02 22:42:36,547 - mmcls - INFO - Epoch [68][4400/5005]\tlr: 1.000e-03, eta: 3:55:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7619\n2023-01-02 22:42:45,405 - mmcls - INFO - Epoch [68][4500/5005]\tlr: 1.000e-03, eta: 3:55:34, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7681\n2023-01-02 22:42:54,437 - mmcls - INFO - Epoch [68][4600/5005]\tlr: 1.000e-03, eta: 3:55:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7910\n2023-01-02 22:43:03,127 - mmcls - INFO - Epoch [68][4700/5005]\tlr: 1.000e-03, eta: 3:55:16, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7864\n2023-01-02 22:43:11,863 - mmcls - INFO - Epoch [68][4800/5005]\tlr: 1.000e-03, eta: 3:55:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7858\n2023-01-02 22:43:20,876 - mmcls - INFO - Epoch [68][4900/5005]\tlr: 1.000e-03, eta: 3:54:59, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7869\n2023-01-02 22:43:29,920 - mmcls - INFO - Epoch [68][5000/5005]\tlr: 1.000e-03, eta: 3:54:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7863\n2023-01-02 22:43:52,536 - mmcls - INFO - Epoch(val) [68][196]\taccuracy_top-1: 62.8000, accuracy_top-5: 83.8520\n2023-01-02 22:44:03,573 - mmcls - INFO - Epoch [69][100/5005]\tlr: 1.000e-03, eta: 3:54:42, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.7917\n2023-01-02 22:44:12,200 - mmcls - INFO - Epoch [69][200/5005]\tlr: 1.000e-03, eta: 3:54:33, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7605\n2023-01-02 22:44:21,122 - mmcls - INFO - Epoch [69][300/5005]\tlr: 1.000e-03, eta: 3:54:24, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7816\n2023-01-02 22:44:30,023 - mmcls - INFO - Epoch [69][400/5005]\tlr: 1.000e-03, eta: 3:54:15, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7613\n2023-01-02 22:44:38,783 - mmcls - INFO - Epoch [69][500/5005]\tlr: 1.000e-03, eta: 3:54:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7821\n2023-01-02 22:44:47,565 - mmcls - INFO - Epoch [69][600/5005]\tlr: 1.000e-03, eta: 3:53:58, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7774\n2023-01-02 22:44:56,535 - mmcls - INFO - Epoch [69][700/5005]\tlr: 1.000e-03, eta: 3:53:49, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7600\n2023-01-02 22:45:05,243 - mmcls - INFO - Epoch [69][800/5005]\tlr: 1.000e-03, eta: 3:53:40, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7811\n2023-01-02 22:45:14,138 - mmcls - INFO - Epoch [69][900/5005]\tlr: 1.000e-03, eta: 3:53:31, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7853\n2023-01-02 22:45:22,616 - mmcls - INFO - Epoch [69][1000/5005]\tlr: 1.000e-03, eta: 3:53:22, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7707\n2023-01-02 22:45:31,301 - mmcls - INFO - Epoch [69][1100/5005]\tlr: 1.000e-03, eta: 3:53:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7808\n2023-01-02 22:45:40,297 - mmcls - INFO - Epoch [69][1200/5005]\tlr: 1.000e-03, eta: 3:53:05, time: 0.090, data_time: 0.000, memory: 1125, loss: 1.7677\n2023-01-02 22:45:49,161 - mmcls - INFO - Epoch [69][1300/5005]\tlr: 1.000e-03, eta: 3:52:56, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7735\n2023-01-02 22:45:57,828 - mmcls - INFO - Epoch [69][1400/5005]\tlr: 1.000e-03, eta: 3:52:47, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7866\n2023-01-02 22:46:06,608 - mmcls - INFO - Epoch [69][1500/5005]\tlr: 1.000e-03, eta: 3:52:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7836\n2023-01-02 22:46:15,302 - mmcls - INFO - Epoch [69][1600/5005]\tlr: 1.000e-03, eta: 3:52:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7771\n2023-01-02 22:46:24,046 - mmcls - INFO - Epoch [69][1700/5005]\tlr: 1.000e-03, eta: 3:52:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7659\n2023-01-02 22:46:32,658 - mmcls - INFO - Epoch [69][1800/5005]\tlr: 1.000e-03, eta: 3:52:12, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7702\n2023-01-02 22:46:41,635 - mmcls - INFO - Epoch [69][1900/5005]\tlr: 1.000e-03, eta: 3:52:03, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7900\n2023-01-02 22:46:50,395 - mmcls - INFO - Epoch [69][2000/5005]\tlr: 1.000e-03, eta: 3:51:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7660\n2023-01-02 22:46:59,225 - mmcls - INFO - Epoch [69][2100/5005]\tlr: 1.000e-03, eta: 3:51:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7560\n2023-01-02 22:47:07,834 - mmcls - INFO - Epoch [69][2200/5005]\tlr: 1.000e-03, eta: 3:51:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7884\n2023-01-02 22:47:16,684 - mmcls - INFO - Epoch [69][2300/5005]\tlr: 1.000e-03, eta: 3:51:28, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7781\n2023-01-02 22:47:25,377 - mmcls - INFO - Epoch [69][2400/5005]\tlr: 1.000e-03, eta: 3:51:19, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7639\n2023-01-02 22:47:33,974 - mmcls - INFO - Epoch [69][2500/5005]\tlr: 1.000e-03, eta: 3:51:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7793\n2023-01-02 22:47:42,675 - mmcls - INFO - Epoch [69][2600/5005]\tlr: 1.000e-03, eta: 3:51:01, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7620\n2023-01-02 22:47:51,233 - mmcls - INFO - Epoch [69][2700/5005]\tlr: 1.000e-03, eta: 3:50:52, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7869\n2023-01-02 22:48:00,060 - mmcls - INFO - Epoch [69][2800/5005]\tlr: 1.000e-03, eta: 3:50:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8169\n2023-01-02 22:48:08,620 - mmcls - INFO - Epoch [69][2900/5005]\tlr: 1.000e-03, eta: 3:50:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7808\n2023-01-02 22:48:17,769 - mmcls - INFO - Epoch [69][3000/5005]\tlr: 1.000e-03, eta: 3:50:26, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7812\n2023-01-02 22:48:26,291 - mmcls - INFO - Epoch [69][3100/5005]\tlr: 1.000e-03, eta: 3:50:17, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7609\n2023-01-02 22:48:35,115 - mmcls - INFO - Epoch [69][3200/5005]\tlr: 1.000e-03, eta: 3:50:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7591\n2023-01-02 22:48:43,782 - mmcls - INFO - Epoch [69][3300/5005]\tlr: 1.000e-03, eta: 3:49:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7616\n2023-01-02 22:48:52,561 - mmcls - INFO - Epoch [69][3400/5005]\tlr: 1.000e-03, eta: 3:49:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7736\n2023-01-02 22:49:01,575 - mmcls - INFO - Epoch [69][3500/5005]\tlr: 1.000e-03, eta: 3:49:42, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7539\n2023-01-02 22:49:10,426 - mmcls - INFO - Epoch [69][3600/5005]\tlr: 1.000e-03, eta: 3:49:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7725\n2023-01-02 22:49:19,352 - mmcls - INFO - Epoch [69][3700/5005]\tlr: 1.000e-03, eta: 3:49:24, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7652\n2023-01-02 22:49:27,924 - mmcls - INFO - Epoch [69][3800/5005]\tlr: 1.000e-03, eta: 3:49:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7585\n2023-01-02 22:49:36,760 - mmcls - INFO - Epoch [69][3900/5005]\tlr: 1.000e-03, eta: 3:49:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7845\n2023-01-02 22:49:45,375 - mmcls - INFO - Epoch [69][4000/5005]\tlr: 1.000e-03, eta: 3:48:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7823\n2023-01-02 22:49:53,788 - mmcls - INFO - Epoch [69][4100/5005]\tlr: 1.000e-03, eta: 3:48:49, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7717\n2023-01-02 22:50:02,639 - mmcls - INFO - Epoch [69][4200/5005]\tlr: 1.000e-03, eta: 3:48:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7710\n2023-01-02 22:50:11,187 - mmcls - INFO - Epoch [69][4300/5005]\tlr: 1.000e-03, eta: 3:48:31, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7787\n2023-01-02 22:50:19,946 - mmcls - INFO - Epoch [69][4400/5005]\tlr: 1.000e-03, eta: 3:48:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7794\n2023-01-02 22:50:28,913 - mmcls - INFO - Epoch [69][4500/5005]\tlr: 1.000e-03, eta: 3:48:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7732\n2023-01-02 22:50:37,756 - mmcls - INFO - Epoch [69][4600/5005]\tlr: 1.000e-03, eta: 3:48:05, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7944\n2023-01-02 22:50:46,375 - mmcls - INFO - Epoch [69][4700/5005]\tlr: 1.000e-03, eta: 3:47:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7648\n2023-01-02 22:50:55,046 - mmcls - INFO - Epoch [69][4800/5005]\tlr: 1.000e-03, eta: 3:47:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7608\n2023-01-02 22:51:03,746 - mmcls - INFO - Epoch [69][4900/5005]\tlr: 1.000e-03, eta: 3:47:38, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7835\n2023-01-02 22:51:12,553 - mmcls - INFO - Epoch [69][5000/5005]\tlr: 1.000e-03, eta: 3:47:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7934\n2023-01-02 22:51:35,231 - mmcls - INFO - Epoch(val) [69][196]\taccuracy_top-1: 62.6220, accuracy_top-5: 83.8120\n2023-01-02 22:51:46,003 - mmcls - INFO - Epoch [70][100/5005]\tlr: 1.000e-03, eta: 3:47:21, time: 0.108, data_time: 0.034, memory: 1125, loss: 1.7690\n2023-01-02 22:51:54,899 - mmcls - INFO - Epoch [70][200/5005]\tlr: 1.000e-03, eta: 3:47:12, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7589\n2023-01-02 22:52:03,741 - mmcls - INFO - Epoch [70][300/5005]\tlr: 1.000e-03, eta: 3:47:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7585\n2023-01-02 22:52:12,500 - mmcls - INFO - Epoch [70][400/5005]\tlr: 1.000e-03, eta: 3:46:55, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7675\n2023-01-02 22:52:21,150 - mmcls - INFO - Epoch [70][500/5005]\tlr: 1.000e-03, eta: 3:46:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7646\n2023-01-02 22:52:29,907 - mmcls - INFO - Epoch [70][600/5005]\tlr: 1.000e-03, eta: 3:46:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7881\n2023-01-02 22:52:38,823 - mmcls - INFO - Epoch [70][700/5005]\tlr: 1.000e-03, eta: 3:46:28, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7577\n2023-01-02 22:52:47,571 - mmcls - INFO - Epoch [70][800/5005]\tlr: 1.000e-03, eta: 3:46:19, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7770\n2023-01-02 22:52:56,447 - mmcls - INFO - Epoch [70][900/5005]\tlr: 1.000e-03, eta: 3:46:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7727\n2023-01-02 22:53:04,892 - mmcls - INFO - Epoch [70][1000/5005]\tlr: 1.000e-03, eta: 3:46:02, time: 0.084, data_time: 0.022, memory: 1125, loss: 1.7678\n2023-01-02 22:53:13,361 - mmcls - INFO - Epoch [70][1100/5005]\tlr: 1.000e-03, eta: 3:45:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7596\n2023-01-02 22:53:22,574 - mmcls - INFO - Epoch [70][1200/5005]\tlr: 1.000e-03, eta: 3:45:44, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7420\n2023-01-02 22:53:31,511 - mmcls - INFO - Epoch [70][1300/5005]\tlr: 1.000e-03, eta: 3:45:35, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7777\n2023-01-02 22:53:40,687 - mmcls - INFO - Epoch [70][1400/5005]\tlr: 1.000e-03, eta: 3:45:27, time: 0.092, data_time: 0.006, memory: 1125, loss: 1.7715\n2023-01-02 22:53:49,171 - mmcls - INFO - Epoch [70][1500/5005]\tlr: 1.000e-03, eta: 3:45:18, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7631\n2023-01-02 22:53:57,880 - mmcls - INFO - Epoch [70][1600/5005]\tlr: 1.000e-03, eta: 3:45:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7616\n2023-01-02 22:54:06,715 - mmcls - INFO - Epoch [70][1700/5005]\tlr: 1.000e-03, eta: 3:45:00, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7656\n2023-01-02 22:54:15,506 - mmcls - INFO - Epoch [70][1800/5005]\tlr: 1.000e-03, eta: 3:44:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7589\n2023-01-02 22:54:24,461 - mmcls - INFO - Epoch [70][1900/5005]\tlr: 1.000e-03, eta: 3:44:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7978\n2023-01-02 22:54:33,207 - mmcls - INFO - Epoch [70][2000/5005]\tlr: 1.000e-03, eta: 3:44:34, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7607\n2023-01-02 22:54:42,112 - mmcls - INFO - Epoch [70][2100/5005]\tlr: 1.000e-03, eta: 3:44:25, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7972\n2023-01-02 22:54:50,556 - mmcls - INFO - Epoch [70][2200/5005]\tlr: 1.000e-03, eta: 3:44:16, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.7778\n2023-01-02 22:54:59,378 - mmcls - INFO - Epoch [70][2300/5005]\tlr: 1.000e-03, eta: 3:44:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7678\n2023-01-02 22:55:08,086 - mmcls - INFO - Epoch [70][2400/5005]\tlr: 1.000e-03, eta: 3:43:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7463\n2023-01-02 22:55:16,957 - mmcls - INFO - Epoch [70][2500/5005]\tlr: 1.000e-03, eta: 3:43:50, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7498\n2023-01-02 22:55:25,748 - mmcls - INFO - Epoch [70][2600/5005]\tlr: 1.000e-03, eta: 3:43:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7716\n2023-01-02 22:55:34,383 - mmcls - INFO - Epoch [70][2700/5005]\tlr: 1.000e-03, eta: 3:43:32, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7673\n2023-01-02 22:55:43,135 - mmcls - INFO - Epoch [70][2800/5005]\tlr: 1.000e-03, eta: 3:43:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7757\n2023-01-02 22:55:51,991 - mmcls - INFO - Epoch [70][2900/5005]\tlr: 1.000e-03, eta: 3:43:14, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7677\n2023-01-02 22:56:00,756 - mmcls - INFO - Epoch [70][3000/5005]\tlr: 1.000e-03, eta: 3:43:06, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7685\n2023-01-02 22:56:09,467 - mmcls - INFO - Epoch [70][3100/5005]\tlr: 1.000e-03, eta: 3:42:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7586\n2023-01-02 22:56:18,287 - mmcls - INFO - Epoch [70][3200/5005]\tlr: 1.000e-03, eta: 3:42:48, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7702\n2023-01-02 22:56:26,811 - mmcls - INFO - Epoch [70][3300/5005]\tlr: 1.000e-03, eta: 3:42:39, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7468\n2023-01-02 22:56:35,489 - mmcls - INFO - Epoch [70][3400/5005]\tlr: 1.000e-03, eta: 3:42:30, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7764\n2023-01-02 22:56:44,226 - mmcls - INFO - Epoch [70][3500/5005]\tlr: 1.000e-03, eta: 3:42:21, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7666\n2023-01-02 22:56:52,893 - mmcls - INFO - Epoch [70][3600/5005]\tlr: 1.000e-03, eta: 3:42:13, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7858\n2023-01-02 22:57:02,210 - mmcls - INFO - Epoch [70][3700/5005]\tlr: 1.000e-03, eta: 3:42:04, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7740\n2023-01-02 22:57:11,189 - mmcls - INFO - Epoch [70][3800/5005]\tlr: 1.000e-03, eta: 3:41:55, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7655\n2023-01-02 22:57:20,164 - mmcls - INFO - Epoch [70][3900/5005]\tlr: 1.000e-03, eta: 3:41:46, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7750\n2023-01-02 22:57:28,866 - mmcls - INFO - Epoch [70][4000/5005]\tlr: 1.000e-03, eta: 3:41:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7795\n2023-01-02 22:57:37,658 - mmcls - INFO - Epoch [70][4100/5005]\tlr: 1.000e-03, eta: 3:41:29, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7590\n2023-01-02 22:57:46,224 - mmcls - INFO - Epoch [70][4200/5005]\tlr: 1.000e-03, eta: 3:41:20, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7980\n2023-01-02 22:57:54,915 - mmcls - INFO - Epoch [70][4300/5005]\tlr: 1.000e-03, eta: 3:41:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7947\n2023-01-02 22:58:03,428 - mmcls - INFO - Epoch [70][4400/5005]\tlr: 1.000e-03, eta: 3:41:02, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7712\n2023-01-02 22:58:12,124 - mmcls - INFO - Epoch [70][4500/5005]\tlr: 1.000e-03, eta: 3:40:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7720\n2023-01-02 22:58:20,899 - mmcls - INFO - Epoch [70][4600/5005]\tlr: 1.000e-03, eta: 3:40:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7963\n2023-01-02 22:58:29,807 - mmcls - INFO - Epoch [70][4700/5005]\tlr: 1.000e-03, eta: 3:40:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7810\n2023-01-02 22:58:38,445 - mmcls - INFO - Epoch [70][4800/5005]\tlr: 1.000e-03, eta: 3:40:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7823\n2023-01-02 22:58:47,018 - mmcls - INFO - Epoch [70][4900/5005]\tlr: 1.000e-03, eta: 3:40:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7854\n2023-01-02 22:58:56,087 - mmcls - INFO - Epoch [70][5000/5005]\tlr: 1.000e-03, eta: 3:40:09, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7901\n2023-01-02 22:59:18,724 - mmcls - INFO - Epoch(val) [70][196]\taccuracy_top-1: 62.7640, accuracy_top-5: 83.8820\n2023-01-02 22:59:29,623 - mmcls - INFO - Epoch [71][100/5005]\tlr: 1.000e-03, eta: 3:40:01, time: 0.109, data_time: 0.032, memory: 1125, loss: 1.7590\n2023-01-02 22:59:38,165 - mmcls - INFO - Epoch [71][200/5005]\tlr: 1.000e-03, eta: 3:39:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7460\n2023-01-02 22:59:47,052 - mmcls - INFO - Epoch [71][300/5005]\tlr: 1.000e-03, eta: 3:39:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7796\n2023-01-02 22:59:55,725 - mmcls - INFO - Epoch [71][400/5005]\tlr: 1.000e-03, eta: 3:39:34, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7453\n2023-01-02 23:00:04,647 - mmcls - INFO - Epoch [71][500/5005]\tlr: 1.000e-03, eta: 3:39:26, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7549\n2023-01-02 23:00:13,158 - mmcls - INFO - Epoch [71][600/5005]\tlr: 1.000e-03, eta: 3:39:17, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7490\n2023-01-02 23:00:21,827 - mmcls - INFO - Epoch [71][700/5005]\tlr: 1.000e-03, eta: 3:39:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7535\n2023-01-02 23:00:30,610 - mmcls - INFO - Epoch [71][800/5005]\tlr: 1.000e-03, eta: 3:38:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7577\n2023-01-02 23:00:39,258 - mmcls - INFO - Epoch [71][900/5005]\tlr: 1.000e-03, eta: 3:38:50, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7674\n2023-01-02 23:00:47,949 - mmcls - INFO - Epoch [71][1000/5005]\tlr: 1.000e-03, eta: 3:38:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7530\n2023-01-02 23:00:56,908 - mmcls - INFO - Epoch [71][1100/5005]\tlr: 1.000e-03, eta: 3:38:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7699\n2023-01-02 23:01:05,539 - mmcls - INFO - Epoch [71][1200/5005]\tlr: 1.000e-03, eta: 3:38:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7648\n2023-01-02 23:01:14,332 - mmcls - INFO - Epoch [71][1300/5005]\tlr: 1.000e-03, eta: 3:38:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7733\n2023-01-02 23:01:23,258 - mmcls - INFO - Epoch [71][1400/5005]\tlr: 1.000e-03, eta: 3:38:06, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7662\n2023-01-02 23:01:32,431 - mmcls - INFO - Epoch [71][1500/5005]\tlr: 1.000e-03, eta: 3:37:57, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7803\n2023-01-02 23:01:41,090 - mmcls - INFO - Epoch [71][1600/5005]\tlr: 1.000e-03, eta: 3:37:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7462\n2023-01-02 23:01:49,992 - mmcls - INFO - Epoch [71][1700/5005]\tlr: 1.000e-03, eta: 3:37:40, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7693\n2023-01-02 23:01:58,590 - mmcls - INFO - Epoch [71][1800/5005]\tlr: 1.000e-03, eta: 3:37:31, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7742\n2023-01-02 23:02:07,191 - mmcls - INFO - Epoch [71][1900/5005]\tlr: 1.000e-03, eta: 3:37:22, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7612\n2023-01-02 23:02:16,074 - mmcls - INFO - Epoch [71][2000/5005]\tlr: 1.000e-03, eta: 3:37:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7940\n2023-01-02 23:02:24,924 - mmcls - INFO - Epoch [71][2100/5005]\tlr: 1.000e-03, eta: 3:37:05, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7921\n2023-01-02 23:02:33,970 - mmcls - INFO - Epoch [71][2200/5005]\tlr: 1.000e-03, eta: 3:36:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7680\n2023-01-02 23:02:43,082 - mmcls - INFO - Epoch [71][2300/5005]\tlr: 1.000e-03, eta: 3:36:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7548\n2023-01-02 23:02:51,732 - mmcls - INFO - Epoch [71][2400/5005]\tlr: 1.000e-03, eta: 3:36:38, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7641\n2023-01-02 23:03:00,393 - mmcls - INFO - Epoch [71][2500/5005]\tlr: 1.000e-03, eta: 3:36:29, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7779\n2023-01-02 23:03:09,115 - mmcls - INFO - Epoch [71][2600/5005]\tlr: 1.000e-03, eta: 3:36:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7607\n2023-01-02 23:03:17,943 - mmcls - INFO - Epoch [71][2700/5005]\tlr: 1.000e-03, eta: 3:36:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7521\n2023-01-02 23:03:26,897 - mmcls - INFO - Epoch [71][2800/5005]\tlr: 1.000e-03, eta: 3:36:03, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7727\n2023-01-02 23:03:35,917 - mmcls - INFO - Epoch [71][2900/5005]\tlr: 1.000e-03, eta: 3:35:54, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7762\n2023-01-02 23:03:44,467 - mmcls - INFO - Epoch [71][3000/5005]\tlr: 1.000e-03, eta: 3:35:46, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7752\n2023-01-02 23:03:53,328 - mmcls - INFO - Epoch [71][3100/5005]\tlr: 1.000e-03, eta: 3:35:37, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7724\n2023-01-02 23:04:02,124 - mmcls - INFO - Epoch [71][3200/5005]\tlr: 1.000e-03, eta: 3:35:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7680\n2023-01-02 23:04:10,865 - mmcls - INFO - Epoch [71][3300/5005]\tlr: 1.000e-03, eta: 3:35:19, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7694\n2023-01-02 23:04:19,431 - mmcls - INFO - Epoch [71][3400/5005]\tlr: 1.000e-03, eta: 3:35:10, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7713\n2023-01-02 23:04:28,116 - mmcls - INFO - Epoch [71][3500/5005]\tlr: 1.000e-03, eta: 3:35:01, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7792\n2023-01-02 23:04:36,886 - mmcls - INFO - Epoch [71][3600/5005]\tlr: 1.000e-03, eta: 3:34:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7755\n2023-01-02 23:04:45,642 - mmcls - INFO - Epoch [71][3700/5005]\tlr: 1.000e-03, eta: 3:34:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7777\n2023-01-02 23:04:54,086 - mmcls - INFO - Epoch [71][3800/5005]\tlr: 1.000e-03, eta: 3:34:35, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7885\n2023-01-02 23:05:02,607 - mmcls - INFO - Epoch [71][3900/5005]\tlr: 1.000e-03, eta: 3:34:26, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7892\n2023-01-02 23:05:11,530 - mmcls - INFO - Epoch [71][4000/5005]\tlr: 1.000e-03, eta: 3:34:17, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7646\n2023-01-02 23:05:20,134 - mmcls - INFO - Epoch [71][4100/5005]\tlr: 1.000e-03, eta: 3:34:08, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7746\n2023-01-02 23:05:28,921 - mmcls - INFO - Epoch [71][4200/5005]\tlr: 1.000e-03, eta: 3:33:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7870\n2023-01-02 23:05:37,569 - mmcls - INFO - Epoch [71][4300/5005]\tlr: 1.000e-03, eta: 3:33:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7694\n2023-01-02 23:05:46,290 - mmcls - INFO - Epoch [71][4400/5005]\tlr: 1.000e-03, eta: 3:33:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7811\n2023-01-02 23:05:55,007 - mmcls - INFO - Epoch [71][4500/5005]\tlr: 1.000e-03, eta: 3:33:33, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7667\n2023-01-02 23:06:03,646 - mmcls - INFO - Epoch [71][4600/5005]\tlr: 1.000e-03, eta: 3:33:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7890\n2023-01-02 23:06:12,578 - mmcls - INFO - Epoch [71][4700/5005]\tlr: 1.000e-03, eta: 3:33:15, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7673\n2023-01-02 23:06:21,355 - mmcls - INFO - Epoch [71][4800/5005]\tlr: 1.000e-03, eta: 3:33:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7789\n2023-01-02 23:06:30,035 - mmcls - INFO - Epoch [71][4900/5005]\tlr: 1.000e-03, eta: 3:32:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7519\n2023-01-02 23:06:38,738 - mmcls - INFO - Epoch [71][5000/5005]\tlr: 1.000e-03, eta: 3:32:49, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7705\n2023-01-02 23:07:01,480 - mmcls - INFO - Epoch(val) [71][196]\taccuracy_top-1: 62.7220, accuracy_top-5: 83.8600\n2023-01-02 23:07:12,593 - mmcls - INFO - Epoch [72][100/5005]\tlr: 1.000e-03, eta: 3:32:40, time: 0.111, data_time: 0.035, memory: 1125, loss: 1.7214\n2023-01-02 23:07:21,666 - mmcls - INFO - Epoch [72][200/5005]\tlr: 1.000e-03, eta: 3:32:32, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7564\n2023-01-02 23:07:30,351 - mmcls - INFO - Epoch [72][300/5005]\tlr: 1.000e-03, eta: 3:32:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7832\n2023-01-02 23:07:39,152 - mmcls - INFO - Epoch [72][400/5005]\tlr: 1.000e-03, eta: 3:32:14, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.7502\n2023-01-02 23:07:47,728 - mmcls - INFO - Epoch [72][500/5005]\tlr: 1.000e-03, eta: 3:32:05, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7670\n2023-01-02 23:07:56,642 - mmcls - INFO - Epoch [72][600/5005]\tlr: 1.000e-03, eta: 3:31:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7298\n2023-01-02 23:08:05,281 - mmcls - INFO - Epoch [72][700/5005]\tlr: 1.000e-03, eta: 3:31:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7819\n2023-01-02 23:08:14,579 - mmcls - INFO - Epoch [72][800/5005]\tlr: 1.000e-03, eta: 3:31:39, time: 0.093, data_time: 0.015, memory: 1125, loss: 1.7524\n2023-01-02 23:08:23,545 - mmcls - INFO - Epoch [72][900/5005]\tlr: 1.000e-03, eta: 3:31:30, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7527\n2023-01-02 23:08:32,158 - mmcls - INFO - Epoch [72][1000/5005]\tlr: 1.000e-03, eta: 3:31:21, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7577\n2023-01-02 23:08:41,217 - mmcls - INFO - Epoch [72][1100/5005]\tlr: 1.000e-03, eta: 3:31:13, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7638\n2023-01-02 23:08:49,814 - mmcls - INFO - Epoch [72][1200/5005]\tlr: 1.000e-03, eta: 3:31:04, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7797\n2023-01-02 23:08:58,484 - mmcls - INFO - Epoch [72][1300/5005]\tlr: 1.000e-03, eta: 3:30:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7612\n2023-01-02 23:09:07,168 - mmcls - INFO - Epoch [72][1400/5005]\tlr: 1.000e-03, eta: 3:30:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7597\n2023-01-02 23:09:16,266 - mmcls - INFO - Epoch [72][1500/5005]\tlr: 1.000e-03, eta: 3:30:37, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7641\n2023-01-02 23:09:25,183 - mmcls - INFO - Epoch [72][1600/5005]\tlr: 1.000e-03, eta: 3:30:29, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7380\n2023-01-02 23:09:33,970 - mmcls - INFO - Epoch [72][1700/5005]\tlr: 1.000e-03, eta: 3:30:20, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7649\n2023-01-02 23:09:42,769 - mmcls - INFO - Epoch [72][1800/5005]\tlr: 1.000e-03, eta: 3:30:11, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7757\n2023-01-02 23:09:51,542 - mmcls - INFO - Epoch [72][1900/5005]\tlr: 1.000e-03, eta: 3:30:02, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7666\n2023-01-02 23:10:00,616 - mmcls - INFO - Epoch [72][2000/5005]\tlr: 1.000e-03, eta: 3:29:54, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7646\n2023-01-02 23:10:09,264 - mmcls - INFO - Epoch [72][2100/5005]\tlr: 1.000e-03, eta: 3:29:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7563\n2023-01-02 23:10:18,460 - mmcls - INFO - Epoch [72][2200/5005]\tlr: 1.000e-03, eta: 3:29:36, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7813\n2023-01-02 23:10:27,276 - mmcls - INFO - Epoch [72][2300/5005]\tlr: 1.000e-03, eta: 3:29:27, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7715\n2023-01-02 23:10:36,111 - mmcls - INFO - Epoch [72][2400/5005]\tlr: 1.000e-03, eta: 3:29:19, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7785\n2023-01-02 23:10:44,870 - mmcls - INFO - Epoch [72][2500/5005]\tlr: 1.000e-03, eta: 3:29:10, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7624\n2023-01-02 23:10:53,589 - mmcls - INFO - Epoch [72][2600/5005]\tlr: 1.000e-03, eta: 3:29:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7515\n2023-01-02 23:11:02,527 - mmcls - INFO - Epoch [72][2700/5005]\tlr: 1.000e-03, eta: 3:28:52, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8053\n2023-01-02 23:11:11,241 - mmcls - INFO - Epoch [72][2800/5005]\tlr: 1.000e-03, eta: 3:28:43, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7673\n2023-01-02 23:11:19,922 - mmcls - INFO - Epoch [72][2900/5005]\tlr: 1.000e-03, eta: 3:28:34, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7697\n2023-01-02 23:11:28,702 - mmcls - INFO - Epoch [72][3000/5005]\tlr: 1.000e-03, eta: 3:28:26, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7698\n2023-01-02 23:11:37,349 - mmcls - INFO - Epoch [72][3100/5005]\tlr: 1.000e-03, eta: 3:28:17, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7657\n2023-01-02 23:11:46,118 - mmcls - INFO - Epoch [72][3200/5005]\tlr: 1.000e-03, eta: 3:28:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7649\n2023-01-02 23:11:55,029 - mmcls - INFO - Epoch [72][3300/5005]\tlr: 1.000e-03, eta: 3:27:59, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7846\n2023-01-02 23:12:03,473 - mmcls - INFO - Epoch [72][3400/5005]\tlr: 1.000e-03, eta: 3:27:50, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7621\n2023-01-02 23:12:12,110 - mmcls - INFO - Epoch [72][3500/5005]\tlr: 1.000e-03, eta: 3:27:41, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7690\n2023-01-02 23:12:20,598 - mmcls - INFO - Epoch [72][3600/5005]\tlr: 1.000e-03, eta: 3:27:32, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7796\n2023-01-02 23:12:29,101 - mmcls - INFO - Epoch [72][3700/5005]\tlr: 1.000e-03, eta: 3:27:24, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.7640\n2023-01-02 23:12:37,949 - mmcls - INFO - Epoch [72][3800/5005]\tlr: 1.000e-03, eta: 3:27:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7578\n2023-01-02 23:12:46,588 - mmcls - INFO - Epoch [72][3900/5005]\tlr: 1.000e-03, eta: 3:27:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7555\n2023-01-02 23:12:55,194 - mmcls - INFO - Epoch [72][4000/5005]\tlr: 1.000e-03, eta: 3:26:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7756\n2023-01-02 23:13:04,096 - mmcls - INFO - Epoch [72][4100/5005]\tlr: 1.000e-03, eta: 3:26:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7537\n2023-01-02 23:13:12,878 - mmcls - INFO - Epoch [72][4200/5005]\tlr: 1.000e-03, eta: 3:26:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7668\n2023-01-02 23:13:21,420 - mmcls - INFO - Epoch [72][4300/5005]\tlr: 1.000e-03, eta: 3:26:31, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7497\n2023-01-02 23:13:30,360 - mmcls - INFO - Epoch [72][4400/5005]\tlr: 1.000e-03, eta: 3:26:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7846\n2023-01-02 23:13:39,101 - mmcls - INFO - Epoch [72][4500/5005]\tlr: 1.000e-03, eta: 3:26:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7806\n2023-01-02 23:13:47,864 - mmcls - INFO - Epoch [72][4600/5005]\tlr: 1.000e-03, eta: 3:26:04, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7738\n2023-01-02 23:13:56,638 - mmcls - INFO - Epoch [72][4700/5005]\tlr: 1.000e-03, eta: 3:25:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7495\n2023-01-02 23:14:05,276 - mmcls - INFO - Epoch [72][4800/5005]\tlr: 1.000e-03, eta: 3:25:47, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7494\n2023-01-02 23:14:14,416 - mmcls - INFO - Epoch [72][4900/5005]\tlr: 1.000e-03, eta: 3:25:38, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7526\n2023-01-02 23:14:23,198 - mmcls - INFO - Epoch [72][5000/5005]\tlr: 1.000e-03, eta: 3:25:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7818\n2023-01-02 23:14:45,791 - mmcls - INFO - Epoch(val) [72][196]\taccuracy_top-1: 62.9620, accuracy_top-5: 83.9620\n2023-01-02 23:14:56,824 - mmcls - INFO - Epoch [73][100/5005]\tlr: 1.000e-03, eta: 3:25:21, time: 0.110, data_time: 0.051, memory: 1125, loss: 1.7471\n2023-01-02 23:15:05,294 - mmcls - INFO - Epoch [73][200/5005]\tlr: 1.000e-03, eta: 3:25:12, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7756\n2023-01-02 23:15:14,027 - mmcls - INFO - Epoch [73][300/5005]\tlr: 1.000e-03, eta: 3:25:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7711\n2023-01-02 23:15:23,076 - mmcls - INFO - Epoch [73][400/5005]\tlr: 1.000e-03, eta: 3:24:54, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7390\n2023-01-02 23:15:32,028 - mmcls - INFO - Epoch [73][500/5005]\tlr: 1.000e-03, eta: 3:24:45, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7747\n2023-01-02 23:15:40,704 - mmcls - INFO - Epoch [73][600/5005]\tlr: 1.000e-03, eta: 3:24:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7796\n2023-01-02 23:15:49,622 - mmcls - INFO - Epoch [73][700/5005]\tlr: 1.000e-03, eta: 3:24:28, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7818\n2023-01-02 23:15:58,264 - mmcls - INFO - Epoch [73][800/5005]\tlr: 1.000e-03, eta: 3:24:19, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7589\n2023-01-02 23:16:07,024 - mmcls - INFO - Epoch [73][900/5005]\tlr: 1.000e-03, eta: 3:24:10, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7713\n2023-01-02 23:16:15,671 - mmcls - INFO - Epoch [73][1000/5005]\tlr: 1.000e-03, eta: 3:24:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7408\n2023-01-02 23:16:24,607 - mmcls - INFO - Epoch [73][1100/5005]\tlr: 1.000e-03, eta: 3:23:52, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7507\n2023-01-02 23:16:33,298 - mmcls - INFO - Epoch [73][1200/5005]\tlr: 1.000e-03, eta: 3:23:44, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7509\n2023-01-02 23:16:41,820 - mmcls - INFO - Epoch [73][1300/5005]\tlr: 1.000e-03, eta: 3:23:35, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7521\n2023-01-02 23:16:50,812 - mmcls - INFO - Epoch [73][1400/5005]\tlr: 1.000e-03, eta: 3:23:26, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7910\n2023-01-02 23:16:59,776 - mmcls - INFO - Epoch [73][1500/5005]\tlr: 1.000e-03, eta: 3:23:17, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7795\n2023-01-02 23:17:08,207 - mmcls - INFO - Epoch [73][1600/5005]\tlr: 1.000e-03, eta: 3:23:08, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7574\n2023-01-02 23:17:17,023 - mmcls - INFO - Epoch [73][1700/5005]\tlr: 1.000e-03, eta: 3:23:00, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7489\n2023-01-02 23:17:25,515 - mmcls - INFO - Epoch [73][1800/5005]\tlr: 1.000e-03, eta: 3:22:51, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7526\n2023-01-02 23:17:34,241 - mmcls - INFO - Epoch [73][1900/5005]\tlr: 1.000e-03, eta: 3:22:42, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7579\n2023-01-02 23:17:42,739 - mmcls - INFO - Epoch [73][2000/5005]\tlr: 1.000e-03, eta: 3:22:33, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7806\n2023-01-02 23:17:51,462 - mmcls - INFO - Epoch [73][2100/5005]\tlr: 1.000e-03, eta: 3:22:24, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7584\n2023-01-02 23:18:00,441 - mmcls - INFO - Epoch [73][2200/5005]\tlr: 1.000e-03, eta: 3:22:15, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7652\n2023-01-02 23:18:08,979 - mmcls - INFO - Epoch [73][2300/5005]\tlr: 1.000e-03, eta: 3:22:06, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7770\n2023-01-02 23:18:17,985 - mmcls - INFO - Epoch [73][2400/5005]\tlr: 1.000e-03, eta: 3:21:58, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7446\n2023-01-02 23:18:26,595 - mmcls - INFO - Epoch [73][2500/5005]\tlr: 1.000e-03, eta: 3:21:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7598\n2023-01-02 23:18:35,425 - mmcls - INFO - Epoch [73][2600/5005]\tlr: 1.000e-03, eta: 3:21:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7438\n2023-01-02 23:18:44,126 - mmcls - INFO - Epoch [73][2700/5005]\tlr: 1.000e-03, eta: 3:21:31, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7495\n2023-01-02 23:18:52,902 - mmcls - INFO - Epoch [73][2800/5005]\tlr: 1.000e-03, eta: 3:21:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7682\n2023-01-02 23:19:01,751 - mmcls - INFO - Epoch [73][2900/5005]\tlr: 1.000e-03, eta: 3:21:14, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7545\n2023-01-02 23:19:10,526 - mmcls - INFO - Epoch [73][3000/5005]\tlr: 1.000e-03, eta: 3:21:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7728\n2023-01-02 23:19:19,335 - mmcls - INFO - Epoch [73][3100/5005]\tlr: 1.000e-03, eta: 3:20:56, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7479\n2023-01-02 23:19:28,044 - mmcls - INFO - Epoch [73][3200/5005]\tlr: 1.000e-03, eta: 3:20:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7769\n2023-01-02 23:19:36,832 - mmcls - INFO - Epoch [73][3300/5005]\tlr: 1.000e-03, eta: 3:20:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7694\n2023-01-02 23:19:45,819 - mmcls - INFO - Epoch [73][3400/5005]\tlr: 1.000e-03, eta: 3:20:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7440\n2023-01-02 23:19:54,934 - mmcls - INFO - Epoch [73][3500/5005]\tlr: 1.000e-03, eta: 3:20:21, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7523\n2023-01-02 23:20:03,619 - mmcls - INFO - Epoch [73][3600/5005]\tlr: 1.000e-03, eta: 3:20:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7668\n2023-01-02 23:20:12,414 - mmcls - INFO - Epoch [73][3700/5005]\tlr: 1.000e-03, eta: 3:20:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7664\n2023-01-02 23:20:21,109 - mmcls - INFO - Epoch [73][3800/5005]\tlr: 1.000e-03, eta: 3:19:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7560\n2023-01-02 23:20:29,834 - mmcls - INFO - Epoch [73][3900/5005]\tlr: 1.000e-03, eta: 3:19:46, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7584\n2023-01-02 23:20:38,537 - mmcls - INFO - Epoch [73][4000/5005]\tlr: 1.000e-03, eta: 3:19:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7747\n2023-01-02 23:20:47,388 - mmcls - INFO - Epoch [73][4100/5005]\tlr: 1.000e-03, eta: 3:19:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7709\n2023-01-02 23:20:55,874 - mmcls - INFO - Epoch [73][4200/5005]\tlr: 1.000e-03, eta: 3:19:19, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7647\n2023-01-02 23:21:04,585 - mmcls - INFO - Epoch [73][4300/5005]\tlr: 1.000e-03, eta: 3:19:10, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7819\n2023-01-02 23:21:13,301 - mmcls - INFO - Epoch [73][4400/5005]\tlr: 1.000e-03, eta: 3:19:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7579\n2023-01-02 23:21:22,010 - mmcls - INFO - Epoch [73][4500/5005]\tlr: 1.000e-03, eta: 3:18:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7653\n2023-01-02 23:21:30,756 - mmcls - INFO - Epoch [73][4600/5005]\tlr: 1.000e-03, eta: 3:18:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7691\n2023-01-02 23:21:39,881 - mmcls - INFO - Epoch [73][4700/5005]\tlr: 1.000e-03, eta: 3:18:35, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7644\n2023-01-02 23:21:48,636 - mmcls - INFO - Epoch [73][4800/5005]\tlr: 1.000e-03, eta: 3:18:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7688\n2023-01-02 23:21:57,595 - mmcls - INFO - Epoch [73][4900/5005]\tlr: 1.000e-03, eta: 3:18:18, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7723\n2023-01-02 23:22:06,194 - mmcls - INFO - Epoch [73][5000/5005]\tlr: 1.000e-03, eta: 3:18:09, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7752\n2023-01-02 23:22:28,729 - mmcls - INFO - Epoch(val) [73][196]\taccuracy_top-1: 62.8440, accuracy_top-5: 83.7000\n2023-01-02 23:22:39,769 - mmcls - INFO - Epoch [74][100/5005]\tlr: 1.000e-03, eta: 3:18:00, time: 0.110, data_time: 0.045, memory: 1125, loss: 1.7377\n2023-01-02 23:22:48,879 - mmcls - INFO - Epoch [74][200/5005]\tlr: 1.000e-03, eta: 3:17:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7510\n2023-01-02 23:22:57,877 - mmcls - INFO - Epoch [74][300/5005]\tlr: 1.000e-03, eta: 3:17:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7456\n2023-01-02 23:23:06,420 - mmcls - INFO - Epoch [74][400/5005]\tlr: 1.000e-03, eta: 3:17:34, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7601\n2023-01-02 23:23:15,057 - mmcls - INFO - Epoch [74][500/5005]\tlr: 1.000e-03, eta: 3:17:25, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7366\n2023-01-02 23:23:24,039 - mmcls - INFO - Epoch [74][600/5005]\tlr: 1.000e-03, eta: 3:17:16, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7640\n2023-01-02 23:23:32,915 - mmcls - INFO - Epoch [74][700/5005]\tlr: 1.000e-03, eta: 3:17:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7518\n2023-01-02 23:23:41,657 - mmcls - INFO - Epoch [74][800/5005]\tlr: 1.000e-03, eta: 3:16:59, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7639\n2023-01-02 23:23:50,146 - mmcls - INFO - Epoch [74][900/5005]\tlr: 1.000e-03, eta: 3:16:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7257\n2023-01-02 23:23:58,771 - mmcls - INFO - Epoch [74][1000/5005]\tlr: 1.000e-03, eta: 3:16:41, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7644\n2023-01-02 23:24:07,620 - mmcls - INFO - Epoch [74][1100/5005]\tlr: 1.000e-03, eta: 3:16:32, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7618\n2023-01-02 23:24:16,442 - mmcls - INFO - Epoch [74][1200/5005]\tlr: 1.000e-03, eta: 3:16:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7527\n2023-01-02 23:24:25,193 - mmcls - INFO - Epoch [74][1300/5005]\tlr: 1.000e-03, eta: 3:16:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7679\n2023-01-02 23:24:34,012 - mmcls - INFO - Epoch [74][1400/5005]\tlr: 1.000e-03, eta: 3:16:06, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7539\n2023-01-02 23:24:42,564 - mmcls - INFO - Epoch [74][1500/5005]\tlr: 1.000e-03, eta: 3:15:57, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7514\n2023-01-02 23:24:51,817 - mmcls - INFO - Epoch [74][1600/5005]\tlr: 1.000e-03, eta: 3:15:48, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7736\n2023-01-02 23:25:00,518 - mmcls - INFO - Epoch [74][1700/5005]\tlr: 1.000e-03, eta: 3:15:39, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7530\n2023-01-02 23:25:09,369 - mmcls - INFO - Epoch [74][1800/5005]\tlr: 1.000e-03, eta: 3:15:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7682\n2023-01-02 23:25:18,191 - mmcls - INFO - Epoch [74][1900/5005]\tlr: 1.000e-03, eta: 3:15:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 1.7459\n2023-01-02 23:25:26,846 - mmcls - INFO - Epoch [74][2000/5005]\tlr: 1.000e-03, eta: 3:15:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7449\n2023-01-02 23:25:35,350 - mmcls - INFO - Epoch [74][2100/5005]\tlr: 1.000e-03, eta: 3:15:04, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7716\n2023-01-02 23:25:44,113 - mmcls - INFO - Epoch [74][2200/5005]\tlr: 1.000e-03, eta: 3:14:55, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7217\n2023-01-02 23:25:52,841 - mmcls - INFO - Epoch [74][2300/5005]\tlr: 1.000e-03, eta: 3:14:46, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7631\n2023-01-02 23:26:01,593 - mmcls - INFO - Epoch [74][2400/5005]\tlr: 1.000e-03, eta: 3:14:38, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7384\n2023-01-02 23:26:10,311 - mmcls - INFO - Epoch [74][2500/5005]\tlr: 1.000e-03, eta: 3:14:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7777\n2023-01-02 23:26:18,924 - mmcls - INFO - Epoch [74][2600/5005]\tlr: 1.000e-03, eta: 3:14:20, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7397\n2023-01-02 23:26:27,659 - mmcls - INFO - Epoch [74][2700/5005]\tlr: 1.000e-03, eta: 3:14:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7505\n2023-01-02 23:26:36,438 - mmcls - INFO - Epoch [74][2800/5005]\tlr: 1.000e-03, eta: 3:14:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7801\n2023-01-02 23:26:45,241 - mmcls - INFO - Epoch [74][2900/5005]\tlr: 1.000e-03, eta: 3:13:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7509\n2023-01-02 23:26:53,944 - mmcls - INFO - Epoch [74][3000/5005]\tlr: 1.000e-03, eta: 3:13:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7530\n2023-01-02 23:27:02,392 - mmcls - INFO - Epoch [74][3100/5005]\tlr: 1.000e-03, eta: 3:13:36, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7540\n2023-01-02 23:27:11,325 - mmcls - INFO - Epoch [74][3200/5005]\tlr: 1.000e-03, eta: 3:13:27, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7809\n2023-01-02 23:27:19,949 - mmcls - INFO - Epoch [74][3300/5005]\tlr: 1.000e-03, eta: 3:13:18, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7552\n2023-01-02 23:27:28,575 - mmcls - INFO - Epoch [74][3400/5005]\tlr: 1.000e-03, eta: 3:13:09, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7789\n2023-01-02 23:27:37,488 - mmcls - INFO - Epoch [74][3500/5005]\tlr: 1.000e-03, eta: 3:13:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7448\n2023-01-02 23:27:46,020 - mmcls - INFO - Epoch [74][3600/5005]\tlr: 1.000e-03, eta: 3:12:52, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7845\n2023-01-02 23:27:54,758 - mmcls - INFO - Epoch [74][3700/5005]\tlr: 1.000e-03, eta: 3:12:43, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7438\n2023-01-02 23:28:03,511 - mmcls - INFO - Epoch [74][3800/5005]\tlr: 1.000e-03, eta: 3:12:34, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7603\n2023-01-02 23:28:12,215 - mmcls - INFO - Epoch [74][3900/5005]\tlr: 1.000e-03, eta: 3:12:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7566\n2023-01-02 23:28:21,230 - mmcls - INFO - Epoch [74][4000/5005]\tlr: 1.000e-03, eta: 3:12:16, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7811\n2023-01-02 23:28:29,822 - mmcls - INFO - Epoch [74][4100/5005]\tlr: 1.000e-03, eta: 3:12:08, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7623\n2023-01-02 23:28:38,773 - mmcls - INFO - Epoch [74][4200/5005]\tlr: 1.000e-03, eta: 3:11:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7671\n2023-01-02 23:28:47,589 - mmcls - INFO - Epoch [74][4300/5005]\tlr: 1.000e-03, eta: 3:11:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7659\n2023-01-02 23:28:56,139 - mmcls - INFO - Epoch [74][4400/5005]\tlr: 1.000e-03, eta: 3:11:41, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7914\n2023-01-02 23:29:05,042 - mmcls - INFO - Epoch [74][4500/5005]\tlr: 1.000e-03, eta: 3:11:32, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7757\n2023-01-02 23:29:13,654 - mmcls - INFO - Epoch [74][4600/5005]\tlr: 1.000e-03, eta: 3:11:24, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7649\n2023-01-02 23:29:22,286 - mmcls - INFO - Epoch [74][4700/5005]\tlr: 1.000e-03, eta: 3:11:15, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7858\n2023-01-02 23:29:30,919 - mmcls - INFO - Epoch [74][4800/5005]\tlr: 1.000e-03, eta: 3:11:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7577\n2023-01-02 23:29:39,894 - mmcls - INFO - Epoch [74][4900/5005]\tlr: 1.000e-03, eta: 3:10:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7786\n2023-01-02 23:29:48,506 - mmcls - INFO - Epoch [74][5000/5005]\tlr: 1.000e-03, eta: 3:10:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7610\n2023-01-02 23:30:11,245 - mmcls - INFO - Epoch(val) [74][196]\taccuracy_top-1: 63.0460, accuracy_top-5: 84.0300\n2023-01-02 23:30:22,032 - mmcls - INFO - Epoch [75][100/5005]\tlr: 1.000e-03, eta: 3:10:40, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.7590\n2023-01-02 23:30:30,997 - mmcls - INFO - Epoch [75][200/5005]\tlr: 1.000e-03, eta: 3:10:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7335\n2023-01-02 23:30:39,547 - mmcls - INFO - Epoch [75][300/5005]\tlr: 1.000e-03, eta: 3:10:22, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7534\n2023-01-02 23:30:48,393 - mmcls - INFO - Epoch [75][400/5005]\tlr: 1.000e-03, eta: 3:10:13, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7456\n2023-01-02 23:30:57,074 - mmcls - INFO - Epoch [75][500/5005]\tlr: 1.000e-03, eta: 3:10:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7608\n2023-01-02 23:31:05,811 - mmcls - INFO - Epoch [75][600/5005]\tlr: 1.000e-03, eta: 3:09:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7531\n2023-01-02 23:31:14,714 - mmcls - INFO - Epoch [75][700/5005]\tlr: 1.000e-03, eta: 3:09:47, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7513\n2023-01-02 23:31:23,440 - mmcls - INFO - Epoch [75][800/5005]\tlr: 1.000e-03, eta: 3:09:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7596\n2023-01-02 23:31:32,463 - mmcls - INFO - Epoch [75][900/5005]\tlr: 1.000e-03, eta: 3:09:29, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7440\n2023-01-02 23:31:41,128 - mmcls - INFO - Epoch [75][1000/5005]\tlr: 1.000e-03, eta: 3:09:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7456\n2023-01-02 23:31:49,841 - mmcls - INFO - Epoch [75][1100/5005]\tlr: 1.000e-03, eta: 3:09:11, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7523\n2023-01-02 23:31:58,623 - mmcls - INFO - Epoch [75][1200/5005]\tlr: 1.000e-03, eta: 3:09:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7589\n2023-01-02 23:32:07,351 - mmcls - INFO - Epoch [75][1300/5005]\tlr: 1.000e-03, eta: 3:08:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7438\n2023-01-02 23:32:16,267 - mmcls - INFO - Epoch [75][1400/5005]\tlr: 1.000e-03, eta: 3:08:45, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7631\n2023-01-02 23:32:24,868 - mmcls - INFO - Epoch [75][1500/5005]\tlr: 1.000e-03, eta: 3:08:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7549\n2023-01-02 23:32:33,583 - mmcls - INFO - Epoch [75][1600/5005]\tlr: 1.000e-03, eta: 3:08:27, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7601\n2023-01-02 23:32:42,347 - mmcls - INFO - Epoch [75][1700/5005]\tlr: 1.000e-03, eta: 3:08:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7639\n2023-01-02 23:32:51,005 - mmcls - INFO - Epoch [75][1800/5005]\tlr: 1.000e-03, eta: 3:08:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7675\n2023-01-02 23:32:59,641 - mmcls - INFO - Epoch [75][1900/5005]\tlr: 1.000e-03, eta: 3:08:01, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7716\n2023-01-02 23:33:08,388 - mmcls - INFO - Epoch [75][2000/5005]\tlr: 1.000e-03, eta: 3:07:52, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7494\n2023-01-02 23:33:17,494 - mmcls - INFO - Epoch [75][2100/5005]\tlr: 1.000e-03, eta: 3:07:43, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7509\n2023-01-02 23:33:26,279 - mmcls - INFO - Epoch [75][2200/5005]\tlr: 1.000e-03, eta: 3:07:35, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7554\n2023-01-02 23:33:35,219 - mmcls - INFO - Epoch [75][2300/5005]\tlr: 1.000e-03, eta: 3:07:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7627\n2023-01-02 23:33:43,867 - mmcls - INFO - Epoch [75][2400/5005]\tlr: 1.000e-03, eta: 3:07:17, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7322\n2023-01-02 23:33:52,557 - mmcls - INFO - Epoch [75][2500/5005]\tlr: 1.000e-03, eta: 3:07:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7877\n2023-01-02 23:34:01,215 - mmcls - INFO - Epoch [75][2600/5005]\tlr: 1.000e-03, eta: 3:06:59, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7495\n2023-01-02 23:34:09,858 - mmcls - INFO - Epoch [75][2700/5005]\tlr: 1.000e-03, eta: 3:06:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7607\n2023-01-02 23:34:18,750 - mmcls - INFO - Epoch [75][2800/5005]\tlr: 1.000e-03, eta: 3:06:42, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7708\n2023-01-02 23:34:27,355 - mmcls - INFO - Epoch [75][2900/5005]\tlr: 1.000e-03, eta: 3:06:33, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7619\n2023-01-02 23:34:35,820 - mmcls - INFO - Epoch [75][3000/5005]\tlr: 1.000e-03, eta: 3:06:24, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7535\n2023-01-02 23:34:44,435 - mmcls - INFO - Epoch [75][3100/5005]\tlr: 1.000e-03, eta: 3:06:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7398\n2023-01-02 23:34:53,177 - mmcls - INFO - Epoch [75][3200/5005]\tlr: 1.000e-03, eta: 3:06:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7551\n2023-01-02 23:35:02,019 - mmcls - INFO - Epoch [75][3300/5005]\tlr: 1.000e-03, eta: 3:05:57, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7627\n2023-01-02 23:35:10,804 - mmcls - INFO - Epoch [75][3400/5005]\tlr: 1.000e-03, eta: 3:05:49, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7498\n2023-01-02 23:35:19,562 - mmcls - INFO - Epoch [75][3500/5005]\tlr: 1.000e-03, eta: 3:05:40, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7439\n2023-01-02 23:35:28,313 - mmcls - INFO - Epoch [75][3600/5005]\tlr: 1.000e-03, eta: 3:05:31, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7538\n2023-01-02 23:35:37,023 - mmcls - INFO - Epoch [75][3700/5005]\tlr: 1.000e-03, eta: 3:05:22, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7634\n2023-01-02 23:35:45,627 - mmcls - INFO - Epoch [75][3800/5005]\tlr: 1.000e-03, eta: 3:05:13, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7577\n2023-01-02 23:35:54,357 - mmcls - INFO - Epoch [75][3900/5005]\tlr: 1.000e-03, eta: 3:05:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7616\n2023-01-02 23:36:03,249 - mmcls - INFO - Epoch [75][4000/5005]\tlr: 1.000e-03, eta: 3:04:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7565\n2023-01-02 23:36:11,924 - mmcls - INFO - Epoch [75][4100/5005]\tlr: 1.000e-03, eta: 3:04:47, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7389\n2023-01-02 23:36:20,659 - mmcls - INFO - Epoch [75][4200/5005]\tlr: 1.000e-03, eta: 3:04:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7428\n2023-01-02 23:36:29,481 - mmcls - INFO - Epoch [75][4300/5005]\tlr: 1.000e-03, eta: 3:04:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7517\n2023-01-02 23:36:38,131 - mmcls - INFO - Epoch [75][4400/5005]\tlr: 1.000e-03, eta: 3:04:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7455\n2023-01-02 23:36:46,903 - mmcls - INFO - Epoch [75][4500/5005]\tlr: 1.000e-03, eta: 3:04:12, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7634\n2023-01-02 23:36:55,804 - mmcls - INFO - Epoch [75][4600/5005]\tlr: 1.000e-03, eta: 3:04:03, time: 0.089, data_time: 0.034, memory: 1125, loss: 1.7654\n2023-01-02 23:37:04,655 - mmcls - INFO - Epoch [75][4700/5005]\tlr: 1.000e-03, eta: 3:03:54, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7528\n2023-01-02 23:37:13,399 - mmcls - INFO - Epoch [75][4800/5005]\tlr: 1.000e-03, eta: 3:03:45, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7571\n2023-01-02 23:37:22,407 - mmcls - INFO - Epoch [75][4900/5005]\tlr: 1.000e-03, eta: 3:03:37, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7500\n2023-01-02 23:37:31,083 - mmcls - INFO - Epoch [75][5000/5005]\tlr: 1.000e-03, eta: 3:03:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7685\n2023-01-02 23:37:53,646 - mmcls - INFO - Epoch(val) [75][196]\taccuracy_top-1: 62.9480, accuracy_top-5: 83.8380\n2023-01-02 23:38:04,747 - mmcls - INFO - Epoch [76][100/5005]\tlr: 1.000e-03, eta: 3:03:19, time: 0.111, data_time: 0.047, memory: 1125, loss: 1.7450\n2023-01-02 23:38:13,250 - mmcls - INFO - Epoch [76][200/5005]\tlr: 1.000e-03, eta: 3:03:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7592\n2023-01-02 23:38:22,184 - mmcls - INFO - Epoch [76][300/5005]\tlr: 1.000e-03, eta: 3:03:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7504\n2023-01-02 23:38:30,838 - mmcls - INFO - Epoch [76][400/5005]\tlr: 1.000e-03, eta: 3:02:53, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7396\n2023-01-02 23:38:39,647 - mmcls - INFO - Epoch [76][500/5005]\tlr: 1.000e-03, eta: 3:02:44, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7469\n2023-01-02 23:38:48,507 - mmcls - INFO - Epoch [76][600/5005]\tlr: 1.000e-03, eta: 3:02:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7322\n2023-01-02 23:38:57,127 - mmcls - INFO - Epoch [76][700/5005]\tlr: 1.000e-03, eta: 3:02:26, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7474\n2023-01-02 23:39:06,166 - mmcls - INFO - Epoch [76][800/5005]\tlr: 1.000e-03, eta: 3:02:17, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7611\n2023-01-02 23:39:15,134 - mmcls - INFO - Epoch [76][900/5005]\tlr: 1.000e-03, eta: 3:02:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7589\n2023-01-02 23:39:23,790 - mmcls - INFO - Epoch [76][1000/5005]\tlr: 1.000e-03, eta: 3:02:00, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7340\n2023-01-02 23:39:32,468 - mmcls - INFO - Epoch [76][1100/5005]\tlr: 1.000e-03, eta: 3:01:51, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7453\n2023-01-02 23:39:41,036 - mmcls - INFO - Epoch [76][1200/5005]\tlr: 1.000e-03, eta: 3:01:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7491\n2023-01-02 23:39:49,776 - mmcls - INFO - Epoch [76][1300/5005]\tlr: 1.000e-03, eta: 3:01:33, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7604\n2023-01-02 23:39:58,416 - mmcls - INFO - Epoch [76][1400/5005]\tlr: 1.000e-03, eta: 3:01:24, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7517\n2023-01-02 23:40:07,491 - mmcls - INFO - Epoch [76][1500/5005]\tlr: 1.000e-03, eta: 3:01:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7490\n2023-01-02 23:40:16,499 - mmcls - INFO - Epoch [76][1600/5005]\tlr: 1.000e-03, eta: 3:01:07, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.7513\n2023-01-02 23:40:25,292 - mmcls - INFO - Epoch [76][1700/5005]\tlr: 1.000e-03, eta: 3:00:58, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7626\n2023-01-02 23:40:34,127 - mmcls - INFO - Epoch [76][1800/5005]\tlr: 1.000e-03, eta: 3:00:49, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7377\n2023-01-02 23:40:42,529 - mmcls - INFO - Epoch [76][1900/5005]\tlr: 1.000e-03, eta: 3:00:41, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7562\n2023-01-02 23:40:51,390 - mmcls - INFO - Epoch [76][2000/5005]\tlr: 1.000e-03, eta: 3:00:32, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7376\n2023-01-02 23:41:00,073 - mmcls - INFO - Epoch [76][2100/5005]\tlr: 1.000e-03, eta: 3:00:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7305\n2023-01-02 23:41:09,183 - mmcls - INFO - Epoch [76][2200/5005]\tlr: 1.000e-03, eta: 3:00:14, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.7531\n2023-01-02 23:41:18,392 - mmcls - INFO - Epoch [76][2300/5005]\tlr: 1.000e-03, eta: 3:00:06, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7468\n2023-01-02 23:41:27,104 - mmcls - INFO - Epoch [76][2400/5005]\tlr: 1.000e-03, eta: 2:59:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7766\n2023-01-02 23:41:36,185 - mmcls - INFO - Epoch [76][2500/5005]\tlr: 1.000e-03, eta: 2:59:48, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.7470\n2023-01-02 23:41:45,156 - mmcls - INFO - Epoch [76][2600/5005]\tlr: 1.000e-03, eta: 2:59:39, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.7531\n2023-01-02 23:41:53,723 - mmcls - INFO - Epoch [76][2700/5005]\tlr: 1.000e-03, eta: 2:59:30, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7517\n2023-01-02 23:42:02,508 - mmcls - INFO - Epoch [76][2800/5005]\tlr: 1.000e-03, eta: 2:59:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7280\n2023-01-02 23:42:11,346 - mmcls - INFO - Epoch [76][2900/5005]\tlr: 1.000e-03, eta: 2:59:13, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7625\n2023-01-02 23:42:20,030 - mmcls - INFO - Epoch [76][3000/5005]\tlr: 1.000e-03, eta: 2:59:04, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7524\n2023-01-02 23:42:28,675 - mmcls - INFO - Epoch [76][3100/5005]\tlr: 1.000e-03, eta: 2:58:55, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7647\n2023-01-02 23:42:37,625 - mmcls - INFO - Epoch [76][3200/5005]\tlr: 1.000e-03, eta: 2:58:46, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7485\n2023-01-02 23:42:46,549 - mmcls - INFO - Epoch [76][3300/5005]\tlr: 1.000e-03, eta: 2:58:38, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7216\n2023-01-02 23:42:55,406 - mmcls - INFO - Epoch [76][3400/5005]\tlr: 1.000e-03, eta: 2:58:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7515\n2023-01-02 23:43:04,158 - mmcls - INFO - Epoch [76][3500/5005]\tlr: 1.000e-03, eta: 2:58:20, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7587\n2023-01-02 23:43:13,279 - mmcls - INFO - Epoch [76][3600/5005]\tlr: 1.000e-03, eta: 2:58:11, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7652\n2023-01-02 23:43:22,207 - mmcls - INFO - Epoch [76][3700/5005]\tlr: 1.000e-03, eta: 2:58:03, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7646\n2023-01-02 23:43:30,906 - mmcls - INFO - Epoch [76][3800/5005]\tlr: 1.000e-03, eta: 2:57:54, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7481\n2023-01-02 23:43:39,692 - mmcls - INFO - Epoch [76][3900/5005]\tlr: 1.000e-03, eta: 2:57:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7589\n2023-01-02 23:43:48,509 - mmcls - INFO - Epoch [76][4000/5005]\tlr: 1.000e-03, eta: 2:57:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7748\n2023-01-02 23:43:57,347 - mmcls - INFO - Epoch [76][4100/5005]\tlr: 1.000e-03, eta: 2:57:27, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7592\n2023-01-02 23:44:06,032 - mmcls - INFO - Epoch [76][4200/5005]\tlr: 1.000e-03, eta: 2:57:19, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7617\n2023-01-02 23:44:14,451 - mmcls - INFO - Epoch [76][4300/5005]\tlr: 1.000e-03, eta: 2:57:10, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7606\n2023-01-02 23:44:23,051 - mmcls - INFO - Epoch [76][4400/5005]\tlr: 1.000e-03, eta: 2:57:01, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7851\n2023-01-02 23:44:31,867 - mmcls - INFO - Epoch [76][4500/5005]\tlr: 1.000e-03, eta: 2:56:52, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7349\n2023-01-02 23:44:40,649 - mmcls - INFO - Epoch [76][4600/5005]\tlr: 1.000e-03, eta: 2:56:43, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7707\n2023-01-02 23:44:49,181 - mmcls - INFO - Epoch [76][4700/5005]\tlr: 1.000e-03, eta: 2:56:34, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7568\n2023-01-02 23:44:58,476 - mmcls - INFO - Epoch [76][4800/5005]\tlr: 1.000e-03, eta: 2:56:26, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7649\n2023-01-02 23:45:07,487 - mmcls - INFO - Epoch [76][4900/5005]\tlr: 1.000e-03, eta: 2:56:17, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7821\n2023-01-02 23:45:16,233 - mmcls - INFO - Epoch [76][5000/5005]\tlr: 1.000e-03, eta: 2:56:08, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7532\n2023-01-02 23:45:38,854 - mmcls - INFO - Epoch(val) [76][196]\taccuracy_top-1: 62.9720, accuracy_top-5: 84.0080\n2023-01-02 23:45:49,881 - mmcls - INFO - Epoch [77][100/5005]\tlr: 1.000e-03, eta: 2:55:59, time: 0.110, data_time: 0.047, memory: 1125, loss: 1.7383\n2023-01-02 23:45:58,776 - mmcls - INFO - Epoch [77][200/5005]\tlr: 1.000e-03, eta: 2:55:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7478\n2023-01-02 23:46:07,468 - mmcls - INFO - Epoch [77][300/5005]\tlr: 1.000e-03, eta: 2:55:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7306\n2023-01-02 23:46:16,519 - mmcls - INFO - Epoch [77][400/5005]\tlr: 1.000e-03, eta: 2:55:33, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7387\n2023-01-02 23:46:25,161 - mmcls - INFO - Epoch [77][500/5005]\tlr: 1.000e-03, eta: 2:55:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7211\n2023-01-02 23:46:33,955 - mmcls - INFO - Epoch [77][600/5005]\tlr: 1.000e-03, eta: 2:55:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7674\n2023-01-02 23:46:42,765 - mmcls - INFO - Epoch [77][700/5005]\tlr: 1.000e-03, eta: 2:55:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7549\n2023-01-02 23:46:51,607 - mmcls - INFO - Epoch [77][800/5005]\tlr: 1.000e-03, eta: 2:54:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7678\n2023-01-02 23:47:00,361 - mmcls - INFO - Epoch [77][900/5005]\tlr: 1.000e-03, eta: 2:54:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7592\n2023-01-02 23:47:08,908 - mmcls - INFO - Epoch [77][1000/5005]\tlr: 1.000e-03, eta: 2:54:40, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7413\n2023-01-02 23:47:17,881 - mmcls - INFO - Epoch [77][1100/5005]\tlr: 1.000e-03, eta: 2:54:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7363\n2023-01-02 23:47:26,581 - mmcls - INFO - Epoch [77][1200/5005]\tlr: 1.000e-03, eta: 2:54:23, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7583\n2023-01-02 23:47:35,377 - mmcls - INFO - Epoch [77][1300/5005]\tlr: 1.000e-03, eta: 2:54:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7492\n2023-01-02 23:47:44,249 - mmcls - INFO - Epoch [77][1400/5005]\tlr: 1.000e-03, eta: 2:54:05, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7449\n2023-01-02 23:47:52,633 - mmcls - INFO - Epoch [77][1500/5005]\tlr: 1.000e-03, eta: 2:53:56, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7373\n2023-01-02 23:48:01,171 - mmcls - INFO - Epoch [77][1600/5005]\tlr: 1.000e-03, eta: 2:53:47, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7533\n2023-01-02 23:48:09,624 - mmcls - INFO - Epoch [77][1700/5005]\tlr: 1.000e-03, eta: 2:53:38, time: 0.084, data_time: 0.016, memory: 1125, loss: 1.7521\n2023-01-02 23:48:18,244 - mmcls - INFO - Epoch [77][1800/5005]\tlr: 1.000e-03, eta: 2:53:29, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7704\n2023-01-02 23:48:26,772 - mmcls - INFO - Epoch [77][1900/5005]\tlr: 1.000e-03, eta: 2:53:21, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7631\n2023-01-02 23:48:35,595 - mmcls - INFO - Epoch [77][2000/5005]\tlr: 1.000e-03, eta: 2:53:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7460\n2023-01-02 23:48:44,112 - mmcls - INFO - Epoch [77][2100/5005]\tlr: 1.000e-03, eta: 2:53:03, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7466\n2023-01-02 23:48:52,913 - mmcls - INFO - Epoch [77][2200/5005]\tlr: 1.000e-03, eta: 2:52:54, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.7690\n2023-01-02 23:49:01,546 - mmcls - INFO - Epoch [77][2300/5005]\tlr: 1.000e-03, eta: 2:52:45, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7404\n2023-01-02 23:49:10,353 - mmcls - INFO - Epoch [77][2400/5005]\tlr: 1.000e-03, eta: 2:52:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7522\n2023-01-02 23:49:19,182 - mmcls - INFO - Epoch [77][2500/5005]\tlr: 1.000e-03, eta: 2:52:28, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7486\n2023-01-02 23:49:27,777 - mmcls - INFO - Epoch [77][2600/5005]\tlr: 1.000e-03, eta: 2:52:19, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7372\n2023-01-02 23:49:36,443 - mmcls - INFO - Epoch [77][2700/5005]\tlr: 1.000e-03, eta: 2:52:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7520\n2023-01-02 23:49:45,103 - mmcls - INFO - Epoch [77][2800/5005]\tlr: 1.000e-03, eta: 2:52:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7461\n2023-01-02 23:49:53,849 - mmcls - INFO - Epoch [77][2900/5005]\tlr: 1.000e-03, eta: 2:51:52, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7608\n2023-01-02 23:50:02,566 - mmcls - INFO - Epoch [77][3000/5005]\tlr: 1.000e-03, eta: 2:51:44, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7512\n2023-01-02 23:50:11,227 - mmcls - INFO - Epoch [77][3100/5005]\tlr: 1.000e-03, eta: 2:51:35, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7572\n2023-01-02 23:50:20,233 - mmcls - INFO - Epoch [77][3200/5005]\tlr: 1.000e-03, eta: 2:51:26, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7637\n2023-01-02 23:50:29,055 - mmcls - INFO - Epoch [77][3300/5005]\tlr: 1.000e-03, eta: 2:51:17, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7483\n2023-01-02 23:50:37,826 - mmcls - INFO - Epoch [77][3400/5005]\tlr: 1.000e-03, eta: 2:51:08, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7593\n2023-01-02 23:50:46,695 - mmcls - INFO - Epoch [77][3500/5005]\tlr: 1.000e-03, eta: 2:51:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7452\n2023-01-02 23:50:55,268 - mmcls - INFO - Epoch [77][3600/5005]\tlr: 1.000e-03, eta: 2:50:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7458\n2023-01-02 23:51:04,058 - mmcls - INFO - Epoch [77][3700/5005]\tlr: 1.000e-03, eta: 2:50:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7621\n2023-01-02 23:51:12,736 - mmcls - INFO - Epoch [77][3800/5005]\tlr: 1.000e-03, eta: 2:50:33, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7643\n2023-01-02 23:51:21,383 - mmcls - INFO - Epoch [77][3900/5005]\tlr: 1.000e-03, eta: 2:50:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7654\n2023-01-02 23:51:30,478 - mmcls - INFO - Epoch [77][4000/5005]\tlr: 1.000e-03, eta: 2:50:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7430\n2023-01-02 23:51:39,371 - mmcls - INFO - Epoch [77][4100/5005]\tlr: 1.000e-03, eta: 2:50:07, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7655\n2023-01-02 23:51:47,949 - mmcls - INFO - Epoch [77][4200/5005]\tlr: 1.000e-03, eta: 2:49:58, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7508\n2023-01-02 23:51:56,848 - mmcls - INFO - Epoch [77][4300/5005]\tlr: 1.000e-03, eta: 2:49:49, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7452\n2023-01-02 23:52:05,789 - mmcls - INFO - Epoch [77][4400/5005]\tlr: 1.000e-03, eta: 2:49:40, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7439\n2023-01-02 23:52:14,367 - mmcls - INFO - Epoch [77][4500/5005]\tlr: 1.000e-03, eta: 2:49:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7711\n2023-01-02 23:52:23,099 - mmcls - INFO - Epoch [77][4600/5005]\tlr: 1.000e-03, eta: 2:49:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7563\n2023-01-02 23:52:31,881 - mmcls - INFO - Epoch [77][4700/5005]\tlr: 1.000e-03, eta: 2:49:14, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7487\n2023-01-02 23:52:40,588 - mmcls - INFO - Epoch [77][4800/5005]\tlr: 1.000e-03, eta: 2:49:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7834\n2023-01-02 23:52:49,544 - mmcls - INFO - Epoch [77][4900/5005]\tlr: 1.000e-03, eta: 2:48:56, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7790\n2023-01-02 23:52:58,517 - mmcls - INFO - Epoch [77][5000/5005]\tlr: 1.000e-03, eta: 2:48:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7687\n2023-01-02 23:53:21,218 - mmcls - INFO - Epoch(val) [77][196]\taccuracy_top-1: 63.0620, accuracy_top-5: 84.0200\n2023-01-02 23:53:31,956 - mmcls - INFO - Epoch [78][100/5005]\tlr: 1.000e-03, eta: 2:48:39, time: 0.107, data_time: 0.036, memory: 1125, loss: 1.7646\n2023-01-02 23:53:40,678 - mmcls - INFO - Epoch [78][200/5005]\tlr: 1.000e-03, eta: 2:48:30, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7272\n2023-01-02 23:53:49,511 - mmcls - INFO - Epoch [78][300/5005]\tlr: 1.000e-03, eta: 2:48:21, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7302\n2023-01-02 23:53:58,127 - mmcls - INFO - Epoch [78][400/5005]\tlr: 1.000e-03, eta: 2:48:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7427\n2023-01-02 23:54:06,708 - mmcls - INFO - Epoch [78][500/5005]\tlr: 1.000e-03, eta: 2:48:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7714\n2023-01-02 23:54:15,520 - mmcls - INFO - Epoch [78][600/5005]\tlr: 1.000e-03, eta: 2:47:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7399\n2023-01-02 23:54:24,271 - mmcls - INFO - Epoch [78][700/5005]\tlr: 1.000e-03, eta: 2:47:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7433\n2023-01-02 23:54:32,915 - mmcls - INFO - Epoch [78][800/5005]\tlr: 1.000e-03, eta: 2:47:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7578\n2023-01-02 23:54:41,570 - mmcls - INFO - Epoch [78][900/5005]\tlr: 1.000e-03, eta: 2:47:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7600\n2023-01-02 23:54:50,068 - mmcls - INFO - Epoch [78][1000/5005]\tlr: 1.000e-03, eta: 2:47:19, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7368\n2023-01-02 23:54:58,773 - mmcls - INFO - Epoch [78][1100/5005]\tlr: 1.000e-03, eta: 2:47:10, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7431\n2023-01-02 23:55:07,415 - mmcls - INFO - Epoch [78][1200/5005]\tlr: 1.000e-03, eta: 2:47:02, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7176\n2023-01-02 23:55:16,544 - mmcls - INFO - Epoch [78][1300/5005]\tlr: 1.000e-03, eta: 2:46:53, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7579\n2023-01-02 23:55:25,476 - mmcls - INFO - Epoch [78][1400/5005]\tlr: 1.000e-03, eta: 2:46:44, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7499\n2023-01-02 23:55:34,040 - mmcls - INFO - Epoch [78][1500/5005]\tlr: 1.000e-03, eta: 2:46:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7423\n2023-01-02 23:55:42,778 - mmcls - INFO - Epoch [78][1600/5005]\tlr: 1.000e-03, eta: 2:46:26, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7470\n2023-01-02 23:55:51,863 - mmcls - INFO - Epoch [78][1700/5005]\tlr: 1.000e-03, eta: 2:46:18, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7379\n2023-01-02 23:56:00,797 - mmcls - INFO - Epoch [78][1800/5005]\tlr: 1.000e-03, eta: 2:46:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7468\n2023-01-02 23:56:09,531 - mmcls - INFO - Epoch [78][1900/5005]\tlr: 1.000e-03, eta: 2:46:00, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7450\n2023-01-02 23:56:18,209 - mmcls - INFO - Epoch [78][2000/5005]\tlr: 1.000e-03, eta: 2:45:51, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7496\n2023-01-02 23:56:27,050 - mmcls - INFO - Epoch [78][2100/5005]\tlr: 1.000e-03, eta: 2:45:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7300\n2023-01-02 23:56:36,017 - mmcls - INFO - Epoch [78][2200/5005]\tlr: 1.000e-03, eta: 2:45:34, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7453\n2023-01-02 23:56:44,854 - mmcls - INFO - Epoch [78][2300/5005]\tlr: 1.000e-03, eta: 2:45:25, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7455\n2023-01-02 23:56:54,107 - mmcls - INFO - Epoch [78][2400/5005]\tlr: 1.000e-03, eta: 2:45:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7272\n2023-01-02 23:57:02,584 - mmcls - INFO - Epoch [78][2500/5005]\tlr: 1.000e-03, eta: 2:45:07, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7569\n2023-01-02 23:57:11,326 - mmcls - INFO - Epoch [78][2600/5005]\tlr: 1.000e-03, eta: 2:44:59, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7505\n2023-01-02 23:57:20,343 - mmcls - INFO - Epoch [78][2700/5005]\tlr: 1.000e-03, eta: 2:44:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7434\n2023-01-02 23:57:28,868 - mmcls - INFO - Epoch [78][2800/5005]\tlr: 1.000e-03, eta: 2:44:41, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7732\n2023-01-02 23:57:37,698 - mmcls - INFO - Epoch [78][2900/5005]\tlr: 1.000e-03, eta: 2:44:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7519\n2023-01-02 23:57:46,661 - mmcls - INFO - Epoch [78][3000/5005]\tlr: 1.000e-03, eta: 2:44:23, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7387\n2023-01-02 23:57:55,392 - mmcls - INFO - Epoch [78][3100/5005]\tlr: 1.000e-03, eta: 2:44:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7385\n2023-01-02 23:58:04,555 - mmcls - INFO - Epoch [78][3200/5005]\tlr: 1.000e-03, eta: 2:44:06, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7464\n2023-01-02 23:58:13,383 - mmcls - INFO - Epoch [78][3300/5005]\tlr: 1.000e-03, eta: 2:43:57, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7337\n2023-01-02 23:58:22,026 - mmcls - INFO - Epoch [78][3400/5005]\tlr: 1.000e-03, eta: 2:43:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7427\n2023-01-02 23:58:30,698 - mmcls - INFO - Epoch [78][3500/5005]\tlr: 1.000e-03, eta: 2:43:39, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7438\n2023-01-02 23:58:39,689 - mmcls - INFO - Epoch [78][3600/5005]\tlr: 1.000e-03, eta: 2:43:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7567\n2023-01-02 23:58:48,512 - mmcls - INFO - Epoch [78][3700/5005]\tlr: 1.000e-03, eta: 2:43:22, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7548\n2023-01-02 23:58:57,147 - mmcls - INFO - Epoch [78][3800/5005]\tlr: 1.000e-03, eta: 2:43:13, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7697\n2023-01-02 23:59:06,091 - mmcls - INFO - Epoch [78][3900/5005]\tlr: 1.000e-03, eta: 2:43:04, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7527\n2023-01-02 23:59:14,602 - mmcls - INFO - Epoch [78][4000/5005]\tlr: 1.000e-03, eta: 2:42:55, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7532\n2023-01-02 23:59:23,228 - mmcls - INFO - Epoch [78][4100/5005]\tlr: 1.000e-03, eta: 2:42:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7305\n2023-01-02 23:59:32,294 - mmcls - INFO - Epoch [78][4200/5005]\tlr: 1.000e-03, eta: 2:42:38, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.7571\n2023-01-02 23:59:41,170 - mmcls - INFO - Epoch [78][4300/5005]\tlr: 1.000e-03, eta: 2:42:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7739\n2023-01-02 23:59:50,052 - mmcls - INFO - Epoch [78][4400/5005]\tlr: 1.000e-03, eta: 2:42:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7605\n2023-01-02 23:59:58,653 - mmcls - INFO - Epoch [78][4500/5005]\tlr: 1.000e-03, eta: 2:42:12, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7714\n2023-01-03 00:00:07,485 - mmcls - INFO - Epoch [78][4600/5005]\tlr: 1.000e-03, eta: 2:42:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7547\n2023-01-03 00:00:16,112 - mmcls - INFO - Epoch [78][4700/5005]\tlr: 1.000e-03, eta: 2:41:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7462\n2023-01-03 00:00:24,726 - mmcls - INFO - Epoch [78][4800/5005]\tlr: 1.000e-03, eta: 2:41:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7486\n2023-01-03 00:00:33,437 - mmcls - INFO - Epoch [78][4900/5005]\tlr: 1.000e-03, eta: 2:41:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7555\n2023-01-03 00:00:42,524 - mmcls - INFO - Epoch [78][5000/5005]\tlr: 1.000e-03, eta: 2:41:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7564\n2023-01-03 00:01:05,081 - mmcls - INFO - Epoch(val) [78][196]\taccuracy_top-1: 63.2260, accuracy_top-5: 83.9840\n2023-01-03 00:01:16,184 - mmcls - INFO - Epoch [79][100/5005]\tlr: 1.000e-03, eta: 2:41:19, time: 0.111, data_time: 0.041, memory: 1125, loss: 1.7701\n2023-01-03 00:01:25,058 - mmcls - INFO - Epoch [79][200/5005]\tlr: 1.000e-03, eta: 2:41:10, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7443\n2023-01-03 00:01:33,809 - mmcls - INFO - Epoch [79][300/5005]\tlr: 1.000e-03, eta: 2:41:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7266\n2023-01-03 00:01:42,502 - mmcls - INFO - Epoch [79][400/5005]\tlr: 1.000e-03, eta: 2:40:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7104\n2023-01-03 00:01:51,560 - mmcls - INFO - Epoch [79][500/5005]\tlr: 1.000e-03, eta: 2:40:44, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7482\n2023-01-03 00:02:00,261 - mmcls - INFO - Epoch [79][600/5005]\tlr: 1.000e-03, eta: 2:40:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7327\n2023-01-03 00:02:09,076 - mmcls - INFO - Epoch [79][700/5005]\tlr: 1.000e-03, eta: 2:40:26, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7371\n2023-01-03 00:02:17,765 - mmcls - INFO - Epoch [79][800/5005]\tlr: 1.000e-03, eta: 2:40:17, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7416\n2023-01-03 00:02:26,623 - mmcls - INFO - Epoch [79][900/5005]\tlr: 1.000e-03, eta: 2:40:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7328\n2023-01-03 00:02:35,166 - mmcls - INFO - Epoch [79][1000/5005]\tlr: 1.000e-03, eta: 2:40:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7510\n2023-01-03 00:02:44,011 - mmcls - INFO - Epoch [79][1100/5005]\tlr: 1.000e-03, eta: 2:39:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7680\n2023-01-03 00:02:52,686 - mmcls - INFO - Epoch [79][1200/5005]\tlr: 1.000e-03, eta: 2:39:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7630\n2023-01-03 00:03:01,564 - mmcls - INFO - Epoch [79][1300/5005]\tlr: 1.000e-03, eta: 2:39:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7406\n2023-01-03 00:03:10,590 - mmcls - INFO - Epoch [79][1400/5005]\tlr: 1.000e-03, eta: 2:39:24, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7422\n2023-01-03 00:03:19,090 - mmcls - INFO - Epoch [79][1500/5005]\tlr: 1.000e-03, eta: 2:39:16, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7360\n2023-01-03 00:03:27,699 - mmcls - INFO - Epoch [79][1600/5005]\tlr: 1.000e-03, eta: 2:39:07, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7649\n2023-01-03 00:03:36,637 - mmcls - INFO - Epoch [79][1700/5005]\tlr: 1.000e-03, eta: 2:38:58, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7502\n2023-01-03 00:03:45,195 - mmcls - INFO - Epoch [79][1800/5005]\tlr: 1.000e-03, eta: 2:38:49, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7608\n2023-01-03 00:03:53,851 - mmcls - INFO - Epoch [79][1900/5005]\tlr: 1.000e-03, eta: 2:38:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7382\n2023-01-03 00:04:02,558 - mmcls - INFO - Epoch [79][2000/5005]\tlr: 1.000e-03, eta: 2:38:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7382\n2023-01-03 00:04:11,184 - mmcls - INFO - Epoch [79][2100/5005]\tlr: 1.000e-03, eta: 2:38:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7658\n2023-01-03 00:04:19,831 - mmcls - INFO - Epoch [79][2200/5005]\tlr: 1.000e-03, eta: 2:38:14, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7484\n2023-01-03 00:04:28,520 - mmcls - INFO - Epoch [79][2300/5005]\tlr: 1.000e-03, eta: 2:38:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7464\n2023-01-03 00:04:37,324 - mmcls - INFO - Epoch [79][2400/5005]\tlr: 1.000e-03, eta: 2:37:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7543\n2023-01-03 00:04:46,005 - mmcls - INFO - Epoch [79][2500/5005]\tlr: 1.000e-03, eta: 2:37:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7426\n2023-01-03 00:04:54,720 - mmcls - INFO - Epoch [79][2600/5005]\tlr: 1.000e-03, eta: 2:37:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7423\n2023-01-03 00:05:03,599 - mmcls - INFO - Epoch [79][2700/5005]\tlr: 1.000e-03, eta: 2:37:30, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7296\n2023-01-03 00:05:12,191 - mmcls - INFO - Epoch [79][2800/5005]\tlr: 1.000e-03, eta: 2:37:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7513\n2023-01-03 00:05:20,990 - mmcls - INFO - Epoch [79][2900/5005]\tlr: 1.000e-03, eta: 2:37:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7635\n2023-01-03 00:05:29,709 - mmcls - INFO - Epoch [79][3000/5005]\tlr: 1.000e-03, eta: 2:37:03, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7448\n2023-01-03 00:05:38,286 - mmcls - INFO - Epoch [79][3100/5005]\tlr: 1.000e-03, eta: 2:36:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7394\n2023-01-03 00:05:47,272 - mmcls - INFO - Epoch [79][3200/5005]\tlr: 1.000e-03, eta: 2:36:46, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7603\n2023-01-03 00:05:55,832 - mmcls - INFO - Epoch [79][3300/5005]\tlr: 1.000e-03, eta: 2:36:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7587\n2023-01-03 00:06:04,684 - mmcls - INFO - Epoch [79][3400/5005]\tlr: 1.000e-03, eta: 2:36:28, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7547\n2023-01-03 00:06:13,431 - mmcls - INFO - Epoch [79][3500/5005]\tlr: 1.000e-03, eta: 2:36:19, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7576\n2023-01-03 00:06:21,811 - mmcls - INFO - Epoch [79][3600/5005]\tlr: 1.000e-03, eta: 2:36:10, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7522\n2023-01-03 00:06:30,986 - mmcls - INFO - Epoch [79][3700/5005]\tlr: 1.000e-03, eta: 2:36:01, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7591\n2023-01-03 00:06:39,889 - mmcls - INFO - Epoch [79][3800/5005]\tlr: 1.000e-03, eta: 2:35:53, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7318\n2023-01-03 00:06:48,519 - mmcls - INFO - Epoch [79][3900/5005]\tlr: 1.000e-03, eta: 2:35:44, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7447\n2023-01-03 00:06:57,145 - mmcls - INFO - Epoch [79][4000/5005]\tlr: 1.000e-03, eta: 2:35:35, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7591\n2023-01-03 00:07:05,721 - mmcls - INFO - Epoch [79][4100/5005]\tlr: 1.000e-03, eta: 2:35:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7472\n2023-01-03 00:07:14,428 - mmcls - INFO - Epoch [79][4200/5005]\tlr: 1.000e-03, eta: 2:35:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7434\n2023-01-03 00:07:23,380 - mmcls - INFO - Epoch [79][4300/5005]\tlr: 1.000e-03, eta: 2:35:09, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7372\n2023-01-03 00:07:31,961 - mmcls - INFO - Epoch [79][4400/5005]\tlr: 1.000e-03, eta: 2:35:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7374\n2023-01-03 00:07:41,138 - mmcls - INFO - Epoch [79][4500/5005]\tlr: 1.000e-03, eta: 2:34:51, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7389\n2023-01-03 00:07:49,810 - mmcls - INFO - Epoch [79][4600/5005]\tlr: 1.000e-03, eta: 2:34:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7690\n2023-01-03 00:07:58,503 - mmcls - INFO - Epoch [79][4700/5005]\tlr: 1.000e-03, eta: 2:34:33, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7490\n2023-01-03 00:08:07,254 - mmcls - INFO - Epoch [79][4800/5005]\tlr: 1.000e-03, eta: 2:34:25, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7448\n2023-01-03 00:08:15,920 - mmcls - INFO - Epoch [79][4900/5005]\tlr: 1.000e-03, eta: 2:34:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7676\n2023-01-03 00:08:24,756 - mmcls - INFO - Epoch [79][5000/5005]\tlr: 1.000e-03, eta: 2:34:07, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7508\n2023-01-03 00:08:47,271 - mmcls - INFO - Epoch(val) [79][196]\taccuracy_top-1: 62.9860, accuracy_top-5: 84.0600\n2023-01-03 00:08:58,078 - mmcls - INFO - Epoch [80][100/5005]\tlr: 1.000e-03, eta: 2:33:58, time: 0.108, data_time: 0.037, memory: 1125, loss: 1.7225\n2023-01-03 00:09:06,590 - mmcls - INFO - Epoch [80][200/5005]\tlr: 1.000e-03, eta: 2:33:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7079\n2023-01-03 00:09:15,148 - mmcls - INFO - Epoch [80][300/5005]\tlr: 1.000e-03, eta: 2:33:40, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7235\n2023-01-03 00:09:23,778 - mmcls - INFO - Epoch [80][400/5005]\tlr: 1.000e-03, eta: 2:33:32, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7348\n2023-01-03 00:09:32,531 - mmcls - INFO - Epoch [80][500/5005]\tlr: 1.000e-03, eta: 2:33:23, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7417\n2023-01-03 00:09:41,247 - mmcls - INFO - Epoch [80][600/5005]\tlr: 1.000e-03, eta: 2:33:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7374\n2023-01-03 00:09:49,759 - mmcls - INFO - Epoch [80][700/5005]\tlr: 1.000e-03, eta: 2:33:05, time: 0.085, data_time: 0.042, memory: 1125, loss: 1.7381\n2023-01-03 00:09:58,423 - mmcls - INFO - Epoch [80][800/5005]\tlr: 1.000e-03, eta: 2:32:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7384\n2023-01-03 00:10:07,151 - mmcls - INFO - Epoch [80][900/5005]\tlr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7485\n2023-01-03 00:10:15,778 - mmcls - INFO - Epoch [80][1000/5005]\tlr: 1.000e-03, eta: 2:32:39, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7119\n2023-01-03 00:10:24,487 - mmcls - INFO - Epoch [80][1100/5005]\tlr: 1.000e-03, eta: 2:32:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7425\n2023-01-03 00:10:32,866 - mmcls - INFO - Epoch [80][1200/5005]\tlr: 1.000e-03, eta: 2:32:21, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7356\n2023-01-03 00:10:41,647 - mmcls - INFO - Epoch [80][1300/5005]\tlr: 1.000e-03, eta: 2:32:12, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7383\n2023-01-03 00:10:50,470 - mmcls - INFO - Epoch [80][1400/5005]\tlr: 1.000e-03, eta: 2:32:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7490\n2023-01-03 00:10:59,326 - mmcls - INFO - Epoch [80][1500/5005]\tlr: 1.000e-03, eta: 2:31:54, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7250\n2023-01-03 00:11:08,093 - mmcls - INFO - Epoch [80][1600/5005]\tlr: 1.000e-03, eta: 2:31:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7314\n2023-01-03 00:11:16,932 - mmcls - INFO - Epoch [80][1700/5005]\tlr: 1.000e-03, eta: 2:31:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7253\n2023-01-03 00:11:25,367 - mmcls - INFO - Epoch [80][1800/5005]\tlr: 1.000e-03, eta: 2:31:28, time: 0.084, data_time: 0.033, memory: 1125, loss: 1.7586\n2023-01-03 00:11:34,583 - mmcls - INFO - Epoch [80][1900/5005]\tlr: 1.000e-03, eta: 2:31:19, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7397\n2023-01-03 00:11:43,367 - mmcls - INFO - Epoch [80][2000/5005]\tlr: 1.000e-03, eta: 2:31:10, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7359\n2023-01-03 00:11:52,597 - mmcls - INFO - Epoch [80][2100/5005]\tlr: 1.000e-03, eta: 2:31:02, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7608\n2023-01-03 00:12:01,784 - mmcls - INFO - Epoch [80][2200/5005]\tlr: 1.000e-03, eta: 2:30:53, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7482\n2023-01-03 00:12:10,400 - mmcls - INFO - Epoch [80][2300/5005]\tlr: 1.000e-03, eta: 2:30:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7699\n2023-01-03 00:12:18,956 - mmcls - INFO - Epoch [80][2400/5005]\tlr: 1.000e-03, eta: 2:30:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7508\n2023-01-03 00:12:27,531 - mmcls - INFO - Epoch [80][2500/5005]\tlr: 1.000e-03, eta: 2:30:27, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7634\n2023-01-03 00:12:36,272 - mmcls - INFO - Epoch [80][2600/5005]\tlr: 1.000e-03, eta: 2:30:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7525\n2023-01-03 00:12:44,865 - mmcls - INFO - Epoch [80][2700/5005]\tlr: 1.000e-03, eta: 2:30:09, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7315\n2023-01-03 00:12:53,570 - mmcls - INFO - Epoch [80][2800/5005]\tlr: 1.000e-03, eta: 2:30:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7633\n2023-01-03 00:13:02,387 - mmcls - INFO - Epoch [80][2900/5005]\tlr: 1.000e-03, eta: 2:29:51, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7510\n2023-01-03 00:13:11,141 - mmcls - INFO - Epoch [80][3000/5005]\tlr: 1.000e-03, eta: 2:29:42, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7537\n2023-01-03 00:13:19,914 - mmcls - INFO - Epoch [80][3100/5005]\tlr: 1.000e-03, eta: 2:29:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7597\n2023-01-03 00:13:28,808 - mmcls - INFO - Epoch [80][3200/5005]\tlr: 1.000e-03, eta: 2:29:25, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7581\n2023-01-03 00:13:37,612 - mmcls - INFO - Epoch [80][3300/5005]\tlr: 1.000e-03, eta: 2:29:16, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7505\n2023-01-03 00:13:46,702 - mmcls - INFO - Epoch [80][3400/5005]\tlr: 1.000e-03, eta: 2:29:07, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7371\n2023-01-03 00:13:55,690 - mmcls - INFO - Epoch [80][3500/5005]\tlr: 1.000e-03, eta: 2:28:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7579\n2023-01-03 00:14:04,544 - mmcls - INFO - Epoch [80][3600/5005]\tlr: 1.000e-03, eta: 2:28:50, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7570\n2023-01-03 00:14:13,591 - mmcls - INFO - Epoch [80][3700/5005]\tlr: 1.000e-03, eta: 2:28:41, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7618\n2023-01-03 00:14:22,218 - mmcls - INFO - Epoch [80][3800/5005]\tlr: 1.000e-03, eta: 2:28:32, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7563\n2023-01-03 00:14:31,077 - mmcls - INFO - Epoch [80][3900/5005]\tlr: 1.000e-03, eta: 2:28:23, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7513\n2023-01-03 00:14:39,645 - mmcls - INFO - Epoch [80][4000/5005]\tlr: 1.000e-03, eta: 2:28:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7534\n2023-01-03 00:14:48,246 - mmcls - INFO - Epoch [80][4100/5005]\tlr: 1.000e-03, eta: 2:28:06, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7335\n2023-01-03 00:14:56,948 - mmcls - INFO - Epoch [80][4200/5005]\tlr: 1.000e-03, eta: 2:27:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7509\n2023-01-03 00:15:05,806 - mmcls - INFO - Epoch [80][4300/5005]\tlr: 1.000e-03, eta: 2:27:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7600\n2023-01-03 00:15:14,594 - mmcls - INFO - Epoch [80][4400/5005]\tlr: 1.000e-03, eta: 2:27:39, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7366\n2023-01-03 00:15:23,514 - mmcls - INFO - Epoch [80][4500/5005]\tlr: 1.000e-03, eta: 2:27:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7744\n2023-01-03 00:15:32,129 - mmcls - INFO - Epoch [80][4600/5005]\tlr: 1.000e-03, eta: 2:27:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7189\n2023-01-03 00:15:40,908 - mmcls - INFO - Epoch [80][4700/5005]\tlr: 1.000e-03, eta: 2:27:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7476\n2023-01-03 00:15:49,695 - mmcls - INFO - Epoch [80][4800/5005]\tlr: 1.000e-03, eta: 2:27:04, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7494\n2023-01-03 00:15:58,426 - mmcls - INFO - Epoch [80][4900/5005]\tlr: 1.000e-03, eta: 2:26:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7691\n2023-01-03 00:16:07,527 - mmcls - INFO - Epoch [80][5000/5005]\tlr: 1.000e-03, eta: 2:26:47, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7618\n2023-01-03 00:16:30,128 - mmcls - INFO - Epoch(val) [80][196]\taccuracy_top-1: 63.1860, accuracy_top-5: 84.1320\n2023-01-03 00:16:41,284 - mmcls - INFO - Epoch [81][100/5005]\tlr: 1.000e-03, eta: 2:26:38, time: 0.111, data_time: 0.045, memory: 1125, loss: 1.7145\n2023-01-03 00:16:50,014 - mmcls - INFO - Epoch [81][200/5005]\tlr: 1.000e-03, eta: 2:26:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7159\n2023-01-03 00:16:58,442 - mmcls - INFO - Epoch [81][300/5005]\tlr: 1.000e-03, eta: 2:26:20, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7319\n2023-01-03 00:17:07,018 - mmcls - INFO - Epoch [81][400/5005]\tlr: 1.000e-03, eta: 2:26:11, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7198\n2023-01-03 00:17:15,573 - mmcls - INFO - Epoch [81][500/5005]\tlr: 1.000e-03, eta: 2:26:02, time: 0.085, data_time: 0.031, memory: 1125, loss: 1.7145\n2023-01-03 00:17:24,494 - mmcls - INFO - Epoch [81][600/5005]\tlr: 1.000e-03, eta: 2:25:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7558\n2023-01-03 00:17:33,241 - mmcls - INFO - Epoch [81][700/5005]\tlr: 1.000e-03, eta: 2:25:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7160\n2023-01-03 00:17:41,870 - mmcls - INFO - Epoch [81][800/5005]\tlr: 1.000e-03, eta: 2:25:36, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7394\n2023-01-03 00:17:50,401 - mmcls - INFO - Epoch [81][900/5005]\tlr: 1.000e-03, eta: 2:25:27, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7362\n2023-01-03 00:17:59,629 - mmcls - INFO - Epoch [81][1000/5005]\tlr: 1.000e-03, eta: 2:25:18, time: 0.092, data_time: 0.008, memory: 1125, loss: 1.7333\n2023-01-03 00:18:08,060 - mmcls - INFO - Epoch [81][1100/5005]\tlr: 1.000e-03, eta: 2:25:10, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7325\n2023-01-03 00:18:16,677 - mmcls - INFO - Epoch [81][1200/5005]\tlr: 1.000e-03, eta: 2:25:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7505\n2023-01-03 00:18:25,636 - mmcls - INFO - Epoch [81][1300/5005]\tlr: 1.000e-03, eta: 2:24:52, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7388\n2023-01-03 00:18:34,275 - mmcls - INFO - Epoch [81][1400/5005]\tlr: 1.000e-03, eta: 2:24:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7320\n2023-01-03 00:18:42,986 - mmcls - INFO - Epoch [81][1500/5005]\tlr: 1.000e-03, eta: 2:24:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7369\n2023-01-03 00:18:51,970 - mmcls - INFO - Epoch [81][1600/5005]\tlr: 1.000e-03, eta: 2:24:26, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7701\n2023-01-03 00:19:00,555 - mmcls - INFO - Epoch [81][1700/5005]\tlr: 1.000e-03, eta: 2:24:17, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7442\n2023-01-03 00:19:09,471 - mmcls - INFO - Epoch [81][1800/5005]\tlr: 1.000e-03, eta: 2:24:08, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7378\n2023-01-03 00:19:18,123 - mmcls - INFO - Epoch [81][1900/5005]\tlr: 1.000e-03, eta: 2:23:59, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7479\n2023-01-03 00:19:27,129 - mmcls - INFO - Epoch [81][2000/5005]\tlr: 1.000e-03, eta: 2:23:50, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7442\n2023-01-03 00:19:35,789 - mmcls - INFO - Epoch [81][2100/5005]\tlr: 1.000e-03, eta: 2:23:41, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7655\n2023-01-03 00:19:44,682 - mmcls - INFO - Epoch [81][2200/5005]\tlr: 1.000e-03, eta: 2:23:33, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7522\n2023-01-03 00:19:53,234 - mmcls - INFO - Epoch [81][2300/5005]\tlr: 1.000e-03, eta: 2:23:24, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7572\n2023-01-03 00:20:02,015 - mmcls - INFO - Epoch [81][2400/5005]\tlr: 1.000e-03, eta: 2:23:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7511\n2023-01-03 00:20:10,669 - mmcls - INFO - Epoch [81][2500/5005]\tlr: 1.000e-03, eta: 2:23:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7349\n2023-01-03 00:20:19,454 - mmcls - INFO - Epoch [81][2600/5005]\tlr: 1.000e-03, eta: 2:22:57, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7154\n2023-01-03 00:20:28,268 - mmcls - INFO - Epoch [81][2700/5005]\tlr: 1.000e-03, eta: 2:22:49, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7390\n2023-01-03 00:20:36,645 - mmcls - INFO - Epoch [81][2800/5005]\tlr: 1.000e-03, eta: 2:22:40, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7447\n2023-01-03 00:20:45,339 - mmcls - INFO - Epoch [81][2900/5005]\tlr: 1.000e-03, eta: 2:22:31, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7278\n2023-01-03 00:20:54,297 - mmcls - INFO - Epoch [81][3000/5005]\tlr: 1.000e-03, eta: 2:22:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7362\n2023-01-03 00:21:03,070 - mmcls - INFO - Epoch [81][3100/5005]\tlr: 1.000e-03, eta: 2:22:13, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7437\n2023-01-03 00:21:11,723 - mmcls - INFO - Epoch [81][3200/5005]\tlr: 1.000e-03, eta: 2:22:05, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7507\n2023-01-03 00:21:20,576 - mmcls - INFO - Epoch [81][3300/5005]\tlr: 1.000e-03, eta: 2:21:56, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7526\n2023-01-03 00:21:29,660 - mmcls - INFO - Epoch [81][3400/5005]\tlr: 1.000e-03, eta: 2:21:47, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7477\n2023-01-03 00:21:38,269 - mmcls - INFO - Epoch [81][3500/5005]\tlr: 1.000e-03, eta: 2:21:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7374\n2023-01-03 00:21:46,851 - mmcls - INFO - Epoch [81][3600/5005]\tlr: 1.000e-03, eta: 2:21:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7253\n2023-01-03 00:21:55,696 - mmcls - INFO - Epoch [81][3700/5005]\tlr: 1.000e-03, eta: 2:21:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7460\n2023-01-03 00:22:04,337 - mmcls - INFO - Epoch [81][3800/5005]\tlr: 1.000e-03, eta: 2:21:12, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7468\n2023-01-03 00:22:13,151 - mmcls - INFO - Epoch [81][3900/5005]\tlr: 1.000e-03, eta: 2:21:03, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7551\n2023-01-03 00:22:22,079 - mmcls - INFO - Epoch [81][4000/5005]\tlr: 1.000e-03, eta: 2:20:54, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7439\n2023-01-03 00:22:30,795 - mmcls - INFO - Epoch [81][4100/5005]\tlr: 1.000e-03, eta: 2:20:45, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7511\n2023-01-03 00:22:39,469 - mmcls - INFO - Epoch [81][4200/5005]\tlr: 1.000e-03, eta: 2:20:36, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7462\n2023-01-03 00:22:48,002 - mmcls - INFO - Epoch [81][4300/5005]\tlr: 1.000e-03, eta: 2:20:28, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7470\n2023-01-03 00:22:56,910 - mmcls - INFO - Epoch [81][4400/5005]\tlr: 1.000e-03, eta: 2:20:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7798\n2023-01-03 00:23:05,585 - mmcls - INFO - Epoch [81][4500/5005]\tlr: 1.000e-03, eta: 2:20:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7560\n2023-01-03 00:23:14,448 - mmcls - INFO - Epoch [81][4600/5005]\tlr: 1.000e-03, eta: 2:20:01, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7688\n2023-01-03 00:23:23,117 - mmcls - INFO - Epoch [81][4700/5005]\tlr: 1.000e-03, eta: 2:19:52, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7477\n2023-01-03 00:23:32,094 - mmcls - INFO - Epoch [81][4800/5005]\tlr: 1.000e-03, eta: 2:19:44, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7476\n2023-01-03 00:23:40,671 - mmcls - INFO - Epoch [81][4900/5005]\tlr: 1.000e-03, eta: 2:19:35, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7515\n2023-01-03 00:23:49,592 - mmcls - INFO - Epoch [81][5000/5005]\tlr: 1.000e-03, eta: 2:19:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7343\n2023-01-03 00:24:12,398 - mmcls - INFO - Epoch(val) [81][196]\taccuracy_top-1: 63.0440, accuracy_top-5: 83.9820\n2023-01-03 00:24:23,254 - mmcls - INFO - Epoch [82][100/5005]\tlr: 1.000e-03, eta: 2:19:17, time: 0.108, data_time: 0.051, memory: 1125, loss: 1.7414\n2023-01-03 00:24:32,128 - mmcls - INFO - Epoch [82][200/5005]\tlr: 1.000e-03, eta: 2:19:08, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7385\n2023-01-03 00:24:41,286 - mmcls - INFO - Epoch [82][300/5005]\tlr: 1.000e-03, eta: 2:19:00, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7606\n2023-01-03 00:24:49,834 - mmcls - INFO - Epoch [82][400/5005]\tlr: 1.000e-03, eta: 2:18:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7288\n2023-01-03 00:24:58,668 - mmcls - INFO - Epoch [82][500/5005]\tlr: 1.000e-03, eta: 2:18:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7429\n2023-01-03 00:25:07,535 - mmcls - INFO - Epoch [82][600/5005]\tlr: 1.000e-03, eta: 2:18:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7300\n2023-01-03 00:25:16,114 - mmcls - INFO - Epoch [82][700/5005]\tlr: 1.000e-03, eta: 2:18:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7190\n2023-01-03 00:25:25,423 - mmcls - INFO - Epoch [82][800/5005]\tlr: 1.000e-03, eta: 2:18:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7190\n2023-01-03 00:25:34,315 - mmcls - INFO - Epoch [82][900/5005]\tlr: 1.000e-03, eta: 2:18:07, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7478\n2023-01-03 00:25:42,992 - mmcls - INFO - Epoch [82][1000/5005]\tlr: 1.000e-03, eta: 2:17:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7067\n2023-01-03 00:25:51,432 - mmcls - INFO - Epoch [82][1100/5005]\tlr: 1.000e-03, eta: 2:17:49, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7315\n2023-01-03 00:26:00,331 - mmcls - INFO - Epoch [82][1200/5005]\tlr: 1.000e-03, eta: 2:17:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7437\n2023-01-03 00:26:09,299 - mmcls - INFO - Epoch [82][1300/5005]\tlr: 1.000e-03, eta: 2:17:32, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7295\n2023-01-03 00:26:17,826 - mmcls - INFO - Epoch [82][1400/5005]\tlr: 1.000e-03, eta: 2:17:23, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7222\n2023-01-03 00:26:26,750 - mmcls - INFO - Epoch [82][1500/5005]\tlr: 1.000e-03, eta: 2:17:14, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7435\n2023-01-03 00:26:35,496 - mmcls - INFO - Epoch [82][1600/5005]\tlr: 1.000e-03, eta: 2:17:05, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7590\n2023-01-03 00:26:44,039 - mmcls - INFO - Epoch [82][1700/5005]\tlr: 1.000e-03, eta: 2:16:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7430\n2023-01-03 00:26:52,737 - mmcls - INFO - Epoch [82][1800/5005]\tlr: 1.000e-03, eta: 2:16:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7390\n2023-01-03 00:27:01,399 - mmcls - INFO - Epoch [82][1900/5005]\tlr: 1.000e-03, eta: 2:16:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7444\n2023-01-03 00:27:10,080 - mmcls - INFO - Epoch [82][2000/5005]\tlr: 1.000e-03, eta: 2:16:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7472\n2023-01-03 00:27:18,720 - mmcls - INFO - Epoch [82][2100/5005]\tlr: 1.000e-03, eta: 2:16:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7213\n2023-01-03 00:27:27,346 - mmcls - INFO - Epoch [82][2200/5005]\tlr: 1.000e-03, eta: 2:16:12, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7261\n2023-01-03 00:27:36,000 - mmcls - INFO - Epoch [82][2300/5005]\tlr: 1.000e-03, eta: 2:16:03, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7383\n2023-01-03 00:27:44,850 - mmcls - INFO - Epoch [82][2400/5005]\tlr: 1.000e-03, eta: 2:15:55, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7457\n2023-01-03 00:27:53,475 - mmcls - INFO - Epoch [82][2500/5005]\tlr: 1.000e-03, eta: 2:15:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7537\n2023-01-03 00:28:02,265 - mmcls - INFO - Epoch [82][2600/5005]\tlr: 1.000e-03, eta: 2:15:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7584\n2023-01-03 00:28:11,268 - mmcls - INFO - Epoch [82][2700/5005]\tlr: 1.000e-03, eta: 2:15:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7230\n2023-01-03 00:28:19,720 - mmcls - INFO - Epoch [82][2800/5005]\tlr: 1.000e-03, eta: 2:15:19, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.7384\n2023-01-03 00:28:28,597 - mmcls - INFO - Epoch [82][2900/5005]\tlr: 1.000e-03, eta: 2:15:11, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7446\n2023-01-03 00:28:37,386 - mmcls - INFO - Epoch [82][3000/5005]\tlr: 1.000e-03, eta: 2:15:02, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7308\n2023-01-03 00:28:46,010 - mmcls - INFO - Epoch [82][3100/5005]\tlr: 1.000e-03, eta: 2:14:53, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7288\n2023-01-03 00:28:54,648 - mmcls - INFO - Epoch [82][3200/5005]\tlr: 1.000e-03, eta: 2:14:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7551\n2023-01-03 00:29:03,414 - mmcls - INFO - Epoch [82][3300/5005]\tlr: 1.000e-03, eta: 2:14:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7398\n2023-01-03 00:29:12,217 - mmcls - INFO - Epoch [82][3400/5005]\tlr: 1.000e-03, eta: 2:14:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7338\n2023-01-03 00:29:20,844 - mmcls - INFO - Epoch [82][3500/5005]\tlr: 1.000e-03, eta: 2:14:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7602\n2023-01-03 00:29:29,300 - mmcls - INFO - Epoch [82][3600/5005]\tlr: 1.000e-03, eta: 2:14:09, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7453\n2023-01-03 00:29:37,990 - mmcls - INFO - Epoch [82][3700/5005]\tlr: 1.000e-03, eta: 2:14:00, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7597\n2023-01-03 00:29:46,902 - mmcls - INFO - Epoch [82][3800/5005]\tlr: 1.000e-03, eta: 2:13:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7550\n2023-01-03 00:29:55,568 - mmcls - INFO - Epoch [82][3900/5005]\tlr: 1.000e-03, eta: 2:13:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7486\n2023-01-03 00:30:04,066 - mmcls - INFO - Epoch [82][4000/5005]\tlr: 1.000e-03, eta: 2:13:34, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7282\n2023-01-03 00:30:13,075 - mmcls - INFO - Epoch [82][4100/5005]\tlr: 1.000e-03, eta: 2:13:25, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7572\n2023-01-03 00:30:21,776 - mmcls - INFO - Epoch [82][4200/5005]\tlr: 1.000e-03, eta: 2:13:16, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7561\n2023-01-03 00:30:30,497 - mmcls - INFO - Epoch [82][4300/5005]\tlr: 1.000e-03, eta: 2:13:07, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7624\n2023-01-03 00:30:39,503 - mmcls - INFO - Epoch [82][4400/5005]\tlr: 1.000e-03, eta: 2:12:58, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7360\n2023-01-03 00:30:48,491 - mmcls - INFO - Epoch [82][4500/5005]\tlr: 1.000e-03, eta: 2:12:50, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7339\n2023-01-03 00:30:56,994 - mmcls - INFO - Epoch [82][4600/5005]\tlr: 1.000e-03, eta: 2:12:41, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7418\n2023-01-03 00:31:05,696 - mmcls - INFO - Epoch [82][4700/5005]\tlr: 1.000e-03, eta: 2:12:32, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.7528\n2023-01-03 00:31:14,610 - mmcls - INFO - Epoch [82][4800/5005]\tlr: 1.000e-03, eta: 2:12:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7628\n2023-01-03 00:31:23,265 - mmcls - INFO - Epoch [82][4900/5005]\tlr: 1.000e-03, eta: 2:12:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7401\n2023-01-03 00:31:32,067 - mmcls - INFO - Epoch [82][5000/5005]\tlr: 1.000e-03, eta: 2:12:06, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7556\n2023-01-03 00:31:54,671 - mmcls - INFO - Epoch(val) [82][196]\taccuracy_top-1: 63.0600, accuracy_top-5: 83.9920\n2023-01-03 00:32:05,527 - mmcls - INFO - Epoch [83][100/5005]\tlr: 1.000e-03, eta: 2:11:57, time: 0.108, data_time: 0.042, memory: 1125, loss: 1.7171\n2023-01-03 00:32:14,387 - mmcls - INFO - Epoch [83][200/5005]\tlr: 1.000e-03, eta: 2:11:48, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7249\n2023-01-03 00:32:23,339 - mmcls - INFO - Epoch [83][300/5005]\tlr: 1.000e-03, eta: 2:11:39, time: 0.089, data_time: 0.037, memory: 1125, loss: 1.7246\n2023-01-03 00:32:32,101 - mmcls - INFO - Epoch [83][400/5005]\tlr: 1.000e-03, eta: 2:11:30, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7267\n2023-01-03 00:32:40,756 - mmcls - INFO - Epoch [83][500/5005]\tlr: 1.000e-03, eta: 2:11:21, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7451\n2023-01-03 00:32:49,588 - mmcls - INFO - Epoch [83][600/5005]\tlr: 1.000e-03, eta: 2:11:13, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7403\n2023-01-03 00:32:58,256 - mmcls - INFO - Epoch [83][700/5005]\tlr: 1.000e-03, eta: 2:11:04, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7390\n2023-01-03 00:33:07,218 - mmcls - INFO - Epoch [83][800/5005]\tlr: 1.000e-03, eta: 2:10:55, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7207\n2023-01-03 00:33:15,978 - mmcls - INFO - Epoch [83][900/5005]\tlr: 1.000e-03, eta: 2:10:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7620\n2023-01-03 00:33:24,571 - mmcls - INFO - Epoch [83][1000/5005]\tlr: 1.000e-03, eta: 2:10:37, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7391\n2023-01-03 00:33:33,308 - mmcls - INFO - Epoch [83][1100/5005]\tlr: 1.000e-03, eta: 2:10:29, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7306\n2023-01-03 00:33:41,961 - mmcls - INFO - Epoch [83][1200/5005]\tlr: 1.000e-03, eta: 2:10:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7399\n2023-01-03 00:33:50,758 - mmcls - INFO - Epoch [83][1300/5005]\tlr: 1.000e-03, eta: 2:10:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7273\n2023-01-03 00:33:59,504 - mmcls - INFO - Epoch [83][1400/5005]\tlr: 1.000e-03, eta: 2:10:02, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7288\n2023-01-03 00:34:08,243 - mmcls - INFO - Epoch [83][1500/5005]\tlr: 1.000e-03, eta: 2:09:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7394\n2023-01-03 00:34:17,074 - mmcls - INFO - Epoch [83][1600/5005]\tlr: 1.000e-03, eta: 2:09:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7668\n2023-01-03 00:34:25,976 - mmcls - INFO - Epoch [83][1700/5005]\tlr: 1.000e-03, eta: 2:09:36, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7093\n2023-01-03 00:34:34,789 - mmcls - INFO - Epoch [83][1800/5005]\tlr: 1.000e-03, eta: 2:09:27, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7498\n2023-01-03 00:34:44,793 - mmcls - INFO - Epoch [83][1900/5005]\tlr: 1.000e-03, eta: 2:09:18, time: 0.100, data_time: 0.006, memory: 1125, loss: 1.7511\n2023-01-03 00:34:53,511 - mmcls - INFO - Epoch [83][2000/5005]\tlr: 1.000e-03, eta: 2:09:10, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7425\n2023-01-03 00:35:02,268 - mmcls - INFO - Epoch [83][2100/5005]\tlr: 1.000e-03, eta: 2:09:01, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7316\n2023-01-03 00:35:11,329 - mmcls - INFO - Epoch [83][2200/5005]\tlr: 1.000e-03, eta: 2:08:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7381\n2023-01-03 00:35:20,000 - mmcls - INFO - Epoch [83][2300/5005]\tlr: 1.000e-03, eta: 2:08:43, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7370\n2023-01-03 00:35:28,764 - mmcls - INFO - Epoch [83][2400/5005]\tlr: 1.000e-03, eta: 2:08:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7483\n2023-01-03 00:35:37,301 - mmcls - INFO - Epoch [83][2500/5005]\tlr: 1.000e-03, eta: 2:08:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7502\n2023-01-03 00:35:45,956 - mmcls - INFO - Epoch [83][2600/5005]\tlr: 1.000e-03, eta: 2:08:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7253\n2023-01-03 00:35:54,730 - mmcls - INFO - Epoch [83][2700/5005]\tlr: 1.000e-03, eta: 2:08:08, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7557\n2023-01-03 00:36:03,951 - mmcls - INFO - Epoch [83][2800/5005]\tlr: 1.000e-03, eta: 2:07:59, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7484\n2023-01-03 00:36:12,783 - mmcls - INFO - Epoch [83][2900/5005]\tlr: 1.000e-03, eta: 2:07:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7411\n2023-01-03 00:36:21,448 - mmcls - INFO - Epoch [83][3000/5005]\tlr: 1.000e-03, eta: 2:07:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7750\n2023-01-03 00:36:30,025 - mmcls - INFO - Epoch [83][3100/5005]\tlr: 1.000e-03, eta: 2:07:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7245\n2023-01-03 00:36:38,549 - mmcls - INFO - Epoch [83][3200/5005]\tlr: 1.000e-03, eta: 2:07:24, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7509\n2023-01-03 00:36:47,326 - mmcls - INFO - Epoch [83][3300/5005]\tlr: 1.000e-03, eta: 2:07:15, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7378\n2023-01-03 00:36:56,091 - mmcls - INFO - Epoch [83][3400/5005]\tlr: 1.000e-03, eta: 2:07:06, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7546\n2023-01-03 00:37:04,856 - mmcls - INFO - Epoch [83][3500/5005]\tlr: 1.000e-03, eta: 2:06:58, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7397\n2023-01-03 00:37:13,533 - mmcls - INFO - Epoch [83][3600/5005]\tlr: 1.000e-03, eta: 2:06:49, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7430\n2023-01-03 00:37:22,257 - mmcls - INFO - Epoch [83][3700/5005]\tlr: 1.000e-03, eta: 2:06:40, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7530\n2023-01-03 00:37:30,822 - mmcls - INFO - Epoch [83][3800/5005]\tlr: 1.000e-03, eta: 2:06:31, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7407\n2023-01-03 00:37:39,602 - mmcls - INFO - Epoch [83][3900/5005]\tlr: 1.000e-03, eta: 2:06:22, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7486\n2023-01-03 00:37:48,635 - mmcls - INFO - Epoch [83][4000/5005]\tlr: 1.000e-03, eta: 2:06:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7603\n2023-01-03 00:37:57,237 - mmcls - INFO - Epoch [83][4100/5005]\tlr: 1.000e-03, eta: 2:06:05, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7393\n2023-01-03 00:38:05,808 - mmcls - INFO - Epoch [83][4200/5005]\tlr: 1.000e-03, eta: 2:05:56, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7625\n2023-01-03 00:38:14,908 - mmcls - INFO - Epoch [83][4300/5005]\tlr: 1.000e-03, eta: 2:05:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7396\n2023-01-03 00:38:23,553 - mmcls - INFO - Epoch [83][4400/5005]\tlr: 1.000e-03, eta: 2:05:38, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7554\n2023-01-03 00:38:32,197 - mmcls - INFO - Epoch [83][4500/5005]\tlr: 1.000e-03, eta: 2:05:29, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7307\n2023-01-03 00:38:41,012 - mmcls - INFO - Epoch [83][4600/5005]\tlr: 1.000e-03, eta: 2:05:21, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7511\n2023-01-03 00:38:49,745 - mmcls - INFO - Epoch [83][4700/5005]\tlr: 1.000e-03, eta: 2:05:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7653\n2023-01-03 00:38:58,535 - mmcls - INFO - Epoch [83][4800/5005]\tlr: 1.000e-03, eta: 2:05:03, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7214\n2023-01-03 00:39:07,148 - mmcls - INFO - Epoch [83][4900/5005]\tlr: 1.000e-03, eta: 2:04:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7509\n2023-01-03 00:39:16,077 - mmcls - INFO - Epoch [83][5000/5005]\tlr: 1.000e-03, eta: 2:04:45, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7317\n2023-01-03 00:39:38,706 - mmcls - INFO - Epoch(val) [83][196]\taccuracy_top-1: 63.1780, accuracy_top-5: 84.1160\n2023-01-03 00:39:49,561 - mmcls - INFO - Epoch [84][100/5005]\tlr: 1.000e-03, eta: 2:04:37, time: 0.108, data_time: 0.064, memory: 1125, loss: 1.7267\n2023-01-03 00:39:58,338 - mmcls - INFO - Epoch [84][200/5005]\tlr: 1.000e-03, eta: 2:04:28, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7071\n2023-01-03 00:40:07,474 - mmcls - INFO - Epoch [84][300/5005]\tlr: 1.000e-03, eta: 2:04:19, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7427\n2023-01-03 00:40:16,271 - mmcls - INFO - Epoch [84][400/5005]\tlr: 1.000e-03, eta: 2:04:10, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7301\n2023-01-03 00:40:25,509 - mmcls - INFO - Epoch [84][500/5005]\tlr: 1.000e-03, eta: 2:04:01, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7083\n2023-01-03 00:40:34,236 - mmcls - INFO - Epoch [84][600/5005]\tlr: 1.000e-03, eta: 2:03:53, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7347\n2023-01-03 00:40:42,989 - mmcls - INFO - Epoch [84][700/5005]\tlr: 1.000e-03, eta: 2:03:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7290\n2023-01-03 00:40:51,800 - mmcls - INFO - Epoch [84][800/5005]\tlr: 1.000e-03, eta: 2:03:35, time: 0.088, data_time: 0.042, memory: 1125, loss: 1.7467\n2023-01-03 00:41:00,626 - mmcls - INFO - Epoch [84][900/5005]\tlr: 1.000e-03, eta: 2:03:26, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7192\n2023-01-03 00:41:09,517 - mmcls - INFO - Epoch [84][1000/5005]\tlr: 1.000e-03, eta: 2:03:18, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7403\n2023-01-03 00:41:18,284 - mmcls - INFO - Epoch [84][1100/5005]\tlr: 1.000e-03, eta: 2:03:09, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.7325\n2023-01-03 00:41:26,894 - mmcls - INFO - Epoch [84][1200/5005]\tlr: 1.000e-03, eta: 2:03:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7385\n2023-01-03 00:41:35,751 - mmcls - INFO - Epoch [84][1300/5005]\tlr: 1.000e-03, eta: 2:02:51, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7306\n2023-01-03 00:41:44,795 - mmcls - INFO - Epoch [84][1400/5005]\tlr: 1.000e-03, eta: 2:02:42, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.7372\n2023-01-03 00:41:53,410 - mmcls - INFO - Epoch [84][1500/5005]\tlr: 1.000e-03, eta: 2:02:33, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7347\n2023-01-03 00:42:02,398 - mmcls - INFO - Epoch [84][1600/5005]\tlr: 1.000e-03, eta: 2:02:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7407\n2023-01-03 00:42:11,198 - mmcls - INFO - Epoch [84][1700/5005]\tlr: 1.000e-03, eta: 2:02:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7408\n2023-01-03 00:42:20,040 - mmcls - INFO - Epoch [84][1800/5005]\tlr: 1.000e-03, eta: 2:02:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7458\n2023-01-03 00:42:28,833 - mmcls - INFO - Epoch [84][1900/5005]\tlr: 1.000e-03, eta: 2:01:58, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7224\n2023-01-03 00:42:37,550 - mmcls - INFO - Epoch [84][2000/5005]\tlr: 1.000e-03, eta: 2:01:50, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7338\n2023-01-03 00:42:46,201 - mmcls - INFO - Epoch [84][2100/5005]\tlr: 1.000e-03, eta: 2:01:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7187\n2023-01-03 00:42:55,290 - mmcls - INFO - Epoch [84][2200/5005]\tlr: 1.000e-03, eta: 2:01:32, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7246\n2023-01-03 00:43:04,071 - mmcls - INFO - Epoch [84][2300/5005]\tlr: 1.000e-03, eta: 2:01:23, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7504\n2023-01-03 00:43:12,716 - mmcls - INFO - Epoch [84][2400/5005]\tlr: 1.000e-03, eta: 2:01:14, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7515\n2023-01-03 00:43:21,294 - mmcls - INFO - Epoch [84][2500/5005]\tlr: 1.000e-03, eta: 2:01:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7343\n2023-01-03 00:43:30,006 - mmcls - INFO - Epoch [84][2600/5005]\tlr: 1.000e-03, eta: 2:00:57, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7357\n2023-01-03 00:43:39,061 - mmcls - INFO - Epoch [84][2700/5005]\tlr: 1.000e-03, eta: 2:00:48, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7546\n2023-01-03 00:43:47,826 - mmcls - INFO - Epoch [84][2800/5005]\tlr: 1.000e-03, eta: 2:00:39, time: 0.088, data_time: 0.042, memory: 1125, loss: 1.7619\n2023-01-03 00:43:56,624 - mmcls - INFO - Epoch [84][2900/5005]\tlr: 1.000e-03, eta: 2:00:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7292\n2023-01-03 00:44:05,378 - mmcls - INFO - Epoch [84][3000/5005]\tlr: 1.000e-03, eta: 2:00:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7210\n2023-01-03 00:44:14,187 - mmcls - INFO - Epoch [84][3100/5005]\tlr: 1.000e-03, eta: 2:00:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7494\n2023-01-03 00:44:22,736 - mmcls - INFO - Epoch [84][3200/5005]\tlr: 1.000e-03, eta: 2:00:04, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7324\n2023-01-03 00:44:31,158 - mmcls - INFO - Epoch [84][3300/5005]\tlr: 1.000e-03, eta: 1:59:55, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7398\n2023-01-03 00:44:39,937 - mmcls - INFO - Epoch [84][3400/5005]\tlr: 1.000e-03, eta: 1:59:46, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7355\n2023-01-03 00:44:48,630 - mmcls - INFO - Epoch [84][3500/5005]\tlr: 1.000e-03, eta: 1:59:37, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7481\n2023-01-03 00:44:57,419 - mmcls - INFO - Epoch [84][3600/5005]\tlr: 1.000e-03, eta: 1:59:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7222\n2023-01-03 00:45:06,059 - mmcls - INFO - Epoch [84][3700/5005]\tlr: 1.000e-03, eta: 1:59:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7262\n2023-01-03 00:45:14,736 - mmcls - INFO - Epoch [84][3800/5005]\tlr: 1.000e-03, eta: 1:59:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7325\n2023-01-03 00:45:23,422 - mmcls - INFO - Epoch [84][3900/5005]\tlr: 1.000e-03, eta: 1:59:02, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7405\n2023-01-03 00:45:32,454 - mmcls - INFO - Epoch [84][4000/5005]\tlr: 1.000e-03, eta: 1:58:53, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7354\n2023-01-03 00:45:41,445 - mmcls - INFO - Epoch [84][4100/5005]\tlr: 1.000e-03, eta: 1:58:45, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7487\n2023-01-03 00:45:50,250 - mmcls - INFO - Epoch [84][4200/5005]\tlr: 1.000e-03, eta: 1:58:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7441\n2023-01-03 00:45:59,378 - mmcls - INFO - Epoch [84][4300/5005]\tlr: 1.000e-03, eta: 1:58:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7373\n2023-01-03 00:46:07,762 - mmcls - INFO - Epoch [84][4400/5005]\tlr: 1.000e-03, eta: 1:58:18, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7598\n2023-01-03 00:46:16,580 - mmcls - INFO - Epoch [84][4500/5005]\tlr: 1.000e-03, eta: 1:58:09, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7119\n2023-01-03 00:46:24,905 - mmcls - INFO - Epoch [84][4600/5005]\tlr: 1.000e-03, eta: 1:58:00, time: 0.083, data_time: 0.017, memory: 1125, loss: 1.7593\n2023-01-03 00:46:33,656 - mmcls - INFO - Epoch [84][4700/5005]\tlr: 1.000e-03, eta: 1:57:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7476\n2023-01-03 00:46:42,397 - mmcls - INFO - Epoch [84][4800/5005]\tlr: 1.000e-03, eta: 1:57:43, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7464\n2023-01-03 00:46:51,099 - mmcls - INFO - Epoch [84][4900/5005]\tlr: 1.000e-03, eta: 1:57:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7322\n2023-01-03 00:46:59,974 - mmcls - INFO - Epoch [84][5000/5005]\tlr: 1.000e-03, eta: 1:57:25, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7637\n2023-01-03 00:47:22,970 - mmcls - INFO - Epoch(val) [84][196]\taccuracy_top-1: 63.1160, accuracy_top-5: 84.0320\n2023-01-03 00:47:33,874 - mmcls - INFO - Epoch [85][100/5005]\tlr: 1.000e-03, eta: 1:57:16, time: 0.109, data_time: 0.032, memory: 1125, loss: 1.7181\n2023-01-03 00:47:42,841 - mmcls - INFO - Epoch [85][200/5005]\tlr: 1.000e-03, eta: 1:57:08, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7298\n2023-01-03 00:47:51,914 - mmcls - INFO - Epoch [85][300/5005]\tlr: 1.000e-03, eta: 1:56:59, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.7174\n2023-01-03 00:48:00,629 - mmcls - INFO - Epoch [85][400/5005]\tlr: 1.000e-03, eta: 1:56:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7504\n2023-01-03 00:48:09,409 - mmcls - INFO - Epoch [85][500/5005]\tlr: 1.000e-03, eta: 1:56:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7228\n2023-01-03 00:48:18,040 - mmcls - INFO - Epoch [85][600/5005]\tlr: 1.000e-03, eta: 1:56:32, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.6925\n2023-01-03 00:48:26,790 - mmcls - INFO - Epoch [85][700/5005]\tlr: 1.000e-03, eta: 1:56:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7447\n2023-01-03 00:48:35,469 - mmcls - INFO - Epoch [85][800/5005]\tlr: 1.000e-03, eta: 1:56:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7476\n2023-01-03 00:48:44,348 - mmcls - INFO - Epoch [85][900/5005]\tlr: 1.000e-03, eta: 1:56:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7258\n2023-01-03 00:48:52,878 - mmcls - INFO - Epoch [85][1000/5005]\tlr: 1.000e-03, eta: 1:55:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7403\n2023-01-03 00:49:01,589 - mmcls - INFO - Epoch [85][1100/5005]\tlr: 1.000e-03, eta: 1:55:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7356\n2023-01-03 00:49:10,305 - mmcls - INFO - Epoch [85][1200/5005]\tlr: 1.000e-03, eta: 1:55:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7201\n2023-01-03 00:49:18,874 - mmcls - INFO - Epoch [85][1300/5005]\tlr: 1.000e-03, eta: 1:55:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7412\n2023-01-03 00:49:27,805 - mmcls - INFO - Epoch [85][1400/5005]\tlr: 1.000e-03, eta: 1:55:22, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7329\n2023-01-03 00:49:36,544 - mmcls - INFO - Epoch [85][1500/5005]\tlr: 1.000e-03, eta: 1:55:13, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7220\n2023-01-03 00:49:45,937 - mmcls - INFO - Epoch [85][1600/5005]\tlr: 1.000e-03, eta: 1:55:04, time: 0.094, data_time: 0.013, memory: 1125, loss: 1.7227\n2023-01-03 00:49:54,631 - mmcls - INFO - Epoch [85][1700/5005]\tlr: 1.000e-03, eta: 1:54:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7251\n2023-01-03 00:50:03,391 - mmcls - INFO - Epoch [85][1800/5005]\tlr: 1.000e-03, eta: 1:54:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7198\n2023-01-03 00:50:11,952 - mmcls - INFO - Epoch [85][1900/5005]\tlr: 1.000e-03, eta: 1:54:38, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7393\n2023-01-03 00:50:20,550 - mmcls - INFO - Epoch [85][2000/5005]\tlr: 1.000e-03, eta: 1:54:29, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7269\n2023-01-03 00:50:29,247 - mmcls - INFO - Epoch [85][2100/5005]\tlr: 1.000e-03, eta: 1:54:20, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7302\n2023-01-03 00:50:38,096 - mmcls - INFO - Epoch [85][2200/5005]\tlr: 1.000e-03, eta: 1:54:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7326\n2023-01-03 00:50:46,712 - mmcls - INFO - Epoch [85][2300/5005]\tlr: 1.000e-03, eta: 1:54:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7379\n2023-01-03 00:50:55,406 - mmcls - INFO - Epoch [85][2400/5005]\tlr: 1.000e-03, eta: 1:53:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7590\n2023-01-03 00:51:04,186 - mmcls - INFO - Epoch [85][2500/5005]\tlr: 1.000e-03, eta: 1:53:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7437\n2023-01-03 00:51:12,692 - mmcls - INFO - Epoch [85][2600/5005]\tlr: 1.000e-03, eta: 1:53:36, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7597\n2023-01-03 00:51:21,328 - mmcls - INFO - Epoch [85][2700/5005]\tlr: 1.000e-03, eta: 1:53:27, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7186\n2023-01-03 00:51:30,077 - mmcls - INFO - Epoch [85][2800/5005]\tlr: 1.000e-03, eta: 1:53:19, time: 0.087, data_time: 0.042, memory: 1125, loss: 1.7282\n2023-01-03 00:51:38,786 - mmcls - INFO - Epoch [85][2900/5005]\tlr: 1.000e-03, eta: 1:53:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7231\n2023-01-03 00:51:47,953 - mmcls - INFO - Epoch [85][3000/5005]\tlr: 1.000e-03, eta: 1:53:01, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.7303\n2023-01-03 00:51:56,781 - mmcls - INFO - Epoch [85][3100/5005]\tlr: 1.000e-03, eta: 1:52:52, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7156\n2023-01-03 00:52:05,458 - mmcls - INFO - Epoch [85][3200/5005]\tlr: 1.000e-03, eta: 1:52:43, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7320\n2023-01-03 00:52:14,275 - mmcls - INFO - Epoch [85][3300/5005]\tlr: 1.000e-03, eta: 1:52:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7759\n2023-01-03 00:52:22,993 - mmcls - INFO - Epoch [85][3400/5005]\tlr: 1.000e-03, eta: 1:52:26, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7197\n2023-01-03 00:52:31,909 - mmcls - INFO - Epoch [85][3500/5005]\tlr: 1.000e-03, eta: 1:52:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7305\n2023-01-03 00:52:40,584 - mmcls - INFO - Epoch [85][3600/5005]\tlr: 1.000e-03, eta: 1:52:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7510\n2023-01-03 00:52:49,211 - mmcls - INFO - Epoch [85][3700/5005]\tlr: 1.000e-03, eta: 1:51:59, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7545\n2023-01-03 00:52:57,780 - mmcls - INFO - Epoch [85][3800/5005]\tlr: 1.000e-03, eta: 1:51:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7315\n2023-01-03 00:53:06,714 - mmcls - INFO - Epoch [85][3900/5005]\tlr: 1.000e-03, eta: 1:51:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7671\n2023-01-03 00:53:15,297 - mmcls - INFO - Epoch [85][4000/5005]\tlr: 1.000e-03, eta: 1:51:33, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7523\n2023-01-03 00:53:24,223 - mmcls - INFO - Epoch [85][4100/5005]\tlr: 1.000e-03, eta: 1:51:24, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7386\n2023-01-03 00:53:32,760 - mmcls - INFO - Epoch [85][4200/5005]\tlr: 1.000e-03, eta: 1:51:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7487\n2023-01-03 00:53:41,341 - mmcls - INFO - Epoch [85][4300/5005]\tlr: 1.000e-03, eta: 1:51:06, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7409\n2023-01-03 00:53:49,872 - mmcls - INFO - Epoch [85][4400/5005]\tlr: 1.000e-03, eta: 1:50:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7494\n2023-01-03 00:53:58,354 - mmcls - INFO - Epoch [85][4500/5005]\tlr: 1.000e-03, eta: 1:50:49, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7540\n2023-01-03 00:54:07,198 - mmcls - INFO - Epoch [85][4600/5005]\tlr: 1.000e-03, eta: 1:50:40, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7714\n2023-01-03 00:54:16,123 - mmcls - INFO - Epoch [85][4700/5005]\tlr: 1.000e-03, eta: 1:50:31, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7380\n2023-01-03 00:54:24,921 - mmcls - INFO - Epoch [85][4800/5005]\tlr: 1.000e-03, eta: 1:50:22, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7235\n2023-01-03 00:54:33,411 - mmcls - INFO - Epoch [85][4900/5005]\tlr: 1.000e-03, eta: 1:50:14, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7473\n2023-01-03 00:54:42,560 - mmcls - INFO - Epoch [85][5000/5005]\tlr: 1.000e-03, eta: 1:50:05, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7447\n2023-01-03 00:55:05,164 - mmcls - INFO - Epoch(val) [85][196]\taccuracy_top-1: 63.1960, accuracy_top-5: 83.8420\n2023-01-03 00:55:16,169 - mmcls - INFO - Epoch [86][100/5005]\tlr: 1.000e-03, eta: 1:49:56, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.7208\n2023-01-03 00:55:24,889 - mmcls - INFO - Epoch [86][200/5005]\tlr: 1.000e-03, eta: 1:49:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7211\n2023-01-03 00:55:33,507 - mmcls - INFO - Epoch [86][300/5005]\tlr: 1.000e-03, eta: 1:49:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7278\n2023-01-03 00:55:42,086 - mmcls - INFO - Epoch [86][400/5005]\tlr: 1.000e-03, eta: 1:49:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7239\n2023-01-03 00:55:51,048 - mmcls - INFO - Epoch [86][500/5005]\tlr: 1.000e-03, eta: 1:49:21, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7160\n2023-01-03 00:55:59,740 - mmcls - INFO - Epoch [86][600/5005]\tlr: 1.000e-03, eta: 1:49:12, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7497\n2023-01-03 00:56:08,467 - mmcls - INFO - Epoch [86][700/5005]\tlr: 1.000e-03, eta: 1:49:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7163\n2023-01-03 00:56:17,305 - mmcls - INFO - Epoch [86][800/5005]\tlr: 1.000e-03, eta: 1:48:54, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7296\n2023-01-03 00:56:26,040 - mmcls - INFO - Epoch [86][900/5005]\tlr: 1.000e-03, eta: 1:48:45, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7131\n2023-01-03 00:56:34,902 - mmcls - INFO - Epoch [86][1000/5005]\tlr: 1.000e-03, eta: 1:48:37, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7430\n2023-01-03 00:56:43,518 - mmcls - INFO - Epoch [86][1100/5005]\tlr: 1.000e-03, eta: 1:48:28, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7388\n2023-01-03 00:56:52,501 - mmcls - INFO - Epoch [86][1200/5005]\tlr: 1.000e-03, eta: 1:48:19, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7209\n2023-01-03 00:57:01,186 - mmcls - INFO - Epoch [86][1300/5005]\tlr: 1.000e-03, eta: 1:48:10, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7336\n2023-01-03 00:57:09,853 - mmcls - INFO - Epoch [86][1400/5005]\tlr: 1.000e-03, eta: 1:48:01, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7201\n2023-01-03 00:57:18,473 - mmcls - INFO - Epoch [86][1500/5005]\tlr: 1.000e-03, eta: 1:47:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7354\n2023-01-03 00:57:27,256 - mmcls - INFO - Epoch [86][1600/5005]\tlr: 1.000e-03, eta: 1:47:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7121\n2023-01-03 00:57:35,888 - mmcls - INFO - Epoch [86][1700/5005]\tlr: 1.000e-03, eta: 1:47:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7508\n2023-01-03 00:57:44,744 - mmcls - INFO - Epoch [86][1800/5005]\tlr: 1.000e-03, eta: 1:47:26, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7353\n2023-01-03 00:57:53,752 - mmcls - INFO - Epoch [86][1900/5005]\tlr: 1.000e-03, eta: 1:47:17, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7315\n2023-01-03 00:58:02,508 - mmcls - INFO - Epoch [86][2000/5005]\tlr: 1.000e-03, eta: 1:47:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7348\n2023-01-03 00:58:11,131 - mmcls - INFO - Epoch [86][2100/5005]\tlr: 1.000e-03, eta: 1:47:00, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7243\n2023-01-03 00:58:19,943 - mmcls - INFO - Epoch [86][2200/5005]\tlr: 1.000e-03, eta: 1:46:51, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7310\n2023-01-03 00:58:28,716 - mmcls - INFO - Epoch [86][2300/5005]\tlr: 1.000e-03, eta: 1:46:42, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7126\n2023-01-03 00:58:37,400 - mmcls - INFO - Epoch [86][2400/5005]\tlr: 1.000e-03, eta: 1:46:33, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7217\n2023-01-03 00:58:46,159 - mmcls - INFO - Epoch [86][2500/5005]\tlr: 1.000e-03, eta: 1:46:25, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7446\n2023-01-03 00:58:55,078 - mmcls - INFO - Epoch [86][2600/5005]\tlr: 1.000e-03, eta: 1:46:16, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7251\n2023-01-03 00:59:03,929 - mmcls - INFO - Epoch [86][2700/5005]\tlr: 1.000e-03, eta: 1:46:07, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7422\n2023-01-03 00:59:12,921 - mmcls - INFO - Epoch [86][2800/5005]\tlr: 1.000e-03, eta: 1:45:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7194\n2023-01-03 00:59:21,670 - mmcls - INFO - Epoch [86][2900/5005]\tlr: 1.000e-03, eta: 1:45:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7161\n2023-01-03 00:59:30,625 - mmcls - INFO - Epoch [86][3000/5005]\tlr: 1.000e-03, eta: 1:45:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7165\n2023-01-03 00:59:39,343 - mmcls - INFO - Epoch [86][3100/5005]\tlr: 1.000e-03, eta: 1:45:32, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7312\n2023-01-03 00:59:48,065 - mmcls - INFO - Epoch [86][3200/5005]\tlr: 1.000e-03, eta: 1:45:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7517\n2023-01-03 00:59:56,992 - mmcls - INFO - Epoch [86][3300/5005]\tlr: 1.000e-03, eta: 1:45:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7347\n2023-01-03 01:00:05,985 - mmcls - INFO - Epoch [86][3400/5005]\tlr: 1.000e-03, eta: 1:45:05, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7366\n2023-01-03 01:00:14,692 - mmcls - INFO - Epoch [86][3500/5005]\tlr: 1.000e-03, eta: 1:44:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7496\n2023-01-03 01:00:23,405 - mmcls - INFO - Epoch [86][3600/5005]\tlr: 1.000e-03, eta: 1:44:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7170\n2023-01-03 01:00:32,020 - mmcls - INFO - Epoch [86][3700/5005]\tlr: 1.000e-03, eta: 1:44:39, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7378\n2023-01-03 01:00:40,790 - mmcls - INFO - Epoch [86][3800/5005]\tlr: 1.000e-03, eta: 1:44:30, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7453\n2023-01-03 01:00:49,459 - mmcls - INFO - Epoch [86][3900/5005]\tlr: 1.000e-03, eta: 1:44:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7447\n2023-01-03 01:00:57,949 - mmcls - INFO - Epoch [86][4000/5005]\tlr: 1.000e-03, eta: 1:44:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7309\n2023-01-03 01:01:06,624 - mmcls - INFO - Epoch [86][4100/5005]\tlr: 1.000e-03, eta: 1:44:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7313\n2023-01-03 01:01:15,380 - mmcls - INFO - Epoch [86][4200/5005]\tlr: 1.000e-03, eta: 1:43:55, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7244\n2023-01-03 01:01:24,420 - mmcls - INFO - Epoch [86][4300/5005]\tlr: 1.000e-03, eta: 1:43:46, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7321\n2023-01-03 01:01:32,935 - mmcls - INFO - Epoch [86][4400/5005]\tlr: 1.000e-03, eta: 1:43:37, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7572\n2023-01-03 01:01:41,525 - mmcls - INFO - Epoch [86][4500/5005]\tlr: 1.000e-03, eta: 1:43:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7512\n2023-01-03 01:01:50,286 - mmcls - INFO - Epoch [86][4600/5005]\tlr: 1.000e-03, eta: 1:43:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7397\n2023-01-03 01:01:59,236 - mmcls - INFO - Epoch [86][4700/5005]\tlr: 1.000e-03, eta: 1:43:11, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7426\n2023-01-03 01:02:08,112 - mmcls - INFO - Epoch [86][4800/5005]\tlr: 1.000e-03, eta: 1:43:02, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7473\n2023-01-03 01:02:16,892 - mmcls - INFO - Epoch [86][4900/5005]\tlr: 1.000e-03, eta: 1:42:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7593\n2023-01-03 01:02:25,770 - mmcls - INFO - Epoch [86][5000/5005]\tlr: 1.000e-03, eta: 1:42:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7524\n2023-01-03 01:02:48,362 - mmcls - INFO - Epoch(val) [86][196]\taccuracy_top-1: 63.2620, accuracy_top-5: 84.0420\n2023-01-03 01:02:59,134 - mmcls - INFO - Epoch [87][100/5005]\tlr: 1.000e-03, eta: 1:42:36, time: 0.107, data_time: 0.048, memory: 1125, loss: 1.7362\n2023-01-03 01:03:07,795 - mmcls - INFO - Epoch [87][200/5005]\tlr: 1.000e-03, eta: 1:42:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7063\n2023-01-03 01:03:16,531 - mmcls - INFO - Epoch [87][300/5005]\tlr: 1.000e-03, eta: 1:42:18, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7236\n2023-01-03 01:03:25,172 - mmcls - INFO - Epoch [87][400/5005]\tlr: 1.000e-03, eta: 1:42:09, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7131\n2023-01-03 01:03:33,753 - mmcls - INFO - Epoch [87][500/5005]\tlr: 1.000e-03, eta: 1:42:00, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7266\n2023-01-03 01:03:42,638 - mmcls - INFO - Epoch [87][600/5005]\tlr: 1.000e-03, eta: 1:41:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7176\n2023-01-03 01:03:51,534 - mmcls - INFO - Epoch [87][700/5005]\tlr: 1.000e-03, eta: 1:41:43, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7306\n2023-01-03 01:04:00,065 - mmcls - INFO - Epoch [87][800/5005]\tlr: 1.000e-03, eta: 1:41:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7071\n2023-01-03 01:04:09,158 - mmcls - INFO - Epoch [87][900/5005]\tlr: 1.000e-03, eta: 1:41:25, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7326\n2023-01-03 01:04:18,233 - mmcls - INFO - Epoch [87][1000/5005]\tlr: 1.000e-03, eta: 1:41:16, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7288\n2023-01-03 01:04:26,790 - mmcls - INFO - Epoch [87][1100/5005]\tlr: 1.000e-03, eta: 1:41:07, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7151\n2023-01-03 01:04:35,592 - mmcls - INFO - Epoch [87][1200/5005]\tlr: 1.000e-03, eta: 1:40:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7191\n2023-01-03 01:04:44,434 - mmcls - INFO - Epoch [87][1300/5005]\tlr: 1.000e-03, eta: 1:40:50, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7365\n2023-01-03 01:04:53,234 - mmcls - INFO - Epoch [87][1400/5005]\tlr: 1.000e-03, eta: 1:40:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7182\n2023-01-03 01:05:02,157 - mmcls - INFO - Epoch [87][1500/5005]\tlr: 1.000e-03, eta: 1:40:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7210\n2023-01-03 01:05:10,681 - mmcls - INFO - Epoch [87][1600/5005]\tlr: 1.000e-03, eta: 1:40:23, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7444\n2023-01-03 01:05:19,339 - mmcls - INFO - Epoch [87][1700/5005]\tlr: 1.000e-03, eta: 1:40:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7478\n2023-01-03 01:05:28,225 - mmcls - INFO - Epoch [87][1800/5005]\tlr: 1.000e-03, eta: 1:40:06, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7180\n2023-01-03 01:05:37,105 - mmcls - INFO - Epoch [87][1900/5005]\tlr: 1.000e-03, eta: 1:39:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7082\n2023-01-03 01:05:45,960 - mmcls - INFO - Epoch [87][2000/5005]\tlr: 1.000e-03, eta: 1:39:48, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7233\n2023-01-03 01:05:54,629 - mmcls - INFO - Epoch [87][2100/5005]\tlr: 1.000e-03, eta: 1:39:39, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7445\n2023-01-03 01:06:03,233 - mmcls - INFO - Epoch [87][2200/5005]\tlr: 1.000e-03, eta: 1:39:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7110\n2023-01-03 01:06:11,534 - mmcls - INFO - Epoch [87][2300/5005]\tlr: 1.000e-03, eta: 1:39:22, time: 0.083, data_time: 0.001, memory: 1125, loss: 1.7380\n2023-01-03 01:06:20,306 - mmcls - INFO - Epoch [87][2400/5005]\tlr: 1.000e-03, eta: 1:39:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7299\n2023-01-03 01:06:29,189 - mmcls - INFO - Epoch [87][2500/5005]\tlr: 1.000e-03, eta: 1:39:04, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7331\n2023-01-03 01:06:38,133 - mmcls - INFO - Epoch [87][2600/5005]\tlr: 1.000e-03, eta: 1:38:55, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7346\n2023-01-03 01:06:46,997 - mmcls - INFO - Epoch [87][2700/5005]\tlr: 1.000e-03, eta: 1:38:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7399\n2023-01-03 01:06:55,662 - mmcls - INFO - Epoch [87][2800/5005]\tlr: 1.000e-03, eta: 1:38:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7416\n2023-01-03 01:07:04,166 - mmcls - INFO - Epoch [87][2900/5005]\tlr: 1.000e-03, eta: 1:38:29, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7453\n2023-01-03 01:07:13,106 - mmcls - INFO - Epoch [87][3000/5005]\tlr: 1.000e-03, eta: 1:38:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7289\n2023-01-03 01:07:21,509 - mmcls - INFO - Epoch [87][3100/5005]\tlr: 1.000e-03, eta: 1:38:11, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7199\n2023-01-03 01:07:30,193 - mmcls - INFO - Epoch [87][3200/5005]\tlr: 1.000e-03, eta: 1:38:03, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7366\n2023-01-03 01:07:38,720 - mmcls - INFO - Epoch [87][3300/5005]\tlr: 1.000e-03, eta: 1:37:54, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7529\n2023-01-03 01:07:47,663 - mmcls - INFO - Epoch [87][3400/5005]\tlr: 1.000e-03, eta: 1:37:45, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7415\n2023-01-03 01:07:56,383 - mmcls - INFO - Epoch [87][3500/5005]\tlr: 1.000e-03, eta: 1:37:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7210\n2023-01-03 01:08:05,472 - mmcls - INFO - Epoch [87][3600/5005]\tlr: 1.000e-03, eta: 1:37:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7549\n2023-01-03 01:08:14,507 - mmcls - INFO - Epoch [87][3700/5005]\tlr: 1.000e-03, eta: 1:37:19, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7373\n2023-01-03 01:08:23,274 - mmcls - INFO - Epoch [87][3800/5005]\tlr: 1.000e-03, eta: 1:37:10, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7517\n2023-01-03 01:08:32,483 - mmcls - INFO - Epoch [87][3900/5005]\tlr: 1.000e-03, eta: 1:37:01, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7453\n2023-01-03 01:08:41,047 - mmcls - INFO - Epoch [87][4000/5005]\tlr: 1.000e-03, eta: 1:36:52, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7114\n2023-01-03 01:08:49,672 - mmcls - INFO - Epoch [87][4100/5005]\tlr: 1.000e-03, eta: 1:36:43, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7546\n2023-01-03 01:08:58,285 - mmcls - INFO - Epoch [87][4200/5005]\tlr: 1.000e-03, eta: 1:36:35, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7331\n2023-01-03 01:09:07,186 - mmcls - INFO - Epoch [87][4300/5005]\tlr: 1.000e-03, eta: 1:36:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7642\n2023-01-03 01:09:16,039 - mmcls - INFO - Epoch [87][4400/5005]\tlr: 1.000e-03, eta: 1:36:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7415\n2023-01-03 01:09:24,621 - mmcls - INFO - Epoch [87][4500/5005]\tlr: 1.000e-03, eta: 1:36:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7430\n2023-01-03 01:09:33,080 - mmcls - INFO - Epoch [87][4600/5005]\tlr: 1.000e-03, eta: 1:35:59, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7415\n2023-01-03 01:09:41,825 - mmcls - INFO - Epoch [87][4700/5005]\tlr: 1.000e-03, eta: 1:35:51, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7317\n2023-01-03 01:09:50,713 - mmcls - INFO - Epoch [87][4800/5005]\tlr: 1.000e-03, eta: 1:35:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7325\n2023-01-03 01:09:59,296 - mmcls - INFO - Epoch [87][4900/5005]\tlr: 1.000e-03, eta: 1:35:33, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7556\n2023-01-03 01:10:08,179 - mmcls - INFO - Epoch [87][5000/5005]\tlr: 1.000e-03, eta: 1:35:24, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7471\n2023-01-03 01:10:30,704 - mmcls - INFO - Epoch(val) [87][196]\taccuracy_top-1: 63.1700, accuracy_top-5: 84.1560\n2023-01-03 01:10:41,921 - mmcls - INFO - Epoch [88][100/5005]\tlr: 1.000e-03, eta: 1:35:15, time: 0.112, data_time: 0.051, memory: 1125, loss: 1.7392\n2023-01-03 01:10:50,327 - mmcls - INFO - Epoch [88][200/5005]\tlr: 1.000e-03, eta: 1:35:06, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7278\n2023-01-03 01:10:59,132 - mmcls - INFO - Epoch [88][300/5005]\tlr: 1.000e-03, eta: 1:34:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7082\n2023-01-03 01:11:08,237 - mmcls - INFO - Epoch [88][400/5005]\tlr: 1.000e-03, eta: 1:34:49, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7159\n2023-01-03 01:11:16,971 - mmcls - INFO - Epoch [88][500/5005]\tlr: 1.000e-03, eta: 1:34:40, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7406\n2023-01-03 01:11:25,423 - mmcls - INFO - Epoch [88][600/5005]\tlr: 1.000e-03, eta: 1:34:31, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7021\n2023-01-03 01:11:34,176 - mmcls - INFO - Epoch [88][700/5005]\tlr: 1.000e-03, eta: 1:34:22, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7450\n2023-01-03 01:11:43,169 - mmcls - INFO - Epoch [88][800/5005]\tlr: 1.000e-03, eta: 1:34:14, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7423\n2023-01-03 01:11:51,875 - mmcls - INFO - Epoch [88][900/5005]\tlr: 1.000e-03, eta: 1:34:05, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7382\n2023-01-03 01:12:00,410 - mmcls - INFO - Epoch [88][1000/5005]\tlr: 1.000e-03, eta: 1:33:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7163\n2023-01-03 01:12:09,077 - mmcls - INFO - Epoch [88][1100/5005]\tlr: 1.000e-03, eta: 1:33:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7299\n2023-01-03 01:12:18,268 - mmcls - INFO - Epoch [88][1200/5005]\tlr: 1.000e-03, eta: 1:33:38, time: 0.092, data_time: 0.027, memory: 1125, loss: 1.7278\n2023-01-03 01:12:26,936 - mmcls - INFO - Epoch [88][1300/5005]\tlr: 1.000e-03, eta: 1:33:30, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7272\n2023-01-03 01:12:35,956 - mmcls - INFO - Epoch [88][1400/5005]\tlr: 1.000e-03, eta: 1:33:21, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7087\n2023-01-03 01:12:44,430 - mmcls - INFO - Epoch [88][1500/5005]\tlr: 1.000e-03, eta: 1:33:12, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7365\n2023-01-03 01:12:53,292 - mmcls - INFO - Epoch [88][1600/5005]\tlr: 1.000e-03, eta: 1:33:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7383\n2023-01-03 01:13:02,009 - mmcls - INFO - Epoch [88][1700/5005]\tlr: 1.000e-03, eta: 1:32:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7379\n2023-01-03 01:13:10,645 - mmcls - INFO - Epoch [88][1800/5005]\tlr: 1.000e-03, eta: 1:32:45, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7460\n2023-01-03 01:13:19,322 - mmcls - INFO - Epoch [88][1900/5005]\tlr: 1.000e-03, eta: 1:32:37, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7512\n2023-01-03 01:13:28,017 - mmcls - INFO - Epoch [88][2000/5005]\tlr: 1.000e-03, eta: 1:32:28, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7197\n2023-01-03 01:13:36,862 - mmcls - INFO - Epoch [88][2100/5005]\tlr: 1.000e-03, eta: 1:32:19, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7069\n2023-01-03 01:13:45,487 - mmcls - INFO - Epoch [88][2200/5005]\tlr: 1.000e-03, eta: 1:32:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7289\n2023-01-03 01:13:54,683 - mmcls - INFO - Epoch [88][2300/5005]\tlr: 1.000e-03, eta: 1:32:01, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7426\n2023-01-03 01:14:03,288 - mmcls - INFO - Epoch [88][2400/5005]\tlr: 1.000e-03, eta: 1:31:53, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7361\n2023-01-03 01:14:12,231 - mmcls - INFO - Epoch [88][2500/5005]\tlr: 1.000e-03, eta: 1:31:44, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7238\n2023-01-03 01:14:21,003 - mmcls - INFO - Epoch [88][2600/5005]\tlr: 1.000e-03, eta: 1:31:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7197\n2023-01-03 01:14:29,758 - mmcls - INFO - Epoch [88][2700/5005]\tlr: 1.000e-03, eta: 1:31:26, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7594\n2023-01-03 01:14:38,423 - mmcls - INFO - Epoch [88][2800/5005]\tlr: 1.000e-03, eta: 1:31:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7375\n2023-01-03 01:14:47,187 - mmcls - INFO - Epoch [88][2900/5005]\tlr: 1.000e-03, eta: 1:31:09, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7278\n2023-01-03 01:14:55,927 - mmcls - INFO - Epoch [88][3000/5005]\tlr: 1.000e-03, eta: 1:31:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7172\n2023-01-03 01:15:04,513 - mmcls - INFO - Epoch [88][3100/5005]\tlr: 1.000e-03, eta: 1:30:51, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7134\n2023-01-03 01:15:13,290 - mmcls - INFO - Epoch [88][3200/5005]\tlr: 1.000e-03, eta: 1:30:42, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7318\n2023-01-03 01:15:22,011 - mmcls - INFO - Epoch [88][3300/5005]\tlr: 1.000e-03, eta: 1:30:33, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7118\n2023-01-03 01:15:30,627 - mmcls - INFO - Epoch [88][3400/5005]\tlr: 1.000e-03, eta: 1:30:25, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7612\n2023-01-03 01:15:39,460 - mmcls - INFO - Epoch [88][3500/5005]\tlr: 1.000e-03, eta: 1:30:16, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.7288\n2023-01-03 01:15:48,205 - mmcls - INFO - Epoch [88][3600/5005]\tlr: 1.000e-03, eta: 1:30:07, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7293\n2023-01-03 01:15:57,201 - mmcls - INFO - Epoch [88][3700/5005]\tlr: 1.000e-03, eta: 1:29:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7400\n2023-01-03 01:16:05,866 - mmcls - INFO - Epoch [88][3800/5005]\tlr: 1.000e-03, eta: 1:29:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7295\n2023-01-03 01:16:14,611 - mmcls - INFO - Epoch [88][3900/5005]\tlr: 1.000e-03, eta: 1:29:41, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7554\n2023-01-03 01:16:23,791 - mmcls - INFO - Epoch [88][4000/5005]\tlr: 1.000e-03, eta: 1:29:32, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7364\n2023-01-03 01:16:32,588 - mmcls - INFO - Epoch [88][4100/5005]\tlr: 1.000e-03, eta: 1:29:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7445\n2023-01-03 01:16:41,193 - mmcls - INFO - Epoch [88][4200/5005]\tlr: 1.000e-03, eta: 1:29:14, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7377\n2023-01-03 01:16:49,840 - mmcls - INFO - Epoch [88][4300/5005]\tlr: 1.000e-03, eta: 1:29:05, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7310\n2023-01-03 01:16:58,500 - mmcls - INFO - Epoch [88][4400/5005]\tlr: 1.000e-03, eta: 1:28:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7451\n2023-01-03 01:17:07,537 - mmcls - INFO - Epoch [88][4500/5005]\tlr: 1.000e-03, eta: 1:28:48, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7120\n2023-01-03 01:17:16,362 - mmcls - INFO - Epoch [88][4600/5005]\tlr: 1.000e-03, eta: 1:28:39, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7353\n2023-01-03 01:17:24,987 - mmcls - INFO - Epoch [88][4700/5005]\tlr: 1.000e-03, eta: 1:28:30, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7436\n2023-01-03 01:17:33,709 - mmcls - INFO - Epoch [88][4800/5005]\tlr: 1.000e-03, eta: 1:28:21, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7512\n2023-01-03 01:17:42,407 - mmcls - INFO - Epoch [88][4900/5005]\tlr: 1.000e-03, eta: 1:28:13, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7276\n2023-01-03 01:17:51,015 - mmcls - INFO - Epoch [88][5000/5005]\tlr: 1.000e-03, eta: 1:28:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7105\n2023-01-03 01:18:13,480 - mmcls - INFO - Epoch(val) [88][196]\taccuracy_top-1: 62.9860, accuracy_top-5: 83.9660\n2023-01-03 01:18:24,435 - mmcls - INFO - Epoch [89][100/5005]\tlr: 1.000e-03, eta: 1:27:55, time: 0.109, data_time: 0.046, memory: 1125, loss: 1.7025\n2023-01-03 01:18:33,337 - mmcls - INFO - Epoch [89][200/5005]\tlr: 1.000e-03, eta: 1:27:46, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7214\n2023-01-03 01:18:41,909 - mmcls - INFO - Epoch [89][300/5005]\tlr: 1.000e-03, eta: 1:27:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7345\n2023-01-03 01:18:50,875 - mmcls - INFO - Epoch [89][400/5005]\tlr: 1.000e-03, eta: 1:27:28, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7194\n2023-01-03 01:18:59,782 - mmcls - INFO - Epoch [89][500/5005]\tlr: 1.000e-03, eta: 1:27:20, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7049\n2023-01-03 01:19:09,068 - mmcls - INFO - Epoch [89][600/5005]\tlr: 1.000e-03, eta: 1:27:11, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.7336\n2023-01-03 01:19:17,752 - mmcls - INFO - Epoch [89][700/5005]\tlr: 1.000e-03, eta: 1:27:02, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7126\n2023-01-03 01:19:26,633 - mmcls - INFO - Epoch [89][800/5005]\tlr: 1.000e-03, eta: 1:26:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7252\n2023-01-03 01:19:35,686 - mmcls - INFO - Epoch [89][900/5005]\tlr: 1.000e-03, eta: 1:26:45, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7156\n2023-01-03 01:19:44,353 - mmcls - INFO - Epoch [89][1000/5005]\tlr: 1.000e-03, eta: 1:26:36, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.6973\n2023-01-03 01:19:53,271 - mmcls - INFO - Epoch [89][1100/5005]\tlr: 1.000e-03, eta: 1:26:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7425\n2023-01-03 01:20:01,827 - mmcls - INFO - Epoch [89][1200/5005]\tlr: 1.000e-03, eta: 1:26:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7224\n2023-01-03 01:20:10,540 - mmcls - INFO - Epoch [89][1300/5005]\tlr: 1.000e-03, eta: 1:26:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7328\n2023-01-03 01:20:19,256 - mmcls - INFO - Epoch [89][1400/5005]\tlr: 1.000e-03, eta: 1:26:00, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7283\n2023-01-03 01:20:27,833 - mmcls - INFO - Epoch [89][1500/5005]\tlr: 1.000e-03, eta: 1:25:52, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7363\n2023-01-03 01:20:36,448 - mmcls - INFO - Epoch [89][1600/5005]\tlr: 1.000e-03, eta: 1:25:43, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7350\n2023-01-03 01:20:44,981 - mmcls - INFO - Epoch [89][1700/5005]\tlr: 1.000e-03, eta: 1:25:34, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7467\n2023-01-03 01:20:53,675 - mmcls - INFO - Epoch [89][1800/5005]\tlr: 1.000e-03, eta: 1:25:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7274\n2023-01-03 01:21:02,339 - mmcls - INFO - Epoch [89][1900/5005]\tlr: 1.000e-03, eta: 1:25:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7513\n2023-01-03 01:21:10,806 - mmcls - INFO - Epoch [89][2000/5005]\tlr: 1.000e-03, eta: 1:25:08, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7440\n2023-01-03 01:21:19,506 - mmcls - INFO - Epoch [89][2100/5005]\tlr: 1.000e-03, eta: 1:24:59, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7018\n2023-01-03 01:21:28,238 - mmcls - INFO - Epoch [89][2200/5005]\tlr: 1.000e-03, eta: 1:24:50, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7499\n2023-01-03 01:21:37,023 - mmcls - INFO - Epoch [89][2300/5005]\tlr: 1.000e-03, eta: 1:24:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7254\n2023-01-03 01:21:45,839 - mmcls - INFO - Epoch [89][2400/5005]\tlr: 1.000e-03, eta: 1:24:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7375\n2023-01-03 01:21:54,622 - mmcls - INFO - Epoch [89][2500/5005]\tlr: 1.000e-03, eta: 1:24:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7375\n2023-01-03 01:22:03,353 - mmcls - INFO - Epoch [89][2600/5005]\tlr: 1.000e-03, eta: 1:24:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7564\n2023-01-03 01:22:12,190 - mmcls - INFO - Epoch [89][2700/5005]\tlr: 1.000e-03, eta: 1:24:06, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7262\n2023-01-03 01:22:21,280 - mmcls - INFO - Epoch [89][2800/5005]\tlr: 1.000e-03, eta: 1:23:57, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7313\n2023-01-03 01:22:29,874 - mmcls - INFO - Epoch [89][2900/5005]\tlr: 1.000e-03, eta: 1:23:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7482\n2023-01-03 01:22:38,508 - mmcls - INFO - Epoch [89][3000/5005]\tlr: 1.000e-03, eta: 1:23:39, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7245\n2023-01-03 01:22:47,176 - mmcls - INFO - Epoch [89][3100/5005]\tlr: 1.000e-03, eta: 1:23:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7463\n2023-01-03 01:22:55,891 - mmcls - INFO - Epoch [89][3200/5005]\tlr: 1.000e-03, eta: 1:23:22, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7258\n2023-01-03 01:23:04,819 - mmcls - INFO - Epoch [89][3300/5005]\tlr: 1.000e-03, eta: 1:23:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7237\n2023-01-03 01:23:13,628 - mmcls - INFO - Epoch [89][3400/5005]\tlr: 1.000e-03, eta: 1:23:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7142\n2023-01-03 01:23:22,426 - mmcls - INFO - Epoch [89][3500/5005]\tlr: 1.000e-03, eta: 1:22:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7287\n2023-01-03 01:23:31,449 - mmcls - INFO - Epoch [89][3600/5005]\tlr: 1.000e-03, eta: 1:22:47, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7216\n2023-01-03 01:23:39,799 - mmcls - INFO - Epoch [89][3700/5005]\tlr: 1.000e-03, eta: 1:22:38, time: 0.083, data_time: 0.030, memory: 1125, loss: 1.7217\n2023-01-03 01:23:48,582 - mmcls - INFO - Epoch [89][3800/5005]\tlr: 1.000e-03, eta: 1:22:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7447\n2023-01-03 01:23:57,372 - mmcls - INFO - Epoch [89][3900/5005]\tlr: 1.000e-03, eta: 1:22:20, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7362\n2023-01-03 01:24:06,284 - mmcls - INFO - Epoch [89][4000/5005]\tlr: 1.000e-03, eta: 1:22:11, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7507\n2023-01-03 01:24:14,762 - mmcls - INFO - Epoch [89][4100/5005]\tlr: 1.000e-03, eta: 1:22:03, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.7293\n2023-01-03 01:24:23,446 - mmcls - INFO - Epoch [89][4200/5005]\tlr: 1.000e-03, eta: 1:21:54, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7457\n2023-01-03 01:24:32,174 - mmcls - INFO - Epoch [89][4300/5005]\tlr: 1.000e-03, eta: 1:21:45, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7242\n2023-01-03 01:24:40,981 - mmcls - INFO - Epoch [89][4400/5005]\tlr: 1.000e-03, eta: 1:21:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6991\n2023-01-03 01:24:49,608 - mmcls - INFO - Epoch [89][4500/5005]\tlr: 1.000e-03, eta: 1:21:27, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7463\n2023-01-03 01:24:58,406 - mmcls - INFO - Epoch [89][4600/5005]\tlr: 1.000e-03, eta: 1:21:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7423\n2023-01-03 01:25:06,892 - mmcls - INFO - Epoch [89][4700/5005]\tlr: 1.000e-03, eta: 1:21:10, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7233\n2023-01-03 01:25:15,635 - mmcls - INFO - Epoch [89][4800/5005]\tlr: 1.000e-03, eta: 1:21:01, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7359\n2023-01-03 01:25:24,333 - mmcls - INFO - Epoch [89][4900/5005]\tlr: 1.000e-03, eta: 1:20:52, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7100\n2023-01-03 01:25:33,104 - mmcls - INFO - Epoch [89][5000/5005]\tlr: 1.000e-03, eta: 1:20:43, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7414\n2023-01-03 01:25:55,678 - mmcls - INFO - Epoch(val) [89][196]\taccuracy_top-1: 63.1020, accuracy_top-5: 83.9760\n2023-01-03 01:26:06,373 - mmcls - INFO - Epoch [90][100/5005]\tlr: 1.000e-03, eta: 1:20:34, time: 0.107, data_time: 0.061, memory: 1125, loss: 1.7329\n2023-01-03 01:26:14,987 - mmcls - INFO - Epoch [90][200/5005]\tlr: 1.000e-03, eta: 1:20:25, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7104\n2023-01-03 01:26:23,718 - mmcls - INFO - Epoch [90][300/5005]\tlr: 1.000e-03, eta: 1:20:17, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7435\n2023-01-03 01:26:32,312 - mmcls - INFO - Epoch [90][400/5005]\tlr: 1.000e-03, eta: 1:20:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7163\n2023-01-03 01:26:40,974 - mmcls - INFO - Epoch [90][500/5005]\tlr: 1.000e-03, eta: 1:19:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7299\n2023-01-03 01:26:49,902 - mmcls - INFO - Epoch [90][600/5005]\tlr: 1.000e-03, eta: 1:19:50, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7321\n2023-01-03 01:26:58,475 - mmcls - INFO - Epoch [90][700/5005]\tlr: 1.000e-03, eta: 1:19:41, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7164\n2023-01-03 01:27:07,041 - mmcls - INFO - Epoch [90][800/5005]\tlr: 1.000e-03, eta: 1:19:33, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7111\n2023-01-03 01:27:15,741 - mmcls - INFO - Epoch [90][900/5005]\tlr: 1.000e-03, eta: 1:19:24, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7269\n2023-01-03 01:27:24,476 - mmcls - INFO - Epoch [90][1000/5005]\tlr: 1.000e-03, eta: 1:19:15, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.6981\n2023-01-03 01:27:33,007 - mmcls - INFO - Epoch [90][1100/5005]\tlr: 1.000e-03, eta: 1:19:06, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7227\n2023-01-03 01:27:41,872 - mmcls - INFO - Epoch [90][1200/5005]\tlr: 1.000e-03, eta: 1:18:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7141\n2023-01-03 01:27:50,447 - mmcls - INFO - Epoch [90][1300/5005]\tlr: 1.000e-03, eta: 1:18:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7461\n2023-01-03 01:27:59,240 - mmcls - INFO - Epoch [90][1400/5005]\tlr: 1.000e-03, eta: 1:18:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7280\n2023-01-03 01:28:08,035 - mmcls - INFO - Epoch [90][1500/5005]\tlr: 1.000e-03, eta: 1:18:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7308\n2023-01-03 01:28:16,807 - mmcls - INFO - Epoch [90][1600/5005]\tlr: 1.000e-03, eta: 1:18:22, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7218\n2023-01-03 01:28:25,716 - mmcls - INFO - Epoch [90][1700/5005]\tlr: 1.000e-03, eta: 1:18:13, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7590\n2023-01-03 01:28:34,652 - mmcls - INFO - Epoch [90][1800/5005]\tlr: 1.000e-03, eta: 1:18:05, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7233\n2023-01-03 01:28:43,195 - mmcls - INFO - Epoch [90][1900/5005]\tlr: 1.000e-03, eta: 1:17:56, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7216\n2023-01-03 01:28:52,201 - mmcls - INFO - Epoch [90][2000/5005]\tlr: 1.000e-03, eta: 1:17:47, time: 0.090, data_time: 0.037, memory: 1125, loss: 1.7398\n2023-01-03 01:29:00,789 - mmcls - INFO - Epoch [90][2100/5005]\tlr: 1.000e-03, eta: 1:17:38, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.7343\n2023-01-03 01:29:09,796 - mmcls - INFO - Epoch [90][2200/5005]\tlr: 1.000e-03, eta: 1:17:29, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7301\n2023-01-03 01:29:18,545 - mmcls - INFO - Epoch [90][2300/5005]\tlr: 1.000e-03, eta: 1:17:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7293\n2023-01-03 01:29:27,211 - mmcls - INFO - Epoch [90][2400/5005]\tlr: 1.000e-03, eta: 1:17:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7374\n2023-01-03 01:29:35,952 - mmcls - INFO - Epoch [90][2500/5005]\tlr: 1.000e-03, eta: 1:17:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7194\n2023-01-03 01:29:44,799 - mmcls - INFO - Epoch [90][2600/5005]\tlr: 1.000e-03, eta: 1:16:54, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7337\n2023-01-03 01:29:53,517 - mmcls - INFO - Epoch [90][2700/5005]\tlr: 1.000e-03, eta: 1:16:45, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7299\n2023-01-03 01:30:02,306 - mmcls - INFO - Epoch [90][2800/5005]\tlr: 1.000e-03, eta: 1:16:37, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7257\n2023-01-03 01:30:11,263 - mmcls - INFO - Epoch [90][2900/5005]\tlr: 1.000e-03, eta: 1:16:28, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7163\n2023-01-03 01:30:19,985 - mmcls - INFO - Epoch [90][3000/5005]\tlr: 1.000e-03, eta: 1:16:19, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7595\n2023-01-03 01:30:28,540 - mmcls - INFO - Epoch [90][3100/5005]\tlr: 1.000e-03, eta: 1:16:10, time: 0.086, data_time: 0.040, memory: 1125, loss: 1.7226\n2023-01-03 01:30:36,982 - mmcls - INFO - Epoch [90][3200/5005]\tlr: 1.000e-03, eta: 1:16:01, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7233\n2023-01-03 01:30:45,733 - mmcls - INFO - Epoch [90][3300/5005]\tlr: 1.000e-03, eta: 1:15:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7322\n2023-01-03 01:30:54,758 - mmcls - INFO - Epoch [90][3400/5005]\tlr: 1.000e-03, eta: 1:15:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7235\n2023-01-03 01:31:03,556 - mmcls - INFO - Epoch [90][3500/5005]\tlr: 1.000e-03, eta: 1:15:35, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7201\n2023-01-03 01:31:12,114 - mmcls - INFO - Epoch [90][3600/5005]\tlr: 1.000e-03, eta: 1:15:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7360\n2023-01-03 01:31:20,644 - mmcls - INFO - Epoch [90][3700/5005]\tlr: 1.000e-03, eta: 1:15:17, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7302\n2023-01-03 01:31:29,350 - mmcls - INFO - Epoch [90][3800/5005]\tlr: 1.000e-03, eta: 1:15:08, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7246\n2023-01-03 01:31:38,410 - mmcls - INFO - Epoch [90][3900/5005]\tlr: 1.000e-03, eta: 1:15:00, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7375\n2023-01-03 01:31:47,350 - mmcls - INFO - Epoch [90][4000/5005]\tlr: 1.000e-03, eta: 1:14:51, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7311\n2023-01-03 01:31:56,367 - mmcls - INFO - Epoch [90][4100/5005]\tlr: 1.000e-03, eta: 1:14:42, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7430\n2023-01-03 01:32:05,167 - mmcls - INFO - Epoch [90][4200/5005]\tlr: 1.000e-03, eta: 1:14:33, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7329\n2023-01-03 01:32:13,809 - mmcls - INFO - Epoch [90][4300/5005]\tlr: 1.000e-03, eta: 1:14:25, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7424\n2023-01-03 01:32:22,503 - mmcls - INFO - Epoch [90][4400/5005]\tlr: 1.000e-03, eta: 1:14:16, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7170\n2023-01-03 01:32:31,244 - mmcls - INFO - Epoch [90][4500/5005]\tlr: 1.000e-03, eta: 1:14:07, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7372\n2023-01-03 01:32:39,954 - mmcls - INFO - Epoch [90][4600/5005]\tlr: 1.000e-03, eta: 1:13:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7216\n2023-01-03 01:32:48,853 - mmcls - INFO - Epoch [90][4700/5005]\tlr: 1.000e-03, eta: 1:13:49, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7563\n2023-01-03 01:32:57,626 - mmcls - INFO - Epoch [90][4800/5005]\tlr: 1.000e-03, eta: 1:13:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7349\n2023-01-03 01:33:06,336 - mmcls - INFO - Epoch [90][4900/5005]\tlr: 1.000e-03, eta: 1:13:32, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7407\n2023-01-03 01:33:14,933 - mmcls - INFO - Epoch [90][5000/5005]\tlr: 1.000e-03, eta: 1:13:23, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7507\n2023-01-03 01:33:37,558 - mmcls - INFO - Epoch(val) [90][196]\taccuracy_top-1: 63.2260, accuracy_top-5: 84.1020\n2023-01-03 01:33:48,341 - mmcls - INFO - Epoch [91][100/5005]\tlr: 1.000e-04, eta: 1:13:14, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.7012\n2023-01-03 01:33:57,017 - mmcls - INFO - Epoch [91][200/5005]\tlr: 1.000e-04, eta: 1:13:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6877\n2023-01-03 01:34:05,950 - mmcls - INFO - Epoch [91][300/5005]\tlr: 1.000e-04, eta: 1:12:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7058\n2023-01-03 01:34:14,491 - mmcls - INFO - Epoch [91][400/5005]\tlr: 1.000e-04, eta: 1:12:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6914\n2023-01-03 01:34:23,185 - mmcls - INFO - Epoch [91][500/5005]\tlr: 1.000e-04, eta: 1:12:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7183\n2023-01-03 01:34:32,119 - mmcls - INFO - Epoch [91][600/5005]\tlr: 1.000e-04, eta: 1:12:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7279\n2023-01-03 01:34:40,987 - mmcls - INFO - Epoch [91][700/5005]\tlr: 1.000e-04, eta: 1:12:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7001\n2023-01-03 01:34:49,743 - mmcls - INFO - Epoch [91][800/5005]\tlr: 1.000e-04, eta: 1:12:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7057\n2023-01-03 01:34:58,708 - mmcls - INFO - Epoch [91][900/5005]\tlr: 1.000e-04, eta: 1:12:03, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7296\n2023-01-03 01:35:07,297 - mmcls - INFO - Epoch [91][1000/5005]\tlr: 1.000e-04, eta: 1:11:55, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6856\n2023-01-03 01:35:15,859 - mmcls - INFO - Epoch [91][1100/5005]\tlr: 1.000e-04, eta: 1:11:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7155\n2023-01-03 01:35:24,581 - mmcls - INFO - Epoch [91][1200/5005]\tlr: 1.000e-04, eta: 1:11:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6945\n2023-01-03 01:35:33,130 - mmcls - INFO - Epoch [91][1300/5005]\tlr: 1.000e-04, eta: 1:11:28, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7180\n2023-01-03 01:35:41,856 - mmcls - INFO - Epoch [91][1400/5005]\tlr: 1.000e-04, eta: 1:11:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6956\n2023-01-03 01:35:50,604 - mmcls - INFO - Epoch [91][1500/5005]\tlr: 1.000e-04, eta: 1:11:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6934\n2023-01-03 01:35:59,333 - mmcls - INFO - Epoch [91][1600/5005]\tlr: 1.000e-04, eta: 1:11:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7170\n2023-01-03 01:36:08,042 - mmcls - INFO - Epoch [91][1700/5005]\tlr: 1.000e-04, eta: 1:10:53, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7154\n2023-01-03 01:36:16,557 - mmcls - INFO - Epoch [91][1800/5005]\tlr: 1.000e-04, eta: 1:10:44, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6991\n2023-01-03 01:36:25,148 - mmcls - INFO - Epoch [91][1900/5005]\tlr: 1.000e-04, eta: 1:10:35, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.6871\n2023-01-03 01:36:33,941 - mmcls - INFO - Epoch [91][2000/5005]\tlr: 1.000e-04, eta: 1:10:27, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7037\n2023-01-03 01:36:42,928 - mmcls - INFO - Epoch [91][2100/5005]\tlr: 1.000e-04, eta: 1:10:18, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.6937\n2023-01-03 01:36:51,778 - mmcls - INFO - Epoch [91][2200/5005]\tlr: 1.000e-04, eta: 1:10:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7015\n2023-01-03 01:37:00,452 - mmcls - INFO - Epoch [91][2300/5005]\tlr: 1.000e-04, eta: 1:10:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7062\n2023-01-03 01:37:09,512 - mmcls - INFO - Epoch [91][2400/5005]\tlr: 1.000e-04, eta: 1:09:51, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.6687\n2023-01-03 01:37:18,052 - mmcls - INFO - Epoch [91][2500/5005]\tlr: 1.000e-04, eta: 1:09:43, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6990\n2023-01-03 01:37:26,841 - mmcls - INFO - Epoch [91][2600/5005]\tlr: 1.000e-04, eta: 1:09:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7100\n2023-01-03 01:37:35,541 - mmcls - INFO - Epoch [91][2700/5005]\tlr: 1.000e-04, eta: 1:09:25, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6909\n2023-01-03 01:37:44,996 - mmcls - INFO - Epoch [91][2800/5005]\tlr: 1.000e-04, eta: 1:09:16, time: 0.095, data_time: 0.003, memory: 1125, loss: 1.7106\n2023-01-03 01:37:53,462 - mmcls - INFO - Epoch [91][2900/5005]\tlr: 1.000e-04, eta: 1:09:07, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7080\n2023-01-03 01:38:02,664 - mmcls - INFO - Epoch [91][3000/5005]\tlr: 1.000e-04, eta: 1:08:59, time: 0.092, data_time: 0.016, memory: 1125, loss: 1.7024\n2023-01-03 01:38:11,062 - mmcls - INFO - Epoch [91][3100/5005]\tlr: 1.000e-04, eta: 1:08:50, time: 0.084, data_time: 0.024, memory: 1125, loss: 1.7033\n2023-01-03 01:38:19,786 - mmcls - INFO - Epoch [91][3200/5005]\tlr: 1.000e-04, eta: 1:08:41, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.6905\n2023-01-03 01:38:28,373 - mmcls - INFO - Epoch [91][3300/5005]\tlr: 1.000e-04, eta: 1:08:32, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6996\n2023-01-03 01:38:37,164 - mmcls - INFO - Epoch [91][3400/5005]\tlr: 1.000e-04, eta: 1:08:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6875\n2023-01-03 01:38:45,866 - mmcls - INFO - Epoch [91][3500/5005]\tlr: 1.000e-04, eta: 1:08:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7032\n2023-01-03 01:38:54,636 - mmcls - INFO - Epoch [91][3600/5005]\tlr: 1.000e-04, eta: 1:08:06, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.6923\n2023-01-03 01:39:03,413 - mmcls - INFO - Epoch [91][3700/5005]\tlr: 1.000e-04, eta: 1:07:57, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7099\n2023-01-03 01:39:12,034 - mmcls - INFO - Epoch [91][3800/5005]\tlr: 1.000e-04, eta: 1:07:48, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6918\n2023-01-03 01:39:21,244 - mmcls - INFO - Epoch [91][3900/5005]\tlr: 1.000e-04, eta: 1:07:39, time: 0.092, data_time: 0.010, memory: 1125, loss: 1.6822\n2023-01-03 01:39:30,043 - mmcls - INFO - Epoch [91][4000/5005]\tlr: 1.000e-04, eta: 1:07:31, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6903\n2023-01-03 01:39:38,687 - mmcls - INFO - Epoch [91][4100/5005]\tlr: 1.000e-04, eta: 1:07:22, time: 0.086, data_time: 0.046, memory: 1125, loss: 1.6972\n2023-01-03 01:39:47,309 - mmcls - INFO - Epoch [91][4200/5005]\tlr: 1.000e-04, eta: 1:07:13, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7015\n2023-01-03 01:39:56,122 - mmcls - INFO - Epoch [91][4300/5005]\tlr: 1.000e-04, eta: 1:07:04, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.6924\n2023-01-03 01:40:05,198 - mmcls - INFO - Epoch [91][4400/5005]\tlr: 1.000e-04, eta: 1:06:55, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7057\n2023-01-03 01:40:14,218 - mmcls - INFO - Epoch [91][4500/5005]\tlr: 1.000e-04, eta: 1:06:47, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7113\n2023-01-03 01:40:23,120 - mmcls - INFO - Epoch [91][4600/5005]\tlr: 1.000e-04, eta: 1:06:38, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7073\n2023-01-03 01:40:31,924 - mmcls - INFO - Epoch [91][4700/5005]\tlr: 1.000e-04, eta: 1:06:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7011\n2023-01-03 01:40:40,475 - mmcls - INFO - Epoch [91][4800/5005]\tlr: 1.000e-04, eta: 1:06:20, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7255\n2023-01-03 01:40:49,250 - mmcls - INFO - Epoch [91][4900/5005]\tlr: 1.000e-04, eta: 1:06:11, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6913\n2023-01-03 01:40:57,915 - mmcls - INFO - Epoch [91][5000/5005]\tlr: 1.000e-04, eta: 1:06:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6937\n2023-01-03 01:41:20,648 - mmcls - INFO - Epoch(val) [91][196]\taccuracy_top-1: 63.4760, accuracy_top-5: 84.2940\n2023-01-03 01:41:31,684 - mmcls - INFO - Epoch [92][100/5005]\tlr: 1.000e-04, eta: 1:05:54, time: 0.110, data_time: 0.058, memory: 1125, loss: 1.6944\n2023-01-03 01:41:40,526 - mmcls - INFO - Epoch [92][200/5005]\tlr: 1.000e-04, eta: 1:05:45, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7090\n2023-01-03 01:41:49,282 - mmcls - INFO - Epoch [92][300/5005]\tlr: 1.000e-04, eta: 1:05:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6862\n2023-01-03 01:41:58,059 - mmcls - INFO - Epoch [92][400/5005]\tlr: 1.000e-04, eta: 1:05:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7106\n2023-01-03 01:42:06,613 - mmcls - INFO - Epoch [92][500/5005]\tlr: 1.000e-04, eta: 1:05:18, time: 0.086, data_time: 0.041, memory: 1125, loss: 1.6793\n2023-01-03 01:42:15,250 - mmcls - INFO - Epoch [92][600/5005]\tlr: 1.000e-04, eta: 1:05:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7058\n2023-01-03 01:42:23,929 - mmcls - INFO - Epoch [92][700/5005]\tlr: 1.000e-04, eta: 1:05:01, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7036\n2023-01-03 01:42:32,830 - mmcls - INFO - Epoch [92][800/5005]\tlr: 1.000e-04, eta: 1:04:52, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6918\n2023-01-03 01:42:41,404 - mmcls - INFO - Epoch [92][900/5005]\tlr: 1.000e-04, eta: 1:04:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6907\n2023-01-03 01:42:49,908 - mmcls - INFO - Epoch [92][1000/5005]\tlr: 1.000e-04, eta: 1:04:34, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6861\n2023-01-03 01:42:58,359 - mmcls - INFO - Epoch [92][1100/5005]\tlr: 1.000e-04, eta: 1:04:25, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.6765\n2023-01-03 01:43:06,998 - mmcls - INFO - Epoch [92][1200/5005]\tlr: 1.000e-04, eta: 1:04:17, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7046\n2023-01-03 01:43:16,047 - mmcls - INFO - Epoch [92][1300/5005]\tlr: 1.000e-04, eta: 1:04:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6839\n2023-01-03 01:43:24,955 - mmcls - INFO - Epoch [92][1400/5005]\tlr: 1.000e-04, eta: 1:03:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6924\n2023-01-03 01:43:34,227 - mmcls - INFO - Epoch [92][1500/5005]\tlr: 1.000e-04, eta: 1:03:50, time: 0.093, data_time: 0.005, memory: 1125, loss: 1.6891\n2023-01-03 01:43:43,103 - mmcls - INFO - Epoch [92][1600/5005]\tlr: 1.000e-04, eta: 1:03:42, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7057\n2023-01-03 01:43:51,747 - mmcls - INFO - Epoch [92][1700/5005]\tlr: 1.000e-04, eta: 1:03:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7164\n2023-01-03 01:44:00,563 - mmcls - INFO - Epoch [92][1800/5005]\tlr: 1.000e-04, eta: 1:03:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6929\n2023-01-03 01:44:09,524 - mmcls - INFO - Epoch [92][1900/5005]\tlr: 1.000e-04, eta: 1:03:15, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6804\n2023-01-03 01:44:18,225 - mmcls - INFO - Epoch [92][2000/5005]\tlr: 1.000e-04, eta: 1:03:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6754\n2023-01-03 01:44:27,078 - mmcls - INFO - Epoch [92][2100/5005]\tlr: 1.000e-04, eta: 1:02:58, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.6950\n2023-01-03 01:44:35,651 - mmcls - INFO - Epoch [92][2200/5005]\tlr: 1.000e-04, eta: 1:02:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7076\n2023-01-03 01:44:44,299 - mmcls - INFO - Epoch [92][2300/5005]\tlr: 1.000e-04, eta: 1:02:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6963\n2023-01-03 01:44:53,556 - mmcls - INFO - Epoch [92][2400/5005]\tlr: 1.000e-04, eta: 1:02:31, time: 0.093, data_time: 0.020, memory: 1125, loss: 1.6975\n2023-01-03 01:45:02,234 - mmcls - INFO - Epoch [92][2500/5005]\tlr: 1.000e-04, eta: 1:02:22, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6798\n2023-01-03 01:45:10,742 - mmcls - INFO - Epoch [92][2600/5005]\tlr: 1.000e-04, eta: 1:02:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7143\n2023-01-03 01:45:19,425 - mmcls - INFO - Epoch [92][2700/5005]\tlr: 1.000e-04, eta: 1:02:05, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.6922\n2023-01-03 01:45:28,203 - mmcls - INFO - Epoch [92][2800/5005]\tlr: 1.000e-04, eta: 1:01:56, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7142\n2023-01-03 01:45:37,103 - mmcls - INFO - Epoch [92][2900/5005]\tlr: 1.000e-04, eta: 1:01:47, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6918\n2023-01-03 01:45:45,842 - mmcls - INFO - Epoch [92][3000/5005]\tlr: 1.000e-04, eta: 1:01:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6697\n2023-01-03 01:45:54,557 - mmcls - INFO - Epoch [92][3100/5005]\tlr: 1.000e-04, eta: 1:01:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7180\n2023-01-03 01:46:02,950 - mmcls - INFO - Epoch [92][3200/5005]\tlr: 1.000e-04, eta: 1:01:21, time: 0.084, data_time: 0.000, memory: 1125, loss: 1.6752\n2023-01-03 01:46:11,622 - mmcls - INFO - Epoch [92][3300/5005]\tlr: 1.000e-04, eta: 1:01:12, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7038\n2023-01-03 01:46:20,083 - mmcls - INFO - Epoch [92][3400/5005]\tlr: 1.000e-04, eta: 1:01:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6924\n2023-01-03 01:46:28,602 - mmcls - INFO - Epoch [92][3500/5005]\tlr: 1.000e-04, eta: 1:00:54, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6915\n2023-01-03 01:46:37,362 - mmcls - INFO - Epoch [92][3600/5005]\tlr: 1.000e-04, eta: 1:00:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6747\n2023-01-03 01:46:46,288 - mmcls - INFO - Epoch [92][3700/5005]\tlr: 1.000e-04, eta: 1:00:37, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6920\n2023-01-03 01:46:54,910 - mmcls - INFO - Epoch [92][3800/5005]\tlr: 1.000e-04, eta: 1:00:28, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7065\n2023-01-03 01:47:03,765 - mmcls - INFO - Epoch [92][3900/5005]\tlr: 1.000e-04, eta: 1:00:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6899\n2023-01-03 01:47:13,095 - mmcls - INFO - Epoch [92][4000/5005]\tlr: 1.000e-04, eta: 1:00:10, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7154\n2023-01-03 01:47:21,666 - mmcls - INFO - Epoch [92][4100/5005]\tlr: 1.000e-04, eta: 1:00:01, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.6879\n2023-01-03 01:47:30,562 - mmcls - INFO - Epoch [92][4200/5005]\tlr: 1.000e-04, eta: 0:59:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7020\n2023-01-03 01:47:39,370 - mmcls - INFO - Epoch [92][4300/5005]\tlr: 1.000e-04, eta: 0:59:44, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7053\n2023-01-03 01:47:48,079 - mmcls - INFO - Epoch [92][4400/5005]\tlr: 1.000e-04, eta: 0:59:35, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6922\n2023-01-03 01:47:56,742 - mmcls - INFO - Epoch [92][4500/5005]\tlr: 1.000e-04, eta: 0:59:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6740\n2023-01-03 01:48:05,443 - mmcls - INFO - Epoch [92][4600/5005]\tlr: 1.000e-04, eta: 0:59:17, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6952\n2023-01-03 01:48:14,067 - mmcls - INFO - Epoch [92][4700/5005]\tlr: 1.000e-04, eta: 0:59:09, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6852\n2023-01-03 01:48:22,716 - mmcls - INFO - Epoch [92][4800/5005]\tlr: 1.000e-04, eta: 0:59:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6902\n2023-01-03 01:48:31,710 - mmcls - INFO - Epoch [92][4900/5005]\tlr: 1.000e-04, eta: 0:58:51, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6873\n2023-01-03 01:48:40,688 - mmcls - INFO - Epoch [92][5000/5005]\tlr: 1.000e-04, eta: 0:58:42, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7019\n2023-01-03 01:49:03,397 - mmcls - INFO - Epoch(val) [92][196]\taccuracy_top-1: 63.6180, accuracy_top-5: 84.3060\n2023-01-03 01:49:14,360 - mmcls - INFO - Epoch [93][100/5005]\tlr: 1.000e-04, eta: 0:58:33, time: 0.110, data_time: 0.029, memory: 1125, loss: 1.6968\n2023-01-03 01:49:23,065 - mmcls - INFO - Epoch [93][200/5005]\tlr: 1.000e-04, eta: 0:58:24, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6873\n2023-01-03 01:49:31,760 - mmcls - INFO - Epoch [93][300/5005]\tlr: 1.000e-04, eta: 0:58:16, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6936\n2023-01-03 01:49:40,395 - mmcls - INFO - Epoch [93][400/5005]\tlr: 1.000e-04, eta: 0:58:07, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7013\n2023-01-03 01:49:49,006 - mmcls - INFO - Epoch [93][500/5005]\tlr: 1.000e-04, eta: 0:57:58, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7129\n2023-01-03 01:49:57,831 - mmcls - INFO - Epoch [93][600/5005]\tlr: 1.000e-04, eta: 0:57:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6798\n2023-01-03 01:50:06,574 - mmcls - INFO - Epoch [93][700/5005]\tlr: 1.000e-04, eta: 0:57:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6868\n2023-01-03 01:50:15,470 - mmcls - INFO - Epoch [93][800/5005]\tlr: 1.000e-04, eta: 0:57:32, time: 0.089, data_time: 0.046, memory: 1125, loss: 1.6784\n2023-01-03 01:50:24,350 - mmcls - INFO - Epoch [93][900/5005]\tlr: 1.000e-04, eta: 0:57:23, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6989\n2023-01-03 01:50:33,414 - mmcls - INFO - Epoch [93][1000/5005]\tlr: 1.000e-04, eta: 0:57:14, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.6899\n2023-01-03 01:50:42,261 - mmcls - INFO - Epoch [93][1100/5005]\tlr: 1.000e-04, eta: 0:57:05, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7069\n2023-01-03 01:50:51,117 - mmcls - INFO - Epoch [93][1200/5005]\tlr: 1.000e-04, eta: 0:56:56, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6999\n2023-01-03 01:50:59,886 - mmcls - INFO - Epoch [93][1300/5005]\tlr: 1.000e-04, eta: 0:56:48, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6721\n2023-01-03 01:51:09,039 - mmcls - INFO - Epoch [93][1400/5005]\tlr: 1.000e-04, eta: 0:56:39, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6813\n2023-01-03 01:51:17,705 - mmcls - INFO - Epoch [93][1500/5005]\tlr: 1.000e-04, eta: 0:56:30, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6758\n2023-01-03 01:51:26,811 - mmcls - INFO - Epoch [93][1600/5005]\tlr: 1.000e-04, eta: 0:56:21, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6944\n2023-01-03 01:51:35,248 - mmcls - INFO - Epoch [93][1700/5005]\tlr: 1.000e-04, eta: 0:56:12, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.7144\n2023-01-03 01:51:44,086 - mmcls - INFO - Epoch [93][1800/5005]\tlr: 1.000e-04, eta: 0:56:04, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6906\n2023-01-03 01:51:52,902 - mmcls - INFO - Epoch [93][1900/5005]\tlr: 1.000e-04, eta: 0:55:55, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.6640\n2023-01-03 01:52:01,392 - mmcls - INFO - Epoch [93][2000/5005]\tlr: 1.000e-04, eta: 0:55:46, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7113\n2023-01-03 01:52:09,894 - mmcls - INFO - Epoch [93][2100/5005]\tlr: 1.000e-04, eta: 0:55:37, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6911\n2023-01-03 01:52:18,820 - mmcls - INFO - Epoch [93][2200/5005]\tlr: 1.000e-04, eta: 0:55:28, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6822\n2023-01-03 01:52:27,702 - mmcls - INFO - Epoch [93][2300/5005]\tlr: 1.000e-04, eta: 0:55:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6813\n2023-01-03 01:52:36,566 - mmcls - INFO - Epoch [93][2400/5005]\tlr: 1.000e-04, eta: 0:55:11, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6701\n2023-01-03 01:52:45,163 - mmcls - INFO - Epoch [93][2500/5005]\tlr: 1.000e-04, eta: 0:55:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7031\n2023-01-03 01:52:53,890 - mmcls - INFO - Epoch [93][2600/5005]\tlr: 1.000e-04, eta: 0:54:53, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7050\n2023-01-03 01:53:02,610 - mmcls - INFO - Epoch [93][2700/5005]\tlr: 1.000e-04, eta: 0:54:44, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6648\n2023-01-03 01:53:11,264 - mmcls - INFO - Epoch [93][2800/5005]\tlr: 1.000e-04, eta: 0:54:36, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.6818\n2023-01-03 01:53:20,364 - mmcls - INFO - Epoch [93][2900/5005]\tlr: 1.000e-04, eta: 0:54:27, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.6924\n2023-01-03 01:53:29,407 - mmcls - INFO - Epoch [93][3000/5005]\tlr: 1.000e-04, eta: 0:54:18, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6957\n2023-01-03 01:53:38,060 - mmcls - INFO - Epoch [93][3100/5005]\tlr: 1.000e-04, eta: 0:54:09, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6956\n2023-01-03 01:53:46,787 - mmcls - INFO - Epoch [93][3200/5005]\tlr: 1.000e-04, eta: 0:54:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7071\n2023-01-03 01:53:55,674 - mmcls - INFO - Epoch [93][3300/5005]\tlr: 1.000e-04, eta: 0:53:52, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7105\n2023-01-03 01:54:04,723 - mmcls - INFO - Epoch [93][3400/5005]\tlr: 1.000e-04, eta: 0:53:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7033\n2023-01-03 01:54:13,257 - mmcls - INFO - Epoch [93][3500/5005]\tlr: 1.000e-04, eta: 0:53:34, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.6716\n2023-01-03 01:54:22,224 - mmcls - INFO - Epoch [93][3600/5005]\tlr: 1.000e-04, eta: 0:53:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.6863\n2023-01-03 01:54:30,946 - mmcls - INFO - Epoch [93][3700/5005]\tlr: 1.000e-04, eta: 0:53:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6596\n2023-01-03 01:54:39,708 - mmcls - INFO - Epoch [93][3800/5005]\tlr: 1.000e-04, eta: 0:53:08, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7098\n2023-01-03 01:54:48,406 - mmcls - INFO - Epoch [93][3900/5005]\tlr: 1.000e-04, eta: 0:52:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6963\n2023-01-03 01:54:56,918 - mmcls - INFO - Epoch [93][4000/5005]\tlr: 1.000e-04, eta: 0:52:50, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6976\n2023-01-03 01:55:05,584 - mmcls - INFO - Epoch [93][4100/5005]\tlr: 1.000e-04, eta: 0:52:41, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7116\n2023-01-03 01:55:14,588 - mmcls - INFO - Epoch [93][4200/5005]\tlr: 1.000e-04, eta: 0:52:32, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6899\n2023-01-03 01:55:23,179 - mmcls - INFO - Epoch [93][4300/5005]\tlr: 1.000e-04, eta: 0:52:24, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.6858\n2023-01-03 01:55:31,917 - mmcls - INFO - Epoch [93][4400/5005]\tlr: 1.000e-04, eta: 0:52:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6727\n2023-01-03 01:55:40,715 - mmcls - INFO - Epoch [93][4500/5005]\tlr: 1.000e-04, eta: 0:52:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6813\n2023-01-03 01:55:49,433 - mmcls - INFO - Epoch [93][4600/5005]\tlr: 1.000e-04, eta: 0:51:57, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6805\n2023-01-03 01:55:58,159 - mmcls - INFO - Epoch [93][4700/5005]\tlr: 1.000e-04, eta: 0:51:48, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.6736\n2023-01-03 01:56:06,721 - mmcls - INFO - Epoch [93][4800/5005]\tlr: 1.000e-04, eta: 0:51:40, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7189\n2023-01-03 01:56:15,490 - mmcls - INFO - Epoch [93][4900/5005]\tlr: 1.000e-04, eta: 0:51:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7171\n2023-01-03 01:56:24,071 - mmcls - INFO - Epoch [93][5000/5005]\tlr: 1.000e-04, eta: 0:51:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6795\n2023-01-03 01:56:46,861 - mmcls - INFO - Epoch(val) [93][196]\taccuracy_top-1: 63.6640, accuracy_top-5: 84.3360\n2023-01-03 01:56:58,132 - mmcls - INFO - Epoch [94][100/5005]\tlr: 1.000e-04, eta: 0:51:13, time: 0.112, data_time: 0.045, memory: 1125, loss: 1.6792\n2023-01-03 01:57:06,759 - mmcls - INFO - Epoch [94][200/5005]\tlr: 1.000e-04, eta: 0:51:04, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.6814\n2023-01-03 01:57:15,553 - mmcls - INFO - Epoch [94][300/5005]\tlr: 1.000e-04, eta: 0:50:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6866\n2023-01-03 01:57:24,595 - mmcls - INFO - Epoch [94][400/5005]\tlr: 1.000e-04, eta: 0:50:47, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6802\n2023-01-03 01:57:33,266 - mmcls - INFO - Epoch [94][500/5005]\tlr: 1.000e-04, eta: 0:50:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6949\n2023-01-03 01:57:41,933 - mmcls - INFO - Epoch [94][600/5005]\tlr: 1.000e-04, eta: 0:50:29, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6812\n2023-01-03 01:57:50,418 - mmcls - INFO - Epoch [94][700/5005]\tlr: 1.000e-04, eta: 0:50:20, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6841\n2023-01-03 01:57:59,199 - mmcls - INFO - Epoch [94][800/5005]\tlr: 1.000e-04, eta: 0:50:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7055\n2023-01-03 01:58:07,860 - mmcls - INFO - Epoch [94][900/5005]\tlr: 1.000e-04, eta: 0:50:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6779\n2023-01-03 01:58:16,755 - mmcls - INFO - Epoch [94][1000/5005]\tlr: 1.000e-04, eta: 0:49:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6768\n2023-01-03 01:58:25,462 - mmcls - INFO - Epoch [94][1100/5005]\tlr: 1.000e-04, eta: 0:49:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7079\n2023-01-03 01:58:34,398 - mmcls - INFO - Epoch [94][1200/5005]\tlr: 1.000e-04, eta: 0:49:36, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7017\n2023-01-03 01:58:43,069 - mmcls - INFO - Epoch [94][1300/5005]\tlr: 1.000e-04, eta: 0:49:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7031\n2023-01-03 01:58:52,220 - mmcls - INFO - Epoch [94][1400/5005]\tlr: 1.000e-04, eta: 0:49:19, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7144\n2023-01-03 01:59:00,701 - mmcls - INFO - Epoch [94][1500/5005]\tlr: 1.000e-04, eta: 0:49:10, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7034\n2023-01-03 01:59:09,459 - mmcls - INFO - Epoch [94][1600/5005]\tlr: 1.000e-04, eta: 0:49:01, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6872\n2023-01-03 01:59:18,200 - mmcls - INFO - Epoch [94][1700/5005]\tlr: 1.000e-04, eta: 0:48:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6910\n2023-01-03 01:59:27,279 - mmcls - INFO - Epoch [94][1800/5005]\tlr: 1.000e-04, eta: 0:48:43, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.6924\n2023-01-03 01:59:35,815 - mmcls - INFO - Epoch [94][1900/5005]\tlr: 1.000e-04, eta: 0:48:35, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6922\n2023-01-03 01:59:44,426 - mmcls - INFO - Epoch [94][2000/5005]\tlr: 1.000e-04, eta: 0:48:26, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7008\n2023-01-03 01:59:53,135 - mmcls - INFO - Epoch [94][2100/5005]\tlr: 1.000e-04, eta: 0:48:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6695\n2023-01-03 02:00:01,939 - mmcls - INFO - Epoch [94][2200/5005]\tlr: 1.000e-04, eta: 0:48:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6896\n2023-01-03 02:00:11,126 - mmcls - INFO - Epoch [94][2300/5005]\tlr: 1.000e-04, eta: 0:47:59, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.7032\n2023-01-03 02:00:19,881 - mmcls - INFO - Epoch [94][2400/5005]\tlr: 1.000e-04, eta: 0:47:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6957\n2023-01-03 02:00:28,348 - mmcls - INFO - Epoch [94][2500/5005]\tlr: 1.000e-04, eta: 0:47:42, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6895\n2023-01-03 02:00:36,930 - mmcls - INFO - Epoch [94][2600/5005]\tlr: 1.000e-04, eta: 0:47:33, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7118\n2023-01-03 02:00:45,494 - mmcls - INFO - Epoch [94][2700/5005]\tlr: 1.000e-04, eta: 0:47:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6663\n2023-01-03 02:00:53,956 - mmcls - INFO - Epoch [94][2800/5005]\tlr: 1.000e-04, eta: 0:47:15, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7031\n2023-01-03 02:01:02,764 - mmcls - INFO - Epoch [94][2900/5005]\tlr: 1.000e-04, eta: 0:47:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6771\n2023-01-03 02:01:11,495 - mmcls - INFO - Epoch [94][3000/5005]\tlr: 1.000e-04, eta: 0:46:58, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6994\n2023-01-03 02:01:20,173 - mmcls - INFO - Epoch [94][3100/5005]\tlr: 1.000e-04, eta: 0:46:49, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6957\n2023-01-03 02:01:29,042 - mmcls - INFO - Epoch [94][3200/5005]\tlr: 1.000e-04, eta: 0:46:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6875\n2023-01-03 02:01:37,618 - mmcls - INFO - Epoch [94][3300/5005]\tlr: 1.000e-04, eta: 0:46:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7009\n2023-01-03 02:01:46,347 - mmcls - INFO - Epoch [94][3400/5005]\tlr: 1.000e-04, eta: 0:46:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6750\n2023-01-03 02:01:55,536 - mmcls - INFO - Epoch [94][3500/5005]\tlr: 1.000e-04, eta: 0:46:14, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6876\n2023-01-03 02:02:04,768 - mmcls - INFO - Epoch [94][3600/5005]\tlr: 1.000e-04, eta: 0:46:05, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6699\n2023-01-03 02:02:13,428 - mmcls - INFO - Epoch [94][3700/5005]\tlr: 1.000e-04, eta: 0:45:56, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6905\n2023-01-03 02:02:22,208 - mmcls - INFO - Epoch [94][3800/5005]\tlr: 1.000e-04, eta: 0:45:47, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.6741\n2023-01-03 02:02:31,169 - mmcls - INFO - Epoch [94][3900/5005]\tlr: 1.000e-04, eta: 0:45:39, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6786\n2023-01-03 02:02:40,047 - mmcls - INFO - Epoch [94][4000/5005]\tlr: 1.000e-04, eta: 0:45:30, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6926\n2023-01-03 02:02:48,586 - mmcls - INFO - Epoch [94][4100/5005]\tlr: 1.000e-04, eta: 0:45:21, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6487\n2023-01-03 02:02:57,579 - mmcls - INFO - Epoch [94][4200/5005]\tlr: 1.000e-04, eta: 0:45:12, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.6870\n2023-01-03 02:03:06,363 - mmcls - INFO - Epoch [94][4300/5005]\tlr: 1.000e-04, eta: 0:45:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7038\n2023-01-03 02:03:15,371 - mmcls - INFO - Epoch [94][4400/5005]\tlr: 1.000e-04, eta: 0:44:55, time: 0.090, data_time: 0.040, memory: 1125, loss: 1.7010\n2023-01-03 02:03:24,175 - mmcls - INFO - Epoch [94][4500/5005]\tlr: 1.000e-04, eta: 0:44:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6944\n2023-01-03 02:03:32,818 - mmcls - INFO - Epoch [94][4600/5005]\tlr: 1.000e-04, eta: 0:44:37, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6863\n2023-01-03 02:03:42,029 - mmcls - INFO - Epoch [94][4700/5005]\tlr: 1.000e-04, eta: 0:44:28, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6943\n2023-01-03 02:03:50,589 - mmcls - INFO - Epoch [94][4800/5005]\tlr: 1.000e-04, eta: 0:44:19, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6964\n2023-01-03 02:03:59,370 - mmcls - INFO - Epoch [94][4900/5005]\tlr: 1.000e-04, eta: 0:44:11, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6843\n2023-01-03 02:04:08,405 - mmcls - INFO - Epoch [94][5000/5005]\tlr: 1.000e-04, eta: 0:44:02, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7006\n2023-01-03 02:04:30,906 - mmcls - INFO - Epoch(val) [94][196]\taccuracy_top-1: 63.5280, accuracy_top-5: 84.1540\n2023-01-03 02:04:41,856 - mmcls - INFO - Epoch [95][100/5005]\tlr: 1.000e-04, eta: 0:43:53, time: 0.109, data_time: 0.050, memory: 1125, loss: 1.6851\n2023-01-03 02:04:50,790 - mmcls - INFO - Epoch [95][200/5005]\tlr: 1.000e-04, eta: 0:43:44, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6811\n2023-01-03 02:04:59,455 - mmcls - INFO - Epoch [95][300/5005]\tlr: 1.000e-04, eta: 0:43:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7138\n2023-01-03 02:05:07,983 - mmcls - INFO - Epoch [95][400/5005]\tlr: 1.000e-04, eta: 0:43:26, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6757\n2023-01-03 02:05:16,748 - mmcls - INFO - Epoch [95][500/5005]\tlr: 1.000e-04, eta: 0:43:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6782\n2023-01-03 02:05:25,789 - mmcls - INFO - Epoch [95][600/5005]\tlr: 1.000e-04, eta: 0:43:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6881\n2023-01-03 02:05:34,294 - mmcls - INFO - Epoch [95][700/5005]\tlr: 1.000e-04, eta: 0:43:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6974\n2023-01-03 02:05:42,899 - mmcls - INFO - Epoch [95][800/5005]\tlr: 1.000e-04, eta: 0:42:51, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.6723\n2023-01-03 02:05:51,604 - mmcls - INFO - Epoch [95][900/5005]\tlr: 1.000e-04, eta: 0:42:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6787\n2023-01-03 02:06:00,468 - mmcls - INFO - Epoch [95][1000/5005]\tlr: 1.000e-04, eta: 0:42:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7076\n2023-01-03 02:06:09,170 - mmcls - INFO - Epoch [95][1100/5005]\tlr: 1.000e-04, eta: 0:42:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6704\n2023-01-03 02:06:17,894 - mmcls - INFO - Epoch [95][1200/5005]\tlr: 1.000e-04, eta: 0:42:16, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6876\n2023-01-03 02:06:26,984 - mmcls - INFO - Epoch [95][1300/5005]\tlr: 1.000e-04, eta: 0:42:07, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6815\n2023-01-03 02:06:36,004 - mmcls - INFO - Epoch [95][1400/5005]\tlr: 1.000e-04, eta: 0:41:58, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6816\n2023-01-03 02:06:44,549 - mmcls - INFO - Epoch [95][1500/5005]\tlr: 1.000e-04, eta: 0:41:49, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6815\n2023-01-03 02:06:53,080 - mmcls - INFO - Epoch [95][1600/5005]\tlr: 1.000e-04, eta: 0:41:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6739\n2023-01-03 02:07:01,952 - mmcls - INFO - Epoch [95][1700/5005]\tlr: 1.000e-04, eta: 0:41:32, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6917\n2023-01-03 02:07:10,914 - mmcls - INFO - Epoch [95][1800/5005]\tlr: 1.000e-04, eta: 0:41:23, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6968\n2023-01-03 02:07:19,698 - mmcls - INFO - Epoch [95][1900/5005]\tlr: 1.000e-04, eta: 0:41:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7058\n2023-01-03 02:07:28,745 - mmcls - INFO - Epoch [95][2000/5005]\tlr: 1.000e-04, eta: 0:41:06, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7054\n2023-01-03 02:07:37,597 - mmcls - INFO - Epoch [95][2100/5005]\tlr: 1.000e-04, eta: 0:40:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6848\n2023-01-03 02:07:46,541 - mmcls - INFO - Epoch [95][2200/5005]\tlr: 1.000e-04, eta: 0:40:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7131\n2023-01-03 02:07:55,113 - mmcls - INFO - Epoch [95][2300/5005]\tlr: 1.000e-04, eta: 0:40:39, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6965\n2023-01-03 02:08:03,978 - mmcls - INFO - Epoch [95][2400/5005]\tlr: 1.000e-04, eta: 0:40:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6817\n2023-01-03 02:08:12,557 - mmcls - INFO - Epoch [95][2500/5005]\tlr: 1.000e-04, eta: 0:40:22, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7002\n2023-01-03 02:08:21,489 - mmcls - INFO - Epoch [95][2600/5005]\tlr: 1.000e-04, eta: 0:40:13, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6864\n2023-01-03 02:08:30,352 - mmcls - INFO - Epoch [95][2700/5005]\tlr: 1.000e-04, eta: 0:40:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.6860\n2023-01-03 02:08:39,098 - mmcls - INFO - Epoch [95][2800/5005]\tlr: 1.000e-04, eta: 0:39:55, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7112\n2023-01-03 02:08:47,759 - mmcls - INFO - Epoch [95][2900/5005]\tlr: 1.000e-04, eta: 0:39:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6996\n2023-01-03 02:08:56,612 - mmcls - INFO - Epoch [95][3000/5005]\tlr: 1.000e-04, eta: 0:39:38, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7053\n2023-01-03 02:09:05,271 - mmcls - INFO - Epoch [95][3100/5005]\tlr: 1.000e-04, eta: 0:39:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6769\n2023-01-03 02:09:14,407 - mmcls - INFO - Epoch [95][3200/5005]\tlr: 1.000e-04, eta: 0:39:20, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6987\n2023-01-03 02:09:23,171 - mmcls - INFO - Epoch [95][3300/5005]\tlr: 1.000e-04, eta: 0:39:11, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6822\n2023-01-03 02:09:31,990 - mmcls - INFO - Epoch [95][3400/5005]\tlr: 1.000e-04, eta: 0:39:02, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7098\n2023-01-03 02:09:40,560 - mmcls - INFO - Epoch [95][3500/5005]\tlr: 1.000e-04, eta: 0:38:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6928\n2023-01-03 02:09:49,678 - mmcls - INFO - Epoch [95][3600/5005]\tlr: 1.000e-04, eta: 0:38:45, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6911\n2023-01-03 02:09:58,726 - mmcls - INFO - Epoch [95][3700/5005]\tlr: 1.000e-04, eta: 0:38:36, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.6872\n2023-01-03 02:10:07,446 - mmcls - INFO - Epoch [95][3800/5005]\tlr: 1.000e-04, eta: 0:38:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6989\n2023-01-03 02:10:16,151 - mmcls - INFO - Epoch [95][3900/5005]\tlr: 1.000e-04, eta: 0:38:18, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.6809\n2023-01-03 02:10:24,820 - mmcls - INFO - Epoch [95][4000/5005]\tlr: 1.000e-04, eta: 0:38:10, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6895\n2023-01-03 02:10:33,631 - mmcls - INFO - Epoch [95][4100/5005]\tlr: 1.000e-04, eta: 0:38:01, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6836\n2023-01-03 02:10:42,067 - mmcls - INFO - Epoch [95][4200/5005]\tlr: 1.000e-04, eta: 0:37:52, time: 0.084, data_time: 0.000, memory: 1125, loss: 1.6847\n2023-01-03 02:10:50,897 - mmcls - INFO - Epoch [95][4300/5005]\tlr: 1.000e-04, eta: 0:37:43, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7125\n2023-01-03 02:10:59,657 - mmcls - INFO - Epoch [95][4400/5005]\tlr: 1.000e-04, eta: 0:37:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6777\n2023-01-03 02:11:08,452 - mmcls - INFO - Epoch [95][4500/5005]\tlr: 1.000e-04, eta: 0:37:26, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7047\n2023-01-03 02:11:17,070 - mmcls - INFO - Epoch [95][4600/5005]\tlr: 1.000e-04, eta: 0:37:17, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.6837\n2023-01-03 02:11:25,712 - mmcls - INFO - Epoch [95][4700/5005]\tlr: 1.000e-04, eta: 0:37:08, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7087\n2023-01-03 02:11:34,851 - mmcls - INFO - Epoch [95][4800/5005]\tlr: 1.000e-04, eta: 0:36:59, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6881\n2023-01-03 02:11:44,100 - mmcls - INFO - Epoch [95][4900/5005]\tlr: 1.000e-04, eta: 0:36:50, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.6896\n2023-01-03 02:11:52,453 - mmcls - INFO - Epoch [95][5000/5005]\tlr: 1.000e-04, eta: 0:36:42, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.6842\n2023-01-03 02:12:15,260 - mmcls - INFO - Epoch(val) [95][196]\taccuracy_top-1: 63.6340, accuracy_top-5: 84.2080\n2023-01-03 02:12:26,335 - mmcls - INFO - Epoch [96][100/5005]\tlr: 1.000e-04, eta: 0:36:32, time: 0.111, data_time: 0.044, memory: 1125, loss: 1.6958\n2023-01-03 02:12:34,885 - mmcls - INFO - Epoch [96][200/5005]\tlr: 1.000e-04, eta: 0:36:24, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6892\n2023-01-03 02:12:43,293 - mmcls - INFO - Epoch [96][300/5005]\tlr: 1.000e-04, eta: 0:36:15, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.6937\n2023-01-03 02:12:52,401 - mmcls - INFO - Epoch [96][400/5005]\tlr: 1.000e-04, eta: 0:36:06, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.6735\n2023-01-03 02:13:01,145 - mmcls - INFO - Epoch [96][500/5005]\tlr: 1.000e-04, eta: 0:35:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6577\n2023-01-03 02:13:09,705 - mmcls - INFO - Epoch [96][600/5005]\tlr: 1.000e-04, eta: 0:35:48, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.6803\n2023-01-03 02:13:18,404 - mmcls - INFO - Epoch [96][700/5005]\tlr: 1.000e-04, eta: 0:35:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6681\n2023-01-03 02:13:27,186 - mmcls - INFO - Epoch [96][800/5005]\tlr: 1.000e-04, eta: 0:35:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7010\n2023-01-03 02:13:35,658 - mmcls - INFO - Epoch [96][900/5005]\tlr: 1.000e-04, eta: 0:35:22, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7067\n2023-01-03 02:13:44,104 - mmcls - INFO - Epoch [96][1000/5005]\tlr: 1.000e-04, eta: 0:35:13, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.6863\n2023-01-03 02:13:52,792 - mmcls - INFO - Epoch [96][1100/5005]\tlr: 1.000e-04, eta: 0:35:04, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.6610\n2023-01-03 02:14:01,372 - mmcls - INFO - Epoch [96][1200/5005]\tlr: 1.000e-04, eta: 0:34:56, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6952\n2023-01-03 02:14:10,045 - mmcls - INFO - Epoch [96][1300/5005]\tlr: 1.000e-04, eta: 0:34:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6953\n2023-01-03 02:14:18,701 - mmcls - INFO - Epoch [96][1400/5005]\tlr: 1.000e-04, eta: 0:34:38, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6883\n2023-01-03 02:14:27,671 - mmcls - INFO - Epoch [96][1500/5005]\tlr: 1.000e-04, eta: 0:34:29, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6909\n2023-01-03 02:14:36,312 - mmcls - INFO - Epoch [96][1600/5005]\tlr: 1.000e-04, eta: 0:34:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7144\n2023-01-03 02:14:45,219 - mmcls - INFO - Epoch [96][1700/5005]\tlr: 1.000e-04, eta: 0:34:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6852\n2023-01-03 02:14:53,990 - mmcls - INFO - Epoch [96][1800/5005]\tlr: 1.000e-04, eta: 0:34:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6798\n2023-01-03 02:15:03,205 - mmcls - INFO - Epoch [96][1900/5005]\tlr: 1.000e-04, eta: 0:33:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7201\n2023-01-03 02:15:12,068 - mmcls - INFO - Epoch [96][2000/5005]\tlr: 1.000e-04, eta: 0:33:45, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6749\n2023-01-03 02:15:20,910 - mmcls - INFO - Epoch [96][2100/5005]\tlr: 1.000e-04, eta: 0:33:36, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.6785\n2023-01-03 02:15:29,892 - mmcls - INFO - Epoch [96][2200/5005]\tlr: 1.000e-04, eta: 0:33:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.6836\n2023-01-03 02:15:38,588 - mmcls - INFO - Epoch [96][2300/5005]\tlr: 1.000e-04, eta: 0:33:19, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7128\n2023-01-03 02:15:47,296 - mmcls - INFO - Epoch [96][2400/5005]\tlr: 1.000e-04, eta: 0:33:10, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6978\n2023-01-03 02:15:55,872 - mmcls - INFO - Epoch [96][2500/5005]\tlr: 1.000e-04, eta: 0:33:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6831\n2023-01-03 02:16:04,542 - mmcls - INFO - Epoch [96][2600/5005]\tlr: 1.000e-04, eta: 0:32:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6774\n2023-01-03 02:16:13,409 - mmcls - INFO - Epoch [96][2700/5005]\tlr: 1.000e-04, eta: 0:32:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6882\n2023-01-03 02:16:21,912 - mmcls - INFO - Epoch [96][2800/5005]\tlr: 1.000e-04, eta: 0:32:35, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.6935\n2023-01-03 02:16:30,648 - mmcls - INFO - Epoch [96][2900/5005]\tlr: 1.000e-04, eta: 0:32:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6883\n2023-01-03 02:16:39,661 - mmcls - INFO - Epoch [96][3000/5005]\tlr: 1.000e-04, eta: 0:32:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7065\n2023-01-03 02:16:48,348 - mmcls - INFO - Epoch [96][3100/5005]\tlr: 1.000e-04, eta: 0:32:08, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.6736\n2023-01-03 02:16:57,337 - mmcls - INFO - Epoch [96][3200/5005]\tlr: 1.000e-04, eta: 0:32:00, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.6852\n2023-01-03 02:17:06,211 - mmcls - INFO - Epoch [96][3300/5005]\tlr: 1.000e-04, eta: 0:31:51, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6713\n2023-01-03 02:17:14,956 - mmcls - INFO - Epoch [96][3400/5005]\tlr: 1.000e-04, eta: 0:31:42, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.6964\n2023-01-03 02:17:23,807 - mmcls - INFO - Epoch [96][3500/5005]\tlr: 1.000e-04, eta: 0:31:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6828\n2023-01-03 02:17:32,757 - mmcls - INFO - Epoch [96][3600/5005]\tlr: 1.000e-04, eta: 0:31:24, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.6919\n2023-01-03 02:17:41,408 - mmcls - INFO - Epoch [96][3700/5005]\tlr: 1.000e-04, eta: 0:31:16, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.6966\n2023-01-03 02:17:50,160 - mmcls - INFO - Epoch [96][3800/5005]\tlr: 1.000e-04, eta: 0:31:07, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.6792\n2023-01-03 02:17:58,783 - mmcls - INFO - Epoch [96][3900/5005]\tlr: 1.000e-04, eta: 0:30:58, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7083\n2023-01-03 02:18:07,738 - mmcls - INFO - Epoch [96][4000/5005]\tlr: 1.000e-04, eta: 0:30:49, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6779\n2023-01-03 02:18:16,407 - mmcls - INFO - Epoch [96][4100/5005]\tlr: 1.000e-04, eta: 0:30:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6905\n2023-01-03 02:18:24,975 - mmcls - INFO - Epoch [96][4200/5005]\tlr: 1.000e-04, eta: 0:30:32, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6992\n2023-01-03 02:18:33,656 - mmcls - INFO - Epoch [96][4300/5005]\tlr: 1.000e-04, eta: 0:30:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6972\n2023-01-03 02:18:42,457 - mmcls - INFO - Epoch [96][4400/5005]\tlr: 1.000e-04, eta: 0:30:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6706\n2023-01-03 02:18:51,564 - mmcls - INFO - Epoch [96][4500/5005]\tlr: 1.000e-04, eta: 0:30:05, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6953\n2023-01-03 02:19:00,332 - mmcls - INFO - Epoch [96][4600/5005]\tlr: 1.000e-04, eta: 0:29:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.6915\n2023-01-03 02:19:08,983 - mmcls - INFO - Epoch [96][4700/5005]\tlr: 1.000e-04, eta: 0:29:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6759\n2023-01-03 02:19:17,580 - mmcls - INFO - Epoch [96][4800/5005]\tlr: 1.000e-04, eta: 0:29:39, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.6843\n2023-01-03 02:19:26,214 - mmcls - INFO - Epoch [96][4900/5005]\tlr: 1.000e-04, eta: 0:29:30, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6742\n2023-01-03 02:19:35,040 - mmcls - INFO - Epoch [96][5000/5005]\tlr: 1.000e-04, eta: 0:29:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6705\n2023-01-03 02:19:57,749 - mmcls - INFO - Epoch(val) [96][196]\taccuracy_top-1: 63.7640, accuracy_top-5: 84.2920\n2023-01-03 02:20:08,886 - mmcls - INFO - Epoch [97][100/5005]\tlr: 1.000e-04, eta: 0:29:12, time: 0.111, data_time: 0.054, memory: 1125, loss: 1.7024\n2023-01-03 02:20:17,458 - mmcls - INFO - Epoch [97][200/5005]\tlr: 1.000e-04, eta: 0:29:03, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6677\n2023-01-03 02:20:25,906 - mmcls - INFO - Epoch [97][300/5005]\tlr: 1.000e-04, eta: 0:28:54, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.6843\n2023-01-03 02:20:34,600 - mmcls - INFO - Epoch [97][400/5005]\tlr: 1.000e-04, eta: 0:28:46, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7039\n2023-01-03 02:20:43,560 - mmcls - INFO - Epoch [97][500/5005]\tlr: 1.000e-04, eta: 0:28:37, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.6835\n2023-01-03 02:20:52,211 - mmcls - INFO - Epoch [97][600/5005]\tlr: 1.000e-04, eta: 0:28:28, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6898\n2023-01-03 02:21:00,821 - mmcls - INFO - Epoch [97][700/5005]\tlr: 1.000e-04, eta: 0:28:19, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.6835\n2023-01-03 02:21:09,476 - mmcls - INFO - Epoch [97][800/5005]\tlr: 1.000e-04, eta: 0:28:10, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7074\n2023-01-03 02:21:18,033 - mmcls - INFO - Epoch [97][900/5005]\tlr: 1.000e-04, eta: 0:28:02, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6881\n2023-01-03 02:21:26,676 - mmcls - INFO - Epoch [97][1000/5005]\tlr: 1.000e-04, eta: 0:27:53, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.6820\n2023-01-03 02:21:35,502 - mmcls - INFO - Epoch [97][1100/5005]\tlr: 1.000e-04, eta: 0:27:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6591\n2023-01-03 02:21:44,262 - mmcls - INFO - Epoch [97][1200/5005]\tlr: 1.000e-04, eta: 0:27:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6876\n2023-01-03 02:21:52,933 - mmcls - INFO - Epoch [97][1300/5005]\tlr: 1.000e-04, eta: 0:27:26, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7092\n2023-01-03 02:22:01,738 - mmcls - INFO - Epoch [97][1400/5005]\tlr: 1.000e-04, eta: 0:27:18, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6696\n2023-01-03 02:22:10,488 - mmcls - INFO - Epoch [97][1500/5005]\tlr: 1.000e-04, eta: 0:27:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6880\n2023-01-03 02:22:19,049 - mmcls - INFO - Epoch [97][1600/5005]\tlr: 1.000e-04, eta: 0:27:00, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6954\n2023-01-03 02:22:27,815 - mmcls - INFO - Epoch [97][1700/5005]\tlr: 1.000e-04, eta: 0:26:51, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.6897\n2023-01-03 02:22:36,689 - mmcls - INFO - Epoch [97][1800/5005]\tlr: 1.000e-04, eta: 0:26:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6931\n2023-01-03 02:22:45,384 - mmcls - INFO - Epoch [97][1900/5005]\tlr: 1.000e-04, eta: 0:26:34, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6825\n2023-01-03 02:22:54,018 - mmcls - INFO - Epoch [97][2000/5005]\tlr: 1.000e-04, eta: 0:26:25, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6952\n2023-01-03 02:23:02,973 - mmcls - INFO - Epoch [97][2100/5005]\tlr: 1.000e-04, eta: 0:26:16, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6950\n2023-01-03 02:23:11,493 - mmcls - INFO - Epoch [97][2200/5005]\tlr: 1.000e-04, eta: 0:26:07, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7080\n2023-01-03 02:23:20,299 - mmcls - INFO - Epoch [97][2300/5005]\tlr: 1.000e-04, eta: 0:25:58, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6822\n2023-01-03 02:23:29,121 - mmcls - INFO - Epoch [97][2400/5005]\tlr: 1.000e-04, eta: 0:25:50, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6784\n2023-01-03 02:23:37,752 - mmcls - INFO - Epoch [97][2500/5005]\tlr: 1.000e-04, eta: 0:25:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6916\n2023-01-03 02:23:46,789 - mmcls - INFO - Epoch [97][2600/5005]\tlr: 1.000e-04, eta: 0:25:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6750\n2023-01-03 02:23:55,618 - mmcls - INFO - Epoch [97][2700/5005]\tlr: 1.000e-04, eta: 0:25:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6911\n2023-01-03 02:24:04,693 - mmcls - INFO - Epoch [97][2800/5005]\tlr: 1.000e-04, eta: 0:25:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6998\n2023-01-03 02:24:13,551 - mmcls - INFO - Epoch [97][2900/5005]\tlr: 1.000e-04, eta: 0:25:06, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.6802\n2023-01-03 02:24:22,225 - mmcls - INFO - Epoch [97][3000/5005]\tlr: 1.000e-04, eta: 0:24:57, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6661\n2023-01-03 02:24:31,141 - mmcls - INFO - Epoch [97][3100/5005]\tlr: 1.000e-04, eta: 0:24:48, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.6800\n2023-01-03 02:24:39,749 - mmcls - INFO - Epoch [97][3200/5005]\tlr: 1.000e-04, eta: 0:24:39, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7104\n2023-01-03 02:24:48,655 - mmcls - INFO - Epoch [97][3300/5005]\tlr: 1.000e-04, eta: 0:24:30, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.6990\n2023-01-03 02:24:57,343 - mmcls - INFO - Epoch [97][3400/5005]\tlr: 1.000e-04, eta: 0:24:22, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6784\n2023-01-03 02:25:06,047 - mmcls - INFO - Epoch [97][3500/5005]\tlr: 1.000e-04, eta: 0:24:13, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7029\n2023-01-03 02:25:14,771 - mmcls - INFO - Epoch [97][3600/5005]\tlr: 1.000e-04, eta: 0:24:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7032\n2023-01-03 02:25:23,380 - mmcls - INFO - Epoch [97][3700/5005]\tlr: 1.000e-04, eta: 0:23:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7054\n2023-01-03 02:25:32,443 - mmcls - INFO - Epoch [97][3800/5005]\tlr: 1.000e-04, eta: 0:23:46, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6876\n2023-01-03 02:25:41,284 - mmcls - INFO - Epoch [97][3900/5005]\tlr: 1.000e-04, eta: 0:23:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6774\n2023-01-03 02:25:49,868 - mmcls - INFO - Epoch [97][4000/5005]\tlr: 1.000e-04, eta: 0:23:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6944\n2023-01-03 02:25:59,056 - mmcls - INFO - Epoch [97][4100/5005]\tlr: 1.000e-04, eta: 0:23:20, time: 0.092, data_time: 0.000, memory: 1125, loss: 1.6693\n2023-01-03 02:26:08,038 - mmcls - INFO - Epoch [97][4200/5005]\tlr: 1.000e-04, eta: 0:23:11, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6891\n2023-01-03 02:26:16,755 - mmcls - INFO - Epoch [97][4300/5005]\tlr: 1.000e-04, eta: 0:23:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6712\n2023-01-03 02:26:25,174 - mmcls - INFO - Epoch [97][4400/5005]\tlr: 1.000e-04, eta: 0:22:54, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7026\n2023-01-03 02:26:33,761 - mmcls - INFO - Epoch [97][4500/5005]\tlr: 1.000e-04, eta: 0:22:45, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6885\n2023-01-03 02:26:42,268 - mmcls - INFO - Epoch [97][4600/5005]\tlr: 1.000e-04, eta: 0:22:36, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7094\n2023-01-03 02:26:51,370 - mmcls - INFO - Epoch [97][4700/5005]\tlr: 1.000e-04, eta: 0:22:27, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6959\n2023-01-03 02:27:00,445 - mmcls - INFO - Epoch [97][4800/5005]\tlr: 1.000e-04, eta: 0:22:18, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6801\n2023-01-03 02:27:09,000 - mmcls - INFO - Epoch [97][4900/5005]\tlr: 1.000e-04, eta: 0:22:10, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6978\n2023-01-03 02:27:18,163 - mmcls - INFO - Epoch [97][5000/5005]\tlr: 1.000e-04, eta: 0:22:01, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.7025\n2023-01-03 02:27:40,818 - mmcls - INFO - Epoch(val) [97][196]\taccuracy_top-1: 63.7580, accuracy_top-5: 84.4780\n2023-01-03 02:27:51,918 - mmcls - INFO - Epoch [98][100/5005]\tlr: 1.000e-04, eta: 0:21:52, time: 0.111, data_time: 0.033, memory: 1125, loss: 1.7087\n2023-01-03 02:28:00,936 - mmcls - INFO - Epoch [98][200/5005]\tlr: 1.000e-04, eta: 0:21:43, time: 0.090, data_time: 0.043, memory: 1125, loss: 1.7063\n2023-01-03 02:28:09,953 - mmcls - INFO - Epoch [98][300/5005]\tlr: 1.000e-04, eta: 0:21:34, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.6886\n2023-01-03 02:28:18,535 - mmcls - INFO - Epoch [98][400/5005]\tlr: 1.000e-04, eta: 0:21:25, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6890\n2023-01-03 02:28:26,997 - mmcls - INFO - Epoch [98][500/5005]\tlr: 1.000e-04, eta: 0:21:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6864\n2023-01-03 02:28:35,578 - mmcls - INFO - Epoch [98][600/5005]\tlr: 1.000e-04, eta: 0:21:08, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.6803\n2023-01-03 02:28:44,000 - mmcls - INFO - Epoch [98][700/5005]\tlr: 1.000e-04, eta: 0:20:59, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.6980\n2023-01-03 02:28:53,280 - mmcls - INFO - Epoch [98][800/5005]\tlr: 1.000e-04, eta: 0:20:50, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.6765\n2023-01-03 02:29:02,130 - mmcls - INFO - Epoch [98][900/5005]\tlr: 1.000e-04, eta: 0:20:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7047\n2023-01-03 02:29:10,914 - mmcls - INFO - Epoch [98][1000/5005]\tlr: 1.000e-04, eta: 0:20:33, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6788\n2023-01-03 02:29:19,420 - mmcls - INFO - Epoch [98][1100/5005]\tlr: 1.000e-04, eta: 0:20:24, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.7008\n2023-01-03 02:29:28,179 - mmcls - INFO - Epoch [98][1200/5005]\tlr: 1.000e-04, eta: 0:20:15, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.6994\n2023-01-03 02:29:37,077 - mmcls - INFO - Epoch [98][1300/5005]\tlr: 1.000e-04, eta: 0:20:06, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.6931\n2023-01-03 02:29:45,871 - mmcls - INFO - Epoch [98][1400/5005]\tlr: 1.000e-04, eta: 0:19:57, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6671\n2023-01-03 02:29:54,303 - mmcls - INFO - Epoch [98][1500/5005]\tlr: 1.000e-04, eta: 0:19:49, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.6609\n2023-01-03 02:30:03,225 - mmcls - INFO - Epoch [98][1600/5005]\tlr: 1.000e-04, eta: 0:19:40, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6857\n2023-01-03 02:30:11,786 - mmcls - INFO - Epoch [98][1700/5005]\tlr: 1.000e-04, eta: 0:19:31, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6782\n2023-01-03 02:30:20,555 - mmcls - INFO - Epoch [98][1800/5005]\tlr: 1.000e-04, eta: 0:19:22, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.6720\n2023-01-03 02:30:29,266 - mmcls - INFO - Epoch [98][1900/5005]\tlr: 1.000e-04, eta: 0:19:13, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6713\n2023-01-03 02:30:38,585 - mmcls - INFO - Epoch [98][2000/5005]\tlr: 1.000e-04, eta: 0:19:05, time: 0.093, data_time: 0.002, memory: 1125, loss: 1.6904\n2023-01-03 02:30:47,224 - mmcls - INFO - Epoch [98][2100/5005]\tlr: 1.000e-04, eta: 0:18:56, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6884\n2023-01-03 02:30:55,844 - mmcls - INFO - Epoch [98][2200/5005]\tlr: 1.000e-04, eta: 0:18:47, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6685\n2023-01-03 02:31:04,700 - mmcls - INFO - Epoch [98][2300/5005]\tlr: 1.000e-04, eta: 0:18:38, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.6833\n2023-01-03 02:31:13,453 - mmcls - INFO - Epoch [98][2400/5005]\tlr: 1.000e-04, eta: 0:18:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6988\n2023-01-03 02:31:22,498 - mmcls - INFO - Epoch [98][2500/5005]\tlr: 1.000e-04, eta: 0:18:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6977\n2023-01-03 02:31:30,730 - mmcls - INFO - Epoch [98][2600/5005]\tlr: 1.000e-04, eta: 0:18:12, time: 0.082, data_time: 0.004, memory: 1125, loss: 1.6824\n2023-01-03 02:31:39,569 - mmcls - INFO - Epoch [98][2700/5005]\tlr: 1.000e-04, eta: 0:18:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6880\n2023-01-03 02:31:48,206 - mmcls - INFO - Epoch [98][2800/5005]\tlr: 1.000e-04, eta: 0:17:54, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6770\n2023-01-03 02:31:56,567 - mmcls - INFO - Epoch [98][2900/5005]\tlr: 1.000e-04, eta: 0:17:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.6612\n2023-01-03 02:32:05,170 - mmcls - INFO - Epoch [98][3000/5005]\tlr: 1.000e-04, eta: 0:17:37, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6671\n2023-01-03 02:32:13,689 - mmcls - INFO - Epoch [98][3100/5005]\tlr: 1.000e-04, eta: 0:17:28, time: 0.085, data_time: 0.030, memory: 1125, loss: 1.6798\n2023-01-03 02:32:22,451 - mmcls - INFO - Epoch [98][3200/5005]\tlr: 1.000e-04, eta: 0:17:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6865\n2023-01-03 02:32:31,328 - mmcls - INFO - Epoch [98][3300/5005]\tlr: 1.000e-04, eta: 0:17:10, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7042\n2023-01-03 02:32:40,036 - mmcls - INFO - Epoch [98][3400/5005]\tlr: 1.000e-04, eta: 0:17:01, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6810\n2023-01-03 02:32:49,235 - mmcls - INFO - Epoch [98][3500/5005]\tlr: 1.000e-04, eta: 0:16:53, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.6574\n2023-01-03 02:32:58,064 - mmcls - INFO - Epoch [98][3600/5005]\tlr: 1.000e-04, eta: 0:16:44, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.6864\n2023-01-03 02:33:06,895 - mmcls - INFO - Epoch [98][3700/5005]\tlr: 1.000e-04, eta: 0:16:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6982\n2023-01-03 02:33:15,400 - mmcls - INFO - Epoch [98][3800/5005]\tlr: 1.000e-04, eta: 0:16:26, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.6712\n2023-01-03 02:33:24,037 - mmcls - INFO - Epoch [98][3900/5005]\tlr: 1.000e-04, eta: 0:16:17, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6808\n2023-01-03 02:33:32,740 - mmcls - INFO - Epoch [98][4000/5005]\tlr: 1.000e-04, eta: 0:16:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6900\n2023-01-03 02:33:41,322 - mmcls - INFO - Epoch [98][4100/5005]\tlr: 1.000e-04, eta: 0:16:00, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6962\n2023-01-03 02:33:50,115 - mmcls - INFO - Epoch [98][4200/5005]\tlr: 1.000e-04, eta: 0:15:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6924\n2023-01-03 02:33:58,891 - mmcls - INFO - Epoch [98][4300/5005]\tlr: 1.000e-04, eta: 0:15:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6742\n2023-01-03 02:34:08,250 - mmcls - INFO - Epoch [98][4400/5005]\tlr: 1.000e-04, eta: 0:15:33, time: 0.093, data_time: 0.007, memory: 1125, loss: 1.6754\n2023-01-03 02:34:17,235 - mmcls - INFO - Epoch [98][4500/5005]\tlr: 1.000e-04, eta: 0:15:25, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6969\n2023-01-03 02:34:25,699 - mmcls - INFO - Epoch [98][4600/5005]\tlr: 1.000e-04, eta: 0:15:16, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.6973\n2023-01-03 02:34:34,338 - mmcls - INFO - Epoch [98][4700/5005]\tlr: 1.000e-04, eta: 0:15:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6973\n2023-01-03 02:34:43,049 - mmcls - INFO - Epoch [98][4800/5005]\tlr: 1.000e-04, eta: 0:14:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6795\n2023-01-03 02:34:51,600 - mmcls - INFO - Epoch [98][4900/5005]\tlr: 1.000e-04, eta: 0:14:49, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7002\n2023-01-03 02:35:00,516 - mmcls - INFO - Epoch [98][5000/5005]\tlr: 1.000e-04, eta: 0:14:41, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.6871\n2023-01-03 02:35:23,545 - mmcls - INFO - Epoch(val) [98][196]\taccuracy_top-1: 63.6820, accuracy_top-5: 84.4020\n2023-01-03 02:35:34,700 - mmcls - INFO - Epoch [99][100/5005]\tlr: 1.000e-04, eta: 0:14:31, time: 0.111, data_time: 0.031, memory: 1125, loss: 1.6862\n2023-01-03 02:35:43,251 - mmcls - INFO - Epoch [99][200/5005]\tlr: 1.000e-04, eta: 0:14:23, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.6923\n2023-01-03 02:35:51,807 - mmcls - INFO - Epoch [99][300/5005]\tlr: 1.000e-04, eta: 0:14:14, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6815\n2023-01-03 02:36:00,958 - mmcls - INFO - Epoch [99][400/5005]\tlr: 1.000e-04, eta: 0:14:05, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.6847\n2023-01-03 02:36:09,540 - mmcls - INFO - Epoch [99][500/5005]\tlr: 1.000e-04, eta: 0:13:56, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6842\n2023-01-03 02:36:18,697 - mmcls - INFO - Epoch [99][600/5005]\tlr: 1.000e-04, eta: 0:13:47, time: 0.092, data_time: 0.023, memory: 1125, loss: 1.6816\n2023-01-03 02:36:27,612 - mmcls - INFO - Epoch [99][700/5005]\tlr: 1.000e-04, eta: 0:13:39, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.6849\n2023-01-03 02:36:36,238 - mmcls - INFO - Epoch [99][800/5005]\tlr: 1.000e-04, eta: 0:13:30, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.6949\n2023-01-03 02:36:44,824 - mmcls - INFO - Epoch [99][900/5005]\tlr: 1.000e-04, eta: 0:13:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6774\n2023-01-03 02:36:53,507 - mmcls - INFO - Epoch [99][1000/5005]\tlr: 1.000e-04, eta: 0:13:12, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6957\n2023-01-03 02:37:02,172 - mmcls - INFO - Epoch [99][1100/5005]\tlr: 1.000e-04, eta: 0:13:03, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6712\n2023-01-03 02:37:11,055 - mmcls - INFO - Epoch [99][1200/5005]\tlr: 1.000e-04, eta: 0:12:55, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6931\n2023-01-03 02:37:19,931 - mmcls - INFO - Epoch [99][1300/5005]\tlr: 1.000e-04, eta: 0:12:46, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7034\n2023-01-03 02:37:28,695 - mmcls - INFO - Epoch [99][1400/5005]\tlr: 1.000e-04, eta: 0:12:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6829\n2023-01-03 02:37:37,402 - mmcls - INFO - Epoch [99][1500/5005]\tlr: 1.000e-04, eta: 0:12:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6837\n2023-01-03 02:37:45,835 - mmcls - INFO - Epoch [99][1600/5005]\tlr: 1.000e-04, eta: 0:12:19, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6825\n2023-01-03 02:37:54,488 - mmcls - INFO - Epoch [99][1700/5005]\tlr: 1.000e-04, eta: 0:12:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6799\n2023-01-03 02:38:03,407 - mmcls - INFO - Epoch [99][1800/5005]\tlr: 1.000e-04, eta: 0:12:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6863\n2023-01-03 02:38:12,123 - mmcls - INFO - Epoch [99][1900/5005]\tlr: 1.000e-04, eta: 0:11:53, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6782\n2023-01-03 02:38:21,052 - mmcls - INFO - Epoch [99][2000/5005]\tlr: 1.000e-04, eta: 0:11:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6640\n2023-01-03 02:38:29,696 - mmcls - INFO - Epoch [99][2100/5005]\tlr: 1.000e-04, eta: 0:11:35, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6685\n2023-01-03 02:38:38,506 - mmcls - INFO - Epoch [99][2200/5005]\tlr: 1.000e-04, eta: 0:11:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6660\n2023-01-03 02:38:47,428 - mmcls - INFO - Epoch [99][2300/5005]\tlr: 1.000e-04, eta: 0:11:18, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.6831\n2023-01-03 02:38:56,224 - mmcls - INFO - Epoch [99][2400/5005]\tlr: 1.000e-04, eta: 0:11:09, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.6704\n2023-01-03 02:39:04,875 - mmcls - INFO - Epoch [99][2500/5005]\tlr: 1.000e-04, eta: 0:11:00, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6981\n2023-01-03 02:39:13,704 - mmcls - INFO - Epoch [99][2600/5005]\tlr: 1.000e-04, eta: 0:10:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6937\n2023-01-03 02:39:22,556 - mmcls - INFO - Epoch [99][2700/5005]\tlr: 1.000e-04, eta: 0:10:43, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7004\n2023-01-03 02:39:31,207 - mmcls - INFO - Epoch [99][2800/5005]\tlr: 1.000e-04, eta: 0:10:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6808\n2023-01-03 02:39:39,829 - mmcls - INFO - Epoch [99][2900/5005]\tlr: 1.000e-04, eta: 0:10:25, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6643\n2023-01-03 02:39:48,362 - mmcls - INFO - Epoch [99][3000/5005]\tlr: 1.000e-04, eta: 0:10:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6922\n2023-01-03 02:39:57,310 - mmcls - INFO - Epoch [99][3100/5005]\tlr: 1.000e-04, eta: 0:10:07, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6887\n2023-01-03 02:40:06,137 - mmcls - INFO - Epoch [99][3200/5005]\tlr: 1.000e-04, eta: 0:09:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6752\n2023-01-03 02:40:14,842 - mmcls - INFO - Epoch [99][3300/5005]\tlr: 1.000e-04, eta: 0:09:50, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6768\n2023-01-03 02:40:23,373 - mmcls - INFO - Epoch [99][3400/5005]\tlr: 1.000e-04, eta: 0:09:41, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6731\n2023-01-03 02:40:31,773 - mmcls - INFO - Epoch [99][3500/5005]\tlr: 1.000e-04, eta: 0:09:32, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7051\n2023-01-03 02:40:40,542 - mmcls - INFO - Epoch [99][3600/5005]\tlr: 1.000e-04, eta: 0:09:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6964\n2023-01-03 02:40:49,505 - mmcls - INFO - Epoch [99][3700/5005]\tlr: 1.000e-04, eta: 0:09:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6656\n2023-01-03 02:40:58,261 - mmcls - INFO - Epoch [99][3800/5005]\tlr: 1.000e-04, eta: 0:09:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6825\n2023-01-03 02:41:07,090 - mmcls - INFO - Epoch [99][3900/5005]\tlr: 1.000e-04, eta: 0:08:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6988\n2023-01-03 02:41:15,693 - mmcls - INFO - Epoch [99][4000/5005]\tlr: 1.000e-04, eta: 0:08:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6853\n2023-01-03 02:41:24,499 - mmcls - INFO - Epoch [99][4100/5005]\tlr: 1.000e-04, eta: 0:08:39, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6851\n2023-01-03 02:41:33,190 - mmcls - INFO - Epoch [99][4200/5005]\tlr: 1.000e-04, eta: 0:08:31, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6889\n2023-01-03 02:41:42,078 - mmcls - INFO - Epoch [99][4300/5005]\tlr: 1.000e-04, eta: 0:08:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6996\n2023-01-03 02:41:51,006 - mmcls - INFO - Epoch [99][4400/5005]\tlr: 1.000e-04, eta: 0:08:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6755\n2023-01-03 02:41:59,652 - mmcls - INFO - Epoch [99][4500/5005]\tlr: 1.000e-04, eta: 0:08:04, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6955\n2023-01-03 02:42:08,636 - mmcls - INFO - Epoch [99][4600/5005]\tlr: 1.000e-04, eta: 0:07:55, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7071\n2023-01-03 02:42:17,438 - mmcls - INFO - Epoch [99][4700/5005]\tlr: 1.000e-04, eta: 0:07:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7034\n2023-01-03 02:42:26,313 - mmcls - INFO - Epoch [99][4800/5005]\tlr: 1.000e-04, eta: 0:07:38, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7006\n2023-01-03 02:42:35,154 - mmcls - INFO - Epoch [99][4900/5005]\tlr: 1.000e-04, eta: 0:07:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6813\n2023-01-03 02:42:43,954 - mmcls - INFO - Epoch [99][5000/5005]\tlr: 1.000e-04, eta: 0:07:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6696\n2023-01-03 02:43:06,635 - mmcls - INFO - Epoch(val) [99][196]\taccuracy_top-1: 63.8040, accuracy_top-5: 84.4540\n2023-01-03 02:43:17,566 - mmcls - INFO - Epoch [100][100/5005]\tlr: 1.000e-04, eta: 0:07:11, time: 0.109, data_time: 0.053, memory: 1125, loss: 1.6779\n2023-01-03 02:43:26,398 - mmcls - INFO - Epoch [100][200/5005]\tlr: 1.000e-04, eta: 0:07:02, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.6805\n2023-01-03 02:43:35,387 - mmcls - INFO - Epoch [100][300/5005]\tlr: 1.000e-04, eta: 0:06:53, time: 0.090, data_time: 0.000, memory: 1125, loss: 1.6904\n2023-01-03 02:43:44,008 - mmcls - INFO - Epoch [100][400/5005]\tlr: 1.000e-04, eta: 0:06:45, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.6909\n2023-01-03 02:43:52,788 - mmcls - INFO - Epoch [100][500/5005]\tlr: 1.000e-04, eta: 0:06:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6876\n2023-01-03 02:44:01,686 - mmcls - INFO - Epoch [100][600/5005]\tlr: 1.000e-04, eta: 0:06:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6883\n2023-01-03 02:44:10,408 - mmcls - INFO - Epoch [100][700/5005]\tlr: 1.000e-04, eta: 0:06:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6804\n2023-01-03 02:44:19,340 - mmcls - INFO - Epoch [100][800/5005]\tlr: 1.000e-04, eta: 0:06:09, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.6706\n2023-01-03 02:44:27,847 - mmcls - INFO - Epoch [100][900/5005]\tlr: 1.000e-04, eta: 0:06:01, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.6950\n2023-01-03 02:44:36,895 - mmcls - INFO - Epoch [100][1000/5005]\tlr: 1.000e-04, eta: 0:05:52, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7045\n2023-01-03 02:44:45,807 - mmcls - INFO - Epoch [100][1100/5005]\tlr: 1.000e-04, eta: 0:05:43, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6766\n2023-01-03 02:44:54,551 - mmcls - INFO - Epoch [100][1200/5005]\tlr: 1.000e-04, eta: 0:05:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6840\n2023-01-03 02:45:03,535 - mmcls - INFO - Epoch [100][1300/5005]\tlr: 1.000e-04, eta: 0:05:25, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6925\n2023-01-03 02:45:12,512 - mmcls - INFO - Epoch [100][1400/5005]\tlr: 1.000e-04, eta: 0:05:17, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6746\n2023-01-03 02:45:20,981 - mmcls - INFO - Epoch [100][1500/5005]\tlr: 1.000e-04, eta: 0:05:08, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.6781\n2023-01-03 02:45:29,964 - mmcls - INFO - Epoch [100][1600/5005]\tlr: 1.000e-04, eta: 0:04:59, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6980\n2023-01-03 02:45:38,479 - mmcls - INFO - Epoch [100][1700/5005]\tlr: 1.000e-04, eta: 0:04:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6976\n2023-01-03 02:45:47,177 - mmcls - INFO - Epoch [100][1800/5005]\tlr: 1.000e-04, eta: 0:04:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6805\n2023-01-03 02:45:55,865 - mmcls - INFO - Epoch [100][1900/5005]\tlr: 1.000e-04, eta: 0:04:33, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6945\n2023-01-03 02:46:04,441 - mmcls - INFO - Epoch [100][2000/5005]\tlr: 1.000e-04, eta: 0:04:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7072\n2023-01-03 02:46:13,167 - mmcls - INFO - Epoch [100][2100/5005]\tlr: 1.000e-04, eta: 0:04:15, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6832\n2023-01-03 02:46:22,054 - mmcls - INFO - Epoch [100][2200/5005]\tlr: 1.000e-04, eta: 0:04:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6751\n2023-01-03 02:46:30,846 - mmcls - INFO - Epoch [100][2300/5005]\tlr: 1.000e-04, eta: 0:03:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6701\n2023-01-03 02:46:39,433 - mmcls - INFO - Epoch [100][2400/5005]\tlr: 1.000e-04, eta: 0:03:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6927\n2023-01-03 02:46:48,165 - mmcls - INFO - Epoch [100][2500/5005]\tlr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6653\n2023-01-03 02:46:56,957 - mmcls - INFO - Epoch [100][2600/5005]\tlr: 1.000e-04, eta: 0:03:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6978\n2023-01-03 02:47:05,896 - mmcls - INFO - Epoch [100][2700/5005]\tlr: 1.000e-04, eta: 0:03:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6745\n2023-01-03 02:47:14,732 - mmcls - INFO - Epoch [100][2800/5005]\tlr: 1.000e-04, eta: 0:03:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6697\n2023-01-03 02:47:23,692 - mmcls - INFO - Epoch [100][2900/5005]\tlr: 1.000e-04, eta: 0:03:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6831\n2023-01-03 02:47:32,522 - mmcls - INFO - Epoch [100][3000/5005]\tlr: 1.000e-04, eta: 0:02:56, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6955\n2023-01-03 02:47:41,337 - mmcls - INFO - Epoch [100][3100/5005]\tlr: 1.000e-04, eta: 0:02:47, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6996\n2023-01-03 02:47:49,830 - mmcls - INFO - Epoch [100][3200/5005]\tlr: 1.000e-04, eta: 0:02:38, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7043\n2023-01-03 02:47:58,788 - mmcls - INFO - Epoch [100][3300/5005]\tlr: 1.000e-04, eta: 0:02:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6863\n2023-01-03 02:48:07,702 - mmcls - INFO - Epoch [100][3400/5005]\tlr: 1.000e-04, eta: 0:02:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7145\n2023-01-03 02:48:16,360 - mmcls - INFO - Epoch [100][3500/5005]\tlr: 1.000e-04, eta: 0:02:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6950\n2023-01-03 02:48:25,099 - mmcls - INFO - Epoch [100][3600/5005]\tlr: 1.000e-04, eta: 0:02:03, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6933\n2023-01-03 02:48:33,888 - mmcls - INFO - Epoch [100][3700/5005]\tlr: 1.000e-04, eta: 0:01:54, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.6865\n2023-01-03 02:48:42,699 - mmcls - INFO - Epoch [100][3800/5005]\tlr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6914\n2023-01-03 02:48:51,346 - mmcls - INFO - Epoch [100][3900/5005]\tlr: 1.000e-04, eta: 0:01:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6628\n2023-01-03 02:49:00,044 - mmcls - INFO - Epoch [100][4000/5005]\tlr: 1.000e-04, eta: 0:01:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6716\n2023-01-03 02:49:08,637 - mmcls - INFO - Epoch [100][4100/5005]\tlr: 1.000e-04, eta: 0:01:19, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6865\n2023-01-03 02:49:17,407 - mmcls - INFO - Epoch [100][4200/5005]\tlr: 1.000e-04, eta: 0:01:10, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6872\n2023-01-03 02:49:26,404 - mmcls - INFO - Epoch [100][4300/5005]\tlr: 1.000e-04, eta: 0:01:02, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6791\n2023-01-03 02:49:35,142 - mmcls - INFO - Epoch [100][4400/5005]\tlr: 1.000e-04, eta: 0:00:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6953\n2023-01-03 02:49:43,687 - mmcls - INFO - Epoch [100][4500/5005]\tlr: 1.000e-04, eta: 0:00:44, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6830\n2023-01-03 02:49:52,276 - mmcls - INFO - Epoch [100][4600/5005]\tlr: 1.000e-04, eta: 0:00:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7026\n2023-01-03 02:50:00,925 - mmcls - INFO - Epoch [100][4700/5005]\tlr: 1.000e-04, eta: 0:00:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6973\n2023-01-03 02:50:09,481 - mmcls - INFO - Epoch [100][4800/5005]\tlr: 1.000e-04, eta: 0:00:18, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6781\n2023-01-03 02:50:18,557 - mmcls - INFO - Epoch [100][4900/5005]\tlr: 1.000e-04, eta: 0:00:09, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.6820\n2023-01-03 02:50:27,240 - mmcls - INFO - Epoch [100][5000/5005]\tlr: 1.000e-04, eta: 0:00:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6544\n2023-01-03 02:50:27,568 - mmcls - INFO - Saving checkpoint at 100 epochs\n2023-01-03 02:50:50,069 - mmcls - INFO - Epoch(val) [100][196]\taccuracy_top-1: 63.6660, accuracy_top-5: 84.3400\n"
  },
  {
    "path": "classification-NO-activation-function/logs/pinet_relu.log",
    "content": "2023-01-03 15:18:24,147 - mmcls - INFO - Environment info:\n------------------------------------------------------------\nsys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 10.2, V10.2.8\nGCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 10.2\nMMClassification: 0.24.0+91b85bb\n------------------------------------------------------------\n\n2023-01-03 15:18:24,147 - mmcls - INFO - Distributed training: True\n2023-01-03 15:18:24,311 - mmcls - INFO - Config:\nmodel = dict(\n    type='ImageClassifier',\n    backbone=dict(\n        type='ResNet',\n        depth=18,\n        num_stages=4,\n        out_indices=(3, ),\n        style='pytorch'),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=1000,\n        in_channels=512,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1)),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=32,\n    workers_per_gpu=2,\n    train=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[30, 60, 90])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=50)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/resnet18_8xb32_in1k_pi'\ngpu_ids = range(0, 8)\n\n2023-01-03 15:18:29,763 - mmcls - INFO - Set random seed to 1668662542, deterministic: False\n2023-01-03 15:18:29,974 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n2023-01-03 15:18:30,059 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\nName of parameter - Initialization information\n\nbackbone.conv1.weight - torch.Size([64, 3, 7, 7]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.0.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.1.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn3.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.0.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.downsample.1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.1.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn3.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.0.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.downsample.1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.1.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn3.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.0.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.downsample.1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.1.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn3.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nhead.fc.weight - torch.Size([1000, 512]): \nNormalInit: mean=0, std=0.01, bias=0 \n\nhead.fc.bias - torch.Size([1000]): \nNormalInit: mean=0, std=0.01, bias=0 \n2023-01-03 15:18:58,454 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi\n2023-01-03 15:18:58,454 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) DistOptimizerHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2023-01-03 15:18:58,455 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs\n2023-01-03 15:18:58,455 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend.\n2023-01-03 15:19:10,547 - mmcls - INFO - Epoch [1][100/5005]\tlr: 1.000e-01, eta: 16:46:24, time: 0.121, data_time: 0.038, memory: 1125, loss: 6.8861\n2023-01-03 15:19:19,638 - mmcls - INFO - Epoch [1][200/5005]\tlr: 1.000e-01, eta: 14:42:08, time: 0.091, data_time: 0.017, memory: 1125, loss: 6.7358\n2023-01-03 15:19:28,497 - mmcls - INFO - Epoch [1][300/5005]\tlr: 1.000e-01, eta: 13:54:09, time: 0.089, data_time: 0.034, memory: 1125, loss: 6.5839\n2023-01-03 15:19:37,552 - mmcls - INFO - Epoch [1][400/5005]\tlr: 1.000e-01, eta: 13:34:05, time: 0.091, data_time: 0.026, memory: 1125, loss: 6.4154\n2023-01-03 15:19:46,252 - mmcls - INFO - Epoch [1][500/5005]\tlr: 1.000e-01, eta: 13:16:00, time: 0.087, data_time: 0.040, memory: 1125, loss: 6.2515\n2023-01-03 15:19:54,938 - mmcls - INFO - Epoch [1][600/5005]\tlr: 1.000e-01, eta: 13:03:57, time: 0.087, data_time: 0.013, memory: 1125, loss: 6.1224\n2023-01-03 15:20:03,842 - mmcls - INFO - Epoch [1][700/5005]\tlr: 1.000e-01, eta: 12:57:42, time: 0.089, data_time: 0.033, memory: 1125, loss: 5.9919\n2023-01-03 15:20:13,331 - mmcls - INFO - Epoch [1][800/5005]\tlr: 1.000e-01, eta: 12:59:12, time: 0.095, data_time: 0.008, memory: 1125, loss: 5.8911\n2023-01-03 15:20:21,998 - mmcls - INFO - Epoch [1][900/5005]\tlr: 1.000e-01, eta: 12:52:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 5.7362\n2023-01-03 15:20:30,562 - mmcls - INFO - Epoch [1][1000/5005]\tlr: 1.000e-01, eta: 12:46:26, time: 0.086, data_time: 0.018, memory: 1125, loss: 5.6388\n2023-01-03 15:20:39,173 - mmcls - INFO - Epoch [1][1100/5005]\tlr: 1.000e-01, eta: 12:41:50, time: 0.086, data_time: 0.028, memory: 1125, loss: 5.5561\n2023-01-03 15:20:47,798 - mmcls - INFO - Epoch [1][1200/5005]\tlr: 1.000e-01, eta: 12:38:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 5.4535\n2023-01-03 15:20:56,687 - mmcls - INFO - Epoch [1][1300/5005]\tlr: 1.000e-01, eta: 12:36:31, time: 0.089, data_time: 0.020, memory: 1125, loss: 5.3708\n2023-01-03 15:21:05,539 - mmcls - INFO - Epoch [1][1400/5005]\tlr: 1.000e-01, eta: 12:34:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 5.3070\n2023-01-03 15:21:14,544 - mmcls - INFO - Epoch [1][1500/5005]\tlr: 1.000e-01, eta: 12:34:18, time: 0.090, data_time: 0.007, memory: 1125, loss: 5.2293\n2023-01-03 15:21:23,635 - mmcls - INFO - Epoch [1][1600/5005]\tlr: 1.000e-01, eta: 12:34:14, time: 0.091, data_time: 0.023, memory: 1125, loss: 5.1703\n2023-01-03 15:21:32,674 - mmcls - INFO - Epoch [1][1700/5005]\tlr: 1.000e-01, eta: 12:33:55, time: 0.090, data_time: 0.036, memory: 1125, loss: 5.1147\n2023-01-03 15:21:41,822 - mmcls - INFO - Epoch [1][1800/5005]\tlr: 1.000e-01, eta: 12:34:09, time: 0.092, data_time: 0.052, memory: 1125, loss: 5.0406\n2023-01-03 15:21:50,808 - mmcls - INFO - Epoch [1][1900/5005]\tlr: 1.000e-01, eta: 12:33:39, time: 0.090, data_time: 0.038, memory: 1125, loss: 4.9875\n2023-01-03 15:21:59,710 - mmcls - INFO - Epoch [1][2000/5005]\tlr: 1.000e-01, eta: 12:32:46, time: 0.089, data_time: 0.022, memory: 1125, loss: 4.9228\n2023-01-03 15:22:08,434 - mmcls - INFO - Epoch [1][2100/5005]\tlr: 1.000e-01, eta: 12:31:16, time: 0.087, data_time: 0.019, memory: 1125, loss: 4.8593\n2023-01-03 15:22:17,296 - mmcls - INFO - Epoch [1][2200/5005]\tlr: 1.000e-01, eta: 12:30:28, time: 0.089, data_time: 0.038, memory: 1125, loss: 4.8095\n2023-01-03 15:22:26,245 - mmcls - INFO - Epoch [1][2300/5005]\tlr: 1.000e-01, eta: 12:29:57, time: 0.089, data_time: 0.033, memory: 1125, loss: 4.7575\n2023-01-03 15:22:34,982 - mmcls - INFO - Epoch [1][2400/5005]\tlr: 1.000e-01, eta: 12:28:48, time: 0.087, data_time: 0.010, memory: 1125, loss: 4.7292\n2023-01-03 15:22:43,903 - mmcls - INFO - Epoch [1][2500/5005]\tlr: 1.000e-01, eta: 12:28:20, time: 0.089, data_time: 0.002, memory: 1125, loss: 4.6630\n2023-01-03 15:22:52,693 - mmcls - INFO - Epoch [1][2600/5005]\tlr: 1.000e-01, eta: 12:27:26, time: 0.088, data_time: 0.011, memory: 1125, loss: 4.6083\n2023-01-03 15:23:01,492 - mmcls - INFO - Epoch [1][2700/5005]\tlr: 1.000e-01, eta: 12:26:38, time: 0.088, data_time: 0.007, memory: 1125, loss: 4.5430\n2023-01-03 15:23:10,562 - mmcls - INFO - Epoch [1][2800/5005]\tlr: 1.000e-01, eta: 12:26:43, time: 0.091, data_time: 0.012, memory: 1125, loss: 4.5334\n2023-01-03 15:23:19,665 - mmcls - INFO - Epoch [1][2900/5005]\tlr: 1.000e-01, eta: 12:26:50, time: 0.091, data_time: 0.040, memory: 1125, loss: 4.4810\n2023-01-03 15:23:28,409 - mmcls - INFO - Epoch [1][3000/5005]\tlr: 1.000e-01, eta: 12:25:57, time: 0.087, data_time: 0.015, memory: 1125, loss: 4.4313\n2023-01-03 15:23:37,023 - mmcls - INFO - Epoch [1][3100/5005]\tlr: 1.000e-01, eta: 12:24:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 4.4097\n2023-01-03 15:23:45,652 - mmcls - INFO - Epoch [1][3200/5005]\tlr: 1.000e-01, eta: 12:23:43, time: 0.086, data_time: 0.010, memory: 1125, loss: 4.3585\n2023-01-03 15:23:54,674 - mmcls - INFO - Epoch [1][3300/5005]\tlr: 1.000e-01, eta: 12:23:41, time: 0.090, data_time: 0.021, memory: 1125, loss: 4.3516\n2023-01-03 15:24:03,561 - mmcls - INFO - Epoch [1][3400/5005]\tlr: 1.000e-01, eta: 12:23:18, time: 0.089, data_time: 0.007, memory: 1125, loss: 4.2911\n2023-01-03 15:24:12,140 - mmcls - INFO - Epoch [1][3500/5005]\tlr: 1.000e-01, eta: 12:22:14, time: 0.086, data_time: 0.024, memory: 1125, loss: 4.2691\n2023-01-03 15:24:20,656 - mmcls - INFO - Epoch [1][3600/5005]\tlr: 1.000e-01, eta: 12:21:04, time: 0.085, data_time: 0.031, memory: 1125, loss: 4.2172\n2023-01-03 15:24:29,396 - mmcls - INFO - Epoch [1][3700/5005]\tlr: 1.000e-01, eta: 12:20:27, time: 0.087, data_time: 0.012, memory: 1125, loss: 4.2102\n2023-01-03 15:24:38,129 - mmcls - INFO - Epoch [1][3800/5005]\tlr: 1.000e-01, eta: 12:19:50, time: 0.087, data_time: 0.015, memory: 1125, loss: 4.1719\n2023-01-03 15:24:46,654 - mmcls - INFO - Epoch [1][3900/5005]\tlr: 1.000e-01, eta: 12:18:49, time: 0.085, data_time: 0.034, memory: 1125, loss: 4.1208\n2023-01-03 15:24:55,282 - mmcls - INFO - Epoch [1][4000/5005]\tlr: 1.000e-01, eta: 12:18:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 4.1009\n2023-01-03 15:25:04,226 - mmcls - INFO - Epoch [1][4100/5005]\tlr: 1.000e-01, eta: 12:17:57, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.0630\n2023-01-03 15:25:12,889 - mmcls - INFO - Epoch [1][4200/5005]\tlr: 1.000e-01, eta: 12:17:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 4.0263\n2023-01-03 15:25:21,532 - mmcls - INFO - Epoch [1][4300/5005]\tlr: 1.000e-01, eta: 12:16:37, time: 0.086, data_time: 0.034, memory: 1125, loss: 3.9998\n2023-01-03 15:25:30,500 - mmcls - INFO - Epoch [1][4400/5005]\tlr: 1.000e-01, eta: 12:16:34, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.0183\n2023-01-03 15:25:39,207 - mmcls - INFO - Epoch [1][4500/5005]\tlr: 1.000e-01, eta: 12:16:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.9670\n2023-01-03 15:25:48,035 - mmcls - INFO - Epoch [1][4600/5005]\tlr: 1.000e-01, eta: 12:15:47, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.9385\n2023-01-03 15:25:56,659 - mmcls - INFO - Epoch [1][4700/5005]\tlr: 1.000e-01, eta: 12:15:07, time: 0.086, data_time: 0.016, memory: 1125, loss: 3.9028\n2023-01-03 15:26:05,493 - mmcls - INFO - Epoch [1][4800/5005]\tlr: 1.000e-01, eta: 12:14:51, time: 0.088, data_time: 0.039, memory: 1125, loss: 3.9059\n2023-01-03 15:26:14,265 - mmcls - INFO - Epoch [1][4900/5005]\tlr: 1.000e-01, eta: 12:14:31, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.8832\n2023-01-03 15:26:23,130 - mmcls - INFO - Epoch [1][5000/5005]\tlr: 1.000e-01, eta: 12:14:19, time: 0.089, data_time: 0.004, memory: 1125, loss: 3.8454\n2023-01-03 15:26:46,332 - mmcls - INFO - Epoch(val) [1][196]\taccuracy_top-1: 26.6600, accuracy_top-5: 50.6340\n2023-01-03 15:26:57,526 - mmcls - INFO - Epoch [2][100/5005]\tlr: 1.000e-01, eta: 12:17:08, time: 0.112, data_time: 0.071, memory: 1125, loss: 3.7910\n2023-01-03 15:27:06,109 - mmcls - INFO - Epoch [2][200/5005]\tlr: 1.000e-01, eta: 12:16:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 3.8017\n2023-01-03 15:27:14,809 - mmcls - INFO - Epoch [2][300/5005]\tlr: 1.000e-01, eta: 12:15:56, time: 0.087, data_time: 0.014, memory: 1125, loss: 3.7630\n2023-01-03 15:27:23,545 - mmcls - INFO - Epoch [2][400/5005]\tlr: 1.000e-01, eta: 12:15:31, time: 0.087, data_time: 0.021, memory: 1125, loss: 3.7589\n2023-01-03 15:27:32,471 - mmcls - INFO - Epoch [2][500/5005]\tlr: 1.000e-01, eta: 12:15:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 3.7228\n2023-01-03 15:27:41,160 - mmcls - INFO - Epoch [2][600/5005]\tlr: 1.000e-01, eta: 12:14:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.7156\n2023-01-03 15:27:50,085 - mmcls - INFO - Epoch [2][700/5005]\tlr: 1.000e-01, eta: 12:14:46, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.7089\n2023-01-03 15:27:58,642 - mmcls - INFO - Epoch [2][800/5005]\tlr: 1.000e-01, eta: 12:14:08, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.6864\n2023-01-03 15:28:07,716 - mmcls - INFO - Epoch [2][900/5005]\tlr: 1.000e-01, eta: 12:14:13, time: 0.091, data_time: 0.013, memory: 1125, loss: 3.6658\n2023-01-03 15:28:16,484 - mmcls - INFO - Epoch [2][1000/5005]\tlr: 1.000e-01, eta: 12:13:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.6299\n2023-01-03 15:28:25,007 - mmcls - INFO - Epoch [2][1100/5005]\tlr: 1.000e-01, eta: 12:13:12, time: 0.085, data_time: 0.002, memory: 1125, loss: 3.6258\n2023-01-03 15:28:33,515 - mmcls - INFO - Epoch [2][1200/5005]\tlr: 1.000e-01, eta: 12:12:32, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.6390\n2023-01-03 15:28:42,283 - mmcls - INFO - Epoch [2][1300/5005]\tlr: 1.000e-01, eta: 12:12:13, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.6106\n2023-01-03 15:28:51,050 - mmcls - INFO - Epoch [2][1400/5005]\tlr: 1.000e-01, eta: 12:11:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.5996\n2023-01-03 15:28:59,647 - mmcls - INFO - Epoch [2][1500/5005]\tlr: 1.000e-01, eta: 12:11:24, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.5724\n2023-01-03 15:29:08,238 - mmcls - INFO - Epoch [2][1600/5005]\tlr: 1.000e-01, eta: 12:10:53, time: 0.086, data_time: 0.018, memory: 1125, loss: 3.5616\n2023-01-03 15:29:17,123 - mmcls - INFO - Epoch [2][1700/5005]\tlr: 1.000e-01, eta: 12:10:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.5777\n2023-01-03 15:29:25,777 - mmcls - INFO - Epoch [2][1800/5005]\tlr: 1.000e-01, eta: 12:10:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.5460\n2023-01-03 15:29:34,310 - mmcls - INFO - Epoch [2][1900/5005]\tlr: 1.000e-01, eta: 12:09:46, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.5145\n2023-01-03 15:29:43,258 - mmcls - INFO - Epoch [2][2000/5005]\tlr: 1.000e-01, eta: 12:09:43, time: 0.090, data_time: 0.003, memory: 1125, loss: 3.5156\n2023-01-03 15:29:51,739 - mmcls - INFO - Epoch [2][2100/5005]\tlr: 1.000e-01, eta: 12:09:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.4966\n2023-01-03 15:30:00,382 - mmcls - INFO - Epoch [2][2200/5005]\tlr: 1.000e-01, eta: 12:08:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 3.4741\n2023-01-03 15:30:09,261 - mmcls - INFO - Epoch [2][2300/5005]\tlr: 1.000e-01, eta: 12:08:35, time: 0.089, data_time: 0.010, memory: 1125, loss: 3.4618\n2023-01-03 15:30:18,124 - mmcls - INFO - Epoch [2][2400/5005]\tlr: 1.000e-01, eta: 12:08:25, time: 0.088, data_time: 0.038, memory: 1125, loss: 3.4485\n2023-01-03 15:30:27,173 - mmcls - INFO - Epoch [2][2500/5005]\tlr: 1.000e-01, eta: 12:08:29, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.4584\n2023-01-03 15:30:36,328 - mmcls - INFO - Epoch [2][2600/5005]\tlr: 1.000e-01, eta: 12:08:38, time: 0.091, data_time: 0.006, memory: 1125, loss: 3.4459\n2023-01-03 15:30:45,538 - mmcls - INFO - Epoch [2][2700/5005]\tlr: 1.000e-01, eta: 12:08:51, time: 0.092, data_time: 0.009, memory: 1125, loss: 3.4440\n2023-01-03 15:30:54,387 - mmcls - INFO - Epoch [2][2800/5005]\tlr: 1.000e-01, eta: 12:08:40, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.4240\n2023-01-03 15:31:03,196 - mmcls - INFO - Epoch [2][2900/5005]\tlr: 1.000e-01, eta: 12:08:27, time: 0.088, data_time: 0.019, memory: 1125, loss: 3.4440\n2023-01-03 15:31:12,485 - mmcls - INFO - Epoch [2][3000/5005]\tlr: 1.000e-01, eta: 12:08:44, time: 0.093, data_time: 0.009, memory: 1125, loss: 3.4074\n2023-01-03 15:31:21,164 - mmcls - INFO - Epoch [2][3100/5005]\tlr: 1.000e-01, eta: 12:08:23, time: 0.087, data_time: 0.012, memory: 1125, loss: 3.3897\n2023-01-03 15:31:29,945 - mmcls - INFO - Epoch [2][3200/5005]\tlr: 1.000e-01, eta: 12:08:09, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.3759\n2023-01-03 15:31:38,538 - mmcls - INFO - Epoch [2][3300/5005]\tlr: 1.000e-01, eta: 12:07:43, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.3747\n2023-01-03 15:31:47,611 - mmcls - INFO - Epoch [2][3400/5005]\tlr: 1.000e-01, eta: 12:07:46, time: 0.091, data_time: 0.015, memory: 1125, loss: 3.3758\n2023-01-03 15:31:56,274 - mmcls - INFO - Epoch [2][3500/5005]\tlr: 1.000e-01, eta: 12:07:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 3.3746\n2023-01-03 15:32:04,916 - mmcls - INFO - Epoch [2][3600/5005]\tlr: 1.000e-01, eta: 12:07:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.3526\n2023-01-03 15:32:13,911 - mmcls - INFO - Epoch [2][3700/5005]\tlr: 1.000e-01, eta: 12:07:01, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.3560\n2023-01-03 15:32:22,491 - mmcls - INFO - Epoch [2][3800/5005]\tlr: 1.000e-01, eta: 12:06:36, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.3498\n2023-01-03 15:32:31,666 - mmcls - INFO - Epoch [2][3900/5005]\tlr: 1.000e-01, eta: 12:06:44, time: 0.092, data_time: 0.006, memory: 1125, loss: 3.3383\n2023-01-03 15:32:40,320 - mmcls - INFO - Epoch [2][4000/5005]\tlr: 1.000e-01, eta: 12:06:24, time: 0.087, data_time: 0.028, memory: 1125, loss: 3.3252\n2023-01-03 15:32:49,268 - mmcls - INFO - Epoch [2][4100/5005]\tlr: 1.000e-01, eta: 12:06:19, time: 0.089, data_time: 0.009, memory: 1125, loss: 3.3006\n2023-01-03 15:32:58,071 - mmcls - INFO - Epoch [2][4200/5005]\tlr: 1.000e-01, eta: 12:06:06, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.2968\n2023-01-03 15:33:06,651 - mmcls - INFO - Epoch [2][4300/5005]\tlr: 1.000e-01, eta: 12:05:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 3.2907\n2023-01-03 15:33:15,499 - mmcls - INFO - Epoch [2][4400/5005]\tlr: 1.000e-01, eta: 12:05:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.2737\n2023-01-03 15:33:24,361 - mmcls - INFO - Epoch [2][4500/5005]\tlr: 1.000e-01, eta: 12:05:23, time: 0.089, data_time: 0.025, memory: 1125, loss: 3.2848\n2023-01-03 15:33:32,922 - mmcls - INFO - Epoch [2][4600/5005]\tlr: 1.000e-01, eta: 12:04:59, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.2837\n2023-01-03 15:33:41,643 - mmcls - INFO - Epoch [2][4700/5005]\tlr: 1.000e-01, eta: 12:04:43, time: 0.087, data_time: 0.011, memory: 1125, loss: 3.2658\n2023-01-03 15:33:50,468 - mmcls - INFO - Epoch [2][4800/5005]\tlr: 1.000e-01, eta: 12:04:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 3.2736\n2023-01-03 15:33:59,221 - mmcls - INFO - Epoch [2][4900/5005]\tlr: 1.000e-01, eta: 12:04:18, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.2593\n2023-01-03 15:34:07,820 - mmcls - INFO - Epoch [2][5000/5005]\tlr: 1.000e-01, eta: 12:03:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.2728\n2023-01-03 15:34:30,821 - mmcls - INFO - Epoch(val) [2][196]\taccuracy_top-1: 35.4840, accuracy_top-5: 61.0780\n2023-01-03 15:34:41,956 - mmcls - INFO - Epoch [3][100/5005]\tlr: 1.000e-01, eta: 12:05:15, time: 0.111, data_time: 0.038, memory: 1125, loss: 3.1983\n2023-01-03 15:34:50,837 - mmcls - INFO - Epoch [3][200/5005]\tlr: 1.000e-01, eta: 12:05:06, time: 0.089, data_time: 0.025, memory: 1125, loss: 3.1895\n2023-01-03 15:34:59,597 - mmcls - INFO - Epoch [3][300/5005]\tlr: 1.000e-01, eta: 12:04:52, time: 0.088, data_time: 0.022, memory: 1125, loss: 3.1871\n2023-01-03 15:35:08,195 - mmcls - INFO - Epoch [3][400/5005]\tlr: 1.000e-01, eta: 12:04:30, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.2168\n2023-01-03 15:35:16,867 - mmcls - INFO - Epoch [3][500/5005]\tlr: 1.000e-01, eta: 12:04:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.1907\n2023-01-03 15:35:25,807 - mmcls - INFO - Epoch [3][600/5005]\tlr: 1.000e-01, eta: 12:04:06, time: 0.089, data_time: 0.007, memory: 1125, loss: 3.1476\n2023-01-03 15:35:34,325 - mmcls - INFO - Epoch [3][700/5005]\tlr: 1.000e-01, eta: 12:03:41, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.1764\n2023-01-03 15:35:42,914 - mmcls - INFO - Epoch [3][800/5005]\tlr: 1.000e-01, eta: 12:03:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.1573\n2023-01-03 15:35:51,483 - mmcls - INFO - Epoch [3][900/5005]\tlr: 1.000e-01, eta: 12:02:58, time: 0.086, data_time: 0.023, memory: 1125, loss: 3.1756\n2023-01-03 15:36:00,125 - mmcls - INFO - Epoch [3][1000/5005]\tlr: 1.000e-01, eta: 12:02:39, time: 0.086, data_time: 0.015, memory: 1125, loss: 3.1709\n2023-01-03 15:36:08,834 - mmcls - INFO - Epoch [3][1100/5005]\tlr: 1.000e-01, eta: 12:02:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.1598\n2023-01-03 15:36:17,666 - mmcls - INFO - Epoch [3][1200/5005]\tlr: 1.000e-01, eta: 12:02:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 3.1340\n2023-01-03 15:36:26,389 - mmcls - INFO - Epoch [3][1300/5005]\tlr: 1.000e-01, eta: 12:01:59, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.1802\n2023-01-03 15:36:35,224 - mmcls - INFO - Epoch [3][1400/5005]\tlr: 1.000e-01, eta: 12:01:49, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.1507\n2023-01-03 15:36:43,903 - mmcls - INFO - Epoch [3][1500/5005]\tlr: 1.000e-01, eta: 12:01:33, time: 0.087, data_time: 0.029, memory: 1125, loss: 3.1183\n2023-01-03 15:36:52,511 - mmcls - INFO - Epoch [3][1600/5005]\tlr: 1.000e-01, eta: 12:01:14, time: 0.086, data_time: 0.030, memory: 1125, loss: 3.1390\n2023-01-03 15:37:01,029 - mmcls - INFO - Epoch [3][1700/5005]\tlr: 1.000e-01, eta: 12:00:51, time: 0.085, data_time: 0.033, memory: 1125, loss: 3.1151\n2023-01-03 15:37:09,885 - mmcls - INFO - Epoch [3][1800/5005]\tlr: 1.000e-01, eta: 12:00:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 3.1254\n2023-01-03 15:37:18,426 - mmcls - INFO - Epoch [3][1900/5005]\tlr: 1.000e-01, eta: 12:00:21, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.1233\n2023-01-03 15:37:27,318 - mmcls - INFO - Epoch [3][2000/5005]\tlr: 1.000e-01, eta: 12:00:14, time: 0.089, data_time: 0.029, memory: 1125, loss: 3.1377\n2023-01-03 15:37:36,024 - mmcls - INFO - Epoch [3][2100/5005]\tlr: 1.000e-01, eta: 11:59:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.0860\n2023-01-03 15:37:44,704 - mmcls - INFO - Epoch [3][2200/5005]\tlr: 1.000e-01, eta: 11:59:44, time: 0.087, data_time: 0.033, memory: 1125, loss: 3.1365\n2023-01-03 15:37:53,558 - mmcls - INFO - Epoch [3][2300/5005]\tlr: 1.000e-01, eta: 11:59:35, time: 0.088, data_time: 0.005, memory: 1125, loss: 3.1126\n2023-01-03 15:38:02,627 - mmcls - INFO - Epoch [3][2400/5005]\tlr: 1.000e-01, eta: 11:59:35, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.0613\n2023-01-03 15:38:11,240 - mmcls - INFO - Epoch [3][2500/5005]\tlr: 1.000e-01, eta: 11:59:17, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1118\n2023-01-03 15:38:20,051 - mmcls - INFO - Epoch [3][2600/5005]\tlr: 1.000e-01, eta: 11:59:07, time: 0.088, data_time: 0.029, memory: 1125, loss: 3.0687\n2023-01-03 15:38:28,804 - mmcls - INFO - Epoch [3][2700/5005]\tlr: 1.000e-01, eta: 11:58:55, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.1012\n2023-01-03 15:38:37,605 - mmcls - INFO - Epoch [3][2800/5005]\tlr: 1.000e-01, eta: 11:58:44, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.0932\n2023-01-03 15:38:46,148 - mmcls - INFO - Epoch [3][2900/5005]\tlr: 1.000e-01, eta: 11:58:24, time: 0.085, data_time: 0.030, memory: 1125, loss: 3.0778\n2023-01-03 15:38:54,981 - mmcls - INFO - Epoch [3][3000/5005]\tlr: 1.000e-01, eta: 11:58:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 3.0865\n2023-01-03 15:39:03,509 - mmcls - INFO - Epoch [3][3100/5005]\tlr: 1.000e-01, eta: 11:57:54, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.0816\n2023-01-03 15:39:12,330 - mmcls - INFO - Epoch [3][3200/5005]\tlr: 1.000e-01, eta: 11:57:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.0725\n2023-01-03 15:39:21,130 - mmcls - INFO - Epoch [3][3300/5005]\tlr: 1.000e-01, eta: 11:57:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0673\n2023-01-03 15:39:29,683 - mmcls - INFO - Epoch [3][3400/5005]\tlr: 1.000e-01, eta: 11:57:15, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0762\n2023-01-03 15:39:38,690 - mmcls - INFO - Epoch [3][3500/5005]\tlr: 1.000e-01, eta: 11:57:13, time: 0.090, data_time: 0.008, memory: 1125, loss: 3.0792\n2023-01-03 15:39:47,289 - mmcls - INFO - Epoch [3][3600/5005]\tlr: 1.000e-01, eta: 11:56:55, time: 0.086, data_time: 0.033, memory: 1125, loss: 3.0612\n2023-01-03 15:39:56,180 - mmcls - INFO - Epoch [3][3700/5005]\tlr: 1.000e-01, eta: 11:56:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 3.0442\n2023-01-03 15:40:05,069 - mmcls - INFO - Epoch [3][3800/5005]\tlr: 1.000e-01, eta: 11:56:42, time: 0.089, data_time: 0.007, memory: 1125, loss: 3.0363\n2023-01-03 15:40:14,011 - mmcls - INFO - Epoch [3][3900/5005]\tlr: 1.000e-01, eta: 11:56:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0170\n2023-01-03 15:40:22,924 - mmcls - INFO - Epoch [3][4000/5005]\tlr: 1.000e-01, eta: 11:56:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0327\n2023-01-03 15:40:31,606 - mmcls - INFO - Epoch [3][4100/5005]\tlr: 1.000e-01, eta: 11:56:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0476\n2023-01-03 15:40:40,797 - mmcls - INFO - Epoch [3][4200/5005]\tlr: 1.000e-01, eta: 11:56:19, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.0443\n2023-01-03 15:40:49,431 - mmcls - INFO - Epoch [3][4300/5005]\tlr: 1.000e-01, eta: 11:56:03, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0229\n2023-01-03 15:40:58,262 - mmcls - INFO - Epoch [3][4400/5005]\tlr: 1.000e-01, eta: 11:55:54, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.0156\n2023-01-03 15:41:06,907 - mmcls - INFO - Epoch [3][4500/5005]\tlr: 1.000e-01, eta: 11:55:39, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0322\n2023-01-03 15:41:15,972 - mmcls - INFO - Epoch [3][4600/5005]\tlr: 1.000e-01, eta: 11:55:38, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.9978\n2023-01-03 15:41:24,734 - mmcls - INFO - Epoch [3][4700/5005]\tlr: 1.000e-01, eta: 11:55:26, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.0369\n2023-01-03 15:41:33,652 - mmcls - INFO - Epoch [3][4800/5005]\tlr: 1.000e-01, eta: 11:55:20, time: 0.089, data_time: 0.016, memory: 1125, loss: 3.0155\n2023-01-03 15:41:42,786 - mmcls - INFO - Epoch [3][4900/5005]\tlr: 1.000e-01, eta: 11:55:21, time: 0.091, data_time: 0.026, memory: 1125, loss: 3.0293\n2023-01-03 15:41:51,486 - mmcls - INFO - Epoch [3][5000/5005]\tlr: 1.000e-01, eta: 11:55:07, time: 0.087, data_time: 0.029, memory: 1125, loss: 3.0136\n2023-01-03 15:42:14,146 - mmcls - INFO - Epoch(val) [3][196]\taccuracy_top-1: 41.0100, accuracy_top-5: 67.1080\n2023-01-03 15:42:25,032 - mmcls - INFO - Epoch [4][100/5005]\tlr: 1.000e-01, eta: 11:55:49, time: 0.109, data_time: 0.048, memory: 1125, loss: 2.9524\n2023-01-03 15:42:34,102 - mmcls - INFO - Epoch [4][200/5005]\tlr: 1.000e-01, eta: 11:55:47, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.9433\n2023-01-03 15:42:43,180 - mmcls - INFO - Epoch [4][300/5005]\tlr: 1.000e-01, eta: 11:55:46, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.9584\n2023-01-03 15:42:51,993 - mmcls - INFO - Epoch [4][400/5005]\tlr: 1.000e-01, eta: 11:55:35, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9773\n2023-01-03 15:43:00,914 - mmcls - INFO - Epoch [4][500/5005]\tlr: 1.000e-01, eta: 11:55:29, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.9513\n2023-01-03 15:43:09,612 - mmcls - INFO - Epoch [4][600/5005]\tlr: 1.000e-01, eta: 11:55:15, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.9697\n2023-01-03 15:43:18,502 - mmcls - INFO - Epoch [4][700/5005]\tlr: 1.000e-01, eta: 11:55:07, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.9516\n2023-01-03 15:43:27,081 - mmcls - INFO - Epoch [4][800/5005]\tlr: 1.000e-01, eta: 11:54:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.9589\n2023-01-03 15:43:36,129 - mmcls - INFO - Epoch [4][900/5005]\tlr: 1.000e-01, eta: 11:54:47, time: 0.090, data_time: 0.034, memory: 1125, loss: 2.9083\n2023-01-03 15:43:45,069 - mmcls - INFO - Epoch [4][1000/5005]\tlr: 1.000e-01, eta: 11:54:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9740\n2023-01-03 15:43:53,729 - mmcls - INFO - Epoch [4][1100/5005]\tlr: 1.000e-01, eta: 11:54:27, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9804\n2023-01-03 15:44:02,621 - mmcls - INFO - Epoch [4][1200/5005]\tlr: 1.000e-01, eta: 11:54:19, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.9395\n2023-01-03 15:44:11,590 - mmcls - INFO - Epoch [4][1300/5005]\tlr: 1.000e-01, eta: 11:54:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9476\n2023-01-03 15:44:20,625 - mmcls - INFO - Epoch [4][1400/5005]\tlr: 1.000e-01, eta: 11:54:10, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9523\n2023-01-03 15:44:29,544 - mmcls - INFO - Epoch [4][1500/5005]\tlr: 1.000e-01, eta: 11:54:03, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.9428\n2023-01-03 15:44:38,487 - mmcls - INFO - Epoch [4][1600/5005]\tlr: 1.000e-01, eta: 11:53:57, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.9393\n2023-01-03 15:44:47,117 - mmcls - INFO - Epoch [4][1700/5005]\tlr: 1.000e-01, eta: 11:53:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9422\n2023-01-03 15:44:55,770 - mmcls - INFO - Epoch [4][1800/5005]\tlr: 1.000e-01, eta: 11:53:27, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9309\n2023-01-03 15:45:04,258 - mmcls - INFO - Epoch [4][1900/5005]\tlr: 1.000e-01, eta: 11:53:08, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.9073\n2023-01-03 15:45:12,933 - mmcls - INFO - Epoch [4][2000/5005]\tlr: 1.000e-01, eta: 11:52:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9317\n2023-01-03 15:45:21,508 - mmcls - INFO - Epoch [4][2100/5005]\tlr: 1.000e-01, eta: 11:52:37, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8988\n2023-01-03 15:45:30,106 - mmcls - INFO - Epoch [4][2200/5005]\tlr: 1.000e-01, eta: 11:52:22, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9402\n2023-01-03 15:45:38,890 - mmcls - INFO - Epoch [4][2300/5005]\tlr: 1.000e-01, eta: 11:52:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.9377\n2023-01-03 15:45:47,698 - mmcls - INFO - Epoch [4][2400/5005]\tlr: 1.000e-01, eta: 11:52:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9206\n2023-01-03 15:45:56,891 - mmcls - INFO - Epoch [4][2500/5005]\tlr: 1.000e-01, eta: 11:52:02, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.9342\n2023-01-03 15:46:05,599 - mmcls - INFO - Epoch [4][2600/5005]\tlr: 1.000e-01, eta: 11:51:50, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9168\n2023-01-03 15:46:14,296 - mmcls - INFO - Epoch [4][2700/5005]\tlr: 1.000e-01, eta: 11:51:37, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9171\n2023-01-03 15:46:22,978 - mmcls - INFO - Epoch [4][2800/5005]\tlr: 1.000e-01, eta: 11:51:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.9262\n2023-01-03 15:46:31,805 - mmcls - INFO - Epoch [4][2900/5005]\tlr: 1.000e-01, eta: 11:51:14, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.9406\n2023-01-03 15:46:40,744 - mmcls - INFO - Epoch [4][3000/5005]\tlr: 1.000e-01, eta: 11:51:08, time: 0.089, data_time: 0.048, memory: 1125, loss: 2.9370\n2023-01-03 15:46:49,471 - mmcls - INFO - Epoch [4][3100/5005]\tlr: 1.000e-01, eta: 11:50:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9111\n2023-01-03 15:46:58,124 - mmcls - INFO - Epoch [4][3200/5005]\tlr: 1.000e-01, eta: 11:50:42, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9217\n2023-01-03 15:47:06,898 - mmcls - INFO - Epoch [4][3300/5005]\tlr: 1.000e-01, eta: 11:50:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9190\n2023-01-03 15:47:15,577 - mmcls - INFO - Epoch [4][3400/5005]\tlr: 1.000e-01, eta: 11:50:18, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.9357\n2023-01-03 15:47:24,562 - mmcls - INFO - Epoch [4][3500/5005]\tlr: 1.000e-01, eta: 11:50:13, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8989\n2023-01-03 15:47:33,165 - mmcls - INFO - Epoch [4][3600/5005]\tlr: 1.000e-01, eta: 11:49:58, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.9365\n2023-01-03 15:47:41,890 - mmcls - INFO - Epoch [4][3700/5005]\tlr: 1.000e-01, eta: 11:49:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9031\n2023-01-03 15:47:50,495 - mmcls - INFO - Epoch [4][3800/5005]\tlr: 1.000e-01, eta: 11:49:31, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.9280\n2023-01-03 15:47:59,327 - mmcls - INFO - Epoch [4][3900/5005]\tlr: 1.000e-01, eta: 11:49:22, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.8919\n2023-01-03 15:48:07,788 - mmcls - INFO - Epoch [4][4000/5005]\tlr: 1.000e-01, eta: 11:49:04, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.9052\n2023-01-03 15:48:16,793 - mmcls - INFO - Epoch [4][4100/5005]\tlr: 1.000e-01, eta: 11:48:59, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8841\n2023-01-03 15:48:25,701 - mmcls - INFO - Epoch [4][4200/5005]\tlr: 1.000e-01, eta: 11:48:52, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8946\n2023-01-03 15:48:34,610 - mmcls - INFO - Epoch [4][4300/5005]\tlr: 1.000e-01, eta: 11:48:45, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8977\n2023-01-03 15:48:43,381 - mmcls - INFO - Epoch [4][4400/5005]\tlr: 1.000e-01, eta: 11:48:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.8860\n2023-01-03 15:48:52,369 - mmcls - INFO - Epoch [4][4500/5005]\tlr: 1.000e-01, eta: 11:48:29, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8708\n2023-01-03 15:49:01,206 - mmcls - INFO - Epoch [4][4600/5005]\tlr: 1.000e-01, eta: 11:48:20, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8940\n2023-01-03 15:49:10,133 - mmcls - INFO - Epoch [4][4700/5005]\tlr: 1.000e-01, eta: 11:48:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9077\n2023-01-03 15:49:19,157 - mmcls - INFO - Epoch [4][4800/5005]\tlr: 1.000e-01, eta: 11:48:09, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.8987\n2023-01-03 15:49:27,994 - mmcls - INFO - Epoch [4][4900/5005]\tlr: 1.000e-01, eta: 11:48:00, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.8931\n2023-01-03 15:49:37,092 - mmcls - INFO - Epoch [4][5000/5005]\tlr: 1.000e-01, eta: 11:47:58, time: 0.091, data_time: 0.035, memory: 1125, loss: 2.8569\n2023-01-03 15:49:59,931 - mmcls - INFO - Epoch(val) [4][196]\taccuracy_top-1: 43.4940, accuracy_top-5: 69.8200\n2023-01-03 15:50:10,849 - mmcls - INFO - Epoch [5][100/5005]\tlr: 1.000e-01, eta: 11:48:27, time: 0.109, data_time: 0.052, memory: 1125, loss: 2.8349\n2023-01-03 15:50:19,702 - mmcls - INFO - Epoch [5][200/5005]\tlr: 1.000e-01, eta: 11:48:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8035\n2023-01-03 15:50:28,694 - mmcls - INFO - Epoch [5][300/5005]\tlr: 1.000e-01, eta: 11:48:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8564\n2023-01-03 15:50:37,329 - mmcls - INFO - Epoch [5][400/5005]\tlr: 1.000e-01, eta: 11:47:59, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.8557\n2023-01-03 15:50:46,065 - mmcls - INFO - Epoch [5][500/5005]\tlr: 1.000e-01, eta: 11:47:47, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.8324\n2023-01-03 15:50:54,876 - mmcls - INFO - Epoch [5][600/5005]\tlr: 1.000e-01, eta: 11:47:37, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8073\n2023-01-03 15:51:03,312 - mmcls - INFO - Epoch [5][700/5005]\tlr: 1.000e-01, eta: 11:47:19, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.8404\n2023-01-03 15:51:12,081 - mmcls - INFO - Epoch [5][800/5005]\tlr: 1.000e-01, eta: 11:47:09, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.8232\n2023-01-03 15:51:20,673 - mmcls - INFO - Epoch [5][900/5005]\tlr: 1.000e-01, eta: 11:46:54, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.8333\n2023-01-03 15:51:29,681 - mmcls - INFO - Epoch [5][1000/5005]\tlr: 1.000e-01, eta: 11:46:49, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8413\n2023-01-03 15:51:38,415 - mmcls - INFO - Epoch [5][1100/5005]\tlr: 1.000e-01, eta: 11:46:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8251\n2023-01-03 15:51:47,106 - mmcls - INFO - Epoch [5][1200/5005]\tlr: 1.000e-01, eta: 11:46:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8240\n2023-01-03 15:51:55,837 - mmcls - INFO - Epoch [5][1300/5005]\tlr: 1.000e-01, eta: 11:46:14, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.8205\n2023-01-03 15:52:04,561 - mmcls - INFO - Epoch [5][1400/5005]\tlr: 1.000e-01, eta: 11:46:02, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.8289\n2023-01-03 15:52:13,358 - mmcls - INFO - Epoch [5][1500/5005]\tlr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8048\n2023-01-03 15:52:22,071 - mmcls - INFO - Epoch [5][1600/5005]\tlr: 1.000e-01, eta: 11:45:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8426\n2023-01-03 15:52:30,671 - mmcls - INFO - Epoch [5][1700/5005]\tlr: 1.000e-01, eta: 11:45:26, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8386\n2023-01-03 15:52:39,250 - mmcls - INFO - Epoch [5][1800/5005]\tlr: 1.000e-01, eta: 11:45:12, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8075\n2023-01-03 15:52:47,998 - mmcls - INFO - Epoch [5][1900/5005]\tlr: 1.000e-01, eta: 11:45:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8372\n2023-01-03 15:52:56,587 - mmcls - INFO - Epoch [5][2000/5005]\tlr: 1.000e-01, eta: 11:44:46, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8068\n2023-01-03 15:53:05,430 - mmcls - INFO - Epoch [5][2100/5005]\tlr: 1.000e-01, eta: 11:44:38, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8410\n2023-01-03 15:53:14,222 - mmcls - INFO - Epoch [5][2200/5005]\tlr: 1.000e-01, eta: 11:44:28, time: 0.088, data_time: 0.048, memory: 1125, loss: 2.8278\n2023-01-03 15:53:23,096 - mmcls - INFO - Epoch [5][2300/5005]\tlr: 1.000e-01, eta: 11:44:20, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.8503\n2023-01-03 15:53:31,593 - mmcls - INFO - Epoch [5][2400/5005]\tlr: 1.000e-01, eta: 11:44:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8163\n2023-01-03 15:53:40,562 - mmcls - INFO - Epoch [5][2500/5005]\tlr: 1.000e-01, eta: 11:43:58, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8316\n2023-01-03 15:53:49,173 - mmcls - INFO - Epoch [5][2600/5005]\tlr: 1.000e-01, eta: 11:43:44, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8203\n2023-01-03 15:53:57,994 - mmcls - INFO - Epoch [5][2700/5005]\tlr: 1.000e-01, eta: 11:43:35, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8392\n2023-01-03 15:54:07,016 - mmcls - INFO - Epoch [5][2800/5005]\tlr: 1.000e-01, eta: 11:43:30, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8401\n2023-01-03 15:54:15,709 - mmcls - INFO - Epoch [5][2900/5005]\tlr: 1.000e-01, eta: 11:43:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8627\n2023-01-03 15:54:24,798 - mmcls - INFO - Epoch [5][3000/5005]\tlr: 1.000e-01, eta: 11:43:14, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.8207\n2023-01-03 15:54:33,531 - mmcls - INFO - Epoch [5][3100/5005]\tlr: 1.000e-01, eta: 11:43:04, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7959\n2023-01-03 15:54:42,221 - mmcls - INFO - Epoch [5][3200/5005]\tlr: 1.000e-01, eta: 11:42:52, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7929\n2023-01-03 15:54:51,242 - mmcls - INFO - Epoch [5][3300/5005]\tlr: 1.000e-01, eta: 11:42:47, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.8216\n2023-01-03 15:55:00,094 - mmcls - INFO - Epoch [5][3400/5005]\tlr: 1.000e-01, eta: 11:42:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7983\n2023-01-03 15:55:08,921 - mmcls - INFO - Epoch [5][3500/5005]\tlr: 1.000e-01, eta: 11:42:29, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8278\n2023-01-03 15:55:17,788 - mmcls - INFO - Epoch [5][3600/5005]\tlr: 1.000e-01, eta: 11:42:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8511\n2023-01-03 15:55:26,364 - mmcls - INFO - Epoch [5][3700/5005]\tlr: 1.000e-01, eta: 11:42:07, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.8386\n2023-01-03 15:55:34,961 - mmcls - INFO - Epoch [5][3800/5005]\tlr: 1.000e-01, eta: 11:41:53, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8179\n2023-01-03 15:55:43,632 - mmcls - INFO - Epoch [5][3900/5005]\tlr: 1.000e-01, eta: 11:41:41, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.8292\n2023-01-03 15:55:52,915 - mmcls - INFO - Epoch [5][4000/5005]\tlr: 1.000e-01, eta: 11:41:41, time: 0.093, data_time: 0.003, memory: 1125, loss: 2.8158\n2023-01-03 15:56:01,580 - mmcls - INFO - Epoch [5][4100/5005]\tlr: 1.000e-01, eta: 11:41:29, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8072\n2023-01-03 15:56:10,241 - mmcls - INFO - Epoch [5][4200/5005]\tlr: 1.000e-01, eta: 11:41:16, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.8145\n2023-01-03 15:56:19,078 - mmcls - INFO - Epoch [5][4300/5005]\tlr: 1.000e-01, eta: 11:41:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8239\n2023-01-03 15:56:27,815 - mmcls - INFO - Epoch [5][4400/5005]\tlr: 1.000e-01, eta: 11:40:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8089\n2023-01-03 15:56:36,358 - mmcls - INFO - Epoch [5][4500/5005]\tlr: 1.000e-01, eta: 11:40:42, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.7913\n2023-01-03 15:56:45,112 - mmcls - INFO - Epoch [5][4600/5005]\tlr: 1.000e-01, eta: 11:40:32, time: 0.087, data_time: 0.029, memory: 1125, loss: 2.7802\n2023-01-03 15:56:53,659 - mmcls - INFO - Epoch [5][4700/5005]\tlr: 1.000e-01, eta: 11:40:18, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.7832\n2023-01-03 15:57:02,454 - mmcls - INFO - Epoch [5][4800/5005]\tlr: 1.000e-01, eta: 11:40:08, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.7884\n2023-01-03 15:57:11,210 - mmcls - INFO - Epoch [5][4900/5005]\tlr: 1.000e-01, eta: 11:39:58, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.7935\n2023-01-03 15:57:20,353 - mmcls - INFO - Epoch [5][5000/5005]\tlr: 1.000e-01, eta: 11:39:55, time: 0.091, data_time: 0.030, memory: 1125, loss: 2.7992\n2023-01-03 15:57:43,010 - mmcls - INFO - Epoch(val) [5][196]\taccuracy_top-1: 44.4220, accuracy_top-5: 70.4080\n2023-01-03 15:57:54,420 - mmcls - INFO - Epoch [6][100/5005]\tlr: 1.000e-01, eta: 11:40:25, time: 0.114, data_time: 0.041, memory: 1125, loss: 2.7526\n2023-01-03 15:58:03,301 - mmcls - INFO - Epoch [6][200/5005]\tlr: 1.000e-01, eta: 11:40:17, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7741\n2023-01-03 15:58:11,994 - mmcls - INFO - Epoch [6][300/5005]\tlr: 1.000e-01, eta: 11:40:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7651\n2023-01-03 15:58:20,835 - mmcls - INFO - Epoch [6][400/5005]\tlr: 1.000e-01, eta: 11:39:57, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.7616\n2023-01-03 15:58:29,356 - mmcls - INFO - Epoch [6][500/5005]\tlr: 1.000e-01, eta: 11:39:42, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.7531\n2023-01-03 15:58:38,340 - mmcls - INFO - Epoch [6][600/5005]\tlr: 1.000e-01, eta: 11:39:36, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7398\n2023-01-03 15:58:47,110 - mmcls - INFO - Epoch [6][700/5005]\tlr: 1.000e-01, eta: 11:39:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7650\n2023-01-03 15:58:55,937 - mmcls - INFO - Epoch [6][800/5005]\tlr: 1.000e-01, eta: 11:39:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7432\n2023-01-03 15:59:04,845 - mmcls - INFO - Epoch [6][900/5005]\tlr: 1.000e-01, eta: 11:39:09, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7584\n2023-01-03 15:59:13,548 - mmcls - INFO - Epoch [6][1000/5005]\tlr: 1.000e-01, eta: 11:38:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7862\n2023-01-03 15:59:22,545 - mmcls - INFO - Epoch [6][1100/5005]\tlr: 1.000e-01, eta: 11:38:52, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7674\n2023-01-03 15:59:31,219 - mmcls - INFO - Epoch [6][1200/5005]\tlr: 1.000e-01, eta: 11:38:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7839\n2023-01-03 15:59:39,916 - mmcls - INFO - Epoch [6][1300/5005]\tlr: 1.000e-01, eta: 11:38:28, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.7966\n2023-01-03 15:59:49,192 - mmcls - INFO - Epoch [6][1400/5005]\tlr: 1.000e-01, eta: 11:38:27, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.7524\n2023-01-03 15:59:58,563 - mmcls - INFO - Epoch [6][1500/5005]\tlr: 1.000e-01, eta: 11:38:28, time: 0.094, data_time: 0.025, memory: 1125, loss: 2.7729\n2023-01-03 16:00:07,498 - mmcls - INFO - Epoch [6][1600/5005]\tlr: 1.000e-01, eta: 11:38:21, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7527\n2023-01-03 16:00:16,175 - mmcls - INFO - Epoch [6][1700/5005]\tlr: 1.000e-01, eta: 11:38:09, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7683\n2023-01-03 16:00:24,862 - mmcls - INFO - Epoch [6][1800/5005]\tlr: 1.000e-01, eta: 11:37:57, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7809\n2023-01-03 16:00:33,468 - mmcls - INFO - Epoch [6][1900/5005]\tlr: 1.000e-01, eta: 11:37:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7685\n2023-01-03 16:00:42,380 - mmcls - INFO - Epoch [6][2000/5005]\tlr: 1.000e-01, eta: 11:37:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7513\n2023-01-03 16:00:51,329 - mmcls - INFO - Epoch [6][2100/5005]\tlr: 1.000e-01, eta: 11:37:30, time: 0.090, data_time: 0.027, memory: 1125, loss: 2.7411\n2023-01-03 16:01:00,205 - mmcls - INFO - Epoch [6][2200/5005]\tlr: 1.000e-01, eta: 11:37:21, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7658\n2023-01-03 16:01:08,833 - mmcls - INFO - Epoch [6][2300/5005]\tlr: 1.000e-01, eta: 11:37:09, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7526\n2023-01-03 16:01:18,064 - mmcls - INFO - Epoch [6][2400/5005]\tlr: 1.000e-01, eta: 11:37:07, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7653\n2023-01-03 16:01:26,606 - mmcls - INFO - Epoch [6][2500/5005]\tlr: 1.000e-01, eta: 11:36:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7744\n2023-01-03 16:01:35,330 - mmcls - INFO - Epoch [6][2600/5005]\tlr: 1.000e-01, eta: 11:36:42, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7556\n2023-01-03 16:01:43,967 - mmcls - INFO - Epoch [6][2700/5005]\tlr: 1.000e-01, eta: 11:36:30, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7828\n2023-01-03 16:01:52,753 - mmcls - INFO - Epoch [6][2800/5005]\tlr: 1.000e-01, eta: 11:36:20, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7650\n2023-01-03 16:02:01,342 - mmcls - INFO - Epoch [6][2900/5005]\tlr: 1.000e-01, eta: 11:36:07, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7545\n2023-01-03 16:02:10,130 - mmcls - INFO - Epoch [6][3000/5005]\tlr: 1.000e-01, eta: 11:35:57, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7498\n2023-01-03 16:02:18,961 - mmcls - INFO - Epoch [6][3100/5005]\tlr: 1.000e-01, eta: 11:35:48, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.7573\n2023-01-03 16:02:27,717 - mmcls - INFO - Epoch [6][3200/5005]\tlr: 1.000e-01, eta: 11:35:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7340\n2023-01-03 16:02:36,389 - mmcls - INFO - Epoch [6][3300/5005]\tlr: 1.000e-01, eta: 11:35:26, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.7580\n2023-01-03 16:02:45,150 - mmcls - INFO - Epoch [6][3400/5005]\tlr: 1.000e-01, eta: 11:35:16, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.7548\n2023-01-03 16:02:53,851 - mmcls - INFO - Epoch [6][3500/5005]\tlr: 1.000e-01, eta: 11:35:05, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7397\n2023-01-03 16:03:02,451 - mmcls - INFO - Epoch [6][3600/5005]\tlr: 1.000e-01, eta: 11:34:52, time: 0.086, data_time: 0.039, memory: 1125, loss: 2.7477\n2023-01-03 16:03:11,373 - mmcls - INFO - Epoch [6][3700/5005]\tlr: 1.000e-01, eta: 11:34:45, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7720\n2023-01-03 16:03:20,174 - mmcls - INFO - Epoch [6][3800/5005]\tlr: 1.000e-01, eta: 11:34:35, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.7651\n2023-01-03 16:03:28,858 - mmcls - INFO - Epoch [6][3900/5005]\tlr: 1.000e-01, eta: 11:34:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7842\n2023-01-03 16:03:37,560 - mmcls - INFO - Epoch [6][4000/5005]\tlr: 1.000e-01, eta: 11:34:13, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.7246\n2023-01-03 16:03:46,557 - mmcls - INFO - Epoch [6][4100/5005]\tlr: 1.000e-01, eta: 11:34:07, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7571\n2023-01-03 16:03:55,378 - mmcls - INFO - Epoch [6][4200/5005]\tlr: 1.000e-01, eta: 11:33:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7526\n2023-01-03 16:04:03,903 - mmcls - INFO - Epoch [6][4300/5005]\tlr: 1.000e-01, eta: 11:33:44, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7691\n2023-01-03 16:04:13,039 - mmcls - INFO - Epoch [6][4400/5005]\tlr: 1.000e-01, eta: 11:33:40, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.7380\n2023-01-03 16:04:21,925 - mmcls - INFO - Epoch [6][4500/5005]\tlr: 1.000e-01, eta: 11:33:32, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7314\n2023-01-03 16:04:31,039 - mmcls - INFO - Epoch [6][4600/5005]\tlr: 1.000e-01, eta: 11:33:28, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.7479\n2023-01-03 16:04:39,741 - mmcls - INFO - Epoch [6][4700/5005]\tlr: 1.000e-01, eta: 11:33:17, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.7504\n2023-01-03 16:04:48,562 - mmcls - INFO - Epoch [6][4800/5005]\tlr: 1.000e-01, eta: 11:33:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7432\n2023-01-03 16:04:57,658 - mmcls - INFO - Epoch [6][4900/5005]\tlr: 1.000e-01, eta: 11:33:03, time: 0.091, data_time: 0.034, memory: 1125, loss: 2.7310\n2023-01-03 16:05:06,459 - mmcls - INFO - Epoch [6][5000/5005]\tlr: 1.000e-01, eta: 11:32:53, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7430\n2023-01-03 16:05:28,978 - mmcls - INFO - Epoch(val) [6][196]\taccuracy_top-1: 44.0160, accuracy_top-5: 70.7660\n2023-01-03 16:05:39,926 - mmcls - INFO - Epoch [7][100/5005]\tlr: 1.000e-01, eta: 11:33:10, time: 0.109, data_time: 0.040, memory: 1125, loss: 2.7128\n2023-01-03 16:05:48,494 - mmcls - INFO - Epoch [7][200/5005]\tlr: 1.000e-01, eta: 11:32:57, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.6944\n2023-01-03 16:05:57,374 - mmcls - INFO - Epoch [7][300/5005]\tlr: 1.000e-01, eta: 11:32:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7195\n2023-01-03 16:06:06,066 - mmcls - INFO - Epoch [7][400/5005]\tlr: 1.000e-01, eta: 11:32:37, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6993\n2023-01-03 16:06:14,653 - mmcls - INFO - Epoch [7][500/5005]\tlr: 1.000e-01, eta: 11:32:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6877\n2023-01-03 16:06:23,505 - mmcls - INFO - Epoch [7][600/5005]\tlr: 1.000e-01, eta: 11:32:16, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.6918\n2023-01-03 16:06:31,976 - mmcls - INFO - Epoch [7][700/5005]\tlr: 1.000e-01, eta: 11:32:01, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7069\n2023-01-03 16:06:41,036 - mmcls - INFO - Epoch [7][800/5005]\tlr: 1.000e-01, eta: 11:31:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.7133\n2023-01-03 16:06:49,738 - mmcls - INFO - Epoch [7][900/5005]\tlr: 1.000e-01, eta: 11:31:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7005\n2023-01-03 16:06:58,453 - mmcls - INFO - Epoch [7][1000/5005]\tlr: 1.000e-01, eta: 11:31:34, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7099\n2023-01-03 16:07:07,262 - mmcls - INFO - Epoch [7][1100/5005]\tlr: 1.000e-01, eta: 11:31:25, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7471\n2023-01-03 16:07:16,279 - mmcls - INFO - Epoch [7][1200/5005]\tlr: 1.000e-01, eta: 11:31:19, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.6998\n2023-01-03 16:07:24,819 - mmcls - INFO - Epoch [7][1300/5005]\tlr: 1.000e-01, eta: 11:31:05, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.7193\n2023-01-03 16:07:33,957 - mmcls - INFO - Epoch [7][1400/5005]\tlr: 1.000e-01, eta: 11:31:01, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7208\n2023-01-03 16:07:42,837 - mmcls - INFO - Epoch [7][1500/5005]\tlr: 1.000e-01, eta: 11:30:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7228\n2023-01-03 16:07:51,437 - mmcls - INFO - Epoch [7][1600/5005]\tlr: 1.000e-01, eta: 11:30:40, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.7159\n2023-01-03 16:08:00,534 - mmcls - INFO - Epoch [7][1700/5005]\tlr: 1.000e-01, eta: 11:30:35, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.6782\n2023-01-03 16:08:09,224 - mmcls - INFO - Epoch [7][1800/5005]\tlr: 1.000e-01, eta: 11:30:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7194\n2023-01-03 16:08:18,041 - mmcls - INFO - Epoch [7][1900/5005]\tlr: 1.000e-01, eta: 11:30:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7155\n2023-01-03 16:08:26,875 - mmcls - INFO - Epoch [7][2000/5005]\tlr: 1.000e-01, eta: 11:30:06, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.7177\n2023-01-03 16:08:35,496 - mmcls - INFO - Epoch [7][2100/5005]\tlr: 1.000e-01, eta: 11:29:54, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7275\n2023-01-03 16:08:44,293 - mmcls - INFO - Epoch [7][2200/5005]\tlr: 1.000e-01, eta: 11:29:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7524\n2023-01-03 16:08:53,084 - mmcls - INFO - Epoch [7][2300/5005]\tlr: 1.000e-01, eta: 11:29:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7250\n2023-01-03 16:09:01,739 - mmcls - INFO - Epoch [7][2400/5005]\tlr: 1.000e-01, eta: 11:29:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6961\n2023-01-03 16:09:10,361 - mmcls - INFO - Epoch [7][2500/5005]\tlr: 1.000e-01, eta: 11:29:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7207\n2023-01-03 16:09:18,754 - mmcls - INFO - Epoch [7][2600/5005]\tlr: 1.000e-01, eta: 11:28:56, time: 0.084, data_time: 0.025, memory: 1125, loss: 2.7360\n2023-01-03 16:09:27,809 - mmcls - INFO - Epoch [7][2700/5005]\tlr: 1.000e-01, eta: 11:28:51, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.7016\n2023-01-03 16:09:36,582 - mmcls - INFO - Epoch [7][2800/5005]\tlr: 1.000e-01, eta: 11:28:41, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.7370\n2023-01-03 16:09:45,142 - mmcls - INFO - Epoch [7][2900/5005]\tlr: 1.000e-01, eta: 11:28:28, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7147\n2023-01-03 16:09:54,062 - mmcls - INFO - Epoch [7][3000/5005]\tlr: 1.000e-01, eta: 11:28:21, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7133\n2023-01-03 16:10:02,961 - mmcls - INFO - Epoch [7][3100/5005]\tlr: 1.000e-01, eta: 11:28:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7160\n2023-01-03 16:10:11,684 - mmcls - INFO - Epoch [7][3200/5005]\tlr: 1.000e-01, eta: 11:28:02, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.6959\n2023-01-03 16:10:20,297 - mmcls - INFO - Epoch [7][3300/5005]\tlr: 1.000e-01, eta: 11:27:50, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.7229\n2023-01-03 16:10:28,981 - mmcls - INFO - Epoch [7][3400/5005]\tlr: 1.000e-01, eta: 11:27:39, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7229\n2023-01-03 16:10:37,854 - mmcls - INFO - Epoch [7][3500/5005]\tlr: 1.000e-01, eta: 11:27:31, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7181\n2023-01-03 16:10:46,884 - mmcls - INFO - Epoch [7][3600/5005]\tlr: 1.000e-01, eta: 11:27:25, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6851\n2023-01-03 16:10:55,761 - mmcls - INFO - Epoch [7][3700/5005]\tlr: 1.000e-01, eta: 11:27:17, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.7074\n2023-01-03 16:11:04,513 - mmcls - INFO - Epoch [7][3800/5005]\tlr: 1.000e-01, eta: 11:27:07, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.7011\n2023-01-03 16:11:13,449 - mmcls - INFO - Epoch [7][3900/5005]\tlr: 1.000e-01, eta: 11:26:59, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.7132\n2023-01-03 16:11:22,329 - mmcls - INFO - Epoch [7][4000/5005]\tlr: 1.000e-01, eta: 11:26:51, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7299\n2023-01-03 16:11:31,113 - mmcls - INFO - Epoch [7][4100/5005]\tlr: 1.000e-01, eta: 11:26:41, time: 0.088, data_time: 0.047, memory: 1125, loss: 2.7286\n2023-01-03 16:11:39,605 - mmcls - INFO - Epoch [7][4200/5005]\tlr: 1.000e-01, eta: 11:26:28, time: 0.085, data_time: 0.041, memory: 1125, loss: 2.7065\n2023-01-03 16:11:48,320 - mmcls - INFO - Epoch [7][4300/5005]\tlr: 1.000e-01, eta: 11:26:17, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7015\n2023-01-03 16:11:57,392 - mmcls - INFO - Epoch [7][4400/5005]\tlr: 1.000e-01, eta: 11:26:12, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7023\n2023-01-03 16:12:06,075 - mmcls - INFO - Epoch [7][4500/5005]\tlr: 1.000e-01, eta: 11:26:01, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7052\n2023-01-03 16:12:14,887 - mmcls - INFO - Epoch [7][4600/5005]\tlr: 1.000e-01, eta: 11:25:52, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6985\n2023-01-03 16:12:23,871 - mmcls - INFO - Epoch [7][4700/5005]\tlr: 1.000e-01, eta: 11:25:45, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.7030\n2023-01-03 16:12:32,893 - mmcls - INFO - Epoch [7][4800/5005]\tlr: 1.000e-01, eta: 11:25:39, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.7198\n2023-01-03 16:12:41,683 - mmcls - INFO - Epoch [7][4900/5005]\tlr: 1.000e-01, eta: 11:25:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6914\n2023-01-03 16:12:50,393 - mmcls - INFO - Epoch [7][5000/5005]\tlr: 1.000e-01, eta: 11:25:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7296\n2023-01-03 16:13:13,128 - mmcls - INFO - Epoch(val) [7][196]\taccuracy_top-1: 46.7100, accuracy_top-5: 72.5680\n2023-01-03 16:13:24,154 - mmcls - INFO - Epoch [8][100/5005]\tlr: 1.000e-01, eta: 11:25:32, time: 0.110, data_time: 0.037, memory: 1125, loss: 2.6507\n2023-01-03 16:13:32,934 - mmcls - INFO - Epoch [8][200/5005]\tlr: 1.000e-01, eta: 11:25:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6699\n2023-01-03 16:13:41,571 - mmcls - INFO - Epoch [8][300/5005]\tlr: 1.000e-01, eta: 11:25:11, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.6612\n2023-01-03 16:13:50,512 - mmcls - INFO - Epoch [8][400/5005]\tlr: 1.000e-01, eta: 11:25:04, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6721\n2023-01-03 16:13:59,262 - mmcls - INFO - Epoch [8][500/5005]\tlr: 1.000e-01, eta: 11:24:54, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.6534\n2023-01-03 16:14:07,780 - mmcls - INFO - Epoch [8][600/5005]\tlr: 1.000e-01, eta: 11:24:41, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.6587\n2023-01-03 16:14:16,594 - mmcls - INFO - Epoch [8][700/5005]\tlr: 1.000e-01, eta: 11:24:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6935\n2023-01-03 16:14:25,488 - mmcls - INFO - Epoch [8][800/5005]\tlr: 1.000e-01, eta: 11:24:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6666\n2023-01-03 16:14:33,977 - mmcls - INFO - Epoch [8][900/5005]\tlr: 1.000e-01, eta: 11:24:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.6889\n2023-01-03 16:14:42,494 - mmcls - INFO - Epoch [8][1000/5005]\tlr: 1.000e-01, eta: 11:23:57, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6657\n2023-01-03 16:14:51,381 - mmcls - INFO - Epoch [8][1100/5005]\tlr: 1.000e-01, eta: 11:23:49, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.6696\n2023-01-03 16:15:00,110 - mmcls - INFO - Epoch [8][1200/5005]\tlr: 1.000e-01, eta: 11:23:39, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.6875\n2023-01-03 16:15:08,923 - mmcls - INFO - Epoch [8][1300/5005]\tlr: 1.000e-01, eta: 11:23:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7078\n2023-01-03 16:15:17,858 - mmcls - INFO - Epoch [8][1400/5005]\tlr: 1.000e-01, eta: 11:23:22, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.6690\n2023-01-03 16:15:26,382 - mmcls - INFO - Epoch [8][1500/5005]\tlr: 1.000e-01, eta: 11:23:09, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.6688\n2023-01-03 16:15:35,211 - mmcls - INFO - Epoch [8][1600/5005]\tlr: 1.000e-01, eta: 11:23:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6945\n2023-01-03 16:15:44,031 - mmcls - INFO - Epoch [8][1700/5005]\tlr: 1.000e-01, eta: 11:22:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6738\n2023-01-03 16:15:52,904 - mmcls - INFO - Epoch [8][1800/5005]\tlr: 1.000e-01, eta: 11:22:43, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.6709\n2023-01-03 16:16:01,896 - mmcls - INFO - Epoch [8][1900/5005]\tlr: 1.000e-01, eta: 11:22:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.6949\n2023-01-03 16:16:10,500 - mmcls - INFO - Epoch [8][2000/5005]\tlr: 1.000e-01, eta: 11:22:24, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6637\n2023-01-03 16:16:19,019 - mmcls - INFO - Epoch [8][2100/5005]\tlr: 1.000e-01, eta: 11:22:12, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.6592\n2023-01-03 16:16:27,546 - mmcls - INFO - Epoch [8][2200/5005]\tlr: 1.000e-01, eta: 11:21:59, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6983\n2023-01-03 16:16:36,133 - mmcls - INFO - Epoch [8][2300/5005]\tlr: 1.000e-01, eta: 11:21:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6541\n2023-01-03 16:16:44,778 - mmcls - INFO - Epoch [8][2400/5005]\tlr: 1.000e-01, eta: 11:21:36, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.6927\n2023-01-03 16:16:53,703 - mmcls - INFO - Epoch [8][2500/5005]\tlr: 1.000e-01, eta: 11:21:28, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6876\n2023-01-03 16:17:02,356 - mmcls - INFO - Epoch [8][2600/5005]\tlr: 1.000e-01, eta: 11:21:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.6946\n2023-01-03 16:17:11,122 - mmcls - INFO - Epoch [8][2700/5005]\tlr: 1.000e-01, eta: 11:21:07, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7066\n2023-01-03 16:17:20,433 - mmcls - INFO - Epoch [8][2800/5005]\tlr: 1.000e-01, eta: 11:21:04, time: 0.093, data_time: 0.008, memory: 1125, loss: 2.6756\n2023-01-03 16:17:29,189 - mmcls - INFO - Epoch [8][2900/5005]\tlr: 1.000e-01, eta: 11:20:55, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.6669\n2023-01-03 16:17:38,147 - mmcls - INFO - Epoch [8][3000/5005]\tlr: 1.000e-01, eta: 11:20:47, time: 0.090, data_time: 0.026, memory: 1125, loss: 2.6775\n2023-01-03 16:17:46,753 - mmcls - INFO - Epoch [8][3100/5005]\tlr: 1.000e-01, eta: 11:20:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6831\n2023-01-03 16:17:55,182 - mmcls - INFO - Epoch [8][3200/5005]\tlr: 1.000e-01, eta: 11:20:22, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.6914\n2023-01-03 16:18:04,094 - mmcls - INFO - Epoch [8][3300/5005]\tlr: 1.000e-01, eta: 11:20:14, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.6446\n2023-01-03 16:18:12,967 - mmcls - INFO - Epoch [8][3400/5005]\tlr: 1.000e-01, eta: 11:20:06, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.6825\n2023-01-03 16:18:21,532 - mmcls - INFO - Epoch [8][3500/5005]\tlr: 1.000e-01, eta: 11:19:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.6664\n2023-01-03 16:18:30,449 - mmcls - INFO - Epoch [8][3600/5005]\tlr: 1.000e-01, eta: 11:19:46, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.6764\n2023-01-03 16:18:39,287 - mmcls - INFO - Epoch [8][3700/5005]\tlr: 1.000e-01, eta: 11:19:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.6769\n2023-01-03 16:18:47,991 - mmcls - INFO - Epoch [8][3800/5005]\tlr: 1.000e-01, eta: 11:19:27, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.6822\n2023-01-03 16:18:56,802 - mmcls - INFO - Epoch [8][3900/5005]\tlr: 1.000e-01, eta: 11:19:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.6877\n2023-01-03 16:19:05,687 - mmcls - INFO - Epoch [8][4000/5005]\tlr: 1.000e-01, eta: 11:19:10, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6935\n2023-01-03 16:19:14,719 - mmcls - INFO - Epoch [8][4100/5005]\tlr: 1.000e-01, eta: 11:19:03, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6538\n2023-01-03 16:19:23,410 - mmcls - INFO - Epoch [8][4200/5005]\tlr: 1.000e-01, eta: 11:18:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6592\n2023-01-03 16:19:31,910 - mmcls - INFO - Epoch [8][4300/5005]\tlr: 1.000e-01, eta: 11:18:40, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.6868\n2023-01-03 16:19:40,619 - mmcls - INFO - Epoch [8][4400/5005]\tlr: 1.000e-01, eta: 11:18:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6806\n2023-01-03 16:19:49,508 - mmcls - INFO - Epoch [8][4500/5005]\tlr: 1.000e-01, eta: 11:18:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6743\n2023-01-03 16:19:58,615 - mmcls - INFO - Epoch [8][4600/5005]\tlr: 1.000e-01, eta: 11:18:16, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.6954\n2023-01-03 16:20:07,543 - mmcls - INFO - Epoch [8][4700/5005]\tlr: 1.000e-01, eta: 11:18:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6731\n2023-01-03 16:20:16,042 - mmcls - INFO - Epoch [8][4800/5005]\tlr: 1.000e-01, eta: 11:17:55, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.6893\n2023-01-03 16:20:25,110 - mmcls - INFO - Epoch [8][4900/5005]\tlr: 1.000e-01, eta: 11:17:49, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.6769\n2023-01-03 16:20:33,840 - mmcls - INFO - Epoch [8][5000/5005]\tlr: 1.000e-01, eta: 11:17:39, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.6838\n2023-01-03 16:20:56,622 - mmcls - INFO - Epoch(val) [8][196]\taccuracy_top-1: 45.5300, accuracy_top-5: 71.7540\n2023-01-03 16:21:07,595 - mmcls - INFO - Epoch [9][100/5005]\tlr: 1.000e-01, eta: 11:17:49, time: 0.109, data_time: 0.038, memory: 1125, loss: 2.6299\n2023-01-03 16:21:16,407 - mmcls - INFO - Epoch [9][200/5005]\tlr: 1.000e-01, eta: 11:17:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.6156\n2023-01-03 16:21:24,916 - mmcls - INFO - Epoch [9][300/5005]\tlr: 1.000e-01, eta: 11:17:28, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6425\n2023-01-03 16:21:33,560 - mmcls - INFO - Epoch [9][400/5005]\tlr: 1.000e-01, eta: 11:17:17, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.6543\n2023-01-03 16:21:42,169 - mmcls - INFO - Epoch [9][500/5005]\tlr: 1.000e-01, eta: 11:17:05, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.6298\n2023-01-03 16:21:50,871 - mmcls - INFO - Epoch [9][600/5005]\tlr: 1.000e-01, eta: 11:16:55, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.6507\n2023-01-03 16:21:59,330 - mmcls - INFO - Epoch [9][700/5005]\tlr: 1.000e-01, eta: 11:16:42, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.6526\n2023-01-03 16:22:07,985 - mmcls - INFO - Epoch [9][800/5005]\tlr: 1.000e-01, eta: 11:16:31, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6183\n2023-01-03 16:22:16,733 - mmcls - INFO - Epoch [9][900/5005]\tlr: 1.000e-01, eta: 11:16:21, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6456\n2023-01-03 16:22:25,728 - mmcls - INFO - Epoch [9][1000/5005]\tlr: 1.000e-01, eta: 11:16:14, time: 0.090, data_time: 0.035, memory: 1125, loss: 2.6258\n2023-01-03 16:22:34,567 - mmcls - INFO - Epoch [9][1100/5005]\tlr: 1.000e-01, eta: 11:16:06, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.6411\n2023-01-03 16:22:43,262 - mmcls - INFO - Epoch [9][1200/5005]\tlr: 1.000e-01, eta: 11:15:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6596\n2023-01-03 16:22:51,992 - mmcls - INFO - Epoch [9][1300/5005]\tlr: 1.000e-01, eta: 11:15:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6807\n2023-01-03 16:23:00,925 - mmcls - INFO - Epoch [9][1400/5005]\tlr: 1.000e-01, eta: 11:15:38, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.6502\n2023-01-03 16:23:09,644 - mmcls - INFO - Epoch [9][1500/5005]\tlr: 1.000e-01, eta: 11:15:27, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.6315\n2023-01-03 16:23:18,298 - mmcls - INFO - Epoch [9][1600/5005]\tlr: 1.000e-01, eta: 11:15:17, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.6434\n2023-01-03 16:23:26,987 - mmcls - INFO - Epoch [9][1700/5005]\tlr: 1.000e-01, eta: 11:15:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6408\n2023-01-03 16:23:36,117 - mmcls - INFO - Epoch [9][1800/5005]\tlr: 1.000e-01, eta: 11:15:01, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.6351\n2023-01-03 16:23:44,613 - mmcls - INFO - Epoch [9][1900/5005]\tlr: 1.000e-01, eta: 11:14:48, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6598\n2023-01-03 16:23:53,664 - mmcls - INFO - Epoch [9][2000/5005]\tlr: 1.000e-01, eta: 11:14:42, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.6395\n2023-01-03 16:24:02,707 - mmcls - INFO - Epoch [9][2100/5005]\tlr: 1.000e-01, eta: 11:14:35, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.6474\n2023-01-03 16:24:11,405 - mmcls - INFO - Epoch [9][2200/5005]\tlr: 1.000e-01, eta: 11:14:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6438\n2023-01-03 16:24:20,192 - mmcls - INFO - Epoch [9][2300/5005]\tlr: 1.000e-01, eta: 11:14:16, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6250\n2023-01-03 16:24:29,063 - mmcls - INFO - Epoch [9][2400/5005]\tlr: 1.000e-01, eta: 11:14:07, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.6557\n2023-01-03 16:24:37,780 - mmcls - INFO - Epoch [9][2500/5005]\tlr: 1.000e-01, eta: 11:13:57, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.6437\n2023-01-03 16:24:46,396 - mmcls - INFO - Epoch [9][2600/5005]\tlr: 1.000e-01, eta: 11:13:46, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.6453\n2023-01-03 16:24:55,054 - mmcls - INFO - Epoch [9][2700/5005]\tlr: 1.000e-01, eta: 11:13:35, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6343\n2023-01-03 16:25:03,727 - mmcls - INFO - Epoch [9][2800/5005]\tlr: 1.000e-01, eta: 11:13:25, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.6383\n2023-01-03 16:25:12,369 - mmcls - INFO - Epoch [9][2900/5005]\tlr: 1.000e-01, eta: 11:13:14, time: 0.086, data_time: 0.037, memory: 1125, loss: 2.6506\n2023-01-03 16:25:21,112 - mmcls - INFO - Epoch [9][3000/5005]\tlr: 1.000e-01, eta: 11:13:04, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.6527\n2023-01-03 16:25:29,593 - mmcls - INFO - Epoch [9][3100/5005]\tlr: 1.000e-01, eta: 11:12:52, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6718\n2023-01-03 16:25:38,549 - mmcls - INFO - Epoch [9][3200/5005]\tlr: 1.000e-01, eta: 11:12:44, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.6633\n2023-01-03 16:25:47,514 - mmcls - INFO - Epoch [9][3300/5005]\tlr: 1.000e-01, eta: 11:12:37, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.6374\n2023-01-03 16:25:56,074 - mmcls - INFO - Epoch [9][3400/5005]\tlr: 1.000e-01, eta: 11:12:25, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6768\n2023-01-03 16:26:05,015 - mmcls - INFO - Epoch [9][3500/5005]\tlr: 1.000e-01, eta: 11:12:18, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6665\n2023-01-03 16:26:14,254 - mmcls - INFO - Epoch [9][3600/5005]\tlr: 1.000e-01, eta: 11:12:13, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.6323\n2023-01-03 16:26:23,268 - mmcls - INFO - Epoch [9][3700/5005]\tlr: 1.000e-01, eta: 11:12:06, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.6450\n2023-01-03 16:26:32,289 - mmcls - INFO - Epoch [9][3800/5005]\tlr: 1.000e-01, eta: 11:11:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.6795\n2023-01-03 16:26:40,835 - mmcls - INFO - Epoch [9][3900/5005]\tlr: 1.000e-01, eta: 11:11:48, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.6294\n2023-01-03 16:26:49,568 - mmcls - INFO - Epoch [9][4000/5005]\tlr: 1.000e-01, eta: 11:11:38, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6336\n2023-01-03 16:26:58,244 - mmcls - INFO - Epoch [9][4100/5005]\tlr: 1.000e-01, eta: 11:11:27, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.6369\n2023-01-03 16:27:07,032 - mmcls - INFO - Epoch [9][4200/5005]\tlr: 1.000e-01, eta: 11:11:18, time: 0.088, data_time: 0.040, memory: 1125, loss: 2.6801\n2023-01-03 16:27:15,691 - mmcls - INFO - Epoch [9][4300/5005]\tlr: 1.000e-01, eta: 11:11:08, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.6137\n2023-01-03 16:27:24,273 - mmcls - INFO - Epoch [9][4400/5005]\tlr: 1.000e-01, eta: 11:10:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6357\n2023-01-03 16:27:32,979 - mmcls - INFO - Epoch [9][4500/5005]\tlr: 1.000e-01, eta: 11:10:46, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6431\n2023-01-03 16:27:41,927 - mmcls - INFO - Epoch [9][4600/5005]\tlr: 1.000e-01, eta: 11:10:38, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.6606\n2023-01-03 16:27:50,904 - mmcls - INFO - Epoch [9][4700/5005]\tlr: 1.000e-01, eta: 11:10:31, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.6576\n2023-01-03 16:27:59,846 - mmcls - INFO - Epoch [9][4800/5005]\tlr: 1.000e-01, eta: 11:10:24, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.6452\n2023-01-03 16:28:08,635 - mmcls - INFO - Epoch [9][4900/5005]\tlr: 1.000e-01, eta: 11:10:14, time: 0.088, data_time: 0.044, memory: 1125, loss: 2.6461\n2023-01-03 16:28:17,249 - mmcls - INFO - Epoch [9][5000/5005]\tlr: 1.000e-01, eta: 11:10:03, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6521\n2023-01-03 16:28:40,196 - mmcls - INFO - Epoch(val) [9][196]\taccuracy_top-1: 46.3660, accuracy_top-5: 72.4300\n2023-01-03 16:28:51,092 - mmcls - INFO - Epoch [10][100/5005]\tlr: 1.000e-01, eta: 11:10:10, time: 0.109, data_time: 0.031, memory: 1125, loss: 2.6027\n2023-01-03 16:28:59,747 - mmcls - INFO - Epoch [10][200/5005]\tlr: 1.000e-01, eta: 11:10:00, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6276\n2023-01-03 16:29:08,382 - mmcls - INFO - Epoch [10][300/5005]\tlr: 1.000e-01, eta: 11:09:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5809\n2023-01-03 16:29:17,222 - mmcls - INFO - Epoch [10][400/5005]\tlr: 1.000e-01, eta: 11:09:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5983\n2023-01-03 16:29:26,276 - mmcls - INFO - Epoch [10][500/5005]\tlr: 1.000e-01, eta: 11:09:33, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.6043\n2023-01-03 16:29:35,003 - mmcls - INFO - Epoch [10][600/5005]\tlr: 1.000e-01, eta: 11:09:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6181\n2023-01-03 16:29:43,738 - mmcls - INFO - Epoch [10][700/5005]\tlr: 1.000e-01, eta: 11:09:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5932\n2023-01-03 16:29:52,709 - mmcls - INFO - Epoch [10][800/5005]\tlr: 1.000e-01, eta: 11:09:06, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.6374\n2023-01-03 16:30:01,662 - mmcls - INFO - Epoch [10][900/5005]\tlr: 1.000e-01, eta: 11:08:59, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.6166\n2023-01-03 16:30:10,333 - mmcls - INFO - Epoch [10][1000/5005]\tlr: 1.000e-01, eta: 11:08:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5883\n2023-01-03 16:30:18,888 - mmcls - INFO - Epoch [10][1100/5005]\tlr: 1.000e-01, eta: 11:08:37, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6196\n2023-01-03 16:30:27,564 - mmcls - INFO - Epoch [10][1200/5005]\tlr: 1.000e-01, eta: 11:08:26, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5982\n2023-01-03 16:30:36,406 - mmcls - INFO - Epoch [10][1300/5005]\tlr: 1.000e-01, eta: 11:08:18, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.6395\n2023-01-03 16:30:45,135 - mmcls - INFO - Epoch [10][1400/5005]\tlr: 1.000e-01, eta: 11:08:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6136\n2023-01-03 16:30:53,927 - mmcls - INFO - Epoch [10][1500/5005]\tlr: 1.000e-01, eta: 11:07:59, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6355\n2023-01-03 16:31:02,612 - mmcls - INFO - Epoch [10][1600/5005]\tlr: 1.000e-01, eta: 11:07:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6475\n2023-01-03 16:31:11,556 - mmcls - INFO - Epoch [10][1700/5005]\tlr: 1.000e-01, eta: 11:07:41, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.6057\n2023-01-03 16:31:20,122 - mmcls - INFO - Epoch [10][1800/5005]\tlr: 1.000e-01, eta: 11:07:29, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6324\n2023-01-03 16:31:28,962 - mmcls - INFO - Epoch [10][1900/5005]\tlr: 1.000e-01, eta: 11:07:21, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6472\n2023-01-03 16:31:37,991 - mmcls - INFO - Epoch [10][2000/5005]\tlr: 1.000e-01, eta: 11:07:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.6168\n2023-01-03 16:31:46,535 - mmcls - INFO - Epoch [10][2100/5005]\tlr: 1.000e-01, eta: 11:07:02, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.6150\n2023-01-03 16:31:55,611 - mmcls - INFO - Epoch [10][2200/5005]\tlr: 1.000e-01, eta: 11:06:56, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.6022\n2023-01-03 16:32:04,119 - mmcls - INFO - Epoch [10][2300/5005]\tlr: 1.000e-01, eta: 11:06:44, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6382\n2023-01-03 16:32:13,221 - mmcls - INFO - Epoch [10][2400/5005]\tlr: 1.000e-01, eta: 11:06:38, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6241\n2023-01-03 16:32:22,021 - mmcls - INFO - Epoch [10][2500/5005]\tlr: 1.000e-01, eta: 11:06:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.6272\n2023-01-03 16:32:30,611 - mmcls - INFO - Epoch [10][2600/5005]\tlr: 1.000e-01, eta: 11:06:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6385\n2023-01-03 16:32:39,493 - mmcls - INFO - Epoch [10][2700/5005]\tlr: 1.000e-01, eta: 11:06:09, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.6159\n2023-01-03 16:32:48,016 - mmcls - INFO - Epoch [10][2800/5005]\tlr: 1.000e-01, eta: 11:05:57, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.6338\n2023-01-03 16:32:56,602 - mmcls - INFO - Epoch [10][2900/5005]\tlr: 1.000e-01, eta: 11:05:46, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6137\n2023-01-03 16:33:05,418 - mmcls - INFO - Epoch [10][3000/5005]\tlr: 1.000e-01, eta: 11:05:37, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6313\n2023-01-03 16:33:14,362 - mmcls - INFO - Epoch [10][3100/5005]\tlr: 1.000e-01, eta: 11:05:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.6090\n2023-01-03 16:33:23,255 - mmcls - INFO - Epoch [10][3200/5005]\tlr: 1.000e-01, eta: 11:05:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6534\n2023-01-03 16:33:32,057 - mmcls - INFO - Epoch [10][3300/5005]\tlr: 1.000e-01, eta: 11:05:12, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6277\n2023-01-03 16:33:40,568 - mmcls - INFO - Epoch [10][3400/5005]\tlr: 1.000e-01, eta: 11:05:00, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.6406\n2023-01-03 16:33:49,693 - mmcls - INFO - Epoch [10][3500/5005]\tlr: 1.000e-01, eta: 11:04:54, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6288\n2023-01-03 16:33:58,513 - mmcls - INFO - Epoch [10][3600/5005]\tlr: 1.000e-01, eta: 11:04:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.6613\n2023-01-03 16:34:06,981 - mmcls - INFO - Epoch [10][3700/5005]\tlr: 1.000e-01, eta: 11:04:33, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6105\n2023-01-03 16:34:15,755 - mmcls - INFO - Epoch [10][3800/5005]\tlr: 1.000e-01, eta: 11:04:24, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.6381\n2023-01-03 16:34:24,617 - mmcls - INFO - Epoch [10][3900/5005]\tlr: 1.000e-01, eta: 11:04:15, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.6182\n2023-01-03 16:34:33,252 - mmcls - INFO - Epoch [10][4000/5005]\tlr: 1.000e-01, eta: 11:04:05, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.6087\n2023-01-03 16:34:42,221 - mmcls - INFO - Epoch [10][4100/5005]\tlr: 1.000e-01, eta: 11:03:57, time: 0.090, data_time: 0.037, memory: 1125, loss: 2.6480\n2023-01-03 16:34:50,935 - mmcls - INFO - Epoch [10][4200/5005]\tlr: 1.000e-01, eta: 11:03:47, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.6403\n2023-01-03 16:34:59,886 - mmcls - INFO - Epoch [10][4300/5005]\tlr: 1.000e-01, eta: 11:03:40, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.6531\n2023-01-03 16:35:08,890 - mmcls - INFO - Epoch [10][4400/5005]\tlr: 1.000e-01, eta: 11:03:33, time: 0.090, data_time: 0.032, memory: 1125, loss: 2.6548\n2023-01-03 16:35:17,886 - mmcls - INFO - Epoch [10][4500/5005]\tlr: 1.000e-01, eta: 11:03:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.6138\n2023-01-03 16:35:26,523 - mmcls - INFO - Epoch [10][4600/5005]\tlr: 1.000e-01, eta: 11:03:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6142\n2023-01-03 16:35:35,193 - mmcls - INFO - Epoch [10][4700/5005]\tlr: 1.000e-01, eta: 11:03:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.6173\n2023-01-03 16:35:43,750 - mmcls - INFO - Epoch [10][4800/5005]\tlr: 1.000e-01, eta: 11:02:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6315\n2023-01-03 16:35:52,497 - mmcls - INFO - Epoch [10][4900/5005]\tlr: 1.000e-01, eta: 11:02:44, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6207\n2023-01-03 16:36:01,417 - mmcls - INFO - Epoch [10][5000/5005]\tlr: 1.000e-01, eta: 11:02:36, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6449\n2023-01-03 16:36:24,017 - mmcls - INFO - Epoch(val) [10][196]\taccuracy_top-1: 47.1720, accuracy_top-5: 73.4980\n2023-01-03 16:36:34,825 - mmcls - INFO - Epoch [11][100/5005]\tlr: 1.000e-01, eta: 11:02:40, time: 0.108, data_time: 0.061, memory: 1125, loss: 2.5712\n2023-01-03 16:36:43,317 - mmcls - INFO - Epoch [11][200/5005]\tlr: 1.000e-01, eta: 11:02:28, time: 0.085, data_time: 0.023, memory: 1125, loss: 2.5676\n2023-01-03 16:36:52,206 - mmcls - INFO - Epoch [11][300/5005]\tlr: 1.000e-01, eta: 11:02:20, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.5800\n2023-01-03 16:37:00,595 - mmcls - INFO - Epoch [11][400/5005]\tlr: 1.000e-01, eta: 11:02:07, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.5926\n2023-01-03 16:37:09,637 - mmcls - INFO - Epoch [11][500/5005]\tlr: 1.000e-01, eta: 11:02:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5609\n2023-01-03 16:37:18,295 - mmcls - INFO - Epoch [11][600/5005]\tlr: 1.000e-01, eta: 11:01:50, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.6096\n2023-01-03 16:37:26,765 - mmcls - INFO - Epoch [11][700/5005]\tlr: 1.000e-01, eta: 11:01:38, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.5962\n2023-01-03 16:37:35,401 - mmcls - INFO - Epoch [11][800/5005]\tlr: 1.000e-01, eta: 11:01:27, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.5938\n2023-01-03 16:37:44,040 - mmcls - INFO - Epoch [11][900/5005]\tlr: 1.000e-01, eta: 11:01:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6052\n2023-01-03 16:37:52,678 - mmcls - INFO - Epoch [11][1000/5005]\tlr: 1.000e-01, eta: 11:01:06, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6059\n2023-01-03 16:38:01,659 - mmcls - INFO - Epoch [11][1100/5005]\tlr: 1.000e-01, eta: 11:00:59, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5805\n2023-01-03 16:38:10,361 - mmcls - INFO - Epoch [11][1200/5005]\tlr: 1.000e-01, eta: 11:00:49, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.5866\n2023-01-03 16:38:19,049 - mmcls - INFO - Epoch [11][1300/5005]\tlr: 1.000e-01, eta: 11:00:39, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.6187\n2023-01-03 16:38:27,803 - mmcls - INFO - Epoch [11][1400/5005]\tlr: 1.000e-01, eta: 11:00:29, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6221\n2023-01-03 16:38:36,417 - mmcls - INFO - Epoch [11][1500/5005]\tlr: 1.000e-01, eta: 11:00:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6244\n2023-01-03 16:38:45,338 - mmcls - INFO - Epoch [11][1600/5005]\tlr: 1.000e-01, eta: 11:00:11, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6339\n2023-01-03 16:38:54,181 - mmcls - INFO - Epoch [11][1700/5005]\tlr: 1.000e-01, eta: 11:00:02, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6037\n2023-01-03 16:39:02,634 - mmcls - INFO - Epoch [11][1800/5005]\tlr: 1.000e-01, eta: 10:59:50, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5895\n2023-01-03 16:39:11,363 - mmcls - INFO - Epoch [11][1900/5005]\tlr: 1.000e-01, eta: 10:59:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.5957\n2023-01-03 16:39:20,084 - mmcls - INFO - Epoch [11][2000/5005]\tlr: 1.000e-01, eta: 10:59:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.6142\n2023-01-03 16:39:29,048 - mmcls - INFO - Epoch [11][2100/5005]\tlr: 1.000e-01, eta: 10:59:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.6024\n2023-01-03 16:39:37,938 - mmcls - INFO - Epoch [11][2200/5005]\tlr: 1.000e-01, eta: 10:59:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6090\n2023-01-03 16:39:46,926 - mmcls - INFO - Epoch [11][2300/5005]\tlr: 1.000e-01, eta: 10:59:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5957\n2023-01-03 16:39:55,637 - mmcls - INFO - Epoch [11][2400/5005]\tlr: 1.000e-01, eta: 10:58:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6168\n2023-01-03 16:40:04,562 - mmcls - INFO - Epoch [11][2500/5005]\tlr: 1.000e-01, eta: 10:58:50, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5991\n2023-01-03 16:40:13,454 - mmcls - INFO - Epoch [11][2600/5005]\tlr: 1.000e-01, eta: 10:58:41, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.5964\n2023-01-03 16:40:22,243 - mmcls - INFO - Epoch [11][2700/5005]\tlr: 1.000e-01, eta: 10:58:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6097\n2023-01-03 16:40:31,075 - mmcls - INFO - Epoch [11][2800/5005]\tlr: 1.000e-01, eta: 10:58:23, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.6229\n2023-01-03 16:40:40,071 - mmcls - INFO - Epoch [11][2900/5005]\tlr: 1.000e-01, eta: 10:58:16, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.6455\n2023-01-03 16:40:49,047 - mmcls - INFO - Epoch [11][3000/5005]\tlr: 1.000e-01, eta: 10:58:08, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.6136\n2023-01-03 16:40:58,175 - mmcls - INFO - Epoch [11][3100/5005]\tlr: 1.000e-01, eta: 10:58:02, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5987\n2023-01-03 16:41:06,956 - mmcls - INFO - Epoch [11][3200/5005]\tlr: 1.000e-01, eta: 10:57:53, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6236\n2023-01-03 16:41:15,639 - mmcls - INFO - Epoch [11][3300/5005]\tlr: 1.000e-01, eta: 10:57:43, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5932\n2023-01-03 16:41:24,595 - mmcls - INFO - Epoch [11][3400/5005]\tlr: 1.000e-01, eta: 10:57:35, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.6171\n2023-01-03 16:41:33,438 - mmcls - INFO - Epoch [11][3500/5005]\tlr: 1.000e-01, eta: 10:57:27, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.6292\n2023-01-03 16:41:42,672 - mmcls - INFO - Epoch [11][3600/5005]\tlr: 1.000e-01, eta: 10:57:21, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.6277\n2023-01-03 16:41:51,374 - mmcls - INFO - Epoch [11][3700/5005]\tlr: 1.000e-01, eta: 10:57:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6283\n2023-01-03 16:42:00,277 - mmcls - INFO - Epoch [11][3800/5005]\tlr: 1.000e-01, eta: 10:57:03, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5890\n2023-01-03 16:42:09,397 - mmcls - INFO - Epoch [11][3900/5005]\tlr: 1.000e-01, eta: 10:56:57, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.6114\n2023-01-03 16:42:18,104 - mmcls - INFO - Epoch [11][4000/5005]\tlr: 1.000e-01, eta: 10:56:47, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.6151\n2023-01-03 16:42:27,259 - mmcls - INFO - Epoch [11][4100/5005]\tlr: 1.000e-01, eta: 10:56:41, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.5854\n2023-01-03 16:42:36,028 - mmcls - INFO - Epoch [11][4200/5005]\tlr: 1.000e-01, eta: 10:56:31, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.6171\n2023-01-03 16:42:44,984 - mmcls - INFO - Epoch [11][4300/5005]\tlr: 1.000e-01, eta: 10:56:24, time: 0.090, data_time: 0.019, memory: 1125, loss: 2.6150\n2023-01-03 16:42:53,616 - mmcls - INFO - Epoch [11][4400/5005]\tlr: 1.000e-01, eta: 10:56:13, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.5974\n2023-01-03 16:43:02,687 - mmcls - INFO - Epoch [11][4500/5005]\tlr: 1.000e-01, eta: 10:56:06, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.6064\n2023-01-03 16:43:11,478 - mmcls - INFO - Epoch [11][4600/5005]\tlr: 1.000e-01, eta: 10:55:57, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.6112\n2023-01-03 16:43:20,205 - mmcls - INFO - Epoch [11][4700/5005]\tlr: 1.000e-01, eta: 10:55:47, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5873\n2023-01-03 16:43:29,324 - mmcls - INFO - Epoch [11][4800/5005]\tlr: 1.000e-01, eta: 10:55:41, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6095\n2023-01-03 16:43:38,221 - mmcls - INFO - Epoch [11][4900/5005]\tlr: 1.000e-01, eta: 10:55:33, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5990\n2023-01-03 16:43:47,346 - mmcls - INFO - Epoch [11][5000/5005]\tlr: 1.000e-01, eta: 10:55:26, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.6335\n2023-01-03 16:44:10,240 - mmcls - INFO - Epoch(val) [11][196]\taccuracy_top-1: 48.3720, accuracy_top-5: 73.9280\n2023-01-03 16:44:21,125 - mmcls - INFO - Epoch [12][100/5005]\tlr: 1.000e-01, eta: 10:55:30, time: 0.109, data_time: 0.036, memory: 1125, loss: 2.5498\n2023-01-03 16:44:29,798 - mmcls - INFO - Epoch [12][200/5005]\tlr: 1.000e-01, eta: 10:55:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5588\n2023-01-03 16:44:39,127 - mmcls - INFO - Epoch [12][300/5005]\tlr: 1.000e-01, eta: 10:55:15, time: 0.093, data_time: 0.034, memory: 1125, loss: 2.5591\n2023-01-03 16:44:48,004 - mmcls - INFO - Epoch [12][400/5005]\tlr: 1.000e-01, eta: 10:55:06, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.5958\n2023-01-03 16:44:56,640 - mmcls - INFO - Epoch [12][500/5005]\tlr: 1.000e-01, eta: 10:54:56, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5451\n2023-01-03 16:45:05,691 - mmcls - INFO - Epoch [12][600/5005]\tlr: 1.000e-01, eta: 10:54:49, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5965\n2023-01-03 16:45:14,379 - mmcls - INFO - Epoch [12][700/5005]\tlr: 1.000e-01, eta: 10:54:39, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5766\n2023-01-03 16:45:23,163 - mmcls - INFO - Epoch [12][800/5005]\tlr: 1.000e-01, eta: 10:54:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5778\n2023-01-03 16:45:32,221 - mmcls - INFO - Epoch [12][900/5005]\tlr: 1.000e-01, eta: 10:54:23, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.5803\n2023-01-03 16:45:40,717 - mmcls - INFO - Epoch [12][1000/5005]\tlr: 1.000e-01, eta: 10:54:11, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.5763\n2023-01-03 16:45:49,672 - mmcls - INFO - Epoch [12][1100/5005]\tlr: 1.000e-01, eta: 10:54:03, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5972\n2023-01-03 16:45:58,482 - mmcls - INFO - Epoch [12][1200/5005]\tlr: 1.000e-01, eta: 10:53:54, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5618\n2023-01-03 16:46:07,490 - mmcls - INFO - Epoch [12][1300/5005]\tlr: 1.000e-01, eta: 10:53:47, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6014\n2023-01-03 16:46:16,391 - mmcls - INFO - Epoch [12][1400/5005]\tlr: 1.000e-01, eta: 10:53:39, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5761\n2023-01-03 16:46:25,277 - mmcls - INFO - Epoch [12][1500/5005]\tlr: 1.000e-01, eta: 10:53:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5813\n2023-01-03 16:46:34,013 - mmcls - INFO - Epoch [12][1600/5005]\tlr: 1.000e-01, eta: 10:53:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5935\n2023-01-03 16:46:42,675 - mmcls - INFO - Epoch [12][1700/5005]\tlr: 1.000e-01, eta: 10:53:10, time: 0.087, data_time: 0.039, memory: 1125, loss: 2.5764\n2023-01-03 16:46:52,043 - mmcls - INFO - Epoch [12][1800/5005]\tlr: 1.000e-01, eta: 10:53:06, time: 0.094, data_time: 0.003, memory: 1125, loss: 2.6027\n2023-01-03 16:47:00,641 - mmcls - INFO - Epoch [12][1900/5005]\tlr: 1.000e-01, eta: 10:52:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6076\n2023-01-03 16:47:09,555 - mmcls - INFO - Epoch [12][2000/5005]\tlr: 1.000e-01, eta: 10:52:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5961\n2023-01-03 16:47:18,377 - mmcls - INFO - Epoch [12][2100/5005]\tlr: 1.000e-01, eta: 10:52:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5979\n2023-01-03 16:47:27,342 - mmcls - INFO - Epoch [12][2200/5005]\tlr: 1.000e-01, eta: 10:52:30, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.5765\n2023-01-03 16:47:36,076 - mmcls - INFO - Epoch [12][2300/5005]\tlr: 1.000e-01, eta: 10:52:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6145\n2023-01-03 16:47:44,695 - mmcls - INFO - Epoch [12][2400/5005]\tlr: 1.000e-01, eta: 10:52:10, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.5929\n2023-01-03 16:47:53,690 - mmcls - INFO - Epoch [12][2500/5005]\tlr: 1.000e-01, eta: 10:52:02, time: 0.090, data_time: 0.031, memory: 1125, loss: 2.5936\n2023-01-03 16:48:02,306 - mmcls - INFO - Epoch [12][2600/5005]\tlr: 1.000e-01, eta: 10:51:52, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6007\n2023-01-03 16:48:11,428 - mmcls - INFO - Epoch [12][2700/5005]\tlr: 1.000e-01, eta: 10:51:45, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.6073\n2023-01-03 16:48:19,960 - mmcls - INFO - Epoch [12][2800/5005]\tlr: 1.000e-01, eta: 10:51:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5808\n2023-01-03 16:48:28,631 - mmcls - INFO - Epoch [12][2900/5005]\tlr: 1.000e-01, eta: 10:51:24, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.6005\n2023-01-03 16:48:37,249 - mmcls - INFO - Epoch [12][3000/5005]\tlr: 1.000e-01, eta: 10:51:14, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.5816\n2023-01-03 16:48:46,076 - mmcls - INFO - Epoch [12][3100/5005]\tlr: 1.000e-01, eta: 10:51:05, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.5967\n2023-01-03 16:48:54,964 - mmcls - INFO - Epoch [12][3200/5005]\tlr: 1.000e-01, eta: 10:50:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6057\n2023-01-03 16:49:03,804 - mmcls - INFO - Epoch [12][3300/5005]\tlr: 1.000e-01, eta: 10:50:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.6072\n2023-01-03 16:49:12,560 - mmcls - INFO - Epoch [12][3400/5005]\tlr: 1.000e-01, eta: 10:50:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5946\n2023-01-03 16:49:21,317 - mmcls - INFO - Epoch [12][3500/5005]\tlr: 1.000e-01, eta: 10:50:29, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.5906\n2023-01-03 16:49:30,091 - mmcls - INFO - Epoch [12][3600/5005]\tlr: 1.000e-01, eta: 10:50:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5885\n2023-01-03 16:49:38,832 - mmcls - INFO - Epoch [12][3700/5005]\tlr: 1.000e-01, eta: 10:50:10, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.6114\n2023-01-03 16:49:47,778 - mmcls - INFO - Epoch [12][3800/5005]\tlr: 1.000e-01, eta: 10:50:02, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5896\n2023-01-03 16:49:56,600 - mmcls - INFO - Epoch [12][3900/5005]\tlr: 1.000e-01, eta: 10:49:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5781\n2023-01-03 16:50:05,660 - mmcls - INFO - Epoch [12][4000/5005]\tlr: 1.000e-01, eta: 10:49:46, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.5769\n2023-01-03 16:50:14,418 - mmcls - INFO - Epoch [12][4100/5005]\tlr: 1.000e-01, eta: 10:49:37, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.5975\n2023-01-03 16:50:23,053 - mmcls - INFO - Epoch [12][4200/5005]\tlr: 1.000e-01, eta: 10:49:26, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.5867\n2023-01-03 16:50:31,806 - mmcls - INFO - Epoch [12][4300/5005]\tlr: 1.000e-01, eta: 10:49:17, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5968\n2023-01-03 16:50:40,553 - mmcls - INFO - Epoch [12][4400/5005]\tlr: 1.000e-01, eta: 10:49:07, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.6266\n2023-01-03 16:50:49,233 - mmcls - INFO - Epoch [12][4500/5005]\tlr: 1.000e-01, eta: 10:48:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5995\n2023-01-03 16:50:58,465 - mmcls - INFO - Epoch [12][4600/5005]\tlr: 1.000e-01, eta: 10:48:52, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.5978\n2023-01-03 16:51:07,318 - mmcls - INFO - Epoch [12][4700/5005]\tlr: 1.000e-01, eta: 10:48:43, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5962\n2023-01-03 16:51:15,934 - mmcls - INFO - Epoch [12][4800/5005]\tlr: 1.000e-01, eta: 10:48:32, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.5826\n2023-01-03 16:51:24,559 - mmcls - INFO - Epoch [12][4900/5005]\tlr: 1.000e-01, eta: 10:48:22, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6016\n2023-01-03 16:51:33,344 - mmcls - INFO - Epoch [12][5000/5005]\tlr: 1.000e-01, eta: 10:48:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5841\n2023-01-03 16:51:55,941 - mmcls - INFO - Epoch(val) [12][196]\taccuracy_top-1: 47.0480, accuracy_top-5: 73.1620\n2023-01-03 16:52:06,761 - mmcls - INFO - Epoch [13][100/5005]\tlr: 1.000e-01, eta: 10:48:15, time: 0.108, data_time: 0.040, memory: 1125, loss: 2.5431\n2023-01-03 16:52:15,280 - mmcls - INFO - Epoch [13][200/5005]\tlr: 1.000e-01, eta: 10:48:04, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.5674\n2023-01-03 16:52:23,849 - mmcls - INFO - Epoch [13][300/5005]\tlr: 1.000e-01, eta: 10:47:53, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5353\n2023-01-03 16:52:32,425 - mmcls - INFO - Epoch [13][400/5005]\tlr: 1.000e-01, eta: 10:47:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5142\n2023-01-03 16:52:41,209 - mmcls - INFO - Epoch [13][500/5005]\tlr: 1.000e-01, eta: 10:47:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5520\n2023-01-03 16:52:49,911 - mmcls - INFO - Epoch [13][600/5005]\tlr: 1.000e-01, eta: 10:47:23, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5595\n2023-01-03 16:52:58,758 - mmcls - INFO - Epoch [13][700/5005]\tlr: 1.000e-01, eta: 10:47:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5419\n2023-01-03 16:53:07,277 - mmcls - INFO - Epoch [13][800/5005]\tlr: 1.000e-01, eta: 10:47:03, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5656\n2023-01-03 16:53:15,931 - mmcls - INFO - Epoch [13][900/5005]\tlr: 1.000e-01, eta: 10:46:53, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5569\n2023-01-03 16:53:24,835 - mmcls - INFO - Epoch [13][1000/5005]\tlr: 1.000e-01, eta: 10:46:45, time: 0.089, data_time: 0.036, memory: 1125, loss: 2.5462\n2023-01-03 16:53:33,662 - mmcls - INFO - Epoch [13][1100/5005]\tlr: 1.000e-01, eta: 10:46:36, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5789\n2023-01-03 16:53:42,242 - mmcls - INFO - Epoch [13][1200/5005]\tlr: 1.000e-01, eta: 10:46:25, time: 0.086, data_time: 0.042, memory: 1125, loss: 2.5714\n2023-01-03 16:53:51,266 - mmcls - INFO - Epoch [13][1300/5005]\tlr: 1.000e-01, eta: 10:46:18, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5763\n2023-01-03 16:54:00,074 - mmcls - INFO - Epoch [13][1400/5005]\tlr: 1.000e-01, eta: 10:46:09, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5639\n2023-01-03 16:54:09,086 - mmcls - INFO - Epoch [13][1500/5005]\tlr: 1.000e-01, eta: 10:46:01, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5854\n2023-01-03 16:54:18,146 - mmcls - INFO - Epoch [13][1600/5005]\tlr: 1.000e-01, eta: 10:45:54, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.5609\n2023-01-03 16:54:27,031 - mmcls - INFO - Epoch [13][1700/5005]\tlr: 1.000e-01, eta: 10:45:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5816\n2023-01-03 16:54:35,795 - mmcls - INFO - Epoch [13][1800/5005]\tlr: 1.000e-01, eta: 10:45:36, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5780\n2023-01-03 16:54:44,604 - mmcls - INFO - Epoch [13][1900/5005]\tlr: 1.000e-01, eta: 10:45:27, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5592\n2023-01-03 16:54:53,433 - mmcls - INFO - Epoch [13][2000/5005]\tlr: 1.000e-01, eta: 10:45:19, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5791\n2023-01-03 16:55:02,149 - mmcls - INFO - Epoch [13][2100/5005]\tlr: 1.000e-01, eta: 10:45:09, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.6032\n2023-01-03 16:55:10,769 - mmcls - INFO - Epoch [13][2200/5005]\tlr: 1.000e-01, eta: 10:44:59, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5689\n2023-01-03 16:55:19,361 - mmcls - INFO - Epoch [13][2300/5005]\tlr: 1.000e-01, eta: 10:44:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5813\n2023-01-03 16:55:28,277 - mmcls - INFO - Epoch [13][2400/5005]\tlr: 1.000e-01, eta: 10:44:40, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.5783\n2023-01-03 16:55:37,023 - mmcls - INFO - Epoch [13][2500/5005]\tlr: 1.000e-01, eta: 10:44:30, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.5807\n2023-01-03 16:55:45,567 - mmcls - INFO - Epoch [13][2600/5005]\tlr: 1.000e-01, eta: 10:44:20, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5885\n2023-01-03 16:55:54,374 - mmcls - INFO - Epoch [13][2700/5005]\tlr: 1.000e-01, eta: 10:44:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5994\n2023-01-03 16:56:02,931 - mmcls - INFO - Epoch [13][2800/5005]\tlr: 1.000e-01, eta: 10:44:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5897\n2023-01-03 16:56:11,668 - mmcls - INFO - Epoch [13][2900/5005]\tlr: 1.000e-01, eta: 10:43:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5748\n2023-01-03 16:56:20,316 - mmcls - INFO - Epoch [13][3000/5005]\tlr: 1.000e-01, eta: 10:43:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5843\n2023-01-03 16:56:29,246 - mmcls - INFO - Epoch [13][3100/5005]\tlr: 1.000e-01, eta: 10:43:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5651\n2023-01-03 16:56:37,939 - mmcls - INFO - Epoch [13][3200/5005]\tlr: 1.000e-01, eta: 10:43:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5790\n2023-01-03 16:56:46,806 - mmcls - INFO - Epoch [13][3300/5005]\tlr: 1.000e-01, eta: 10:43:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5809\n2023-01-03 16:56:55,408 - mmcls - INFO - Epoch [13][3400/5005]\tlr: 1.000e-01, eta: 10:43:03, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5844\n2023-01-03 16:57:03,966 - mmcls - INFO - Epoch [13][3500/5005]\tlr: 1.000e-01, eta: 10:42:53, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.5749\n2023-01-03 16:57:12,794 - mmcls - INFO - Epoch [13][3600/5005]\tlr: 1.000e-01, eta: 10:42:44, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.5700\n2023-01-03 16:57:21,572 - mmcls - INFO - Epoch [13][3700/5005]\tlr: 1.000e-01, eta: 10:42:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5711\n2023-01-03 16:57:30,100 - mmcls - INFO - Epoch [13][3800/5005]\tlr: 1.000e-01, eta: 10:42:24, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.5479\n2023-01-03 16:57:39,143 - mmcls - INFO - Epoch [13][3900/5005]\tlr: 1.000e-01, eta: 10:42:17, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5781\n2023-01-03 16:57:48,125 - mmcls - INFO - Epoch [13][4000/5005]\tlr: 1.000e-01, eta: 10:42:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5953\n2023-01-03 16:57:57,158 - mmcls - INFO - Epoch [13][4100/5005]\tlr: 1.000e-01, eta: 10:42:01, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.5915\n2023-01-03 16:58:05,959 - mmcls - INFO - Epoch [13][4200/5005]\tlr: 1.000e-01, eta: 10:41:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5958\n2023-01-03 16:58:14,557 - mmcls - INFO - Epoch [13][4300/5005]\tlr: 1.000e-01, eta: 10:41:42, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.5825\n2023-01-03 16:58:23,482 - mmcls - INFO - Epoch [13][4400/5005]\tlr: 1.000e-01, eta: 10:41:34, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.5761\n2023-01-03 16:58:32,252 - mmcls - INFO - Epoch [13][4500/5005]\tlr: 1.000e-01, eta: 10:41:24, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5753\n2023-01-03 16:58:41,172 - mmcls - INFO - Epoch [13][4600/5005]\tlr: 1.000e-01, eta: 10:41:16, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5774\n2023-01-03 16:58:49,526 - mmcls - INFO - Epoch [13][4700/5005]\tlr: 1.000e-01, eta: 10:41:04, time: 0.083, data_time: 0.009, memory: 1125, loss: 2.6008\n2023-01-03 16:58:58,489 - mmcls - INFO - Epoch [13][4800/5005]\tlr: 1.000e-01, eta: 10:40:56, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5778\n2023-01-03 16:59:07,222 - mmcls - INFO - Epoch [13][4900/5005]\tlr: 1.000e-01, eta: 10:40:47, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.5831\n2023-01-03 16:59:16,202 - mmcls - INFO - Epoch [13][5000/5005]\tlr: 1.000e-01, eta: 10:40:39, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5547\n2023-01-03 16:59:38,884 - mmcls - INFO - Epoch(val) [13][196]\taccuracy_top-1: 48.3680, accuracy_top-5: 74.2320\n2023-01-03 16:59:49,916 - mmcls - INFO - Epoch [14][100/5005]\tlr: 1.000e-01, eta: 10:40:41, time: 0.110, data_time: 0.059, memory: 1125, loss: 2.5312\n2023-01-03 16:59:59,377 - mmcls - INFO - Epoch [14][200/5005]\tlr: 1.000e-01, eta: 10:40:37, time: 0.095, data_time: 0.012, memory: 1125, loss: 2.5460\n2023-01-03 17:00:08,018 - mmcls - INFO - Epoch [14][300/5005]\tlr: 1.000e-01, eta: 10:40:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5286\n2023-01-03 17:00:16,987 - mmcls - INFO - Epoch [14][400/5005]\tlr: 1.000e-01, eta: 10:40:19, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.5242\n2023-01-03 17:00:25,634 - mmcls - INFO - Epoch [14][500/5005]\tlr: 1.000e-01, eta: 10:40:09, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5494\n2023-01-03 17:00:34,491 - mmcls - INFO - Epoch [14][600/5005]\tlr: 1.000e-01, eta: 10:40:00, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.5302\n2023-01-03 17:00:43,379 - mmcls - INFO - Epoch [14][700/5005]\tlr: 1.000e-01, eta: 10:39:51, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5678\n2023-01-03 17:00:51,801 - mmcls - INFO - Epoch [14][800/5005]\tlr: 1.000e-01, eta: 10:39:40, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.5367\n2023-01-03 17:01:01,130 - mmcls - INFO - Epoch [14][900/5005]\tlr: 1.000e-01, eta: 10:39:34, time: 0.093, data_time: 0.012, memory: 1125, loss: 2.5874\n2023-01-03 17:01:09,936 - mmcls - INFO - Epoch [14][1000/5005]\tlr: 1.000e-01, eta: 10:39:25, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5757\n2023-01-03 17:01:18,522 - mmcls - INFO - Epoch [14][1100/5005]\tlr: 1.000e-01, eta: 10:39:15, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5730\n2023-01-03 17:01:27,073 - mmcls - INFO - Epoch [14][1200/5005]\tlr: 1.000e-01, eta: 10:39:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5545\n2023-01-03 17:01:35,886 - mmcls - INFO - Epoch [14][1300/5005]\tlr: 1.000e-01, eta: 10:38:55, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5566\n2023-01-03 17:01:44,534 - mmcls - INFO - Epoch [14][1400/5005]\tlr: 1.000e-01, eta: 10:38:45, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.5551\n2023-01-03 17:01:53,233 - mmcls - INFO - Epoch [14][1500/5005]\tlr: 1.000e-01, eta: 10:38:36, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5712\n2023-01-03 17:02:01,646 - mmcls - INFO - Epoch [14][1600/5005]\tlr: 1.000e-01, eta: 10:38:24, time: 0.084, data_time: 0.009, memory: 1125, loss: 2.5793\n2023-01-03 17:02:10,514 - mmcls - INFO - Epoch [14][1700/5005]\tlr: 1.000e-01, eta: 10:38:16, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5459\n2023-01-03 17:02:19,322 - mmcls - INFO - Epoch [14][1800/5005]\tlr: 1.000e-01, eta: 10:38:07, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5984\n2023-01-03 17:02:27,959 - mmcls - INFO - Epoch [14][1900/5005]\tlr: 1.000e-01, eta: 10:37:56, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5631\n2023-01-03 17:02:36,683 - mmcls - INFO - Epoch [14][2000/5005]\tlr: 1.000e-01, eta: 10:37:47, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5897\n2023-01-03 17:02:45,547 - mmcls - INFO - Epoch [14][2100/5005]\tlr: 1.000e-01, eta: 10:37:38, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.5611\n2023-01-03 17:02:54,367 - mmcls - INFO - Epoch [14][2200/5005]\tlr: 1.000e-01, eta: 10:37:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5602\n2023-01-03 17:03:03,231 - mmcls - INFO - Epoch [14][2300/5005]\tlr: 1.000e-01, eta: 10:37:21, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5393\n2023-01-03 17:03:11,919 - mmcls - INFO - Epoch [14][2400/5005]\tlr: 1.000e-01, eta: 10:37:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5683\n2023-01-03 17:03:20,478 - mmcls - INFO - Epoch [14][2500/5005]\tlr: 1.000e-01, eta: 10:37:00, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5583\n2023-01-03 17:03:29,129 - mmcls - INFO - Epoch [14][2600/5005]\tlr: 1.000e-01, eta: 10:36:50, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5570\n2023-01-03 17:03:37,882 - mmcls - INFO - Epoch [14][2700/5005]\tlr: 1.000e-01, eta: 10:36:41, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.5555\n2023-01-03 17:03:46,500 - mmcls - INFO - Epoch [14][2800/5005]\tlr: 1.000e-01, eta: 10:36:31, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.5458\n2023-01-03 17:03:55,371 - mmcls - INFO - Epoch [14][2900/5005]\tlr: 1.000e-01, eta: 10:36:22, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.5659\n2023-01-03 17:04:04,358 - mmcls - INFO - Epoch [14][3000/5005]\tlr: 1.000e-01, eta: 10:36:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5642\n2023-01-03 17:04:12,925 - mmcls - INFO - Epoch [14][3100/5005]\tlr: 1.000e-01, eta: 10:36:04, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5541\n2023-01-03 17:04:21,541 - mmcls - INFO - Epoch [14][3200/5005]\tlr: 1.000e-01, eta: 10:35:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5332\n2023-01-03 17:04:30,375 - mmcls - INFO - Epoch [14][3300/5005]\tlr: 1.000e-01, eta: 10:35:45, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5599\n2023-01-03 17:04:39,235 - mmcls - INFO - Epoch [14][3400/5005]\tlr: 1.000e-01, eta: 10:35:37, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5605\n2023-01-03 17:04:47,815 - mmcls - INFO - Epoch [14][3500/5005]\tlr: 1.000e-01, eta: 10:35:26, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5305\n2023-01-03 17:04:56,608 - mmcls - INFO - Epoch [14][3600/5005]\tlr: 1.000e-01, eta: 10:35:17, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5897\n2023-01-03 17:05:05,431 - mmcls - INFO - Epoch [14][3700/5005]\tlr: 1.000e-01, eta: 10:35:08, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.5935\n2023-01-03 17:05:14,185 - mmcls - INFO - Epoch [14][3800/5005]\tlr: 1.000e-01, eta: 10:34:59, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5882\n2023-01-03 17:05:23,017 - mmcls - INFO - Epoch [14][3900/5005]\tlr: 1.000e-01, eta: 10:34:50, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.5675\n2023-01-03 17:05:31,519 - mmcls - INFO - Epoch [14][4000/5005]\tlr: 1.000e-01, eta: 10:34:39, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5772\n2023-01-03 17:05:40,528 - mmcls - INFO - Epoch [14][4100/5005]\tlr: 1.000e-01, eta: 10:34:32, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.5594\n2023-01-03 17:05:49,417 - mmcls - INFO - Epoch [14][4200/5005]\tlr: 1.000e-01, eta: 10:34:23, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.5483\n2023-01-03 17:05:58,051 - mmcls - INFO - Epoch [14][4300/5005]\tlr: 1.000e-01, eta: 10:34:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5690\n2023-01-03 17:06:07,125 - mmcls - INFO - Epoch [14][4400/5005]\tlr: 1.000e-01, eta: 10:34:06, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.5626\n2023-01-03 17:06:16,050 - mmcls - INFO - Epoch [14][4500/5005]\tlr: 1.000e-01, eta: 10:33:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5985\n2023-01-03 17:06:24,847 - mmcls - INFO - Epoch [14][4600/5005]\tlr: 1.000e-01, eta: 10:33:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5664\n2023-01-03 17:06:33,372 - mmcls - INFO - Epoch [14][4700/5005]\tlr: 1.000e-01, eta: 10:33:38, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.5969\n2023-01-03 17:06:42,404 - mmcls - INFO - Epoch [14][4800/5005]\tlr: 1.000e-01, eta: 10:33:30, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.5566\n2023-01-03 17:06:51,044 - mmcls - INFO - Epoch [14][4900/5005]\tlr: 1.000e-01, eta: 10:33:20, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5908\n2023-01-03 17:06:59,641 - mmcls - INFO - Epoch [14][5000/5005]\tlr: 1.000e-01, eta: 10:33:10, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5567\n2023-01-03 17:07:22,235 - mmcls - INFO - Epoch(val) [14][196]\taccuracy_top-1: 48.7580, accuracy_top-5: 74.3940\n2023-01-03 17:07:33,311 - mmcls - INFO - Epoch [15][100/5005]\tlr: 1.000e-01, eta: 10:33:12, time: 0.111, data_time: 0.064, memory: 1125, loss: 2.5225\n2023-01-03 17:07:42,384 - mmcls - INFO - Epoch [15][200/5005]\tlr: 1.000e-01, eta: 10:33:04, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.5285\n2023-01-03 17:07:51,085 - mmcls - INFO - Epoch [15][300/5005]\tlr: 1.000e-01, eta: 10:32:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4920\n2023-01-03 17:08:00,157 - mmcls - INFO - Epoch [15][400/5005]\tlr: 1.000e-01, eta: 10:32:48, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.5218\n2023-01-03 17:08:08,919 - mmcls - INFO - Epoch [15][500/5005]\tlr: 1.000e-01, eta: 10:32:38, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5355\n2023-01-03 17:08:17,671 - mmcls - INFO - Epoch [15][600/5005]\tlr: 1.000e-01, eta: 10:32:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5498\n2023-01-03 17:08:27,247 - mmcls - INFO - Epoch [15][700/5005]\tlr: 1.000e-01, eta: 10:32:25, time: 0.096, data_time: 0.012, memory: 1125, loss: 2.5154\n2023-01-03 17:08:36,476 - mmcls - INFO - Epoch [15][800/5005]\tlr: 1.000e-01, eta: 10:32:18, time: 0.092, data_time: 0.032, memory: 1125, loss: 2.5216\n2023-01-03 17:08:45,059 - mmcls - INFO - Epoch [15][900/5005]\tlr: 1.000e-01, eta: 10:32:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5596\n2023-01-03 17:08:53,949 - mmcls - INFO - Epoch [15][1000/5005]\tlr: 1.000e-01, eta: 10:31:59, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.5598\n2023-01-03 17:09:02,916 - mmcls - INFO - Epoch [15][1100/5005]\tlr: 1.000e-01, eta: 10:31:51, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.5478\n2023-01-03 17:09:11,508 - mmcls - INFO - Epoch [15][1200/5005]\tlr: 1.000e-01, eta: 10:31:41, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.5347\n2023-01-03 17:09:20,380 - mmcls - INFO - Epoch [15][1300/5005]\tlr: 1.000e-01, eta: 10:31:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5539\n2023-01-03 17:09:29,315 - mmcls - INFO - Epoch [15][1400/5005]\tlr: 1.000e-01, eta: 10:31:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.5666\n2023-01-03 17:09:37,994 - mmcls - INFO - Epoch [15][1500/5005]\tlr: 1.000e-01, eta: 10:31:15, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5309\n2023-01-03 17:09:46,661 - mmcls - INFO - Epoch [15][1600/5005]\tlr: 1.000e-01, eta: 10:31:05, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5400\n2023-01-03 17:09:55,467 - mmcls - INFO - Epoch [15][1700/5005]\tlr: 1.000e-01, eta: 10:30:56, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.5454\n2023-01-03 17:10:04,073 - mmcls - INFO - Epoch [15][1800/5005]\tlr: 1.000e-01, eta: 10:30:46, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.5504\n2023-01-03 17:10:12,892 - mmcls - INFO - Epoch [15][1900/5005]\tlr: 1.000e-01, eta: 10:30:37, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5488\n2023-01-03 17:10:21,641 - mmcls - INFO - Epoch [15][2000/5005]\tlr: 1.000e-01, eta: 10:30:27, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.5643\n2023-01-03 17:10:30,517 - mmcls - INFO - Epoch [15][2100/5005]\tlr: 1.000e-01, eta: 10:30:19, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.5452\n2023-01-03 17:10:39,536 - mmcls - INFO - Epoch [15][2200/5005]\tlr: 1.000e-01, eta: 10:30:11, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.5606\n2023-01-03 17:10:48,410 - mmcls - INFO - Epoch [15][2300/5005]\tlr: 1.000e-01, eta: 10:30:03, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.5617\n2023-01-03 17:10:57,106 - mmcls - INFO - Epoch [15][2400/5005]\tlr: 1.000e-01, eta: 10:29:53, time: 0.087, data_time: 0.043, memory: 1125, loss: 2.5581\n2023-01-03 17:11:05,927 - mmcls - INFO - Epoch [15][2500/5005]\tlr: 1.000e-01, eta: 10:29:44, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5588\n2023-01-03 17:11:15,103 - mmcls - INFO - Epoch [15][2600/5005]\tlr: 1.000e-01, eta: 10:29:37, time: 0.092, data_time: 0.011, memory: 1125, loss: 2.5286\n2023-01-03 17:11:23,676 - mmcls - INFO - Epoch [15][2700/5005]\tlr: 1.000e-01, eta: 10:29:27, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.5651\n2023-01-03 17:11:32,357 - mmcls - INFO - Epoch [15][2800/5005]\tlr: 1.000e-01, eta: 10:29:17, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.5624\n2023-01-03 17:11:41,168 - mmcls - INFO - Epoch [15][2900/5005]\tlr: 1.000e-01, eta: 10:29:08, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.5500\n2023-01-03 17:11:50,097 - mmcls - INFO - Epoch [15][3000/5005]\tlr: 1.000e-01, eta: 10:29:00, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.5401\n2023-01-03 17:11:58,911 - mmcls - INFO - Epoch [15][3100/5005]\tlr: 1.000e-01, eta: 10:28:51, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.5669\n2023-01-03 17:12:07,663 - mmcls - INFO - Epoch [15][3200/5005]\tlr: 1.000e-01, eta: 10:28:42, time: 0.088, data_time: 0.041, memory: 1125, loss: 2.5574\n2023-01-03 17:12:16,322 - mmcls - INFO - Epoch [15][3300/5005]\tlr: 1.000e-01, eta: 10:28:32, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.5520\n2023-01-03 17:12:24,974 - mmcls - INFO - Epoch [15][3400/5005]\tlr: 1.000e-01, eta: 10:28:22, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5374\n2023-01-03 17:12:33,750 - mmcls - INFO - Epoch [15][3500/5005]\tlr: 1.000e-01, eta: 10:28:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5436\n2023-01-03 17:12:42,253 - mmcls - INFO - Epoch [15][3600/5005]\tlr: 1.000e-01, eta: 10:28:02, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.5544\n2023-01-03 17:12:51,389 - mmcls - INFO - Epoch [15][3700/5005]\tlr: 1.000e-01, eta: 10:27:55, time: 0.091, data_time: 0.049, memory: 1125, loss: 2.5489\n2023-01-03 17:12:59,903 - mmcls - INFO - Epoch [15][3800/5005]\tlr: 1.000e-01, eta: 10:27:45, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5535\n2023-01-03 17:13:08,517 - mmcls - INFO - Epoch [15][3900/5005]\tlr: 1.000e-01, eta: 10:27:34, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5689\n2023-01-03 17:13:17,536 - mmcls - INFO - Epoch [15][4000/5005]\tlr: 1.000e-01, eta: 10:27:27, time: 0.090, data_time: 0.043, memory: 1125, loss: 2.5539\n2023-01-03 17:13:26,227 - mmcls - INFO - Epoch [15][4100/5005]\tlr: 1.000e-01, eta: 10:27:17, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.5852\n2023-01-03 17:13:35,360 - mmcls - INFO - Epoch [15][4200/5005]\tlr: 1.000e-01, eta: 10:27:10, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.5228\n2023-01-03 17:13:44,156 - mmcls - INFO - Epoch [15][4300/5005]\tlr: 1.000e-01, eta: 10:27:01, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5906\n2023-01-03 17:13:52,863 - mmcls - INFO - Epoch [15][4400/5005]\tlr: 1.000e-01, eta: 10:26:51, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5627\n2023-01-03 17:14:01,512 - mmcls - INFO - Epoch [15][4500/5005]\tlr: 1.000e-01, eta: 10:26:42, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5623\n2023-01-03 17:14:10,257 - mmcls - INFO - Epoch [15][4600/5005]\tlr: 1.000e-01, eta: 10:26:32, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5434\n2023-01-03 17:14:19,084 - mmcls - INFO - Epoch [15][4700/5005]\tlr: 1.000e-01, eta: 10:26:23, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5791\n2023-01-03 17:14:27,921 - mmcls - INFO - Epoch [15][4800/5005]\tlr: 1.000e-01, eta: 10:26:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5484\n2023-01-03 17:14:36,403 - mmcls - INFO - Epoch [15][4900/5005]\tlr: 1.000e-01, eta: 10:26:04, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5470\n2023-01-03 17:14:45,017 - mmcls - INFO - Epoch [15][5000/5005]\tlr: 1.000e-01, eta: 10:25:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5674\n2023-01-03 17:15:07,604 - mmcls - INFO - Epoch(val) [15][196]\taccuracy_top-1: 47.4920, accuracy_top-5: 73.5260\n2023-01-03 17:15:18,513 - mmcls - INFO - Epoch [16][100/5005]\tlr: 1.000e-01, eta: 10:25:54, time: 0.109, data_time: 0.046, memory: 1125, loss: 2.4889\n2023-01-03 17:15:27,373 - mmcls - INFO - Epoch [16][200/5005]\tlr: 1.000e-01, eta: 10:25:45, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4954\n2023-01-03 17:15:36,146 - mmcls - INFO - Epoch [16][300/5005]\tlr: 1.000e-01, eta: 10:25:36, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5112\n2023-01-03 17:15:44,778 - mmcls - INFO - Epoch [16][400/5005]\tlr: 1.000e-01, eta: 10:25:26, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5407\n2023-01-03 17:15:53,603 - mmcls - INFO - Epoch [16][500/5005]\tlr: 1.000e-01, eta: 10:25:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5093\n2023-01-03 17:16:02,530 - mmcls - INFO - Epoch [16][600/5005]\tlr: 1.000e-01, eta: 10:25:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5153\n2023-01-03 17:16:11,348 - mmcls - INFO - Epoch [16][700/5005]\tlr: 1.000e-01, eta: 10:25:00, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.5004\n2023-01-03 17:16:19,964 - mmcls - INFO - Epoch [16][800/5005]\tlr: 1.000e-01, eta: 10:24:50, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.4996\n2023-01-03 17:16:28,638 - mmcls - INFO - Epoch [16][900/5005]\tlr: 1.000e-01, eta: 10:24:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.5450\n2023-01-03 17:16:37,348 - mmcls - INFO - Epoch [16][1000/5005]\tlr: 1.000e-01, eta: 10:24:31, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5120\n2023-01-03 17:16:46,017 - mmcls - INFO - Epoch [16][1100/5005]\tlr: 1.000e-01, eta: 10:24:21, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5518\n2023-01-03 17:16:54,838 - mmcls - INFO - Epoch [16][1200/5005]\tlr: 1.000e-01, eta: 10:24:12, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5509\n2023-01-03 17:17:03,596 - mmcls - INFO - Epoch [16][1300/5005]\tlr: 1.000e-01, eta: 10:24:03, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5316\n2023-01-03 17:17:12,347 - mmcls - INFO - Epoch [16][1400/5005]\tlr: 1.000e-01, eta: 10:23:54, time: 0.087, data_time: 0.038, memory: 1125, loss: 2.5425\n2023-01-03 17:17:21,420 - mmcls - INFO - Epoch [16][1500/5005]\tlr: 1.000e-01, eta: 10:23:46, time: 0.091, data_time: 0.015, memory: 1125, loss: 2.5496\n2023-01-03 17:17:30,044 - mmcls - INFO - Epoch [16][1600/5005]\tlr: 1.000e-01, eta: 10:23:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.5414\n2023-01-03 17:17:38,885 - mmcls - INFO - Epoch [16][1700/5005]\tlr: 1.000e-01, eta: 10:23:27, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5349\n2023-01-03 17:17:47,823 - mmcls - INFO - Epoch [16][1800/5005]\tlr: 1.000e-01, eta: 10:23:19, time: 0.089, data_time: 0.040, memory: 1125, loss: 2.5585\n2023-01-03 17:17:56,503 - mmcls - INFO - Epoch [16][1900/5005]\tlr: 1.000e-01, eta: 10:23:09, time: 0.087, data_time: 0.043, memory: 1125, loss: 2.5296\n2023-01-03 17:18:05,134 - mmcls - INFO - Epoch [16][2000/5005]\tlr: 1.000e-01, eta: 10:23:00, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5451\n2023-01-03 17:18:13,694 - mmcls - INFO - Epoch [16][2100/5005]\tlr: 1.000e-01, eta: 10:22:49, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.5493\n2023-01-03 17:18:22,826 - mmcls - INFO - Epoch [16][2200/5005]\tlr: 1.000e-01, eta: 10:22:42, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.5553\n2023-01-03 17:18:31,389 - mmcls - INFO - Epoch [16][2300/5005]\tlr: 1.000e-01, eta: 10:22:32, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5483\n2023-01-03 17:18:40,085 - mmcls - INFO - Epoch [16][2400/5005]\tlr: 1.000e-01, eta: 10:22:22, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5533\n2023-01-03 17:18:48,767 - mmcls - INFO - Epoch [16][2500/5005]\tlr: 1.000e-01, eta: 10:22:13, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5545\n2023-01-03 17:18:58,029 - mmcls - INFO - Epoch [16][2600/5005]\tlr: 1.000e-01, eta: 10:22:06, time: 0.093, data_time: 0.030, memory: 1125, loss: 2.5485\n2023-01-03 17:19:06,966 - mmcls - INFO - Epoch [16][2700/5005]\tlr: 1.000e-01, eta: 10:21:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5332\n2023-01-03 17:19:15,451 - mmcls - INFO - Epoch [16][2800/5005]\tlr: 1.000e-01, eta: 10:21:47, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.5665\n2023-01-03 17:19:24,214 - mmcls - INFO - Epoch [16][2900/5005]\tlr: 1.000e-01, eta: 10:21:38, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5204\n2023-01-03 17:19:32,895 - mmcls - INFO - Epoch [16][3000/5005]\tlr: 1.000e-01, eta: 10:21:28, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5250\n2023-01-03 17:19:41,649 - mmcls - INFO - Epoch [16][3100/5005]\tlr: 1.000e-01, eta: 10:21:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5398\n2023-01-03 17:19:50,228 - mmcls - INFO - Epoch [16][3200/5005]\tlr: 1.000e-01, eta: 10:21:09, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.5071\n2023-01-03 17:19:59,111 - mmcls - INFO - Epoch [16][3300/5005]\tlr: 1.000e-01, eta: 10:21:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5658\n2023-01-03 17:20:07,815 - mmcls - INFO - Epoch [16][3400/5005]\tlr: 1.000e-01, eta: 10:20:51, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5130\n2023-01-03 17:20:16,597 - mmcls - INFO - Epoch [16][3500/5005]\tlr: 1.000e-01, eta: 10:20:42, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.5622\n2023-01-03 17:20:25,495 - mmcls - INFO - Epoch [16][3600/5005]\tlr: 1.000e-01, eta: 10:20:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5596\n2023-01-03 17:20:34,467 - mmcls - INFO - Epoch [16][3700/5005]\tlr: 1.000e-01, eta: 10:20:25, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.5494\n2023-01-03 17:20:43,164 - mmcls - INFO - Epoch [16][3800/5005]\tlr: 1.000e-01, eta: 10:20:16, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5525\n2023-01-03 17:20:51,535 - mmcls - INFO - Epoch [16][3900/5005]\tlr: 1.000e-01, eta: 10:20:05, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.5430\n2023-01-03 17:21:00,055 - mmcls - INFO - Epoch [16][4000/5005]\tlr: 1.000e-01, eta: 10:19:54, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.5457\n2023-01-03 17:21:08,845 - mmcls - INFO - Epoch [16][4100/5005]\tlr: 1.000e-01, eta: 10:19:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.5614\n2023-01-03 17:21:18,110 - mmcls - INFO - Epoch [16][4200/5005]\tlr: 1.000e-01, eta: 10:19:39, time: 0.093, data_time: 0.007, memory: 1125, loss: 2.5472\n2023-01-03 17:21:26,628 - mmcls - INFO - Epoch [16][4300/5005]\tlr: 1.000e-01, eta: 10:19:28, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.5241\n2023-01-03 17:21:35,474 - mmcls - INFO - Epoch [16][4400/5005]\tlr: 1.000e-01, eta: 10:19:19, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5229\n2023-01-03 17:21:44,298 - mmcls - INFO - Epoch [16][4500/5005]\tlr: 1.000e-01, eta: 10:19:11, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5866\n2023-01-03 17:21:53,041 - mmcls - INFO - Epoch [16][4600/5005]\tlr: 1.000e-01, eta: 10:19:01, time: 0.087, data_time: 0.029, memory: 1125, loss: 2.5573\n2023-01-03 17:22:01,945 - mmcls - INFO - Epoch [16][4700/5005]\tlr: 1.000e-01, eta: 10:18:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5492\n2023-01-03 17:22:10,659 - mmcls - INFO - Epoch [16][4800/5005]\tlr: 1.000e-01, eta: 10:18:43, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5615\n2023-01-03 17:22:19,841 - mmcls - INFO - Epoch [16][4900/5005]\tlr: 1.000e-01, eta: 10:18:37, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.5421\n2023-01-03 17:22:28,559 - mmcls - INFO - Epoch [16][5000/5005]\tlr: 1.000e-01, eta: 10:18:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5454\n2023-01-03 17:22:51,370 - mmcls - INFO - Epoch(val) [16][196]\taccuracy_top-1: 47.9200, accuracy_top-5: 74.1040\n2023-01-03 17:23:02,087 - mmcls - INFO - Epoch [17][100/5005]\tlr: 1.000e-01, eta: 10:18:25, time: 0.107, data_time: 0.047, memory: 1125, loss: 2.5069\n2023-01-03 17:23:10,838 - mmcls - INFO - Epoch [17][200/5005]\tlr: 1.000e-01, eta: 10:18:16, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5141\n2023-01-03 17:23:19,480 - mmcls - INFO - Epoch [17][300/5005]\tlr: 1.000e-01, eta: 10:18:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4727\n2023-01-03 17:23:28,096 - mmcls - INFO - Epoch [17][400/5005]\tlr: 1.000e-01, eta: 10:17:56, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.5135\n2023-01-03 17:23:37,140 - mmcls - INFO - Epoch [17][500/5005]\tlr: 1.000e-01, eta: 10:17:49, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5067\n2023-01-03 17:23:45,930 - mmcls - INFO - Epoch [17][600/5005]\tlr: 1.000e-01, eta: 10:17:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5294\n2023-01-03 17:23:54,631 - mmcls - INFO - Epoch [17][700/5005]\tlr: 1.000e-01, eta: 10:17:30, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.5320\n2023-01-03 17:24:03,506 - mmcls - INFO - Epoch [17][800/5005]\tlr: 1.000e-01, eta: 10:17:22, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5231\n2023-01-03 17:24:12,177 - mmcls - INFO - Epoch [17][900/5005]\tlr: 1.000e-01, eta: 10:17:12, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5195\n2023-01-03 17:24:20,895 - mmcls - INFO - Epoch [17][1000/5005]\tlr: 1.000e-01, eta: 10:17:02, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5340\n2023-01-03 17:24:30,079 - mmcls - INFO - Epoch [17][1100/5005]\tlr: 1.000e-01, eta: 10:16:56, time: 0.092, data_time: 0.027, memory: 1125, loss: 2.5233\n2023-01-03 17:24:38,594 - mmcls - INFO - Epoch [17][1200/5005]\tlr: 1.000e-01, eta: 10:16:45, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4994\n2023-01-03 17:24:47,277 - mmcls - INFO - Epoch [17][1300/5005]\tlr: 1.000e-01, eta: 10:16:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5382\n2023-01-03 17:24:55,918 - mmcls - INFO - Epoch [17][1400/5005]\tlr: 1.000e-01, eta: 10:16:26, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5373\n2023-01-03 17:25:04,689 - mmcls - INFO - Epoch [17][1500/5005]\tlr: 1.000e-01, eta: 10:16:17, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.4976\n2023-01-03 17:25:13,343 - mmcls - INFO - Epoch [17][1600/5005]\tlr: 1.000e-01, eta: 10:16:07, time: 0.086, data_time: 0.044, memory: 1125, loss: 2.5370\n2023-01-03 17:25:22,338 - mmcls - INFO - Epoch [17][1700/5005]\tlr: 1.000e-01, eta: 10:15:59, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.5272\n2023-01-03 17:25:30,949 - mmcls - INFO - Epoch [17][1800/5005]\tlr: 1.000e-01, eta: 10:15:49, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5186\n2023-01-03 17:25:39,883 - mmcls - INFO - Epoch [17][1900/5005]\tlr: 1.000e-01, eta: 10:15:41, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5262\n2023-01-03 17:25:48,509 - mmcls - INFO - Epoch [17][2000/5005]\tlr: 1.000e-01, eta: 10:15:31, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5137\n2023-01-03 17:25:57,314 - mmcls - INFO - Epoch [17][2100/5005]\tlr: 1.000e-01, eta: 10:15:22, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5227\n2023-01-03 17:26:05,947 - mmcls - INFO - Epoch [17][2200/5005]\tlr: 1.000e-01, eta: 10:15:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5140\n2023-01-03 17:26:14,602 - mmcls - INFO - Epoch [17][2300/5005]\tlr: 1.000e-01, eta: 10:15:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5181\n2023-01-03 17:26:23,285 - mmcls - INFO - Epoch [17][2400/5005]\tlr: 1.000e-01, eta: 10:14:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5374\n2023-01-03 17:26:32,003 - mmcls - INFO - Epoch [17][2500/5005]\tlr: 1.000e-01, eta: 10:14:43, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.5357\n2023-01-03 17:26:40,951 - mmcls - INFO - Epoch [17][2600/5005]\tlr: 1.000e-01, eta: 10:14:35, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5697\n2023-01-03 17:26:49,918 - mmcls - INFO - Epoch [17][2700/5005]\tlr: 1.000e-01, eta: 10:14:27, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5304\n2023-01-03 17:26:58,819 - mmcls - INFO - Epoch [17][2800/5005]\tlr: 1.000e-01, eta: 10:14:19, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5441\n2023-01-03 17:27:07,853 - mmcls - INFO - Epoch [17][2900/5005]\tlr: 1.000e-01, eta: 10:14:11, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5316\n2023-01-03 17:27:16,574 - mmcls - INFO - Epoch [17][3000/5005]\tlr: 1.000e-01, eta: 10:14:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5314\n2023-01-03 17:27:25,549 - mmcls - INFO - Epoch [17][3100/5005]\tlr: 1.000e-01, eta: 10:13:53, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.5252\n2023-01-03 17:27:34,390 - mmcls - INFO - Epoch [17][3200/5005]\tlr: 1.000e-01, eta: 10:13:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5217\n2023-01-03 17:27:43,120 - mmcls - INFO - Epoch [17][3300/5005]\tlr: 1.000e-01, eta: 10:13:35, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.5275\n2023-01-03 17:27:52,029 - mmcls - INFO - Epoch [17][3400/5005]\tlr: 1.000e-01, eta: 10:13:27, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5239\n2023-01-03 17:28:01,190 - mmcls - INFO - Epoch [17][3500/5005]\tlr: 1.000e-01, eta: 10:13:20, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.5434\n2023-01-03 17:28:09,740 - mmcls - INFO - Epoch [17][3600/5005]\tlr: 1.000e-01, eta: 10:13:10, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.5491\n2023-01-03 17:28:18,346 - mmcls - INFO - Epoch [17][3700/5005]\tlr: 1.000e-01, eta: 10:13:00, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5532\n2023-01-03 17:28:27,209 - mmcls - INFO - Epoch [17][3800/5005]\tlr: 1.000e-01, eta: 10:12:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5699\n2023-01-03 17:28:36,094 - mmcls - INFO - Epoch [17][3900/5005]\tlr: 1.000e-01, eta: 10:12:42, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.5372\n2023-01-03 17:28:44,860 - mmcls - INFO - Epoch [17][4000/5005]\tlr: 1.000e-01, eta: 10:12:33, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5386\n2023-01-03 17:28:53,541 - mmcls - INFO - Epoch [17][4100/5005]\tlr: 1.000e-01, eta: 10:12:24, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5270\n2023-01-03 17:29:02,282 - mmcls - INFO - Epoch [17][4200/5005]\tlr: 1.000e-01, eta: 10:12:15, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5371\n2023-01-03 17:29:10,900 - mmcls - INFO - Epoch [17][4300/5005]\tlr: 1.000e-01, eta: 10:12:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5393\n2023-01-03 17:29:19,635 - mmcls - INFO - Epoch [17][4400/5005]\tlr: 1.000e-01, eta: 10:11:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5315\n2023-01-03 17:29:28,482 - mmcls - INFO - Epoch [17][4500/5005]\tlr: 1.000e-01, eta: 10:11:47, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5283\n2023-01-03 17:29:37,374 - mmcls - INFO - Epoch [17][4600/5005]\tlr: 1.000e-01, eta: 10:11:38, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5665\n2023-01-03 17:29:46,523 - mmcls - INFO - Epoch [17][4700/5005]\tlr: 1.000e-01, eta: 10:11:31, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.5196\n2023-01-03 17:29:55,192 - mmcls - INFO - Epoch [17][4800/5005]\tlr: 1.000e-01, eta: 10:11:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5328\n2023-01-03 17:30:03,990 - mmcls - INFO - Epoch [17][4900/5005]\tlr: 1.000e-01, eta: 10:11:12, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.5348\n2023-01-03 17:30:12,700 - mmcls - INFO - Epoch [17][5000/5005]\tlr: 1.000e-01, eta: 10:11:03, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5491\n2023-01-03 17:30:35,397 - mmcls - INFO - Epoch(val) [17][196]\taccuracy_top-1: 49.7740, accuracy_top-5: 75.5680\n2023-01-03 17:30:46,151 - mmcls - INFO - Epoch [18][100/5005]\tlr: 1.000e-01, eta: 10:11:01, time: 0.107, data_time: 0.060, memory: 1125, loss: 2.4843\n2023-01-03 17:30:55,063 - mmcls - INFO - Epoch [18][200/5005]\tlr: 1.000e-01, eta: 10:10:52, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4970\n2023-01-03 17:31:04,047 - mmcls - INFO - Epoch [18][300/5005]\tlr: 1.000e-01, eta: 10:10:44, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.4932\n2023-01-03 17:31:12,699 - mmcls - INFO - Epoch [18][400/5005]\tlr: 1.000e-01, eta: 10:10:35, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4998\n2023-01-03 17:31:21,445 - mmcls - INFO - Epoch [18][500/5005]\tlr: 1.000e-01, eta: 10:10:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5124\n2023-01-03 17:31:30,263 - mmcls - INFO - Epoch [18][600/5005]\tlr: 1.000e-01, eta: 10:10:17, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5247\n2023-01-03 17:31:39,175 - mmcls - INFO - Epoch [18][700/5005]\tlr: 1.000e-01, eta: 10:10:08, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.4798\n2023-01-03 17:31:48,105 - mmcls - INFO - Epoch [18][800/5005]\tlr: 1.000e-01, eta: 10:10:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.4789\n2023-01-03 17:31:57,174 - mmcls - INFO - Epoch [18][900/5005]\tlr: 1.000e-01, eta: 10:09:52, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.5098\n2023-01-03 17:32:05,713 - mmcls - INFO - Epoch [18][1000/5005]\tlr: 1.000e-01, eta: 10:09:42, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.5020\n2023-01-03 17:32:14,469 - mmcls - INFO - Epoch [18][1100/5005]\tlr: 1.000e-01, eta: 10:09:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5327\n2023-01-03 17:32:22,993 - mmcls - INFO - Epoch [18][1200/5005]\tlr: 1.000e-01, eta: 10:09:22, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5354\n2023-01-03 17:32:32,073 - mmcls - INFO - Epoch [18][1300/5005]\tlr: 1.000e-01, eta: 10:09:15, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.5186\n2023-01-03 17:32:40,612 - mmcls - INFO - Epoch [18][1400/5005]\tlr: 1.000e-01, eta: 10:09:05, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.5309\n2023-01-03 17:32:49,437 - mmcls - INFO - Epoch [18][1500/5005]\tlr: 1.000e-01, eta: 10:08:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.5012\n2023-01-03 17:32:58,298 - mmcls - INFO - Epoch [18][1600/5005]\tlr: 1.000e-01, eta: 10:08:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5319\n2023-01-03 17:33:07,151 - mmcls - INFO - Epoch [18][1700/5005]\tlr: 1.000e-01, eta: 10:08:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5188\n2023-01-03 17:33:16,078 - mmcls - INFO - Epoch [18][1800/5005]\tlr: 1.000e-01, eta: 10:08:30, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5203\n2023-01-03 17:33:24,929 - mmcls - INFO - Epoch [18][1900/5005]\tlr: 1.000e-01, eta: 10:08:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5200\n2023-01-03 17:33:33,763 - mmcls - INFO - Epoch [18][2000/5005]\tlr: 1.000e-01, eta: 10:08:13, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5088\n2023-01-03 17:33:42,518 - mmcls - INFO - Epoch [18][2100/5005]\tlr: 1.000e-01, eta: 10:08:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5158\n2023-01-03 17:33:51,048 - mmcls - INFO - Epoch [18][2200/5005]\tlr: 1.000e-01, eta: 10:07:53, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.5466\n2023-01-03 17:33:59,830 - mmcls - INFO - Epoch [18][2300/5005]\tlr: 1.000e-01, eta: 10:07:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5006\n2023-01-03 17:34:08,756 - mmcls - INFO - Epoch [18][2400/5005]\tlr: 1.000e-01, eta: 10:07:36, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5201\n2023-01-03 17:34:17,278 - mmcls - INFO - Epoch [18][2500/5005]\tlr: 1.000e-01, eta: 10:07:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5298\n2023-01-03 17:34:26,199 - mmcls - INFO - Epoch [18][2600/5005]\tlr: 1.000e-01, eta: 10:07:17, time: 0.089, data_time: 0.043, memory: 1125, loss: 2.5447\n2023-01-03 17:34:34,954 - mmcls - INFO - Epoch [18][2700/5005]\tlr: 1.000e-01, eta: 10:07:08, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5343\n2023-01-03 17:34:43,485 - mmcls - INFO - Epoch [18][2800/5005]\tlr: 1.000e-01, eta: 10:06:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4876\n2023-01-03 17:34:52,345 - mmcls - INFO - Epoch [18][2900/5005]\tlr: 1.000e-01, eta: 10:06:49, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.5133\n2023-01-03 17:35:00,977 - mmcls - INFO - Epoch [18][3000/5005]\tlr: 1.000e-01, eta: 10:06:39, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5208\n2023-01-03 17:35:09,716 - mmcls - INFO - Epoch [18][3100/5005]\tlr: 1.000e-01, eta: 10:06:30, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.5168\n2023-01-03 17:35:18,585 - mmcls - INFO - Epoch [18][3200/5005]\tlr: 1.000e-01, eta: 10:06:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5095\n2023-01-03 17:35:27,307 - mmcls - INFO - Epoch [18][3300/5005]\tlr: 1.000e-01, eta: 10:06:12, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5385\n2023-01-03 17:35:36,017 - mmcls - INFO - Epoch [18][3400/5005]\tlr: 1.000e-01, eta: 10:06:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5201\n2023-01-03 17:35:44,816 - mmcls - INFO - Epoch [18][3500/5005]\tlr: 1.000e-01, eta: 10:05:54, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5241\n2023-01-03 17:35:53,526 - mmcls - INFO - Epoch [18][3600/5005]\tlr: 1.000e-01, eta: 10:05:44, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.5185\n2023-01-03 17:36:02,298 - mmcls - INFO - Epoch [18][3700/5005]\tlr: 1.000e-01, eta: 10:05:35, time: 0.088, data_time: 0.041, memory: 1125, loss: 2.5527\n2023-01-03 17:36:10,956 - mmcls - INFO - Epoch [18][3800/5005]\tlr: 1.000e-01, eta: 10:05:26, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.5370\n2023-01-03 17:36:19,669 - mmcls - INFO - Epoch [18][3900/5005]\tlr: 1.000e-01, eta: 10:05:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5342\n2023-01-03 17:36:28,558 - mmcls - INFO - Epoch [18][4000/5005]\tlr: 1.000e-01, eta: 10:05:08, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.5186\n2023-01-03 17:36:37,242 - mmcls - INFO - Epoch [18][4100/5005]\tlr: 1.000e-01, eta: 10:04:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5354\n2023-01-03 17:36:46,184 - mmcls - INFO - Epoch [18][4200/5005]\tlr: 1.000e-01, eta: 10:04:50, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5506\n2023-01-03 17:36:55,045 - mmcls - INFO - Epoch [18][4300/5005]\tlr: 1.000e-01, eta: 10:04:41, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5157\n2023-01-03 17:37:03,816 - mmcls - INFO - Epoch [18][4400/5005]\tlr: 1.000e-01, eta: 10:04:32, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5500\n2023-01-03 17:37:12,316 - mmcls - INFO - Epoch [18][4500/5005]\tlr: 1.000e-01, eta: 10:04:22, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.5153\n2023-01-03 17:37:21,415 - mmcls - INFO - Epoch [18][4600/5005]\tlr: 1.000e-01, eta: 10:04:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.5452\n2023-01-03 17:37:30,112 - mmcls - INFO - Epoch [18][4700/5005]\tlr: 1.000e-01, eta: 10:04:05, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5506\n2023-01-03 17:37:39,092 - mmcls - INFO - Epoch [18][4800/5005]\tlr: 1.000e-01, eta: 10:03:57, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.5042\n2023-01-03 17:37:47,951 - mmcls - INFO - Epoch [18][4900/5005]\tlr: 1.000e-01, eta: 10:03:48, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.5521\n2023-01-03 17:37:56,665 - mmcls - INFO - Epoch [18][5000/5005]\tlr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5304\n2023-01-03 17:38:19,270 - mmcls - INFO - Epoch(val) [18][196]\taccuracy_top-1: 48.8620, accuracy_top-5: 74.6160\n2023-01-03 17:38:30,141 - mmcls - INFO - Epoch [19][100/5005]\tlr: 1.000e-01, eta: 10:03:37, time: 0.109, data_time: 0.041, memory: 1125, loss: 2.4707\n2023-01-03 17:38:38,874 - mmcls - INFO - Epoch [19][200/5005]\tlr: 1.000e-01, eta: 10:03:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4771\n2023-01-03 17:38:48,089 - mmcls - INFO - Epoch [19][300/5005]\tlr: 1.000e-01, eta: 10:03:20, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.4861\n2023-01-03 17:38:56,742 - mmcls - INFO - Epoch [19][400/5005]\tlr: 1.000e-01, eta: 10:03:11, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5079\n2023-01-03 17:39:05,357 - mmcls - INFO - Epoch [19][500/5005]\tlr: 1.000e-01, eta: 10:03:01, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.4876\n2023-01-03 17:39:14,113 - mmcls - INFO - Epoch [19][600/5005]\tlr: 1.000e-01, eta: 10:02:52, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5190\n2023-01-03 17:39:22,925 - mmcls - INFO - Epoch [19][700/5005]\tlr: 1.000e-01, eta: 10:02:43, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4887\n2023-01-03 17:39:31,739 - mmcls - INFO - Epoch [19][800/5005]\tlr: 1.000e-01, eta: 10:02:34, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4846\n2023-01-03 17:39:40,301 - mmcls - INFO - Epoch [19][900/5005]\tlr: 1.000e-01, eta: 10:02:24, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.4904\n2023-01-03 17:39:49,023 - mmcls - INFO - Epoch [19][1000/5005]\tlr: 1.000e-01, eta: 10:02:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5144\n2023-01-03 17:39:57,717 - mmcls - INFO - Epoch [19][1100/5005]\tlr: 1.000e-01, eta: 10:02:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5110\n2023-01-03 17:40:06,492 - mmcls - INFO - Epoch [19][1200/5005]\tlr: 1.000e-01, eta: 10:01:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5075\n2023-01-03 17:40:15,106 - mmcls - INFO - Epoch [19][1300/5005]\tlr: 1.000e-01, eta: 10:01:46, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.5036\n2023-01-03 17:40:23,790 - mmcls - INFO - Epoch [19][1400/5005]\tlr: 1.000e-01, eta: 10:01:37, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4986\n2023-01-03 17:40:32,400 - mmcls - INFO - Epoch [19][1500/5005]\tlr: 1.000e-01, eta: 10:01:27, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4973\n2023-01-03 17:40:41,276 - mmcls - INFO - Epoch [19][1600/5005]\tlr: 1.000e-01, eta: 10:01:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5371\n2023-01-03 17:40:49,936 - mmcls - INFO - Epoch [19][1700/5005]\tlr: 1.000e-01, eta: 10:01:09, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5210\n2023-01-03 17:40:59,035 - mmcls - INFO - Epoch [19][1800/5005]\tlr: 1.000e-01, eta: 10:01:01, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.4950\n2023-01-03 17:41:07,865 - mmcls - INFO - Epoch [19][1900/5005]\tlr: 1.000e-01, eta: 10:00:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5077\n2023-01-03 17:41:16,708 - mmcls - INFO - Epoch [19][2000/5005]\tlr: 1.000e-01, eta: 10:00:44, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4882\n2023-01-03 17:41:25,266 - mmcls - INFO - Epoch [19][2100/5005]\tlr: 1.000e-01, eta: 10:00:34, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4995\n2023-01-03 17:41:34,078 - mmcls - INFO - Epoch [19][2200/5005]\tlr: 1.000e-01, eta: 10:00:25, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5402\n2023-01-03 17:41:42,623 - mmcls - INFO - Epoch [19][2300/5005]\tlr: 1.000e-01, eta: 10:00:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5050\n2023-01-03 17:41:51,272 - mmcls - INFO - Epoch [19][2400/5005]\tlr: 1.000e-01, eta: 10:00:05, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5127\n2023-01-03 17:42:00,342 - mmcls - INFO - Epoch [19][2500/5005]\tlr: 1.000e-01, eta: 9:59:58, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.5271\n2023-01-03 17:42:09,043 - mmcls - INFO - Epoch [19][2600/5005]\tlr: 1.000e-01, eta: 9:59:48, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4995\n2023-01-03 17:42:17,771 - mmcls - INFO - Epoch [19][2700/5005]\tlr: 1.000e-01, eta: 9:59:39, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5255\n2023-01-03 17:42:27,128 - mmcls - INFO - Epoch [19][2800/5005]\tlr: 1.000e-01, eta: 9:59:32, time: 0.094, data_time: 0.016, memory: 1125, loss: 2.5362\n2023-01-03 17:42:35,554 - mmcls - INFO - Epoch [19][2900/5005]\tlr: 1.000e-01, eta: 9:59:22, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.5273\n2023-01-03 17:42:44,228 - mmcls - INFO - Epoch [19][3000/5005]\tlr: 1.000e-01, eta: 9:59:12, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5070\n2023-01-03 17:42:53,157 - mmcls - INFO - Epoch [19][3100/5005]\tlr: 1.000e-01, eta: 9:59:04, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.5304\n2023-01-03 17:43:01,877 - mmcls - INFO - Epoch [19][3200/5005]\tlr: 1.000e-01, eta: 9:58:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.5170\n2023-01-03 17:43:11,045 - mmcls - INFO - Epoch [19][3300/5005]\tlr: 1.000e-01, eta: 9:58:47, time: 0.092, data_time: 0.015, memory: 1125, loss: 2.5371\n2023-01-03 17:43:19,752 - mmcls - INFO - Epoch [19][3400/5005]\tlr: 1.000e-01, eta: 9:58:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5241\n2023-01-03 17:43:28,567 - mmcls - INFO - Epoch [19][3500/5005]\tlr: 1.000e-01, eta: 9:58:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5156\n2023-01-03 17:43:37,206 - mmcls - INFO - Epoch [19][3600/5005]\tlr: 1.000e-01, eta: 9:58:20, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.5135\n2023-01-03 17:43:46,070 - mmcls - INFO - Epoch [19][3700/5005]\tlr: 1.000e-01, eta: 9:58:11, time: 0.089, data_time: 0.026, memory: 1125, loss: 2.5030\n2023-01-03 17:43:54,961 - mmcls - INFO - Epoch [19][3800/5005]\tlr: 1.000e-01, eta: 9:58:02, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.5119\n2023-01-03 17:44:03,538 - mmcls - INFO - Epoch [19][3900/5005]\tlr: 1.000e-01, eta: 9:57:52, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.5330\n2023-01-03 17:44:12,320 - mmcls - INFO - Epoch [19][4000/5005]\tlr: 1.000e-01, eta: 9:57:43, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5176\n2023-01-03 17:44:20,899 - mmcls - INFO - Epoch [19][4100/5005]\tlr: 1.000e-01, eta: 9:57:34, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5204\n2023-01-03 17:44:29,681 - mmcls - INFO - Epoch [19][4200/5005]\tlr: 1.000e-01, eta: 9:57:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5464\n2023-01-03 17:44:38,603 - mmcls - INFO - Epoch [19][4300/5005]\tlr: 1.000e-01, eta: 9:57:16, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5357\n2023-01-03 17:44:47,330 - mmcls - INFO - Epoch [19][4400/5005]\tlr: 1.000e-01, eta: 9:57:07, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.5255\n2023-01-03 17:44:56,046 - mmcls - INFO - Epoch [19][4500/5005]\tlr: 1.000e-01, eta: 9:56:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5273\n2023-01-03 17:45:04,726 - mmcls - INFO - Epoch [19][4600/5005]\tlr: 1.000e-01, eta: 9:56:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5359\n2023-01-03 17:45:13,525 - mmcls - INFO - Epoch [19][4700/5005]\tlr: 1.000e-01, eta: 9:56:39, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5111\n2023-01-03 17:45:22,277 - mmcls - INFO - Epoch [19][4800/5005]\tlr: 1.000e-01, eta: 9:56:30, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.5130\n2023-01-03 17:45:31,134 - mmcls - INFO - Epoch [19][4900/5005]\tlr: 1.000e-01, eta: 9:56:21, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5230\n2023-01-03 17:45:40,382 - mmcls - INFO - Epoch [19][5000/5005]\tlr: 1.000e-01, eta: 9:56:14, time: 0.093, data_time: 0.020, memory: 1125, loss: 2.5393\n2023-01-03 17:46:03,183 - mmcls - INFO - Epoch(val) [19][196]\taccuracy_top-1: 49.7360, accuracy_top-5: 75.2660\n2023-01-03 17:46:14,342 - mmcls - INFO - Epoch [20][100/5005]\tlr: 1.000e-01, eta: 9:56:13, time: 0.111, data_time: 0.040, memory: 1125, loss: 2.4676\n2023-01-03 17:46:23,506 - mmcls - INFO - Epoch [20][200/5005]\tlr: 1.000e-01, eta: 9:56:06, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.4770\n2023-01-03 17:46:32,152 - mmcls - INFO - Epoch [20][300/5005]\tlr: 1.000e-01, eta: 9:55:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4562\n2023-01-03 17:46:40,815 - mmcls - INFO - Epoch [20][400/5005]\tlr: 1.000e-01, eta: 9:55:47, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.4487\n2023-01-03 17:46:49,440 - mmcls - INFO - Epoch [20][500/5005]\tlr: 1.000e-01, eta: 9:55:37, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4869\n2023-01-03 17:46:58,113 - mmcls - INFO - Epoch [20][600/5005]\tlr: 1.000e-01, eta: 9:55:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4805\n2023-01-03 17:47:06,765 - mmcls - INFO - Epoch [20][700/5005]\tlr: 1.000e-01, eta: 9:55:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4788\n2023-01-03 17:47:15,331 - mmcls - INFO - Epoch [20][800/5005]\tlr: 1.000e-01, eta: 9:55:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4781\n2023-01-03 17:47:24,253 - mmcls - INFO - Epoch [20][900/5005]\tlr: 1.000e-01, eta: 9:54:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4711\n2023-01-03 17:47:33,058 - mmcls - INFO - Epoch [20][1000/5005]\tlr: 1.000e-01, eta: 9:54:51, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4810\n2023-01-03 17:47:41,689 - mmcls - INFO - Epoch [20][1100/5005]\tlr: 1.000e-01, eta: 9:54:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4973\n2023-01-03 17:47:50,757 - mmcls - INFO - Epoch [20][1200/5005]\tlr: 1.000e-01, eta: 9:54:33, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.4989\n2023-01-03 17:47:59,586 - mmcls - INFO - Epoch [20][1300/5005]\tlr: 1.000e-01, eta: 9:54:24, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.4867\n2023-01-03 17:48:08,413 - mmcls - INFO - Epoch [20][1400/5005]\tlr: 1.000e-01, eta: 9:54:15, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4976\n2023-01-03 17:48:16,929 - mmcls - INFO - Epoch [20][1500/5005]\tlr: 1.000e-01, eta: 9:54:05, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5270\n2023-01-03 17:48:25,959 - mmcls - INFO - Epoch [20][1600/5005]\tlr: 1.000e-01, eta: 9:53:57, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4991\n2023-01-03 17:48:34,952 - mmcls - INFO - Epoch [20][1700/5005]\tlr: 1.000e-01, eta: 9:53:49, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5057\n2023-01-03 17:48:43,570 - mmcls - INFO - Epoch [20][1800/5005]\tlr: 1.000e-01, eta: 9:53:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5065\n2023-01-03 17:48:52,114 - mmcls - INFO - Epoch [20][1900/5005]\tlr: 1.000e-01, eta: 9:53:30, time: 0.085, data_time: 0.037, memory: 1125, loss: 2.4983\n2023-01-03 17:49:00,893 - mmcls - INFO - Epoch [20][2000/5005]\tlr: 1.000e-01, eta: 9:53:21, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.5206\n2023-01-03 17:49:09,697 - mmcls - INFO - Epoch [20][2100/5005]\tlr: 1.000e-01, eta: 9:53:12, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.5334\n2023-01-03 17:49:18,235 - mmcls - INFO - Epoch [20][2200/5005]\tlr: 1.000e-01, eta: 9:53:02, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5017\n2023-01-03 17:49:27,028 - mmcls - INFO - Epoch [20][2300/5005]\tlr: 1.000e-01, eta: 9:52:53, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4857\n2023-01-03 17:49:35,518 - mmcls - INFO - Epoch [20][2400/5005]\tlr: 1.000e-01, eta: 9:52:42, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5075\n2023-01-03 17:49:44,216 - mmcls - INFO - Epoch [20][2500/5005]\tlr: 1.000e-01, eta: 9:52:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5254\n2023-01-03 17:49:52,986 - mmcls - INFO - Epoch [20][2600/5005]\tlr: 1.000e-01, eta: 9:52:24, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.4849\n2023-01-03 17:50:01,811 - mmcls - INFO - Epoch [20][2700/5005]\tlr: 1.000e-01, eta: 9:52:15, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5166\n2023-01-03 17:50:10,521 - mmcls - INFO - Epoch [20][2800/5005]\tlr: 1.000e-01, eta: 9:52:06, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.4934\n2023-01-03 17:50:19,126 - mmcls - INFO - Epoch [20][2900/5005]\tlr: 1.000e-01, eta: 9:51:56, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5356\n2023-01-03 17:50:27,863 - mmcls - INFO - Epoch [20][3000/5005]\tlr: 1.000e-01, eta: 9:51:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5087\n2023-01-03 17:50:36,626 - mmcls - INFO - Epoch [20][3100/5005]\tlr: 1.000e-01, eta: 9:51:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5262\n2023-01-03 17:50:45,257 - mmcls - INFO - Epoch [20][3200/5005]\tlr: 1.000e-01, eta: 9:51:28, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.5332\n2023-01-03 17:50:53,819 - mmcls - INFO - Epoch [20][3300/5005]\tlr: 1.000e-01, eta: 9:51:18, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.5276\n2023-01-03 17:51:02,504 - mmcls - INFO - Epoch [20][3400/5005]\tlr: 1.000e-01, eta: 9:51:09, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5014\n2023-01-03 17:51:11,260 - mmcls - INFO - Epoch [20][3500/5005]\tlr: 1.000e-01, eta: 9:51:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5173\n2023-01-03 17:51:20,470 - mmcls - INFO - Epoch [20][3600/5005]\tlr: 1.000e-01, eta: 9:50:53, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.5208\n2023-01-03 17:51:29,075 - mmcls - INFO - Epoch [20][3700/5005]\tlr: 1.000e-01, eta: 9:50:43, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5268\n2023-01-03 17:51:37,811 - mmcls - INFO - Epoch [20][3800/5005]\tlr: 1.000e-01, eta: 9:50:34, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5313\n2023-01-03 17:51:46,554 - mmcls - INFO - Epoch [20][3900/5005]\tlr: 1.000e-01, eta: 9:50:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4961\n2023-01-03 17:51:55,291 - mmcls - INFO - Epoch [20][4000/5005]\tlr: 1.000e-01, eta: 9:50:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4862\n2023-01-03 17:52:04,058 - mmcls - INFO - Epoch [20][4100/5005]\tlr: 1.000e-01, eta: 9:50:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5202\n2023-01-03 17:52:12,730 - mmcls - INFO - Epoch [20][4200/5005]\tlr: 1.000e-01, eta: 9:49:57, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5327\n2023-01-03 17:52:21,275 - mmcls - INFO - Epoch [20][4300/5005]\tlr: 1.000e-01, eta: 9:49:47, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.5290\n2023-01-03 17:52:30,003 - mmcls - INFO - Epoch [20][4400/5005]\tlr: 1.000e-01, eta: 9:49:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5026\n2023-01-03 17:52:38,657 - mmcls - INFO - Epoch [20][4500/5005]\tlr: 1.000e-01, eta: 9:49:28, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5069\n2023-01-03 17:52:47,543 - mmcls - INFO - Epoch [20][4600/5005]\tlr: 1.000e-01, eta: 9:49:20, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5005\n2023-01-03 17:52:56,450 - mmcls - INFO - Epoch [20][4700/5005]\tlr: 1.000e-01, eta: 9:49:11, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.5160\n2023-01-03 17:53:05,470 - mmcls - INFO - Epoch [20][4800/5005]\tlr: 1.000e-01, eta: 9:49:03, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5339\n2023-01-03 17:53:14,461 - mmcls - INFO - Epoch [20][4900/5005]\tlr: 1.000e-01, eta: 9:48:55, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5054\n2023-01-03 17:53:23,135 - mmcls - INFO - Epoch [20][5000/5005]\tlr: 1.000e-01, eta: 9:48:46, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.4840\n2023-01-03 17:53:46,787 - mmcls - INFO - Epoch(val) [20][196]\taccuracy_top-1: 47.7040, accuracy_top-5: 74.1900\n2023-01-03 17:53:57,801 - mmcls - INFO - Epoch [21][100/5005]\tlr: 1.000e-01, eta: 9:48:43, time: 0.110, data_time: 0.045, memory: 1125, loss: 2.4738\n2023-01-03 17:54:06,634 - mmcls - INFO - Epoch [21][200/5005]\tlr: 1.000e-01, eta: 9:48:34, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4647\n2023-01-03 17:54:15,360 - mmcls - INFO - Epoch [21][300/5005]\tlr: 1.000e-01, eta: 9:48:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4659\n2023-01-03 17:54:24,155 - mmcls - INFO - Epoch [21][400/5005]\tlr: 1.000e-01, eta: 9:48:16, time: 0.088, data_time: 0.032, memory: 1125, loss: 2.4900\n2023-01-03 17:54:32,873 - mmcls - INFO - Epoch [21][500/5005]\tlr: 1.000e-01, eta: 9:48:07, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4758\n2023-01-03 17:54:41,543 - mmcls - INFO - Epoch [21][600/5005]\tlr: 1.000e-01, eta: 9:47:58, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.4613\n2023-01-03 17:54:50,407 - mmcls - INFO - Epoch [21][700/5005]\tlr: 1.000e-01, eta: 9:47:49, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.4927\n2023-01-03 17:54:59,027 - mmcls - INFO - Epoch [21][800/5005]\tlr: 1.000e-01, eta: 9:47:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4955\n2023-01-03 17:55:07,753 - mmcls - INFO - Epoch [21][900/5005]\tlr: 1.000e-01, eta: 9:47:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4549\n2023-01-03 17:55:17,476 - mmcls - INFO - Epoch [21][1000/5005]\tlr: 1.000e-01, eta: 9:47:25, time: 0.097, data_time: 0.003, memory: 1125, loss: 2.4975\n2023-01-03 17:55:27,059 - mmcls - INFO - Epoch [21][1100/5005]\tlr: 1.000e-01, eta: 9:47:19, time: 0.096, data_time: 0.019, memory: 1125, loss: 2.5158\n2023-01-03 17:55:35,938 - mmcls - INFO - Epoch [21][1200/5005]\tlr: 1.000e-01, eta: 9:47:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.4996\n2023-01-03 17:55:44,788 - mmcls - INFO - Epoch [21][1300/5005]\tlr: 1.000e-01, eta: 9:47:02, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.5054\n2023-01-03 17:55:53,397 - mmcls - INFO - Epoch [21][1400/5005]\tlr: 1.000e-01, eta: 9:46:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5180\n2023-01-03 17:56:02,288 - mmcls - INFO - Epoch [21][1500/5005]\tlr: 1.000e-01, eta: 9:46:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4792\n2023-01-03 17:56:10,908 - mmcls - INFO - Epoch [21][1600/5005]\tlr: 1.000e-01, eta: 9:46:34, time: 0.086, data_time: 0.038, memory: 1125, loss: 2.4847\n2023-01-03 17:56:19,786 - mmcls - INFO - Epoch [21][1700/5005]\tlr: 1.000e-01, eta: 9:46:25, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.4916\n2023-01-03 17:56:28,699 - mmcls - INFO - Epoch [21][1800/5005]\tlr: 1.000e-01, eta: 9:46:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4921\n2023-01-03 17:56:37,570 - mmcls - INFO - Epoch [21][1900/5005]\tlr: 1.000e-01, eta: 9:46:08, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5107\n2023-01-03 17:56:46,260 - mmcls - INFO - Epoch [21][2000/5005]\tlr: 1.000e-01, eta: 9:45:59, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5145\n2023-01-03 17:56:55,381 - mmcls - INFO - Epoch [21][2100/5005]\tlr: 1.000e-01, eta: 9:45:51, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5135\n2023-01-03 17:57:04,205 - mmcls - INFO - Epoch [21][2200/5005]\tlr: 1.000e-01, eta: 9:45:42, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.5145\n2023-01-03 17:57:13,096 - mmcls - INFO - Epoch [21][2300/5005]\tlr: 1.000e-01, eta: 9:45:34, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.5043\n2023-01-03 17:57:21,838 - mmcls - INFO - Epoch [21][2400/5005]\tlr: 1.000e-01, eta: 9:45:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5251\n2023-01-03 17:57:30,387 - mmcls - INFO - Epoch [21][2500/5005]\tlr: 1.000e-01, eta: 9:45:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5300\n2023-01-03 17:57:39,074 - mmcls - INFO - Epoch [21][2600/5005]\tlr: 1.000e-01, eta: 9:45:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4991\n2023-01-03 17:57:47,736 - mmcls - INFO - Epoch [21][2700/5005]\tlr: 1.000e-01, eta: 9:44:56, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4946\n2023-01-03 17:57:56,840 - mmcls - INFO - Epoch [21][2800/5005]\tlr: 1.000e-01, eta: 9:44:48, time: 0.091, data_time: 0.036, memory: 1125, loss: 2.5009\n2023-01-03 17:58:05,361 - mmcls - INFO - Epoch [21][2900/5005]\tlr: 1.000e-01, eta: 9:44:38, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.5092\n2023-01-03 17:58:14,238 - mmcls - INFO - Epoch [21][3000/5005]\tlr: 1.000e-01, eta: 9:44:29, time: 0.089, data_time: 0.047, memory: 1125, loss: 2.4958\n2023-01-03 17:58:22,717 - mmcls - INFO - Epoch [21][3100/5005]\tlr: 1.000e-01, eta: 9:44:19, time: 0.085, data_time: 0.025, memory: 1125, loss: 2.4936\n2023-01-03 17:58:31,426 - mmcls - INFO - Epoch [21][3200/5005]\tlr: 1.000e-01, eta: 9:44:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4906\n2023-01-03 17:58:40,318 - mmcls - INFO - Epoch [21][3300/5005]\tlr: 1.000e-01, eta: 9:44:01, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5112\n2023-01-03 17:58:49,390 - mmcls - INFO - Epoch [21][3400/5005]\tlr: 1.000e-01, eta: 9:43:53, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.4957\n2023-01-03 17:58:58,186 - mmcls - INFO - Epoch [21][3500/5005]\tlr: 1.000e-01, eta: 9:43:44, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.5107\n2023-01-03 17:59:06,829 - mmcls - INFO - Epoch [21][3600/5005]\tlr: 1.000e-01, eta: 9:43:35, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.5142\n2023-01-03 17:59:15,490 - mmcls - INFO - Epoch [21][3700/5005]\tlr: 1.000e-01, eta: 9:43:25, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5194\n2023-01-03 17:59:24,557 - mmcls - INFO - Epoch [21][3800/5005]\tlr: 1.000e-01, eta: 9:43:18, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.5152\n2023-01-03 17:59:33,197 - mmcls - INFO - Epoch [21][3900/5005]\tlr: 1.000e-01, eta: 9:43:08, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.5061\n2023-01-03 17:59:41,871 - mmcls - INFO - Epoch [21][4000/5005]\tlr: 1.000e-01, eta: 9:42:59, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.4794\n2023-01-03 17:59:51,214 - mmcls - INFO - Epoch [21][4100/5005]\tlr: 1.000e-01, eta: 9:42:52, time: 0.093, data_time: 0.009, memory: 1125, loss: 2.4835\n2023-01-03 17:59:59,727 - mmcls - INFO - Epoch [21][4200/5005]\tlr: 1.000e-01, eta: 9:42:42, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.4850\n2023-01-03 18:00:08,577 - mmcls - INFO - Epoch [21][4300/5005]\tlr: 1.000e-01, eta: 9:42:33, time: 0.089, data_time: 0.034, memory: 1125, loss: 2.5192\n2023-01-03 18:00:17,271 - mmcls - INFO - Epoch [21][4400/5005]\tlr: 1.000e-01, eta: 9:42:24, time: 0.087, data_time: 0.044, memory: 1125, loss: 2.5108\n2023-01-03 18:00:26,044 - mmcls - INFO - Epoch [21][4500/5005]\tlr: 1.000e-01, eta: 9:42:15, time: 0.088, data_time: 0.045, memory: 1125, loss: 2.5186\n2023-01-03 18:00:35,022 - mmcls - INFO - Epoch [21][4600/5005]\tlr: 1.000e-01, eta: 9:42:06, time: 0.090, data_time: 0.027, memory: 1125, loss: 2.5093\n2023-01-03 18:00:44,118 - mmcls - INFO - Epoch [21][4700/5005]\tlr: 1.000e-01, eta: 9:41:59, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.5042\n2023-01-03 18:00:53,210 - mmcls - INFO - Epoch [21][4800/5005]\tlr: 1.000e-01, eta: 9:41:51, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.4921\n2023-01-03 18:01:02,667 - mmcls - INFO - Epoch [21][4900/5005]\tlr: 1.000e-01, eta: 9:41:44, time: 0.095, data_time: 0.025, memory: 1125, loss: 2.5075\n2023-01-03 18:01:11,469 - mmcls - INFO - Epoch [21][5000/5005]\tlr: 1.000e-01, eta: 9:41:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.5337\n2023-01-03 18:01:34,729 - mmcls - INFO - Epoch(val) [21][196]\taccuracy_top-1: 46.5660, accuracy_top-5: 72.8020\n2023-01-03 18:01:45,656 - mmcls - INFO - Epoch [22][100/5005]\tlr: 1.000e-01, eta: 9:41:32, time: 0.109, data_time: 0.047, memory: 1125, loss: 2.4889\n2023-01-03 18:01:54,673 - mmcls - INFO - Epoch [22][200/5005]\tlr: 1.000e-01, eta: 9:41:24, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.4466\n2023-01-03 18:02:03,655 - mmcls - INFO - Epoch [22][300/5005]\tlr: 1.000e-01, eta: 9:41:16, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.4409\n2023-01-03 18:02:12,275 - mmcls - INFO - Epoch [22][400/5005]\tlr: 1.000e-01, eta: 9:41:06, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4582\n2023-01-03 18:02:21,218 - mmcls - INFO - Epoch [22][500/5005]\tlr: 1.000e-01, eta: 9:40:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5021\n2023-01-03 18:02:29,785 - mmcls - INFO - Epoch [22][600/5005]\tlr: 1.000e-01, eta: 9:40:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4575\n2023-01-03 18:02:38,645 - mmcls - INFO - Epoch [22][700/5005]\tlr: 1.000e-01, eta: 9:40:39, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5022\n2023-01-03 18:02:47,268 - mmcls - INFO - Epoch [22][800/5005]\tlr: 1.000e-01, eta: 9:40:30, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4639\n2023-01-03 18:02:56,057 - mmcls - INFO - Epoch [22][900/5005]\tlr: 1.000e-01, eta: 9:40:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4777\n2023-01-03 18:03:04,671 - mmcls - INFO - Epoch [22][1000/5005]\tlr: 1.000e-01, eta: 9:40:11, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.4578\n2023-01-03 18:03:13,508 - mmcls - INFO - Epoch [22][1100/5005]\tlr: 1.000e-01, eta: 9:40:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4717\n2023-01-03 18:03:22,076 - mmcls - INFO - Epoch [22][1200/5005]\tlr: 1.000e-01, eta: 9:39:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4639\n2023-01-03 18:03:30,724 - mmcls - INFO - Epoch [22][1300/5005]\tlr: 1.000e-01, eta: 9:39:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4984\n2023-01-03 18:03:39,552 - mmcls - INFO - Epoch [22][1400/5005]\tlr: 1.000e-01, eta: 9:39:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4698\n2023-01-03 18:03:48,513 - mmcls - INFO - Epoch [22][1500/5005]\tlr: 1.000e-01, eta: 9:39:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4773\n2023-01-03 18:03:57,525 - mmcls - INFO - Epoch [22][1600/5005]\tlr: 1.000e-01, eta: 9:39:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4726\n2023-01-03 18:04:06,352 - mmcls - INFO - Epoch [22][1700/5005]\tlr: 1.000e-01, eta: 9:39:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5041\n2023-01-03 18:04:15,132 - mmcls - INFO - Epoch [22][1800/5005]\tlr: 1.000e-01, eta: 9:39:00, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4931\n2023-01-03 18:04:23,772 - mmcls - INFO - Epoch [22][1900/5005]\tlr: 1.000e-01, eta: 9:38:51, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5117\n2023-01-03 18:04:32,359 - mmcls - INFO - Epoch [22][2000/5005]\tlr: 1.000e-01, eta: 9:38:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5255\n2023-01-03 18:04:40,923 - mmcls - INFO - Epoch [22][2100/5005]\tlr: 1.000e-01, eta: 9:38:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4949\n2023-01-03 18:04:49,514 - mmcls - INFO - Epoch [22][2200/5005]\tlr: 1.000e-01, eta: 9:38:21, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4868\n2023-01-03 18:04:58,187 - mmcls - INFO - Epoch [22][2300/5005]\tlr: 1.000e-01, eta: 9:38:12, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5026\n2023-01-03 18:05:07,018 - mmcls - INFO - Epoch [22][2400/5005]\tlr: 1.000e-01, eta: 9:38:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4806\n2023-01-03 18:05:15,695 - mmcls - INFO - Epoch [22][2500/5005]\tlr: 1.000e-01, eta: 9:37:54, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5253\n2023-01-03 18:05:24,414 - mmcls - INFO - Epoch [22][2600/5005]\tlr: 1.000e-01, eta: 9:37:45, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5046\n2023-01-03 18:05:33,383 - mmcls - INFO - Epoch [22][2700/5005]\tlr: 1.000e-01, eta: 9:37:36, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.5198\n2023-01-03 18:05:41,913 - mmcls - INFO - Epoch [22][2800/5005]\tlr: 1.000e-01, eta: 9:37:26, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.4883\n2023-01-03 18:05:50,856 - mmcls - INFO - Epoch [22][2900/5005]\tlr: 1.000e-01, eta: 9:37:18, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5121\n2023-01-03 18:05:59,741 - mmcls - INFO - Epoch [22][3000/5005]\tlr: 1.000e-01, eta: 9:37:09, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.5062\n2023-01-03 18:06:08,319 - mmcls - INFO - Epoch [22][3100/5005]\tlr: 1.000e-01, eta: 9:37:00, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4698\n2023-01-03 18:06:16,927 - mmcls - INFO - Epoch [22][3200/5005]\tlr: 1.000e-01, eta: 9:36:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5288\n2023-01-03 18:06:25,544 - mmcls - INFO - Epoch [22][3300/5005]\tlr: 1.000e-01, eta: 9:36:40, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5068\n2023-01-03 18:06:34,198 - mmcls - INFO - Epoch [22][3400/5005]\tlr: 1.000e-01, eta: 9:36:31, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4945\n2023-01-03 18:06:42,766 - mmcls - INFO - Epoch [22][3500/5005]\tlr: 1.000e-01, eta: 9:36:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4979\n2023-01-03 18:06:51,389 - mmcls - INFO - Epoch [22][3600/5005]\tlr: 1.000e-01, eta: 9:36:12, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4692\n2023-01-03 18:07:00,024 - mmcls - INFO - Epoch [22][3700/5005]\tlr: 1.000e-01, eta: 9:36:02, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.5201\n2023-01-03 18:07:08,934 - mmcls - INFO - Epoch [22][3800/5005]\tlr: 1.000e-01, eta: 9:35:54, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5015\n2023-01-03 18:07:17,690 - mmcls - INFO - Epoch [22][3900/5005]\tlr: 1.000e-01, eta: 9:35:45, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5203\n2023-01-03 18:07:26,237 - mmcls - INFO - Epoch [22][4000/5005]\tlr: 1.000e-01, eta: 9:35:35, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.5030\n2023-01-03 18:07:34,873 - mmcls - INFO - Epoch [22][4100/5005]\tlr: 1.000e-01, eta: 9:35:25, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5259\n2023-01-03 18:07:43,756 - mmcls - INFO - Epoch [22][4200/5005]\tlr: 1.000e-01, eta: 9:35:17, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.4722\n2023-01-03 18:07:52,348 - mmcls - INFO - Epoch [22][4300/5005]\tlr: 1.000e-01, eta: 9:35:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5055\n2023-01-03 18:08:00,996 - mmcls - INFO - Epoch [22][4400/5005]\tlr: 1.000e-01, eta: 9:34:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4837\n2023-01-03 18:08:09,832 - mmcls - INFO - Epoch [22][4500/5005]\tlr: 1.000e-01, eta: 9:34:49, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5145\n2023-01-03 18:08:18,652 - mmcls - INFO - Epoch [22][4600/5005]\tlr: 1.000e-01, eta: 9:34:40, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4822\n2023-01-03 18:08:27,354 - mmcls - INFO - Epoch [22][4700/5005]\tlr: 1.000e-01, eta: 9:34:31, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5097\n2023-01-03 18:08:36,462 - mmcls - INFO - Epoch [22][4800/5005]\tlr: 1.000e-01, eta: 9:34:23, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.5113\n2023-01-03 18:08:45,597 - mmcls - INFO - Epoch [22][4900/5005]\tlr: 1.000e-01, eta: 9:34:15, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.4939\n2023-01-03 18:08:54,860 - mmcls - INFO - Epoch [22][5000/5005]\tlr: 1.000e-01, eta: 9:34:08, time: 0.093, data_time: 0.010, memory: 1125, loss: 2.4850\n2023-01-03 18:09:18,130 - mmcls - INFO - Epoch(val) [22][196]\taccuracy_top-1: 49.0780, accuracy_top-5: 75.0660\n2023-01-03 18:09:29,322 - mmcls - INFO - Epoch [23][100/5005]\tlr: 1.000e-01, eta: 9:34:05, time: 0.112, data_time: 0.065, memory: 1125, loss: 2.4451\n2023-01-03 18:09:38,183 - mmcls - INFO - Epoch [23][200/5005]\tlr: 1.000e-01, eta: 9:33:57, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4714\n2023-01-03 18:09:47,022 - mmcls - INFO - Epoch [23][300/5005]\tlr: 1.000e-01, eta: 9:33:48, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4865\n2023-01-03 18:09:55,673 - mmcls - INFO - Epoch [23][400/5005]\tlr: 1.000e-01, eta: 9:33:38, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4538\n2023-01-03 18:10:04,295 - mmcls - INFO - Epoch [23][500/5005]\tlr: 1.000e-01, eta: 9:33:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4925\n2023-01-03 18:10:13,006 - mmcls - INFO - Epoch [23][600/5005]\tlr: 1.000e-01, eta: 9:33:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4754\n2023-01-03 18:10:21,720 - mmcls - INFO - Epoch [23][700/5005]\tlr: 1.000e-01, eta: 9:33:10, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.4378\n2023-01-03 18:10:30,377 - mmcls - INFO - Epoch [23][800/5005]\tlr: 1.000e-01, eta: 9:33:01, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4526\n2023-01-03 18:10:39,225 - mmcls - INFO - Epoch [23][900/5005]\tlr: 1.000e-01, eta: 9:32:52, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.4916\n2023-01-03 18:10:47,921 - mmcls - INFO - Epoch [23][1000/5005]\tlr: 1.000e-01, eta: 9:32:43, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.4933\n2023-01-03 18:10:56,858 - mmcls - INFO - Epoch [23][1100/5005]\tlr: 1.000e-01, eta: 9:32:35, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4840\n2023-01-03 18:11:05,802 - mmcls - INFO - Epoch [23][1200/5005]\tlr: 1.000e-01, eta: 9:32:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4670\n2023-01-03 18:11:14,347 - mmcls - INFO - Epoch [23][1300/5005]\tlr: 1.000e-01, eta: 9:32:16, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.4863\n2023-01-03 18:11:23,009 - mmcls - INFO - Epoch [23][1400/5005]\tlr: 1.000e-01, eta: 9:32:07, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4743\n2023-01-03 18:11:31,798 - mmcls - INFO - Epoch [23][1500/5005]\tlr: 1.000e-01, eta: 9:31:58, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4854\n2023-01-03 18:11:40,346 - mmcls - INFO - Epoch [23][1600/5005]\tlr: 1.000e-01, eta: 9:31:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4616\n2023-01-03 18:11:48,849 - mmcls - INFO - Epoch [23][1700/5005]\tlr: 1.000e-01, eta: 9:31:38, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.5021\n2023-01-03 18:11:57,719 - mmcls - INFO - Epoch [23][1800/5005]\tlr: 1.000e-01, eta: 9:31:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4935\n2023-01-03 18:12:06,269 - mmcls - INFO - Epoch [23][1900/5005]\tlr: 1.000e-01, eta: 9:31:20, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.4816\n2023-01-03 18:12:15,011 - mmcls - INFO - Epoch [23][2000/5005]\tlr: 1.000e-01, eta: 9:31:11, time: 0.087, data_time: 0.039, memory: 1125, loss: 2.4590\n2023-01-03 18:12:23,528 - mmcls - INFO - Epoch [23][2100/5005]\tlr: 1.000e-01, eta: 9:31:01, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.4875\n2023-01-03 18:12:32,266 - mmcls - INFO - Epoch [23][2200/5005]\tlr: 1.000e-01, eta: 9:30:52, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4705\n2023-01-03 18:12:40,931 - mmcls - INFO - Epoch [23][2300/5005]\tlr: 1.000e-01, eta: 9:30:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.4937\n2023-01-03 18:12:49,514 - mmcls - INFO - Epoch [23][2400/5005]\tlr: 1.000e-01, eta: 9:30:33, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.4882\n2023-01-03 18:12:58,227 - mmcls - INFO - Epoch [23][2500/5005]\tlr: 1.000e-01, eta: 9:30:23, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4973\n2023-01-03 18:13:07,098 - mmcls - INFO - Epoch [23][2600/5005]\tlr: 1.000e-01, eta: 9:30:15, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.4896\n2023-01-03 18:13:16,126 - mmcls - INFO - Epoch [23][2700/5005]\tlr: 1.000e-01, eta: 9:30:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.4777\n2023-01-03 18:13:24,744 - mmcls - INFO - Epoch [23][2800/5005]\tlr: 1.000e-01, eta: 9:29:57, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4789\n2023-01-03 18:13:33,595 - mmcls - INFO - Epoch [23][2900/5005]\tlr: 1.000e-01, eta: 9:29:48, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.5164\n2023-01-03 18:13:42,400 - mmcls - INFO - Epoch [23][3000/5005]\tlr: 1.000e-01, eta: 9:29:39, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4900\n2023-01-03 18:13:51,137 - mmcls - INFO - Epoch [23][3100/5005]\tlr: 1.000e-01, eta: 9:29:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4822\n2023-01-03 18:13:59,637 - mmcls - INFO - Epoch [23][3200/5005]\tlr: 1.000e-01, eta: 9:29:20, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.5035\n2023-01-03 18:14:08,808 - mmcls - INFO - Epoch [23][3300/5005]\tlr: 1.000e-01, eta: 9:29:13, time: 0.092, data_time: 0.010, memory: 1125, loss: 2.5184\n2023-01-03 18:14:17,944 - mmcls - INFO - Epoch [23][3400/5005]\tlr: 1.000e-01, eta: 9:29:05, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.5107\n2023-01-03 18:14:26,517 - mmcls - INFO - Epoch [23][3500/5005]\tlr: 1.000e-01, eta: 9:28:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5056\n2023-01-03 18:14:35,516 - mmcls - INFO - Epoch [23][3600/5005]\tlr: 1.000e-01, eta: 9:28:47, time: 0.090, data_time: 0.033, memory: 1125, loss: 2.4482\n2023-01-03 18:14:44,523 - mmcls - INFO - Epoch [23][3700/5005]\tlr: 1.000e-01, eta: 9:28:39, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.5066\n2023-01-03 18:14:53,140 - mmcls - INFO - Epoch [23][3800/5005]\tlr: 1.000e-01, eta: 9:28:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5036\n2023-01-03 18:15:01,602 - mmcls - INFO - Epoch [23][3900/5005]\tlr: 1.000e-01, eta: 9:28:19, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.4802\n2023-01-03 18:15:10,501 - mmcls - INFO - Epoch [23][4000/5005]\tlr: 1.000e-01, eta: 9:28:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4868\n2023-01-03 18:15:19,064 - mmcls - INFO - Epoch [23][4100/5005]\tlr: 1.000e-01, eta: 9:28:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5075\n2023-01-03 18:15:28,059 - mmcls - INFO - Epoch [23][4200/5005]\tlr: 1.000e-01, eta: 9:27:53, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.5320\n2023-01-03 18:15:36,647 - mmcls - INFO - Epoch [23][4300/5005]\tlr: 1.000e-01, eta: 9:27:43, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.4966\n2023-01-03 18:15:45,410 - mmcls - INFO - Epoch [23][4400/5005]\tlr: 1.000e-01, eta: 9:27:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4830\n2023-01-03 18:15:54,593 - mmcls - INFO - Epoch [23][4500/5005]\tlr: 1.000e-01, eta: 9:27:27, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.4827\n2023-01-03 18:16:03,386 - mmcls - INFO - Epoch [23][4600/5005]\tlr: 1.000e-01, eta: 9:27:18, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4820\n2023-01-03 18:16:12,589 - mmcls - INFO - Epoch [23][4700/5005]\tlr: 1.000e-01, eta: 9:27:10, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.5341\n2023-01-03 18:16:21,061 - mmcls - INFO - Epoch [23][4800/5005]\tlr: 1.000e-01, eta: 9:27:00, time: 0.085, data_time: 0.024, memory: 1125, loss: 2.5038\n2023-01-03 18:16:29,657 - mmcls - INFO - Epoch [23][4900/5005]\tlr: 1.000e-01, eta: 9:26:50, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.5028\n2023-01-03 18:16:38,611 - mmcls - INFO - Epoch [23][5000/5005]\tlr: 1.000e-01, eta: 9:26:42, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.4934\n2023-01-03 18:17:01,470 - mmcls - INFO - Epoch(val) [23][196]\taccuracy_top-1: 49.7680, accuracy_top-5: 75.2340\n2023-01-03 18:17:12,633 - mmcls - INFO - Epoch [24][100/5005]\tlr: 1.000e-01, eta: 9:26:39, time: 0.111, data_time: 0.045, memory: 1125, loss: 2.4585\n2023-01-03 18:17:21,542 - mmcls - INFO - Epoch [24][200/5005]\tlr: 1.000e-01, eta: 9:26:31, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4244\n2023-01-03 18:17:30,336 - mmcls - INFO - Epoch [24][300/5005]\tlr: 1.000e-01, eta: 9:26:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4720\n2023-01-03 18:17:39,271 - mmcls - INFO - Epoch [24][400/5005]\tlr: 1.000e-01, eta: 9:26:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4548\n2023-01-03 18:17:47,925 - mmcls - INFO - Epoch [24][500/5005]\tlr: 1.000e-01, eta: 9:26:04, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.4624\n2023-01-03 18:17:56,606 - mmcls - INFO - Epoch [24][600/5005]\tlr: 1.000e-01, eta: 9:25:54, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4110\n2023-01-03 18:18:05,426 - mmcls - INFO - Epoch [24][700/5005]\tlr: 1.000e-01, eta: 9:25:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4599\n2023-01-03 18:18:13,915 - mmcls - INFO - Epoch [24][800/5005]\tlr: 1.000e-01, eta: 9:25:36, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4388\n2023-01-03 18:18:22,620 - mmcls - INFO - Epoch [24][900/5005]\tlr: 1.000e-01, eta: 9:25:26, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4467\n2023-01-03 18:18:31,437 - mmcls - INFO - Epoch [24][1000/5005]\tlr: 1.000e-01, eta: 9:25:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4785\n2023-01-03 18:18:40,342 - mmcls - INFO - Epoch [24][1100/5005]\tlr: 1.000e-01, eta: 9:25:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4775\n2023-01-03 18:18:49,503 - mmcls - INFO - Epoch [24][1200/5005]\tlr: 1.000e-01, eta: 9:25:01, time: 0.092, data_time: 0.029, memory: 1125, loss: 2.4680\n2023-01-03 18:18:58,350 - mmcls - INFO - Epoch [24][1300/5005]\tlr: 1.000e-01, eta: 9:24:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4861\n2023-01-03 18:19:06,968 - mmcls - INFO - Epoch [24][1400/5005]\tlr: 1.000e-01, eta: 9:24:43, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4482\n2023-01-03 18:19:16,325 - mmcls - INFO - Epoch [24][1500/5005]\tlr: 1.000e-01, eta: 9:24:36, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.4902\n2023-01-03 18:19:25,360 - mmcls - INFO - Epoch [24][1600/5005]\tlr: 1.000e-01, eta: 9:24:28, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4945\n2023-01-03 18:19:33,878 - mmcls - INFO - Epoch [24][1700/5005]\tlr: 1.000e-01, eta: 9:24:18, time: 0.085, data_time: 0.041, memory: 1125, loss: 2.4742\n2023-01-03 18:19:42,670 - mmcls - INFO - Epoch [24][1800/5005]\tlr: 1.000e-01, eta: 9:24:09, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.4624\n2023-01-03 18:19:51,703 - mmcls - INFO - Epoch [24][1900/5005]\tlr: 1.000e-01, eta: 9:24:01, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.5059\n2023-01-03 18:20:00,186 - mmcls - INFO - Epoch [24][2000/5005]\tlr: 1.000e-01, eta: 9:23:51, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4887\n2023-01-03 18:20:09,026 - mmcls - INFO - Epoch [24][2100/5005]\tlr: 1.000e-01, eta: 9:23:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4851\n2023-01-03 18:20:17,446 - mmcls - INFO - Epoch [24][2200/5005]\tlr: 1.000e-01, eta: 9:23:32, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.4963\n2023-01-03 18:20:26,122 - mmcls - INFO - Epoch [24][2300/5005]\tlr: 1.000e-01, eta: 9:23:23, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.4598\n2023-01-03 18:20:34,848 - mmcls - INFO - Epoch [24][2400/5005]\tlr: 1.000e-01, eta: 9:23:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4823\n2023-01-03 18:20:43,394 - mmcls - INFO - Epoch [24][2500/5005]\tlr: 1.000e-01, eta: 9:23:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4856\n2023-01-03 18:20:52,121 - mmcls - INFO - Epoch [24][2600/5005]\tlr: 1.000e-01, eta: 9:22:55, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4674\n2023-01-03 18:21:00,730 - mmcls - INFO - Epoch [24][2700/5005]\tlr: 1.000e-01, eta: 9:22:45, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.5126\n2023-01-03 18:21:09,716 - mmcls - INFO - Epoch [24][2800/5005]\tlr: 1.000e-01, eta: 9:22:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5032\n2023-01-03 18:21:18,670 - mmcls - INFO - Epoch [24][2900/5005]\tlr: 1.000e-01, eta: 9:22:28, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4873\n2023-01-03 18:21:27,332 - mmcls - INFO - Epoch [24][3000/5005]\tlr: 1.000e-01, eta: 9:22:19, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.4590\n2023-01-03 18:21:35,850 - mmcls - INFO - Epoch [24][3100/5005]\tlr: 1.000e-01, eta: 9:22:09, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.5078\n2023-01-03 18:21:44,319 - mmcls - INFO - Epoch [24][3200/5005]\tlr: 1.000e-01, eta: 9:21:59, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5089\n2023-01-03 18:21:52,801 - mmcls - INFO - Epoch [24][3300/5005]\tlr: 1.000e-01, eta: 9:21:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4903\n2023-01-03 18:22:01,435 - mmcls - INFO - Epoch [24][3400/5005]\tlr: 1.000e-01, eta: 9:21:40, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4787\n2023-01-03 18:22:10,104 - mmcls - INFO - Epoch [24][3500/5005]\tlr: 1.000e-01, eta: 9:21:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4839\n2023-01-03 18:22:18,831 - mmcls - INFO - Epoch [24][3600/5005]\tlr: 1.000e-01, eta: 9:21:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4906\n2023-01-03 18:22:27,905 - mmcls - INFO - Epoch [24][3700/5005]\tlr: 1.000e-01, eta: 9:21:14, time: 0.091, data_time: 0.022, memory: 1125, loss: 2.5108\n2023-01-03 18:22:36,390 - mmcls - INFO - Epoch [24][3800/5005]\tlr: 1.000e-01, eta: 9:21:04, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.4799\n2023-01-03 18:22:45,190 - mmcls - INFO - Epoch [24][3900/5005]\tlr: 1.000e-01, eta: 9:20:55, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4888\n2023-01-03 18:22:54,026 - mmcls - INFO - Epoch [24][4000/5005]\tlr: 1.000e-01, eta: 9:20:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5014\n2023-01-03 18:23:02,596 - mmcls - INFO - Epoch [24][4100/5005]\tlr: 1.000e-01, eta: 9:20:36, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4844\n2023-01-03 18:23:11,154 - mmcls - INFO - Epoch [24][4200/5005]\tlr: 1.000e-01, eta: 9:20:27, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.4890\n2023-01-03 18:23:19,868 - mmcls - INFO - Epoch [24][4300/5005]\tlr: 1.000e-01, eta: 9:20:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5042\n2023-01-03 18:23:28,696 - mmcls - INFO - Epoch [24][4400/5005]\tlr: 1.000e-01, eta: 9:20:09, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4625\n2023-01-03 18:23:37,401 - mmcls - INFO - Epoch [24][4500/5005]\tlr: 1.000e-01, eta: 9:19:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5186\n2023-01-03 18:23:45,991 - mmcls - INFO - Epoch [24][4600/5005]\tlr: 1.000e-01, eta: 9:19:50, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4956\n2023-01-03 18:23:54,718 - mmcls - INFO - Epoch [24][4700/5005]\tlr: 1.000e-01, eta: 9:19:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4955\n2023-01-03 18:24:03,449 - mmcls - INFO - Epoch [24][4800/5005]\tlr: 1.000e-01, eta: 9:19:32, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5144\n2023-01-03 18:24:12,276 - mmcls - INFO - Epoch [24][4900/5005]\tlr: 1.000e-01, eta: 9:19:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4792\n2023-01-03 18:24:21,149 - mmcls - INFO - Epoch [24][5000/5005]\tlr: 1.000e-01, eta: 9:19:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4988\n2023-01-03 18:24:43,953 - mmcls - INFO - Epoch(val) [24][196]\taccuracy_top-1: 49.7260, accuracy_top-5: 75.3100\n2023-01-03 18:24:54,898 - mmcls - INFO - Epoch [25][100/5005]\tlr: 1.000e-01, eta: 9:19:10, time: 0.109, data_time: 0.044, memory: 1125, loss: 2.4409\n2023-01-03 18:25:03,508 - mmcls - INFO - Epoch [25][200/5005]\tlr: 1.000e-01, eta: 9:19:01, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4468\n2023-01-03 18:25:12,078 - mmcls - INFO - Epoch [25][300/5005]\tlr: 1.000e-01, eta: 9:18:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4567\n2023-01-03 18:25:20,819 - mmcls - INFO - Epoch [25][400/5005]\tlr: 1.000e-01, eta: 9:18:42, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.4392\n2023-01-03 18:25:29,601 - mmcls - INFO - Epoch [25][500/5005]\tlr: 1.000e-01, eta: 9:18:33, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.4403\n2023-01-03 18:25:38,173 - mmcls - INFO - Epoch [25][600/5005]\tlr: 1.000e-01, eta: 9:18:23, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4658\n2023-01-03 18:25:46,821 - mmcls - INFO - Epoch [25][700/5005]\tlr: 1.000e-01, eta: 9:18:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.4490\n2023-01-03 18:25:55,495 - mmcls - INFO - Epoch [25][800/5005]\tlr: 1.000e-01, eta: 9:18:05, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4378\n2023-01-03 18:26:04,312 - mmcls - INFO - Epoch [25][900/5005]\tlr: 1.000e-01, eta: 9:17:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4507\n2023-01-03 18:26:13,035 - mmcls - INFO - Epoch [25][1000/5005]\tlr: 1.000e-01, eta: 9:17:47, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4547\n2023-01-03 18:26:21,725 - mmcls - INFO - Epoch [25][1100/5005]\tlr: 1.000e-01, eta: 9:17:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4724\n2023-01-03 18:26:30,653 - mmcls - INFO - Epoch [25][1200/5005]\tlr: 1.000e-01, eta: 9:17:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5059\n2023-01-03 18:26:39,310 - mmcls - INFO - Epoch [25][1300/5005]\tlr: 1.000e-01, eta: 9:17:20, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4559\n2023-01-03 18:26:47,722 - mmcls - INFO - Epoch [25][1400/5005]\tlr: 1.000e-01, eta: 9:17:10, time: 0.084, data_time: 0.021, memory: 1125, loss: 2.4552\n2023-01-03 18:26:56,614 - mmcls - INFO - Epoch [25][1500/5005]\tlr: 1.000e-01, eta: 9:17:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4475\n2023-01-03 18:27:05,450 - mmcls - INFO - Epoch [25][1600/5005]\tlr: 1.000e-01, eta: 9:16:52, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4475\n2023-01-03 18:27:14,138 - mmcls - INFO - Epoch [25][1700/5005]\tlr: 1.000e-01, eta: 9:16:43, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.4761\n2023-01-03 18:27:22,686 - mmcls - INFO - Epoch [25][1800/5005]\tlr: 1.000e-01, eta: 9:16:33, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4815\n2023-01-03 18:27:31,563 - mmcls - INFO - Epoch [25][1900/5005]\tlr: 1.000e-01, eta: 9:16:25, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.4796\n2023-01-03 18:27:40,210 - mmcls - INFO - Epoch [25][2000/5005]\tlr: 1.000e-01, eta: 9:16:15, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4854\n2023-01-03 18:27:48,873 - mmcls - INFO - Epoch [25][2100/5005]\tlr: 1.000e-01, eta: 9:16:06, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4829\n2023-01-03 18:27:57,509 - mmcls - INFO - Epoch [25][2200/5005]\tlr: 1.000e-01, eta: 9:15:57, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4813\n2023-01-03 18:28:06,056 - mmcls - INFO - Epoch [25][2300/5005]\tlr: 1.000e-01, eta: 9:15:47, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.4901\n2023-01-03 18:28:14,873 - mmcls - INFO - Epoch [25][2400/5005]\tlr: 1.000e-01, eta: 9:15:38, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4609\n2023-01-03 18:28:23,933 - mmcls - INFO - Epoch [25][2500/5005]\tlr: 1.000e-01, eta: 9:15:30, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.4790\n2023-01-03 18:28:32,698 - mmcls - INFO - Epoch [25][2600/5005]\tlr: 1.000e-01, eta: 9:15:21, time: 0.088, data_time: 0.035, memory: 1125, loss: 2.4719\n2023-01-03 18:28:41,333 - mmcls - INFO - Epoch [25][2700/5005]\tlr: 1.000e-01, eta: 9:15:12, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4874\n2023-01-03 18:28:50,232 - mmcls - INFO - Epoch [25][2800/5005]\tlr: 1.000e-01, eta: 9:15:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4687\n2023-01-03 18:28:59,040 - mmcls - INFO - Epoch [25][2900/5005]\tlr: 1.000e-01, eta: 9:14:54, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.4741\n2023-01-03 18:29:07,567 - mmcls - INFO - Epoch [25][3000/5005]\tlr: 1.000e-01, eta: 9:14:44, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4804\n2023-01-03 18:29:16,225 - mmcls - INFO - Epoch [25][3100/5005]\tlr: 1.000e-01, eta: 9:14:35, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.4741\n2023-01-03 18:29:24,798 - mmcls - INFO - Epoch [25][3200/5005]\tlr: 1.000e-01, eta: 9:14:26, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4782\n2023-01-03 18:29:33,757 - mmcls - INFO - Epoch [25][3300/5005]\tlr: 1.000e-01, eta: 9:14:17, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.4937\n2023-01-03 18:29:42,590 - mmcls - INFO - Epoch [25][3400/5005]\tlr: 1.000e-01, eta: 9:14:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4843\n2023-01-03 18:29:51,386 - mmcls - INFO - Epoch [25][3500/5005]\tlr: 1.000e-01, eta: 9:13:59, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4755\n2023-01-03 18:30:00,086 - mmcls - INFO - Epoch [25][3600/5005]\tlr: 1.000e-01, eta: 9:13:50, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4868\n2023-01-03 18:30:08,665 - mmcls - INFO - Epoch [25][3700/5005]\tlr: 1.000e-01, eta: 9:13:41, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4860\n2023-01-03 18:30:17,401 - mmcls - INFO - Epoch [25][3800/5005]\tlr: 1.000e-01, eta: 9:13:32, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4749\n2023-01-03 18:30:26,188 - mmcls - INFO - Epoch [25][3900/5005]\tlr: 1.000e-01, eta: 9:13:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4644\n2023-01-03 18:30:35,095 - mmcls - INFO - Epoch [25][4000/5005]\tlr: 1.000e-01, eta: 9:13:14, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5005\n2023-01-03 18:30:43,662 - mmcls - INFO - Epoch [25][4100/5005]\tlr: 1.000e-01, eta: 9:13:05, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.4765\n2023-01-03 18:30:52,310 - mmcls - INFO - Epoch [25][4200/5005]\tlr: 1.000e-01, eta: 9:12:55, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4845\n2023-01-03 18:31:00,969 - mmcls - INFO - Epoch [25][4300/5005]\tlr: 1.000e-01, eta: 9:12:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4960\n2023-01-03 18:31:09,803 - mmcls - INFO - Epoch [25][4400/5005]\tlr: 1.000e-01, eta: 9:12:37, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.4648\n2023-01-03 18:31:18,881 - mmcls - INFO - Epoch [25][4500/5005]\tlr: 1.000e-01, eta: 9:12:29, time: 0.091, data_time: 0.040, memory: 1125, loss: 2.4740\n2023-01-03 18:31:27,594 - mmcls - INFO - Epoch [25][4600/5005]\tlr: 1.000e-01, eta: 9:12:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4863\n2023-01-03 18:31:36,477 - mmcls - INFO - Epoch [25][4700/5005]\tlr: 1.000e-01, eta: 9:12:11, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5008\n2023-01-03 18:31:45,234 - mmcls - INFO - Epoch [25][4800/5005]\tlr: 1.000e-01, eta: 9:12:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5047\n2023-01-03 18:31:53,699 - mmcls - INFO - Epoch [25][4900/5005]\tlr: 1.000e-01, eta: 9:11:52, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4934\n2023-01-03 18:32:02,451 - mmcls - INFO - Epoch [25][5000/5005]\tlr: 1.000e-01, eta: 9:11:44, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4675\n2023-01-03 18:32:25,037 - mmcls - INFO - Epoch(val) [25][196]\taccuracy_top-1: 49.6840, accuracy_top-5: 75.4460\n2023-01-03 18:32:36,096 - mmcls - INFO - Epoch [26][100/5005]\tlr: 1.000e-01, eta: 9:11:40, time: 0.110, data_time: 0.037, memory: 1125, loss: 2.4666\n2023-01-03 18:32:44,750 - mmcls - INFO - Epoch [26][200/5005]\tlr: 1.000e-01, eta: 9:11:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4385\n2023-01-03 18:32:53,436 - mmcls - INFO - Epoch [26][300/5005]\tlr: 1.000e-01, eta: 9:11:21, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4471\n2023-01-03 18:33:02,052 - mmcls - INFO - Epoch [26][400/5005]\tlr: 1.000e-01, eta: 9:11:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4207\n2023-01-03 18:33:10,850 - mmcls - INFO - Epoch [26][500/5005]\tlr: 1.000e-01, eta: 9:11:03, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4489\n2023-01-03 18:33:19,697 - mmcls - INFO - Epoch [26][600/5005]\tlr: 1.000e-01, eta: 9:10:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4251\n2023-01-03 18:33:28,494 - mmcls - INFO - Epoch [26][700/5005]\tlr: 1.000e-01, eta: 9:10:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4675\n2023-01-03 18:33:37,476 - mmcls - INFO - Epoch [26][800/5005]\tlr: 1.000e-01, eta: 9:10:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4434\n2023-01-03 18:33:46,426 - mmcls - INFO - Epoch [26][900/5005]\tlr: 1.000e-01, eta: 9:10:28, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.4636\n2023-01-03 18:33:55,492 - mmcls - INFO - Epoch [26][1000/5005]\tlr: 1.000e-01, eta: 9:10:20, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.4489\n2023-01-03 18:34:04,350 - mmcls - INFO - Epoch [26][1100/5005]\tlr: 1.000e-01, eta: 9:10:11, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4594\n2023-01-03 18:34:13,019 - mmcls - INFO - Epoch [26][1200/5005]\tlr: 1.000e-01, eta: 9:10:02, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4572\n2023-01-03 18:34:21,787 - mmcls - INFO - Epoch [26][1300/5005]\tlr: 1.000e-01, eta: 9:09:53, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4605\n2023-01-03 18:34:30,522 - mmcls - INFO - Epoch [26][1400/5005]\tlr: 1.000e-01, eta: 9:09:44, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4730\n2023-01-03 18:34:39,612 - mmcls - INFO - Epoch [26][1500/5005]\tlr: 1.000e-01, eta: 9:09:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.4736\n2023-01-03 18:34:48,455 - mmcls - INFO - Epoch [26][1600/5005]\tlr: 1.000e-01, eta: 9:09:27, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4542\n2023-01-03 18:34:57,159 - mmcls - INFO - Epoch [26][1700/5005]\tlr: 1.000e-01, eta: 9:09:18, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4880\n2023-01-03 18:35:06,213 - mmcls - INFO - Epoch [26][1800/5005]\tlr: 1.000e-01, eta: 9:09:10, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.4768\n2023-01-03 18:35:15,126 - mmcls - INFO - Epoch [26][1900/5005]\tlr: 1.000e-01, eta: 9:09:02, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.4428\n2023-01-03 18:35:24,299 - mmcls - INFO - Epoch [26][2000/5005]\tlr: 1.000e-01, eta: 9:08:54, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.5018\n2023-01-03 18:35:32,963 - mmcls - INFO - Epoch [26][2100/5005]\tlr: 1.000e-01, eta: 9:08:44, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4821\n2023-01-03 18:35:41,667 - mmcls - INFO - Epoch [26][2200/5005]\tlr: 1.000e-01, eta: 9:08:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4727\n2023-01-03 18:35:50,485 - mmcls - INFO - Epoch [26][2300/5005]\tlr: 1.000e-01, eta: 9:08:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4845\n2023-01-03 18:35:59,087 - mmcls - INFO - Epoch [26][2400/5005]\tlr: 1.000e-01, eta: 9:08:17, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4747\n2023-01-03 18:36:07,724 - mmcls - INFO - Epoch [26][2500/5005]\tlr: 1.000e-01, eta: 9:08:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4614\n2023-01-03 18:36:16,424 - mmcls - INFO - Epoch [26][2600/5005]\tlr: 1.000e-01, eta: 9:07:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4568\n2023-01-03 18:36:25,197 - mmcls - INFO - Epoch [26][2700/5005]\tlr: 1.000e-01, eta: 9:07:49, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4834\n2023-01-03 18:36:33,714 - mmcls - INFO - Epoch [26][2800/5005]\tlr: 1.000e-01, eta: 9:07:40, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.4690\n2023-01-03 18:36:42,631 - mmcls - INFO - Epoch [26][2900/5005]\tlr: 1.000e-01, eta: 9:07:31, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4640\n2023-01-03 18:36:51,518 - mmcls - INFO - Epoch [26][3000/5005]\tlr: 1.000e-01, eta: 9:07:23, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4611\n2023-01-03 18:37:00,147 - mmcls - INFO - Epoch [26][3100/5005]\tlr: 1.000e-01, eta: 9:07:13, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5033\n2023-01-03 18:37:08,733 - mmcls - INFO - Epoch [26][3200/5005]\tlr: 1.000e-01, eta: 9:07:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4669\n2023-01-03 18:37:17,336 - mmcls - INFO - Epoch [26][3300/5005]\tlr: 1.000e-01, eta: 9:06:54, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4741\n2023-01-03 18:37:25,780 - mmcls - INFO - Epoch [26][3400/5005]\tlr: 1.000e-01, eta: 9:06:44, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.5001\n2023-01-03 18:37:34,641 - mmcls - INFO - Epoch [26][3500/5005]\tlr: 1.000e-01, eta: 9:06:36, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4983\n2023-01-03 18:37:43,247 - mmcls - INFO - Epoch [26][3600/5005]\tlr: 1.000e-01, eta: 9:06:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4783\n2023-01-03 18:37:51,834 - mmcls - INFO - Epoch [26][3700/5005]\tlr: 1.000e-01, eta: 9:06:17, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4742\n2023-01-03 18:38:00,671 - mmcls - INFO - Epoch [26][3800/5005]\tlr: 1.000e-01, eta: 9:06:08, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4713\n2023-01-03 18:38:09,232 - mmcls - INFO - Epoch [26][3900/5005]\tlr: 1.000e-01, eta: 9:05:58, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.4846\n2023-01-03 18:38:18,106 - mmcls - INFO - Epoch [26][4000/5005]\tlr: 1.000e-01, eta: 9:05:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4938\n2023-01-03 18:38:27,024 - mmcls - INFO - Epoch [26][4100/5005]\tlr: 1.000e-01, eta: 9:05:41, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.4846\n2023-01-03 18:38:35,617 - mmcls - INFO - Epoch [26][4200/5005]\tlr: 1.000e-01, eta: 9:05:32, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4601\n2023-01-03 18:38:44,181 - mmcls - INFO - Epoch [26][4300/5005]\tlr: 1.000e-01, eta: 9:05:22, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4395\n2023-01-03 18:38:52,747 - mmcls - INFO - Epoch [26][4400/5005]\tlr: 1.000e-01, eta: 9:05:13, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4701\n2023-01-03 18:39:01,589 - mmcls - INFO - Epoch [26][4500/5005]\tlr: 1.000e-01, eta: 9:05:04, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4958\n2023-01-03 18:39:10,281 - mmcls - INFO - Epoch [26][4600/5005]\tlr: 1.000e-01, eta: 9:04:55, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.5130\n2023-01-03 18:39:18,950 - mmcls - INFO - Epoch [26][4700/5005]\tlr: 1.000e-01, eta: 9:04:45, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5025\n2023-01-03 18:39:27,589 - mmcls - INFO - Epoch [26][4800/5005]\tlr: 1.000e-01, eta: 9:04:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4855\n2023-01-03 18:39:36,186 - mmcls - INFO - Epoch [26][4900/5005]\tlr: 1.000e-01, eta: 9:04:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4833\n2023-01-03 18:39:44,650 - mmcls - INFO - Epoch [26][5000/5005]\tlr: 1.000e-01, eta: 9:04:17, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.4745\n2023-01-03 18:40:07,128 - mmcls - INFO - Epoch(val) [26][196]\taccuracy_top-1: 49.6120, accuracy_top-5: 75.5400\n2023-01-03 18:40:17,924 - mmcls - INFO - Epoch [27][100/5005]\tlr: 1.000e-01, eta: 9:04:12, time: 0.108, data_time: 0.041, memory: 1125, loss: 2.4060\n2023-01-03 18:40:26,529 - mmcls - INFO - Epoch [27][200/5005]\tlr: 1.000e-01, eta: 9:04:03, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4177\n2023-01-03 18:40:35,281 - mmcls - INFO - Epoch [27][300/5005]\tlr: 1.000e-01, eta: 9:03:53, time: 0.087, data_time: 0.048, memory: 1125, loss: 2.4497\n2023-01-03 18:40:43,910 - mmcls - INFO - Epoch [27][400/5005]\tlr: 1.000e-01, eta: 9:03:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4486\n2023-01-03 18:40:52,685 - mmcls - INFO - Epoch [27][500/5005]\tlr: 1.000e-01, eta: 9:03:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4565\n2023-01-03 18:41:01,599 - mmcls - INFO - Epoch [27][600/5005]\tlr: 1.000e-01, eta: 9:03:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4610\n2023-01-03 18:41:10,265 - mmcls - INFO - Epoch [27][700/5005]\tlr: 1.000e-01, eta: 9:03:17, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4651\n2023-01-03 18:41:19,022 - mmcls - INFO - Epoch [27][800/5005]\tlr: 1.000e-01, eta: 9:03:08, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4214\n2023-01-03 18:41:27,603 - mmcls - INFO - Epoch [27][900/5005]\tlr: 1.000e-01, eta: 9:02:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4570\n2023-01-03 18:41:36,229 - mmcls - INFO - Epoch [27][1000/5005]\tlr: 1.000e-01, eta: 9:02:50, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4565\n2023-01-03 18:41:45,076 - mmcls - INFO - Epoch [27][1100/5005]\tlr: 1.000e-01, eta: 9:02:41, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4708\n2023-01-03 18:41:54,195 - mmcls - INFO - Epoch [27][1200/5005]\tlr: 1.000e-01, eta: 9:02:33, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.4845\n2023-01-03 18:42:02,899 - mmcls - INFO - Epoch [27][1300/5005]\tlr: 1.000e-01, eta: 9:02:24, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4743\n2023-01-03 18:42:11,583 - mmcls - INFO - Epoch [27][1400/5005]\tlr: 1.000e-01, eta: 9:02:15, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4677\n2023-01-03 18:42:20,314 - mmcls - INFO - Epoch [27][1500/5005]\tlr: 1.000e-01, eta: 9:02:05, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.4613\n2023-01-03 18:42:28,974 - mmcls - INFO - Epoch [27][1600/5005]\tlr: 1.000e-01, eta: 9:01:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4686\n2023-01-03 18:42:37,745 - mmcls - INFO - Epoch [27][1700/5005]\tlr: 1.000e-01, eta: 9:01:47, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4536\n2023-01-03 18:42:46,397 - mmcls - INFO - Epoch [27][1800/5005]\tlr: 1.000e-01, eta: 9:01:38, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.4606\n2023-01-03 18:42:55,292 - mmcls - INFO - Epoch [27][1900/5005]\tlr: 1.000e-01, eta: 9:01:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4851\n2023-01-03 18:43:03,971 - mmcls - INFO - Epoch [27][2000/5005]\tlr: 1.000e-01, eta: 9:01:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4842\n2023-01-03 18:43:12,952 - mmcls - INFO - Epoch [27][2100/5005]\tlr: 1.000e-01, eta: 9:01:12, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.4575\n2023-01-03 18:43:21,740 - mmcls - INFO - Epoch [27][2200/5005]\tlr: 1.000e-01, eta: 9:01:03, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4703\n2023-01-03 18:43:30,506 - mmcls - INFO - Epoch [27][2300/5005]\tlr: 1.000e-01, eta: 9:00:54, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.5015\n2023-01-03 18:43:39,481 - mmcls - INFO - Epoch [27][2400/5005]\tlr: 1.000e-01, eta: 9:00:46, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.4773\n2023-01-03 18:43:48,066 - mmcls - INFO - Epoch [27][2500/5005]\tlr: 1.000e-01, eta: 9:00:36, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4747\n2023-01-03 18:43:57,225 - mmcls - INFO - Epoch [27][2600/5005]\tlr: 1.000e-01, eta: 9:00:28, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.4499\n2023-01-03 18:44:05,908 - mmcls - INFO - Epoch [27][2700/5005]\tlr: 1.000e-01, eta: 9:00:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4696\n2023-01-03 18:44:14,773 - mmcls - INFO - Epoch [27][2800/5005]\tlr: 1.000e-01, eta: 9:00:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.4934\n2023-01-03 18:44:23,790 - mmcls - INFO - Epoch [27][2900/5005]\tlr: 1.000e-01, eta: 9:00:02, time: 0.090, data_time: 0.039, memory: 1125, loss: 2.4545\n2023-01-03 18:44:32,534 - mmcls - INFO - Epoch [27][3000/5005]\tlr: 1.000e-01, eta: 8:59:53, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.4620\n2023-01-03 18:44:41,056 - mmcls - INFO - Epoch [27][3100/5005]\tlr: 1.000e-01, eta: 8:59:43, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4614\n2023-01-03 18:44:49,614 - mmcls - INFO - Epoch [27][3200/5005]\tlr: 1.000e-01, eta: 8:59:34, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4778\n2023-01-03 18:44:58,243 - mmcls - INFO - Epoch [27][3300/5005]\tlr: 1.000e-01, eta: 8:59:25, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4830\n2023-01-03 18:45:06,894 - mmcls - INFO - Epoch [27][3400/5005]\tlr: 1.000e-01, eta: 8:59:15, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4949\n2023-01-03 18:45:15,788 - mmcls - INFO - Epoch [27][3500/5005]\tlr: 1.000e-01, eta: 8:59:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4766\n2023-01-03 18:45:24,694 - mmcls - INFO - Epoch [27][3600/5005]\tlr: 1.000e-01, eta: 8:58:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4711\n2023-01-03 18:45:33,473 - mmcls - INFO - Epoch [27][3700/5005]\tlr: 1.000e-01, eta: 8:58:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.4850\n2023-01-03 18:45:41,962 - mmcls - INFO - Epoch [27][3800/5005]\tlr: 1.000e-01, eta: 8:58:39, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.4964\n2023-01-03 18:45:50,649 - mmcls - INFO - Epoch [27][3900/5005]\tlr: 1.000e-01, eta: 8:58:30, time: 0.087, data_time: 0.041, memory: 1125, loss: 2.4689\n2023-01-03 18:45:59,220 - mmcls - INFO - Epoch [27][4000/5005]\tlr: 1.000e-01, eta: 8:58:21, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4734\n2023-01-03 18:46:08,258 - mmcls - INFO - Epoch [27][4100/5005]\tlr: 1.000e-01, eta: 8:58:13, time: 0.090, data_time: 0.025, memory: 1125, loss: 2.4834\n2023-01-03 18:46:17,130 - mmcls - INFO - Epoch [27][4200/5005]\tlr: 1.000e-01, eta: 8:58:04, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.4616\n2023-01-03 18:46:25,592 - mmcls - INFO - Epoch [27][4300/5005]\tlr: 1.000e-01, eta: 8:57:54, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.4690\n2023-01-03 18:46:34,340 - mmcls - INFO - Epoch [27][4400/5005]\tlr: 1.000e-01, eta: 8:57:45, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4783\n2023-01-03 18:46:42,924 - mmcls - INFO - Epoch [27][4500/5005]\tlr: 1.000e-01, eta: 8:57:36, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4934\n2023-01-03 18:46:51,742 - mmcls - INFO - Epoch [27][4600/5005]\tlr: 1.000e-01, eta: 8:57:27, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.4607\n2023-01-03 18:47:00,445 - mmcls - INFO - Epoch [27][4700/5005]\tlr: 1.000e-01, eta: 8:57:18, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4509\n2023-01-03 18:47:09,181 - mmcls - INFO - Epoch [27][4800/5005]\tlr: 1.000e-01, eta: 8:57:09, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4825\n2023-01-03 18:47:17,987 - mmcls - INFO - Epoch [27][4900/5005]\tlr: 1.000e-01, eta: 8:57:00, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4597\n2023-01-03 18:47:26,675 - mmcls - INFO - Epoch [27][5000/5005]\tlr: 1.000e-01, eta: 8:56:51, time: 0.087, data_time: 0.040, memory: 1125, loss: 2.4762\n2023-01-03 18:47:49,380 - mmcls - INFO - Epoch(val) [27][196]\taccuracy_top-1: 49.4140, accuracy_top-5: 75.2940\n2023-01-03 18:48:00,634 - mmcls - INFO - Epoch [28][100/5005]\tlr: 1.000e-01, eta: 8:56:47, time: 0.112, data_time: 0.043, memory: 1125, loss: 2.4336\n2023-01-03 18:48:09,582 - mmcls - INFO - Epoch [28][200/5005]\tlr: 1.000e-01, eta: 8:56:38, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.3921\n2023-01-03 18:48:18,137 - mmcls - INFO - Epoch [28][300/5005]\tlr: 1.000e-01, eta: 8:56:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4400\n2023-01-03 18:48:26,808 - mmcls - INFO - Epoch [28][400/5005]\tlr: 1.000e-01, eta: 8:56:20, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4329\n2023-01-03 18:48:35,402 - mmcls - INFO - Epoch [28][500/5005]\tlr: 1.000e-01, eta: 8:56:10, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.4214\n2023-01-03 18:48:44,147 - mmcls - INFO - Epoch [28][600/5005]\tlr: 1.000e-01, eta: 8:56:01, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4253\n2023-01-03 18:48:52,758 - mmcls - INFO - Epoch [28][700/5005]\tlr: 1.000e-01, eta: 8:55:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4590\n2023-01-03 18:49:01,473 - mmcls - INFO - Epoch [28][800/5005]\tlr: 1.000e-01, eta: 8:55:43, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4542\n2023-01-03 18:49:10,610 - mmcls - INFO - Epoch [28][900/5005]\tlr: 1.000e-01, eta: 8:55:35, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.4320\n2023-01-03 18:49:19,365 - mmcls - INFO - Epoch [28][1000/5005]\tlr: 1.000e-01, eta: 8:55:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4603\n2023-01-03 18:49:28,087 - mmcls - INFO - Epoch [28][1100/5005]\tlr: 1.000e-01, eta: 8:55:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4716\n2023-01-03 18:49:36,899 - mmcls - INFO - Epoch [28][1200/5005]\tlr: 1.000e-01, eta: 8:55:08, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4667\n2023-01-03 18:49:45,715 - mmcls - INFO - Epoch [28][1300/5005]\tlr: 1.000e-01, eta: 8:54:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4747\n2023-01-03 18:49:54,425 - mmcls - INFO - Epoch [28][1400/5005]\tlr: 1.000e-01, eta: 8:54:50, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4747\n2023-01-03 18:50:03,020 - mmcls - INFO - Epoch [28][1500/5005]\tlr: 1.000e-01, eta: 8:54:40, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4670\n2023-01-03 18:50:11,722 - mmcls - INFO - Epoch [28][1600/5005]\tlr: 1.000e-01, eta: 8:54:31, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4698\n2023-01-03 18:50:20,646 - mmcls - INFO - Epoch [28][1700/5005]\tlr: 1.000e-01, eta: 8:54:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.4606\n2023-01-03 18:50:29,566 - mmcls - INFO - Epoch [28][1800/5005]\tlr: 1.000e-01, eta: 8:54:14, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.4540\n2023-01-03 18:50:38,562 - mmcls - INFO - Epoch [28][1900/5005]\tlr: 1.000e-01, eta: 8:54:06, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.4409\n2023-01-03 18:50:47,429 - mmcls - INFO - Epoch [28][2000/5005]\tlr: 1.000e-01, eta: 8:53:57, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.4751\n2023-01-03 18:50:56,165 - mmcls - INFO - Epoch [28][2100/5005]\tlr: 1.000e-01, eta: 8:53:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4394\n2023-01-03 18:51:04,736 - mmcls - INFO - Epoch [28][2200/5005]\tlr: 1.000e-01, eta: 8:53:39, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.4641\n2023-01-03 18:51:13,332 - mmcls - INFO - Epoch [28][2300/5005]\tlr: 1.000e-01, eta: 8:53:29, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4544\n2023-01-03 18:51:22,060 - mmcls - INFO - Epoch [28][2400/5005]\tlr: 1.000e-01, eta: 8:53:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4638\n2023-01-03 18:51:30,770 - mmcls - INFO - Epoch [28][2500/5005]\tlr: 1.000e-01, eta: 8:53:11, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4461\n2023-01-03 18:51:39,406 - mmcls - INFO - Epoch [28][2600/5005]\tlr: 1.000e-01, eta: 8:53:02, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4757\n2023-01-03 18:51:48,046 - mmcls - INFO - Epoch [28][2700/5005]\tlr: 1.000e-01, eta: 8:52:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4634\n2023-01-03 18:51:56,659 - mmcls - INFO - Epoch [28][2800/5005]\tlr: 1.000e-01, eta: 8:52:43, time: 0.086, data_time: 0.039, memory: 1125, loss: 2.4615\n2023-01-03 18:52:05,239 - mmcls - INFO - Epoch [28][2900/5005]\tlr: 1.000e-01, eta: 8:52:34, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.4883\n2023-01-03 18:52:14,685 - mmcls - INFO - Epoch [28][3000/5005]\tlr: 1.000e-01, eta: 8:52:27, time: 0.095, data_time: 0.002, memory: 1125, loss: 2.4788\n2023-01-03 18:52:23,226 - mmcls - INFO - Epoch [28][3100/5005]\tlr: 1.000e-01, eta: 8:52:17, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.4797\n2023-01-03 18:52:31,935 - mmcls - INFO - Epoch [28][3200/5005]\tlr: 1.000e-01, eta: 8:52:08, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4515\n2023-01-03 18:52:40,683 - mmcls - INFO - Epoch [28][3300/5005]\tlr: 1.000e-01, eta: 8:51:59, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4924\n2023-01-03 18:52:49,334 - mmcls - INFO - Epoch [28][3400/5005]\tlr: 1.000e-01, eta: 8:51:50, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4346\n2023-01-03 18:52:58,143 - mmcls - INFO - Epoch [28][3500/5005]\tlr: 1.000e-01, eta: 8:51:41, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4703\n2023-01-03 18:53:06,926 - mmcls - INFO - Epoch [28][3600/5005]\tlr: 1.000e-01, eta: 8:51:32, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.4504\n2023-01-03 18:53:15,780 - mmcls - INFO - Epoch [28][3700/5005]\tlr: 1.000e-01, eta: 8:51:23, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4727\n2023-01-03 18:53:24,508 - mmcls - INFO - Epoch [28][3800/5005]\tlr: 1.000e-01, eta: 8:51:14, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4996\n2023-01-03 18:53:33,196 - mmcls - INFO - Epoch [28][3900/5005]\tlr: 1.000e-01, eta: 8:51:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4984\n2023-01-03 18:53:42,242 - mmcls - INFO - Epoch [28][4000/5005]\tlr: 1.000e-01, eta: 8:50:57, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.5004\n2023-01-03 18:53:50,990 - mmcls - INFO - Epoch [28][4100/5005]\tlr: 1.000e-01, eta: 8:50:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4883\n2023-01-03 18:53:59,741 - mmcls - INFO - Epoch [28][4200/5005]\tlr: 1.000e-01, eta: 8:50:39, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4933\n2023-01-03 18:54:08,503 - mmcls - INFO - Epoch [28][4300/5005]\tlr: 1.000e-01, eta: 8:50:30, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.4947\n2023-01-03 18:54:17,307 - mmcls - INFO - Epoch [28][4400/5005]\tlr: 1.000e-01, eta: 8:50:21, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.4963\n2023-01-03 18:54:26,312 - mmcls - INFO - Epoch [28][4500/5005]\tlr: 1.000e-01, eta: 8:50:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.4481\n2023-01-03 18:54:35,086 - mmcls - INFO - Epoch [28][4600/5005]\tlr: 1.000e-01, eta: 8:50:04, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4534\n2023-01-03 18:54:43,845 - mmcls - INFO - Epoch [28][4700/5005]\tlr: 1.000e-01, eta: 8:49:55, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4965\n2023-01-03 18:54:52,399 - mmcls - INFO - Epoch [28][4800/5005]\tlr: 1.000e-01, eta: 8:49:45, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4827\n2023-01-03 18:55:01,238 - mmcls - INFO - Epoch [28][4900/5005]\tlr: 1.000e-01, eta: 8:49:37, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.4775\n2023-01-03 18:55:10,067 - mmcls - INFO - Epoch [28][5000/5005]\tlr: 1.000e-01, eta: 8:49:28, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4828\n2023-01-03 18:55:32,652 - mmcls - INFO - Epoch(val) [28][196]\taccuracy_top-1: 50.1920, accuracy_top-5: 75.6660\n2023-01-03 18:55:43,602 - mmcls - INFO - Epoch [29][100/5005]\tlr: 1.000e-01, eta: 8:49:23, time: 0.109, data_time: 0.035, memory: 1125, loss: 2.4173\n2023-01-03 18:55:52,059 - mmcls - INFO - Epoch [29][200/5005]\tlr: 1.000e-01, eta: 8:49:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.4073\n2023-01-03 18:56:00,888 - mmcls - INFO - Epoch [29][300/5005]\tlr: 1.000e-01, eta: 8:49:04, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4149\n2023-01-03 18:56:09,389 - mmcls - INFO - Epoch [29][400/5005]\tlr: 1.000e-01, eta: 8:48:55, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4338\n2023-01-03 18:56:18,388 - mmcls - INFO - Epoch [29][500/5005]\tlr: 1.000e-01, eta: 8:48:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.4161\n2023-01-03 18:56:27,098 - mmcls - INFO - Epoch [29][600/5005]\tlr: 1.000e-01, eta: 8:48:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4258\n2023-01-03 18:56:35,955 - mmcls - INFO - Epoch [29][700/5005]\tlr: 1.000e-01, eta: 8:48:29, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4835\n2023-01-03 18:56:44,528 - mmcls - INFO - Epoch [29][800/5005]\tlr: 1.000e-01, eta: 8:48:19, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4405\n2023-01-03 18:56:53,449 - mmcls - INFO - Epoch [29][900/5005]\tlr: 1.000e-01, eta: 8:48:11, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.4460\n2023-01-03 18:57:02,001 - mmcls - INFO - Epoch [29][1000/5005]\tlr: 1.000e-01, eta: 8:48:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4438\n2023-01-03 18:57:10,798 - mmcls - INFO - Epoch [29][1100/5005]\tlr: 1.000e-01, eta: 8:47:52, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4494\n2023-01-03 18:57:19,737 - mmcls - INFO - Epoch [29][1200/5005]\tlr: 1.000e-01, eta: 8:47:44, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4476\n2023-01-03 18:57:28,434 - mmcls - INFO - Epoch [29][1300/5005]\tlr: 1.000e-01, eta: 8:47:35, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4589\n2023-01-03 18:57:37,249 - mmcls - INFO - Epoch [29][1400/5005]\tlr: 1.000e-01, eta: 8:47:26, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4397\n2023-01-03 18:57:45,704 - mmcls - INFO - Epoch [29][1500/5005]\tlr: 1.000e-01, eta: 8:47:16, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.4678\n2023-01-03 18:57:54,758 - mmcls - INFO - Epoch [29][1600/5005]\tlr: 1.000e-01, eta: 8:47:08, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.4243\n2023-01-03 18:58:03,373 - mmcls - INFO - Epoch [29][1700/5005]\tlr: 1.000e-01, eta: 8:46:58, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4635\n2023-01-03 18:58:12,213 - mmcls - INFO - Epoch [29][1800/5005]\tlr: 1.000e-01, eta: 8:46:50, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.4426\n2023-01-03 18:58:20,864 - mmcls - INFO - Epoch [29][1900/5005]\tlr: 1.000e-01, eta: 8:46:40, time: 0.086, data_time: 0.049, memory: 1125, loss: 2.4565\n2023-01-03 18:58:29,468 - mmcls - INFO - Epoch [29][2000/5005]\tlr: 1.000e-01, eta: 8:46:31, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4434\n2023-01-03 18:58:38,340 - mmcls - INFO - Epoch [29][2100/5005]\tlr: 1.000e-01, eta: 8:46:22, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4814\n2023-01-03 18:58:47,090 - mmcls - INFO - Epoch [29][2200/5005]\tlr: 1.000e-01, eta: 8:46:13, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4747\n2023-01-03 18:58:55,875 - mmcls - INFO - Epoch [29][2300/5005]\tlr: 1.000e-01, eta: 8:46:05, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.4850\n2023-01-03 18:59:04,658 - mmcls - INFO - Epoch [29][2400/5005]\tlr: 1.000e-01, eta: 8:45:56, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4653\n2023-01-03 18:59:13,593 - mmcls - INFO - Epoch [29][2500/5005]\tlr: 1.000e-01, eta: 8:45:47, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4179\n2023-01-03 18:59:22,263 - mmcls - INFO - Epoch [29][2600/5005]\tlr: 1.000e-01, eta: 8:45:38, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4753\n2023-01-03 18:59:30,899 - mmcls - INFO - Epoch [29][2700/5005]\tlr: 1.000e-01, eta: 8:45:29, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.4609\n2023-01-03 18:59:39,813 - mmcls - INFO - Epoch [29][2800/5005]\tlr: 1.000e-01, eta: 8:45:20, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.4555\n2023-01-03 18:59:48,774 - mmcls - INFO - Epoch [29][2900/5005]\tlr: 1.000e-01, eta: 8:45:12, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4798\n2023-01-03 18:59:57,475 - mmcls - INFO - Epoch [29][3000/5005]\tlr: 1.000e-01, eta: 8:45:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4395\n2023-01-03 19:00:06,153 - mmcls - INFO - Epoch [29][3100/5005]\tlr: 1.000e-01, eta: 8:44:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4757\n2023-01-03 19:00:14,770 - mmcls - INFO - Epoch [29][3200/5005]\tlr: 1.000e-01, eta: 8:44:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4551\n2023-01-03 19:00:23,643 - mmcls - INFO - Epoch [29][3300/5005]\tlr: 1.000e-01, eta: 8:44:35, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.4830\n2023-01-03 19:00:32,397 - mmcls - INFO - Epoch [29][3400/5005]\tlr: 1.000e-01, eta: 8:44:26, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4536\n2023-01-03 19:00:41,274 - mmcls - INFO - Epoch [29][3500/5005]\tlr: 1.000e-01, eta: 8:44:18, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.4734\n2023-01-03 19:00:50,353 - mmcls - INFO - Epoch [29][3600/5005]\tlr: 1.000e-01, eta: 8:44:10, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.4828\n2023-01-03 19:00:59,058 - mmcls - INFO - Epoch [29][3700/5005]\tlr: 1.000e-01, eta: 8:44:00, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.4831\n2023-01-03 19:01:07,783 - mmcls - INFO - Epoch [29][3800/5005]\tlr: 1.000e-01, eta: 8:43:51, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4479\n2023-01-03 19:01:16,545 - mmcls - INFO - Epoch [29][3900/5005]\tlr: 1.000e-01, eta: 8:43:42, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.4647\n2023-01-03 19:01:25,328 - mmcls - INFO - Epoch [29][4000/5005]\tlr: 1.000e-01, eta: 8:43:34, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.4584\n2023-01-03 19:01:34,131 - mmcls - INFO - Epoch [29][4100/5005]\tlr: 1.000e-01, eta: 8:43:25, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4763\n2023-01-03 19:01:42,670 - mmcls - INFO - Epoch [29][4200/5005]\tlr: 1.000e-01, eta: 8:43:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4795\n2023-01-03 19:01:51,128 - mmcls - INFO - Epoch [29][4300/5005]\tlr: 1.000e-01, eta: 8:43:06, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.4671\n2023-01-03 19:02:00,080 - mmcls - INFO - Epoch [29][4400/5005]\tlr: 1.000e-01, eta: 8:42:57, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.4679\n2023-01-03 19:02:08,864 - mmcls - INFO - Epoch [29][4500/5005]\tlr: 1.000e-01, eta: 8:42:48, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4749\n2023-01-03 19:02:17,871 - mmcls - INFO - Epoch [29][4600/5005]\tlr: 1.000e-01, eta: 8:42:40, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.4828\n2023-01-03 19:02:26,440 - mmcls - INFO - Epoch [29][4700/5005]\tlr: 1.000e-01, eta: 8:42:30, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4683\n2023-01-03 19:02:35,193 - mmcls - INFO - Epoch [29][4800/5005]\tlr: 1.000e-01, eta: 8:42:21, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.4820\n2023-01-03 19:02:43,741 - mmcls - INFO - Epoch [29][4900/5005]\tlr: 1.000e-01, eta: 8:42:12, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.4665\n2023-01-03 19:02:52,419 - mmcls - INFO - Epoch [29][5000/5005]\tlr: 1.000e-01, eta: 8:42:03, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.4986\n2023-01-03 19:03:14,956 - mmcls - INFO - Epoch(val) [29][196]\taccuracy_top-1: 50.3220, accuracy_top-5: 75.8180\n2023-01-03 19:03:25,993 - mmcls - INFO - Epoch [30][100/5005]\tlr: 1.000e-01, eta: 8:41:58, time: 0.110, data_time: 0.059, memory: 1125, loss: 2.4273\n2023-01-03 19:03:34,789 - mmcls - INFO - Epoch [30][200/5005]\tlr: 1.000e-01, eta: 8:41:49, time: 0.088, data_time: 0.042, memory: 1125, loss: 2.3969\n2023-01-03 19:03:43,633 - mmcls - INFO - Epoch [30][300/5005]\tlr: 1.000e-01, eta: 8:41:40, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4378\n2023-01-03 19:03:52,426 - mmcls - INFO - Epoch [30][400/5005]\tlr: 1.000e-01, eta: 8:41:31, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4223\n2023-01-03 19:04:01,045 - mmcls - INFO - Epoch [30][500/5005]\tlr: 1.000e-01, eta: 8:41:22, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4359\n2023-01-03 19:04:09,870 - mmcls - INFO - Epoch [30][600/5005]\tlr: 1.000e-01, eta: 8:41:13, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4445\n2023-01-03 19:04:18,797 - mmcls - INFO - Epoch [30][700/5005]\tlr: 1.000e-01, eta: 8:41:05, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.4217\n2023-01-03 19:04:27,609 - mmcls - INFO - Epoch [30][800/5005]\tlr: 1.000e-01, eta: 8:40:56, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.3872\n2023-01-03 19:04:36,273 - mmcls - INFO - Epoch [30][900/5005]\tlr: 1.000e-01, eta: 8:40:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4273\n2023-01-03 19:04:44,855 - mmcls - INFO - Epoch [30][1000/5005]\tlr: 1.000e-01, eta: 8:40:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4686\n2023-01-03 19:04:53,504 - mmcls - INFO - Epoch [30][1100/5005]\tlr: 1.000e-01, eta: 8:40:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4481\n2023-01-03 19:05:02,259 - mmcls - INFO - Epoch [30][1200/5005]\tlr: 1.000e-01, eta: 8:40:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4502\n2023-01-03 19:05:10,853 - mmcls - INFO - Epoch [30][1300/5005]\tlr: 1.000e-01, eta: 8:40:10, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4398\n2023-01-03 19:05:19,499 - mmcls - INFO - Epoch [30][1400/5005]\tlr: 1.000e-01, eta: 8:40:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4497\n2023-01-03 19:05:28,361 - mmcls - INFO - Epoch [30][1500/5005]\tlr: 1.000e-01, eta: 8:39:52, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.4404\n2023-01-03 19:05:36,964 - mmcls - INFO - Epoch [30][1600/5005]\tlr: 1.000e-01, eta: 8:39:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.4744\n2023-01-03 19:05:46,068 - mmcls - INFO - Epoch [30][1700/5005]\tlr: 1.000e-01, eta: 8:39:34, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.4670\n2023-01-03 19:05:54,718 - mmcls - INFO - Epoch [30][1800/5005]\tlr: 1.000e-01, eta: 8:39:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4501\n2023-01-03 19:06:03,343 - mmcls - INFO - Epoch [30][1900/5005]\tlr: 1.000e-01, eta: 8:39:16, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4158\n2023-01-03 19:06:12,079 - mmcls - INFO - Epoch [30][2000/5005]\tlr: 1.000e-01, eta: 8:39:07, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4745\n2023-01-03 19:06:20,502 - mmcls - INFO - Epoch [30][2100/5005]\tlr: 1.000e-01, eta: 8:38:57, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.4541\n2023-01-03 19:06:29,413 - mmcls - INFO - Epoch [30][2200/5005]\tlr: 1.000e-01, eta: 8:38:49, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4729\n2023-01-03 19:06:38,239 - mmcls - INFO - Epoch [30][2300/5005]\tlr: 1.000e-01, eta: 8:38:40, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4445\n2023-01-03 19:06:47,337 - mmcls - INFO - Epoch [30][2400/5005]\tlr: 1.000e-01, eta: 8:38:32, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.4574\n2023-01-03 19:06:56,267 - mmcls - INFO - Epoch [30][2500/5005]\tlr: 1.000e-01, eta: 8:38:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4522\n2023-01-03 19:07:04,790 - mmcls - INFO - Epoch [30][2600/5005]\tlr: 1.000e-01, eta: 8:38:14, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4699\n2023-01-03 19:07:13,504 - mmcls - INFO - Epoch [30][2700/5005]\tlr: 1.000e-01, eta: 8:38:05, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.4572\n2023-01-03 19:07:22,118 - mmcls - INFO - Epoch [30][2800/5005]\tlr: 1.000e-01, eta: 8:37:55, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4553\n2023-01-03 19:07:30,901 - mmcls - INFO - Epoch [30][2900/5005]\tlr: 1.000e-01, eta: 8:37:46, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.4565\n2023-01-03 19:07:39,699 - mmcls - INFO - Epoch [30][3000/5005]\tlr: 1.000e-01, eta: 8:37:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4464\n2023-01-03 19:07:48,430 - mmcls - INFO - Epoch [30][3100/5005]\tlr: 1.000e-01, eta: 8:37:28, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.4704\n2023-01-03 19:07:57,386 - mmcls - INFO - Epoch [30][3200/5005]\tlr: 1.000e-01, eta: 8:37:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4867\n2023-01-03 19:08:06,217 - mmcls - INFO - Epoch [30][3300/5005]\tlr: 1.000e-01, eta: 8:37:11, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.4865\n2023-01-03 19:08:14,854 - mmcls - INFO - Epoch [30][3400/5005]\tlr: 1.000e-01, eta: 8:37:02, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4581\n2023-01-03 19:08:23,570 - mmcls - INFO - Epoch [30][3500/5005]\tlr: 1.000e-01, eta: 8:36:53, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.4734\n2023-01-03 19:08:32,329 - mmcls - INFO - Epoch [30][3600/5005]\tlr: 1.000e-01, eta: 8:36:44, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.4667\n2023-01-03 19:08:41,008 - mmcls - INFO - Epoch [30][3700/5005]\tlr: 1.000e-01, eta: 8:36:35, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.4819\n2023-01-03 19:08:49,783 - mmcls - INFO - Epoch [30][3800/5005]\tlr: 1.000e-01, eta: 8:36:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4833\n2023-01-03 19:08:59,107 - mmcls - INFO - Epoch [30][3900/5005]\tlr: 1.000e-01, eta: 8:36:18, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.4787\n2023-01-03 19:09:07,742 - mmcls - INFO - Epoch [30][4000/5005]\tlr: 1.000e-01, eta: 8:36:09, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4935\n2023-01-03 19:09:16,285 - mmcls - INFO - Epoch [30][4100/5005]\tlr: 1.000e-01, eta: 8:36:00, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4465\n2023-01-03 19:09:25,113 - mmcls - INFO - Epoch [30][4200/5005]\tlr: 1.000e-01, eta: 8:35:51, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.4759\n2023-01-03 19:09:33,773 - mmcls - INFO - Epoch [30][4300/5005]\tlr: 1.000e-01, eta: 8:35:42, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.4669\n2023-01-03 19:09:42,992 - mmcls - INFO - Epoch [30][4400/5005]\tlr: 1.000e-01, eta: 8:35:34, time: 0.092, data_time: 0.039, memory: 1125, loss: 2.4685\n2023-01-03 19:09:52,150 - mmcls - INFO - Epoch [30][4500/5005]\tlr: 1.000e-01, eta: 8:35:26, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.4483\n2023-01-03 19:10:00,981 - mmcls - INFO - Epoch [30][4600/5005]\tlr: 1.000e-01, eta: 8:35:17, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4932\n2023-01-03 19:10:09,747 - mmcls - INFO - Epoch [30][4700/5005]\tlr: 1.000e-01, eta: 8:35:08, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4515\n2023-01-03 19:10:18,458 - mmcls - INFO - Epoch [30][4800/5005]\tlr: 1.000e-01, eta: 8:34:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5007\n2023-01-03 19:10:27,094 - mmcls - INFO - Epoch [30][4900/5005]\tlr: 1.000e-01, eta: 8:34:50, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.4805\n2023-01-03 19:10:35,821 - mmcls - INFO - Epoch [30][5000/5005]\tlr: 1.000e-01, eta: 8:34:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4751\n2023-01-03 19:10:58,494 - mmcls - INFO - Epoch(val) [30][196]\taccuracy_top-1: 46.9700, accuracy_top-5: 72.9040\n2023-01-03 19:11:09,659 - mmcls - INFO - Epoch [31][100/5005]\tlr: 1.000e-02, eta: 8:34:36, time: 0.111, data_time: 0.040, memory: 1125, loss: 2.2557\n2023-01-03 19:11:18,561 - mmcls - INFO - Epoch [31][200/5005]\tlr: 1.000e-02, eta: 8:34:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.1786\n2023-01-03 19:11:27,349 - mmcls - INFO - Epoch [31][300/5005]\tlr: 1.000e-02, eta: 8:34:18, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.1189\n2023-01-03 19:11:36,025 - mmcls - INFO - Epoch [31][400/5005]\tlr: 1.000e-02, eta: 8:34:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0838\n2023-01-03 19:11:44,704 - mmcls - INFO - Epoch [31][500/5005]\tlr: 1.000e-02, eta: 8:34:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0906\n2023-01-03 19:11:53,400 - mmcls - INFO - Epoch [31][600/5005]\tlr: 1.000e-02, eta: 8:33:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0444\n2023-01-03 19:12:02,362 - mmcls - INFO - Epoch [31][700/5005]\tlr: 1.000e-02, eta: 8:33:43, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.0668\n2023-01-03 19:12:10,952 - mmcls - INFO - Epoch [31][800/5005]\tlr: 1.000e-02, eta: 8:33:33, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.0317\n2023-01-03 19:12:19,373 - mmcls - INFO - Epoch [31][900/5005]\tlr: 1.000e-02, eta: 8:33:24, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0448\n2023-01-03 19:12:28,260 - mmcls - INFO - Epoch [31][1000/5005]\tlr: 1.000e-02, eta: 8:33:15, time: 0.089, data_time: 0.033, memory: 1125, loss: 2.0175\n2023-01-03 19:12:37,164 - mmcls - INFO - Epoch [31][1100/5005]\tlr: 1.000e-02, eta: 8:33:06, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0101\n2023-01-03 19:12:45,881 - mmcls - INFO - Epoch [31][1200/5005]\tlr: 1.000e-02, eta: 8:32:57, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0004\n2023-01-03 19:12:54,716 - mmcls - INFO - Epoch [31][1300/5005]\tlr: 1.000e-02, eta: 8:32:48, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.9840\n2023-01-03 19:13:03,115 - mmcls - INFO - Epoch [31][1400/5005]\tlr: 1.000e-02, eta: 8:32:39, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0180\n2023-01-03 19:13:11,838 - mmcls - INFO - Epoch [31][1500/5005]\tlr: 1.000e-02, eta: 8:32:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.9879\n2023-01-03 19:13:20,835 - mmcls - INFO - Epoch [31][1600/5005]\tlr: 1.000e-02, eta: 8:32:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.9743\n2023-01-03 19:13:29,642 - mmcls - INFO - Epoch [31][1700/5005]\tlr: 1.000e-02, eta: 8:32:12, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9919\n2023-01-03 19:13:38,551 - mmcls - INFO - Epoch [31][1800/5005]\tlr: 1.000e-02, eta: 8:32:04, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.9727\n2023-01-03 19:13:47,284 - mmcls - INFO - Epoch [31][1900/5005]\tlr: 1.000e-02, eta: 8:31:55, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.9597\n2023-01-03 19:13:55,741 - mmcls - INFO - Epoch [31][2000/5005]\tlr: 1.000e-02, eta: 8:31:45, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.9560\n2023-01-03 19:14:04,286 - mmcls - INFO - Epoch [31][2100/5005]\tlr: 1.000e-02, eta: 8:31:36, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.9648\n2023-01-03 19:14:13,041 - mmcls - INFO - Epoch [31][2200/5005]\tlr: 1.000e-02, eta: 8:31:27, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.9568\n2023-01-03 19:14:21,870 - mmcls - INFO - Epoch [31][2300/5005]\tlr: 1.000e-02, eta: 8:31:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.9658\n2023-01-03 19:14:30,442 - mmcls - INFO - Epoch [31][2400/5005]\tlr: 1.000e-02, eta: 8:31:09, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.9481\n2023-01-03 19:14:39,178 - mmcls - INFO - Epoch [31][2500/5005]\tlr: 1.000e-02, eta: 8:31:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.9395\n2023-01-03 19:14:47,847 - mmcls - INFO - Epoch [31][2600/5005]\tlr: 1.000e-02, eta: 8:30:50, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.9490\n2023-01-03 19:14:56,549 - mmcls - INFO - Epoch [31][2700/5005]\tlr: 1.000e-02, eta: 8:30:41, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9550\n2023-01-03 19:15:05,191 - mmcls - INFO - Epoch [31][2800/5005]\tlr: 1.000e-02, eta: 8:30:32, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.9406\n2023-01-03 19:15:13,946 - mmcls - INFO - Epoch [31][2900/5005]\tlr: 1.000e-02, eta: 8:30:23, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.9724\n2023-01-03 19:15:22,695 - mmcls - INFO - Epoch [31][3000/5005]\tlr: 1.000e-02, eta: 8:30:14, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.9386\n2023-01-03 19:15:31,293 - mmcls - INFO - Epoch [31][3100/5005]\tlr: 1.000e-02, eta: 8:30:05, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.9372\n2023-01-03 19:15:40,424 - mmcls - INFO - Epoch [31][3200/5005]\tlr: 1.000e-02, eta: 8:29:57, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.9343\n2023-01-03 19:15:49,042 - mmcls - INFO - Epoch [31][3300/5005]\tlr: 1.000e-02, eta: 8:29:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9299\n2023-01-03 19:15:57,560 - mmcls - INFO - Epoch [31][3400/5005]\tlr: 1.000e-02, eta: 8:29:38, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.9193\n2023-01-03 19:16:06,149 - mmcls - INFO - Epoch [31][3500/5005]\tlr: 1.000e-02, eta: 8:29:29, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.9427\n2023-01-03 19:16:14,773 - mmcls - INFO - Epoch [31][3600/5005]\tlr: 1.000e-02, eta: 8:29:20, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.9535\n2023-01-03 19:16:23,421 - mmcls - INFO - Epoch [31][3700/5005]\tlr: 1.000e-02, eta: 8:29:10, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.9280\n2023-01-03 19:16:32,134 - mmcls - INFO - Epoch [31][3800/5005]\tlr: 1.000e-02, eta: 8:29:01, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.9223\n2023-01-03 19:16:41,070 - mmcls - INFO - Epoch [31][3900/5005]\tlr: 1.000e-02, eta: 8:28:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.9143\n2023-01-03 19:16:49,945 - mmcls - INFO - Epoch [31][4000/5005]\tlr: 1.000e-02, eta: 8:28:44, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.9153\n2023-01-03 19:16:58,726 - mmcls - INFO - Epoch [31][4100/5005]\tlr: 1.000e-02, eta: 8:28:35, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.9092\n2023-01-03 19:17:07,304 - mmcls - INFO - Epoch [31][4200/5005]\tlr: 1.000e-02, eta: 8:28:26, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.9158\n2023-01-03 19:17:16,148 - mmcls - INFO - Epoch [31][4300/5005]\tlr: 1.000e-02, eta: 8:28:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9052\n2023-01-03 19:17:24,690 - mmcls - INFO - Epoch [31][4400/5005]\tlr: 1.000e-02, eta: 8:28:08, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.9074\n2023-01-03 19:17:33,341 - mmcls - INFO - Epoch [31][4500/5005]\tlr: 1.000e-02, eta: 8:27:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9164\n2023-01-03 19:17:42,173 - mmcls - INFO - Epoch [31][4600/5005]\tlr: 1.000e-02, eta: 8:27:50, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8893\n2023-01-03 19:17:50,865 - mmcls - INFO - Epoch [31][4700/5005]\tlr: 1.000e-02, eta: 8:27:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.9108\n2023-01-03 19:17:59,604 - mmcls - INFO - Epoch [31][4800/5005]\tlr: 1.000e-02, eta: 8:27:32, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.9105\n2023-01-03 19:18:08,018 - mmcls - INFO - Epoch [31][4900/5005]\tlr: 1.000e-02, eta: 8:27:22, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.9110\n2023-01-03 19:18:16,783 - mmcls - INFO - Epoch [31][5000/5005]\tlr: 1.000e-02, eta: 8:27:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9080\n2023-01-03 19:18:39,450 - mmcls - INFO - Epoch(val) [31][196]\taccuracy_top-1: 62.3760, accuracy_top-5: 84.6060\n2023-01-03 19:18:50,607 - mmcls - INFO - Epoch [32][100/5005]\tlr: 1.000e-02, eta: 8:27:08, time: 0.111, data_time: 0.038, memory: 1125, loss: 1.8718\n2023-01-03 19:18:59,469 - mmcls - INFO - Epoch [32][200/5005]\tlr: 1.000e-02, eta: 8:26:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8586\n2023-01-03 19:19:08,193 - mmcls - INFO - Epoch [32][300/5005]\tlr: 1.000e-02, eta: 8:26:50, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.8798\n2023-01-03 19:19:17,082 - mmcls - INFO - Epoch [32][400/5005]\tlr: 1.000e-02, eta: 8:26:42, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8583\n2023-01-03 19:19:25,761 - mmcls - INFO - Epoch [32][500/5005]\tlr: 1.000e-02, eta: 8:26:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.8783\n2023-01-03 19:19:34,229 - mmcls - INFO - Epoch [32][600/5005]\tlr: 1.000e-02, eta: 8:26:23, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8832\n2023-01-03 19:19:42,987 - mmcls - INFO - Epoch [32][700/5005]\tlr: 1.000e-02, eta: 8:26:14, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.8495\n2023-01-03 19:19:52,222 - mmcls - INFO - Epoch [32][800/5005]\tlr: 1.000e-02, eta: 8:26:06, time: 0.092, data_time: 0.006, memory: 1125, loss: 1.8735\n2023-01-03 19:20:00,852 - mmcls - INFO - Epoch [32][900/5005]\tlr: 1.000e-02, eta: 8:25:57, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.8772\n2023-01-03 19:20:09,527 - mmcls - INFO - Epoch [32][1000/5005]\tlr: 1.000e-02, eta: 8:25:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.8563\n2023-01-03 19:20:18,397 - mmcls - INFO - Epoch [32][1100/5005]\tlr: 1.000e-02, eta: 8:25:39, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.8574\n2023-01-03 19:20:27,205 - mmcls - INFO - Epoch [32][1200/5005]\tlr: 1.000e-02, eta: 8:25:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8640\n2023-01-03 19:20:36,254 - mmcls - INFO - Epoch [32][1300/5005]\tlr: 1.000e-02, eta: 8:25:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8644\n2023-01-03 19:20:45,287 - mmcls - INFO - Epoch [32][1400/5005]\tlr: 1.000e-02, eta: 8:25:14, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.8634\n2023-01-03 19:20:54,053 - mmcls - INFO - Epoch [32][1500/5005]\tlr: 1.000e-02, eta: 8:25:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8477\n2023-01-03 19:21:02,778 - mmcls - INFO - Epoch [32][1600/5005]\tlr: 1.000e-02, eta: 8:24:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8458\n2023-01-03 19:21:11,374 - mmcls - INFO - Epoch [32][1700/5005]\tlr: 1.000e-02, eta: 8:24:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8645\n2023-01-03 19:21:19,868 - mmcls - INFO - Epoch [32][1800/5005]\tlr: 1.000e-02, eta: 8:24:37, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8569\n2023-01-03 19:21:28,833 - mmcls - INFO - Epoch [32][1900/5005]\tlr: 1.000e-02, eta: 8:24:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.8505\n2023-01-03 19:21:37,493 - mmcls - INFO - Epoch [32][2000/5005]\tlr: 1.000e-02, eta: 8:24:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8520\n2023-01-03 19:21:46,423 - mmcls - INFO - Epoch [32][2100/5005]\tlr: 1.000e-02, eta: 8:24:11, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.8789\n2023-01-03 19:21:55,395 - mmcls - INFO - Epoch [32][2200/5005]\tlr: 1.000e-02, eta: 8:24:02, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.8466\n2023-01-03 19:22:03,968 - mmcls - INFO - Epoch [32][2300/5005]\tlr: 1.000e-02, eta: 8:23:53, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8420\n2023-01-03 19:22:13,000 - mmcls - INFO - Epoch [32][2400/5005]\tlr: 1.000e-02, eta: 8:23:45, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.8273\n2023-01-03 19:22:21,704 - mmcls - INFO - Epoch [32][2500/5005]\tlr: 1.000e-02, eta: 8:23:35, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.8549\n2023-01-03 19:22:30,549 - mmcls - INFO - Epoch [32][2600/5005]\tlr: 1.000e-02, eta: 8:23:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8507\n2023-01-03 19:22:39,082 - mmcls - INFO - Epoch [32][2700/5005]\tlr: 1.000e-02, eta: 8:23:17, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8431\n2023-01-03 19:22:47,760 - mmcls - INFO - Epoch [32][2800/5005]\tlr: 1.000e-02, eta: 8:23:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8313\n2023-01-03 19:22:56,509 - mmcls - INFO - Epoch [32][2900/5005]\tlr: 1.000e-02, eta: 8:22:59, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.8497\n2023-01-03 19:23:05,487 - mmcls - INFO - Epoch [32][3000/5005]\tlr: 1.000e-02, eta: 8:22:51, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.8420\n2023-01-03 19:23:14,183 - mmcls - INFO - Epoch [32][3100/5005]\tlr: 1.000e-02, eta: 8:22:42, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8451\n2023-01-03 19:23:22,953 - mmcls - INFO - Epoch [32][3200/5005]\tlr: 1.000e-02, eta: 8:22:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8400\n2023-01-03 19:23:31,558 - mmcls - INFO - Epoch [32][3300/5005]\tlr: 1.000e-02, eta: 8:22:24, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8627\n2023-01-03 19:23:40,163 - mmcls - INFO - Epoch [32][3400/5005]\tlr: 1.000e-02, eta: 8:22:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8520\n2023-01-03 19:23:48,995 - mmcls - INFO - Epoch [32][3500/5005]\tlr: 1.000e-02, eta: 8:22:06, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8491\n2023-01-03 19:23:57,615 - mmcls - INFO - Epoch [32][3600/5005]\tlr: 1.000e-02, eta: 8:21:56, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.8534\n2023-01-03 19:24:06,285 - mmcls - INFO - Epoch [32][3700/5005]\tlr: 1.000e-02, eta: 8:21:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.8401\n2023-01-03 19:24:15,157 - mmcls - INFO - Epoch [32][3800/5005]\tlr: 1.000e-02, eta: 8:21:38, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.8516\n2023-01-03 19:24:23,914 - mmcls - INFO - Epoch [32][3900/5005]\tlr: 1.000e-02, eta: 8:21:30, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.8589\n2023-01-03 19:24:32,318 - mmcls - INFO - Epoch [32][4000/5005]\tlr: 1.000e-02, eta: 8:21:20, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.8520\n2023-01-03 19:24:40,840 - mmcls - INFO - Epoch [32][4100/5005]\tlr: 1.000e-02, eta: 8:21:10, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.8409\n2023-01-03 19:24:49,442 - mmcls - INFO - Epoch [32][4200/5005]\tlr: 1.000e-02, eta: 8:21:01, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.8470\n2023-01-03 19:24:58,090 - mmcls - INFO - Epoch [32][4300/5005]\tlr: 1.000e-02, eta: 8:20:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.8543\n2023-01-03 19:25:06,750 - mmcls - INFO - Epoch [32][4400/5005]\tlr: 1.000e-02, eta: 8:20:43, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8567\n2023-01-03 19:25:15,598 - mmcls - INFO - Epoch [32][4500/5005]\tlr: 1.000e-02, eta: 8:20:34, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.8385\n2023-01-03 19:25:24,351 - mmcls - INFO - Epoch [32][4600/5005]\tlr: 1.000e-02, eta: 8:20:25, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.8474\n2023-01-03 19:25:32,989 - mmcls - INFO - Epoch [32][4700/5005]\tlr: 1.000e-02, eta: 8:20:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8342\n2023-01-03 19:25:41,865 - mmcls - INFO - Epoch [32][4800/5005]\tlr: 1.000e-02, eta: 8:20:07, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.8492\n2023-01-03 19:25:50,252 - mmcls - INFO - Epoch [32][4900/5005]\tlr: 1.000e-02, eta: 8:19:58, time: 0.084, data_time: 0.035, memory: 1125, loss: 1.8506\n2023-01-03 19:25:58,883 - mmcls - INFO - Epoch [32][5000/5005]\tlr: 1.000e-02, eta: 8:19:48, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.8370\n2023-01-03 19:26:21,518 - mmcls - INFO - Epoch(val) [32][196]\taccuracy_top-1: 63.2260, accuracy_top-5: 85.3400\n2023-01-03 19:26:32,625 - mmcls - INFO - Epoch [33][100/5005]\tlr: 1.000e-02, eta: 8:19:43, time: 0.111, data_time: 0.058, memory: 1125, loss: 1.8012\n2023-01-03 19:26:41,318 - mmcls - INFO - Epoch [33][200/5005]\tlr: 1.000e-02, eta: 8:19:34, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.8229\n2023-01-03 19:26:49,946 - mmcls - INFO - Epoch [33][300/5005]\tlr: 1.000e-02, eta: 8:19:25, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.8209\n2023-01-03 19:26:58,932 - mmcls - INFO - Epoch [33][400/5005]\tlr: 1.000e-02, eta: 8:19:16, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.8154\n2023-01-03 19:27:07,550 - mmcls - INFO - Epoch [33][500/5005]\tlr: 1.000e-02, eta: 8:19:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8085\n2023-01-03 19:27:16,624 - mmcls - INFO - Epoch [33][600/5005]\tlr: 1.000e-02, eta: 8:18:59, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.8110\n2023-01-03 19:27:25,292 - mmcls - INFO - Epoch [33][700/5005]\tlr: 1.000e-02, eta: 8:18:50, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8085\n2023-01-03 19:27:34,262 - mmcls - INFO - Epoch [33][800/5005]\tlr: 1.000e-02, eta: 8:18:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8108\n2023-01-03 19:27:42,900 - mmcls - INFO - Epoch [33][900/5005]\tlr: 1.000e-02, eta: 8:18:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8256\n2023-01-03 19:27:51,535 - mmcls - INFO - Epoch [33][1000/5005]\tlr: 1.000e-02, eta: 8:18:23, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7979\n2023-01-03 19:28:00,234 - mmcls - INFO - Epoch [33][1100/5005]\tlr: 1.000e-02, eta: 8:18:14, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8059\n2023-01-03 19:28:08,950 - mmcls - INFO - Epoch [33][1200/5005]\tlr: 1.000e-02, eta: 8:18:05, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.7758\n2023-01-03 19:28:17,781 - mmcls - INFO - Epoch [33][1300/5005]\tlr: 1.000e-02, eta: 8:17:56, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8378\n2023-01-03 19:28:26,327 - mmcls - INFO - Epoch [33][1400/5005]\tlr: 1.000e-02, eta: 8:17:47, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.7962\n2023-01-03 19:28:35,263 - mmcls - INFO - Epoch [33][1500/5005]\tlr: 1.000e-02, eta: 8:17:38, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8103\n2023-01-03 19:28:44,099 - mmcls - INFO - Epoch [33][1600/5005]\tlr: 1.000e-02, eta: 8:17:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.8185\n2023-01-03 19:28:52,769 - mmcls - INFO - Epoch [33][1700/5005]\tlr: 1.000e-02, eta: 8:17:20, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8091\n2023-01-03 19:29:01,422 - mmcls - INFO - Epoch [33][1800/5005]\tlr: 1.000e-02, eta: 8:17:11, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8391\n2023-01-03 19:29:10,471 - mmcls - INFO - Epoch [33][1900/5005]\tlr: 1.000e-02, eta: 8:17:03, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7966\n2023-01-03 19:29:19,778 - mmcls - INFO - Epoch [33][2000/5005]\tlr: 1.000e-02, eta: 8:16:55, time: 0.093, data_time: 0.012, memory: 1125, loss: 1.8244\n2023-01-03 19:29:28,276 - mmcls - INFO - Epoch [33][2100/5005]\tlr: 1.000e-02, eta: 8:16:45, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.8073\n2023-01-03 19:29:36,924 - mmcls - INFO - Epoch [33][2200/5005]\tlr: 1.000e-02, eta: 8:16:36, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8112\n2023-01-03 19:29:45,433 - mmcls - INFO - Epoch [33][2300/5005]\tlr: 1.000e-02, eta: 8:16:27, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8126\n2023-01-03 19:29:54,418 - mmcls - INFO - Epoch [33][2400/5005]\tlr: 1.000e-02, eta: 8:16:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8043\n2023-01-03 19:30:03,154 - mmcls - INFO - Epoch [33][2500/5005]\tlr: 1.000e-02, eta: 8:16:09, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7801\n2023-01-03 19:30:11,813 - mmcls - INFO - Epoch [33][2600/5005]\tlr: 1.000e-02, eta: 8:16:00, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.8038\n2023-01-03 19:30:20,638 - mmcls - INFO - Epoch [33][2700/5005]\tlr: 1.000e-02, eta: 8:15:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8174\n2023-01-03 19:30:29,506 - mmcls - INFO - Epoch [33][2800/5005]\tlr: 1.000e-02, eta: 8:15:43, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7970\n2023-01-03 19:30:38,522 - mmcls - INFO - Epoch [33][2900/5005]\tlr: 1.000e-02, eta: 8:15:34, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.8239\n2023-01-03 19:30:47,229 - mmcls - INFO - Epoch [33][3000/5005]\tlr: 1.000e-02, eta: 8:15:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8221\n2023-01-03 19:30:55,942 - mmcls - INFO - Epoch [33][3100/5005]\tlr: 1.000e-02, eta: 8:15:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7986\n2023-01-03 19:31:04,943 - mmcls - INFO - Epoch [33][3200/5005]\tlr: 1.000e-02, eta: 8:15:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.8320\n2023-01-03 19:31:13,723 - mmcls - INFO - Epoch [33][3300/5005]\tlr: 1.000e-02, eta: 8:14:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8210\n2023-01-03 19:31:22,414 - mmcls - INFO - Epoch [33][3400/5005]\tlr: 1.000e-02, eta: 8:14:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8149\n2023-01-03 19:31:31,364 - mmcls - INFO - Epoch [33][3500/5005]\tlr: 1.000e-02, eta: 8:14:42, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.8344\n2023-01-03 19:31:39,920 - mmcls - INFO - Epoch [33][3600/5005]\tlr: 1.000e-02, eta: 8:14:32, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.8074\n2023-01-03 19:31:48,548 - mmcls - INFO - Epoch [33][3700/5005]\tlr: 1.000e-02, eta: 8:14:23, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8154\n2023-01-03 19:31:57,529 - mmcls - INFO - Epoch [33][3800/5005]\tlr: 1.000e-02, eta: 8:14:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8274\n2023-01-03 19:32:06,263 - mmcls - INFO - Epoch [33][3900/5005]\tlr: 1.000e-02, eta: 8:14:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8077\n2023-01-03 19:32:15,234 - mmcls - INFO - Epoch [33][4000/5005]\tlr: 1.000e-02, eta: 8:13:57, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.8193\n2023-01-03 19:32:23,905 - mmcls - INFO - Epoch [33][4100/5005]\tlr: 1.000e-02, eta: 8:13:48, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.8122\n2023-01-03 19:32:32,689 - mmcls - INFO - Epoch [33][4200/5005]\tlr: 1.000e-02, eta: 8:13:39, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.7952\n2023-01-03 19:32:41,768 - mmcls - INFO - Epoch [33][4300/5005]\tlr: 1.000e-02, eta: 8:13:31, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.8143\n2023-01-03 19:32:50,551 - mmcls - INFO - Epoch [33][4400/5005]\tlr: 1.000e-02, eta: 8:13:22, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7978\n2023-01-03 19:32:59,173 - mmcls - INFO - Epoch [33][4500/5005]\tlr: 1.000e-02, eta: 8:13:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7861\n2023-01-03 19:33:07,917 - mmcls - INFO - Epoch [33][4600/5005]\tlr: 1.000e-02, eta: 8:13:04, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.8240\n2023-01-03 19:33:16,673 - mmcls - INFO - Epoch [33][4700/5005]\tlr: 1.000e-02, eta: 8:12:55, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.8143\n2023-01-03 19:33:25,437 - mmcls - INFO - Epoch [33][4800/5005]\tlr: 1.000e-02, eta: 8:12:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8051\n2023-01-03 19:33:34,271 - mmcls - INFO - Epoch [33][4900/5005]\tlr: 1.000e-02, eta: 8:12:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7947\n2023-01-03 19:33:43,283 - mmcls - INFO - Epoch [33][5000/5005]\tlr: 1.000e-02, eta: 8:12:29, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.8114\n2023-01-03 19:34:05,803 - mmcls - INFO - Epoch(val) [33][196]\taccuracy_top-1: 63.7280, accuracy_top-5: 85.5700\n2023-01-03 19:34:16,909 - mmcls - INFO - Epoch [34][100/5005]\tlr: 1.000e-02, eta: 8:12:23, time: 0.111, data_time: 0.057, memory: 1125, loss: 1.7634\n2023-01-03 19:34:25,970 - mmcls - INFO - Epoch [34][200/5005]\tlr: 1.000e-02, eta: 8:12:15, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7934\n2023-01-03 19:34:34,731 - mmcls - INFO - Epoch [34][300/5005]\tlr: 1.000e-02, eta: 8:12:06, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7722\n2023-01-03 19:34:43,264 - mmcls - INFO - Epoch [34][400/5005]\tlr: 1.000e-02, eta: 8:11:57, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7744\n2023-01-03 19:34:52,117 - mmcls - INFO - Epoch [34][500/5005]\tlr: 1.000e-02, eta: 8:11:48, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7721\n2023-01-03 19:35:00,670 - mmcls - INFO - Epoch [34][600/5005]\tlr: 1.000e-02, eta: 8:11:39, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.7615\n2023-01-03 19:35:09,608 - mmcls - INFO - Epoch [34][700/5005]\tlr: 1.000e-02, eta: 8:11:30, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7742\n2023-01-03 19:35:18,348 - mmcls - INFO - Epoch [34][800/5005]\tlr: 1.000e-02, eta: 8:11:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7853\n2023-01-03 19:35:27,497 - mmcls - INFO - Epoch [34][900/5005]\tlr: 1.000e-02, eta: 8:11:13, time: 0.091, data_time: 0.029, memory: 1125, loss: 1.7705\n2023-01-03 19:35:35,999 - mmcls - INFO - Epoch [34][1000/5005]\tlr: 1.000e-02, eta: 8:11:03, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7951\n2023-01-03 19:35:44,557 - mmcls - INFO - Epoch [34][1100/5005]\tlr: 1.000e-02, eta: 8:10:54, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7821\n2023-01-03 19:35:53,117 - mmcls - INFO - Epoch [34][1200/5005]\tlr: 1.000e-02, eta: 8:10:45, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7529\n2023-01-03 19:36:01,729 - mmcls - INFO - Epoch [34][1300/5005]\tlr: 1.000e-02, eta: 8:10:36, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7708\n2023-01-03 19:36:10,189 - mmcls - INFO - Epoch [34][1400/5005]\tlr: 1.000e-02, eta: 8:10:26, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7824\n2023-01-03 19:36:18,726 - mmcls - INFO - Epoch [34][1500/5005]\tlr: 1.000e-02, eta: 8:10:17, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7848\n2023-01-03 19:36:27,234 - mmcls - INFO - Epoch [34][1600/5005]\tlr: 1.000e-02, eta: 8:10:07, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7826\n2023-01-03 19:36:36,318 - mmcls - INFO - Epoch [34][1700/5005]\tlr: 1.000e-02, eta: 8:09:59, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7969\n2023-01-03 19:36:44,881 - mmcls - INFO - Epoch [34][1800/5005]\tlr: 1.000e-02, eta: 8:09:50, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7789\n2023-01-03 19:36:53,644 - mmcls - INFO - Epoch [34][1900/5005]\tlr: 1.000e-02, eta: 8:09:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7977\n2023-01-03 19:37:02,369 - mmcls - INFO - Epoch [34][2000/5005]\tlr: 1.000e-02, eta: 8:09:32, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7776\n2023-01-03 19:37:10,966 - mmcls - INFO - Epoch [34][2100/5005]\tlr: 1.000e-02, eta: 8:09:23, time: 0.086, data_time: 0.051, memory: 1125, loss: 1.7694\n2023-01-03 19:37:19,759 - mmcls - INFO - Epoch [34][2200/5005]\tlr: 1.000e-02, eta: 8:09:14, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7812\n2023-01-03 19:37:28,613 - mmcls - INFO - Epoch [34][2300/5005]\tlr: 1.000e-02, eta: 8:09:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7672\n2023-01-03 19:37:37,280 - mmcls - INFO - Epoch [34][2400/5005]\tlr: 1.000e-02, eta: 8:08:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7848\n2023-01-03 19:37:46,026 - mmcls - INFO - Epoch [34][2500/5005]\tlr: 1.000e-02, eta: 8:08:47, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7945\n2023-01-03 19:37:54,761 - mmcls - INFO - Epoch [34][2600/5005]\tlr: 1.000e-02, eta: 8:08:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7779\n2023-01-03 19:38:03,122 - mmcls - INFO - Epoch [34][2700/5005]\tlr: 1.000e-02, eta: 8:08:28, time: 0.084, data_time: 0.015, memory: 1125, loss: 1.7794\n2023-01-03 19:38:12,036 - mmcls - INFO - Epoch [34][2800/5005]\tlr: 1.000e-02, eta: 8:08:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7776\n2023-01-03 19:38:20,855 - mmcls - INFO - Epoch [34][2900/5005]\tlr: 1.000e-02, eta: 8:08:11, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7796\n2023-01-03 19:38:29,351 - mmcls - INFO - Epoch [34][3000/5005]\tlr: 1.000e-02, eta: 8:08:01, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7611\n2023-01-03 19:38:37,984 - mmcls - INFO - Epoch [34][3100/5005]\tlr: 1.000e-02, eta: 8:07:52, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7895\n2023-01-03 19:38:46,466 - mmcls - INFO - Epoch [34][3200/5005]\tlr: 1.000e-02, eta: 8:07:43, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7862\n2023-01-03 19:38:55,348 - mmcls - INFO - Epoch [34][3300/5005]\tlr: 1.000e-02, eta: 8:07:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7737\n2023-01-03 19:39:03,896 - mmcls - INFO - Epoch [34][3400/5005]\tlr: 1.000e-02, eta: 8:07:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7867\n2023-01-03 19:39:12,394 - mmcls - INFO - Epoch [34][3500/5005]\tlr: 1.000e-02, eta: 8:07:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7728\n2023-01-03 19:39:21,367 - mmcls - INFO - Epoch [34][3600/5005]\tlr: 1.000e-02, eta: 8:07:07, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7750\n2023-01-03 19:39:30,250 - mmcls - INFO - Epoch [34][3700/5005]\tlr: 1.000e-02, eta: 8:06:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7638\n2023-01-03 19:39:39,104 - mmcls - INFO - Epoch [34][3800/5005]\tlr: 1.000e-02, eta: 8:06:49, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7708\n2023-01-03 19:39:48,041 - mmcls - INFO - Epoch [34][3900/5005]\tlr: 1.000e-02, eta: 8:06:41, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7711\n2023-01-03 19:39:56,869 - mmcls - INFO - Epoch [34][4000/5005]\tlr: 1.000e-02, eta: 8:06:32, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7752\n2023-01-03 19:40:05,597 - mmcls - INFO - Epoch [34][4100/5005]\tlr: 1.000e-02, eta: 8:06:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7807\n2023-01-03 19:40:14,417 - mmcls - INFO - Epoch [34][4200/5005]\tlr: 1.000e-02, eta: 8:06:14, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7668\n2023-01-03 19:40:23,064 - mmcls - INFO - Epoch [34][4300/5005]\tlr: 1.000e-02, eta: 8:06:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7986\n2023-01-03 19:40:31,935 - mmcls - INFO - Epoch [34][4400/5005]\tlr: 1.000e-02, eta: 8:05:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7574\n2023-01-03 19:40:40,763 - mmcls - INFO - Epoch [34][4500/5005]\tlr: 1.000e-02, eta: 8:05:48, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7783\n2023-01-03 19:40:49,814 - mmcls - INFO - Epoch [34][4600/5005]\tlr: 1.000e-02, eta: 8:05:39, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7792\n2023-01-03 19:40:58,619 - mmcls - INFO - Epoch [34][4700/5005]\tlr: 1.000e-02, eta: 8:05:31, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7902\n2023-01-03 19:41:07,194 - mmcls - INFO - Epoch [34][4800/5005]\tlr: 1.000e-02, eta: 8:05:21, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7546\n2023-01-03 19:41:15,993 - mmcls - INFO - Epoch [34][4900/5005]\tlr: 1.000e-02, eta: 8:05:13, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7959\n2023-01-03 19:41:24,709 - mmcls - INFO - Epoch [34][5000/5005]\tlr: 1.000e-02, eta: 8:05:04, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7859\n2023-01-03 19:41:47,420 - mmcls - INFO - Epoch(val) [34][196]\taccuracy_top-1: 64.5200, accuracy_top-5: 86.0300\n2023-01-03 19:41:58,225 - mmcls - INFO - Epoch [35][100/5005]\tlr: 1.000e-02, eta: 8:04:57, time: 0.108, data_time: 0.038, memory: 1125, loss: 1.7345\n2023-01-03 19:42:07,341 - mmcls - INFO - Epoch [35][200/5005]\tlr: 1.000e-02, eta: 8:04:49, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.7472\n2023-01-03 19:42:16,183 - mmcls - INFO - Epoch [35][300/5005]\tlr: 1.000e-02, eta: 8:04:40, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7373\n2023-01-03 19:42:25,026 - mmcls - INFO - Epoch [35][400/5005]\tlr: 1.000e-02, eta: 8:04:31, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7534\n2023-01-03 19:42:33,812 - mmcls - INFO - Epoch [35][500/5005]\tlr: 1.000e-02, eta: 8:04:23, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7741\n2023-01-03 19:42:42,411 - mmcls - INFO - Epoch [35][600/5005]\tlr: 1.000e-02, eta: 8:04:13, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7265\n2023-01-03 19:42:51,099 - mmcls - INFO - Epoch [35][700/5005]\tlr: 1.000e-02, eta: 8:04:04, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7323\n2023-01-03 19:42:59,710 - mmcls - INFO - Epoch [35][800/5005]\tlr: 1.000e-02, eta: 8:03:55, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7747\n2023-01-03 19:43:08,329 - mmcls - INFO - Epoch [35][900/5005]\tlr: 1.000e-02, eta: 8:03:46, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7518\n2023-01-03 19:43:17,085 - mmcls - INFO - Epoch [35][1000/5005]\tlr: 1.000e-02, eta: 8:03:37, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7550\n2023-01-03 19:43:25,931 - mmcls - INFO - Epoch [35][1100/5005]\tlr: 1.000e-02, eta: 8:03:28, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7555\n2023-01-03 19:43:34,880 - mmcls - INFO - Epoch [35][1200/5005]\tlr: 1.000e-02, eta: 8:03:20, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7648\n2023-01-03 19:43:43,632 - mmcls - INFO - Epoch [35][1300/5005]\tlr: 1.000e-02, eta: 8:03:11, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7739\n2023-01-03 19:43:52,255 - mmcls - INFO - Epoch [35][1400/5005]\tlr: 1.000e-02, eta: 8:03:02, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7436\n2023-01-03 19:44:00,898 - mmcls - INFO - Epoch [35][1500/5005]\tlr: 1.000e-02, eta: 8:02:53, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7925\n2023-01-03 19:44:09,470 - mmcls - INFO - Epoch [35][1600/5005]\tlr: 1.000e-02, eta: 8:02:43, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7518\n2023-01-03 19:44:18,272 - mmcls - INFO - Epoch [35][1700/5005]\tlr: 1.000e-02, eta: 8:02:34, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7373\n2023-01-03 19:44:27,348 - mmcls - INFO - Epoch [35][1800/5005]\tlr: 1.000e-02, eta: 8:02:26, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7597\n2023-01-03 19:44:35,780 - mmcls - INFO - Epoch [35][1900/5005]\tlr: 1.000e-02, eta: 8:02:17, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7482\n2023-01-03 19:44:44,298 - mmcls - INFO - Epoch [35][2000/5005]\tlr: 1.000e-02, eta: 8:02:07, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7246\n2023-01-03 19:44:52,819 - mmcls - INFO - Epoch [35][2100/5005]\tlr: 1.000e-02, eta: 8:01:58, time: 0.085, data_time: 0.035, memory: 1125, loss: 1.7591\n2023-01-03 19:45:01,525 - mmcls - INFO - Epoch [35][2200/5005]\tlr: 1.000e-02, eta: 8:01:49, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7607\n2023-01-03 19:45:10,284 - mmcls - INFO - Epoch [35][2300/5005]\tlr: 1.000e-02, eta: 8:01:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7872\n2023-01-03 19:45:18,996 - mmcls - INFO - Epoch [35][2400/5005]\tlr: 1.000e-02, eta: 8:01:31, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7522\n2023-01-03 19:45:28,013 - mmcls - INFO - Epoch [35][2500/5005]\tlr: 1.000e-02, eta: 8:01:23, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7764\n2023-01-03 19:45:36,648 - mmcls - INFO - Epoch [35][2600/5005]\tlr: 1.000e-02, eta: 8:01:13, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7714\n2023-01-03 19:45:45,565 - mmcls - INFO - Epoch [35][2700/5005]\tlr: 1.000e-02, eta: 8:01:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7670\n2023-01-03 19:45:53,986 - mmcls - INFO - Epoch [35][2800/5005]\tlr: 1.000e-02, eta: 8:00:55, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7599\n2023-01-03 19:46:02,587 - mmcls - INFO - Epoch [35][2900/5005]\tlr: 1.000e-02, eta: 8:00:46, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7389\n2023-01-03 19:46:11,471 - mmcls - INFO - Epoch [35][3000/5005]\tlr: 1.000e-02, eta: 8:00:37, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7713\n2023-01-03 19:46:20,041 - mmcls - INFO - Epoch [35][3100/5005]\tlr: 1.000e-02, eta: 8:00:28, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7595\n2023-01-03 19:46:29,334 - mmcls - INFO - Epoch [35][3200/5005]\tlr: 1.000e-02, eta: 8:00:20, time: 0.093, data_time: 0.026, memory: 1125, loss: 1.7871\n2023-01-03 19:46:37,874 - mmcls - INFO - Epoch [35][3300/5005]\tlr: 1.000e-02, eta: 8:00:11, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7762\n2023-01-03 19:46:46,410 - mmcls - INFO - Epoch [35][3400/5005]\tlr: 1.000e-02, eta: 8:00:02, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7620\n2023-01-03 19:46:55,182 - mmcls - INFO - Epoch [35][3500/5005]\tlr: 1.000e-02, eta: 7:59:53, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.7819\n2023-01-03 19:47:03,848 - mmcls - INFO - Epoch [35][3600/5005]\tlr: 1.000e-02, eta: 7:59:44, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7861\n2023-01-03 19:47:12,629 - mmcls - INFO - Epoch [35][3700/5005]\tlr: 1.000e-02, eta: 7:59:35, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7819\n2023-01-03 19:47:21,205 - mmcls - INFO - Epoch [35][3800/5005]\tlr: 1.000e-02, eta: 7:59:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7716\n2023-01-03 19:47:29,906 - mmcls - INFO - Epoch [35][3900/5005]\tlr: 1.000e-02, eta: 7:59:16, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7684\n2023-01-03 19:47:38,716 - mmcls - INFO - Epoch [35][4000/5005]\tlr: 1.000e-02, eta: 7:59:08, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7631\n2023-01-03 19:47:47,394 - mmcls - INFO - Epoch [35][4100/5005]\tlr: 1.000e-02, eta: 7:58:59, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7569\n2023-01-03 19:47:56,175 - mmcls - INFO - Epoch [35][4200/5005]\tlr: 1.000e-02, eta: 7:58:50, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7626\n2023-01-03 19:48:04,525 - mmcls - INFO - Epoch [35][4300/5005]\tlr: 1.000e-02, eta: 7:58:40, time: 0.083, data_time: 0.016, memory: 1125, loss: 1.7680\n2023-01-03 19:48:13,335 - mmcls - INFO - Epoch [35][4400/5005]\tlr: 1.000e-02, eta: 7:58:31, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7598\n2023-01-03 19:48:22,042 - mmcls - INFO - Epoch [35][4500/5005]\tlr: 1.000e-02, eta: 7:58:22, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7583\n2023-01-03 19:48:30,612 - mmcls - INFO - Epoch [35][4600/5005]\tlr: 1.000e-02, eta: 7:58:13, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7434\n2023-01-03 19:48:39,334 - mmcls - INFO - Epoch [35][4700/5005]\tlr: 1.000e-02, eta: 7:58:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7724\n2023-01-03 19:48:48,350 - mmcls - INFO - Epoch [35][4800/5005]\tlr: 1.000e-02, eta: 7:57:56, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7572\n2023-01-03 19:48:57,131 - mmcls - INFO - Epoch [35][4900/5005]\tlr: 1.000e-02, eta: 7:57:47, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7600\n2023-01-03 19:49:05,807 - mmcls - INFO - Epoch [35][5000/5005]\tlr: 1.000e-02, eta: 7:57:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7742\n2023-01-03 19:49:28,417 - mmcls - INFO - Epoch(val) [35][196]\taccuracy_top-1: 64.2960, accuracy_top-5: 86.1160\n2023-01-03 19:49:39,427 - mmcls - INFO - Epoch [36][100/5005]\tlr: 1.000e-02, eta: 7:57:32, time: 0.110, data_time: 0.061, memory: 1125, loss: 1.7245\n2023-01-03 19:49:47,963 - mmcls - INFO - Epoch [36][200/5005]\tlr: 1.000e-02, eta: 7:57:22, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7420\n2023-01-03 19:49:56,793 - mmcls - INFO - Epoch [36][300/5005]\tlr: 1.000e-02, eta: 7:57:14, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7289\n2023-01-03 19:50:05,233 - mmcls - INFO - Epoch [36][400/5005]\tlr: 1.000e-02, eta: 7:57:04, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7254\n2023-01-03 19:50:13,917 - mmcls - INFO - Epoch [36][500/5005]\tlr: 1.000e-02, eta: 7:56:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7337\n2023-01-03 19:50:22,490 - mmcls - INFO - Epoch [36][600/5005]\tlr: 1.000e-02, eta: 7:56:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7290\n2023-01-03 19:50:31,157 - mmcls - INFO - Epoch [36][700/5005]\tlr: 1.000e-02, eta: 7:56:37, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7361\n2023-01-03 19:50:39,717 - mmcls - INFO - Epoch [36][800/5005]\tlr: 1.000e-02, eta: 7:56:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7475\n2023-01-03 19:50:48,455 - mmcls - INFO - Epoch [36][900/5005]\tlr: 1.000e-02, eta: 7:56:18, time: 0.087, data_time: 0.048, memory: 1125, loss: 1.7400\n2023-01-03 19:50:57,010 - mmcls - INFO - Epoch [36][1000/5005]\tlr: 1.000e-02, eta: 7:56:09, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.7555\n2023-01-03 19:51:05,998 - mmcls - INFO - Epoch [36][1100/5005]\tlr: 1.000e-02, eta: 7:56:01, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7535\n2023-01-03 19:51:14,681 - mmcls - INFO - Epoch [36][1200/5005]\tlr: 1.000e-02, eta: 7:55:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7284\n2023-01-03 19:51:23,435 - mmcls - INFO - Epoch [36][1300/5005]\tlr: 1.000e-02, eta: 7:55:43, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.7511\n2023-01-03 19:51:32,231 - mmcls - INFO - Epoch [36][1400/5005]\tlr: 1.000e-02, eta: 7:55:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7555\n2023-01-03 19:51:41,347 - mmcls - INFO - Epoch [36][1500/5005]\tlr: 1.000e-02, eta: 7:55:26, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7306\n2023-01-03 19:51:50,040 - mmcls - INFO - Epoch [36][1600/5005]\tlr: 1.000e-02, eta: 7:55:17, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7379\n2023-01-03 19:51:58,896 - mmcls - INFO - Epoch [36][1700/5005]\tlr: 1.000e-02, eta: 7:55:08, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.7369\n2023-01-03 19:52:07,986 - mmcls - INFO - Epoch [36][1800/5005]\tlr: 1.000e-02, eta: 7:55:00, time: 0.091, data_time: 0.032, memory: 1125, loss: 1.7402\n2023-01-03 19:52:16,640 - mmcls - INFO - Epoch [36][1900/5005]\tlr: 1.000e-02, eta: 7:54:51, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7309\n2023-01-03 19:52:25,286 - mmcls - INFO - Epoch [36][2000/5005]\tlr: 1.000e-02, eta: 7:54:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7557\n2023-01-03 19:52:34,281 - mmcls - INFO - Epoch [36][2100/5005]\tlr: 1.000e-02, eta: 7:54:33, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7489\n2023-01-03 19:52:42,970 - mmcls - INFO - Epoch [36][2200/5005]\tlr: 1.000e-02, eta: 7:54:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7519\n2023-01-03 19:52:51,922 - mmcls - INFO - Epoch [36][2300/5005]\tlr: 1.000e-02, eta: 7:54:15, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7228\n2023-01-03 19:53:00,574 - mmcls - INFO - Epoch [36][2400/5005]\tlr: 1.000e-02, eta: 7:54:06, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7364\n2023-01-03 19:53:09,287 - mmcls - INFO - Epoch [36][2500/5005]\tlr: 1.000e-02, eta: 7:53:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7528\n2023-01-03 19:53:17,791 - mmcls - INFO - Epoch [36][2600/5005]\tlr: 1.000e-02, eta: 7:53:48, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7534\n2023-01-03 19:53:26,541 - mmcls - INFO - Epoch [36][2700/5005]\tlr: 1.000e-02, eta: 7:53:39, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7411\n2023-01-03 19:53:35,277 - mmcls - INFO - Epoch [36][2800/5005]\tlr: 1.000e-02, eta: 7:53:30, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7502\n2023-01-03 19:53:43,946 - mmcls - INFO - Epoch [36][2900/5005]\tlr: 1.000e-02, eta: 7:53:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7479\n2023-01-03 19:53:52,629 - mmcls - INFO - Epoch [36][3000/5005]\tlr: 1.000e-02, eta: 7:53:12, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.7377\n2023-01-03 19:54:01,184 - mmcls - INFO - Epoch [36][3100/5005]\tlr: 1.000e-02, eta: 7:53:03, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7454\n2023-01-03 19:54:10,165 - mmcls - INFO - Epoch [36][3200/5005]\tlr: 1.000e-02, eta: 7:52:54, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7445\n2023-01-03 19:54:18,946 - mmcls - INFO - Epoch [36][3300/5005]\tlr: 1.000e-02, eta: 7:52:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7665\n2023-01-03 19:54:27,537 - mmcls - INFO - Epoch [36][3400/5005]\tlr: 1.000e-02, eta: 7:52:36, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7433\n2023-01-03 19:54:36,225 - mmcls - INFO - Epoch [36][3500/5005]\tlr: 1.000e-02, eta: 7:52:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7442\n2023-01-03 19:54:44,952 - mmcls - INFO - Epoch [36][3600/5005]\tlr: 1.000e-02, eta: 7:52:18, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7670\n2023-01-03 19:54:53,754 - mmcls - INFO - Epoch [36][3700/5005]\tlr: 1.000e-02, eta: 7:52:09, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7510\n2023-01-03 19:55:02,278 - mmcls - INFO - Epoch [36][3800/5005]\tlr: 1.000e-02, eta: 7:52:00, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7393\n2023-01-03 19:55:11,296 - mmcls - INFO - Epoch [36][3900/5005]\tlr: 1.000e-02, eta: 7:51:52, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7751\n2023-01-03 19:55:19,842 - mmcls - INFO - Epoch [36][4000/5005]\tlr: 1.000e-02, eta: 7:51:42, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7665\n2023-01-03 19:55:28,418 - mmcls - INFO - Epoch [36][4100/5005]\tlr: 1.000e-02, eta: 7:51:33, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7552\n2023-01-03 19:55:36,986 - mmcls - INFO - Epoch [36][4200/5005]\tlr: 1.000e-02, eta: 7:51:24, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7634\n2023-01-03 19:55:46,317 - mmcls - INFO - Epoch [36][4300/5005]\tlr: 1.000e-02, eta: 7:51:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7477\n2023-01-03 19:55:55,110 - mmcls - INFO - Epoch [36][4400/5005]\tlr: 1.000e-02, eta: 7:51:07, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7908\n2023-01-03 19:56:03,760 - mmcls - INFO - Epoch [36][4500/5005]\tlr: 1.000e-02, eta: 7:50:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7517\n2023-01-03 19:56:12,679 - mmcls - INFO - Epoch [36][4600/5005]\tlr: 1.000e-02, eta: 7:50:49, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7685\n2023-01-03 19:56:21,890 - mmcls - INFO - Epoch [36][4700/5005]\tlr: 1.000e-02, eta: 7:50:41, time: 0.092, data_time: 0.013, memory: 1125, loss: 1.7532\n2023-01-03 19:56:30,548 - mmcls - INFO - Epoch [36][4800/5005]\tlr: 1.000e-02, eta: 7:50:32, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7479\n2023-01-03 19:56:39,205 - mmcls - INFO - Epoch [36][4900/5005]\tlr: 1.000e-02, eta: 7:50:23, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7493\n2023-01-03 19:56:47,872 - mmcls - INFO - Epoch [36][5000/5005]\tlr: 1.000e-02, eta: 7:50:14, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7515\n2023-01-03 19:57:10,623 - mmcls - INFO - Epoch(val) [36][196]\taccuracy_top-1: 64.5340, accuracy_top-5: 86.2000\n2023-01-03 19:57:21,422 - mmcls - INFO - Epoch [37][100/5005]\tlr: 1.000e-02, eta: 7:50:08, time: 0.108, data_time: 0.045, memory: 1125, loss: 1.7054\n2023-01-03 19:57:30,102 - mmcls - INFO - Epoch [37][200/5005]\tlr: 1.000e-02, eta: 7:49:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7250\n2023-01-03 19:57:39,021 - mmcls - INFO - Epoch [37][300/5005]\tlr: 1.000e-02, eta: 7:49:50, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6999\n2023-01-03 19:57:47,691 - mmcls - INFO - Epoch [37][400/5005]\tlr: 1.000e-02, eta: 7:49:41, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7194\n2023-01-03 19:57:56,176 - mmcls - INFO - Epoch [37][500/5005]\tlr: 1.000e-02, eta: 7:49:31, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7364\n2023-01-03 19:58:04,973 - mmcls - INFO - Epoch [37][600/5005]\tlr: 1.000e-02, eta: 7:49:23, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7205\n2023-01-03 19:58:13,837 - mmcls - INFO - Epoch [37][700/5005]\tlr: 1.000e-02, eta: 7:49:14, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7482\n2023-01-03 19:58:22,752 - mmcls - INFO - Epoch [37][800/5005]\tlr: 1.000e-02, eta: 7:49:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7326\n2023-01-03 19:58:31,732 - mmcls - INFO - Epoch [37][900/5005]\tlr: 1.000e-02, eta: 7:48:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7598\n2023-01-03 19:58:40,346 - mmcls - INFO - Epoch [37][1000/5005]\tlr: 1.000e-02, eta: 7:48:48, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7329\n2023-01-03 19:58:49,008 - mmcls - INFO - Epoch [37][1100/5005]\tlr: 1.000e-02, eta: 7:48:39, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7448\n2023-01-03 19:58:57,821 - mmcls - INFO - Epoch [37][1200/5005]\tlr: 1.000e-02, eta: 7:48:30, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7475\n2023-01-03 19:59:06,294 - mmcls - INFO - Epoch [37][1300/5005]\tlr: 1.000e-02, eta: 7:48:20, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7391\n2023-01-03 19:59:15,110 - mmcls - INFO - Epoch [37][1400/5005]\tlr: 1.000e-02, eta: 7:48:12, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7120\n2023-01-03 19:59:23,977 - mmcls - INFO - Epoch [37][1500/5005]\tlr: 1.000e-02, eta: 7:48:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7431\n2023-01-03 19:59:32,713 - mmcls - INFO - Epoch [37][1600/5005]\tlr: 1.000e-02, eta: 7:47:54, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7522\n2023-01-03 19:59:41,355 - mmcls - INFO - Epoch [37][1700/5005]\tlr: 1.000e-02, eta: 7:47:45, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7336\n2023-01-03 19:59:50,283 - mmcls - INFO - Epoch [37][1800/5005]\tlr: 1.000e-02, eta: 7:47:36, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7106\n2023-01-03 19:59:58,890 - mmcls - INFO - Epoch [37][1900/5005]\tlr: 1.000e-02, eta: 7:47:27, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7263\n2023-01-03 20:00:07,569 - mmcls - INFO - Epoch [37][2000/5005]\tlr: 1.000e-02, eta: 7:47:18, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7182\n2023-01-03 20:00:16,349 - mmcls - INFO - Epoch [37][2100/5005]\tlr: 1.000e-02, eta: 7:47:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7329\n2023-01-03 20:00:25,080 - mmcls - INFO - Epoch [37][2200/5005]\tlr: 1.000e-02, eta: 7:47:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7462\n2023-01-03 20:00:33,717 - mmcls - INFO - Epoch [37][2300/5005]\tlr: 1.000e-02, eta: 7:46:51, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7517\n2023-01-03 20:00:42,486 - mmcls - INFO - Epoch [37][2400/5005]\tlr: 1.000e-02, eta: 7:46:42, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7785\n2023-01-03 20:00:51,288 - mmcls - INFO - Epoch [37][2500/5005]\tlr: 1.000e-02, eta: 7:46:33, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7466\n2023-01-03 20:01:00,096 - mmcls - INFO - Epoch [37][2600/5005]\tlr: 1.000e-02, eta: 7:46:25, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7278\n2023-01-03 20:01:09,050 - mmcls - INFO - Epoch [37][2700/5005]\tlr: 1.000e-02, eta: 7:46:16, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7274\n2023-01-03 20:01:17,941 - mmcls - INFO - Epoch [37][2800/5005]\tlr: 1.000e-02, eta: 7:46:07, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7652\n2023-01-03 20:01:26,550 - mmcls - INFO - Epoch [37][2900/5005]\tlr: 1.000e-02, eta: 7:45:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7417\n2023-01-03 20:01:35,701 - mmcls - INFO - Epoch [37][3000/5005]\tlr: 1.000e-02, eta: 7:45:50, time: 0.092, data_time: 0.023, memory: 1125, loss: 1.7328\n2023-01-03 20:01:44,306 - mmcls - INFO - Epoch [37][3100/5005]\tlr: 1.000e-02, eta: 7:45:41, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7357\n2023-01-03 20:01:53,209 - mmcls - INFO - Epoch [37][3200/5005]\tlr: 1.000e-02, eta: 7:45:32, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7368\n2023-01-03 20:02:02,045 - mmcls - INFO - Epoch [37][3300/5005]\tlr: 1.000e-02, eta: 7:45:23, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7352\n2023-01-03 20:02:10,774 - mmcls - INFO - Epoch [37][3400/5005]\tlr: 1.000e-02, eta: 7:45:14, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7380\n2023-01-03 20:02:19,433 - mmcls - INFO - Epoch [37][3500/5005]\tlr: 1.000e-02, eta: 7:45:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7409\n2023-01-03 20:02:28,357 - mmcls - INFO - Epoch [37][3600/5005]\tlr: 1.000e-02, eta: 7:44:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7408\n2023-01-03 20:02:37,106 - mmcls - INFO - Epoch [37][3700/5005]\tlr: 1.000e-02, eta: 7:44:48, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7698\n2023-01-03 20:02:45,635 - mmcls - INFO - Epoch [37][3800/5005]\tlr: 1.000e-02, eta: 7:44:39, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7296\n2023-01-03 20:02:54,474 - mmcls - INFO - Epoch [37][3900/5005]\tlr: 1.000e-02, eta: 7:44:30, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7543\n2023-01-03 20:03:03,325 - mmcls - INFO - Epoch [37][4000/5005]\tlr: 1.000e-02, eta: 7:44:21, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7624\n2023-01-03 20:03:12,085 - mmcls - INFO - Epoch [37][4100/5005]\tlr: 1.000e-02, eta: 7:44:12, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7437\n2023-01-03 20:03:20,987 - mmcls - INFO - Epoch [37][4200/5005]\tlr: 1.000e-02, eta: 7:44:04, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7299\n2023-01-03 20:03:29,473 - mmcls - INFO - Epoch [37][4300/5005]\tlr: 1.000e-02, eta: 7:43:54, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7375\n2023-01-03 20:03:38,305 - mmcls - INFO - Epoch [37][4400/5005]\tlr: 1.000e-02, eta: 7:43:45, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7319\n2023-01-03 20:03:47,247 - mmcls - INFO - Epoch [37][4500/5005]\tlr: 1.000e-02, eta: 7:43:37, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.7526\n2023-01-03 20:03:55,953 - mmcls - INFO - Epoch [37][4600/5005]\tlr: 1.000e-02, eta: 7:43:28, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7539\n2023-01-03 20:04:04,589 - mmcls - INFO - Epoch [37][4700/5005]\tlr: 1.000e-02, eta: 7:43:19, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7513\n2023-01-03 20:04:13,230 - mmcls - INFO - Epoch [37][4800/5005]\tlr: 1.000e-02, eta: 7:43:10, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7662\n2023-01-03 20:04:22,102 - mmcls - INFO - Epoch [37][4900/5005]\tlr: 1.000e-02, eta: 7:43:01, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7517\n2023-01-03 20:04:30,704 - mmcls - INFO - Epoch [37][5000/5005]\tlr: 1.000e-02, eta: 7:42:52, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7303\n2023-01-03 20:04:53,215 - mmcls - INFO - Epoch(val) [37][196]\taccuracy_top-1: 64.9800, accuracy_top-5: 86.3740\n2023-01-03 20:05:04,186 - mmcls - INFO - Epoch [38][100/5005]\tlr: 1.000e-02, eta: 7:42:45, time: 0.110, data_time: 0.071, memory: 1125, loss: 1.7130\n2023-01-03 20:05:12,953 - mmcls - INFO - Epoch [38][200/5005]\tlr: 1.000e-02, eta: 7:42:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7127\n2023-01-03 20:05:21,618 - mmcls - INFO - Epoch [38][300/5005]\tlr: 1.000e-02, eta: 7:42:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7261\n2023-01-03 20:05:30,384 - mmcls - INFO - Epoch [38][400/5005]\tlr: 1.000e-02, eta: 7:42:19, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.7131\n2023-01-03 20:05:39,487 - mmcls - INFO - Epoch [38][500/5005]\tlr: 1.000e-02, eta: 7:42:10, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.7394\n2023-01-03 20:05:48,385 - mmcls - INFO - Epoch [38][600/5005]\tlr: 1.000e-02, eta: 7:42:02, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7134\n2023-01-03 20:05:56,921 - mmcls - INFO - Epoch [38][700/5005]\tlr: 1.000e-02, eta: 7:41:52, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7221\n2023-01-03 20:06:05,485 - mmcls - INFO - Epoch [38][800/5005]\tlr: 1.000e-02, eta: 7:41:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7016\n2023-01-03 20:06:14,136 - mmcls - INFO - Epoch [38][900/5005]\tlr: 1.000e-02, eta: 7:41:34, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7117\n2023-01-03 20:06:22,652 - mmcls - INFO - Epoch [38][1000/5005]\tlr: 1.000e-02, eta: 7:41:25, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7094\n2023-01-03 20:06:31,251 - mmcls - INFO - Epoch [38][1100/5005]\tlr: 1.000e-02, eta: 7:41:16, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7096\n2023-01-03 20:06:40,220 - mmcls - INFO - Epoch [38][1200/5005]\tlr: 1.000e-02, eta: 7:41:07, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.7414\n2023-01-03 20:06:49,108 - mmcls - INFO - Epoch [38][1300/5005]\tlr: 1.000e-02, eta: 7:40:58, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7305\n2023-01-03 20:06:57,725 - mmcls - INFO - Epoch [38][1400/5005]\tlr: 1.000e-02, eta: 7:40:49, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7154\n2023-01-03 20:07:06,365 - mmcls - INFO - Epoch [38][1500/5005]\tlr: 1.000e-02, eta: 7:40:40, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7347\n2023-01-03 20:07:15,045 - mmcls - INFO - Epoch [38][1600/5005]\tlr: 1.000e-02, eta: 7:40:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7314\n2023-01-03 20:07:23,912 - mmcls - INFO - Epoch [38][1700/5005]\tlr: 1.000e-02, eta: 7:40:22, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7449\n2023-01-03 20:07:32,667 - mmcls - INFO - Epoch [38][1800/5005]\tlr: 1.000e-02, eta: 7:40:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7350\n2023-01-03 20:07:41,421 - mmcls - INFO - Epoch [38][1900/5005]\tlr: 1.000e-02, eta: 7:40:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7345\n2023-01-03 20:07:50,222 - mmcls - INFO - Epoch [38][2000/5005]\tlr: 1.000e-02, eta: 7:39:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7039\n2023-01-03 20:07:59,038 - mmcls - INFO - Epoch [38][2100/5005]\tlr: 1.000e-02, eta: 7:39:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7233\n2023-01-03 20:08:07,852 - mmcls - INFO - Epoch [38][2200/5005]\tlr: 1.000e-02, eta: 7:39:38, time: 0.088, data_time: 0.045, memory: 1125, loss: 1.7443\n2023-01-03 20:08:16,647 - mmcls - INFO - Epoch [38][2300/5005]\tlr: 1.000e-02, eta: 7:39:29, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7112\n2023-01-03 20:08:25,780 - mmcls - INFO - Epoch [38][2400/5005]\tlr: 1.000e-02, eta: 7:39:21, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7327\n2023-01-03 20:08:34,368 - mmcls - INFO - Epoch [38][2500/5005]\tlr: 1.000e-02, eta: 7:39:12, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7399\n2023-01-03 20:08:43,125 - mmcls - INFO - Epoch [38][2600/5005]\tlr: 1.000e-02, eta: 7:39:03, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7173\n2023-01-03 20:08:51,599 - mmcls - INFO - Epoch [38][2700/5005]\tlr: 1.000e-02, eta: 7:38:54, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7632\n2023-01-03 20:09:00,300 - mmcls - INFO - Epoch [38][2800/5005]\tlr: 1.000e-02, eta: 7:38:45, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7493\n2023-01-03 20:09:08,997 - mmcls - INFO - Epoch [38][2900/5005]\tlr: 1.000e-02, eta: 7:38:36, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7275\n2023-01-03 20:09:17,540 - mmcls - INFO - Epoch [38][3000/5005]\tlr: 1.000e-02, eta: 7:38:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7362\n2023-01-03 20:09:26,172 - mmcls - INFO - Epoch [38][3100/5005]\tlr: 1.000e-02, eta: 7:38:17, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7095\n2023-01-03 20:09:35,108 - mmcls - INFO - Epoch [38][3200/5005]\tlr: 1.000e-02, eta: 7:38:09, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7526\n2023-01-03 20:09:44,022 - mmcls - INFO - Epoch [38][3300/5005]\tlr: 1.000e-02, eta: 7:38:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7615\n2023-01-03 20:09:52,853 - mmcls - INFO - Epoch [38][3400/5005]\tlr: 1.000e-02, eta: 7:37:51, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7407\n2023-01-03 20:10:01,405 - mmcls - INFO - Epoch [38][3500/5005]\tlr: 1.000e-02, eta: 7:37:42, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7353\n2023-01-03 20:10:10,180 - mmcls - INFO - Epoch [38][3600/5005]\tlr: 1.000e-02, eta: 7:37:33, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7579\n2023-01-03 20:10:18,771 - mmcls - INFO - Epoch [38][3700/5005]\tlr: 1.000e-02, eta: 7:37:24, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7395\n2023-01-03 20:10:27,651 - mmcls - INFO - Epoch [38][3800/5005]\tlr: 1.000e-02, eta: 7:37:15, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7404\n2023-01-03 20:10:36,290 - mmcls - INFO - Epoch [38][3900/5005]\tlr: 1.000e-02, eta: 7:37:06, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7138\n2023-01-03 20:10:44,994 - mmcls - INFO - Epoch [38][4000/5005]\tlr: 1.000e-02, eta: 7:36:57, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7337\n2023-01-03 20:10:53,470 - mmcls - INFO - Epoch [38][4100/5005]\tlr: 1.000e-02, eta: 7:36:48, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7374\n2023-01-03 20:11:02,253 - mmcls - INFO - Epoch [38][4200/5005]\tlr: 1.000e-02, eta: 7:36:39, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7562\n2023-01-03 20:11:10,803 - mmcls - INFO - Epoch [38][4300/5005]\tlr: 1.000e-02, eta: 7:36:30, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7431\n2023-01-03 20:11:19,739 - mmcls - INFO - Epoch [38][4400/5005]\tlr: 1.000e-02, eta: 7:36:21, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7370\n2023-01-03 20:11:28,391 - mmcls - INFO - Epoch [38][4500/5005]\tlr: 1.000e-02, eta: 7:36:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7451\n2023-01-03 20:11:37,104 - mmcls - INFO - Epoch [38][4600/5005]\tlr: 1.000e-02, eta: 7:36:03, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7303\n2023-01-03 20:11:45,870 - mmcls - INFO - Epoch [38][4700/5005]\tlr: 1.000e-02, eta: 7:35:54, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7670\n2023-01-03 20:11:54,653 - mmcls - INFO - Epoch [38][4800/5005]\tlr: 1.000e-02, eta: 7:35:46, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7397\n2023-01-03 20:12:03,299 - mmcls - INFO - Epoch [38][4900/5005]\tlr: 1.000e-02, eta: 7:35:36, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7476\n2023-01-03 20:12:11,962 - mmcls - INFO - Epoch [38][5000/5005]\tlr: 1.000e-02, eta: 7:35:27, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7382\n2023-01-03 20:12:34,505 - mmcls - INFO - Epoch(val) [38][196]\taccuracy_top-1: 64.9120, accuracy_top-5: 86.2980\n2023-01-03 20:12:45,544 - mmcls - INFO - Epoch [39][100/5005]\tlr: 1.000e-02, eta: 7:35:21, time: 0.110, data_time: 0.070, memory: 1125, loss: 1.7145\n2023-01-03 20:12:54,068 - mmcls - INFO - Epoch [39][200/5005]\tlr: 1.000e-02, eta: 7:35:12, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7068\n2023-01-03 20:13:02,867 - mmcls - INFO - Epoch [39][300/5005]\tlr: 1.000e-02, eta: 7:35:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6880\n2023-01-03 20:13:11,727 - mmcls - INFO - Epoch [39][400/5005]\tlr: 1.000e-02, eta: 7:34:54, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.7112\n2023-01-03 20:13:20,439 - mmcls - INFO - Epoch [39][500/5005]\tlr: 1.000e-02, eta: 7:34:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7033\n2023-01-03 20:13:29,385 - mmcls - INFO - Epoch [39][600/5005]\tlr: 1.000e-02, eta: 7:34:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7126\n2023-01-03 20:13:38,103 - mmcls - INFO - Epoch [39][700/5005]\tlr: 1.000e-02, eta: 7:34:28, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7237\n2023-01-03 20:13:46,860 - mmcls - INFO - Epoch [39][800/5005]\tlr: 1.000e-02, eta: 7:34:19, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7228\n2023-01-03 20:13:55,748 - mmcls - INFO - Epoch [39][900/5005]\tlr: 1.000e-02, eta: 7:34:10, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7294\n2023-01-03 20:14:04,269 - mmcls - INFO - Epoch [39][1000/5005]\tlr: 1.000e-02, eta: 7:34:01, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7205\n2023-01-03 20:14:13,266 - mmcls - INFO - Epoch [39][1100/5005]\tlr: 1.000e-02, eta: 7:33:52, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7034\n2023-01-03 20:14:21,933 - mmcls - INFO - Epoch [39][1200/5005]\tlr: 1.000e-02, eta: 7:33:43, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7436\n2023-01-03 20:14:30,686 - mmcls - INFO - Epoch [39][1300/5005]\tlr: 1.000e-02, eta: 7:33:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7120\n2023-01-03 20:14:39,397 - mmcls - INFO - Epoch [39][1400/5005]\tlr: 1.000e-02, eta: 7:33:26, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7371\n2023-01-03 20:14:48,229 - mmcls - INFO - Epoch [39][1500/5005]\tlr: 1.000e-02, eta: 7:33:17, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7315\n2023-01-03 20:14:57,011 - mmcls - INFO - Epoch [39][1600/5005]\tlr: 1.000e-02, eta: 7:33:08, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.7161\n2023-01-03 20:15:05,773 - mmcls - INFO - Epoch [39][1700/5005]\tlr: 1.000e-02, eta: 7:32:59, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7433\n2023-01-03 20:15:14,443 - mmcls - INFO - Epoch [39][1800/5005]\tlr: 1.000e-02, eta: 7:32:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7392\n2023-01-03 20:15:23,391 - mmcls - INFO - Epoch [39][1900/5005]\tlr: 1.000e-02, eta: 7:32:41, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7192\n2023-01-03 20:15:32,060 - mmcls - INFO - Epoch [39][2000/5005]\tlr: 1.000e-02, eta: 7:32:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7239\n2023-01-03 20:15:40,774 - mmcls - INFO - Epoch [39][2100/5005]\tlr: 1.000e-02, eta: 7:32:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7116\n2023-01-03 20:15:49,672 - mmcls - INFO - Epoch [39][2200/5005]\tlr: 1.000e-02, eta: 7:32:15, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7289\n2023-01-03 20:15:58,237 - mmcls - INFO - Epoch [39][2300/5005]\tlr: 1.000e-02, eta: 7:32:06, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7189\n2023-01-03 20:16:07,107 - mmcls - INFO - Epoch [39][2400/5005]\tlr: 1.000e-02, eta: 7:31:57, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7380\n2023-01-03 20:16:15,818 - mmcls - INFO - Epoch [39][2500/5005]\tlr: 1.000e-02, eta: 7:31:48, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7046\n2023-01-03 20:16:24,349 - mmcls - INFO - Epoch [39][2600/5005]\tlr: 1.000e-02, eta: 7:31:39, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7485\n2023-01-03 20:16:33,267 - mmcls - INFO - Epoch [39][2700/5005]\tlr: 1.000e-02, eta: 7:31:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7299\n2023-01-03 20:16:42,206 - mmcls - INFO - Epoch [39][2800/5005]\tlr: 1.000e-02, eta: 7:31:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7464\n2023-01-03 20:16:50,917 - mmcls - INFO - Epoch [39][2900/5005]\tlr: 1.000e-02, eta: 7:31:12, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7654\n2023-01-03 20:16:59,832 - mmcls - INFO - Epoch [39][3000/5005]\tlr: 1.000e-02, eta: 7:31:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7321\n2023-01-03 20:17:08,513 - mmcls - INFO - Epoch [39][3100/5005]\tlr: 1.000e-02, eta: 7:30:55, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7208\n2023-01-03 20:17:17,007 - mmcls - INFO - Epoch [39][3200/5005]\tlr: 1.000e-02, eta: 7:30:46, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7202\n2023-01-03 20:17:25,522 - mmcls - INFO - Epoch [39][3300/5005]\tlr: 1.000e-02, eta: 7:30:36, time: 0.085, data_time: 0.035, memory: 1125, loss: 1.7439\n2023-01-03 20:17:34,215 - mmcls - INFO - Epoch [39][3400/5005]\tlr: 1.000e-02, eta: 7:30:27, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7180\n2023-01-03 20:17:43,094 - mmcls - INFO - Epoch [39][3500/5005]\tlr: 1.000e-02, eta: 7:30:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7478\n2023-01-03 20:17:51,849 - mmcls - INFO - Epoch [39][3600/5005]\tlr: 1.000e-02, eta: 7:30:10, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7645\n2023-01-03 20:18:00,464 - mmcls - INFO - Epoch [39][3700/5005]\tlr: 1.000e-02, eta: 7:30:01, time: 0.086, data_time: 0.038, memory: 1125, loss: 1.7353\n2023-01-03 20:18:09,350 - mmcls - INFO - Epoch [39][3800/5005]\tlr: 1.000e-02, eta: 7:29:52, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7532\n2023-01-03 20:18:18,142 - mmcls - INFO - Epoch [39][3900/5005]\tlr: 1.000e-02, eta: 7:29:43, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7345\n2023-01-03 20:18:27,055 - mmcls - INFO - Epoch [39][4000/5005]\tlr: 1.000e-02, eta: 7:29:34, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7435\n2023-01-03 20:18:36,130 - mmcls - INFO - Epoch [39][4100/5005]\tlr: 1.000e-02, eta: 7:29:26, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7380\n2023-01-03 20:18:44,709 - mmcls - INFO - Epoch [39][4200/5005]\tlr: 1.000e-02, eta: 7:29:17, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7449\n2023-01-03 20:18:53,226 - mmcls - INFO - Epoch [39][4300/5005]\tlr: 1.000e-02, eta: 7:29:08, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7268\n2023-01-03 20:19:01,843 - mmcls - INFO - Epoch [39][4400/5005]\tlr: 1.000e-02, eta: 7:28:58, time: 0.086, data_time: 0.041, memory: 1125, loss: 1.7411\n2023-01-03 20:19:10,395 - mmcls - INFO - Epoch [39][4500/5005]\tlr: 1.000e-02, eta: 7:28:49, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7574\n2023-01-03 20:19:18,779 - mmcls - INFO - Epoch [39][4600/5005]\tlr: 1.000e-02, eta: 7:28:40, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7451\n2023-01-03 20:19:27,779 - mmcls - INFO - Epoch [39][4700/5005]\tlr: 1.000e-02, eta: 7:28:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7316\n2023-01-03 20:19:36,416 - mmcls - INFO - Epoch [39][4800/5005]\tlr: 1.000e-02, eta: 7:28:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7284\n2023-01-03 20:19:45,007 - mmcls - INFO - Epoch [39][4900/5005]\tlr: 1.000e-02, eta: 7:28:13, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7360\n2023-01-03 20:19:54,160 - mmcls - INFO - Epoch [39][5000/5005]\tlr: 1.000e-02, eta: 7:28:05, time: 0.092, data_time: 0.016, memory: 1125, loss: 1.7353\n2023-01-03 20:20:16,715 - mmcls - INFO - Epoch(val) [39][196]\taccuracy_top-1: 64.2780, accuracy_top-5: 85.8920\n2023-01-03 20:20:27,703 - mmcls - INFO - Epoch [40][100/5005]\tlr: 1.000e-02, eta: 7:27:58, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.6982\n2023-01-03 20:20:36,639 - mmcls - INFO - Epoch [40][200/5005]\tlr: 1.000e-02, eta: 7:27:50, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7123\n2023-01-03 20:20:45,212 - mmcls - INFO - Epoch [40][300/5005]\tlr: 1.000e-02, eta: 7:27:40, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7267\n2023-01-03 20:20:54,143 - mmcls - INFO - Epoch [40][400/5005]\tlr: 1.000e-02, eta: 7:27:32, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7003\n2023-01-03 20:21:02,745 - mmcls - INFO - Epoch [40][500/5005]\tlr: 1.000e-02, eta: 7:27:23, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7144\n2023-01-03 20:21:11,378 - mmcls - INFO - Epoch [40][600/5005]\tlr: 1.000e-02, eta: 7:27:14, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7069\n2023-01-03 20:21:20,358 - mmcls - INFO - Epoch [40][700/5005]\tlr: 1.000e-02, eta: 7:27:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6935\n2023-01-03 20:21:28,904 - mmcls - INFO - Epoch [40][800/5005]\tlr: 1.000e-02, eta: 7:26:56, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7113\n2023-01-03 20:21:37,717 - mmcls - INFO - Epoch [40][900/5005]\tlr: 1.000e-02, eta: 7:26:47, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7076\n2023-01-03 20:21:46,528 - mmcls - INFO - Epoch [40][1000/5005]\tlr: 1.000e-02, eta: 7:26:38, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7131\n2023-01-03 20:21:55,291 - mmcls - INFO - Epoch [40][1100/5005]\tlr: 1.000e-02, eta: 7:26:29, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7173\n2023-01-03 20:22:05,116 - mmcls - INFO - Epoch [40][1200/5005]\tlr: 1.000e-02, eta: 7:26:22, time: 0.098, data_time: 0.004, memory: 1125, loss: 1.7301\n2023-01-03 20:22:13,829 - mmcls - INFO - Epoch [40][1300/5005]\tlr: 1.000e-02, eta: 7:26:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7226\n2023-01-03 20:22:22,709 - mmcls - INFO - Epoch [40][1400/5005]\tlr: 1.000e-02, eta: 7:26:05, time: 0.089, data_time: 0.038, memory: 1125, loss: 1.7054\n2023-01-03 20:22:31,310 - mmcls - INFO - Epoch [40][1500/5005]\tlr: 1.000e-02, eta: 7:25:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7402\n2023-01-03 20:22:40,268 - mmcls - INFO - Epoch [40][1600/5005]\tlr: 1.000e-02, eta: 7:25:47, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7034\n2023-01-03 20:22:48,670 - mmcls - INFO - Epoch [40][1700/5005]\tlr: 1.000e-02, eta: 7:25:37, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7382\n2023-01-03 20:22:57,657 - mmcls - INFO - Epoch [40][1800/5005]\tlr: 1.000e-02, eta: 7:25:29, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7028\n2023-01-03 20:23:06,645 - mmcls - INFO - Epoch [40][1900/5005]\tlr: 1.000e-02, eta: 7:25:20, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7216\n2023-01-03 20:23:15,075 - mmcls - INFO - Epoch [40][2000/5005]\tlr: 1.000e-02, eta: 7:25:11, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7137\n2023-01-03 20:23:24,016 - mmcls - INFO - Epoch [40][2100/5005]\tlr: 1.000e-02, eta: 7:25:02, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7117\n2023-01-03 20:23:32,548 - mmcls - INFO - Epoch [40][2200/5005]\tlr: 1.000e-02, eta: 7:24:53, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7321\n2023-01-03 20:23:41,027 - mmcls - INFO - Epoch [40][2300/5005]\tlr: 1.000e-02, eta: 7:24:44, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7078\n2023-01-03 20:23:49,849 - mmcls - INFO - Epoch [40][2400/5005]\tlr: 1.000e-02, eta: 7:24:35, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7315\n2023-01-03 20:23:58,619 - mmcls - INFO - Epoch [40][2500/5005]\tlr: 1.000e-02, eta: 7:24:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7433\n2023-01-03 20:24:07,449 - mmcls - INFO - Epoch [40][2600/5005]\tlr: 1.000e-02, eta: 7:24:17, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7253\n2023-01-03 20:24:16,357 - mmcls - INFO - Epoch [40][2700/5005]\tlr: 1.000e-02, eta: 7:24:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7388\n2023-01-03 20:24:25,429 - mmcls - INFO - Epoch [40][2800/5005]\tlr: 1.000e-02, eta: 7:24:00, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.7216\n2023-01-03 20:24:34,230 - mmcls - INFO - Epoch [40][2900/5005]\tlr: 1.000e-02, eta: 7:23:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7246\n2023-01-03 20:24:42,845 - mmcls - INFO - Epoch [40][3000/5005]\tlr: 1.000e-02, eta: 7:23:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7335\n2023-01-03 20:24:51,564 - mmcls - INFO - Epoch [40][3100/5005]\tlr: 1.000e-02, eta: 7:23:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7459\n2023-01-03 20:25:00,283 - mmcls - INFO - Epoch [40][3200/5005]\tlr: 1.000e-02, eta: 7:23:25, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7461\n2023-01-03 20:25:09,060 - mmcls - INFO - Epoch [40][3300/5005]\tlr: 1.000e-02, eta: 7:23:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7285\n2023-01-03 20:25:17,823 - mmcls - INFO - Epoch [40][3400/5005]\tlr: 1.000e-02, eta: 7:23:07, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7383\n2023-01-03 20:25:26,574 - mmcls - INFO - Epoch [40][3500/5005]\tlr: 1.000e-02, eta: 7:22:58, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7475\n2023-01-03 20:25:35,377 - mmcls - INFO - Epoch [40][3600/5005]\tlr: 1.000e-02, eta: 7:22:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7431\n2023-01-03 20:25:43,912 - mmcls - INFO - Epoch [40][3700/5005]\tlr: 1.000e-02, eta: 7:22:40, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.7339\n2023-01-03 20:25:52,449 - mmcls - INFO - Epoch [40][3800/5005]\tlr: 1.000e-02, eta: 7:22:31, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7231\n2023-01-03 20:26:01,409 - mmcls - INFO - Epoch [40][3900/5005]\tlr: 1.000e-02, eta: 7:22:22, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7321\n2023-01-03 20:26:10,105 - mmcls - INFO - Epoch [40][4000/5005]\tlr: 1.000e-02, eta: 7:22:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7078\n2023-01-03 20:26:18,921 - mmcls - INFO - Epoch [40][4100/5005]\tlr: 1.000e-02, eta: 7:22:04, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7243\n2023-01-03 20:26:27,414 - mmcls - INFO - Epoch [40][4200/5005]\tlr: 1.000e-02, eta: 7:21:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7209\n2023-01-03 20:26:35,989 - mmcls - INFO - Epoch [40][4300/5005]\tlr: 1.000e-02, eta: 7:21:46, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7288\n2023-01-03 20:26:44,829 - mmcls - INFO - Epoch [40][4400/5005]\tlr: 1.000e-02, eta: 7:21:37, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7425\n2023-01-03 20:26:53,546 - mmcls - INFO - Epoch [40][4500/5005]\tlr: 1.000e-02, eta: 7:21:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7613\n2023-01-03 20:27:02,401 - mmcls - INFO - Epoch [40][4600/5005]\tlr: 1.000e-02, eta: 7:21:19, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.7430\n2023-01-03 20:27:10,982 - mmcls - INFO - Epoch [40][4700/5005]\tlr: 1.000e-02, eta: 7:21:10, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7199\n2023-01-03 20:27:19,820 - mmcls - INFO - Epoch [40][4800/5005]\tlr: 1.000e-02, eta: 7:21:02, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7478\n2023-01-03 20:27:28,398 - mmcls - INFO - Epoch [40][4900/5005]\tlr: 1.000e-02, eta: 7:20:52, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7105\n2023-01-03 20:27:37,330 - mmcls - INFO - Epoch [40][5000/5005]\tlr: 1.000e-02, eta: 7:20:44, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.7391\n2023-01-03 20:27:59,912 - mmcls - INFO - Epoch(val) [40][196]\taccuracy_top-1: 64.6040, accuracy_top-5: 86.2000\n2023-01-03 20:28:10,858 - mmcls - INFO - Epoch [41][100/5005]\tlr: 1.000e-02, eta: 7:20:37, time: 0.109, data_time: 0.047, memory: 1125, loss: 1.7075\n2023-01-03 20:28:19,749 - mmcls - INFO - Epoch [41][200/5005]\tlr: 1.000e-02, eta: 7:20:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6807\n2023-01-03 20:28:28,500 - mmcls - INFO - Epoch [41][300/5005]\tlr: 1.000e-02, eta: 7:20:20, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7203\n2023-01-03 20:28:37,517 - mmcls - INFO - Epoch [41][400/5005]\tlr: 1.000e-02, eta: 7:20:11, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7004\n2023-01-03 20:28:46,103 - mmcls - INFO - Epoch [41][500/5005]\tlr: 1.000e-02, eta: 7:20:02, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7198\n2023-01-03 20:28:54,788 - mmcls - INFO - Epoch [41][600/5005]\tlr: 1.000e-02, eta: 7:19:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7165\n2023-01-03 20:29:03,779 - mmcls - INFO - Epoch [41][700/5005]\tlr: 1.000e-02, eta: 7:19:44, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7144\n2023-01-03 20:29:12,851 - mmcls - INFO - Epoch [41][800/5005]\tlr: 1.000e-02, eta: 7:19:36, time: 0.091, data_time: 0.034, memory: 1125, loss: 1.7315\n2023-01-03 20:29:21,623 - mmcls - INFO - Epoch [41][900/5005]\tlr: 1.000e-02, eta: 7:19:27, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7099\n2023-01-03 20:29:30,325 - mmcls - INFO - Epoch [41][1000/5005]\tlr: 1.000e-02, eta: 7:19:18, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7453\n2023-01-03 20:29:38,834 - mmcls - INFO - Epoch [41][1100/5005]\tlr: 1.000e-02, eta: 7:19:09, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7066\n2023-01-03 20:29:47,958 - mmcls - INFO - Epoch [41][1200/5005]\tlr: 1.000e-02, eta: 7:19:01, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7143\n2023-01-03 20:29:56,582 - mmcls - INFO - Epoch [41][1300/5005]\tlr: 1.000e-02, eta: 7:18:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7325\n2023-01-03 20:30:05,390 - mmcls - INFO - Epoch [41][1400/5005]\tlr: 1.000e-02, eta: 7:18:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7210\n2023-01-03 20:30:14,062 - mmcls - INFO - Epoch [41][1500/5005]\tlr: 1.000e-02, eta: 7:18:34, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7192\n2023-01-03 20:30:22,967 - mmcls - INFO - Epoch [41][1600/5005]\tlr: 1.000e-02, eta: 7:18:25, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7419\n2023-01-03 20:30:31,721 - mmcls - INFO - Epoch [41][1700/5005]\tlr: 1.000e-02, eta: 7:18:16, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7391\n2023-01-03 20:30:40,690 - mmcls - INFO - Epoch [41][1800/5005]\tlr: 1.000e-02, eta: 7:18:08, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6987\n2023-01-03 20:30:49,419 - mmcls - INFO - Epoch [41][1900/5005]\tlr: 1.000e-02, eta: 7:17:59, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.7103\n2023-01-03 20:30:57,864 - mmcls - INFO - Epoch [41][2000/5005]\tlr: 1.000e-02, eta: 7:17:49, time: 0.084, data_time: 0.021, memory: 1125, loss: 1.7321\n2023-01-03 20:31:06,830 - mmcls - INFO - Epoch [41][2100/5005]\tlr: 1.000e-02, eta: 7:17:41, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7463\n2023-01-03 20:31:15,958 - mmcls - INFO - Epoch [41][2200/5005]\tlr: 1.000e-02, eta: 7:17:32, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7363\n2023-01-03 20:31:24,521 - mmcls - INFO - Epoch [41][2300/5005]\tlr: 1.000e-02, eta: 7:17:23, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7351\n2023-01-03 20:31:33,243 - mmcls - INFO - Epoch [41][2400/5005]\tlr: 1.000e-02, eta: 7:17:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7306\n2023-01-03 20:31:41,908 - mmcls - INFO - Epoch [41][2500/5005]\tlr: 1.000e-02, eta: 7:17:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7208\n2023-01-03 20:31:50,617 - mmcls - INFO - Epoch [41][2600/5005]\tlr: 1.000e-02, eta: 7:16:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7175\n2023-01-03 20:31:59,355 - mmcls - INFO - Epoch [41][2700/5005]\tlr: 1.000e-02, eta: 7:16:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7420\n2023-01-03 20:32:08,288 - mmcls - INFO - Epoch [41][2800/5005]\tlr: 1.000e-02, eta: 7:16:39, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7032\n2023-01-03 20:32:16,973 - mmcls - INFO - Epoch [41][2900/5005]\tlr: 1.000e-02, eta: 7:16:30, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7285\n2023-01-03 20:32:25,557 - mmcls - INFO - Epoch [41][3000/5005]\tlr: 1.000e-02, eta: 7:16:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7289\n2023-01-03 20:32:34,325 - mmcls - INFO - Epoch [41][3100/5005]\tlr: 1.000e-02, eta: 7:16:12, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7446\n2023-01-03 20:32:43,078 - mmcls - INFO - Epoch [41][3200/5005]\tlr: 1.000e-02, eta: 7:16:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7481\n2023-01-03 20:32:51,747 - mmcls - INFO - Epoch [41][3300/5005]\tlr: 1.000e-02, eta: 7:15:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7416\n2023-01-03 20:33:00,487 - mmcls - INFO - Epoch [41][3400/5005]\tlr: 1.000e-02, eta: 7:15:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7347\n2023-01-03 20:33:09,168 - mmcls - INFO - Epoch [41][3500/5005]\tlr: 1.000e-02, eta: 7:15:36, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7579\n2023-01-03 20:33:17,879 - mmcls - INFO - Epoch [41][3600/5005]\tlr: 1.000e-02, eta: 7:15:27, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7435\n2023-01-03 20:33:26,572 - mmcls - INFO - Epoch [41][3700/5005]\tlr: 1.000e-02, eta: 7:15:18, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7700\n2023-01-03 20:33:35,750 - mmcls - INFO - Epoch [41][3800/5005]\tlr: 1.000e-02, eta: 7:15:10, time: 0.092, data_time: 0.024, memory: 1125, loss: 1.7363\n2023-01-03 20:33:44,311 - mmcls - INFO - Epoch [41][3900/5005]\tlr: 1.000e-02, eta: 7:15:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7160\n2023-01-03 20:33:53,026 - mmcls - INFO - Epoch [41][4000/5005]\tlr: 1.000e-02, eta: 7:14:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7329\n2023-01-03 20:34:01,747 - mmcls - INFO - Epoch [41][4100/5005]\tlr: 1.000e-02, eta: 7:14:43, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7060\n2023-01-03 20:34:10,369 - mmcls - INFO - Epoch [41][4200/5005]\tlr: 1.000e-02, eta: 7:14:34, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7406\n2023-01-03 20:34:19,185 - mmcls - INFO - Epoch [41][4300/5005]\tlr: 1.000e-02, eta: 7:14:25, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7447\n2023-01-03 20:34:28,061 - mmcls - INFO - Epoch [41][4400/5005]\tlr: 1.000e-02, eta: 7:14:16, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7548\n2023-01-03 20:34:36,915 - mmcls - INFO - Epoch [41][4500/5005]\tlr: 1.000e-02, eta: 7:14:07, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7391\n2023-01-03 20:34:45,701 - mmcls - INFO - Epoch [41][4600/5005]\tlr: 1.000e-02, eta: 7:13:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7442\n2023-01-03 20:34:54,589 - mmcls - INFO - Epoch [41][4700/5005]\tlr: 1.000e-02, eta: 7:13:50, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7283\n2023-01-03 20:35:03,370 - mmcls - INFO - Epoch [41][4800/5005]\tlr: 1.000e-02, eta: 7:13:41, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7581\n2023-01-03 20:35:11,983 - mmcls - INFO - Epoch [41][4900/5005]\tlr: 1.000e-02, eta: 7:13:32, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7542\n2023-01-03 20:35:20,589 - mmcls - INFO - Epoch [41][5000/5005]\tlr: 1.000e-02, eta: 7:13:23, time: 0.086, data_time: 0.052, memory: 1125, loss: 1.7251\n2023-01-03 20:35:43,188 - mmcls - INFO - Epoch(val) [41][196]\taccuracy_top-1: 64.7300, accuracy_top-5: 86.3380\n2023-01-03 20:35:54,334 - mmcls - INFO - Epoch [42][100/5005]\tlr: 1.000e-02, eta: 7:13:16, time: 0.111, data_time: 0.044, memory: 1125, loss: 1.7042\n2023-01-03 20:36:03,286 - mmcls - INFO - Epoch [42][200/5005]\tlr: 1.000e-02, eta: 7:13:08, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6857\n2023-01-03 20:36:12,343 - mmcls - INFO - Epoch [42][300/5005]\tlr: 1.000e-02, eta: 7:12:59, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.6795\n2023-01-03 20:36:21,295 - mmcls - INFO - Epoch [42][400/5005]\tlr: 1.000e-02, eta: 7:12:51, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6846\n2023-01-03 20:36:29,966 - mmcls - INFO - Epoch [42][500/5005]\tlr: 1.000e-02, eta: 7:12:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7066\n2023-01-03 20:36:38,379 - mmcls - INFO - Epoch [42][600/5005]\tlr: 1.000e-02, eta: 7:12:32, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.6939\n2023-01-03 20:36:47,069 - mmcls - INFO - Epoch [42][700/5005]\tlr: 1.000e-02, eta: 7:12:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7012\n2023-01-03 20:36:55,726 - mmcls - INFO - Epoch [42][800/5005]\tlr: 1.000e-02, eta: 7:12:14, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7099\n2023-01-03 20:37:04,333 - mmcls - INFO - Epoch [42][900/5005]\tlr: 1.000e-02, eta: 7:12:05, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7142\n2023-01-03 20:37:13,267 - mmcls - INFO - Epoch [42][1000/5005]\tlr: 1.000e-02, eta: 7:11:57, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7026\n2023-01-03 20:37:21,898 - mmcls - INFO - Epoch [42][1100/5005]\tlr: 1.000e-02, eta: 7:11:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7163\n2023-01-03 20:37:30,600 - mmcls - INFO - Epoch [42][1200/5005]\tlr: 1.000e-02, eta: 7:11:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.6988\n2023-01-03 20:37:39,556 - mmcls - INFO - Epoch [42][1300/5005]\tlr: 1.000e-02, eta: 7:11:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7055\n2023-01-03 20:37:48,557 - mmcls - INFO - Epoch [42][1400/5005]\tlr: 1.000e-02, eta: 7:11:21, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7091\n2023-01-03 20:37:57,480 - mmcls - INFO - Epoch [42][1500/5005]\tlr: 1.000e-02, eta: 7:11:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6926\n2023-01-03 20:38:06,113 - mmcls - INFO - Epoch [42][1600/5005]\tlr: 1.000e-02, eta: 7:11:04, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7217\n2023-01-03 20:38:14,964 - mmcls - INFO - Epoch [42][1700/5005]\tlr: 1.000e-02, eta: 7:10:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7293\n2023-01-03 20:38:23,933 - mmcls - INFO - Epoch [42][1800/5005]\tlr: 1.000e-02, eta: 7:10:46, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7335\n2023-01-03 20:38:32,890 - mmcls - INFO - Epoch [42][1900/5005]\tlr: 1.000e-02, eta: 7:10:38, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7005\n2023-01-03 20:38:41,641 - mmcls - INFO - Epoch [42][2000/5005]\tlr: 1.000e-02, eta: 7:10:29, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7035\n2023-01-03 20:38:50,519 - mmcls - INFO - Epoch [42][2100/5005]\tlr: 1.000e-02, eta: 7:10:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7303\n2023-01-03 20:38:59,176 - mmcls - INFO - Epoch [42][2200/5005]\tlr: 1.000e-02, eta: 7:10:11, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7196\n2023-01-03 20:39:07,976 - mmcls - INFO - Epoch [42][2300/5005]\tlr: 1.000e-02, eta: 7:10:02, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7250\n2023-01-03 20:39:17,083 - mmcls - INFO - Epoch [42][2400/5005]\tlr: 1.000e-02, eta: 7:09:54, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7319\n2023-01-03 20:39:25,803 - mmcls - INFO - Epoch [42][2500/5005]\tlr: 1.000e-02, eta: 7:09:45, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7093\n2023-01-03 20:39:34,604 - mmcls - INFO - Epoch [42][2600/5005]\tlr: 1.000e-02, eta: 7:09:36, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7122\n2023-01-03 20:39:43,262 - mmcls - INFO - Epoch [42][2700/5005]\tlr: 1.000e-02, eta: 7:09:27, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7312\n2023-01-03 20:39:52,277 - mmcls - INFO - Epoch [42][2800/5005]\tlr: 1.000e-02, eta: 7:09:19, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.7281\n2023-01-03 20:40:00,822 - mmcls - INFO - Epoch [42][2900/5005]\tlr: 1.000e-02, eta: 7:09:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7403\n2023-01-03 20:40:10,204 - mmcls - INFO - Epoch [42][3000/5005]\tlr: 1.000e-02, eta: 7:09:02, time: 0.094, data_time: 0.009, memory: 1125, loss: 1.7328\n2023-01-03 20:40:19,081 - mmcls - INFO - Epoch [42][3100/5005]\tlr: 1.000e-02, eta: 7:08:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7228\n2023-01-03 20:40:28,214 - mmcls - INFO - Epoch [42][3200/5005]\tlr: 1.000e-02, eta: 7:08:45, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7289\n2023-01-03 20:40:36,947 - mmcls - INFO - Epoch [42][3300/5005]\tlr: 1.000e-02, eta: 7:08:36, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7444\n2023-01-03 20:40:45,963 - mmcls - INFO - Epoch [42][3400/5005]\tlr: 1.000e-02, eta: 7:08:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7497\n2023-01-03 20:40:54,759 - mmcls - INFO - Epoch [42][3500/5005]\tlr: 1.000e-02, eta: 7:08:18, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7356\n2023-01-03 20:41:03,554 - mmcls - INFO - Epoch [42][3600/5005]\tlr: 1.000e-02, eta: 7:08:09, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7365\n2023-01-03 20:41:12,264 - mmcls - INFO - Epoch [42][3700/5005]\tlr: 1.000e-02, eta: 7:08:01, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7451\n2023-01-03 20:41:20,910 - mmcls - INFO - Epoch [42][3800/5005]\tlr: 1.000e-02, eta: 7:07:51, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7430\n2023-01-03 20:41:29,707 - mmcls - INFO - Epoch [42][3900/5005]\tlr: 1.000e-02, eta: 7:07:43, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.7556\n2023-01-03 20:41:38,250 - mmcls - INFO - Epoch [42][4000/5005]\tlr: 1.000e-02, eta: 7:07:33, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7130\n2023-01-03 20:41:47,180 - mmcls - INFO - Epoch [42][4100/5005]\tlr: 1.000e-02, eta: 7:07:25, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7435\n2023-01-03 20:41:56,207 - mmcls - INFO - Epoch [42][4200/5005]\tlr: 1.000e-02, eta: 7:07:16, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7336\n2023-01-03 20:42:04,801 - mmcls - INFO - Epoch [42][4300/5005]\tlr: 1.000e-02, eta: 7:07:07, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7324\n2023-01-03 20:42:13,415 - mmcls - INFO - Epoch [42][4400/5005]\tlr: 1.000e-02, eta: 7:06:58, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7531\n2023-01-03 20:42:22,098 - mmcls - INFO - Epoch [42][4500/5005]\tlr: 1.000e-02, eta: 7:06:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7485\n2023-01-03 20:42:30,912 - mmcls - INFO - Epoch [42][4600/5005]\tlr: 1.000e-02, eta: 7:06:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7421\n2023-01-03 20:42:39,660 - mmcls - INFO - Epoch [42][4700/5005]\tlr: 1.000e-02, eta: 7:06:31, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.7395\n2023-01-03 20:42:48,294 - mmcls - INFO - Epoch [42][4800/5005]\tlr: 1.000e-02, eta: 7:06:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7459\n2023-01-03 20:42:56,798 - mmcls - INFO - Epoch [42][4900/5005]\tlr: 1.000e-02, eta: 7:06:13, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7511\n2023-01-03 20:43:05,448 - mmcls - INFO - Epoch [42][5000/5005]\tlr: 1.000e-02, eta: 7:06:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7402\n2023-01-03 20:43:28,169 - mmcls - INFO - Epoch(val) [42][196]\taccuracy_top-1: 64.6720, accuracy_top-5: 86.0440\n2023-01-03 20:43:38,936 - mmcls - INFO - Epoch [43][100/5005]\tlr: 1.000e-02, eta: 7:05:57, time: 0.107, data_time: 0.050, memory: 1125, loss: 1.7031\n2023-01-03 20:43:47,696 - mmcls - INFO - Epoch [43][200/5005]\tlr: 1.000e-02, eta: 7:05:48, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6738\n2023-01-03 20:43:56,239 - mmcls - INFO - Epoch [43][300/5005]\tlr: 1.000e-02, eta: 7:05:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7166\n2023-01-03 20:44:04,870 - mmcls - INFO - Epoch [43][400/5005]\tlr: 1.000e-02, eta: 7:05:30, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6941\n2023-01-03 20:44:14,021 - mmcls - INFO - Epoch [43][500/5005]\tlr: 1.000e-02, eta: 7:05:22, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.7095\n2023-01-03 20:44:22,849 - mmcls - INFO - Epoch [43][600/5005]\tlr: 1.000e-02, eta: 7:05:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6816\n2023-01-03 20:44:31,485 - mmcls - INFO - Epoch [43][700/5005]\tlr: 1.000e-02, eta: 7:05:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7203\n2023-01-03 20:44:40,140 - mmcls - INFO - Epoch [43][800/5005]\tlr: 1.000e-02, eta: 7:04:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7191\n2023-01-03 20:44:49,034 - mmcls - INFO - Epoch [43][900/5005]\tlr: 1.000e-02, eta: 7:04:46, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7088\n2023-01-03 20:44:57,675 - mmcls - INFO - Epoch [43][1000/5005]\tlr: 1.000e-02, eta: 7:04:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7323\n2023-01-03 20:45:06,205 - mmcls - INFO - Epoch [43][1100/5005]\tlr: 1.000e-02, eta: 7:04:28, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7310\n2023-01-03 20:45:15,117 - mmcls - INFO - Epoch [43][1200/5005]\tlr: 1.000e-02, eta: 7:04:19, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7127\n2023-01-03 20:45:24,088 - mmcls - INFO - Epoch [43][1300/5005]\tlr: 1.000e-02, eta: 7:04:11, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7207\n2023-01-03 20:45:32,835 - mmcls - INFO - Epoch [43][1400/5005]\tlr: 1.000e-02, eta: 7:04:02, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7169\n2023-01-03 20:45:41,749 - mmcls - INFO - Epoch [43][1500/5005]\tlr: 1.000e-02, eta: 7:03:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7163\n2023-01-03 20:45:50,717 - mmcls - INFO - Epoch [43][1600/5005]\tlr: 1.000e-02, eta: 7:03:44, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.7166\n2023-01-03 20:45:59,278 - mmcls - INFO - Epoch [43][1700/5005]\tlr: 1.000e-02, eta: 7:03:35, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7387\n2023-01-03 20:46:08,030 - mmcls - INFO - Epoch [43][1800/5005]\tlr: 1.000e-02, eta: 7:03:26, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7264\n2023-01-03 20:46:16,715 - mmcls - INFO - Epoch [43][1900/5005]\tlr: 1.000e-02, eta: 7:03:17, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7187\n2023-01-03 20:46:25,426 - mmcls - INFO - Epoch [43][2000/5005]\tlr: 1.000e-02, eta: 7:03:09, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7186\n2023-01-03 20:46:34,160 - mmcls - INFO - Epoch [43][2100/5005]\tlr: 1.000e-02, eta: 7:03:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7117\n2023-01-03 20:46:42,972 - mmcls - INFO - Epoch [43][2200/5005]\tlr: 1.000e-02, eta: 7:02:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7239\n2023-01-03 20:46:51,894 - mmcls - INFO - Epoch [43][2300/5005]\tlr: 1.000e-02, eta: 7:02:42, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7134\n2023-01-03 20:47:00,334 - mmcls - INFO - Epoch [43][2400/5005]\tlr: 1.000e-02, eta: 7:02:33, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7356\n2023-01-03 20:47:09,088 - mmcls - INFO - Epoch [43][2500/5005]\tlr: 1.000e-02, eta: 7:02:24, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7474\n2023-01-03 20:47:17,645 - mmcls - INFO - Epoch [43][2600/5005]\tlr: 1.000e-02, eta: 7:02:15, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7188\n2023-01-03 20:47:26,620 - mmcls - INFO - Epoch [43][2700/5005]\tlr: 1.000e-02, eta: 7:02:06, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.7379\n2023-01-03 20:47:35,219 - mmcls - INFO - Epoch [43][2800/5005]\tlr: 1.000e-02, eta: 7:01:57, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7368\n2023-01-03 20:47:43,935 - mmcls - INFO - Epoch [43][2900/5005]\tlr: 1.000e-02, eta: 7:01:48, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7331\n2023-01-03 20:47:52,972 - mmcls - INFO - Epoch [43][3000/5005]\tlr: 1.000e-02, eta: 7:01:40, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7262\n2023-01-03 20:48:01,961 - mmcls - INFO - Epoch [43][3100/5005]\tlr: 1.000e-02, eta: 7:01:31, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7299\n2023-01-03 20:48:10,766 - mmcls - INFO - Epoch [43][3200/5005]\tlr: 1.000e-02, eta: 7:01:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7301\n2023-01-03 20:48:19,772 - mmcls - INFO - Epoch [43][3300/5005]\tlr: 1.000e-02, eta: 7:01:14, time: 0.090, data_time: 0.021, memory: 1125, loss: 1.7306\n2023-01-03 20:48:28,421 - mmcls - INFO - Epoch [43][3400/5005]\tlr: 1.000e-02, eta: 7:01:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7339\n2023-01-03 20:48:37,396 - mmcls - INFO - Epoch [43][3500/5005]\tlr: 1.000e-02, eta: 7:00:56, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7378\n2023-01-03 20:48:46,245 - mmcls - INFO - Epoch [43][3600/5005]\tlr: 1.000e-02, eta: 7:00:47, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7451\n2023-01-03 20:48:54,928 - mmcls - INFO - Epoch [43][3700/5005]\tlr: 1.000e-02, eta: 7:00:38, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7414\n2023-01-03 20:49:03,798 - mmcls - INFO - Epoch [43][3800/5005]\tlr: 1.000e-02, eta: 7:00:30, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7340\n2023-01-03 20:49:12,394 - mmcls - INFO - Epoch [43][3900/5005]\tlr: 1.000e-02, eta: 7:00:21, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7233\n2023-01-03 20:49:21,823 - mmcls - INFO - Epoch [43][4000/5005]\tlr: 1.000e-02, eta: 7:00:13, time: 0.094, data_time: 0.004, memory: 1125, loss: 1.7236\n2023-01-03 20:49:30,332 - mmcls - INFO - Epoch [43][4100/5005]\tlr: 1.000e-02, eta: 7:00:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7171\n2023-01-03 20:49:38,988 - mmcls - INFO - Epoch [43][4200/5005]\tlr: 1.000e-02, eta: 6:59:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7512\n2023-01-03 20:49:47,628 - mmcls - INFO - Epoch [43][4300/5005]\tlr: 1.000e-02, eta: 6:59:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7254\n2023-01-03 20:49:56,225 - mmcls - INFO - Epoch [43][4400/5005]\tlr: 1.000e-02, eta: 6:59:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7500\n2023-01-03 20:50:05,020 - mmcls - INFO - Epoch [43][4500/5005]\tlr: 1.000e-02, eta: 6:59:28, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7132\n2023-01-03 20:50:13,848 - mmcls - INFO - Epoch [43][4600/5005]\tlr: 1.000e-02, eta: 6:59:19, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7354\n2023-01-03 20:50:22,560 - mmcls - INFO - Epoch [43][4700/5005]\tlr: 1.000e-02, eta: 6:59:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7374\n2023-01-03 20:50:31,397 - mmcls - INFO - Epoch [43][4800/5005]\tlr: 1.000e-02, eta: 6:59:01, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7352\n2023-01-03 20:50:40,240 - mmcls - INFO - Epoch [43][4900/5005]\tlr: 1.000e-02, eta: 6:58:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7201\n2023-01-03 20:50:49,000 - mmcls - INFO - Epoch [43][5000/5005]\tlr: 1.000e-02, eta: 6:58:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7250\n2023-01-03 20:51:11,659 - mmcls - INFO - Epoch(val) [43][196]\taccuracy_top-1: 64.4740, accuracy_top-5: 86.1340\n2023-01-03 20:51:23,205 - mmcls - INFO - Epoch [44][100/5005]\tlr: 1.000e-02, eta: 6:58:37, time: 0.115, data_time: 0.036, memory: 1125, loss: 1.6715\n2023-01-03 20:51:31,646 - mmcls - INFO - Epoch [44][200/5005]\tlr: 1.000e-02, eta: 6:58:28, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6755\n2023-01-03 20:51:40,344 - mmcls - INFO - Epoch [44][300/5005]\tlr: 1.000e-02, eta: 6:58:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6888\n2023-01-03 20:51:49,091 - mmcls - INFO - Epoch [44][400/5005]\tlr: 1.000e-02, eta: 6:58:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6966\n2023-01-03 20:51:57,977 - mmcls - INFO - Epoch [44][500/5005]\tlr: 1.000e-02, eta: 6:58:01, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.7141\n2023-01-03 20:52:06,908 - mmcls - INFO - Epoch [44][600/5005]\tlr: 1.000e-02, eta: 6:57:53, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7130\n2023-01-03 20:52:15,574 - mmcls - INFO - Epoch [44][700/5005]\tlr: 1.000e-02, eta: 6:57:44, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7115\n2023-01-03 20:52:24,307 - mmcls - INFO - Epoch [44][800/5005]\tlr: 1.000e-02, eta: 6:57:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7189\n2023-01-03 20:52:32,882 - mmcls - INFO - Epoch [44][900/5005]\tlr: 1.000e-02, eta: 6:57:26, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7456\n2023-01-03 20:52:41,420 - mmcls - INFO - Epoch [44][1000/5005]\tlr: 1.000e-02, eta: 6:57:17, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7136\n2023-01-03 20:52:50,711 - mmcls - INFO - Epoch [44][1100/5005]\tlr: 1.000e-02, eta: 6:57:08, time: 0.093, data_time: 0.015, memory: 1125, loss: 1.7100\n2023-01-03 20:52:59,449 - mmcls - INFO - Epoch [44][1200/5005]\tlr: 1.000e-02, eta: 6:56:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7206\n2023-01-03 20:53:08,330 - mmcls - INFO - Epoch [44][1300/5005]\tlr: 1.000e-02, eta: 6:56:51, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7248\n2023-01-03 20:53:16,980 - mmcls - INFO - Epoch [44][1400/5005]\tlr: 1.000e-02, eta: 6:56:42, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7227\n2023-01-03 20:53:25,904 - mmcls - INFO - Epoch [44][1500/5005]\tlr: 1.000e-02, eta: 6:56:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7225\n2023-01-03 20:53:34,411 - mmcls - INFO - Epoch [44][1600/5005]\tlr: 1.000e-02, eta: 6:56:24, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7125\n2023-01-03 20:53:42,984 - mmcls - INFO - Epoch [44][1700/5005]\tlr: 1.000e-02, eta: 6:56:15, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7244\n2023-01-03 20:53:51,767 - mmcls - INFO - Epoch [44][1800/5005]\tlr: 1.000e-02, eta: 6:56:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7465\n2023-01-03 20:54:00,230 - mmcls - INFO - Epoch [44][1900/5005]\tlr: 1.000e-02, eta: 6:55:57, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7223\n2023-01-03 20:54:08,975 - mmcls - INFO - Epoch [44][2000/5005]\tlr: 1.000e-02, eta: 6:55:48, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7353\n2023-01-03 20:54:17,447 - mmcls - INFO - Epoch [44][2100/5005]\tlr: 1.000e-02, eta: 6:55:39, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7469\n2023-01-03 20:54:26,257 - mmcls - INFO - Epoch [44][2200/5005]\tlr: 1.000e-02, eta: 6:55:30, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7492\n2023-01-03 20:54:35,050 - mmcls - INFO - Epoch [44][2300/5005]\tlr: 1.000e-02, eta: 6:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7133\n2023-01-03 20:54:43,661 - mmcls - INFO - Epoch [44][2400/5005]\tlr: 1.000e-02, eta: 6:55:12, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7227\n2023-01-03 20:54:52,394 - mmcls - INFO - Epoch [44][2500/5005]\tlr: 1.000e-02, eta: 6:55:03, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7175\n2023-01-03 20:55:00,944 - mmcls - INFO - Epoch [44][2600/5005]\tlr: 1.000e-02, eta: 6:54:54, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7123\n2023-01-03 20:55:09,722 - mmcls - INFO - Epoch [44][2700/5005]\tlr: 1.000e-02, eta: 6:54:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7136\n2023-01-03 20:55:18,609 - mmcls - INFO - Epoch [44][2800/5005]\tlr: 1.000e-02, eta: 6:54:36, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7345\n2023-01-03 20:55:27,295 - mmcls - INFO - Epoch [44][2900/5005]\tlr: 1.000e-02, eta: 6:54:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7403\n2023-01-03 20:55:35,744 - mmcls - INFO - Epoch [44][3000/5005]\tlr: 1.000e-02, eta: 6:54:18, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7423\n2023-01-03 20:55:44,441 - mmcls - INFO - Epoch [44][3100/5005]\tlr: 1.000e-02, eta: 6:54:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7420\n2023-01-03 20:55:53,252 - mmcls - INFO - Epoch [44][3200/5005]\tlr: 1.000e-02, eta: 6:54:00, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7623\n2023-01-03 20:56:02,209 - mmcls - INFO - Epoch [44][3300/5005]\tlr: 1.000e-02, eta: 6:53:52, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7232\n2023-01-03 20:56:11,275 - mmcls - INFO - Epoch [44][3400/5005]\tlr: 1.000e-02, eta: 6:53:43, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7301\n2023-01-03 20:56:19,926 - mmcls - INFO - Epoch [44][3500/5005]\tlr: 1.000e-02, eta: 6:53:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7068\n2023-01-03 20:56:28,731 - mmcls - INFO - Epoch [44][3600/5005]\tlr: 1.000e-02, eta: 6:53:25, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7417\n2023-01-03 20:56:37,264 - mmcls - INFO - Epoch [44][3700/5005]\tlr: 1.000e-02, eta: 6:53:16, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7296\n2023-01-03 20:56:46,086 - mmcls - INFO - Epoch [44][3800/5005]\tlr: 1.000e-02, eta: 6:53:07, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7319\n2023-01-03 20:56:54,630 - mmcls - INFO - Epoch [44][3900/5005]\tlr: 1.000e-02, eta: 6:52:58, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7434\n2023-01-03 20:57:03,418 - mmcls - INFO - Epoch [44][4000/5005]\tlr: 1.000e-02, eta: 6:52:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7221\n2023-01-03 20:57:11,991 - mmcls - INFO - Epoch [44][4100/5005]\tlr: 1.000e-02, eta: 6:52:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7315\n2023-01-03 20:57:20,737 - mmcls - INFO - Epoch [44][4200/5005]\tlr: 1.000e-02, eta: 6:52:31, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7236\n2023-01-03 20:57:29,872 - mmcls - INFO - Epoch [44][4300/5005]\tlr: 1.000e-02, eta: 6:52:23, time: 0.091, data_time: 0.032, memory: 1125, loss: 1.7383\n2023-01-03 20:57:38,291 - mmcls - INFO - Epoch [44][4400/5005]\tlr: 1.000e-02, eta: 6:52:14, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7390\n2023-01-03 20:57:47,126 - mmcls - INFO - Epoch [44][4500/5005]\tlr: 1.000e-02, eta: 6:52:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7149\n2023-01-03 20:57:55,782 - mmcls - INFO - Epoch [44][4600/5005]\tlr: 1.000e-02, eta: 6:51:56, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7247\n2023-01-03 20:58:04,721 - mmcls - INFO - Epoch [44][4700/5005]\tlr: 1.000e-02, eta: 6:51:47, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7331\n2023-01-03 20:58:13,749 - mmcls - INFO - Epoch [44][4800/5005]\tlr: 1.000e-02, eta: 6:51:39, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7249\n2023-01-03 20:58:22,419 - mmcls - INFO - Epoch [44][4900/5005]\tlr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7102\n2023-01-03 20:58:31,325 - mmcls - INFO - Epoch [44][5000/5005]\tlr: 1.000e-02, eta: 6:51:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7456\n2023-01-03 20:58:53,970 - mmcls - INFO - Epoch(val) [44][196]\taccuracy_top-1: 64.3180, accuracy_top-5: 86.0940\n2023-01-03 20:59:05,030 - mmcls - INFO - Epoch [45][100/5005]\tlr: 1.000e-02, eta: 6:51:14, time: 0.110, data_time: 0.049, memory: 1125, loss: 1.7200\n2023-01-03 20:59:13,876 - mmcls - INFO - Epoch [45][200/5005]\tlr: 1.000e-02, eta: 6:51:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6925\n2023-01-03 20:59:22,290 - mmcls - INFO - Epoch [45][300/5005]\tlr: 1.000e-02, eta: 6:50:56, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7039\n2023-01-03 20:59:31,143 - mmcls - INFO - Epoch [45][400/5005]\tlr: 1.000e-02, eta: 6:50:47, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.6994\n2023-01-03 20:59:39,850 - mmcls - INFO - Epoch [45][500/5005]\tlr: 1.000e-02, eta: 6:50:38, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7104\n2023-01-03 20:59:48,923 - mmcls - INFO - Epoch [45][600/5005]\tlr: 1.000e-02, eta: 6:50:30, time: 0.091, data_time: 0.036, memory: 1125, loss: 1.6932\n2023-01-03 20:59:57,567 - mmcls - INFO - Epoch [45][700/5005]\tlr: 1.000e-02, eta: 6:50:21, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7276\n2023-01-03 21:00:06,730 - mmcls - INFO - Epoch [45][800/5005]\tlr: 1.000e-02, eta: 6:50:13, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.7069\n2023-01-03 21:00:15,305 - mmcls - INFO - Epoch [45][900/5005]\tlr: 1.000e-02, eta: 6:50:04, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7220\n2023-01-03 21:00:24,176 - mmcls - INFO - Epoch [45][1000/5005]\tlr: 1.000e-02, eta: 6:49:55, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7166\n2023-01-03 21:00:33,073 - mmcls - INFO - Epoch [45][1100/5005]\tlr: 1.000e-02, eta: 6:49:46, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7210\n2023-01-03 21:00:41,779 - mmcls - INFO - Epoch [45][1200/5005]\tlr: 1.000e-02, eta: 6:49:37, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6991\n2023-01-03 21:00:50,725 - mmcls - INFO - Epoch [45][1300/5005]\tlr: 1.000e-02, eta: 6:49:29, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7408\n2023-01-03 21:00:59,358 - mmcls - INFO - Epoch [45][1400/5005]\tlr: 1.000e-02, eta: 6:49:19, time: 0.086, data_time: 0.039, memory: 1125, loss: 1.7213\n2023-01-03 21:01:07,968 - mmcls - INFO - Epoch [45][1500/5005]\tlr: 1.000e-02, eta: 6:49:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7252\n2023-01-03 21:01:17,092 - mmcls - INFO - Epoch [45][1600/5005]\tlr: 1.000e-02, eta: 6:49:02, time: 0.091, data_time: 0.035, memory: 1125, loss: 1.7287\n2023-01-03 21:01:25,627 - mmcls - INFO - Epoch [45][1700/5005]\tlr: 1.000e-02, eta: 6:48:53, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7240\n2023-01-03 21:01:34,202 - mmcls - INFO - Epoch [45][1800/5005]\tlr: 1.000e-02, eta: 6:48:44, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7195\n2023-01-03 21:01:43,330 - mmcls - INFO - Epoch [45][1900/5005]\tlr: 1.000e-02, eta: 6:48:35, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.7134\n2023-01-03 21:01:51,879 - mmcls - INFO - Epoch [45][2000/5005]\tlr: 1.000e-02, eta: 6:48:26, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7398\n2023-01-03 21:02:00,956 - mmcls - INFO - Epoch [45][2100/5005]\tlr: 1.000e-02, eta: 6:48:18, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7131\n2023-01-03 21:02:09,902 - mmcls - INFO - Epoch [45][2200/5005]\tlr: 1.000e-02, eta: 6:48:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7241\n2023-01-03 21:02:18,291 - mmcls - INFO - Epoch [45][2300/5005]\tlr: 1.000e-02, eta: 6:48:00, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7447\n2023-01-03 21:02:26,987 - mmcls - INFO - Epoch [45][2400/5005]\tlr: 1.000e-02, eta: 6:47:51, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7265\n2023-01-03 21:02:36,209 - mmcls - INFO - Epoch [45][2500/5005]\tlr: 1.000e-02, eta: 6:47:43, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.6994\n2023-01-03 21:02:45,119 - mmcls - INFO - Epoch [45][2600/5005]\tlr: 1.000e-02, eta: 6:47:34, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7297\n2023-01-03 21:02:53,756 - mmcls - INFO - Epoch [45][2700/5005]\tlr: 1.000e-02, eta: 6:47:25, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.7348\n2023-01-03 21:03:02,470 - mmcls - INFO - Epoch [45][2800/5005]\tlr: 1.000e-02, eta: 6:47:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7393\n2023-01-03 21:03:11,213 - mmcls - INFO - Epoch [45][2900/5005]\tlr: 1.000e-02, eta: 6:47:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7623\n2023-01-03 21:03:19,824 - mmcls - INFO - Epoch [45][3000/5005]\tlr: 1.000e-02, eta: 6:46:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7277\n2023-01-03 21:03:28,543 - mmcls - INFO - Epoch [45][3100/5005]\tlr: 1.000e-02, eta: 6:46:49, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7418\n2023-01-03 21:03:37,211 - mmcls - INFO - Epoch [45][3200/5005]\tlr: 1.000e-02, eta: 6:46:40, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7300\n2023-01-03 21:03:45,807 - mmcls - INFO - Epoch [45][3300/5005]\tlr: 1.000e-02, eta: 6:46:31, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7194\n2023-01-03 21:03:54,660 - mmcls - INFO - Epoch [45][3400/5005]\tlr: 1.000e-02, eta: 6:46:22, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7235\n2023-01-03 21:04:03,529 - mmcls - INFO - Epoch [45][3500/5005]\tlr: 1.000e-02, eta: 6:46:14, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7212\n2023-01-03 21:04:12,450 - mmcls - INFO - Epoch [45][3600/5005]\tlr: 1.000e-02, eta: 6:46:05, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7167\n2023-01-03 21:04:21,115 - mmcls - INFO - Epoch [45][3700/5005]\tlr: 1.000e-02, eta: 6:45:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7323\n2023-01-03 21:04:30,025 - mmcls - INFO - Epoch [45][3800/5005]\tlr: 1.000e-02, eta: 6:45:47, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7232\n2023-01-03 21:04:38,693 - mmcls - INFO - Epoch [45][3900/5005]\tlr: 1.000e-02, eta: 6:45:38, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7487\n2023-01-03 21:04:47,216 - mmcls - INFO - Epoch [45][4000/5005]\tlr: 1.000e-02, eta: 6:45:29, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7196\n2023-01-03 21:04:55,930 - mmcls - INFO - Epoch [45][4100/5005]\tlr: 1.000e-02, eta: 6:45:20, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7478\n2023-01-03 21:05:04,729 - mmcls - INFO - Epoch [45][4200/5005]\tlr: 1.000e-02, eta: 6:45:11, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7304\n2023-01-03 21:05:13,355 - mmcls - INFO - Epoch [45][4300/5005]\tlr: 1.000e-02, eta: 6:45:02, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7296\n2023-01-03 21:05:22,333 - mmcls - INFO - Epoch [45][4400/5005]\tlr: 1.000e-02, eta: 6:44:54, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7409\n2023-01-03 21:05:30,962 - mmcls - INFO - Epoch [45][4500/5005]\tlr: 1.000e-02, eta: 6:44:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7392\n2023-01-03 21:05:39,501 - mmcls - INFO - Epoch [45][4600/5005]\tlr: 1.000e-02, eta: 6:44:36, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7414\n2023-01-03 21:05:48,311 - mmcls - INFO - Epoch [45][4700/5005]\tlr: 1.000e-02, eta: 6:44:27, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7561\n2023-01-03 21:05:57,107 - mmcls - INFO - Epoch [45][4800/5005]\tlr: 1.000e-02, eta: 6:44:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7320\n2023-01-03 21:06:05,632 - mmcls - INFO - Epoch [45][4900/5005]\tlr: 1.000e-02, eta: 6:44:09, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7365\n2023-01-03 21:06:14,545 - mmcls - INFO - Epoch [45][5000/5005]\tlr: 1.000e-02, eta: 6:44:00, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7334\n2023-01-03 21:06:37,443 - mmcls - INFO - Epoch(val) [45][196]\taccuracy_top-1: 64.6480, accuracy_top-5: 86.1700\n2023-01-03 21:06:48,540 - mmcls - INFO - Epoch [46][100/5005]\tlr: 1.000e-02, eta: 6:43:53, time: 0.111, data_time: 0.055, memory: 1125, loss: 1.6955\n2023-01-03 21:06:57,221 - mmcls - INFO - Epoch [46][200/5005]\tlr: 1.000e-02, eta: 6:43:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6923\n2023-01-03 21:07:05,861 - mmcls - INFO - Epoch [46][300/5005]\tlr: 1.000e-02, eta: 6:43:35, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7075\n2023-01-03 21:07:14,604 - mmcls - INFO - Epoch [46][400/5005]\tlr: 1.000e-02, eta: 6:43:26, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7148\n2023-01-03 21:07:23,341 - mmcls - INFO - Epoch [46][500/5005]\tlr: 1.000e-02, eta: 6:43:17, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.6816\n2023-01-03 21:07:32,074 - mmcls - INFO - Epoch [46][600/5005]\tlr: 1.000e-02, eta: 6:43:09, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6901\n2023-01-03 21:07:41,095 - mmcls - INFO - Epoch [46][700/5005]\tlr: 1.000e-02, eta: 6:43:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6996\n2023-01-03 21:07:50,119 - mmcls - INFO - Epoch [46][800/5005]\tlr: 1.000e-02, eta: 6:42:51, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7075\n2023-01-03 21:07:58,956 - mmcls - INFO - Epoch [46][900/5005]\tlr: 1.000e-02, eta: 6:42:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7195\n2023-01-03 21:08:07,807 - mmcls - INFO - Epoch [46][1000/5005]\tlr: 1.000e-02, eta: 6:42:34, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.6967\n2023-01-03 21:08:16,351 - mmcls - INFO - Epoch [46][1100/5005]\tlr: 1.000e-02, eta: 6:42:25, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7184\n2023-01-03 21:08:25,186 - mmcls - INFO - Epoch [46][1200/5005]\tlr: 1.000e-02, eta: 6:42:16, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7105\n2023-01-03 21:08:34,020 - mmcls - INFO - Epoch [46][1300/5005]\tlr: 1.000e-02, eta: 6:42:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7223\n2023-01-03 21:08:42,599 - mmcls - INFO - Epoch [46][1400/5005]\tlr: 1.000e-02, eta: 6:41:58, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7307\n2023-01-03 21:08:51,662 - mmcls - INFO - Epoch [46][1500/5005]\tlr: 1.000e-02, eta: 6:41:50, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7157\n2023-01-03 21:09:00,337 - mmcls - INFO - Epoch [46][1600/5005]\tlr: 1.000e-02, eta: 6:41:41, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7043\n2023-01-03 21:09:09,171 - mmcls - INFO - Epoch [46][1700/5005]\tlr: 1.000e-02, eta: 6:41:32, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.7114\n2023-01-03 21:09:17,873 - mmcls - INFO - Epoch [46][1800/5005]\tlr: 1.000e-02, eta: 6:41:23, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.7307\n2023-01-03 21:09:26,550 - mmcls - INFO - Epoch [46][1900/5005]\tlr: 1.000e-02, eta: 6:41:14, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7117\n2023-01-03 21:09:35,255 - mmcls - INFO - Epoch [46][2000/5005]\tlr: 1.000e-02, eta: 6:41:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7247\n2023-01-03 21:09:43,758 - mmcls - INFO - Epoch [46][2100/5005]\tlr: 1.000e-02, eta: 6:40:56, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7253\n2023-01-03 21:09:52,530 - mmcls - INFO - Epoch [46][2200/5005]\tlr: 1.000e-02, eta: 6:40:47, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7042\n2023-01-03 21:10:01,156 - mmcls - INFO - Epoch [46][2300/5005]\tlr: 1.000e-02, eta: 6:40:38, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7224\n2023-01-03 21:10:09,732 - mmcls - INFO - Epoch [46][2400/5005]\tlr: 1.000e-02, eta: 6:40:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7071\n2023-01-03 21:10:18,522 - mmcls - INFO - Epoch [46][2500/5005]\tlr: 1.000e-02, eta: 6:40:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7203\n2023-01-03 21:10:27,121 - mmcls - INFO - Epoch [46][2600/5005]\tlr: 1.000e-02, eta: 6:40:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.6972\n2023-01-03 21:10:36,107 - mmcls - INFO - Epoch [46][2700/5005]\tlr: 1.000e-02, eta: 6:40:02, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7085\n2023-01-03 21:10:44,815 - mmcls - INFO - Epoch [46][2800/5005]\tlr: 1.000e-02, eta: 6:39:54, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7305\n2023-01-03 21:10:53,598 - mmcls - INFO - Epoch [46][2900/5005]\tlr: 1.000e-02, eta: 6:39:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7343\n2023-01-03 21:11:02,733 - mmcls - INFO - Epoch [46][3000/5005]\tlr: 1.000e-02, eta: 6:39:36, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7472\n2023-01-03 21:11:11,323 - mmcls - INFO - Epoch [46][3100/5005]\tlr: 1.000e-02, eta: 6:39:27, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7423\n2023-01-03 21:11:19,942 - mmcls - INFO - Epoch [46][3200/5005]\tlr: 1.000e-02, eta: 6:39:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7358\n2023-01-03 21:11:28,686 - mmcls - INFO - Epoch [46][3300/5005]\tlr: 1.000e-02, eta: 6:39:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7475\n2023-01-03 21:11:37,625 - mmcls - INFO - Epoch [46][3400/5005]\tlr: 1.000e-02, eta: 6:39:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7341\n2023-01-03 21:11:46,423 - mmcls - INFO - Epoch [46][3500/5005]\tlr: 1.000e-02, eta: 6:38:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7309\n2023-01-03 21:11:54,994 - mmcls - INFO - Epoch [46][3600/5005]\tlr: 1.000e-02, eta: 6:38:43, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7360\n2023-01-03 21:12:04,006 - mmcls - INFO - Epoch [46][3700/5005]\tlr: 1.000e-02, eta: 6:38:34, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.7541\n2023-01-03 21:12:13,001 - mmcls - INFO - Epoch [46][3800/5005]\tlr: 1.000e-02, eta: 6:38:26, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7227\n2023-01-03 21:12:21,903 - mmcls - INFO - Epoch [46][3900/5005]\tlr: 1.000e-02, eta: 6:38:17, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7297\n2023-01-03 21:12:30,628 - mmcls - INFO - Epoch [46][4000/5005]\tlr: 1.000e-02, eta: 6:38:08, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7267\n2023-01-03 21:12:39,531 - mmcls - INFO - Epoch [46][4100/5005]\tlr: 1.000e-02, eta: 6:37:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7332\n2023-01-03 21:12:48,142 - mmcls - INFO - Epoch [46][4200/5005]\tlr: 1.000e-02, eta: 6:37:50, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7465\n2023-01-03 21:12:57,234 - mmcls - INFO - Epoch [46][4300/5005]\tlr: 1.000e-02, eta: 6:37:42, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7527\n2023-01-03 21:13:06,154 - mmcls - INFO - Epoch [46][4400/5005]\tlr: 1.000e-02, eta: 6:37:33, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7196\n2023-01-03 21:13:14,839 - mmcls - INFO - Epoch [46][4500/5005]\tlr: 1.000e-02, eta: 6:37:24, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7316\n2023-01-03 21:13:23,443 - mmcls - INFO - Epoch [46][4600/5005]\tlr: 1.000e-02, eta: 6:37:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7514\n2023-01-03 21:13:32,260 - mmcls - INFO - Epoch [46][4700/5005]\tlr: 1.000e-02, eta: 6:37:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7323\n2023-01-03 21:13:40,855 - mmcls - INFO - Epoch [46][4800/5005]\tlr: 1.000e-02, eta: 6:36:57, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7312\n2023-01-03 21:13:49,490 - mmcls - INFO - Epoch [46][4900/5005]\tlr: 1.000e-02, eta: 6:36:48, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7721\n2023-01-03 21:13:58,233 - mmcls - INFO - Epoch [46][5000/5005]\tlr: 1.000e-02, eta: 6:36:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7184\n2023-01-03 21:14:20,799 - mmcls - INFO - Epoch(val) [46][196]\taccuracy_top-1: 64.3080, accuracy_top-5: 85.9420\n2023-01-03 21:14:31,850 - mmcls - INFO - Epoch [47][100/5005]\tlr: 1.000e-02, eta: 6:36:32, time: 0.110, data_time: 0.068, memory: 1125, loss: 1.7024\n2023-01-03 21:14:40,646 - mmcls - INFO - Epoch [47][200/5005]\tlr: 1.000e-02, eta: 6:36:23, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6739\n2023-01-03 21:14:49,141 - mmcls - INFO - Epoch [47][300/5005]\tlr: 1.000e-02, eta: 6:36:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7146\n2023-01-03 21:14:57,735 - mmcls - INFO - Epoch [47][400/5005]\tlr: 1.000e-02, eta: 6:36:05, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7310\n2023-01-03 21:15:06,371 - mmcls - INFO - Epoch [47][500/5005]\tlr: 1.000e-02, eta: 6:35:56, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7096\n2023-01-03 21:15:15,165 - mmcls - INFO - Epoch [47][600/5005]\tlr: 1.000e-02, eta: 6:35:47, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7260\n2023-01-03 21:15:23,856 - mmcls - INFO - Epoch [47][700/5005]\tlr: 1.000e-02, eta: 6:35:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7128\n2023-01-03 21:15:32,547 - mmcls - INFO - Epoch [47][800/5005]\tlr: 1.000e-02, eta: 6:35:29, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7227\n2023-01-03 21:15:41,410 - mmcls - INFO - Epoch [47][900/5005]\tlr: 1.000e-02, eta: 6:35:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7230\n2023-01-03 21:15:49,857 - mmcls - INFO - Epoch [47][1000/5005]\tlr: 1.000e-02, eta: 6:35:12, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7357\n2023-01-03 21:15:58,677 - mmcls - INFO - Epoch [47][1100/5005]\tlr: 1.000e-02, eta: 6:35:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7140\n2023-01-03 21:16:07,674 - mmcls - INFO - Epoch [47][1200/5005]\tlr: 1.000e-02, eta: 6:34:54, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.6946\n2023-01-03 21:16:16,288 - mmcls - INFO - Epoch [47][1300/5005]\tlr: 1.000e-02, eta: 6:34:45, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7194\n2023-01-03 21:16:25,319 - mmcls - INFO - Epoch [47][1400/5005]\tlr: 1.000e-02, eta: 6:34:37, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7030\n2023-01-03 21:16:33,943 - mmcls - INFO - Epoch [47][1500/5005]\tlr: 1.000e-02, eta: 6:34:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7093\n2023-01-03 21:16:42,680 - mmcls - INFO - Epoch [47][1600/5005]\tlr: 1.000e-02, eta: 6:34:19, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7157\n2023-01-03 21:16:51,549 - mmcls - INFO - Epoch [47][1700/5005]\tlr: 1.000e-02, eta: 6:34:10, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7218\n2023-01-03 21:16:59,910 - mmcls - INFO - Epoch [47][1800/5005]\tlr: 1.000e-02, eta: 6:34:01, time: 0.084, data_time: 0.030, memory: 1125, loss: 1.7259\n2023-01-03 21:17:08,705 - mmcls - INFO - Epoch [47][1900/5005]\tlr: 1.000e-02, eta: 6:33:52, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7245\n2023-01-03 21:17:17,518 - mmcls - INFO - Epoch [47][2000/5005]\tlr: 1.000e-02, eta: 6:33:43, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7194\n2023-01-03 21:17:26,495 - mmcls - INFO - Epoch [47][2100/5005]\tlr: 1.000e-02, eta: 6:33:34, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7371\n2023-01-03 21:17:35,453 - mmcls - INFO - Epoch [47][2200/5005]\tlr: 1.000e-02, eta: 6:33:26, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7460\n2023-01-03 21:17:44,192 - mmcls - INFO - Epoch [47][2300/5005]\tlr: 1.000e-02, eta: 6:33:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7111\n2023-01-03 21:17:52,811 - mmcls - INFO - Epoch [47][2400/5005]\tlr: 1.000e-02, eta: 6:33:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7072\n2023-01-03 21:18:01,538 - mmcls - INFO - Epoch [47][2500/5005]\tlr: 1.000e-02, eta: 6:32:59, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7401\n2023-01-03 21:18:10,769 - mmcls - INFO - Epoch [47][2600/5005]\tlr: 1.000e-02, eta: 6:32:51, time: 0.092, data_time: 0.010, memory: 1125, loss: 1.7364\n2023-01-03 21:18:19,234 - mmcls - INFO - Epoch [47][2700/5005]\tlr: 1.000e-02, eta: 6:32:41, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7140\n2023-01-03 21:18:28,095 - mmcls - INFO - Epoch [47][2800/5005]\tlr: 1.000e-02, eta: 6:32:33, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7373\n2023-01-03 21:18:36,873 - mmcls - INFO - Epoch [47][2900/5005]\tlr: 1.000e-02, eta: 6:32:24, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7180\n2023-01-03 21:18:45,407 - mmcls - INFO - Epoch [47][3000/5005]\tlr: 1.000e-02, eta: 6:32:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7217\n2023-01-03 21:18:54,074 - mmcls - INFO - Epoch [47][3100/5005]\tlr: 1.000e-02, eta: 6:32:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7250\n2023-01-03 21:19:02,776 - mmcls - INFO - Epoch [47][3200/5005]\tlr: 1.000e-02, eta: 6:31:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7514\n2023-01-03 21:19:11,501 - mmcls - INFO - Epoch [47][3300/5005]\tlr: 1.000e-02, eta: 6:31:48, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7312\n2023-01-03 21:19:20,499 - mmcls - INFO - Epoch [47][3400/5005]\tlr: 1.000e-02, eta: 6:31:39, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7233\n2023-01-03 21:19:29,345 - mmcls - INFO - Epoch [47][3500/5005]\tlr: 1.000e-02, eta: 6:31:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7079\n2023-01-03 21:19:37,807 - mmcls - INFO - Epoch [47][3600/5005]\tlr: 1.000e-02, eta: 6:31:21, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7261\n2023-01-03 21:19:46,742 - mmcls - INFO - Epoch [47][3700/5005]\tlr: 1.000e-02, eta: 6:31:13, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7317\n2023-01-03 21:19:55,984 - mmcls - INFO - Epoch [47][3800/5005]\tlr: 1.000e-02, eta: 6:31:04, time: 0.092, data_time: 0.029, memory: 1125, loss: 1.7227\n2023-01-03 21:20:04,898 - mmcls - INFO - Epoch [47][3900/5005]\tlr: 1.000e-02, eta: 6:30:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7280\n2023-01-03 21:20:13,425 - mmcls - INFO - Epoch [47][4000/5005]\tlr: 1.000e-02, eta: 6:30:47, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7417\n2023-01-03 21:20:22,449 - mmcls - INFO - Epoch [47][4100/5005]\tlr: 1.000e-02, eta: 6:30:38, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7539\n2023-01-03 21:20:31,368 - mmcls - INFO - Epoch [47][4200/5005]\tlr: 1.000e-02, eta: 6:30:29, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7508\n2023-01-03 21:20:40,478 - mmcls - INFO - Epoch [47][4300/5005]\tlr: 1.000e-02, eta: 6:30:21, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7254\n2023-01-03 21:20:49,108 - mmcls - INFO - Epoch [47][4400/5005]\tlr: 1.000e-02, eta: 6:30:12, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.7450\n2023-01-03 21:20:57,892 - mmcls - INFO - Epoch [47][4500/5005]\tlr: 1.000e-02, eta: 6:30:03, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.7413\n2023-01-03 21:21:06,486 - mmcls - INFO - Epoch [47][4600/5005]\tlr: 1.000e-02, eta: 6:29:54, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7505\n2023-01-03 21:21:15,029 - mmcls - INFO - Epoch [47][4700/5005]\tlr: 1.000e-02, eta: 6:29:45, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.7256\n2023-01-03 21:21:23,909 - mmcls - INFO - Epoch [47][4800/5005]\tlr: 1.000e-02, eta: 6:29:36, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7354\n2023-01-03 21:21:32,562 - mmcls - INFO - Epoch [47][4900/5005]\tlr: 1.000e-02, eta: 6:29:27, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7415\n2023-01-03 21:21:41,493 - mmcls - INFO - Epoch [47][5000/5005]\tlr: 1.000e-02, eta: 6:29:19, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7445\n2023-01-03 21:22:04,056 - mmcls - INFO - Epoch(val) [47][196]\taccuracy_top-1: 64.6660, accuracy_top-5: 86.1820\n2023-01-03 21:22:14,965 - mmcls - INFO - Epoch [48][100/5005]\tlr: 1.000e-02, eta: 6:29:11, time: 0.109, data_time: 0.040, memory: 1125, loss: 1.7123\n2023-01-03 21:22:23,435 - mmcls - INFO - Epoch [48][200/5005]\tlr: 1.000e-02, eta: 6:29:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6883\n2023-01-03 21:22:32,077 - mmcls - INFO - Epoch [48][300/5005]\tlr: 1.000e-02, eta: 6:28:53, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6966\n2023-01-03 21:22:40,785 - mmcls - INFO - Epoch [48][400/5005]\tlr: 1.000e-02, eta: 6:28:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7019\n2023-01-03 21:22:49,417 - mmcls - INFO - Epoch [48][500/5005]\tlr: 1.000e-02, eta: 6:28:35, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7034\n2023-01-03 21:22:58,299 - mmcls - INFO - Epoch [48][600/5005]\tlr: 1.000e-02, eta: 6:28:26, time: 0.089, data_time: 0.042, memory: 1125, loss: 1.7205\n2023-01-03 21:23:07,025 - mmcls - INFO - Epoch [48][700/5005]\tlr: 1.000e-02, eta: 6:28:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7125\n2023-01-03 21:23:15,602 - mmcls - INFO - Epoch [48][800/5005]\tlr: 1.000e-02, eta: 6:28:08, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7172\n2023-01-03 21:23:24,649 - mmcls - INFO - Epoch [48][900/5005]\tlr: 1.000e-02, eta: 6:28:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7005\n2023-01-03 21:23:33,504 - mmcls - INFO - Epoch [48][1000/5005]\tlr: 1.000e-02, eta: 6:27:51, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7334\n2023-01-03 21:23:42,335 - mmcls - INFO - Epoch [48][1100/5005]\tlr: 1.000e-02, eta: 6:27:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7373\n2023-01-03 21:23:50,949 - mmcls - INFO - Epoch [48][1200/5005]\tlr: 1.000e-02, eta: 6:27:33, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7387\n2023-01-03 21:23:59,907 - mmcls - INFO - Epoch [48][1300/5005]\tlr: 1.000e-02, eta: 6:27:25, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7258\n2023-01-03 21:24:08,714 - mmcls - INFO - Epoch [48][1400/5005]\tlr: 1.000e-02, eta: 6:27:16, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7102\n2023-01-03 21:24:17,300 - mmcls - INFO - Epoch [48][1500/5005]\tlr: 1.000e-02, eta: 6:27:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7248\n2023-01-03 21:24:25,929 - mmcls - INFO - Epoch [48][1600/5005]\tlr: 1.000e-02, eta: 6:26:58, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7188\n2023-01-03 21:24:34,602 - mmcls - INFO - Epoch [48][1700/5005]\tlr: 1.000e-02, eta: 6:26:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7028\n2023-01-03 21:24:43,754 - mmcls - INFO - Epoch [48][1800/5005]\tlr: 1.000e-02, eta: 6:26:40, time: 0.092, data_time: 0.021, memory: 1125, loss: 1.6933\n2023-01-03 21:24:52,581 - mmcls - INFO - Epoch [48][1900/5005]\tlr: 1.000e-02, eta: 6:26:32, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7458\n2023-01-03 21:25:01,220 - mmcls - INFO - Epoch [48][2000/5005]\tlr: 1.000e-02, eta: 6:26:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7197\n2023-01-03 21:25:10,260 - mmcls - INFO - Epoch [48][2100/5005]\tlr: 1.000e-02, eta: 6:26:14, time: 0.090, data_time: 0.032, memory: 1125, loss: 1.7242\n2023-01-03 21:25:18,983 - mmcls - INFO - Epoch [48][2200/5005]\tlr: 1.000e-02, eta: 6:26:05, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7247\n2023-01-03 21:25:27,629 - mmcls - INFO - Epoch [48][2300/5005]\tlr: 1.000e-02, eta: 6:25:56, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7288\n2023-01-03 21:25:36,517 - mmcls - INFO - Epoch [48][2400/5005]\tlr: 1.000e-02, eta: 6:25:47, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7284\n2023-01-03 21:25:45,449 - mmcls - INFO - Epoch [48][2500/5005]\tlr: 1.000e-02, eta: 6:25:39, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7405\n2023-01-03 21:25:54,161 - mmcls - INFO - Epoch [48][2600/5005]\tlr: 1.000e-02, eta: 6:25:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7399\n2023-01-03 21:26:02,780 - mmcls - INFO - Epoch [48][2700/5005]\tlr: 1.000e-02, eta: 6:25:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7196\n2023-01-03 21:26:11,563 - mmcls - INFO - Epoch [48][2800/5005]\tlr: 1.000e-02, eta: 6:25:12, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7219\n2023-01-03 21:26:20,023 - mmcls - INFO - Epoch [48][2900/5005]\tlr: 1.000e-02, eta: 6:25:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7273\n2023-01-03 21:26:28,677 - mmcls - INFO - Epoch [48][3000/5005]\tlr: 1.000e-02, eta: 6:24:54, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.7411\n2023-01-03 21:26:37,381 - mmcls - INFO - Epoch [48][3100/5005]\tlr: 1.000e-02, eta: 6:24:45, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7289\n2023-01-03 21:26:46,190 - mmcls - INFO - Epoch [48][3200/5005]\tlr: 1.000e-02, eta: 6:24:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7194\n2023-01-03 21:26:55,228 - mmcls - INFO - Epoch [48][3300/5005]\tlr: 1.000e-02, eta: 6:24:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7382\n2023-01-03 21:27:04,283 - mmcls - INFO - Epoch [48][3400/5005]\tlr: 1.000e-02, eta: 6:24:19, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7447\n2023-01-03 21:27:13,200 - mmcls - INFO - Epoch [48][3500/5005]\tlr: 1.000e-02, eta: 6:24:10, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7136\n2023-01-03 21:27:21,754 - mmcls - INFO - Epoch [48][3600/5005]\tlr: 1.000e-02, eta: 6:24:01, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7494\n2023-01-03 21:27:30,304 - mmcls - INFO - Epoch [48][3700/5005]\tlr: 1.000e-02, eta: 6:23:52, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7385\n2023-01-03 21:27:39,362 - mmcls - INFO - Epoch [48][3800/5005]\tlr: 1.000e-02, eta: 6:23:44, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.7243\n2023-01-03 21:27:48,437 - mmcls - INFO - Epoch [48][3900/5005]\tlr: 1.000e-02, eta: 6:23:35, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7223\n2023-01-03 21:27:57,302 - mmcls - INFO - Epoch [48][4000/5005]\tlr: 1.000e-02, eta: 6:23:26, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7170\n2023-01-03 21:28:05,973 - mmcls - INFO - Epoch [48][4100/5005]\tlr: 1.000e-02, eta: 6:23:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7205\n2023-01-03 21:28:15,152 - mmcls - INFO - Epoch [48][4200/5005]\tlr: 1.000e-02, eta: 6:23:09, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7375\n2023-01-03 21:28:24,099 - mmcls - INFO - Epoch [48][4300/5005]\tlr: 1.000e-02, eta: 6:23:00, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7423\n2023-01-03 21:28:32,777 - mmcls - INFO - Epoch [48][4400/5005]\tlr: 1.000e-02, eta: 6:22:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7358\n2023-01-03 21:28:41,462 - mmcls - INFO - Epoch [48][4500/5005]\tlr: 1.000e-02, eta: 6:22:43, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7399\n2023-01-03 21:28:50,389 - mmcls - INFO - Epoch [48][4600/5005]\tlr: 1.000e-02, eta: 6:22:34, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7329\n2023-01-03 21:28:59,315 - mmcls - INFO - Epoch [48][4700/5005]\tlr: 1.000e-02, eta: 6:22:25, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7114\n2023-01-03 21:29:07,732 - mmcls - INFO - Epoch [48][4800/5005]\tlr: 1.000e-02, eta: 6:22:16, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7435\n2023-01-03 21:29:16,427 - mmcls - INFO - Epoch [48][4900/5005]\tlr: 1.000e-02, eta: 6:22:07, time: 0.087, data_time: 0.046, memory: 1125, loss: 1.7069\n2023-01-03 21:29:25,239 - mmcls - INFO - Epoch [48][5000/5005]\tlr: 1.000e-02, eta: 6:21:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7612\n2023-01-03 21:29:47,823 - mmcls - INFO - Epoch(val) [48][196]\taccuracy_top-1: 64.6300, accuracy_top-5: 86.1240\n2023-01-03 21:29:58,702 - mmcls - INFO - Epoch [49][100/5005]\tlr: 1.000e-02, eta: 6:21:51, time: 0.109, data_time: 0.054, memory: 1125, loss: 1.7008\n2023-01-03 21:30:07,518 - mmcls - INFO - Epoch [49][200/5005]\tlr: 1.000e-02, eta: 6:21:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6749\n2023-01-03 21:30:16,222 - mmcls - INFO - Epoch [49][300/5005]\tlr: 1.000e-02, eta: 6:21:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7261\n2023-01-03 21:30:25,218 - mmcls - INFO - Epoch [49][400/5005]\tlr: 1.000e-02, eta: 6:21:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6991\n2023-01-03 21:30:34,182 - mmcls - INFO - Epoch [49][500/5005]\tlr: 1.000e-02, eta: 6:21:16, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6971\n2023-01-03 21:30:42,837 - mmcls - INFO - Epoch [49][600/5005]\tlr: 1.000e-02, eta: 6:21:07, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7031\n2023-01-03 21:30:51,664 - mmcls - INFO - Epoch [49][700/5005]\tlr: 1.000e-02, eta: 6:20:58, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7088\n2023-01-03 21:31:00,659 - mmcls - INFO - Epoch [49][800/5005]\tlr: 1.000e-02, eta: 6:20:49, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7008\n2023-01-03 21:31:09,442 - mmcls - INFO - Epoch [49][900/5005]\tlr: 1.000e-02, eta: 6:20:41, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6974\n2023-01-03 21:31:18,090 - mmcls - INFO - Epoch [49][1000/5005]\tlr: 1.000e-02, eta: 6:20:32, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.7140\n2023-01-03 21:31:26,561 - mmcls - INFO - Epoch [49][1100/5005]\tlr: 1.000e-02, eta: 6:20:22, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6746\n2023-01-03 21:31:35,349 - mmcls - INFO - Epoch [49][1200/5005]\tlr: 1.000e-02, eta: 6:20:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6920\n2023-01-03 21:31:43,968 - mmcls - INFO - Epoch [49][1300/5005]\tlr: 1.000e-02, eta: 6:20:05, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7237\n2023-01-03 21:31:52,422 - mmcls - INFO - Epoch [49][1400/5005]\tlr: 1.000e-02, eta: 6:19:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7479\n2023-01-03 21:32:01,336 - mmcls - INFO - Epoch [49][1500/5005]\tlr: 1.000e-02, eta: 6:19:47, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.7069\n2023-01-03 21:32:10,137 - mmcls - INFO - Epoch [49][1600/5005]\tlr: 1.000e-02, eta: 6:19:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7030\n2023-01-03 21:32:18,957 - mmcls - INFO - Epoch [49][1700/5005]\tlr: 1.000e-02, eta: 6:19:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7034\n2023-01-03 21:32:28,386 - mmcls - INFO - Epoch [49][1800/5005]\tlr: 1.000e-02, eta: 6:19:21, time: 0.094, data_time: 0.018, memory: 1125, loss: 1.7099\n2023-01-03 21:32:37,358 - mmcls - INFO - Epoch [49][1900/5005]\tlr: 1.000e-02, eta: 6:19:12, time: 0.090, data_time: 0.044, memory: 1125, loss: 1.7264\n2023-01-03 21:32:46,057 - mmcls - INFO - Epoch [49][2000/5005]\tlr: 1.000e-02, eta: 6:19:03, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7275\n2023-01-03 21:32:54,872 - mmcls - INFO - Epoch [49][2100/5005]\tlr: 1.000e-02, eta: 6:18:55, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7341\n2023-01-03 21:33:03,457 - mmcls - INFO - Epoch [49][2200/5005]\tlr: 1.000e-02, eta: 6:18:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7365\n2023-01-03 21:33:12,616 - mmcls - INFO - Epoch [49][2300/5005]\tlr: 1.000e-02, eta: 6:18:37, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7278\n2023-01-03 21:33:21,429 - mmcls - INFO - Epoch [49][2400/5005]\tlr: 1.000e-02, eta: 6:18:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6848\n2023-01-03 21:33:30,418 - mmcls - INFO - Epoch [49][2500/5005]\tlr: 1.000e-02, eta: 6:18:20, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7045\n2023-01-03 21:33:39,107 - mmcls - INFO - Epoch [49][2600/5005]\tlr: 1.000e-02, eta: 6:18:11, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7061\n2023-01-03 21:33:47,792 - mmcls - INFO - Epoch [49][2700/5005]\tlr: 1.000e-02, eta: 6:18:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7266\n2023-01-03 21:33:56,364 - mmcls - INFO - Epoch [49][2800/5005]\tlr: 1.000e-02, eta: 6:17:53, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7284\n2023-01-03 21:34:05,212 - mmcls - INFO - Epoch [49][2900/5005]\tlr: 1.000e-02, eta: 6:17:44, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7353\n2023-01-03 21:34:14,290 - mmcls - INFO - Epoch [49][3000/5005]\tlr: 1.000e-02, eta: 6:17:36, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.7229\n2023-01-03 21:34:22,895 - mmcls - INFO - Epoch [49][3100/5005]\tlr: 1.000e-02, eta: 6:17:26, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7212\n2023-01-03 21:34:31,356 - mmcls - INFO - Epoch [49][3200/5005]\tlr: 1.000e-02, eta: 6:17:17, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7137\n2023-01-03 21:34:40,023 - mmcls - INFO - Epoch [49][3300/5005]\tlr: 1.000e-02, eta: 6:17:08, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7199\n2023-01-03 21:34:48,722 - mmcls - INFO - Epoch [49][3400/5005]\tlr: 1.000e-02, eta: 6:16:59, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7347\n2023-01-03 21:34:57,242 - mmcls - INFO - Epoch [49][3500/5005]\tlr: 1.000e-02, eta: 6:16:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7501\n2023-01-03 21:35:06,205 - mmcls - INFO - Epoch [49][3600/5005]\tlr: 1.000e-02, eta: 6:16:42, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7378\n2023-01-03 21:35:14,977 - mmcls - INFO - Epoch [49][3700/5005]\tlr: 1.000e-02, eta: 6:16:33, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7512\n2023-01-03 21:35:23,493 - mmcls - INFO - Epoch [49][3800/5005]\tlr: 1.000e-02, eta: 6:16:24, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7247\n2023-01-03 21:35:32,711 - mmcls - INFO - Epoch [49][3900/5005]\tlr: 1.000e-02, eta: 6:16:15, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7332\n2023-01-03 21:35:41,632 - mmcls - INFO - Epoch [49][4000/5005]\tlr: 1.000e-02, eta: 6:16:07, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7434\n2023-01-03 21:35:50,229 - mmcls - INFO - Epoch [49][4100/5005]\tlr: 1.000e-02, eta: 6:15:58, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7569\n2023-01-03 21:35:59,476 - mmcls - INFO - Epoch [49][4200/5005]\tlr: 1.000e-02, eta: 6:15:49, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7499\n2023-01-03 21:36:08,003 - mmcls - INFO - Epoch [49][4300/5005]\tlr: 1.000e-02, eta: 6:15:40, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7537\n2023-01-03 21:36:16,718 - mmcls - INFO - Epoch [49][4400/5005]\tlr: 1.000e-02, eta: 6:15:31, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7592\n2023-01-03 21:36:25,376 - mmcls - INFO - Epoch [49][4500/5005]\tlr: 1.000e-02, eta: 6:15:22, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7331\n2023-01-03 21:36:34,079 - mmcls - INFO - Epoch [49][4600/5005]\tlr: 1.000e-02, eta: 6:15:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7367\n2023-01-03 21:36:42,654 - mmcls - INFO - Epoch [49][4700/5005]\tlr: 1.000e-02, eta: 6:15:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7375\n2023-01-03 21:36:51,409 - mmcls - INFO - Epoch [49][4800/5005]\tlr: 1.000e-02, eta: 6:14:55, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.7453\n2023-01-03 21:37:00,595 - mmcls - INFO - Epoch [49][4900/5005]\tlr: 1.000e-02, eta: 6:14:47, time: 0.092, data_time: 0.038, memory: 1125, loss: 1.7353\n2023-01-03 21:37:09,256 - mmcls - INFO - Epoch [49][5000/5005]\tlr: 1.000e-02, eta: 6:14:38, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7321\n2023-01-03 21:37:31,741 - mmcls - INFO - Epoch(val) [49][196]\taccuracy_top-1: 64.5960, accuracy_top-5: 85.9360\n2023-01-03 21:37:42,889 - mmcls - INFO - Epoch [50][100/5005]\tlr: 1.000e-02, eta: 6:14:31, time: 0.111, data_time: 0.040, memory: 1125, loss: 1.7059\n2023-01-03 21:37:51,263 - mmcls - INFO - Epoch [50][200/5005]\tlr: 1.000e-02, eta: 6:14:22, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.6966\n2023-01-03 21:38:00,090 - mmcls - INFO - Epoch [50][300/5005]\tlr: 1.000e-02, eta: 6:14:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6930\n2023-01-03 21:38:08,777 - mmcls - INFO - Epoch [50][400/5005]\tlr: 1.000e-02, eta: 6:14:04, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7074\n2023-01-03 21:38:17,370 - mmcls - INFO - Epoch [50][500/5005]\tlr: 1.000e-02, eta: 6:13:55, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7077\n2023-01-03 21:38:26,214 - mmcls - INFO - Epoch [50][600/5005]\tlr: 1.000e-02, eta: 6:13:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7089\n2023-01-03 21:38:34,603 - mmcls - INFO - Epoch [50][700/5005]\tlr: 1.000e-02, eta: 6:13:37, time: 0.084, data_time: 0.034, memory: 1125, loss: 1.7079\n2023-01-03 21:38:43,530 - mmcls - INFO - Epoch [50][800/5005]\tlr: 1.000e-02, eta: 6:13:28, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7097\n2023-01-03 21:38:52,368 - mmcls - INFO - Epoch [50][900/5005]\tlr: 1.000e-02, eta: 6:13:19, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7172\n2023-01-03 21:39:00,994 - mmcls - INFO - Epoch [50][1000/5005]\tlr: 1.000e-02, eta: 6:13:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7158\n2023-01-03 21:39:09,745 - mmcls - INFO - Epoch [50][1100/5005]\tlr: 1.000e-02, eta: 6:13:02, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7060\n2023-01-03 21:39:18,745 - mmcls - INFO - Epoch [50][1200/5005]\tlr: 1.000e-02, eta: 6:12:53, time: 0.090, data_time: 0.037, memory: 1125, loss: 1.7127\n2023-01-03 21:39:27,578 - mmcls - INFO - Epoch [50][1300/5005]\tlr: 1.000e-02, eta: 6:12:44, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7026\n2023-01-03 21:39:36,645 - mmcls - INFO - Epoch [50][1400/5005]\tlr: 1.000e-02, eta: 6:12:36, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.6875\n2023-01-03 21:39:45,438 - mmcls - INFO - Epoch [50][1500/5005]\tlr: 1.000e-02, eta: 6:12:27, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7471\n2023-01-03 21:39:54,070 - mmcls - INFO - Epoch [50][1600/5005]\tlr: 1.000e-02, eta: 6:12:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7038\n2023-01-03 21:40:02,935 - mmcls - INFO - Epoch [50][1700/5005]\tlr: 1.000e-02, eta: 6:12:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7236\n2023-01-03 21:40:11,772 - mmcls - INFO - Epoch [50][1800/5005]\tlr: 1.000e-02, eta: 6:12:00, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7230\n2023-01-03 21:40:20,456 - mmcls - INFO - Epoch [50][1900/5005]\tlr: 1.000e-02, eta: 6:11:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7095\n2023-01-03 21:40:29,164 - mmcls - INFO - Epoch [50][2000/5005]\tlr: 1.000e-02, eta: 6:11:42, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7313\n2023-01-03 21:40:38,009 - mmcls - INFO - Epoch [50][2100/5005]\tlr: 1.000e-02, eta: 6:11:34, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7299\n2023-01-03 21:40:47,186 - mmcls - INFO - Epoch [50][2200/5005]\tlr: 1.000e-02, eta: 6:11:25, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7227\n2023-01-03 21:40:55,829 - mmcls - INFO - Epoch [50][2300/5005]\tlr: 1.000e-02, eta: 6:11:16, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7168\n2023-01-03 21:41:04,706 - mmcls - INFO - Epoch [50][2400/5005]\tlr: 1.000e-02, eta: 6:11:07, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7252\n2023-01-03 21:41:13,208 - mmcls - INFO - Epoch [50][2500/5005]\tlr: 1.000e-02, eta: 6:10:58, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.7116\n2023-01-03 21:41:21,939 - mmcls - INFO - Epoch [50][2600/5005]\tlr: 1.000e-02, eta: 6:10:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7103\n2023-01-03 21:41:30,983 - mmcls - INFO - Epoch [50][2700/5005]\tlr: 1.000e-02, eta: 6:10:41, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7246\n2023-01-03 21:41:39,962 - mmcls - INFO - Epoch [50][2800/5005]\tlr: 1.000e-02, eta: 6:10:32, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.7118\n2023-01-03 21:41:48,526 - mmcls - INFO - Epoch [50][2900/5005]\tlr: 1.000e-02, eta: 6:10:23, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7344\n2023-01-03 21:41:57,294 - mmcls - INFO - Epoch [50][3000/5005]\tlr: 1.000e-02, eta: 6:10:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7070\n2023-01-03 21:42:06,184 - mmcls - INFO - Epoch [50][3100/5005]\tlr: 1.000e-02, eta: 6:10:06, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7436\n2023-01-03 21:42:15,189 - mmcls - INFO - Epoch [50][3200/5005]\tlr: 1.000e-02, eta: 6:09:57, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7480\n2023-01-03 21:42:23,850 - mmcls - INFO - Epoch [50][3300/5005]\tlr: 1.000e-02, eta: 6:09:48, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7366\n2023-01-03 21:42:32,582 - mmcls - INFO - Epoch [50][3400/5005]\tlr: 1.000e-02, eta: 6:09:39, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7202\n2023-01-03 21:42:41,328 - mmcls - INFO - Epoch [50][3500/5005]\tlr: 1.000e-02, eta: 6:09:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7288\n2023-01-03 21:42:50,136 - mmcls - INFO - Epoch [50][3600/5005]\tlr: 1.000e-02, eta: 6:09:22, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7333\n2023-01-03 21:42:59,065 - mmcls - INFO - Epoch [50][3700/5005]\tlr: 1.000e-02, eta: 6:09:13, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7493\n2023-01-03 21:43:07,912 - mmcls - INFO - Epoch [50][3800/5005]\tlr: 1.000e-02, eta: 6:09:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7153\n2023-01-03 21:43:16,925 - mmcls - INFO - Epoch [50][3900/5005]\tlr: 1.000e-02, eta: 6:08:56, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7276\n2023-01-03 21:43:25,667 - mmcls - INFO - Epoch [50][4000/5005]\tlr: 1.000e-02, eta: 6:08:47, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7414\n2023-01-03 21:43:34,498 - mmcls - INFO - Epoch [50][4100/5005]\tlr: 1.000e-02, eta: 6:08:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7266\n2023-01-03 21:43:43,071 - mmcls - INFO - Epoch [50][4200/5005]\tlr: 1.000e-02, eta: 6:08:29, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7198\n2023-01-03 21:43:51,734 - mmcls - INFO - Epoch [50][4300/5005]\tlr: 1.000e-02, eta: 6:08:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7313\n2023-01-03 21:44:00,459 - mmcls - INFO - Epoch [50][4400/5005]\tlr: 1.000e-02, eta: 6:08:11, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7363\n2023-01-03 21:44:09,301 - mmcls - INFO - Epoch [50][4500/5005]\tlr: 1.000e-02, eta: 6:08:02, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7334\n2023-01-03 21:44:17,830 - mmcls - INFO - Epoch [50][4600/5005]\tlr: 1.000e-02, eta: 6:07:53, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7284\n2023-01-03 21:44:26,833 - mmcls - INFO - Epoch [50][4700/5005]\tlr: 1.000e-02, eta: 6:07:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7419\n2023-01-03 21:44:35,597 - mmcls - INFO - Epoch [50][4800/5005]\tlr: 1.000e-02, eta: 6:07:36, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7283\n2023-01-03 21:44:44,476 - mmcls - INFO - Epoch [50][4900/5005]\tlr: 1.000e-02, eta: 6:07:27, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7337\n2023-01-03 21:44:53,320 - mmcls - INFO - Epoch [50][5000/5005]\tlr: 1.000e-02, eta: 6:07:18, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.7425\n2023-01-03 21:44:53,667 - mmcls - INFO - Saving checkpoint at 50 epochs\n2023-01-03 21:45:16,150 - mmcls - INFO - Epoch(val) [50][196]\taccuracy_top-1: 63.8300, accuracy_top-5: 85.6040\n2023-01-03 21:45:27,094 - mmcls - INFO - Epoch [51][100/5005]\tlr: 1.000e-02, eta: 6:07:11, time: 0.109, data_time: 0.051, memory: 1125, loss: 1.7148\n2023-01-03 21:45:35,898 - mmcls - INFO - Epoch [51][200/5005]\tlr: 1.000e-02, eta: 6:07:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7035\n2023-01-03 21:45:44,974 - mmcls - INFO - Epoch [51][300/5005]\tlr: 1.000e-02, eta: 6:06:53, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7075\n2023-01-03 21:45:53,533 - mmcls - INFO - Epoch [51][400/5005]\tlr: 1.000e-02, eta: 6:06:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6907\n2023-01-03 21:46:02,014 - mmcls - INFO - Epoch [51][500/5005]\tlr: 1.000e-02, eta: 6:06:35, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6981\n2023-01-03 21:46:10,919 - mmcls - INFO - Epoch [51][600/5005]\tlr: 1.000e-02, eta: 6:06:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6828\n2023-01-03 21:46:19,681 - mmcls - INFO - Epoch [51][700/5005]\tlr: 1.000e-02, eta: 6:06:17, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6754\n2023-01-03 21:46:28,746 - mmcls - INFO - Epoch [51][800/5005]\tlr: 1.000e-02, eta: 6:06:09, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7112\n2023-01-03 21:46:37,386 - mmcls - INFO - Epoch [51][900/5005]\tlr: 1.000e-02, eta: 6:06:00, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7080\n2023-01-03 21:46:46,303 - mmcls - INFO - Epoch [51][1000/5005]\tlr: 1.000e-02, eta: 6:05:51, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.7069\n2023-01-03 21:46:55,229 - mmcls - INFO - Epoch [51][1100/5005]\tlr: 1.000e-02, eta: 6:05:43, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7014\n2023-01-03 21:47:04,562 - mmcls - INFO - Epoch [51][1200/5005]\tlr: 1.000e-02, eta: 6:05:34, time: 0.093, data_time: 0.012, memory: 1125, loss: 1.6961\n2023-01-03 21:47:12,971 - mmcls - INFO - Epoch [51][1300/5005]\tlr: 1.000e-02, eta: 6:05:25, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7039\n2023-01-03 21:47:21,807 - mmcls - INFO - Epoch [51][1400/5005]\tlr: 1.000e-02, eta: 6:05:16, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7174\n2023-01-03 21:47:30,954 - mmcls - INFO - Epoch [51][1500/5005]\tlr: 1.000e-02, eta: 6:05:08, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7152\n2023-01-03 21:47:39,863 - mmcls - INFO - Epoch [51][1600/5005]\tlr: 1.000e-02, eta: 6:04:59, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6933\n2023-01-03 21:47:48,535 - mmcls - INFO - Epoch [51][1700/5005]\tlr: 1.000e-02, eta: 6:04:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7169\n2023-01-03 21:47:57,187 - mmcls - INFO - Epoch [51][1800/5005]\tlr: 1.000e-02, eta: 6:04:41, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7142\n2023-01-03 21:48:06,115 - mmcls - INFO - Epoch [51][1900/5005]\tlr: 1.000e-02, eta: 6:04:33, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7180\n2023-01-03 21:48:14,909 - mmcls - INFO - Epoch [51][2000/5005]\tlr: 1.000e-02, eta: 6:04:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7144\n2023-01-03 21:48:23,427 - mmcls - INFO - Epoch [51][2100/5005]\tlr: 1.000e-02, eta: 6:04:15, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7178\n2023-01-03 21:48:32,521 - mmcls - INFO - Epoch [51][2200/5005]\tlr: 1.000e-02, eta: 6:04:06, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7273\n2023-01-03 21:48:40,972 - mmcls - INFO - Epoch [51][2300/5005]\tlr: 1.000e-02, eta: 6:03:57, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7127\n2023-01-03 21:48:49,591 - mmcls - INFO - Epoch [51][2400/5005]\tlr: 1.000e-02, eta: 6:03:48, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7318\n2023-01-03 21:48:58,251 - mmcls - INFO - Epoch [51][2500/5005]\tlr: 1.000e-02, eta: 6:03:39, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6834\n2023-01-03 21:49:07,097 - mmcls - INFO - Epoch [51][2600/5005]\tlr: 1.000e-02, eta: 6:03:30, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7306\n2023-01-03 21:49:15,844 - mmcls - INFO - Epoch [51][2700/5005]\tlr: 1.000e-02, eta: 6:03:21, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7175\n2023-01-03 21:49:24,512 - mmcls - INFO - Epoch [51][2800/5005]\tlr: 1.000e-02, eta: 6:03:12, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6985\n2023-01-03 21:49:33,139 - mmcls - INFO - Epoch [51][2900/5005]\tlr: 1.000e-02, eta: 6:03:03, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7207\n2023-01-03 21:49:41,984 - mmcls - INFO - Epoch [51][3000/5005]\tlr: 1.000e-02, eta: 6:02:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7063\n2023-01-03 21:49:50,776 - mmcls - INFO - Epoch [51][3100/5005]\tlr: 1.000e-02, eta: 6:02:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7350\n2023-01-03 21:49:59,554 - mmcls - INFO - Epoch [51][3200/5005]\tlr: 1.000e-02, eta: 6:02:37, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7285\n2023-01-03 21:50:08,359 - mmcls - INFO - Epoch [51][3300/5005]\tlr: 1.000e-02, eta: 6:02:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7429\n2023-01-03 21:50:16,890 - mmcls - INFO - Epoch [51][3400/5005]\tlr: 1.000e-02, eta: 6:02:19, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7255\n2023-01-03 21:50:25,765 - mmcls - INFO - Epoch [51][3500/5005]\tlr: 1.000e-02, eta: 6:02:10, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7383\n2023-01-03 21:50:34,876 - mmcls - INFO - Epoch [51][3600/5005]\tlr: 1.000e-02, eta: 6:02:02, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7373\n2023-01-03 21:50:44,012 - mmcls - INFO - Epoch [51][3700/5005]\tlr: 1.000e-02, eta: 6:01:53, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7419\n2023-01-03 21:50:52,786 - mmcls - INFO - Epoch [51][3800/5005]\tlr: 1.000e-02, eta: 6:01:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7498\n2023-01-03 21:51:01,650 - mmcls - INFO - Epoch [51][3900/5005]\tlr: 1.000e-02, eta: 6:01:36, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7328\n2023-01-03 21:51:10,308 - mmcls - INFO - Epoch [51][4000/5005]\tlr: 1.000e-02, eta: 6:01:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7247\n2023-01-03 21:51:19,020 - mmcls - INFO - Epoch [51][4100/5005]\tlr: 1.000e-02, eta: 6:01:18, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7461\n2023-01-03 21:51:27,637 - mmcls - INFO - Epoch [51][4200/5005]\tlr: 1.000e-02, eta: 6:01:09, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7041\n2023-01-03 21:51:36,393 - mmcls - INFO - Epoch [51][4300/5005]\tlr: 1.000e-02, eta: 6:01:00, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.6927\n2023-01-03 21:51:45,495 - mmcls - INFO - Epoch [51][4400/5005]\tlr: 1.000e-02, eta: 6:00:52, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7281\n2023-01-03 21:51:54,214 - mmcls - INFO - Epoch [51][4500/5005]\tlr: 1.000e-02, eta: 6:00:43, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7229\n2023-01-03 21:52:02,963 - mmcls - INFO - Epoch [51][4600/5005]\tlr: 1.000e-02, eta: 6:00:34, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7395\n2023-01-03 21:52:11,590 - mmcls - INFO - Epoch [51][4700/5005]\tlr: 1.000e-02, eta: 6:00:25, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7577\n2023-01-03 21:52:20,287 - mmcls - INFO - Epoch [51][4800/5005]\tlr: 1.000e-02, eta: 6:00:16, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7219\n2023-01-03 21:52:29,021 - mmcls - INFO - Epoch [51][4900/5005]\tlr: 1.000e-02, eta: 6:00:07, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7326\n2023-01-03 21:52:37,911 - mmcls - INFO - Epoch [51][5000/5005]\tlr: 1.000e-02, eta: 5:59:58, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7263\n2023-01-03 21:53:00,456 - mmcls - INFO - Epoch(val) [51][196]\taccuracy_top-1: 64.0660, accuracy_top-5: 85.8940\n2023-01-03 21:53:11,311 - mmcls - INFO - Epoch [52][100/5005]\tlr: 1.000e-02, eta: 5:59:51, time: 0.108, data_time: 0.050, memory: 1125, loss: 1.7022\n2023-01-03 21:53:20,014 - mmcls - INFO - Epoch [52][200/5005]\tlr: 1.000e-02, eta: 5:59:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7064\n2023-01-03 21:53:28,676 - mmcls - INFO - Epoch [52][300/5005]\tlr: 1.000e-02, eta: 5:59:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6920\n2023-01-03 21:53:37,441 - mmcls - INFO - Epoch [52][400/5005]\tlr: 1.000e-02, eta: 5:59:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6810\n2023-01-03 21:53:47,114 - mmcls - INFO - Epoch [52][500/5005]\tlr: 1.000e-02, eta: 5:59:16, time: 0.097, data_time: 0.001, memory: 1125, loss: 1.7284\n2023-01-03 21:53:55,694 - mmcls - INFO - Epoch [52][600/5005]\tlr: 1.000e-02, eta: 5:59:07, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6988\n2023-01-03 21:54:04,792 - mmcls - INFO - Epoch [52][700/5005]\tlr: 1.000e-02, eta: 5:58:58, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7017\n2023-01-03 21:54:13,529 - mmcls - INFO - Epoch [52][800/5005]\tlr: 1.000e-02, eta: 5:58:49, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6986\n2023-01-03 21:54:22,057 - mmcls - INFO - Epoch [52][900/5005]\tlr: 1.000e-02, eta: 5:58:40, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7121\n2023-01-03 21:54:30,741 - mmcls - INFO - Epoch [52][1000/5005]\tlr: 1.000e-02, eta: 5:58:31, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7000\n2023-01-03 21:54:39,835 - mmcls - INFO - Epoch [52][1100/5005]\tlr: 1.000e-02, eta: 5:58:23, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.7130\n2023-01-03 21:54:48,695 - mmcls - INFO - Epoch [52][1200/5005]\tlr: 1.000e-02, eta: 5:58:14, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7172\n2023-01-03 21:54:57,557 - mmcls - INFO - Epoch [52][1300/5005]\tlr: 1.000e-02, eta: 5:58:05, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7187\n2023-01-03 21:55:06,449 - mmcls - INFO - Epoch [52][1400/5005]\tlr: 1.000e-02, eta: 5:57:57, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7008\n2023-01-03 21:55:15,308 - mmcls - INFO - Epoch [52][1500/5005]\tlr: 1.000e-02, eta: 5:57:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7051\n2023-01-03 21:55:24,216 - mmcls - INFO - Epoch [52][1600/5005]\tlr: 1.000e-02, eta: 5:57:39, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6977\n2023-01-03 21:55:33,074 - mmcls - INFO - Epoch [52][1700/5005]\tlr: 1.000e-02, eta: 5:57:30, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7201\n2023-01-03 21:55:41,856 - mmcls - INFO - Epoch [52][1800/5005]\tlr: 1.000e-02, eta: 5:57:22, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7022\n2023-01-03 21:55:50,780 - mmcls - INFO - Epoch [52][1900/5005]\tlr: 1.000e-02, eta: 5:57:13, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7088\n2023-01-03 21:55:59,703 - mmcls - INFO - Epoch [52][2000/5005]\tlr: 1.000e-02, eta: 5:57:04, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7225\n2023-01-03 21:56:08,823 - mmcls - INFO - Epoch [52][2100/5005]\tlr: 1.000e-02, eta: 5:56:56, time: 0.091, data_time: 0.045, memory: 1125, loss: 1.7056\n2023-01-03 21:56:17,606 - mmcls - INFO - Epoch [52][2200/5005]\tlr: 1.000e-02, eta: 5:56:47, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7145\n2023-01-03 21:56:26,424 - mmcls - INFO - Epoch [52][2300/5005]\tlr: 1.000e-02, eta: 5:56:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7134\n2023-01-03 21:56:35,238 - mmcls - INFO - Epoch [52][2400/5005]\tlr: 1.000e-02, eta: 5:56:29, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.7173\n2023-01-03 21:56:43,872 - mmcls - INFO - Epoch [52][2500/5005]\tlr: 1.000e-02, eta: 5:56:20, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7245\n2023-01-03 21:56:52,454 - mmcls - INFO - Epoch [52][2600/5005]\tlr: 1.000e-02, eta: 5:56:11, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7021\n2023-01-03 21:57:01,491 - mmcls - INFO - Epoch [52][2700/5005]\tlr: 1.000e-02, eta: 5:56:03, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7173\n2023-01-03 21:57:10,263 - mmcls - INFO - Epoch [52][2800/5005]\tlr: 1.000e-02, eta: 5:55:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7471\n2023-01-03 21:57:19,205 - mmcls - INFO - Epoch [52][2900/5005]\tlr: 1.000e-02, eta: 5:55:45, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7222\n2023-01-03 21:57:27,925 - mmcls - INFO - Epoch [52][3000/5005]\tlr: 1.000e-02, eta: 5:55:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7003\n2023-01-03 21:57:36,414 - mmcls - INFO - Epoch [52][3100/5005]\tlr: 1.000e-02, eta: 5:55:27, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6760\n2023-01-03 21:57:45,342 - mmcls - INFO - Epoch [52][3200/5005]\tlr: 1.000e-02, eta: 5:55:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7141\n2023-01-03 21:57:53,990 - mmcls - INFO - Epoch [52][3300/5005]\tlr: 1.000e-02, eta: 5:55:09, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7260\n2023-01-03 21:58:02,644 - mmcls - INFO - Epoch [52][3400/5005]\tlr: 1.000e-02, eta: 5:55:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7405\n2023-01-03 21:58:11,441 - mmcls - INFO - Epoch [52][3500/5005]\tlr: 1.000e-02, eta: 5:54:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7242\n2023-01-03 21:58:20,372 - mmcls - INFO - Epoch [52][3600/5005]\tlr: 1.000e-02, eta: 5:54:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7419\n2023-01-03 21:58:29,170 - mmcls - INFO - Epoch [52][3700/5005]\tlr: 1.000e-02, eta: 5:54:34, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.6962\n2023-01-03 21:58:37,629 - mmcls - INFO - Epoch [52][3800/5005]\tlr: 1.000e-02, eta: 5:54:25, time: 0.085, data_time: 0.032, memory: 1125, loss: 1.7537\n2023-01-03 21:58:46,161 - mmcls - INFO - Epoch [52][3900/5005]\tlr: 1.000e-02, eta: 5:54:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7388\n2023-01-03 21:58:54,974 - mmcls - INFO - Epoch [52][4000/5005]\tlr: 1.000e-02, eta: 5:54:07, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7319\n2023-01-03 21:59:03,754 - mmcls - INFO - Epoch [52][4100/5005]\tlr: 1.000e-02, eta: 5:53:58, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7345\n2023-01-03 21:59:12,424 - mmcls - INFO - Epoch [52][4200/5005]\tlr: 1.000e-02, eta: 5:53:49, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7168\n2023-01-03 21:59:21,206 - mmcls - INFO - Epoch [52][4300/5005]\tlr: 1.000e-02, eta: 5:53:41, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7335\n2023-01-03 21:59:29,941 - mmcls - INFO - Epoch [52][4400/5005]\tlr: 1.000e-02, eta: 5:53:32, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7061\n2023-01-03 21:59:38,605 - mmcls - INFO - Epoch [52][4500/5005]\tlr: 1.000e-02, eta: 5:53:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7245\n2023-01-03 21:59:47,119 - mmcls - INFO - Epoch [52][4600/5005]\tlr: 1.000e-02, eta: 5:53:14, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7543\n2023-01-03 21:59:55,559 - mmcls - INFO - Epoch [52][4700/5005]\tlr: 1.000e-02, eta: 5:53:05, time: 0.084, data_time: 0.028, memory: 1125, loss: 1.7177\n2023-01-03 22:00:04,431 - mmcls - INFO - Epoch [52][4800/5005]\tlr: 1.000e-02, eta: 5:52:56, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7294\n2023-01-03 22:00:12,938 - mmcls - INFO - Epoch [52][4900/5005]\tlr: 1.000e-02, eta: 5:52:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7697\n2023-01-03 22:00:21,713 - mmcls - INFO - Epoch [52][5000/5005]\tlr: 1.000e-02, eta: 5:52:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7303\n2023-01-03 22:00:44,332 - mmcls - INFO - Epoch(val) [52][196]\taccuracy_top-1: 64.2520, accuracy_top-5: 85.8620\n2023-01-03 22:00:55,273 - mmcls - INFO - Epoch [53][100/5005]\tlr: 1.000e-02, eta: 5:52:30, time: 0.109, data_time: 0.035, memory: 1125, loss: 1.7015\n2023-01-03 22:01:04,049 - mmcls - INFO - Epoch [53][200/5005]\tlr: 1.000e-02, eta: 5:52:21, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6840\n2023-01-03 22:01:12,788 - mmcls - INFO - Epoch [53][300/5005]\tlr: 1.000e-02, eta: 5:52:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6948\n2023-01-03 22:01:21,463 - mmcls - INFO - Epoch [53][400/5005]\tlr: 1.000e-02, eta: 5:52:04, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6987\n2023-01-03 22:01:30,089 - mmcls - INFO - Epoch [53][500/5005]\tlr: 1.000e-02, eta: 5:51:55, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6875\n2023-01-03 22:01:38,751 - mmcls - INFO - Epoch [53][600/5005]\tlr: 1.000e-02, eta: 5:51:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7027\n2023-01-03 22:01:47,567 - mmcls - INFO - Epoch [53][700/5005]\tlr: 1.000e-02, eta: 5:51:37, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7098\n2023-01-03 22:01:56,839 - mmcls - INFO - Epoch [53][800/5005]\tlr: 1.000e-02, eta: 5:51:28, time: 0.093, data_time: 0.007, memory: 1125, loss: 1.7254\n2023-01-03 22:02:05,996 - mmcls - INFO - Epoch [53][900/5005]\tlr: 1.000e-02, eta: 5:51:20, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6707\n2023-01-03 22:02:14,668 - mmcls - INFO - Epoch [53][1000/5005]\tlr: 1.000e-02, eta: 5:51:11, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7221\n2023-01-03 22:02:23,537 - mmcls - INFO - Epoch [53][1100/5005]\tlr: 1.000e-02, eta: 5:51:02, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6990\n2023-01-03 22:02:32,321 - mmcls - INFO - Epoch [53][1200/5005]\tlr: 1.000e-02, eta: 5:50:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6916\n2023-01-03 22:02:41,103 - mmcls - INFO - Epoch [53][1300/5005]\tlr: 1.000e-02, eta: 5:50:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7199\n2023-01-03 22:02:50,193 - mmcls - INFO - Epoch [53][1400/5005]\tlr: 1.000e-02, eta: 5:50:36, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6973\n2023-01-03 22:02:58,722 - mmcls - INFO - Epoch [53][1500/5005]\tlr: 1.000e-02, eta: 5:50:27, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6917\n2023-01-03 22:03:07,451 - mmcls - INFO - Epoch [53][1600/5005]\tlr: 1.000e-02, eta: 5:50:18, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7040\n2023-01-03 22:03:16,302 - mmcls - INFO - Epoch [53][1700/5005]\tlr: 1.000e-02, eta: 5:50:09, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7033\n2023-01-03 22:03:24,773 - mmcls - INFO - Epoch [53][1800/5005]\tlr: 1.000e-02, eta: 5:50:00, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.6923\n2023-01-03 22:03:33,873 - mmcls - INFO - Epoch [53][1900/5005]\tlr: 1.000e-02, eta: 5:49:52, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.7159\n2023-01-03 22:03:42,727 - mmcls - INFO - Epoch [53][2000/5005]\tlr: 1.000e-02, eta: 5:49:43, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7457\n2023-01-03 22:03:51,542 - mmcls - INFO - Epoch [53][2100/5005]\tlr: 1.000e-02, eta: 5:49:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7169\n2023-01-03 22:04:00,167 - mmcls - INFO - Epoch [53][2200/5005]\tlr: 1.000e-02, eta: 5:49:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7211\n2023-01-03 22:04:08,701 - mmcls - INFO - Epoch [53][2300/5005]\tlr: 1.000e-02, eta: 5:49:16, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7406\n2023-01-03 22:04:17,713 - mmcls - INFO - Epoch [53][2400/5005]\tlr: 1.000e-02, eta: 5:49:07, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7419\n2023-01-03 22:04:26,592 - mmcls - INFO - Epoch [53][2500/5005]\tlr: 1.000e-02, eta: 5:48:59, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7138\n2023-01-03 22:04:35,268 - mmcls - INFO - Epoch [53][2600/5005]\tlr: 1.000e-02, eta: 5:48:50, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7195\n2023-01-03 22:04:44,168 - mmcls - INFO - Epoch [53][2700/5005]\tlr: 1.000e-02, eta: 5:48:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7058\n2023-01-03 22:04:53,261 - mmcls - INFO - Epoch [53][2800/5005]\tlr: 1.000e-02, eta: 5:48:32, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.7175\n2023-01-03 22:05:02,052 - mmcls - INFO - Epoch [53][2900/5005]\tlr: 1.000e-02, eta: 5:48:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7139\n2023-01-03 22:05:11,180 - mmcls - INFO - Epoch [53][3000/5005]\tlr: 1.000e-02, eta: 5:48:15, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.7237\n2023-01-03 22:05:19,657 - mmcls - INFO - Epoch [53][3100/5005]\tlr: 1.000e-02, eta: 5:48:06, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7516\n2023-01-03 22:05:28,245 - mmcls - INFO - Epoch [53][3200/5005]\tlr: 1.000e-02, eta: 5:47:57, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.7213\n2023-01-03 22:05:37,235 - mmcls - INFO - Epoch [53][3300/5005]\tlr: 1.000e-02, eta: 5:47:48, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7129\n2023-01-03 22:05:45,697 - mmcls - INFO - Epoch [53][3400/5005]\tlr: 1.000e-02, eta: 5:47:39, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7288\n2023-01-03 22:05:54,574 - mmcls - INFO - Epoch [53][3500/5005]\tlr: 1.000e-02, eta: 5:47:31, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7450\n2023-01-03 22:06:03,224 - mmcls - INFO - Epoch [53][3600/5005]\tlr: 1.000e-02, eta: 5:47:22, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7305\n2023-01-03 22:06:11,865 - mmcls - INFO - Epoch [53][3700/5005]\tlr: 1.000e-02, eta: 5:47:13, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7380\n2023-01-03 22:06:21,162 - mmcls - INFO - Epoch [53][3800/5005]\tlr: 1.000e-02, eta: 5:47:04, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.7151\n2023-01-03 22:06:29,915 - mmcls - INFO - Epoch [53][3900/5005]\tlr: 1.000e-02, eta: 5:46:55, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7248\n2023-01-03 22:06:38,538 - mmcls - INFO - Epoch [53][4000/5005]\tlr: 1.000e-02, eta: 5:46:46, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7247\n2023-01-03 22:06:47,358 - mmcls - INFO - Epoch [53][4100/5005]\tlr: 1.000e-02, eta: 5:46:38, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7373\n2023-01-03 22:06:56,040 - mmcls - INFO - Epoch [53][4200/5005]\tlr: 1.000e-02, eta: 5:46:29, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7332\n2023-01-03 22:07:04,656 - mmcls - INFO - Epoch [53][4300/5005]\tlr: 1.000e-02, eta: 5:46:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7264\n2023-01-03 22:07:13,488 - mmcls - INFO - Epoch [53][4400/5005]\tlr: 1.000e-02, eta: 5:46:11, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7348\n2023-01-03 22:07:22,098 - mmcls - INFO - Epoch [53][4500/5005]\tlr: 1.000e-02, eta: 5:46:02, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7267\n2023-01-03 22:07:30,815 - mmcls - INFO - Epoch [53][4600/5005]\tlr: 1.000e-02, eta: 5:45:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7350\n2023-01-03 22:07:39,617 - mmcls - INFO - Epoch [53][4700/5005]\tlr: 1.000e-02, eta: 5:45:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7325\n2023-01-03 22:07:48,143 - mmcls - INFO - Epoch [53][4800/5005]\tlr: 1.000e-02, eta: 5:45:35, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7168\n2023-01-03 22:07:56,820 - mmcls - INFO - Epoch [53][4900/5005]\tlr: 1.000e-02, eta: 5:45:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7385\n2023-01-03 22:08:05,640 - mmcls - INFO - Epoch [53][5000/5005]\tlr: 1.000e-02, eta: 5:45:17, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7078\n2023-01-03 22:08:28,185 - mmcls - INFO - Epoch(val) [53][196]\taccuracy_top-1: 64.2380, accuracy_top-5: 86.0000\n2023-01-03 22:08:39,143 - mmcls - INFO - Epoch [54][100/5005]\tlr: 1.000e-02, eta: 5:45:10, time: 0.109, data_time: 0.052, memory: 1125, loss: 1.6891\n2023-01-03 22:08:47,547 - mmcls - INFO - Epoch [54][200/5005]\tlr: 1.000e-02, eta: 5:45:01, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7105\n2023-01-03 22:08:56,618 - mmcls - INFO - Epoch [54][300/5005]\tlr: 1.000e-02, eta: 5:44:52, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.6945\n2023-01-03 22:09:05,186 - mmcls - INFO - Epoch [54][400/5005]\tlr: 1.000e-02, eta: 5:44:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7007\n2023-01-03 22:09:14,115 - mmcls - INFO - Epoch [54][500/5005]\tlr: 1.000e-02, eta: 5:44:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6955\n2023-01-03 22:09:22,827 - mmcls - INFO - Epoch [54][600/5005]\tlr: 1.000e-02, eta: 5:44:25, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7024\n2023-01-03 22:09:31,555 - mmcls - INFO - Epoch [54][700/5005]\tlr: 1.000e-02, eta: 5:44:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6756\n2023-01-03 22:09:40,550 - mmcls - INFO - Epoch [54][800/5005]\tlr: 1.000e-02, eta: 5:44:08, time: 0.090, data_time: 0.035, memory: 1125, loss: 1.6853\n2023-01-03 22:09:49,191 - mmcls - INFO - Epoch [54][900/5005]\tlr: 1.000e-02, eta: 5:43:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6701\n2023-01-03 22:09:57,989 - mmcls - INFO - Epoch [54][1000/5005]\tlr: 1.000e-02, eta: 5:43:50, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7188\n2023-01-03 22:10:07,000 - mmcls - INFO - Epoch [54][1100/5005]\tlr: 1.000e-02, eta: 5:43:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7043\n2023-01-03 22:10:15,822 - mmcls - INFO - Epoch [54][1200/5005]\tlr: 1.000e-02, eta: 5:43:33, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6801\n2023-01-03 22:10:24,362 - mmcls - INFO - Epoch [54][1300/5005]\tlr: 1.000e-02, eta: 5:43:24, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7210\n2023-01-03 22:10:33,401 - mmcls - INFO - Epoch [54][1400/5005]\tlr: 1.000e-02, eta: 5:43:15, time: 0.090, data_time: 0.021, memory: 1125, loss: 1.7240\n2023-01-03 22:10:42,379 - mmcls - INFO - Epoch [54][1500/5005]\tlr: 1.000e-02, eta: 5:43:06, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.7057\n2023-01-03 22:10:51,163 - mmcls - INFO - Epoch [54][1600/5005]\tlr: 1.000e-02, eta: 5:42:57, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7033\n2023-01-03 22:10:59,973 - mmcls - INFO - Epoch [54][1700/5005]\tlr: 1.000e-02, eta: 5:42:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7050\n2023-01-03 22:11:08,537 - mmcls - INFO - Epoch [54][1800/5005]\tlr: 1.000e-02, eta: 5:42:40, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7000\n2023-01-03 22:11:17,516 - mmcls - INFO - Epoch [54][1900/5005]\tlr: 1.000e-02, eta: 5:42:31, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7041\n2023-01-03 22:11:26,258 - mmcls - INFO - Epoch [54][2000/5005]\tlr: 1.000e-02, eta: 5:42:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7306\n2023-01-03 22:11:34,795 - mmcls - INFO - Epoch [54][2100/5005]\tlr: 1.000e-02, eta: 5:42:13, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7246\n2023-01-03 22:11:43,399 - mmcls - INFO - Epoch [54][2200/5005]\tlr: 1.000e-02, eta: 5:42:04, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7176\n2023-01-03 22:11:52,405 - mmcls - INFO - Epoch [54][2300/5005]\tlr: 1.000e-02, eta: 5:41:55, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7101\n2023-01-03 22:12:01,387 - mmcls - INFO - Epoch [54][2400/5005]\tlr: 1.000e-02, eta: 5:41:47, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.7326\n2023-01-03 22:12:10,040 - mmcls - INFO - Epoch [54][2500/5005]\tlr: 1.000e-02, eta: 5:41:38, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7269\n2023-01-03 22:12:18,698 - mmcls - INFO - Epoch [54][2600/5005]\tlr: 1.000e-02, eta: 5:41:29, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.7454\n2023-01-03 22:12:27,289 - mmcls - INFO - Epoch [54][2700/5005]\tlr: 1.000e-02, eta: 5:41:20, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7208\n2023-01-03 22:12:35,757 - mmcls - INFO - Epoch [54][2800/5005]\tlr: 1.000e-02, eta: 5:41:11, time: 0.085, data_time: 0.036, memory: 1125, loss: 1.7151\n2023-01-03 22:12:44,481 - mmcls - INFO - Epoch [54][2900/5005]\tlr: 1.000e-02, eta: 5:41:02, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7021\n2023-01-03 22:12:53,253 - mmcls - INFO - Epoch [54][3000/5005]\tlr: 1.000e-02, eta: 5:40:53, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6895\n2023-01-03 22:13:01,928 - mmcls - INFO - Epoch [54][3100/5005]\tlr: 1.000e-02, eta: 5:40:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7154\n2023-01-03 22:13:10,611 - mmcls - INFO - Epoch [54][3200/5005]\tlr: 1.000e-02, eta: 5:40:35, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7014\n2023-01-03 22:13:19,172 - mmcls - INFO - Epoch [54][3300/5005]\tlr: 1.000e-02, eta: 5:40:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7329\n2023-01-03 22:13:28,050 - mmcls - INFO - Epoch [54][3400/5005]\tlr: 1.000e-02, eta: 5:40:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7147\n2023-01-03 22:13:36,952 - mmcls - INFO - Epoch [54][3500/5005]\tlr: 1.000e-02, eta: 5:40:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7169\n2023-01-03 22:13:45,553 - mmcls - INFO - Epoch [54][3600/5005]\tlr: 1.000e-02, eta: 5:40:00, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7254\n2023-01-03 22:13:54,222 - mmcls - INFO - Epoch [54][3700/5005]\tlr: 1.000e-02, eta: 5:39:51, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7071\n2023-01-03 22:14:02,891 - mmcls - INFO - Epoch [54][3800/5005]\tlr: 1.000e-02, eta: 5:39:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7377\n2023-01-03 22:14:11,492 - mmcls - INFO - Epoch [54][3900/5005]\tlr: 1.000e-02, eta: 5:39:33, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7188\n2023-01-03 22:14:20,267 - mmcls - INFO - Epoch [54][4000/5005]\tlr: 1.000e-02, eta: 5:39:24, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7321\n2023-01-03 22:14:28,963 - mmcls - INFO - Epoch [54][4100/5005]\tlr: 1.000e-02, eta: 5:39:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7520\n2023-01-03 22:14:37,757 - mmcls - INFO - Epoch [54][4200/5005]\tlr: 1.000e-02, eta: 5:39:06, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7339\n2023-01-03 22:14:46,731 - mmcls - INFO - Epoch [54][4300/5005]\tlr: 1.000e-02, eta: 5:38:58, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.7309\n2023-01-03 22:14:55,415 - mmcls - INFO - Epoch [54][4400/5005]\tlr: 1.000e-02, eta: 5:38:49, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7315\n2023-01-03 22:15:04,263 - mmcls - INFO - Epoch [54][4500/5005]\tlr: 1.000e-02, eta: 5:38:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7314\n2023-01-03 22:15:13,292 - mmcls - INFO - Epoch [54][4600/5005]\tlr: 1.000e-02, eta: 5:38:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7301\n2023-01-03 22:15:22,001 - mmcls - INFO - Epoch [54][4700/5005]\tlr: 1.000e-02, eta: 5:38:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7465\n2023-01-03 22:15:30,554 - mmcls - INFO - Epoch [54][4800/5005]\tlr: 1.000e-02, eta: 5:38:14, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7335\n2023-01-03 22:15:39,603 - mmcls - INFO - Epoch [54][4900/5005]\tlr: 1.000e-02, eta: 5:38:05, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.7279\n2023-01-03 22:15:48,335 - mmcls - INFO - Epoch [54][5000/5005]\tlr: 1.000e-02, eta: 5:37:56, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7396\n2023-01-03 22:16:11,044 - mmcls - INFO - Epoch(val) [54][196]\taccuracy_top-1: 64.8520, accuracy_top-5: 86.1600\n2023-01-03 22:16:22,028 - mmcls - INFO - Epoch [55][100/5005]\tlr: 1.000e-02, eta: 5:37:48, time: 0.110, data_time: 0.042, memory: 1125, loss: 1.6594\n2023-01-03 22:16:30,850 - mmcls - INFO - Epoch [55][200/5005]\tlr: 1.000e-02, eta: 5:37:39, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7022\n2023-01-03 22:16:39,367 - mmcls - INFO - Epoch [55][300/5005]\tlr: 1.000e-02, eta: 5:37:30, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6875\n2023-01-03 22:16:47,755 - mmcls - INFO - Epoch [55][400/5005]\tlr: 1.000e-02, eta: 5:37:21, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6792\n2023-01-03 22:16:56,416 - mmcls - INFO - Epoch [55][500/5005]\tlr: 1.000e-02, eta: 5:37:12, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6678\n2023-01-03 22:17:04,949 - mmcls - INFO - Epoch [55][600/5005]\tlr: 1.000e-02, eta: 5:37:03, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6858\n2023-01-03 22:17:13,678 - mmcls - INFO - Epoch [55][700/5005]\tlr: 1.000e-02, eta: 5:36:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6798\n2023-01-03 22:17:22,354 - mmcls - INFO - Epoch [55][800/5005]\tlr: 1.000e-02, eta: 5:36:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6927\n2023-01-03 22:17:30,911 - mmcls - INFO - Epoch [55][900/5005]\tlr: 1.000e-02, eta: 5:36:36, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7005\n2023-01-03 22:17:39,938 - mmcls - INFO - Epoch [55][1000/5005]\tlr: 1.000e-02, eta: 5:36:28, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.6980\n2023-01-03 22:17:48,669 - mmcls - INFO - Epoch [55][1100/5005]\tlr: 1.000e-02, eta: 5:36:19, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.6816\n2023-01-03 22:17:57,624 - mmcls - INFO - Epoch [55][1200/5005]\tlr: 1.000e-02, eta: 5:36:10, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7014\n2023-01-03 22:18:06,698 - mmcls - INFO - Epoch [55][1300/5005]\tlr: 1.000e-02, eta: 5:36:02, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.6998\n2023-01-03 22:18:15,479 - mmcls - INFO - Epoch [55][1400/5005]\tlr: 1.000e-02, eta: 5:35:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6800\n2023-01-03 22:18:24,439 - mmcls - INFO - Epoch [55][1500/5005]\tlr: 1.000e-02, eta: 5:35:44, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7136\n2023-01-03 22:18:33,135 - mmcls - INFO - Epoch [55][1600/5005]\tlr: 1.000e-02, eta: 5:35:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7123\n2023-01-03 22:18:41,942 - mmcls - INFO - Epoch [55][1700/5005]\tlr: 1.000e-02, eta: 5:35:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7064\n2023-01-03 22:18:50,618 - mmcls - INFO - Epoch [55][1800/5005]\tlr: 1.000e-02, eta: 5:35:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7063\n2023-01-03 22:18:59,252 - mmcls - INFO - Epoch [55][1900/5005]\tlr: 1.000e-02, eta: 5:35:09, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7126\n2023-01-03 22:19:08,061 - mmcls - INFO - Epoch [55][2000/5005]\tlr: 1.000e-02, eta: 5:35:00, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7163\n2023-01-03 22:19:17,037 - mmcls - INFO - Epoch [55][2100/5005]\tlr: 1.000e-02, eta: 5:34:51, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7093\n2023-01-03 22:19:25,930 - mmcls - INFO - Epoch [55][2200/5005]\tlr: 1.000e-02, eta: 5:34:42, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.7042\n2023-01-03 22:19:34,522 - mmcls - INFO - Epoch [55][2300/5005]\tlr: 1.000e-02, eta: 5:34:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7476\n2023-01-03 22:19:43,388 - mmcls - INFO - Epoch [55][2400/5005]\tlr: 1.000e-02, eta: 5:34:25, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7193\n2023-01-03 22:19:52,031 - mmcls - INFO - Epoch [55][2500/5005]\tlr: 1.000e-02, eta: 5:34:16, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6960\n2023-01-03 22:20:00,685 - mmcls - INFO - Epoch [55][2600/5005]\tlr: 1.000e-02, eta: 5:34:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7007\n2023-01-03 22:20:09,674 - mmcls - INFO - Epoch [55][2700/5005]\tlr: 1.000e-02, eta: 5:33:58, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7281\n2023-01-03 22:20:18,805 - mmcls - INFO - Epoch [55][2800/5005]\tlr: 1.000e-02, eta: 5:33:50, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7297\n2023-01-03 22:20:27,782 - mmcls - INFO - Epoch [55][2900/5005]\tlr: 1.000e-02, eta: 5:33:41, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7115\n2023-01-03 22:20:36,296 - mmcls - INFO - Epoch [55][3000/5005]\tlr: 1.000e-02, eta: 5:33:32, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7285\n2023-01-03 22:20:45,394 - mmcls - INFO - Epoch [55][3100/5005]\tlr: 1.000e-02, eta: 5:33:23, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7075\n2023-01-03 22:20:54,400 - mmcls - INFO - Epoch [55][3200/5005]\tlr: 1.000e-02, eta: 5:33:15, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7054\n2023-01-03 22:21:03,278 - mmcls - INFO - Epoch [55][3300/5005]\tlr: 1.000e-02, eta: 5:33:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6944\n2023-01-03 22:21:12,229 - mmcls - INFO - Epoch [55][3400/5005]\tlr: 1.000e-02, eta: 5:32:57, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.7448\n2023-01-03 22:21:21,155 - mmcls - INFO - Epoch [55][3500/5005]\tlr: 1.000e-02, eta: 5:32:48, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7489\n2023-01-03 22:21:29,857 - mmcls - INFO - Epoch [55][3600/5005]\tlr: 1.000e-02, eta: 5:32:40, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7094\n2023-01-03 22:21:38,709 - mmcls - INFO - Epoch [55][3700/5005]\tlr: 1.000e-02, eta: 5:32:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6976\n2023-01-03 22:21:47,305 - mmcls - INFO - Epoch [55][3800/5005]\tlr: 1.000e-02, eta: 5:32:22, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7244\n2023-01-03 22:21:56,088 - mmcls - INFO - Epoch [55][3900/5005]\tlr: 1.000e-02, eta: 5:32:13, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7363\n2023-01-03 22:22:04,773 - mmcls - INFO - Epoch [55][4000/5005]\tlr: 1.000e-02, eta: 5:32:04, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7236\n2023-01-03 22:22:13,405 - mmcls - INFO - Epoch [55][4100/5005]\tlr: 1.000e-02, eta: 5:31:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7361\n2023-01-03 22:22:21,954 - mmcls - INFO - Epoch [55][4200/5005]\tlr: 1.000e-02, eta: 5:31:46, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7390\n2023-01-03 22:22:30,468 - mmcls - INFO - Epoch [55][4300/5005]\tlr: 1.000e-02, eta: 5:31:37, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7292\n2023-01-03 22:22:39,124 - mmcls - INFO - Epoch [55][4400/5005]\tlr: 1.000e-02, eta: 5:31:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7350\n2023-01-03 22:22:47,732 - mmcls - INFO - Epoch [55][4500/5005]\tlr: 1.000e-02, eta: 5:31:19, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7437\n2023-01-03 22:22:56,486 - mmcls - INFO - Epoch [55][4600/5005]\tlr: 1.000e-02, eta: 5:31:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7514\n2023-01-03 22:23:05,000 - mmcls - INFO - Epoch [55][4700/5005]\tlr: 1.000e-02, eta: 5:31:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7292\n2023-01-03 22:23:13,728 - mmcls - INFO - Epoch [55][4800/5005]\tlr: 1.000e-02, eta: 5:30:52, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.6986\n2023-01-03 22:23:22,556 - mmcls - INFO - Epoch [55][4900/5005]\tlr: 1.000e-02, eta: 5:30:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7291\n2023-01-03 22:23:31,117 - mmcls - INFO - Epoch [55][5000/5005]\tlr: 1.000e-02, eta: 5:30:35, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7377\n2023-01-03 22:23:53,809 - mmcls - INFO - Epoch(val) [55][196]\taccuracy_top-1: 64.3660, accuracy_top-5: 85.9840\n2023-01-03 22:24:04,832 - mmcls - INFO - Epoch [56][100/5005]\tlr: 1.000e-02, eta: 5:30:27, time: 0.110, data_time: 0.068, memory: 1125, loss: 1.6785\n2023-01-03 22:24:13,397 - mmcls - INFO - Epoch [56][200/5005]\tlr: 1.000e-02, eta: 5:30:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6721\n2023-01-03 22:24:22,271 - mmcls - INFO - Epoch [56][300/5005]\tlr: 1.000e-02, eta: 5:30:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6759\n2023-01-03 22:24:31,389 - mmcls - INFO - Epoch [56][400/5005]\tlr: 1.000e-02, eta: 5:30:00, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.6730\n2023-01-03 22:24:40,000 - mmcls - INFO - Epoch [56][500/5005]\tlr: 1.000e-02, eta: 5:29:52, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6921\n2023-01-03 22:24:48,561 - mmcls - INFO - Epoch [56][600/5005]\tlr: 1.000e-02, eta: 5:29:43, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6836\n2023-01-03 22:24:57,104 - mmcls - INFO - Epoch [56][700/5005]\tlr: 1.000e-02, eta: 5:29:33, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6855\n2023-01-03 22:25:06,414 - mmcls - INFO - Epoch [56][800/5005]\tlr: 1.000e-02, eta: 5:29:25, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.6991\n2023-01-03 22:25:15,312 - mmcls - INFO - Epoch [56][900/5005]\tlr: 1.000e-02, eta: 5:29:16, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.6858\n2023-01-03 22:25:24,061 - mmcls - INFO - Epoch [56][1000/5005]\tlr: 1.000e-02, eta: 5:29:07, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7041\n2023-01-03 22:25:32,879 - mmcls - INFO - Epoch [56][1100/5005]\tlr: 1.000e-02, eta: 5:28:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6985\n2023-01-03 22:25:41,754 - mmcls - INFO - Epoch [56][1200/5005]\tlr: 1.000e-02, eta: 5:28:50, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.7047\n2023-01-03 22:25:50,559 - mmcls - INFO - Epoch [56][1300/5005]\tlr: 1.000e-02, eta: 5:28:41, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6972\n2023-01-03 22:25:59,430 - mmcls - INFO - Epoch [56][1400/5005]\tlr: 1.000e-02, eta: 5:28:32, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7286\n2023-01-03 22:26:08,061 - mmcls - INFO - Epoch [56][1500/5005]\tlr: 1.000e-02, eta: 5:28:23, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6891\n2023-01-03 22:26:16,835 - mmcls - INFO - Epoch [56][1600/5005]\tlr: 1.000e-02, eta: 5:28:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6979\n2023-01-03 22:26:25,299 - mmcls - INFO - Epoch [56][1700/5005]\tlr: 1.000e-02, eta: 5:28:06, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.6906\n2023-01-03 22:26:34,021 - mmcls - INFO - Epoch [56][1800/5005]\tlr: 1.000e-02, eta: 5:27:57, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7258\n2023-01-03 22:26:42,650 - mmcls - INFO - Epoch [56][1900/5005]\tlr: 1.000e-02, eta: 5:27:48, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7056\n2023-01-03 22:26:51,135 - mmcls - INFO - Epoch [56][2000/5005]\tlr: 1.000e-02, eta: 5:27:39, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.7178\n2023-01-03 22:26:59,743 - mmcls - INFO - Epoch [56][2100/5005]\tlr: 1.000e-02, eta: 5:27:30, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7026\n2023-01-03 22:27:08,516 - mmcls - INFO - Epoch [56][2200/5005]\tlr: 1.000e-02, eta: 5:27:21, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7172\n2023-01-03 22:27:17,322 - mmcls - INFO - Epoch [56][2300/5005]\tlr: 1.000e-02, eta: 5:27:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7190\n2023-01-03 22:27:25,783 - mmcls - INFO - Epoch [56][2400/5005]\tlr: 1.000e-02, eta: 5:27:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7246\n2023-01-03 22:27:34,365 - mmcls - INFO - Epoch [56][2500/5005]\tlr: 1.000e-02, eta: 5:26:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7044\n2023-01-03 22:27:43,061 - mmcls - INFO - Epoch [56][2600/5005]\tlr: 1.000e-02, eta: 5:26:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7099\n2023-01-03 22:27:51,527 - mmcls - INFO - Epoch [56][2700/5005]\tlr: 1.000e-02, eta: 5:26:36, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7443\n2023-01-03 22:28:00,628 - mmcls - INFO - Epoch [56][2800/5005]\tlr: 1.000e-02, eta: 5:26:27, time: 0.091, data_time: 0.022, memory: 1125, loss: 1.7345\n2023-01-03 22:28:09,362 - mmcls - INFO - Epoch [56][2900/5005]\tlr: 1.000e-02, eta: 5:26:19, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7083\n2023-01-03 22:28:18,224 - mmcls - INFO - Epoch [56][3000/5005]\tlr: 1.000e-02, eta: 5:26:10, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7162\n2023-01-03 22:28:26,863 - mmcls - INFO - Epoch [56][3100/5005]\tlr: 1.000e-02, eta: 5:26:01, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7262\n2023-01-03 22:28:35,362 - mmcls - INFO - Epoch [56][3200/5005]\tlr: 1.000e-02, eta: 5:25:52, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7081\n2023-01-03 22:28:44,241 - mmcls - INFO - Epoch [56][3300/5005]\tlr: 1.000e-02, eta: 5:25:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7130\n2023-01-03 22:28:52,844 - mmcls - INFO - Epoch [56][3400/5005]\tlr: 1.000e-02, eta: 5:25:34, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6964\n2023-01-03 22:29:01,628 - mmcls - INFO - Epoch [56][3500/5005]\tlr: 1.000e-02, eta: 5:25:25, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7123\n2023-01-03 22:29:10,485 - mmcls - INFO - Epoch [56][3600/5005]\tlr: 1.000e-02, eta: 5:25:16, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7168\n2023-01-03 22:29:19,264 - mmcls - INFO - Epoch [56][3700/5005]\tlr: 1.000e-02, eta: 5:25:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7282\n2023-01-03 22:29:27,921 - mmcls - INFO - Epoch [56][3800/5005]\tlr: 1.000e-02, eta: 5:24:59, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7237\n2023-01-03 22:29:36,750 - mmcls - INFO - Epoch [56][3900/5005]\tlr: 1.000e-02, eta: 5:24:50, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7072\n2023-01-03 22:29:45,606 - mmcls - INFO - Epoch [56][4000/5005]\tlr: 1.000e-02, eta: 5:24:41, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7144\n2023-01-03 22:29:54,161 - mmcls - INFO - Epoch [56][4100/5005]\tlr: 1.000e-02, eta: 5:24:32, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7612\n2023-01-03 22:30:03,290 - mmcls - INFO - Epoch [56][4200/5005]\tlr: 1.000e-02, eta: 5:24:24, time: 0.091, data_time: 0.035, memory: 1125, loss: 1.7361\n2023-01-03 22:30:12,018 - mmcls - INFO - Epoch [56][4300/5005]\tlr: 1.000e-02, eta: 5:24:15, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7038\n2023-01-03 22:30:20,793 - mmcls - INFO - Epoch [56][4400/5005]\tlr: 1.000e-02, eta: 5:24:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7344\n2023-01-03 22:30:29,525 - mmcls - INFO - Epoch [56][4500/5005]\tlr: 1.000e-02, eta: 5:23:57, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7268\n2023-01-03 22:30:38,434 - mmcls - INFO - Epoch [56][4600/5005]\tlr: 1.000e-02, eta: 5:23:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7292\n2023-01-03 22:30:47,054 - mmcls - INFO - Epoch [56][4700/5005]\tlr: 1.000e-02, eta: 5:23:39, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7083\n2023-01-03 22:30:55,821 - mmcls - INFO - Epoch [56][4800/5005]\tlr: 1.000e-02, eta: 5:23:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7325\n2023-01-03 22:31:04,704 - mmcls - INFO - Epoch [56][4900/5005]\tlr: 1.000e-02, eta: 5:23:22, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7329\n2023-01-03 22:31:13,421 - mmcls - INFO - Epoch [56][5000/5005]\tlr: 1.000e-02, eta: 5:23:13, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.7182\n2023-01-03 22:31:36,179 - mmcls - INFO - Epoch(val) [56][196]\taccuracy_top-1: 64.3240, accuracy_top-5: 86.0060\n2023-01-03 22:31:47,037 - mmcls - INFO - Epoch [57][100/5005]\tlr: 1.000e-02, eta: 5:23:05, time: 0.108, data_time: 0.037, memory: 1125, loss: 1.6682\n2023-01-03 22:31:55,634 - mmcls - INFO - Epoch [57][200/5005]\tlr: 1.000e-02, eta: 5:22:56, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.6713\n2023-01-03 22:32:04,576 - mmcls - INFO - Epoch [57][300/5005]\tlr: 1.000e-02, eta: 5:22:47, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.6915\n2023-01-03 22:32:13,481 - mmcls - INFO - Epoch [57][400/5005]\tlr: 1.000e-02, eta: 5:22:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6847\n2023-01-03 22:32:22,145 - mmcls - INFO - Epoch [57][500/5005]\tlr: 1.000e-02, eta: 5:22:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6921\n2023-01-03 22:32:31,037 - mmcls - INFO - Epoch [57][600/5005]\tlr: 1.000e-02, eta: 5:22:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6810\n2023-01-03 22:32:39,684 - mmcls - INFO - Epoch [57][700/5005]\tlr: 1.000e-02, eta: 5:22:12, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7080\n2023-01-03 22:32:48,902 - mmcls - INFO - Epoch [57][800/5005]\tlr: 1.000e-02, eta: 5:22:03, time: 0.092, data_time: 0.033, memory: 1125, loss: 1.6926\n2023-01-03 22:32:57,634 - mmcls - INFO - Epoch [57][900/5005]\tlr: 1.000e-02, eta: 5:21:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6717\n2023-01-03 22:33:06,428 - mmcls - INFO - Epoch [57][1000/5005]\tlr: 1.000e-02, eta: 5:21:46, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7044\n2023-01-03 22:33:15,254 - mmcls - INFO - Epoch [57][1100/5005]\tlr: 1.000e-02, eta: 5:21:37, time: 0.088, data_time: 0.046, memory: 1125, loss: 1.6918\n2023-01-03 22:33:23,876 - mmcls - INFO - Epoch [57][1200/5005]\tlr: 1.000e-02, eta: 5:21:28, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6773\n2023-01-03 22:33:32,810 - mmcls - INFO - Epoch [57][1300/5005]\tlr: 1.000e-02, eta: 5:21:19, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7259\n2023-01-03 22:33:41,754 - mmcls - INFO - Epoch [57][1400/5005]\tlr: 1.000e-02, eta: 5:21:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7184\n2023-01-03 22:33:50,613 - mmcls - INFO - Epoch [57][1500/5005]\tlr: 1.000e-02, eta: 5:21:02, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7104\n2023-01-03 22:33:59,332 - mmcls - INFO - Epoch [57][1600/5005]\tlr: 1.000e-02, eta: 5:20:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6809\n2023-01-03 22:34:08,770 - mmcls - INFO - Epoch [57][1700/5005]\tlr: 1.000e-02, eta: 5:20:45, time: 0.094, data_time: 0.008, memory: 1125, loss: 1.7151\n2023-01-03 22:34:17,534 - mmcls - INFO - Epoch [57][1800/5005]\tlr: 1.000e-02, eta: 5:20:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7029\n2023-01-03 22:34:26,148 - mmcls - INFO - Epoch [57][1900/5005]\tlr: 1.000e-02, eta: 5:20:27, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6806\n2023-01-03 22:34:34,809 - mmcls - INFO - Epoch [57][2000/5005]\tlr: 1.000e-02, eta: 5:20:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7210\n2023-01-03 22:34:43,457 - mmcls - INFO - Epoch [57][2100/5005]\tlr: 1.000e-02, eta: 5:20:09, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7005\n2023-01-03 22:34:52,075 - mmcls - INFO - Epoch [57][2200/5005]\tlr: 1.000e-02, eta: 5:20:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7238\n2023-01-03 22:35:00,953 - mmcls - INFO - Epoch [57][2300/5005]\tlr: 1.000e-02, eta: 5:19:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6902\n2023-01-03 22:35:09,984 - mmcls - INFO - Epoch [57][2400/5005]\tlr: 1.000e-02, eta: 5:19:43, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6950\n2023-01-03 22:35:18,930 - mmcls - INFO - Epoch [57][2500/5005]\tlr: 1.000e-02, eta: 5:19:34, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7030\n2023-01-03 22:35:27,692 - mmcls - INFO - Epoch [57][2600/5005]\tlr: 1.000e-02, eta: 5:19:25, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7328\n2023-01-03 22:35:36,256 - mmcls - INFO - Epoch [57][2700/5005]\tlr: 1.000e-02, eta: 5:19:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7376\n2023-01-03 22:35:44,868 - mmcls - INFO - Epoch [57][2800/5005]\tlr: 1.000e-02, eta: 5:19:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7178\n2023-01-03 22:35:53,692 - mmcls - INFO - Epoch [57][2900/5005]\tlr: 1.000e-02, eta: 5:18:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7292\n2023-01-03 22:36:02,378 - mmcls - INFO - Epoch [57][3000/5005]\tlr: 1.000e-02, eta: 5:18:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7217\n2023-01-03 22:36:11,154 - mmcls - INFO - Epoch [57][3100/5005]\tlr: 1.000e-02, eta: 5:18:41, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6983\n2023-01-03 22:36:19,847 - mmcls - INFO - Epoch [57][3200/5005]\tlr: 1.000e-02, eta: 5:18:32, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7136\n2023-01-03 22:36:28,498 - mmcls - INFO - Epoch [57][3300/5005]\tlr: 1.000e-02, eta: 5:18:23, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7069\n2023-01-03 22:36:37,349 - mmcls - INFO - Epoch [57][3400/5005]\tlr: 1.000e-02, eta: 5:18:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7043\n2023-01-03 22:36:46,436 - mmcls - INFO - Epoch [57][3500/5005]\tlr: 1.000e-02, eta: 5:18:05, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7233\n2023-01-03 22:36:55,324 - mmcls - INFO - Epoch [57][3600/5005]\tlr: 1.000e-02, eta: 5:17:57, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7242\n2023-01-03 22:37:04,505 - mmcls - INFO - Epoch [57][3700/5005]\tlr: 1.000e-02, eta: 5:17:48, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.7095\n2023-01-03 22:37:13,332 - mmcls - INFO - Epoch [57][3800/5005]\tlr: 1.000e-02, eta: 5:17:39, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7143\n2023-01-03 22:37:21,913 - mmcls - INFO - Epoch [57][3900/5005]\tlr: 1.000e-02, eta: 5:17:30, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7275\n2023-01-03 22:37:30,649 - mmcls - INFO - Epoch [57][4000/5005]\tlr: 1.000e-02, eta: 5:17:22, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7198\n2023-01-03 22:37:39,416 - mmcls - INFO - Epoch [57][4100/5005]\tlr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7171\n2023-01-03 22:37:47,856 - mmcls - INFO - Epoch [57][4200/5005]\tlr: 1.000e-02, eta: 5:17:04, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7192\n2023-01-03 22:37:56,550 - mmcls - INFO - Epoch [57][4300/5005]\tlr: 1.000e-02, eta: 5:16:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7234\n2023-01-03 22:38:05,890 - mmcls - INFO - Epoch [57][4400/5005]\tlr: 1.000e-02, eta: 5:16:46, time: 0.093, data_time: 0.031, memory: 1125, loss: 1.7280\n2023-01-03 22:38:14,464 - mmcls - INFO - Epoch [57][4500/5005]\tlr: 1.000e-02, eta: 5:16:37, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7180\n2023-01-03 22:38:23,171 - mmcls - INFO - Epoch [57][4600/5005]\tlr: 1.000e-02, eta: 5:16:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7045\n2023-01-03 22:38:32,197 - mmcls - INFO - Epoch [57][4700/5005]\tlr: 1.000e-02, eta: 5:16:20, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7189\n2023-01-03 22:38:41,458 - mmcls - INFO - Epoch [57][4800/5005]\tlr: 1.000e-02, eta: 5:16:11, time: 0.093, data_time: 0.020, memory: 1125, loss: 1.7300\n2023-01-03 22:38:50,120 - mmcls - INFO - Epoch [57][4900/5005]\tlr: 1.000e-02, eta: 5:16:02, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7159\n2023-01-03 22:38:58,701 - mmcls - INFO - Epoch [57][5000/5005]\tlr: 1.000e-02, eta: 5:15:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7464\n2023-01-03 22:39:21,392 - mmcls - INFO - Epoch(val) [57][196]\taccuracy_top-1: 64.9520, accuracy_top-5: 86.3100\n2023-01-03 22:39:32,348 - mmcls - INFO - Epoch [58][100/5005]\tlr: 1.000e-02, eta: 5:15:45, time: 0.109, data_time: 0.053, memory: 1125, loss: 1.6588\n2023-01-03 22:39:41,026 - mmcls - INFO - Epoch [58][200/5005]\tlr: 1.000e-02, eta: 5:15:37, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6869\n2023-01-03 22:39:49,877 - mmcls - INFO - Epoch [58][300/5005]\tlr: 1.000e-02, eta: 5:15:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6649\n2023-01-03 22:39:58,610 - mmcls - INFO - Epoch [58][400/5005]\tlr: 1.000e-02, eta: 5:15:19, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6670\n2023-01-03 22:40:07,153 - mmcls - INFO - Epoch [58][500/5005]\tlr: 1.000e-02, eta: 5:15:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6908\n2023-01-03 22:40:15,629 - mmcls - INFO - Epoch [58][600/5005]\tlr: 1.000e-02, eta: 5:15:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6897\n2023-01-03 22:40:24,395 - mmcls - INFO - Epoch [58][700/5005]\tlr: 1.000e-02, eta: 5:14:52, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6908\n2023-01-03 22:40:33,588 - mmcls - INFO - Epoch [58][800/5005]\tlr: 1.000e-02, eta: 5:14:43, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7041\n2023-01-03 22:40:42,574 - mmcls - INFO - Epoch [58][900/5005]\tlr: 1.000e-02, eta: 5:14:35, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.6956\n2023-01-03 22:40:51,454 - mmcls - INFO - Epoch [58][1000/5005]\tlr: 1.000e-02, eta: 5:14:26, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6851\n2023-01-03 22:41:00,362 - mmcls - INFO - Epoch [58][1100/5005]\tlr: 1.000e-02, eta: 5:14:17, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.6978\n2023-01-03 22:41:09,148 - mmcls - INFO - Epoch [58][1200/5005]\tlr: 1.000e-02, eta: 5:14:08, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6887\n2023-01-03 22:41:18,274 - mmcls - INFO - Epoch [58][1300/5005]\tlr: 1.000e-02, eta: 5:14:00, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6937\n2023-01-03 22:41:27,152 - mmcls - INFO - Epoch [58][1400/5005]\tlr: 1.000e-02, eta: 5:13:51, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.6991\n2023-01-03 22:41:35,720 - mmcls - INFO - Epoch [58][1500/5005]\tlr: 1.000e-02, eta: 5:13:42, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7134\n2023-01-03 22:41:44,173 - mmcls - INFO - Epoch [58][1600/5005]\tlr: 1.000e-02, eta: 5:13:33, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.7051\n2023-01-03 22:41:52,930 - mmcls - INFO - Epoch [58][1700/5005]\tlr: 1.000e-02, eta: 5:13:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7142\n2023-01-03 22:42:01,455 - mmcls - INFO - Epoch [58][1800/5005]\tlr: 1.000e-02, eta: 5:13:15, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7051\n2023-01-03 22:42:10,282 - mmcls - INFO - Epoch [58][1900/5005]\tlr: 1.000e-02, eta: 5:13:06, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.6800\n2023-01-03 22:42:19,050 - mmcls - INFO - Epoch [58][2000/5005]\tlr: 1.000e-02, eta: 5:12:58, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6991\n2023-01-03 22:42:28,262 - mmcls - INFO - Epoch [58][2100/5005]\tlr: 1.000e-02, eta: 5:12:49, time: 0.092, data_time: 0.019, memory: 1125, loss: 1.7033\n2023-01-03 22:42:37,069 - mmcls - INFO - Epoch [58][2200/5005]\tlr: 1.000e-02, eta: 5:12:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7260\n2023-01-03 22:42:45,614 - mmcls - INFO - Epoch [58][2300/5005]\tlr: 1.000e-02, eta: 5:12:31, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7104\n2023-01-03 22:42:54,373 - mmcls - INFO - Epoch [58][2400/5005]\tlr: 1.000e-02, eta: 5:12:22, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.6960\n2023-01-03 22:43:03,253 - mmcls - INFO - Epoch [58][2500/5005]\tlr: 1.000e-02, eta: 5:12:14, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.7235\n2023-01-03 22:43:12,329 - mmcls - INFO - Epoch [58][2600/5005]\tlr: 1.000e-02, eta: 5:12:05, time: 0.091, data_time: 0.041, memory: 1125, loss: 1.7177\n2023-01-03 22:43:20,872 - mmcls - INFO - Epoch [58][2700/5005]\tlr: 1.000e-02, eta: 5:11:56, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7056\n2023-01-03 22:43:29,418 - mmcls - INFO - Epoch [58][2800/5005]\tlr: 1.000e-02, eta: 5:11:47, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.6832\n2023-01-03 22:43:38,111 - mmcls - INFO - Epoch [58][2900/5005]\tlr: 1.000e-02, eta: 5:11:38, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7258\n2023-01-03 22:43:46,621 - mmcls - INFO - Epoch [58][3000/5005]\tlr: 1.000e-02, eta: 5:11:29, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7164\n2023-01-03 22:43:55,241 - mmcls - INFO - Epoch [58][3100/5005]\tlr: 1.000e-02, eta: 5:11:20, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7269\n2023-01-03 22:44:04,136 - mmcls - INFO - Epoch [58][3200/5005]\tlr: 1.000e-02, eta: 5:11:11, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7213\n2023-01-03 22:44:13,089 - mmcls - INFO - Epoch [58][3300/5005]\tlr: 1.000e-02, eta: 5:11:03, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7379\n2023-01-03 22:44:21,653 - mmcls - INFO - Epoch [58][3400/5005]\tlr: 1.000e-02, eta: 5:10:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7115\n2023-01-03 22:44:30,633 - mmcls - INFO - Epoch [58][3500/5005]\tlr: 1.000e-02, eta: 5:10:45, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6927\n2023-01-03 22:44:39,060 - mmcls - INFO - Epoch [58][3600/5005]\tlr: 1.000e-02, eta: 5:10:36, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.7267\n2023-01-03 22:44:47,803 - mmcls - INFO - Epoch [58][3700/5005]\tlr: 1.000e-02, eta: 5:10:27, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7159\n2023-01-03 22:44:57,069 - mmcls - INFO - Epoch [58][3800/5005]\tlr: 1.000e-02, eta: 5:10:19, time: 0.093, data_time: 0.023, memory: 1125, loss: 1.7268\n2023-01-03 22:45:05,720 - mmcls - INFO - Epoch [58][3900/5005]\tlr: 1.000e-02, eta: 5:10:10, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7130\n2023-01-03 22:45:14,238 - mmcls - INFO - Epoch [58][4000/5005]\tlr: 1.000e-02, eta: 5:10:01, time: 0.085, data_time: 0.042, memory: 1125, loss: 1.7202\n2023-01-03 22:45:23,450 - mmcls - INFO - Epoch [58][4100/5005]\tlr: 1.000e-02, eta: 5:09:52, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.7310\n2023-01-03 22:45:31,962 - mmcls - INFO - Epoch [58][4200/5005]\tlr: 1.000e-02, eta: 5:09:43, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7444\n2023-01-03 22:45:40,655 - mmcls - INFO - Epoch [58][4300/5005]\tlr: 1.000e-02, eta: 5:09:34, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6941\n2023-01-03 22:45:49,518 - mmcls - INFO - Epoch [58][4400/5005]\tlr: 1.000e-02, eta: 5:09:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7168\n2023-01-03 22:45:58,234 - mmcls - INFO - Epoch [58][4500/5005]\tlr: 1.000e-02, eta: 5:09:17, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7431\n2023-01-03 22:46:07,124 - mmcls - INFO - Epoch [58][4600/5005]\tlr: 1.000e-02, eta: 5:09:08, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7375\n2023-01-03 22:46:16,004 - mmcls - INFO - Epoch [58][4700/5005]\tlr: 1.000e-02, eta: 5:08:59, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7182\n2023-01-03 22:46:24,777 - mmcls - INFO - Epoch [58][4800/5005]\tlr: 1.000e-02, eta: 5:08:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7133\n2023-01-03 22:46:33,598 - mmcls - INFO - Epoch [58][4900/5005]\tlr: 1.000e-02, eta: 5:08:42, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7401\n2023-01-03 22:46:41,878 - mmcls - INFO - Epoch [58][5000/5005]\tlr: 1.000e-02, eta: 5:08:32, time: 0.083, data_time: 0.011, memory: 1125, loss: 1.7174\n2023-01-03 22:47:04,887 - mmcls - INFO - Epoch(val) [58][196]\taccuracy_top-1: 64.5600, accuracy_top-5: 85.8420\n2023-01-03 22:47:15,746 - mmcls - INFO - Epoch [59][100/5005]\tlr: 1.000e-02, eta: 5:08:24, time: 0.108, data_time: 0.056, memory: 1125, loss: 1.6690\n2023-01-03 22:47:24,598 - mmcls - INFO - Epoch [59][200/5005]\tlr: 1.000e-02, eta: 5:08:15, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.6632\n2023-01-03 22:47:33,327 - mmcls - INFO - Epoch [59][300/5005]\tlr: 1.000e-02, eta: 5:08:07, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6868\n2023-01-03 22:47:42,433 - mmcls - INFO - Epoch [59][400/5005]\tlr: 1.000e-02, eta: 5:07:58, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.6897\n2023-01-03 22:47:51,207 - mmcls - INFO - Epoch [59][500/5005]\tlr: 1.000e-02, eta: 5:07:49, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7004\n2023-01-03 22:47:59,734 - mmcls - INFO - Epoch [59][600/5005]\tlr: 1.000e-02, eta: 5:07:40, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6868\n2023-01-03 22:48:08,530 - mmcls - INFO - Epoch [59][700/5005]\tlr: 1.000e-02, eta: 5:07:31, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.6984\n2023-01-03 22:48:17,327 - mmcls - INFO - Epoch [59][800/5005]\tlr: 1.000e-02, eta: 5:07:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6671\n2023-01-03 22:48:26,122 - mmcls - INFO - Epoch [59][900/5005]\tlr: 1.000e-02, eta: 5:07:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7039\n2023-01-03 22:48:34,838 - mmcls - INFO - Epoch [59][1000/5005]\tlr: 1.000e-02, eta: 5:07:05, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7005\n2023-01-03 22:48:43,554 - mmcls - INFO - Epoch [59][1100/5005]\tlr: 1.000e-02, eta: 5:06:56, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.6879\n2023-01-03 22:48:52,118 - mmcls - INFO - Epoch [59][1200/5005]\tlr: 1.000e-02, eta: 5:06:47, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6851\n2023-01-03 22:49:01,096 - mmcls - INFO - Epoch [59][1300/5005]\tlr: 1.000e-02, eta: 5:06:38, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6915\n2023-01-03 22:49:09,694 - mmcls - INFO - Epoch [59][1400/5005]\tlr: 1.000e-02, eta: 5:06:29, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7276\n2023-01-03 22:49:18,463 - mmcls - INFO - Epoch [59][1500/5005]\tlr: 1.000e-02, eta: 5:06:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7272\n2023-01-03 22:49:27,389 - mmcls - INFO - Epoch [59][1600/5005]\tlr: 1.000e-02, eta: 5:06:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7049\n2023-01-03 22:49:36,141 - mmcls - INFO - Epoch [59][1700/5005]\tlr: 1.000e-02, eta: 5:06:03, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7007\n2023-01-03 22:49:45,316 - mmcls - INFO - Epoch [59][1800/5005]\tlr: 1.000e-02, eta: 5:05:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7300\n2023-01-03 22:49:54,154 - mmcls - INFO - Epoch [59][1900/5005]\tlr: 1.000e-02, eta: 5:05:46, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7244\n2023-01-03 22:50:03,027 - mmcls - INFO - Epoch [59][2000/5005]\tlr: 1.000e-02, eta: 5:05:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7206\n2023-01-03 22:50:12,087 - mmcls - INFO - Epoch [59][2100/5005]\tlr: 1.000e-02, eta: 5:05:28, time: 0.091, data_time: 0.022, memory: 1125, loss: 1.7107\n2023-01-03 22:50:20,703 - mmcls - INFO - Epoch [59][2200/5005]\tlr: 1.000e-02, eta: 5:05:19, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6863\n2023-01-03 22:50:29,757 - mmcls - INFO - Epoch [59][2300/5005]\tlr: 1.000e-02, eta: 5:05:11, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7243\n2023-01-03 22:50:38,268 - mmcls - INFO - Epoch [59][2400/5005]\tlr: 1.000e-02, eta: 5:05:02, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7032\n2023-01-03 22:50:47,138 - mmcls - INFO - Epoch [59][2500/5005]\tlr: 1.000e-02, eta: 5:04:53, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6895\n2023-01-03 22:50:55,787 - mmcls - INFO - Epoch [59][2600/5005]\tlr: 1.000e-02, eta: 5:04:44, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6998\n2023-01-03 22:51:04,496 - mmcls - INFO - Epoch [59][2700/5005]\tlr: 1.000e-02, eta: 5:04:35, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7026\n2023-01-03 22:51:13,066 - mmcls - INFO - Epoch [59][2800/5005]\tlr: 1.000e-02, eta: 5:04:26, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7093\n2023-01-03 22:51:21,940 - mmcls - INFO - Epoch [59][2900/5005]\tlr: 1.000e-02, eta: 5:04:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6967\n2023-01-03 22:51:30,751 - mmcls - INFO - Epoch [59][3000/5005]\tlr: 1.000e-02, eta: 5:04:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7075\n2023-01-03 22:51:39,744 - mmcls - INFO - Epoch [59][3100/5005]\tlr: 1.000e-02, eta: 5:04:00, time: 0.090, data_time: 0.044, memory: 1125, loss: 1.7190\n2023-01-03 22:51:48,325 - mmcls - INFO - Epoch [59][3200/5005]\tlr: 1.000e-02, eta: 5:03:51, time: 0.086, data_time: 0.044, memory: 1125, loss: 1.6823\n2023-01-03 22:51:57,007 - mmcls - INFO - Epoch [59][3300/5005]\tlr: 1.000e-02, eta: 5:03:42, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7047\n2023-01-03 22:52:05,839 - mmcls - INFO - Epoch [59][3400/5005]\tlr: 1.000e-02, eta: 5:03:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7052\n2023-01-03 22:52:14,444 - mmcls - INFO - Epoch [59][3500/5005]\tlr: 1.000e-02, eta: 5:03:24, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.6989\n2023-01-03 22:52:22,983 - mmcls - INFO - Epoch [59][3600/5005]\tlr: 1.000e-02, eta: 5:03:15, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6866\n2023-01-03 22:52:31,769 - mmcls - INFO - Epoch [59][3700/5005]\tlr: 1.000e-02, eta: 5:03:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6921\n2023-01-03 22:52:40,503 - mmcls - INFO - Epoch [59][3800/5005]\tlr: 1.000e-02, eta: 5:02:58, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6987\n2023-01-03 22:52:49,384 - mmcls - INFO - Epoch [59][3900/5005]\tlr: 1.000e-02, eta: 5:02:49, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7369\n2023-01-03 22:52:58,254 - mmcls - INFO - Epoch [59][4000/5005]\tlr: 1.000e-02, eta: 5:02:40, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7192\n2023-01-03 22:53:06,663 - mmcls - INFO - Epoch [59][4100/5005]\tlr: 1.000e-02, eta: 5:02:31, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7136\n2023-01-03 22:53:15,371 - mmcls - INFO - Epoch [59][4200/5005]\tlr: 1.000e-02, eta: 5:02:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7202\n2023-01-03 22:53:24,149 - mmcls - INFO - Epoch [59][4300/5005]\tlr: 1.000e-02, eta: 5:02:13, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.6987\n2023-01-03 22:53:33,008 - mmcls - INFO - Epoch [59][4400/5005]\tlr: 1.000e-02, eta: 5:02:05, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7312\n2023-01-03 22:53:41,846 - mmcls - INFO - Epoch [59][4500/5005]\tlr: 1.000e-02, eta: 5:01:56, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7305\n2023-01-03 22:53:51,239 - mmcls - INFO - Epoch [59][4600/5005]\tlr: 1.000e-02, eta: 5:01:47, time: 0.094, data_time: 0.049, memory: 1125, loss: 1.7369\n2023-01-03 22:53:59,894 - mmcls - INFO - Epoch [59][4700/5005]\tlr: 1.000e-02, eta: 5:01:38, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6947\n2023-01-03 22:54:08,991 - mmcls - INFO - Epoch [59][4800/5005]\tlr: 1.000e-02, eta: 5:01:30, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7252\n2023-01-03 22:54:17,653 - mmcls - INFO - Epoch [59][4900/5005]\tlr: 1.000e-02, eta: 5:01:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7104\n2023-01-03 22:54:26,488 - mmcls - INFO - Epoch [59][5000/5005]\tlr: 1.000e-02, eta: 5:01:12, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7263\n2023-01-03 22:54:49,113 - mmcls - INFO - Epoch(val) [59][196]\taccuracy_top-1: 64.8380, accuracy_top-5: 86.2560\n2023-01-03 22:55:00,327 - mmcls - INFO - Epoch [60][100/5005]\tlr: 1.000e-02, eta: 5:01:04, time: 0.112, data_time: 0.059, memory: 1125, loss: 1.6720\n2023-01-03 22:55:09,223 - mmcls - INFO - Epoch [60][200/5005]\tlr: 1.000e-02, eta: 5:00:55, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.6973\n2023-01-03 22:55:18,289 - mmcls - INFO - Epoch [60][300/5005]\tlr: 1.000e-02, eta: 5:00:47, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.6854\n2023-01-03 22:55:26,750 - mmcls - INFO - Epoch [60][400/5005]\tlr: 1.000e-02, eta: 5:00:38, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7162\n2023-01-03 22:55:35,584 - mmcls - INFO - Epoch [60][500/5005]\tlr: 1.000e-02, eta: 5:00:29, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6852\n2023-01-03 22:55:44,251 - mmcls - INFO - Epoch [60][600/5005]\tlr: 1.000e-02, eta: 5:00:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6818\n2023-01-03 22:55:52,943 - mmcls - INFO - Epoch [60][700/5005]\tlr: 1.000e-02, eta: 5:00:11, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6736\n2023-01-03 22:56:01,749 - mmcls - INFO - Epoch [60][800/5005]\tlr: 1.000e-02, eta: 5:00:02, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.6885\n2023-01-03 22:56:10,340 - mmcls - INFO - Epoch [60][900/5005]\tlr: 1.000e-02, eta: 4:59:53, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.6882\n2023-01-03 22:56:18,801 - mmcls - INFO - Epoch [60][1000/5005]\tlr: 1.000e-02, eta: 4:59:44, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.7145\n2023-01-03 22:56:27,869 - mmcls - INFO - Epoch [60][1100/5005]\tlr: 1.000e-02, eta: 4:59:36, time: 0.091, data_time: 0.036, memory: 1125, loss: 1.6719\n2023-01-03 22:56:37,267 - mmcls - INFO - Epoch [60][1200/5005]\tlr: 1.000e-02, eta: 4:59:27, time: 0.094, data_time: 0.018, memory: 1125, loss: 1.7053\n2023-01-03 22:56:45,845 - mmcls - INFO - Epoch [60][1300/5005]\tlr: 1.000e-02, eta: 4:59:18, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6853\n2023-01-03 22:56:54,525 - mmcls - INFO - Epoch [60][1400/5005]\tlr: 1.000e-02, eta: 4:59:10, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6966\n2023-01-03 22:57:03,547 - mmcls - INFO - Epoch [60][1500/5005]\tlr: 1.000e-02, eta: 4:59:01, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7284\n2023-01-03 22:57:12,686 - mmcls - INFO - Epoch [60][1600/5005]\tlr: 1.000e-02, eta: 4:58:52, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6891\n2023-01-03 22:57:21,982 - mmcls - INFO - Epoch [60][1700/5005]\tlr: 1.000e-02, eta: 4:58:44, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.6939\n2023-01-03 22:57:30,724 - mmcls - INFO - Epoch [60][1800/5005]\tlr: 1.000e-02, eta: 4:58:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7088\n2023-01-03 22:57:39,391 - mmcls - INFO - Epoch [60][1900/5005]\tlr: 1.000e-02, eta: 4:58:26, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6877\n2023-01-03 22:57:48,494 - mmcls - INFO - Epoch [60][2000/5005]\tlr: 1.000e-02, eta: 4:58:17, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7129\n2023-01-03 22:57:57,209 - mmcls - INFO - Epoch [60][2100/5005]\tlr: 1.000e-02, eta: 4:58:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6929\n2023-01-03 22:58:05,698 - mmcls - INFO - Epoch [60][2200/5005]\tlr: 1.000e-02, eta: 4:58:00, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6952\n2023-01-03 22:58:14,641 - mmcls - INFO - Epoch [60][2300/5005]\tlr: 1.000e-02, eta: 4:57:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7084\n2023-01-03 22:58:23,439 - mmcls - INFO - Epoch [60][2400/5005]\tlr: 1.000e-02, eta: 4:57:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7009\n2023-01-03 22:58:31,860 - mmcls - INFO - Epoch [60][2500/5005]\tlr: 1.000e-02, eta: 4:57:33, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.6993\n2023-01-03 22:58:40,784 - mmcls - INFO - Epoch [60][2600/5005]\tlr: 1.000e-02, eta: 4:57:24, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7020\n2023-01-03 22:58:49,604 - mmcls - INFO - Epoch [60][2700/5005]\tlr: 1.000e-02, eta: 4:57:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7092\n2023-01-03 22:58:58,056 - mmcls - INFO - Epoch [60][2800/5005]\tlr: 1.000e-02, eta: 4:57:06, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7260\n2023-01-03 22:59:07,308 - mmcls - INFO - Epoch [60][2900/5005]\tlr: 1.000e-02, eta: 4:56:58, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.7302\n2023-01-03 22:59:16,077 - mmcls - INFO - Epoch [60][3000/5005]\tlr: 1.000e-02, eta: 4:56:49, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.6914\n2023-01-03 22:59:24,779 - mmcls - INFO - Epoch [60][3100/5005]\tlr: 1.000e-02, eta: 4:56:40, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7303\n2023-01-03 22:59:33,824 - mmcls - INFO - Epoch [60][3200/5005]\tlr: 1.000e-02, eta: 4:56:31, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.7060\n2023-01-03 22:59:42,929 - mmcls - INFO - Epoch [60][3300/5005]\tlr: 1.000e-02, eta: 4:56:23, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.7232\n2023-01-03 22:59:51,517 - mmcls - INFO - Epoch [60][3400/5005]\tlr: 1.000e-02, eta: 4:56:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7225\n2023-01-03 23:00:00,191 - mmcls - INFO - Epoch [60][3500/5005]\tlr: 1.000e-02, eta: 4:56:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7130\n2023-01-03 23:00:09,062 - mmcls - INFO - Epoch [60][3600/5005]\tlr: 1.000e-02, eta: 4:55:56, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7257\n2023-01-03 23:00:17,668 - mmcls - INFO - Epoch [60][3700/5005]\tlr: 1.000e-02, eta: 4:55:47, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7262\n2023-01-03 23:00:26,272 - mmcls - INFO - Epoch [60][3800/5005]\tlr: 1.000e-02, eta: 4:55:38, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7019\n2023-01-03 23:00:34,926 - mmcls - INFO - Epoch [60][3900/5005]\tlr: 1.000e-02, eta: 4:55:29, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.7153\n2023-01-03 23:00:43,581 - mmcls - INFO - Epoch [60][4000/5005]\tlr: 1.000e-02, eta: 4:55:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7129\n2023-01-03 23:00:52,466 - mmcls - INFO - Epoch [60][4100/5005]\tlr: 1.000e-02, eta: 4:55:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7137\n2023-01-03 23:01:01,562 - mmcls - INFO - Epoch [60][4200/5005]\tlr: 1.000e-02, eta: 4:55:03, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6994\n2023-01-03 23:01:10,105 - mmcls - INFO - Epoch [60][4300/5005]\tlr: 1.000e-02, eta: 4:54:54, time: 0.085, data_time: 0.031, memory: 1125, loss: 1.7449\n2023-01-03 23:01:19,003 - mmcls - INFO - Epoch [60][4400/5005]\tlr: 1.000e-02, eta: 4:54:45, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7293\n2023-01-03 23:01:27,789 - mmcls - INFO - Epoch [60][4500/5005]\tlr: 1.000e-02, eta: 4:54:37, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7170\n2023-01-03 23:01:36,462 - mmcls - INFO - Epoch [60][4600/5005]\tlr: 1.000e-02, eta: 4:54:28, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7053\n2023-01-03 23:01:44,994 - mmcls - INFO - Epoch [60][4700/5005]\tlr: 1.000e-02, eta: 4:54:19, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7260\n2023-01-03 23:01:53,962 - mmcls - INFO - Epoch [60][4800/5005]\tlr: 1.000e-02, eta: 4:54:10, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7207\n2023-01-03 23:02:02,731 - mmcls - INFO - Epoch [60][4900/5005]\tlr: 1.000e-02, eta: 4:54:01, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7183\n2023-01-03 23:02:11,408 - mmcls - INFO - Epoch [60][5000/5005]\tlr: 1.000e-02, eta: 4:53:52, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7110\n2023-01-03 23:02:34,061 - mmcls - INFO - Epoch(val) [60][196]\taccuracy_top-1: 64.5160, accuracy_top-5: 86.0500\n2023-01-03 23:02:44,912 - mmcls - INFO - Epoch [61][100/5005]\tlr: 1.000e-03, eta: 4:53:44, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.6283\n2023-01-03 23:02:53,634 - mmcls - INFO - Epoch [61][200/5005]\tlr: 1.000e-03, eta: 4:53:35, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6135\n2023-01-03 23:03:02,558 - mmcls - INFO - Epoch [61][300/5005]\tlr: 1.000e-03, eta: 4:53:27, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.6041\n2023-01-03 23:03:11,309 - mmcls - INFO - Epoch [61][400/5005]\tlr: 1.000e-03, eta: 4:53:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.5656\n2023-01-03 23:03:20,062 - mmcls - INFO - Epoch [61][500/5005]\tlr: 1.000e-03, eta: 4:53:09, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5597\n2023-01-03 23:03:28,887 - mmcls - INFO - Epoch [61][600/5005]\tlr: 1.000e-03, eta: 4:53:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.5589\n2023-01-03 23:03:37,544 - mmcls - INFO - Epoch [61][700/5005]\tlr: 1.000e-03, eta: 4:52:51, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.5348\n2023-01-03 23:03:46,696 - mmcls - INFO - Epoch [61][800/5005]\tlr: 1.000e-03, eta: 4:52:43, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.5601\n2023-01-03 23:03:55,267 - mmcls - INFO - Epoch [61][900/5005]\tlr: 1.000e-03, eta: 4:52:34, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.5592\n2023-01-03 23:04:03,736 - mmcls - INFO - Epoch [61][1000/5005]\tlr: 1.000e-03, eta: 4:52:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.5277\n2023-01-03 23:04:12,433 - mmcls - INFO - Epoch [61][1100/5005]\tlr: 1.000e-03, eta: 4:52:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.5190\n2023-01-03 23:04:21,194 - mmcls - INFO - Epoch [61][1200/5005]\tlr: 1.000e-03, eta: 4:52:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.5361\n2023-01-03 23:04:30,023 - mmcls - INFO - Epoch [61][1300/5005]\tlr: 1.000e-03, eta: 4:51:58, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5436\n2023-01-03 23:04:38,526 - mmcls - INFO - Epoch [61][1400/5005]\tlr: 1.000e-03, eta: 4:51:49, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.5230\n2023-01-03 23:04:47,395 - mmcls - INFO - Epoch [61][1500/5005]\tlr: 1.000e-03, eta: 4:51:40, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.5286\n2023-01-03 23:04:56,088 - mmcls - INFO - Epoch [61][1600/5005]\tlr: 1.000e-03, eta: 4:51:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.5275\n2023-01-03 23:05:04,750 - mmcls - INFO - Epoch [61][1700/5005]\tlr: 1.000e-03, eta: 4:51:22, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.5388\n2023-01-03 23:05:13,402 - mmcls - INFO - Epoch [61][1800/5005]\tlr: 1.000e-03, eta: 4:51:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.5344\n2023-01-03 23:05:22,358 - mmcls - INFO - Epoch [61][1900/5005]\tlr: 1.000e-03, eta: 4:51:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.5154\n2023-01-03 23:05:30,761 - mmcls - INFO - Epoch [61][2000/5005]\tlr: 1.000e-03, eta: 4:50:56, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.5379\n2023-01-03 23:05:39,468 - mmcls - INFO - Epoch [61][2100/5005]\tlr: 1.000e-03, eta: 4:50:47, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.5126\n2023-01-03 23:05:48,333 - mmcls - INFO - Epoch [61][2200/5005]\tlr: 1.000e-03, eta: 4:50:38, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.5258\n2023-01-03 23:05:56,938 - mmcls - INFO - Epoch [61][2300/5005]\tlr: 1.000e-03, eta: 4:50:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.5283\n2023-01-03 23:06:05,649 - mmcls - INFO - Epoch [61][2400/5005]\tlr: 1.000e-03, eta: 4:50:20, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.5089\n2023-01-03 23:06:14,506 - mmcls - INFO - Epoch [61][2500/5005]\tlr: 1.000e-03, eta: 4:50:12, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.5145\n2023-01-03 23:06:23,367 - mmcls - INFO - Epoch [61][2600/5005]\tlr: 1.000e-03, eta: 4:50:03, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.5124\n2023-01-03 23:06:32,212 - mmcls - INFO - Epoch [61][2700/5005]\tlr: 1.000e-03, eta: 4:49:54, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4983\n2023-01-03 23:06:41,029 - mmcls - INFO - Epoch [61][2800/5005]\tlr: 1.000e-03, eta: 4:49:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5002\n2023-01-03 23:06:49,746 - mmcls - INFO - Epoch [61][2900/5005]\tlr: 1.000e-03, eta: 4:49:36, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.5000\n2023-01-03 23:06:58,543 - mmcls - INFO - Epoch [61][3000/5005]\tlr: 1.000e-03, eta: 4:49:27, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.5094\n2023-01-03 23:07:07,186 - mmcls - INFO - Epoch [61][3100/5005]\tlr: 1.000e-03, eta: 4:49:19, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.5018\n2023-01-03 23:07:16,023 - mmcls - INFO - Epoch [61][3200/5005]\tlr: 1.000e-03, eta: 4:49:10, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4998\n2023-01-03 23:07:24,678 - mmcls - INFO - Epoch [61][3300/5005]\tlr: 1.000e-03, eta: 4:49:01, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.5159\n2023-01-03 23:07:33,493 - mmcls - INFO - Epoch [61][3400/5005]\tlr: 1.000e-03, eta: 4:48:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5100\n2023-01-03 23:07:42,398 - mmcls - INFO - Epoch [61][3500/5005]\tlr: 1.000e-03, eta: 4:48:43, time: 0.089, data_time: 0.036, memory: 1125, loss: 1.5207\n2023-01-03 23:07:51,054 - mmcls - INFO - Epoch [61][3600/5005]\tlr: 1.000e-03, eta: 4:48:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.5188\n2023-01-03 23:08:00,195 - mmcls - INFO - Epoch [61][3700/5005]\tlr: 1.000e-03, eta: 4:48:26, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.5077\n2023-01-03 23:08:09,189 - mmcls - INFO - Epoch [61][3800/5005]\tlr: 1.000e-03, eta: 4:48:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4894\n2023-01-03 23:08:18,039 - mmcls - INFO - Epoch [61][3900/5005]\tlr: 1.000e-03, eta: 4:48:08, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.5112\n2023-01-03 23:08:26,845 - mmcls - INFO - Epoch [61][4000/5005]\tlr: 1.000e-03, eta: 4:48:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.5190\n2023-01-03 23:08:35,496 - mmcls - INFO - Epoch [61][4100/5005]\tlr: 1.000e-03, eta: 4:47:51, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.5382\n2023-01-03 23:08:44,081 - mmcls - INFO - Epoch [61][4200/5005]\tlr: 1.000e-03, eta: 4:47:42, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.4943\n2023-01-03 23:08:52,779 - mmcls - INFO - Epoch [61][4300/5005]\tlr: 1.000e-03, eta: 4:47:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4910\n2023-01-03 23:09:01,514 - mmcls - INFO - Epoch [61][4400/5005]\tlr: 1.000e-03, eta: 4:47:24, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4794\n2023-01-03 23:09:10,319 - mmcls - INFO - Epoch [61][4500/5005]\tlr: 1.000e-03, eta: 4:47:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4930\n2023-01-03 23:09:18,953 - mmcls - INFO - Epoch [61][4600/5005]\tlr: 1.000e-03, eta: 4:47:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.5025\n2023-01-03 23:09:27,942 - mmcls - INFO - Epoch [61][4700/5005]\tlr: 1.000e-03, eta: 4:46:58, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4746\n2023-01-03 23:09:36,785 - mmcls - INFO - Epoch [61][4800/5005]\tlr: 1.000e-03, eta: 4:46:49, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.5194\n2023-01-03 23:09:45,620 - mmcls - INFO - Epoch [61][4900/5005]\tlr: 1.000e-03, eta: 4:46:40, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4992\n2023-01-03 23:09:54,190 - mmcls - INFO - Epoch [61][5000/5005]\tlr: 1.000e-03, eta: 4:46:31, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.5144\n2023-01-03 23:10:16,850 - mmcls - INFO - Epoch(val) [61][196]\taccuracy_top-1: 68.3360, accuracy_top-5: 88.3880\n2023-01-03 23:10:28,154 - mmcls - INFO - Epoch [62][100/5005]\tlr: 1.000e-03, eta: 4:46:23, time: 0.113, data_time: 0.073, memory: 1125, loss: 1.4986\n2023-01-03 23:10:36,892 - mmcls - INFO - Epoch [62][200/5005]\tlr: 1.000e-03, eta: 4:46:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.5084\n2023-01-03 23:10:45,960 - mmcls - INFO - Epoch [62][300/5005]\tlr: 1.000e-03, eta: 4:46:06, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4721\n2023-01-03 23:10:54,683 - mmcls - INFO - Epoch [62][400/5005]\tlr: 1.000e-03, eta: 4:45:57, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4802\n2023-01-03 23:11:03,497 - mmcls - INFO - Epoch [62][500/5005]\tlr: 1.000e-03, eta: 4:45:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4736\n2023-01-03 23:11:12,002 - mmcls - INFO - Epoch [62][600/5005]\tlr: 1.000e-03, eta: 4:45:39, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.4710\n2023-01-03 23:11:20,744 - mmcls - INFO - Epoch [62][700/5005]\tlr: 1.000e-03, eta: 4:45:30, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4802\n2023-01-03 23:11:29,460 - mmcls - INFO - Epoch [62][800/5005]\tlr: 1.000e-03, eta: 4:45:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4640\n2023-01-03 23:11:38,052 - mmcls - INFO - Epoch [62][900/5005]\tlr: 1.000e-03, eta: 4:45:12, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4786\n2023-01-03 23:11:46,869 - mmcls - INFO - Epoch [62][1000/5005]\tlr: 1.000e-03, eta: 4:45:03, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4941\n2023-01-03 23:11:55,899 - mmcls - INFO - Epoch [62][1100/5005]\tlr: 1.000e-03, eta: 4:44:55, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.4792\n2023-01-03 23:12:04,935 - mmcls - INFO - Epoch [62][1200/5005]\tlr: 1.000e-03, eta: 4:44:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4749\n2023-01-03 23:12:13,522 - mmcls - INFO - Epoch [62][1300/5005]\tlr: 1.000e-03, eta: 4:44:37, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.4854\n2023-01-03 23:12:22,126 - mmcls - INFO - Epoch [62][1400/5005]\tlr: 1.000e-03, eta: 4:44:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4839\n2023-01-03 23:12:30,899 - mmcls - INFO - Epoch [62][1500/5005]\tlr: 1.000e-03, eta: 4:44:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4791\n2023-01-03 23:12:39,553 - mmcls - INFO - Epoch [62][1600/5005]\tlr: 1.000e-03, eta: 4:44:10, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4800\n2023-01-03 23:12:48,260 - mmcls - INFO - Epoch [62][1700/5005]\tlr: 1.000e-03, eta: 4:44:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4981\n2023-01-03 23:12:57,017 - mmcls - INFO - Epoch [62][1800/5005]\tlr: 1.000e-03, eta: 4:43:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4967\n2023-01-03 23:13:05,793 - mmcls - INFO - Epoch [62][1900/5005]\tlr: 1.000e-03, eta: 4:43:44, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.5065\n2023-01-03 23:13:14,337 - mmcls - INFO - Epoch [62][2000/5005]\tlr: 1.000e-03, eta: 4:43:35, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4792\n2023-01-03 23:13:22,881 - mmcls - INFO - Epoch [62][2100/5005]\tlr: 1.000e-03, eta: 4:43:26, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.4731\n2023-01-03 23:13:31,645 - mmcls - INFO - Epoch [62][2200/5005]\tlr: 1.000e-03, eta: 4:43:17, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4993\n2023-01-03 23:13:40,658 - mmcls - INFO - Epoch [62][2300/5005]\tlr: 1.000e-03, eta: 4:43:08, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.4810\n2023-01-03 23:13:49,410 - mmcls - INFO - Epoch [62][2400/5005]\tlr: 1.000e-03, eta: 4:43:00, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4652\n2023-01-03 23:13:57,918 - mmcls - INFO - Epoch [62][2500/5005]\tlr: 1.000e-03, eta: 4:42:51, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4905\n2023-01-03 23:14:06,359 - mmcls - INFO - Epoch [62][2600/5005]\tlr: 1.000e-03, eta: 4:42:42, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.4600\n2023-01-03 23:14:15,051 - mmcls - INFO - Epoch [62][2700/5005]\tlr: 1.000e-03, eta: 4:42:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4658\n2023-01-03 23:14:23,954 - mmcls - INFO - Epoch [62][2800/5005]\tlr: 1.000e-03, eta: 4:42:24, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4620\n2023-01-03 23:14:32,859 - mmcls - INFO - Epoch [62][2900/5005]\tlr: 1.000e-03, eta: 4:42:15, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4917\n2023-01-03 23:14:41,668 - mmcls - INFO - Epoch [62][3000/5005]\tlr: 1.000e-03, eta: 4:42:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4850\n2023-01-03 23:14:50,488 - mmcls - INFO - Epoch [62][3100/5005]\tlr: 1.000e-03, eta: 4:41:58, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4776\n2023-01-03 23:14:59,126 - mmcls - INFO - Epoch [62][3200/5005]\tlr: 1.000e-03, eta: 4:41:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4743\n2023-01-03 23:15:07,717 - mmcls - INFO - Epoch [62][3300/5005]\tlr: 1.000e-03, eta: 4:41:40, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4988\n2023-01-03 23:15:16,378 - mmcls - INFO - Epoch [62][3400/5005]\tlr: 1.000e-03, eta: 4:41:31, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.4860\n2023-01-03 23:15:25,064 - mmcls - INFO - Epoch [62][3500/5005]\tlr: 1.000e-03, eta: 4:41:22, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.4669\n2023-01-03 23:15:33,863 - mmcls - INFO - Epoch [62][3600/5005]\tlr: 1.000e-03, eta: 4:41:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4848\n2023-01-03 23:15:42,493 - mmcls - INFO - Epoch [62][3700/5005]\tlr: 1.000e-03, eta: 4:41:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4887\n2023-01-03 23:15:51,805 - mmcls - INFO - Epoch [62][3800/5005]\tlr: 1.000e-03, eta: 4:40:56, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.4792\n2023-01-03 23:16:00,602 - mmcls - INFO - Epoch [62][3900/5005]\tlr: 1.000e-03, eta: 4:40:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4845\n2023-01-03 23:16:09,303 - mmcls - INFO - Epoch [62][4000/5005]\tlr: 1.000e-03, eta: 4:40:38, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4770\n2023-01-03 23:16:18,069 - mmcls - INFO - Epoch [62][4100/5005]\tlr: 1.000e-03, eta: 4:40:29, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.4832\n2023-01-03 23:16:26,916 - mmcls - INFO - Epoch [62][4200/5005]\tlr: 1.000e-03, eta: 4:40:20, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4889\n2023-01-03 23:16:35,769 - mmcls - INFO - Epoch [62][4300/5005]\tlr: 1.000e-03, eta: 4:40:12, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4559\n2023-01-03 23:16:44,374 - mmcls - INFO - Epoch [62][4400/5005]\tlr: 1.000e-03, eta: 4:40:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4939\n2023-01-03 23:16:52,929 - mmcls - INFO - Epoch [62][4500/5005]\tlr: 1.000e-03, eta: 4:39:54, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4576\n2023-01-03 23:17:01,750 - mmcls - INFO - Epoch [62][4600/5005]\tlr: 1.000e-03, eta: 4:39:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4716\n2023-01-03 23:17:10,626 - mmcls - INFO - Epoch [62][4700/5005]\tlr: 1.000e-03, eta: 4:39:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4879\n2023-01-03 23:17:19,580 - mmcls - INFO - Epoch [62][4800/5005]\tlr: 1.000e-03, eta: 4:39:28, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4642\n2023-01-03 23:17:27,999 - mmcls - INFO - Epoch [62][4900/5005]\tlr: 1.000e-03, eta: 4:39:18, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.4651\n2023-01-03 23:17:36,945 - mmcls - INFO - Epoch [62][5000/5005]\tlr: 1.000e-03, eta: 4:39:10, time: 0.089, data_time: 0.045, memory: 1125, loss: 1.4673\n2023-01-03 23:17:59,575 - mmcls - INFO - Epoch(val) [62][196]\taccuracy_top-1: 68.7500, accuracy_top-5: 88.5480\n2023-01-03 23:18:10,440 - mmcls - INFO - Epoch [63][100/5005]\tlr: 1.000e-03, eta: 4:39:01, time: 0.108, data_time: 0.049, memory: 1125, loss: 1.4619\n2023-01-03 23:18:19,199 - mmcls - INFO - Epoch [63][200/5005]\tlr: 1.000e-03, eta: 4:38:53, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4429\n2023-01-03 23:18:27,801 - mmcls - INFO - Epoch [63][300/5005]\tlr: 1.000e-03, eta: 4:38:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4700\n2023-01-03 23:18:36,668 - mmcls - INFO - Epoch [63][400/5005]\tlr: 1.000e-03, eta: 4:38:35, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4708\n2023-01-03 23:18:45,377 - mmcls - INFO - Epoch [63][500/5005]\tlr: 1.000e-03, eta: 4:38:26, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.4471\n2023-01-03 23:18:54,232 - mmcls - INFO - Epoch [63][600/5005]\tlr: 1.000e-03, eta: 4:38:17, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4494\n2023-01-03 23:19:03,156 - mmcls - INFO - Epoch [63][700/5005]\tlr: 1.000e-03, eta: 4:38:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4483\n2023-01-03 23:19:12,290 - mmcls - INFO - Epoch [63][800/5005]\tlr: 1.000e-03, eta: 4:38:00, time: 0.091, data_time: 0.024, memory: 1125, loss: 1.4691\n2023-01-03 23:19:20,876 - mmcls - INFO - Epoch [63][900/5005]\tlr: 1.000e-03, eta: 4:37:51, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4788\n2023-01-03 23:19:29,481 - mmcls - INFO - Epoch [63][1000/5005]\tlr: 1.000e-03, eta: 4:37:42, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.4729\n2023-01-03 23:19:38,173 - mmcls - INFO - Epoch [63][1100/5005]\tlr: 1.000e-03, eta: 4:37:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4687\n2023-01-03 23:19:46,803 - mmcls - INFO - Epoch [63][1200/5005]\tlr: 1.000e-03, eta: 4:37:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4619\n2023-01-03 23:19:55,560 - mmcls - INFO - Epoch [63][1300/5005]\tlr: 1.000e-03, eta: 4:37:15, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4661\n2023-01-03 23:20:04,161 - mmcls - INFO - Epoch [63][1400/5005]\tlr: 1.000e-03, eta: 4:37:06, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4866\n2023-01-03 23:20:13,025 - mmcls - INFO - Epoch [63][1500/5005]\tlr: 1.000e-03, eta: 4:36:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4728\n2023-01-03 23:20:21,611 - mmcls - INFO - Epoch [63][1600/5005]\tlr: 1.000e-03, eta: 4:36:49, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4841\n2023-01-03 23:20:30,570 - mmcls - INFO - Epoch [63][1700/5005]\tlr: 1.000e-03, eta: 4:36:40, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.4570\n2023-01-03 23:20:39,358 - mmcls - INFO - Epoch [63][1800/5005]\tlr: 1.000e-03, eta: 4:36:31, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4892\n2023-01-03 23:20:48,205 - mmcls - INFO - Epoch [63][1900/5005]\tlr: 1.000e-03, eta: 4:36:22, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4488\n2023-01-03 23:20:56,966 - mmcls - INFO - Epoch [63][2000/5005]\tlr: 1.000e-03, eta: 4:36:14, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4649\n2023-01-03 23:21:05,589 - mmcls - INFO - Epoch [63][2100/5005]\tlr: 1.000e-03, eta: 4:36:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4584\n2023-01-03 23:21:14,312 - mmcls - INFO - Epoch [63][2200/5005]\tlr: 1.000e-03, eta: 4:35:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4751\n2023-01-03 23:21:23,128 - mmcls - INFO - Epoch [63][2300/5005]\tlr: 1.000e-03, eta: 4:35:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4610\n2023-01-03 23:21:31,866 - mmcls - INFO - Epoch [63][2400/5005]\tlr: 1.000e-03, eta: 4:35:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4725\n2023-01-03 23:21:40,639 - mmcls - INFO - Epoch [63][2500/5005]\tlr: 1.000e-03, eta: 4:35:29, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4823\n2023-01-03 23:21:49,411 - mmcls - INFO - Epoch [63][2600/5005]\tlr: 1.000e-03, eta: 4:35:21, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4793\n2023-01-03 23:21:58,092 - mmcls - INFO - Epoch [63][2700/5005]\tlr: 1.000e-03, eta: 4:35:12, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4817\n2023-01-03 23:22:06,877 - mmcls - INFO - Epoch [63][2800/5005]\tlr: 1.000e-03, eta: 4:35:03, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4635\n2023-01-03 23:22:15,415 - mmcls - INFO - Epoch [63][2900/5005]\tlr: 1.000e-03, eta: 4:34:54, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4556\n2023-01-03 23:22:24,262 - mmcls - INFO - Epoch [63][3000/5005]\tlr: 1.000e-03, eta: 4:34:45, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4557\n2023-01-03 23:22:33,251 - mmcls - INFO - Epoch [63][3100/5005]\tlr: 1.000e-03, eta: 4:34:36, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4729\n2023-01-03 23:22:41,795 - mmcls - INFO - Epoch [63][3200/5005]\tlr: 1.000e-03, eta: 4:34:27, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.4793\n2023-01-03 23:22:50,560 - mmcls - INFO - Epoch [63][3300/5005]\tlr: 1.000e-03, eta: 4:34:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4470\n2023-01-03 23:22:59,302 - mmcls - INFO - Epoch [63][3400/5005]\tlr: 1.000e-03, eta: 4:34:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4843\n2023-01-03 23:23:08,159 - mmcls - INFO - Epoch [63][3500/5005]\tlr: 1.000e-03, eta: 4:34:01, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.4615\n2023-01-03 23:23:16,707 - mmcls - INFO - Epoch [63][3600/5005]\tlr: 1.000e-03, eta: 4:33:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4507\n2023-01-03 23:23:25,489 - mmcls - INFO - Epoch [63][3700/5005]\tlr: 1.000e-03, eta: 4:33:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4596\n2023-01-03 23:23:34,185 - mmcls - INFO - Epoch [63][3800/5005]\tlr: 1.000e-03, eta: 4:33:34, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4546\n2023-01-03 23:23:42,884 - mmcls - INFO - Epoch [63][3900/5005]\tlr: 1.000e-03, eta: 4:33:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4694\n2023-01-03 23:23:51,944 - mmcls - INFO - Epoch [63][4000/5005]\tlr: 1.000e-03, eta: 4:33:17, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.4525\n2023-01-03 23:24:00,519 - mmcls - INFO - Epoch [63][4100/5005]\tlr: 1.000e-03, eta: 4:33:08, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4648\n2023-01-03 23:24:09,060 - mmcls - INFO - Epoch [63][4200/5005]\tlr: 1.000e-03, eta: 4:32:59, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4452\n2023-01-03 23:24:17,672 - mmcls - INFO - Epoch [63][4300/5005]\tlr: 1.000e-03, eta: 4:32:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4483\n2023-01-03 23:24:26,111 - mmcls - INFO - Epoch [63][4400/5005]\tlr: 1.000e-03, eta: 4:32:41, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.4475\n2023-01-03 23:24:34,929 - mmcls - INFO - Epoch [63][4500/5005]\tlr: 1.000e-03, eta: 4:32:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4418\n2023-01-03 23:24:44,141 - mmcls - INFO - Epoch [63][4600/5005]\tlr: 1.000e-03, eta: 4:32:24, time: 0.092, data_time: 0.032, memory: 1125, loss: 1.4586\n2023-01-03 23:24:52,909 - mmcls - INFO - Epoch [63][4700/5005]\tlr: 1.000e-03, eta: 4:32:15, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4796\n2023-01-03 23:25:01,653 - mmcls - INFO - Epoch [63][4800/5005]\tlr: 1.000e-03, eta: 4:32:06, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4465\n2023-01-03 23:25:10,301 - mmcls - INFO - Epoch [63][4900/5005]\tlr: 1.000e-03, eta: 4:31:57, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.4712\n2023-01-03 23:25:19,149 - mmcls - INFO - Epoch [63][5000/5005]\tlr: 1.000e-03, eta: 4:31:48, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4494\n2023-01-03 23:25:41,757 - mmcls - INFO - Epoch(val) [63][196]\taccuracy_top-1: 68.8780, accuracy_top-5: 88.6620\n2023-01-03 23:25:53,136 - mmcls - INFO - Epoch [64][100/5005]\tlr: 1.000e-03, eta: 4:31:40, time: 0.113, data_time: 0.046, memory: 1125, loss: 1.4389\n2023-01-03 23:26:02,168 - mmcls - INFO - Epoch [64][200/5005]\tlr: 1.000e-03, eta: 4:31:32, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4589\n2023-01-03 23:26:10,952 - mmcls - INFO - Epoch [64][300/5005]\tlr: 1.000e-03, eta: 4:31:23, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4566\n2023-01-03 23:26:19,664 - mmcls - INFO - Epoch [64][400/5005]\tlr: 1.000e-03, eta: 4:31:14, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.4428\n2023-01-03 23:26:28,428 - mmcls - INFO - Epoch [64][500/5005]\tlr: 1.000e-03, eta: 4:31:05, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.4601\n2023-01-03 23:26:37,266 - mmcls - INFO - Epoch [64][600/5005]\tlr: 1.000e-03, eta: 4:30:56, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4593\n2023-01-03 23:26:46,456 - mmcls - INFO - Epoch [64][700/5005]\tlr: 1.000e-03, eta: 4:30:48, time: 0.092, data_time: 0.043, memory: 1125, loss: 1.4541\n2023-01-03 23:26:55,625 - mmcls - INFO - Epoch [64][800/5005]\tlr: 1.000e-03, eta: 4:30:39, time: 0.092, data_time: 0.031, memory: 1125, loss: 1.4450\n2023-01-03 23:27:04,362 - mmcls - INFO - Epoch [64][900/5005]\tlr: 1.000e-03, eta: 4:30:30, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4629\n2023-01-03 23:27:12,922 - mmcls - INFO - Epoch [64][1000/5005]\tlr: 1.000e-03, eta: 4:30:21, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4560\n2023-01-03 23:27:21,679 - mmcls - INFO - Epoch [64][1100/5005]\tlr: 1.000e-03, eta: 4:30:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4704\n2023-01-03 23:27:30,244 - mmcls - INFO - Epoch [64][1200/5005]\tlr: 1.000e-03, eta: 4:30:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4691\n2023-01-03 23:27:38,998 - mmcls - INFO - Epoch [64][1300/5005]\tlr: 1.000e-03, eta: 4:29:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4450\n2023-01-03 23:27:47,569 - mmcls - INFO - Epoch [64][1400/5005]\tlr: 1.000e-03, eta: 4:29:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4727\n2023-01-03 23:27:56,263 - mmcls - INFO - Epoch [64][1500/5005]\tlr: 1.000e-03, eta: 4:29:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4681\n2023-01-03 23:28:04,989 - mmcls - INFO - Epoch [64][1600/5005]\tlr: 1.000e-03, eta: 4:29:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4439\n2023-01-03 23:28:13,689 - mmcls - INFO - Epoch [64][1700/5005]\tlr: 1.000e-03, eta: 4:29:19, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4622\n2023-01-03 23:28:22,233 - mmcls - INFO - Epoch [64][1800/5005]\tlr: 1.000e-03, eta: 4:29:10, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4460\n2023-01-03 23:28:31,055 - mmcls - INFO - Epoch [64][1900/5005]\tlr: 1.000e-03, eta: 4:29:01, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4583\n2023-01-03 23:28:39,926 - mmcls - INFO - Epoch [64][2000/5005]\tlr: 1.000e-03, eta: 4:28:53, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.4679\n2023-01-03 23:28:48,483 - mmcls - INFO - Epoch [64][2100/5005]\tlr: 1.000e-03, eta: 4:28:44, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4334\n2023-01-03 23:28:57,091 - mmcls - INFO - Epoch [64][2200/5005]\tlr: 1.000e-03, eta: 4:28:35, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4512\n2023-01-03 23:29:06,213 - mmcls - INFO - Epoch [64][2300/5005]\tlr: 1.000e-03, eta: 4:28:26, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4527\n2023-01-03 23:29:14,918 - mmcls - INFO - Epoch [64][2400/5005]\tlr: 1.000e-03, eta: 4:28:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4403\n2023-01-03 23:29:23,917 - mmcls - INFO - Epoch [64][2500/5005]\tlr: 1.000e-03, eta: 4:28:08, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4340\n2023-01-03 23:29:32,826 - mmcls - INFO - Epoch [64][2600/5005]\tlr: 1.000e-03, eta: 4:28:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.4771\n2023-01-03 23:29:41,449 - mmcls - INFO - Epoch [64][2700/5005]\tlr: 1.000e-03, eta: 4:27:51, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4599\n2023-01-03 23:29:50,076 - mmcls - INFO - Epoch [64][2800/5005]\tlr: 1.000e-03, eta: 4:27:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4509\n2023-01-03 23:29:58,504 - mmcls - INFO - Epoch [64][2900/5005]\tlr: 1.000e-03, eta: 4:27:33, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.4660\n2023-01-03 23:30:07,569 - mmcls - INFO - Epoch [64][3000/5005]\tlr: 1.000e-03, eta: 4:27:24, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.4419\n2023-01-03 23:30:16,166 - mmcls - INFO - Epoch [64][3100/5005]\tlr: 1.000e-03, eta: 4:27:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.4461\n2023-01-03 23:30:24,695 - mmcls - INFO - Epoch [64][3200/5005]\tlr: 1.000e-03, eta: 4:27:06, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.4484\n2023-01-03 23:30:33,709 - mmcls - INFO - Epoch [64][3300/5005]\tlr: 1.000e-03, eta: 4:26:58, time: 0.090, data_time: 0.052, memory: 1125, loss: 1.4363\n2023-01-03 23:30:42,171 - mmcls - INFO - Epoch [64][3400/5005]\tlr: 1.000e-03, eta: 4:26:49, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4517\n2023-01-03 23:30:50,751 - mmcls - INFO - Epoch [64][3500/5005]\tlr: 1.000e-03, eta: 4:26:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4717\n2023-01-03 23:30:59,623 - mmcls - INFO - Epoch [64][3600/5005]\tlr: 1.000e-03, eta: 4:26:31, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.4499\n2023-01-03 23:31:08,649 - mmcls - INFO - Epoch [64][3700/5005]\tlr: 1.000e-03, eta: 4:26:22, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.4453\n2023-01-03 23:31:17,319 - mmcls - INFO - Epoch [64][3800/5005]\tlr: 1.000e-03, eta: 4:26:13, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4348\n2023-01-03 23:31:25,976 - mmcls - INFO - Epoch [64][3900/5005]\tlr: 1.000e-03, eta: 4:26:04, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.4453\n2023-01-03 23:31:34,639 - mmcls - INFO - Epoch [64][4000/5005]\tlr: 1.000e-03, eta: 4:25:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4558\n2023-01-03 23:31:43,566 - mmcls - INFO - Epoch [64][4100/5005]\tlr: 1.000e-03, eta: 4:25:47, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4280\n2023-01-03 23:31:52,183 - mmcls - INFO - Epoch [64][4200/5005]\tlr: 1.000e-03, eta: 4:25:38, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.4327\n2023-01-03 23:32:00,914 - mmcls - INFO - Epoch [64][4300/5005]\tlr: 1.000e-03, eta: 4:25:29, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4536\n2023-01-03 23:32:09,575 - mmcls - INFO - Epoch [64][4400/5005]\tlr: 1.000e-03, eta: 4:25:20, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4705\n2023-01-03 23:32:18,370 - mmcls - INFO - Epoch [64][4500/5005]\tlr: 1.000e-03, eta: 4:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4321\n2023-01-03 23:32:27,343 - mmcls - INFO - Epoch [64][4600/5005]\tlr: 1.000e-03, eta: 4:25:03, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4392\n2023-01-03 23:32:36,281 - mmcls - INFO - Epoch [64][4700/5005]\tlr: 1.000e-03, eta: 4:24:54, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4664\n2023-01-03 23:32:45,131 - mmcls - INFO - Epoch [64][4800/5005]\tlr: 1.000e-03, eta: 4:24:45, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4359\n2023-01-03 23:32:53,730 - mmcls - INFO - Epoch [64][4900/5005]\tlr: 1.000e-03, eta: 4:24:36, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4369\n2023-01-03 23:33:02,534 - mmcls - INFO - Epoch [64][5000/5005]\tlr: 1.000e-03, eta: 4:24:27, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4600\n2023-01-03 23:33:24,988 - mmcls - INFO - Epoch(val) [64][196]\taccuracy_top-1: 68.9920, accuracy_top-5: 88.7240\n2023-01-03 23:33:35,960 - mmcls - INFO - Epoch [65][100/5005]\tlr: 1.000e-03, eta: 4:24:19, time: 0.109, data_time: 0.061, memory: 1125, loss: 1.4263\n2023-01-03 23:33:44,831 - mmcls - INFO - Epoch [65][200/5005]\tlr: 1.000e-03, eta: 4:24:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4266\n2023-01-03 23:33:53,850 - mmcls - INFO - Epoch [65][300/5005]\tlr: 1.000e-03, eta: 4:24:02, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4500\n2023-01-03 23:34:02,733 - mmcls - INFO - Epoch [65][400/5005]\tlr: 1.000e-03, eta: 4:23:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4210\n2023-01-03 23:34:11,536 - mmcls - INFO - Epoch [65][500/5005]\tlr: 1.000e-03, eta: 4:23:44, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4414\n2023-01-03 23:34:20,398 - mmcls - INFO - Epoch [65][600/5005]\tlr: 1.000e-03, eta: 4:23:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.4423\n2023-01-03 23:34:28,906 - mmcls - INFO - Epoch [65][700/5005]\tlr: 1.000e-03, eta: 4:23:26, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4636\n2023-01-03 23:34:37,468 - mmcls - INFO - Epoch [65][800/5005]\tlr: 1.000e-03, eta: 4:23:17, time: 0.086, data_time: 0.046, memory: 1125, loss: 1.4319\n2023-01-03 23:34:46,512 - mmcls - INFO - Epoch [65][900/5005]\tlr: 1.000e-03, eta: 4:23:09, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.4500\n2023-01-03 23:34:55,306 - mmcls - INFO - Epoch [65][1000/5005]\tlr: 1.000e-03, eta: 4:23:00, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4358\n2023-01-03 23:35:04,313 - mmcls - INFO - Epoch [65][1100/5005]\tlr: 1.000e-03, eta: 4:22:51, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.4608\n2023-01-03 23:35:12,788 - mmcls - INFO - Epoch [65][1200/5005]\tlr: 1.000e-03, eta: 4:22:42, time: 0.085, data_time: 0.029, memory: 1125, loss: 1.4507\n2023-01-03 23:35:21,478 - mmcls - INFO - Epoch [65][1300/5005]\tlr: 1.000e-03, eta: 4:22:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4600\n2023-01-03 23:35:30,127 - mmcls - INFO - Epoch [65][1400/5005]\tlr: 1.000e-03, eta: 4:22:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4208\n2023-01-03 23:35:38,954 - mmcls - INFO - Epoch [65][1500/5005]\tlr: 1.000e-03, eta: 4:22:16, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4649\n2023-01-03 23:35:47,630 - mmcls - INFO - Epoch [65][1600/5005]\tlr: 1.000e-03, eta: 4:22:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4376\n2023-01-03 23:35:56,271 - mmcls - INFO - Epoch [65][1700/5005]\tlr: 1.000e-03, eta: 4:21:58, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4662\n2023-01-03 23:36:05,119 - mmcls - INFO - Epoch [65][1800/5005]\tlr: 1.000e-03, eta: 4:21:49, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4399\n2023-01-03 23:36:13,819 - mmcls - INFO - Epoch [65][1900/5005]\tlr: 1.000e-03, eta: 4:21:40, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4388\n2023-01-03 23:36:22,644 - mmcls - INFO - Epoch [65][2000/5005]\tlr: 1.000e-03, eta: 4:21:31, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4286\n2023-01-03 23:36:31,316 - mmcls - INFO - Epoch [65][2100/5005]\tlr: 1.000e-03, eta: 4:21:23, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.4642\n2023-01-03 23:36:40,620 - mmcls - INFO - Epoch [65][2200/5005]\tlr: 1.000e-03, eta: 4:21:14, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.4427\n2023-01-03 23:36:49,365 - mmcls - INFO - Epoch [65][2300/5005]\tlr: 1.000e-03, eta: 4:21:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.4274\n2023-01-03 23:36:58,064 - mmcls - INFO - Epoch [65][2400/5005]\tlr: 1.000e-03, eta: 4:20:56, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.4442\n2023-01-03 23:37:06,510 - mmcls - INFO - Epoch [65][2500/5005]\tlr: 1.000e-03, eta: 4:20:47, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4463\n2023-01-03 23:37:15,045 - mmcls - INFO - Epoch [65][2600/5005]\tlr: 1.000e-03, eta: 4:20:38, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.4543\n2023-01-03 23:37:23,496 - mmcls - INFO - Epoch [65][2700/5005]\tlr: 1.000e-03, eta: 4:20:29, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.4446\n2023-01-03 23:37:32,238 - mmcls - INFO - Epoch [65][2800/5005]\tlr: 1.000e-03, eta: 4:20:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4266\n2023-01-03 23:37:41,181 - mmcls - INFO - Epoch [65][2900/5005]\tlr: 1.000e-03, eta: 4:20:12, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.4548\n2023-01-03 23:37:49,921 - mmcls - INFO - Epoch [65][3000/5005]\tlr: 1.000e-03, eta: 4:20:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4177\n2023-01-03 23:37:58,603 - mmcls - INFO - Epoch [65][3100/5005]\tlr: 1.000e-03, eta: 4:19:54, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4428\n2023-01-03 23:38:07,514 - mmcls - INFO - Epoch [65][3200/5005]\tlr: 1.000e-03, eta: 4:19:45, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.4424\n2023-01-03 23:38:16,266 - mmcls - INFO - Epoch [65][3300/5005]\tlr: 1.000e-03, eta: 4:19:36, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4507\n2023-01-03 23:38:24,865 - mmcls - INFO - Epoch [65][3400/5005]\tlr: 1.000e-03, eta: 4:19:28, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4520\n2023-01-03 23:38:33,762 - mmcls - INFO - Epoch [65][3500/5005]\tlr: 1.000e-03, eta: 4:19:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4292\n2023-01-03 23:38:42,319 - mmcls - INFO - Epoch [65][3600/5005]\tlr: 1.000e-03, eta: 4:19:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4105\n2023-01-03 23:38:51,053 - mmcls - INFO - Epoch [65][3700/5005]\tlr: 1.000e-03, eta: 4:19:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4363\n2023-01-03 23:38:59,720 - mmcls - INFO - Epoch [65][3800/5005]\tlr: 1.000e-03, eta: 4:18:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4311\n2023-01-03 23:39:08,776 - mmcls - INFO - Epoch [65][3900/5005]\tlr: 1.000e-03, eta: 4:18:43, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.4172\n2023-01-03 23:39:17,355 - mmcls - INFO - Epoch [65][4000/5005]\tlr: 1.000e-03, eta: 4:18:34, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4441\n2023-01-03 23:39:25,774 - mmcls - INFO - Epoch [65][4100/5005]\tlr: 1.000e-03, eta: 4:18:25, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.4217\n2023-01-03 23:39:34,731 - mmcls - INFO - Epoch [65][4200/5005]\tlr: 1.000e-03, eta: 4:18:17, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4184\n2023-01-03 23:39:43,640 - mmcls - INFO - Epoch [65][4300/5005]\tlr: 1.000e-03, eta: 4:18:08, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4228\n2023-01-03 23:39:52,126 - mmcls - INFO - Epoch [65][4400/5005]\tlr: 1.000e-03, eta: 4:17:59, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.4301\n2023-01-03 23:40:00,909 - mmcls - INFO - Epoch [65][4500/5005]\tlr: 1.000e-03, eta: 4:17:50, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4379\n2023-01-03 23:40:09,774 - mmcls - INFO - Epoch [65][4600/5005]\tlr: 1.000e-03, eta: 4:17:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4501\n2023-01-03 23:40:18,627 - mmcls - INFO - Epoch [65][4700/5005]\tlr: 1.000e-03, eta: 4:17:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4402\n2023-01-03 23:40:27,354 - mmcls - INFO - Epoch [65][4800/5005]\tlr: 1.000e-03, eta: 4:17:24, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4499\n2023-01-03 23:40:36,079 - mmcls - INFO - Epoch [65][4900/5005]\tlr: 1.000e-03, eta: 4:17:15, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.4500\n2023-01-03 23:40:44,881 - mmcls - INFO - Epoch [65][5000/5005]\tlr: 1.000e-03, eta: 4:17:06, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.4313\n2023-01-03 23:41:07,643 - mmcls - INFO - Epoch(val) [65][196]\taccuracy_top-1: 69.0700, accuracy_top-5: 88.7920\n2023-01-03 23:41:19,063 - mmcls - INFO - Epoch [66][100/5005]\tlr: 1.000e-03, eta: 4:16:58, time: 0.114, data_time: 0.049, memory: 1125, loss: 1.4354\n2023-01-03 23:41:28,122 - mmcls - INFO - Epoch [66][200/5005]\tlr: 1.000e-03, eta: 4:16:49, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.4568\n2023-01-03 23:41:36,689 - mmcls - INFO - Epoch [66][300/5005]\tlr: 1.000e-03, eta: 4:16:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4310\n2023-01-03 23:41:45,558 - mmcls - INFO - Epoch [66][400/5005]\tlr: 1.000e-03, eta: 4:16:32, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4344\n2023-01-03 23:41:54,167 - mmcls - INFO - Epoch [66][500/5005]\tlr: 1.000e-03, eta: 4:16:23, time: 0.086, data_time: 0.040, memory: 1125, loss: 1.4281\n2023-01-03 23:42:02,704 - mmcls - INFO - Epoch [66][600/5005]\tlr: 1.000e-03, eta: 4:16:14, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4235\n2023-01-03 23:42:11,509 - mmcls - INFO - Epoch [66][700/5005]\tlr: 1.000e-03, eta: 4:16:05, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4222\n2023-01-03 23:42:20,061 - mmcls - INFO - Epoch [66][800/5005]\tlr: 1.000e-03, eta: 4:15:56, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.4353\n2023-01-03 23:42:28,759 - mmcls - INFO - Epoch [66][900/5005]\tlr: 1.000e-03, eta: 4:15:47, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.4420\n2023-01-03 23:42:37,540 - mmcls - INFO - Epoch [66][1000/5005]\tlr: 1.000e-03, eta: 4:15:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4245\n2023-01-03 23:42:46,502 - mmcls - INFO - Epoch [66][1100/5005]\tlr: 1.000e-03, eta: 4:15:30, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4432\n2023-01-03 23:42:55,223 - mmcls - INFO - Epoch [66][1200/5005]\tlr: 1.000e-03, eta: 4:15:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.4472\n2023-01-03 23:43:03,873 - mmcls - INFO - Epoch [66][1300/5005]\tlr: 1.000e-03, eta: 4:15:12, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4264\n2023-01-03 23:43:12,730 - mmcls - INFO - Epoch [66][1400/5005]\tlr: 1.000e-03, eta: 4:15:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4446\n2023-01-03 23:43:21,519 - mmcls - INFO - Epoch [66][1500/5005]\tlr: 1.000e-03, eta: 4:14:54, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4362\n2023-01-03 23:43:30,149 - mmcls - INFO - Epoch [66][1600/5005]\tlr: 1.000e-03, eta: 4:14:45, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4340\n2023-01-03 23:43:38,842 - mmcls - INFO - Epoch [66][1700/5005]\tlr: 1.000e-03, eta: 4:14:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4386\n2023-01-03 23:43:47,580 - mmcls - INFO - Epoch [66][1800/5005]\tlr: 1.000e-03, eta: 4:14:28, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.4263\n2023-01-03 23:43:56,514 - mmcls - INFO - Epoch [66][1900/5005]\tlr: 1.000e-03, eta: 4:14:19, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.4281\n2023-01-03 23:44:05,358 - mmcls - INFO - Epoch [66][2000/5005]\tlr: 1.000e-03, eta: 4:14:10, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4237\n2023-01-03 23:44:13,974 - mmcls - INFO - Epoch [66][2100/5005]\tlr: 1.000e-03, eta: 4:14:01, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4385\n2023-01-03 23:44:22,626 - mmcls - INFO - Epoch [66][2200/5005]\tlr: 1.000e-03, eta: 4:13:52, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4253\n2023-01-03 23:44:31,320 - mmcls - INFO - Epoch [66][2300/5005]\tlr: 1.000e-03, eta: 4:13:43, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4462\n2023-01-03 23:44:40,149 - mmcls - INFO - Epoch [66][2400/5005]\tlr: 1.000e-03, eta: 4:13:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4282\n2023-01-03 23:44:49,084 - mmcls - INFO - Epoch [66][2500/5005]\tlr: 1.000e-03, eta: 4:13:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4085\n2023-01-03 23:44:57,527 - mmcls - INFO - Epoch [66][2600/5005]\tlr: 1.000e-03, eta: 4:13:17, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4559\n2023-01-03 23:45:06,367 - mmcls - INFO - Epoch [66][2700/5005]\tlr: 1.000e-03, eta: 4:13:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4109\n2023-01-03 23:45:15,025 - mmcls - INFO - Epoch [66][2800/5005]\tlr: 1.000e-03, eta: 4:12:59, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4282\n2023-01-03 23:45:23,741 - mmcls - INFO - Epoch [66][2900/5005]\tlr: 1.000e-03, eta: 4:12:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4347\n2023-01-03 23:45:32,601 - mmcls - INFO - Epoch [66][3000/5005]\tlr: 1.000e-03, eta: 4:12:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.4026\n2023-01-03 23:45:41,063 - mmcls - INFO - Epoch [66][3100/5005]\tlr: 1.000e-03, eta: 4:12:33, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.4372\n2023-01-03 23:45:50,078 - mmcls - INFO - Epoch [66][3200/5005]\tlr: 1.000e-03, eta: 4:12:24, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4525\n2023-01-03 23:45:58,974 - mmcls - INFO - Epoch [66][3300/5005]\tlr: 1.000e-03, eta: 4:12:15, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.4380\n2023-01-03 23:46:07,815 - mmcls - INFO - Epoch [66][3400/5005]\tlr: 1.000e-03, eta: 4:12:06, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.4070\n2023-01-03 23:46:16,502 - mmcls - INFO - Epoch [66][3500/5005]\tlr: 1.000e-03, eta: 4:11:58, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4153\n2023-01-03 23:46:25,438 - mmcls - INFO - Epoch [66][3600/5005]\tlr: 1.000e-03, eta: 4:11:49, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.4349\n2023-01-03 23:46:34,313 - mmcls - INFO - Epoch [66][3700/5005]\tlr: 1.000e-03, eta: 4:11:40, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.4253\n2023-01-03 23:46:43,031 - mmcls - INFO - Epoch [66][3800/5005]\tlr: 1.000e-03, eta: 4:11:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4346\n2023-01-03 23:46:51,617 - mmcls - INFO - Epoch [66][3900/5005]\tlr: 1.000e-03, eta: 4:11:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4329\n2023-01-03 23:47:00,389 - mmcls - INFO - Epoch [66][4000/5005]\tlr: 1.000e-03, eta: 4:11:13, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4554\n2023-01-03 23:47:09,023 - mmcls - INFO - Epoch [66][4100/5005]\tlr: 1.000e-03, eta: 4:11:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4368\n2023-01-03 23:47:17,551 - mmcls - INFO - Epoch [66][4200/5005]\tlr: 1.000e-03, eta: 4:10:56, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.4250\n2023-01-03 23:47:26,542 - mmcls - INFO - Epoch [66][4300/5005]\tlr: 1.000e-03, eta: 4:10:47, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4387\n2023-01-03 23:47:35,085 - mmcls - INFO - Epoch [66][4400/5005]\tlr: 1.000e-03, eta: 4:10:38, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4378\n2023-01-03 23:47:43,923 - mmcls - INFO - Epoch [66][4500/5005]\tlr: 1.000e-03, eta: 4:10:29, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.4590\n2023-01-03 23:47:52,918 - mmcls - INFO - Epoch [66][4600/5005]\tlr: 1.000e-03, eta: 4:10:20, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4352\n2023-01-03 23:48:01,702 - mmcls - INFO - Epoch [66][4700/5005]\tlr: 1.000e-03, eta: 4:10:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4331\n2023-01-03 23:48:10,314 - mmcls - INFO - Epoch [66][4800/5005]\tlr: 1.000e-03, eta: 4:10:03, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4303\n2023-01-03 23:48:19,102 - mmcls - INFO - Epoch [66][4900/5005]\tlr: 1.000e-03, eta: 4:09:54, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4367\n2023-01-03 23:48:27,945 - mmcls - INFO - Epoch [66][5000/5005]\tlr: 1.000e-03, eta: 4:09:45, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4495\n2023-01-03 23:48:50,698 - mmcls - INFO - Epoch(val) [66][196]\taccuracy_top-1: 69.0620, accuracy_top-5: 88.8260\n2023-01-03 23:49:01,962 - mmcls - INFO - Epoch [67][100/5005]\tlr: 1.000e-03, eta: 4:09:37, time: 0.112, data_time: 0.063, memory: 1125, loss: 1.4379\n2023-01-03 23:49:10,582 - mmcls - INFO - Epoch [67][200/5005]\tlr: 1.000e-03, eta: 4:09:28, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3932\n2023-01-03 23:49:19,333 - mmcls - INFO - Epoch [67][300/5005]\tlr: 1.000e-03, eta: 4:09:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4217\n2023-01-03 23:49:27,971 - mmcls - INFO - Epoch [67][400/5005]\tlr: 1.000e-03, eta: 4:09:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4378\n2023-01-03 23:49:36,654 - mmcls - INFO - Epoch [67][500/5005]\tlr: 1.000e-03, eta: 4:09:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4187\n2023-01-03 23:49:45,441 - mmcls - INFO - Epoch [67][600/5005]\tlr: 1.000e-03, eta: 4:08:53, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.4383\n2023-01-03 23:49:54,168 - mmcls - INFO - Epoch [67][700/5005]\tlr: 1.000e-03, eta: 4:08:44, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4201\n2023-01-03 23:50:03,047 - mmcls - INFO - Epoch [67][800/5005]\tlr: 1.000e-03, eta: 4:08:35, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4134\n2023-01-03 23:50:11,692 - mmcls - INFO - Epoch [67][900/5005]\tlr: 1.000e-03, eta: 4:08:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4331\n2023-01-03 23:50:20,345 - mmcls - INFO - Epoch [67][1000/5005]\tlr: 1.000e-03, eta: 4:08:17, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4169\n2023-01-03 23:50:29,573 - mmcls - INFO - Epoch [67][1100/5005]\tlr: 1.000e-03, eta: 4:08:09, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.4231\n2023-01-03 23:50:38,215 - mmcls - INFO - Epoch [67][1200/5005]\tlr: 1.000e-03, eta: 4:08:00, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4385\n2023-01-03 23:50:46,733 - mmcls - INFO - Epoch [67][1300/5005]\tlr: 1.000e-03, eta: 4:07:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4295\n2023-01-03 23:50:55,304 - mmcls - INFO - Epoch [67][1400/5005]\tlr: 1.000e-03, eta: 4:07:42, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4241\n2023-01-03 23:51:04,184 - mmcls - INFO - Epoch [67][1500/5005]\tlr: 1.000e-03, eta: 4:07:33, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.4371\n2023-01-03 23:51:13,110 - mmcls - INFO - Epoch [67][1600/5005]\tlr: 1.000e-03, eta: 4:07:24, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4379\n2023-01-03 23:51:21,730 - mmcls - INFO - Epoch [67][1700/5005]\tlr: 1.000e-03, eta: 4:07:15, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.4300\n2023-01-03 23:51:30,402 - mmcls - INFO - Epoch [67][1800/5005]\tlr: 1.000e-03, eta: 4:07:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4319\n2023-01-03 23:51:39,834 - mmcls - INFO - Epoch [67][1900/5005]\tlr: 1.000e-03, eta: 4:06:58, time: 0.094, data_time: 0.001, memory: 1125, loss: 1.4100\n2023-01-03 23:51:48,551 - mmcls - INFO - Epoch [67][2000/5005]\tlr: 1.000e-03, eta: 4:06:49, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.4168\n2023-01-03 23:51:57,737 - mmcls - INFO - Epoch [67][2100/5005]\tlr: 1.000e-03, eta: 4:06:41, time: 0.092, data_time: 0.044, memory: 1125, loss: 1.4124\n2023-01-03 23:52:06,468 - mmcls - INFO - Epoch [67][2200/5005]\tlr: 1.000e-03, eta: 4:06:32, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4264\n2023-01-03 23:52:15,030 - mmcls - INFO - Epoch [67][2300/5005]\tlr: 1.000e-03, eta: 4:06:23, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4288\n2023-01-03 23:52:23,438 - mmcls - INFO - Epoch [67][2400/5005]\tlr: 1.000e-03, eta: 4:06:14, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.4401\n2023-01-03 23:52:32,294 - mmcls - INFO - Epoch [67][2500/5005]\tlr: 1.000e-03, eta: 4:06:05, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4250\n2023-01-03 23:52:40,874 - mmcls - INFO - Epoch [67][2600/5005]\tlr: 1.000e-03, eta: 4:05:56, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4040\n2023-01-03 23:52:49,828 - mmcls - INFO - Epoch [67][2700/5005]\tlr: 1.000e-03, eta: 4:05:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4242\n2023-01-03 23:52:58,322 - mmcls - INFO - Epoch [67][2800/5005]\tlr: 1.000e-03, eta: 4:05:38, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4404\n2023-01-03 23:53:06,881 - mmcls - INFO - Epoch [67][2900/5005]\tlr: 1.000e-03, eta: 4:05:29, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.4214\n2023-01-03 23:53:15,657 - mmcls - INFO - Epoch [67][3000/5005]\tlr: 1.000e-03, eta: 4:05:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4173\n2023-01-03 23:53:25,017 - mmcls - INFO - Epoch [67][3100/5005]\tlr: 1.000e-03, eta: 4:05:12, time: 0.094, data_time: 0.012, memory: 1125, loss: 1.4345\n2023-01-03 23:53:33,681 - mmcls - INFO - Epoch [67][3200/5005]\tlr: 1.000e-03, eta: 4:05:03, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4368\n2023-01-03 23:53:42,731 - mmcls - INFO - Epoch [67][3300/5005]\tlr: 1.000e-03, eta: 4:04:55, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.4307\n2023-01-03 23:53:51,446 - mmcls - INFO - Epoch [67][3400/5005]\tlr: 1.000e-03, eta: 4:04:46, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4367\n2023-01-03 23:54:00,169 - mmcls - INFO - Epoch [67][3500/5005]\tlr: 1.000e-03, eta: 4:04:37, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.4065\n2023-01-03 23:54:08,716 - mmcls - INFO - Epoch [67][3600/5005]\tlr: 1.000e-03, eta: 4:04:28, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4313\n2023-01-03 23:54:17,490 - mmcls - INFO - Epoch [67][3700/5005]\tlr: 1.000e-03, eta: 4:04:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4400\n2023-01-03 23:54:26,290 - mmcls - INFO - Epoch [67][3800/5005]\tlr: 1.000e-03, eta: 4:04:10, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4201\n2023-01-03 23:54:34,824 - mmcls - INFO - Epoch [67][3900/5005]\tlr: 1.000e-03, eta: 4:04:01, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.4126\n2023-01-03 23:54:43,584 - mmcls - INFO - Epoch [67][4000/5005]\tlr: 1.000e-03, eta: 4:03:53, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.4387\n2023-01-03 23:54:52,277 - mmcls - INFO - Epoch [67][4100/5005]\tlr: 1.000e-03, eta: 4:03:44, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4352\n2023-01-03 23:55:00,851 - mmcls - INFO - Epoch [67][4200/5005]\tlr: 1.000e-03, eta: 4:03:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.4348\n2023-01-03 23:55:09,737 - mmcls - INFO - Epoch [67][4300/5005]\tlr: 1.000e-03, eta: 4:03:26, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4288\n2023-01-03 23:55:18,157 - mmcls - INFO - Epoch [67][4400/5005]\tlr: 1.000e-03, eta: 4:03:17, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.4384\n2023-01-03 23:55:26,633 - mmcls - INFO - Epoch [67][4500/5005]\tlr: 1.000e-03, eta: 4:03:08, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4200\n2023-01-03 23:55:35,362 - mmcls - INFO - Epoch [67][4600/5005]\tlr: 1.000e-03, eta: 4:02:59, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4328\n2023-01-03 23:55:44,146 - mmcls - INFO - Epoch [67][4700/5005]\tlr: 1.000e-03, eta: 4:02:50, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4343\n2023-01-03 23:55:53,459 - mmcls - INFO - Epoch [67][4800/5005]\tlr: 1.000e-03, eta: 4:02:42, time: 0.093, data_time: 0.029, memory: 1125, loss: 1.4412\n2023-01-03 23:56:02,289 - mmcls - INFO - Epoch [67][4900/5005]\tlr: 1.000e-03, eta: 4:02:33, time: 0.088, data_time: 0.000, memory: 1125, loss: 1.4252\n2023-01-03 23:56:11,075 - mmcls - INFO - Epoch [67][5000/5005]\tlr: 1.000e-03, eta: 4:02:24, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4171\n2023-01-03 23:56:33,743 - mmcls - INFO - Epoch(val) [67][196]\taccuracy_top-1: 69.0900, accuracy_top-5: 88.8860\n2023-01-03 23:56:44,693 - mmcls - INFO - Epoch [68][100/5005]\tlr: 1.000e-03, eta: 4:02:16, time: 0.109, data_time: 0.059, memory: 1125, loss: 1.4405\n2023-01-03 23:56:53,527 - mmcls - INFO - Epoch [68][200/5005]\tlr: 1.000e-03, eta: 4:02:07, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.4083\n2023-01-03 23:57:02,180 - mmcls - INFO - Epoch [68][300/5005]\tlr: 1.000e-03, eta: 4:01:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4224\n2023-01-03 23:57:10,857 - mmcls - INFO - Epoch [68][400/5005]\tlr: 1.000e-03, eta: 4:01:49, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4263\n2023-01-03 23:57:19,492 - mmcls - INFO - Epoch [68][500/5005]\tlr: 1.000e-03, eta: 4:01:40, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4198\n2023-01-03 23:57:28,256 - mmcls - INFO - Epoch [68][600/5005]\tlr: 1.000e-03, eta: 4:01:31, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4080\n2023-01-03 23:57:36,865 - mmcls - INFO - Epoch [68][700/5005]\tlr: 1.000e-03, eta: 4:01:23, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4353\n2023-01-03 23:57:45,472 - mmcls - INFO - Epoch [68][800/5005]\tlr: 1.000e-03, eta: 4:01:14, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4277\n2023-01-03 23:57:54,133 - mmcls - INFO - Epoch [68][900/5005]\tlr: 1.000e-03, eta: 4:01:05, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.4256\n2023-01-03 23:58:02,941 - mmcls - INFO - Epoch [68][1000/5005]\tlr: 1.000e-03, eta: 4:00:56, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4082\n2023-01-03 23:58:11,355 - mmcls - INFO - Epoch [68][1100/5005]\tlr: 1.000e-03, eta: 4:00:47, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.4131\n2023-01-03 23:58:20,443 - mmcls - INFO - Epoch [68][1200/5005]\tlr: 1.000e-03, eta: 4:00:38, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.4130\n2023-01-03 23:58:28,981 - mmcls - INFO - Epoch [68][1300/5005]\tlr: 1.000e-03, eta: 4:00:29, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4000\n2023-01-03 23:58:37,618 - mmcls - INFO - Epoch [68][1400/5005]\tlr: 1.000e-03, eta: 4:00:20, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4062\n2023-01-03 23:58:46,164 - mmcls - INFO - Epoch [68][1500/5005]\tlr: 1.000e-03, eta: 4:00:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4437\n2023-01-03 23:58:55,095 - mmcls - INFO - Epoch [68][1600/5005]\tlr: 1.000e-03, eta: 4:00:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4139\n2023-01-03 23:59:03,790 - mmcls - INFO - Epoch [68][1700/5005]\tlr: 1.000e-03, eta: 3:59:54, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.4224\n2023-01-03 23:59:12,550 - mmcls - INFO - Epoch [68][1800/5005]\tlr: 1.000e-03, eta: 3:59:45, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4113\n2023-01-03 23:59:21,244 - mmcls - INFO - Epoch [68][1900/5005]\tlr: 1.000e-03, eta: 3:59:36, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.4153\n2023-01-03 23:59:29,769 - mmcls - INFO - Epoch [68][2000/5005]\tlr: 1.000e-03, eta: 3:59:27, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4481\n2023-01-03 23:59:38,891 - mmcls - INFO - Epoch [68][2100/5005]\tlr: 1.000e-03, eta: 3:59:19, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.4094\n2023-01-03 23:59:47,532 - mmcls - INFO - Epoch [68][2200/5005]\tlr: 1.000e-03, eta: 3:59:10, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4163\n2023-01-03 23:59:56,444 - mmcls - INFO - Epoch [68][2300/5005]\tlr: 1.000e-03, eta: 3:59:01, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.4101\n2023-01-04 00:00:05,407 - mmcls - INFO - Epoch [68][2400/5005]\tlr: 1.000e-03, eta: 3:58:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4314\n2023-01-04 00:00:14,007 - mmcls - INFO - Epoch [68][2500/5005]\tlr: 1.000e-03, eta: 3:58:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4217\n2023-01-04 00:00:22,841 - mmcls - INFO - Epoch [68][2600/5005]\tlr: 1.000e-03, eta: 3:58:35, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4155\n2023-01-04 00:00:31,685 - mmcls - INFO - Epoch [68][2700/5005]\tlr: 1.000e-03, eta: 3:58:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4129\n2023-01-04 00:00:40,473 - mmcls - INFO - Epoch [68][2800/5005]\tlr: 1.000e-03, eta: 3:58:17, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4291\n2023-01-04 00:00:49,263 - mmcls - INFO - Epoch [68][2900/5005]\tlr: 1.000e-03, eta: 3:58:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4075\n2023-01-04 00:00:57,843 - mmcls - INFO - Epoch [68][3000/5005]\tlr: 1.000e-03, eta: 3:57:59, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.4181\n2023-01-04 00:01:06,662 - mmcls - INFO - Epoch [68][3100/5005]\tlr: 1.000e-03, eta: 3:57:50, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4138\n2023-01-04 00:01:15,340 - mmcls - INFO - Epoch [68][3200/5005]\tlr: 1.000e-03, eta: 3:57:42, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4199\n2023-01-04 00:01:24,313 - mmcls - INFO - Epoch [68][3300/5005]\tlr: 1.000e-03, eta: 3:57:33, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4200\n2023-01-04 00:01:33,110 - mmcls - INFO - Epoch [68][3400/5005]\tlr: 1.000e-03, eta: 3:57:24, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4176\n2023-01-04 00:01:41,940 - mmcls - INFO - Epoch [68][3500/5005]\tlr: 1.000e-03, eta: 3:57:15, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.4258\n2023-01-04 00:01:50,694 - mmcls - INFO - Epoch [68][3600/5005]\tlr: 1.000e-03, eta: 3:57:06, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.4284\n2023-01-04 00:02:00,040 - mmcls - INFO - Epoch [68][3700/5005]\tlr: 1.000e-03, eta: 3:56:58, time: 0.093, data_time: 0.033, memory: 1125, loss: 1.4259\n2023-01-04 00:02:08,782 - mmcls - INFO - Epoch [68][3800/5005]\tlr: 1.000e-03, eta: 3:56:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4507\n2023-01-04 00:02:17,560 - mmcls - INFO - Epoch [68][3900/5005]\tlr: 1.000e-03, eta: 3:56:40, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4036\n2023-01-04 00:02:26,301 - mmcls - INFO - Epoch [68][4000/5005]\tlr: 1.000e-03, eta: 3:56:31, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4207\n2023-01-04 00:02:35,381 - mmcls - INFO - Epoch [68][4100/5005]\tlr: 1.000e-03, eta: 3:56:23, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.4213\n2023-01-04 00:02:44,176 - mmcls - INFO - Epoch [68][4200/5005]\tlr: 1.000e-03, eta: 3:56:14, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.4167\n2023-01-04 00:02:53,100 - mmcls - INFO - Epoch [68][4300/5005]\tlr: 1.000e-03, eta: 3:56:05, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4228\n2023-01-04 00:03:01,678 - mmcls - INFO - Epoch [68][4400/5005]\tlr: 1.000e-03, eta: 3:55:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4143\n2023-01-04 00:03:10,581 - mmcls - INFO - Epoch [68][4500/5005]\tlr: 1.000e-03, eta: 3:55:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4415\n2023-01-04 00:03:19,365 - mmcls - INFO - Epoch [68][4600/5005]\tlr: 1.000e-03, eta: 3:55:39, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.4210\n2023-01-04 00:03:28,504 - mmcls - INFO - Epoch [68][4700/5005]\tlr: 1.000e-03, eta: 3:55:30, time: 0.091, data_time: 0.025, memory: 1125, loss: 1.4229\n2023-01-04 00:03:37,279 - mmcls - INFO - Epoch [68][4800/5005]\tlr: 1.000e-03, eta: 3:55:21, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4253\n2023-01-04 00:03:46,021 - mmcls - INFO - Epoch [68][4900/5005]\tlr: 1.000e-03, eta: 3:55:12, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4212\n2023-01-04 00:03:55,072 - mmcls - INFO - Epoch [68][5000/5005]\tlr: 1.000e-03, eta: 3:55:04, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.4328\n2023-01-04 00:04:17,896 - mmcls - INFO - Epoch(val) [68][196]\taccuracy_top-1: 69.2260, accuracy_top-5: 88.8140\n2023-01-04 00:04:28,673 - mmcls - INFO - Epoch [69][100/5005]\tlr: 1.000e-03, eta: 3:54:55, time: 0.108, data_time: 0.042, memory: 1125, loss: 1.4226\n2023-01-04 00:04:37,279 - mmcls - INFO - Epoch [69][200/5005]\tlr: 1.000e-03, eta: 3:54:46, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4170\n2023-01-04 00:04:46,044 - mmcls - INFO - Epoch [69][300/5005]\tlr: 1.000e-03, eta: 3:54:37, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4101\n2023-01-04 00:04:54,641 - mmcls - INFO - Epoch [69][400/5005]\tlr: 1.000e-03, eta: 3:54:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4260\n2023-01-04 00:05:03,348 - mmcls - INFO - Epoch [69][500/5005]\tlr: 1.000e-03, eta: 3:54:20, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4113\n2023-01-04 00:05:12,033 - mmcls - INFO - Epoch [69][600/5005]\tlr: 1.000e-03, eta: 3:54:11, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.4073\n2023-01-04 00:05:21,134 - mmcls - INFO - Epoch [69][700/5005]\tlr: 1.000e-03, eta: 3:54:02, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.4033\n2023-01-04 00:05:29,809 - mmcls - INFO - Epoch [69][800/5005]\tlr: 1.000e-03, eta: 3:53:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4279\n2023-01-04 00:05:38,517 - mmcls - INFO - Epoch [69][900/5005]\tlr: 1.000e-03, eta: 3:53:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4259\n2023-01-04 00:05:47,416 - mmcls - INFO - Epoch [69][1000/5005]\tlr: 1.000e-03, eta: 3:53:36, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4265\n2023-01-04 00:05:56,107 - mmcls - INFO - Epoch [69][1100/5005]\tlr: 1.000e-03, eta: 3:53:27, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4235\n2023-01-04 00:06:04,623 - mmcls - INFO - Epoch [69][1200/5005]\tlr: 1.000e-03, eta: 3:53:18, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4272\n2023-01-04 00:06:13,367 - mmcls - INFO - Epoch [69][1300/5005]\tlr: 1.000e-03, eta: 3:53:09, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4168\n2023-01-04 00:06:21,999 - mmcls - INFO - Epoch [69][1400/5005]\tlr: 1.000e-03, eta: 3:53:00, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4169\n2023-01-04 00:06:30,798 - mmcls - INFO - Epoch [69][1500/5005]\tlr: 1.000e-03, eta: 3:52:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4309\n2023-01-04 00:06:39,575 - mmcls - INFO - Epoch [69][1600/5005]\tlr: 1.000e-03, eta: 3:52:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4195\n2023-01-04 00:06:48,110 - mmcls - INFO - Epoch [69][1700/5005]\tlr: 1.000e-03, eta: 3:52:34, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.4098\n2023-01-04 00:06:56,788 - mmcls - INFO - Epoch [69][1800/5005]\tlr: 1.000e-03, eta: 3:52:25, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4131\n2023-01-04 00:07:05,722 - mmcls - INFO - Epoch [69][1900/5005]\tlr: 1.000e-03, eta: 3:52:16, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.4134\n2023-01-04 00:07:14,661 - mmcls - INFO - Epoch [69][2000/5005]\tlr: 1.000e-03, eta: 3:52:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4094\n2023-01-04 00:07:23,225 - mmcls - INFO - Epoch [69][2100/5005]\tlr: 1.000e-03, eta: 3:51:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4257\n2023-01-04 00:07:31,922 - mmcls - INFO - Epoch [69][2200/5005]\tlr: 1.000e-03, eta: 3:51:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3967\n2023-01-04 00:07:40,610 - mmcls - INFO - Epoch [69][2300/5005]\tlr: 1.000e-03, eta: 3:51:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4378\n2023-01-04 00:07:49,609 - mmcls - INFO - Epoch [69][2400/5005]\tlr: 1.000e-03, eta: 3:51:32, time: 0.090, data_time: 0.050, memory: 1125, loss: 1.3966\n2023-01-04 00:07:58,078 - mmcls - INFO - Epoch [69][2500/5005]\tlr: 1.000e-03, eta: 3:51:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4183\n2023-01-04 00:08:06,936 - mmcls - INFO - Epoch [69][2600/5005]\tlr: 1.000e-03, eta: 3:51:14, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4019\n2023-01-04 00:08:15,848 - mmcls - INFO - Epoch [69][2700/5005]\tlr: 1.000e-03, eta: 3:51:05, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.4072\n2023-01-04 00:08:24,682 - mmcls - INFO - Epoch [69][2800/5005]\tlr: 1.000e-03, eta: 3:50:57, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4007\n2023-01-04 00:08:33,441 - mmcls - INFO - Epoch [69][2900/5005]\tlr: 1.000e-03, eta: 3:50:48, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.4218\n2023-01-04 00:08:41,936 - mmcls - INFO - Epoch [69][3000/5005]\tlr: 1.000e-03, eta: 3:50:39, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.4055\n2023-01-04 00:08:50,530 - mmcls - INFO - Epoch [69][3100/5005]\tlr: 1.000e-03, eta: 3:50:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4109\n2023-01-04 00:08:59,446 - mmcls - INFO - Epoch [69][3200/5005]\tlr: 1.000e-03, eta: 3:50:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4304\n2023-01-04 00:09:08,215 - mmcls - INFO - Epoch [69][3300/5005]\tlr: 1.000e-03, eta: 3:50:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.4091\n2023-01-04 00:09:16,804 - mmcls - INFO - Epoch [69][3400/5005]\tlr: 1.000e-03, eta: 3:50:03, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4279\n2023-01-04 00:09:25,291 - mmcls - INFO - Epoch [69][3500/5005]\tlr: 1.000e-03, eta: 3:49:54, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4168\n2023-01-04 00:09:34,180 - mmcls - INFO - Epoch [69][3600/5005]\tlr: 1.000e-03, eta: 3:49:46, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4238\n2023-01-04 00:09:42,630 - mmcls - INFO - Epoch [69][3700/5005]\tlr: 1.000e-03, eta: 3:49:37, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4274\n2023-01-04 00:09:51,365 - mmcls - INFO - Epoch [69][3800/5005]\tlr: 1.000e-03, eta: 3:49:28, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.4342\n2023-01-04 00:10:00,139 - mmcls - INFO - Epoch [69][3900/5005]\tlr: 1.000e-03, eta: 3:49:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4298\n2023-01-04 00:10:08,921 - mmcls - INFO - Epoch [69][4000/5005]\tlr: 1.000e-03, eta: 3:49:10, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4114\n2023-01-04 00:10:17,757 - mmcls - INFO - Epoch [69][4100/5005]\tlr: 1.000e-03, eta: 3:49:01, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.4229\n2023-01-04 00:10:26,656 - mmcls - INFO - Epoch [69][4200/5005]\tlr: 1.000e-03, eta: 3:48:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4154\n2023-01-04 00:10:35,515 - mmcls - INFO - Epoch [69][4300/5005]\tlr: 1.000e-03, eta: 3:48:44, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4045\n2023-01-04 00:10:44,147 - mmcls - INFO - Epoch [69][4400/5005]\tlr: 1.000e-03, eta: 3:48:35, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4159\n2023-01-04 00:10:53,004 - mmcls - INFO - Epoch [69][4500/5005]\tlr: 1.000e-03, eta: 3:48:26, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4331\n2023-01-04 00:11:01,691 - mmcls - INFO - Epoch [69][4600/5005]\tlr: 1.000e-03, eta: 3:48:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4211\n2023-01-04 00:11:10,294 - mmcls - INFO - Epoch [69][4700/5005]\tlr: 1.000e-03, eta: 3:48:08, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4293\n2023-01-04 00:11:18,848 - mmcls - INFO - Epoch [69][4800/5005]\tlr: 1.000e-03, eta: 3:48:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4114\n2023-01-04 00:11:27,432 - mmcls - INFO - Epoch [69][4900/5005]\tlr: 1.000e-03, eta: 3:47:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4105\n2023-01-04 00:11:36,393 - mmcls - INFO - Epoch [69][5000/5005]\tlr: 1.000e-03, eta: 3:47:42, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4157\n2023-01-04 00:11:59,013 - mmcls - INFO - Epoch(val) [69][196]\taccuracy_top-1: 69.1820, accuracy_top-5: 88.9480\n2023-01-04 00:12:09,859 - mmcls - INFO - Epoch [70][100/5005]\tlr: 1.000e-03, eta: 3:47:33, time: 0.108, data_time: 0.031, memory: 1125, loss: 1.4192\n2023-01-04 00:12:18,515 - mmcls - INFO - Epoch [70][200/5005]\tlr: 1.000e-03, eta: 3:47:24, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3991\n2023-01-04 00:12:27,175 - mmcls - INFO - Epoch [70][300/5005]\tlr: 1.000e-03, eta: 3:47:16, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4116\n2023-01-04 00:12:35,886 - mmcls - INFO - Epoch [70][400/5005]\tlr: 1.000e-03, eta: 3:47:07, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.3977\n2023-01-04 00:12:44,591 - mmcls - INFO - Epoch [70][500/5005]\tlr: 1.000e-03, eta: 3:46:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4026\n2023-01-04 00:12:53,314 - mmcls - INFO - Epoch [70][600/5005]\tlr: 1.000e-03, eta: 3:46:49, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4196\n2023-01-04 00:13:02,018 - mmcls - INFO - Epoch [70][700/5005]\tlr: 1.000e-03, eta: 3:46:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4027\n2023-01-04 00:13:10,621 - mmcls - INFO - Epoch [70][800/5005]\tlr: 1.000e-03, eta: 3:46:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4158\n2023-01-04 00:13:19,221 - mmcls - INFO - Epoch [70][900/5005]\tlr: 1.000e-03, eta: 3:46:22, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4041\n2023-01-04 00:13:28,079 - mmcls - INFO - Epoch [70][1000/5005]\tlr: 1.000e-03, eta: 3:46:14, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3915\n2023-01-04 00:13:36,887 - mmcls - INFO - Epoch [70][1100/5005]\tlr: 1.000e-03, eta: 3:46:05, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4174\n2023-01-04 00:13:45,682 - mmcls - INFO - Epoch [70][1200/5005]\tlr: 1.000e-03, eta: 3:45:56, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4116\n2023-01-04 00:13:54,551 - mmcls - INFO - Epoch [70][1300/5005]\tlr: 1.000e-03, eta: 3:45:47, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3962\n2023-01-04 00:14:03,062 - mmcls - INFO - Epoch [70][1400/5005]\tlr: 1.000e-03, eta: 3:45:38, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4066\n2023-01-04 00:14:11,539 - mmcls - INFO - Epoch [70][1500/5005]\tlr: 1.000e-03, eta: 3:45:29, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.4251\n2023-01-04 00:14:20,301 - mmcls - INFO - Epoch [70][1600/5005]\tlr: 1.000e-03, eta: 3:45:21, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4198\n2023-01-04 00:14:29,131 - mmcls - INFO - Epoch [70][1700/5005]\tlr: 1.000e-03, eta: 3:45:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4193\n2023-01-04 00:14:37,821 - mmcls - INFO - Epoch [70][1800/5005]\tlr: 1.000e-03, eta: 3:45:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4201\n2023-01-04 00:14:46,533 - mmcls - INFO - Epoch [70][1900/5005]\tlr: 1.000e-03, eta: 3:44:54, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3916\n2023-01-04 00:14:55,347 - mmcls - INFO - Epoch [70][2000/5005]\tlr: 1.000e-03, eta: 3:44:45, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4104\n2023-01-04 00:15:04,332 - mmcls - INFO - Epoch [70][2100/5005]\tlr: 1.000e-03, eta: 3:44:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.4086\n2023-01-04 00:15:13,025 - mmcls - INFO - Epoch [70][2200/5005]\tlr: 1.000e-03, eta: 3:44:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3921\n2023-01-04 00:15:21,521 - mmcls - INFO - Epoch [70][2300/5005]\tlr: 1.000e-03, eta: 3:44:19, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.4084\n2023-01-04 00:15:30,656 - mmcls - INFO - Epoch [70][2400/5005]\tlr: 1.000e-03, eta: 3:44:10, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.4072\n2023-01-04 00:15:39,619 - mmcls - INFO - Epoch [70][2500/5005]\tlr: 1.000e-03, eta: 3:44:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.4152\n2023-01-04 00:15:48,051 - mmcls - INFO - Epoch [70][2600/5005]\tlr: 1.000e-03, eta: 3:43:52, time: 0.084, data_time: 0.019, memory: 1125, loss: 1.4124\n2023-01-04 00:15:56,761 - mmcls - INFO - Epoch [70][2700/5005]\tlr: 1.000e-03, eta: 3:43:43, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.4172\n2023-01-04 00:16:05,340 - mmcls - INFO - Epoch [70][2800/5005]\tlr: 1.000e-03, eta: 3:43:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3955\n2023-01-04 00:16:14,756 - mmcls - INFO - Epoch [70][2900/5005]\tlr: 1.000e-03, eta: 3:43:26, time: 0.094, data_time: 0.028, memory: 1125, loss: 1.4015\n2023-01-04 00:16:23,498 - mmcls - INFO - Epoch [70][3000/5005]\tlr: 1.000e-03, eta: 3:43:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4198\n2023-01-04 00:16:32,123 - mmcls - INFO - Epoch [70][3100/5005]\tlr: 1.000e-03, eta: 3:43:08, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4355\n2023-01-04 00:16:40,775 - mmcls - INFO - Epoch [70][3200/5005]\tlr: 1.000e-03, eta: 3:42:59, time: 0.086, data_time: 0.043, memory: 1125, loss: 1.3939\n2023-01-04 00:16:49,671 - mmcls - INFO - Epoch [70][3300/5005]\tlr: 1.000e-03, eta: 3:42:51, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3957\n2023-01-04 00:16:58,642 - mmcls - INFO - Epoch [70][3400/5005]\tlr: 1.000e-03, eta: 3:42:42, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4170\n2023-01-04 00:17:07,572 - mmcls - INFO - Epoch [70][3500/5005]\tlr: 1.000e-03, eta: 3:42:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4066\n2023-01-04 00:17:16,289 - mmcls - INFO - Epoch [70][3600/5005]\tlr: 1.000e-03, eta: 3:42:24, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4020\n2023-01-04 00:17:25,231 - mmcls - INFO - Epoch [70][3700/5005]\tlr: 1.000e-03, eta: 3:42:16, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4058\n2023-01-04 00:17:34,170 - mmcls - INFO - Epoch [70][3800/5005]\tlr: 1.000e-03, eta: 3:42:07, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4445\n2023-01-04 00:17:42,915 - mmcls - INFO - Epoch [70][3900/5005]\tlr: 1.000e-03, eta: 3:41:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4267\n2023-01-04 00:17:51,574 - mmcls - INFO - Epoch [70][4000/5005]\tlr: 1.000e-03, eta: 3:41:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4113\n2023-01-04 00:18:00,237 - mmcls - INFO - Epoch [70][4100/5005]\tlr: 1.000e-03, eta: 3:41:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4263\n2023-01-04 00:18:09,016 - mmcls - INFO - Epoch [70][4200/5005]\tlr: 1.000e-03, eta: 3:41:31, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4090\n2023-01-04 00:18:17,670 - mmcls - INFO - Epoch [70][4300/5005]\tlr: 1.000e-03, eta: 3:41:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4200\n2023-01-04 00:18:26,781 - mmcls - INFO - Epoch [70][4400/5005]\tlr: 1.000e-03, eta: 3:41:14, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.4274\n2023-01-04 00:18:35,281 - mmcls - INFO - Epoch [70][4500/5005]\tlr: 1.000e-03, eta: 3:41:05, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4201\n2023-01-04 00:18:44,079 - mmcls - INFO - Epoch [70][4600/5005]\tlr: 1.000e-03, eta: 3:40:56, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4132\n2023-01-04 00:18:52,891 - mmcls - INFO - Epoch [70][4700/5005]\tlr: 1.000e-03, eta: 3:40:47, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.4157\n2023-01-04 00:19:02,424 - mmcls - INFO - Epoch [70][4800/5005]\tlr: 1.000e-03, eta: 3:40:39, time: 0.095, data_time: 0.010, memory: 1125, loss: 1.4310\n2023-01-04 00:19:11,054 - mmcls - INFO - Epoch [70][4900/5005]\tlr: 1.000e-03, eta: 3:40:30, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3992\n2023-01-04 00:19:19,809 - mmcls - INFO - Epoch [70][5000/5005]\tlr: 1.000e-03, eta: 3:40:21, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4129\n2023-01-04 00:19:42,430 - mmcls - INFO - Epoch(val) [70][196]\taccuracy_top-1: 69.2200, accuracy_top-5: 88.9600\n2023-01-04 00:19:53,740 - mmcls - INFO - Epoch [71][100/5005]\tlr: 1.000e-03, eta: 3:40:13, time: 0.113, data_time: 0.060, memory: 1125, loss: 1.4010\n2023-01-04 00:20:02,540 - mmcls - INFO - Epoch [71][200/5005]\tlr: 1.000e-03, eta: 3:40:04, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3886\n2023-01-04 00:20:11,380 - mmcls - INFO - Epoch [71][300/5005]\tlr: 1.000e-03, eta: 3:39:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4070\n2023-01-04 00:20:19,963 - mmcls - INFO - Epoch [71][400/5005]\tlr: 1.000e-03, eta: 3:39:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4049\n2023-01-04 00:20:28,793 - mmcls - INFO - Epoch [71][500/5005]\tlr: 1.000e-03, eta: 3:39:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4139\n2023-01-04 00:20:37,481 - mmcls - INFO - Epoch [71][600/5005]\tlr: 1.000e-03, eta: 3:39:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4149\n2023-01-04 00:20:46,275 - mmcls - INFO - Epoch [71][700/5005]\tlr: 1.000e-03, eta: 3:39:20, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3838\n2023-01-04 00:20:54,901 - mmcls - INFO - Epoch [71][800/5005]\tlr: 1.000e-03, eta: 3:39:11, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4279\n2023-01-04 00:21:03,771 - mmcls - INFO - Epoch [71][900/5005]\tlr: 1.000e-03, eta: 3:39:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4223\n2023-01-04 00:21:12,301 - mmcls - INFO - Epoch [71][1000/5005]\tlr: 1.000e-03, eta: 3:38:53, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4181\n2023-01-04 00:21:21,282 - mmcls - INFO - Epoch [71][1100/5005]\tlr: 1.000e-03, eta: 3:38:45, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4139\n2023-01-04 00:21:30,153 - mmcls - INFO - Epoch [71][1200/5005]\tlr: 1.000e-03, eta: 3:38:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3987\n2023-01-04 00:21:38,714 - mmcls - INFO - Epoch [71][1300/5005]\tlr: 1.000e-03, eta: 3:38:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4183\n2023-01-04 00:21:47,461 - mmcls - INFO - Epoch [71][1400/5005]\tlr: 1.000e-03, eta: 3:38:18, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4000\n2023-01-04 00:21:56,208 - mmcls - INFO - Epoch [71][1500/5005]\tlr: 1.000e-03, eta: 3:38:09, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4129\n2023-01-04 00:22:04,996 - mmcls - INFO - Epoch [71][1600/5005]\tlr: 1.000e-03, eta: 3:38:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4201\n2023-01-04 00:22:13,793 - mmcls - INFO - Epoch [71][1700/5005]\tlr: 1.000e-03, eta: 3:37:52, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4208\n2023-01-04 00:22:22,750 - mmcls - INFO - Epoch [71][1800/5005]\tlr: 1.000e-03, eta: 3:37:43, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.4007\n2023-01-04 00:22:31,533 - mmcls - INFO - Epoch [71][1900/5005]\tlr: 1.000e-03, eta: 3:37:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4241\n2023-01-04 00:22:40,231 - mmcls - INFO - Epoch [71][2000/5005]\tlr: 1.000e-03, eta: 3:37:25, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3888\n2023-01-04 00:22:49,329 - mmcls - INFO - Epoch [71][2100/5005]\tlr: 1.000e-03, eta: 3:37:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.4156\n2023-01-04 00:22:57,884 - mmcls - INFO - Epoch [71][2200/5005]\tlr: 1.000e-03, eta: 3:37:08, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4017\n2023-01-04 00:23:06,538 - mmcls - INFO - Epoch [71][2300/5005]\tlr: 1.000e-03, eta: 3:36:59, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3824\n2023-01-04 00:23:15,156 - mmcls - INFO - Epoch [71][2400/5005]\tlr: 1.000e-03, eta: 3:36:50, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4150\n2023-01-04 00:23:24,385 - mmcls - INFO - Epoch [71][2500/5005]\tlr: 1.000e-03, eta: 3:36:41, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.4084\n2023-01-04 00:23:33,355 - mmcls - INFO - Epoch [71][2600/5005]\tlr: 1.000e-03, eta: 3:36:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.4013\n2023-01-04 00:23:42,050 - mmcls - INFO - Epoch [71][2700/5005]\tlr: 1.000e-03, eta: 3:36:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4096\n2023-01-04 00:23:50,595 - mmcls - INFO - Epoch [71][2800/5005]\tlr: 1.000e-03, eta: 3:36:15, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4079\n2023-01-04 00:23:59,199 - mmcls - INFO - Epoch [71][2900/5005]\tlr: 1.000e-03, eta: 3:36:06, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.4078\n2023-01-04 00:24:07,801 - mmcls - INFO - Epoch [71][3000/5005]\tlr: 1.000e-03, eta: 3:35:57, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4094\n2023-01-04 00:24:16,517 - mmcls - INFO - Epoch [71][3100/5005]\tlr: 1.000e-03, eta: 3:35:48, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4094\n2023-01-04 00:24:25,302 - mmcls - INFO - Epoch [71][3200/5005]\tlr: 1.000e-03, eta: 3:35:39, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4178\n2023-01-04 00:24:34,086 - mmcls - INFO - Epoch [71][3300/5005]\tlr: 1.000e-03, eta: 3:35:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4048\n2023-01-04 00:24:42,857 - mmcls - INFO - Epoch [71][3400/5005]\tlr: 1.000e-03, eta: 3:35:22, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4163\n2023-01-04 00:24:51,278 - mmcls - INFO - Epoch [71][3500/5005]\tlr: 1.000e-03, eta: 3:35:13, time: 0.084, data_time: 0.033, memory: 1125, loss: 1.4133\n2023-01-04 00:24:59,915 - mmcls - INFO - Epoch [71][3600/5005]\tlr: 1.000e-03, eta: 3:35:04, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4096\n2023-01-04 00:25:08,823 - mmcls - INFO - Epoch [71][3700/5005]\tlr: 1.000e-03, eta: 3:34:55, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.4095\n2023-01-04 00:25:17,652 - mmcls - INFO - Epoch [71][3800/5005]\tlr: 1.000e-03, eta: 3:34:46, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3941\n2023-01-04 00:25:26,407 - mmcls - INFO - Epoch [71][3900/5005]\tlr: 1.000e-03, eta: 3:34:37, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.4025\n2023-01-04 00:25:35,245 - mmcls - INFO - Epoch [71][4000/5005]\tlr: 1.000e-03, eta: 3:34:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3983\n2023-01-04 00:25:43,989 - mmcls - INFO - Epoch [71][4100/5005]\tlr: 1.000e-03, eta: 3:34:20, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3853\n2023-01-04 00:25:52,593 - mmcls - INFO - Epoch [71][4200/5005]\tlr: 1.000e-03, eta: 3:34:11, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.4176\n2023-01-04 00:26:01,306 - mmcls - INFO - Epoch [71][4300/5005]\tlr: 1.000e-03, eta: 3:34:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4093\n2023-01-04 00:26:09,812 - mmcls - INFO - Epoch [71][4400/5005]\tlr: 1.000e-03, eta: 3:33:53, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4106\n2023-01-04 00:26:18,604 - mmcls - INFO - Epoch [71][4500/5005]\tlr: 1.000e-03, eta: 3:33:44, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4034\n2023-01-04 00:26:27,246 - mmcls - INFO - Epoch [71][4600/5005]\tlr: 1.000e-03, eta: 3:33:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4013\n2023-01-04 00:26:36,053 - mmcls - INFO - Epoch [71][4700/5005]\tlr: 1.000e-03, eta: 3:33:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4260\n2023-01-04 00:26:44,906 - mmcls - INFO - Epoch [71][4800/5005]\tlr: 1.000e-03, eta: 3:33:18, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4033\n2023-01-04 00:26:53,834 - mmcls - INFO - Epoch [71][4900/5005]\tlr: 1.000e-03, eta: 3:33:09, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.3958\n2023-01-04 00:27:02,593 - mmcls - INFO - Epoch [71][5000/5005]\tlr: 1.000e-03, eta: 3:33:00, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3908\n2023-01-04 00:27:25,251 - mmcls - INFO - Epoch(val) [71][196]\taccuracy_top-1: 69.3400, accuracy_top-5: 88.9820\n2023-01-04 00:27:36,528 - mmcls - INFO - Epoch [72][100/5005]\tlr: 1.000e-03, eta: 3:32:52, time: 0.113, data_time: 0.050, memory: 1125, loss: 1.4024\n2023-01-04 00:27:45,436 - mmcls - INFO - Epoch [72][200/5005]\tlr: 1.000e-03, eta: 3:32:43, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4078\n2023-01-04 00:27:54,308 - mmcls - INFO - Epoch [72][300/5005]\tlr: 1.000e-03, eta: 3:32:34, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.3750\n2023-01-04 00:28:03,138 - mmcls - INFO - Epoch [72][400/5005]\tlr: 1.000e-03, eta: 3:32:25, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.4112\n2023-01-04 00:28:11,746 - mmcls - INFO - Epoch [72][500/5005]\tlr: 1.000e-03, eta: 3:32:17, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3915\n2023-01-04 00:28:20,714 - mmcls - INFO - Epoch [72][600/5005]\tlr: 1.000e-03, eta: 3:32:08, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4030\n2023-01-04 00:28:29,753 - mmcls - INFO - Epoch [72][700/5005]\tlr: 1.000e-03, eta: 3:31:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.3987\n2023-01-04 00:28:38,544 - mmcls - INFO - Epoch [72][800/5005]\tlr: 1.000e-03, eta: 3:31:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4003\n2023-01-04 00:28:47,111 - mmcls - INFO - Epoch [72][900/5005]\tlr: 1.000e-03, eta: 3:31:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4124\n2023-01-04 00:28:55,713 - mmcls - INFO - Epoch [72][1000/5005]\tlr: 1.000e-03, eta: 3:31:33, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3983\n2023-01-04 00:29:04,392 - mmcls - INFO - Epoch [72][1100/5005]\tlr: 1.000e-03, eta: 3:31:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4065\n2023-01-04 00:29:13,232 - mmcls - INFO - Epoch [72][1200/5005]\tlr: 1.000e-03, eta: 3:31:15, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4137\n2023-01-04 00:29:21,781 - mmcls - INFO - Epoch [72][1300/5005]\tlr: 1.000e-03, eta: 3:31:06, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.4109\n2023-01-04 00:29:30,372 - mmcls - INFO - Epoch [72][1400/5005]\tlr: 1.000e-03, eta: 3:30:57, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3715\n2023-01-04 00:29:39,072 - mmcls - INFO - Epoch [72][1500/5005]\tlr: 1.000e-03, eta: 3:30:48, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3957\n2023-01-04 00:29:47,658 - mmcls - INFO - Epoch [72][1600/5005]\tlr: 1.000e-03, eta: 3:30:39, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3950\n2023-01-04 00:29:56,514 - mmcls - INFO - Epoch [72][1700/5005]\tlr: 1.000e-03, eta: 3:30:31, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4104\n2023-01-04 00:30:05,286 - mmcls - INFO - Epoch [72][1800/5005]\tlr: 1.000e-03, eta: 3:30:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3973\n2023-01-04 00:30:13,954 - mmcls - INFO - Epoch [72][1900/5005]\tlr: 1.000e-03, eta: 3:30:13, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3902\n2023-01-04 00:30:22,544 - mmcls - INFO - Epoch [72][2000/5005]\tlr: 1.000e-03, eta: 3:30:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4124\n2023-01-04 00:30:30,992 - mmcls - INFO - Epoch [72][2100/5005]\tlr: 1.000e-03, eta: 3:29:55, time: 0.084, data_time: 0.034, memory: 1125, loss: 1.3812\n2023-01-04 00:30:39,611 - mmcls - INFO - Epoch [72][2200/5005]\tlr: 1.000e-03, eta: 3:29:46, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4027\n2023-01-04 00:30:48,192 - mmcls - INFO - Epoch [72][2300/5005]\tlr: 1.000e-03, eta: 3:29:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3990\n2023-01-04 00:30:56,813 - mmcls - INFO - Epoch [72][2400/5005]\tlr: 1.000e-03, eta: 3:29:28, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4039\n2023-01-04 00:31:05,720 - mmcls - INFO - Epoch [72][2500/5005]\tlr: 1.000e-03, eta: 3:29:20, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3929\n2023-01-04 00:31:14,349 - mmcls - INFO - Epoch [72][2600/5005]\tlr: 1.000e-03, eta: 3:29:11, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4067\n2023-01-04 00:31:23,297 - mmcls - INFO - Epoch [72][2700/5005]\tlr: 1.000e-03, eta: 3:29:02, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4295\n2023-01-04 00:31:32,122 - mmcls - INFO - Epoch [72][2800/5005]\tlr: 1.000e-03, eta: 3:28:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4057\n2023-01-04 00:31:41,091 - mmcls - INFO - Epoch [72][2900/5005]\tlr: 1.000e-03, eta: 3:28:44, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.3886\n2023-01-04 00:31:49,822 - mmcls - INFO - Epoch [72][3000/5005]\tlr: 1.000e-03, eta: 3:28:36, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4209\n2023-01-04 00:31:58,783 - mmcls - INFO - Epoch [72][3100/5005]\tlr: 1.000e-03, eta: 3:28:27, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3952\n2023-01-04 00:32:07,653 - mmcls - INFO - Epoch [72][3200/5005]\tlr: 1.000e-03, eta: 3:28:18, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.4046\n2023-01-04 00:32:16,475 - mmcls - INFO - Epoch [72][3300/5005]\tlr: 1.000e-03, eta: 3:28:09, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4121\n2023-01-04 00:32:25,303 - mmcls - INFO - Epoch [72][3400/5005]\tlr: 1.000e-03, eta: 3:28:00, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.4208\n2023-01-04 00:32:34,070 - mmcls - INFO - Epoch [72][3500/5005]\tlr: 1.000e-03, eta: 3:27:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3901\n2023-01-04 00:32:42,857 - mmcls - INFO - Epoch [72][3600/5005]\tlr: 1.000e-03, eta: 3:27:43, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.4057\n2023-01-04 00:32:51,921 - mmcls - INFO - Epoch [72][3700/5005]\tlr: 1.000e-03, eta: 3:27:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3942\n2023-01-04 00:33:00,683 - mmcls - INFO - Epoch [72][3800/5005]\tlr: 1.000e-03, eta: 3:27:25, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4171\n2023-01-04 00:33:09,592 - mmcls - INFO - Epoch [72][3900/5005]\tlr: 1.000e-03, eta: 3:27:17, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3993\n2023-01-04 00:33:18,484 - mmcls - INFO - Epoch [72][4000/5005]\tlr: 1.000e-03, eta: 3:27:08, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3972\n2023-01-04 00:33:27,290 - mmcls - INFO - Epoch [72][4100/5005]\tlr: 1.000e-03, eta: 3:26:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4079\n2023-01-04 00:33:35,736 - mmcls - INFO - Epoch [72][4200/5005]\tlr: 1.000e-03, eta: 3:26:50, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.3942\n2023-01-04 00:33:44,649 - mmcls - INFO - Epoch [72][4300/5005]\tlr: 1.000e-03, eta: 3:26:41, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.4066\n2023-01-04 00:33:53,824 - mmcls - INFO - Epoch [72][4400/5005]\tlr: 1.000e-03, eta: 3:26:33, time: 0.092, data_time: 0.049, memory: 1125, loss: 1.3790\n2023-01-04 00:34:02,371 - mmcls - INFO - Epoch [72][4500/5005]\tlr: 1.000e-03, eta: 3:26:24, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3920\n2023-01-04 00:34:11,097 - mmcls - INFO - Epoch [72][4600/5005]\tlr: 1.000e-03, eta: 3:26:15, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4075\n2023-01-04 00:34:19,583 - mmcls - INFO - Epoch [72][4700/5005]\tlr: 1.000e-03, eta: 3:26:06, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4036\n2023-01-04 00:34:28,699 - mmcls - INFO - Epoch [72][4800/5005]\tlr: 1.000e-03, eta: 3:25:57, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3979\n2023-01-04 00:34:37,284 - mmcls - INFO - Epoch [72][4900/5005]\tlr: 1.000e-03, eta: 3:25:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3980\n2023-01-04 00:34:46,313 - mmcls - INFO - Epoch [72][5000/5005]\tlr: 1.000e-03, eta: 3:25:40, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4079\n2023-01-04 00:35:08,940 - mmcls - INFO - Epoch(val) [72][196]\taccuracy_top-1: 69.4520, accuracy_top-5: 88.9200\n2023-01-04 00:35:20,229 - mmcls - INFO - Epoch [73][100/5005]\tlr: 1.000e-03, eta: 3:25:31, time: 0.113, data_time: 0.027, memory: 1125, loss: 1.3947\n2023-01-04 00:35:28,962 - mmcls - INFO - Epoch [73][200/5005]\tlr: 1.000e-03, eta: 3:25:22, time: 0.087, data_time: 0.044, memory: 1125, loss: 1.4068\n2023-01-04 00:35:37,756 - mmcls - INFO - Epoch [73][300/5005]\tlr: 1.000e-03, eta: 3:25:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3850\n2023-01-04 00:35:46,197 - mmcls - INFO - Epoch [73][400/5005]\tlr: 1.000e-03, eta: 3:25:05, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.3794\n2023-01-04 00:35:54,983 - mmcls - INFO - Epoch [73][500/5005]\tlr: 1.000e-03, eta: 3:24:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3831\n2023-01-04 00:36:03,643 - mmcls - INFO - Epoch [73][600/5005]\tlr: 1.000e-03, eta: 3:24:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3862\n2023-01-04 00:36:12,400 - mmcls - INFO - Epoch [73][700/5005]\tlr: 1.000e-03, eta: 3:24:38, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3975\n2023-01-04 00:36:21,301 - mmcls - INFO - Epoch [73][800/5005]\tlr: 1.000e-03, eta: 3:24:29, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3841\n2023-01-04 00:36:30,205 - mmcls - INFO - Epoch [73][900/5005]\tlr: 1.000e-03, eta: 3:24:21, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4023\n2023-01-04 00:36:38,895 - mmcls - INFO - Epoch [73][1000/5005]\tlr: 1.000e-03, eta: 3:24:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4112\n2023-01-04 00:36:47,361 - mmcls - INFO - Epoch [73][1100/5005]\tlr: 1.000e-03, eta: 3:24:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3955\n2023-01-04 00:36:56,000 - mmcls - INFO - Epoch [73][1200/5005]\tlr: 1.000e-03, eta: 3:23:54, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.4273\n2023-01-04 00:37:04,956 - mmcls - INFO - Epoch [73][1300/5005]\tlr: 1.000e-03, eta: 3:23:45, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4148\n2023-01-04 00:37:13,561 - mmcls - INFO - Epoch [73][1400/5005]\tlr: 1.000e-03, eta: 3:23:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3999\n2023-01-04 00:37:22,418 - mmcls - INFO - Epoch [73][1500/5005]\tlr: 1.000e-03, eta: 3:23:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4161\n2023-01-04 00:37:30,992 - mmcls - INFO - Epoch [73][1600/5005]\tlr: 1.000e-03, eta: 3:23:19, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3783\n2023-01-04 00:37:39,717 - mmcls - INFO - Epoch [73][1700/5005]\tlr: 1.000e-03, eta: 3:23:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4056\n2023-01-04 00:37:48,374 - mmcls - INFO - Epoch [73][1800/5005]\tlr: 1.000e-03, eta: 3:23:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4047\n2023-01-04 00:37:56,809 - mmcls - INFO - Epoch [73][1900/5005]\tlr: 1.000e-03, eta: 3:22:52, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.4025\n2023-01-04 00:38:05,762 - mmcls - INFO - Epoch [73][2000/5005]\tlr: 1.000e-03, eta: 3:22:43, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3847\n2023-01-04 00:38:14,166 - mmcls - INFO - Epoch [73][2100/5005]\tlr: 1.000e-03, eta: 3:22:34, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.4125\n2023-01-04 00:38:22,776 - mmcls - INFO - Epoch [73][2200/5005]\tlr: 1.000e-03, eta: 3:22:25, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4050\n2023-01-04 00:38:31,834 - mmcls - INFO - Epoch [73][2300/5005]\tlr: 1.000e-03, eta: 3:22:17, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4258\n2023-01-04 00:38:40,875 - mmcls - INFO - Epoch [73][2400/5005]\tlr: 1.000e-03, eta: 3:22:08, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4109\n2023-01-04 00:38:49,823 - mmcls - INFO - Epoch [73][2500/5005]\tlr: 1.000e-03, eta: 3:21:59, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3674\n2023-01-04 00:38:58,740 - mmcls - INFO - Epoch [73][2600/5005]\tlr: 1.000e-03, eta: 3:21:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4015\n2023-01-04 00:39:07,603 - mmcls - INFO - Epoch [73][2700/5005]\tlr: 1.000e-03, eta: 3:21:42, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4116\n2023-01-04 00:39:16,496 - mmcls - INFO - Epoch [73][2800/5005]\tlr: 1.000e-03, eta: 3:21:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3854\n2023-01-04 00:39:25,321 - mmcls - INFO - Epoch [73][2900/5005]\tlr: 1.000e-03, eta: 3:21:24, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3820\n2023-01-04 00:39:33,997 - mmcls - INFO - Epoch [73][3000/5005]\tlr: 1.000e-03, eta: 3:21:15, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3914\n2023-01-04 00:39:42,738 - mmcls - INFO - Epoch [73][3100/5005]\tlr: 1.000e-03, eta: 3:21:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3940\n2023-01-04 00:39:51,298 - mmcls - INFO - Epoch [73][3200/5005]\tlr: 1.000e-03, eta: 3:20:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4128\n2023-01-04 00:39:59,844 - mmcls - INFO - Epoch [73][3300/5005]\tlr: 1.000e-03, eta: 3:20:49, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.4014\n2023-01-04 00:40:08,583 - mmcls - INFO - Epoch [73][3400/5005]\tlr: 1.000e-03, eta: 3:20:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3974\n2023-01-04 00:40:17,317 - mmcls - INFO - Epoch [73][3500/5005]\tlr: 1.000e-03, eta: 3:20:31, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.4066\n2023-01-04 00:40:26,059 - mmcls - INFO - Epoch [73][3600/5005]\tlr: 1.000e-03, eta: 3:20:22, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.4057\n2023-01-04 00:40:34,911 - mmcls - INFO - Epoch [73][3700/5005]\tlr: 1.000e-03, eta: 3:20:13, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4154\n2023-01-04 00:40:43,875 - mmcls - INFO - Epoch [73][3800/5005]\tlr: 1.000e-03, eta: 3:20:05, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4068\n2023-01-04 00:40:52,643 - mmcls - INFO - Epoch [73][3900/5005]\tlr: 1.000e-03, eta: 3:19:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3852\n2023-01-04 00:41:01,231 - mmcls - INFO - Epoch [73][4000/5005]\tlr: 1.000e-03, eta: 3:19:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4176\n2023-01-04 00:41:09,930 - mmcls - INFO - Epoch [73][4100/5005]\tlr: 1.000e-03, eta: 3:19:38, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3988\n2023-01-04 00:41:18,685 - mmcls - INFO - Epoch [73][4200/5005]\tlr: 1.000e-03, eta: 3:19:29, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.3719\n2023-01-04 00:41:27,878 - mmcls - INFO - Epoch [73][4300/5005]\tlr: 1.000e-03, eta: 3:19:21, time: 0.092, data_time: 0.052, memory: 1125, loss: 1.3945\n2023-01-04 00:41:36,822 - mmcls - INFO - Epoch [73][4400/5005]\tlr: 1.000e-03, eta: 3:19:12, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.4001\n2023-01-04 00:41:45,648 - mmcls - INFO - Epoch [73][4500/5005]\tlr: 1.000e-03, eta: 3:19:03, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3974\n2023-01-04 00:41:54,723 - mmcls - INFO - Epoch [73][4600/5005]\tlr: 1.000e-03, eta: 3:18:54, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.4292\n2023-01-04 00:42:03,413 - mmcls - INFO - Epoch [73][4700/5005]\tlr: 1.000e-03, eta: 3:18:45, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4011\n2023-01-04 00:42:11,979 - mmcls - INFO - Epoch [73][4800/5005]\tlr: 1.000e-03, eta: 3:18:36, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3826\n2023-01-04 00:42:20,994 - mmcls - INFO - Epoch [73][4900/5005]\tlr: 1.000e-03, eta: 3:18:28, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4084\n2023-01-04 00:42:29,890 - mmcls - INFO - Epoch [73][5000/5005]\tlr: 1.000e-03, eta: 3:18:19, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4025\n2023-01-04 00:42:52,529 - mmcls - INFO - Epoch(val) [73][196]\taccuracy_top-1: 69.5740, accuracy_top-5: 89.0040\n2023-01-04 00:43:03,654 - mmcls - INFO - Epoch [74][100/5005]\tlr: 1.000e-03, eta: 3:18:10, time: 0.111, data_time: 0.058, memory: 1125, loss: 1.4043\n2023-01-04 00:43:12,505 - mmcls - INFO - Epoch [74][200/5005]\tlr: 1.000e-03, eta: 3:18:02, time: 0.089, data_time: 0.049, memory: 1125, loss: 1.3879\n2023-01-04 00:43:21,560 - mmcls - INFO - Epoch [74][300/5005]\tlr: 1.000e-03, eta: 3:17:53, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3886\n2023-01-04 00:43:30,502 - mmcls - INFO - Epoch [74][400/5005]\tlr: 1.000e-03, eta: 3:17:44, time: 0.089, data_time: 0.048, memory: 1125, loss: 1.3952\n2023-01-04 00:43:39,056 - mmcls - INFO - Epoch [74][500/5005]\tlr: 1.000e-03, eta: 3:17:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3846\n2023-01-04 00:43:47,996 - mmcls - INFO - Epoch [74][600/5005]\tlr: 1.000e-03, eta: 3:17:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3889\n2023-01-04 00:43:56,833 - mmcls - INFO - Epoch [74][700/5005]\tlr: 1.000e-03, eta: 3:17:18, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.3912\n2023-01-04 00:44:05,467 - mmcls - INFO - Epoch [74][800/5005]\tlr: 1.000e-03, eta: 3:17:09, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4111\n2023-01-04 00:44:14,145 - mmcls - INFO - Epoch [74][900/5005]\tlr: 1.000e-03, eta: 3:17:00, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4092\n2023-01-04 00:44:22,765 - mmcls - INFO - Epoch [74][1000/5005]\tlr: 1.000e-03, eta: 3:16:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4032\n2023-01-04 00:44:31,814 - mmcls - INFO - Epoch [74][1100/5005]\tlr: 1.000e-03, eta: 3:16:42, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3746\n2023-01-04 00:44:40,439 - mmcls - INFO - Epoch [74][1200/5005]\tlr: 1.000e-03, eta: 3:16:34, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3749\n2023-01-04 00:44:49,532 - mmcls - INFO - Epoch [74][1300/5005]\tlr: 1.000e-03, eta: 3:16:25, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.4032\n2023-01-04 00:44:58,121 - mmcls - INFO - Epoch [74][1400/5005]\tlr: 1.000e-03, eta: 3:16:16, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3722\n2023-01-04 00:45:07,215 - mmcls - INFO - Epoch [74][1500/5005]\tlr: 1.000e-03, eta: 3:16:07, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3889\n2023-01-04 00:45:15,666 - mmcls - INFO - Epoch [74][1600/5005]\tlr: 1.000e-03, eta: 3:15:58, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.4006\n2023-01-04 00:45:24,222 - mmcls - INFO - Epoch [74][1700/5005]\tlr: 1.000e-03, eta: 3:15:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3943\n2023-01-04 00:45:33,240 - mmcls - INFO - Epoch [74][1800/5005]\tlr: 1.000e-03, eta: 3:15:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3676\n2023-01-04 00:45:42,579 - mmcls - INFO - Epoch [74][1900/5005]\tlr: 1.000e-03, eta: 3:15:32, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.4084\n2023-01-04 00:45:51,282 - mmcls - INFO - Epoch [74][2000/5005]\tlr: 1.000e-03, eta: 3:15:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3934\n2023-01-04 00:45:59,920 - mmcls - INFO - Epoch [74][2100/5005]\tlr: 1.000e-03, eta: 3:15:14, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.4030\n2023-01-04 00:46:08,677 - mmcls - INFO - Epoch [74][2200/5005]\tlr: 1.000e-03, eta: 3:15:06, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4113\n2023-01-04 00:46:17,465 - mmcls - INFO - Epoch [74][2300/5005]\tlr: 1.000e-03, eta: 3:14:57, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4018\n2023-01-04 00:46:26,448 - mmcls - INFO - Epoch [74][2400/5005]\tlr: 1.000e-03, eta: 3:14:48, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3933\n2023-01-04 00:46:35,302 - mmcls - INFO - Epoch [74][2500/5005]\tlr: 1.000e-03, eta: 3:14:39, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.4128\n2023-01-04 00:46:43,896 - mmcls - INFO - Epoch [74][2600/5005]\tlr: 1.000e-03, eta: 3:14:30, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3947\n2023-01-04 00:46:52,830 - mmcls - INFO - Epoch [74][2700/5005]\tlr: 1.000e-03, eta: 3:14:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3683\n2023-01-04 00:47:01,628 - mmcls - INFO - Epoch [74][2800/5005]\tlr: 1.000e-03, eta: 3:14:13, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4068\n2023-01-04 00:47:10,220 - mmcls - INFO - Epoch [74][2900/5005]\tlr: 1.000e-03, eta: 3:14:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3811\n2023-01-04 00:47:18,770 - mmcls - INFO - Epoch [74][3000/5005]\tlr: 1.000e-03, eta: 3:13:55, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.4055\n2023-01-04 00:47:27,345 - mmcls - INFO - Epoch [74][3100/5005]\tlr: 1.000e-03, eta: 3:13:46, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3970\n2023-01-04 00:47:35,878 - mmcls - INFO - Epoch [74][3200/5005]\tlr: 1.000e-03, eta: 3:13:37, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3836\n2023-01-04 00:47:44,460 - mmcls - INFO - Epoch [74][3300/5005]\tlr: 1.000e-03, eta: 3:13:28, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3935\n2023-01-04 00:47:53,285 - mmcls - INFO - Epoch [74][3400/5005]\tlr: 1.000e-03, eta: 3:13:19, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.4117\n2023-01-04 00:48:01,609 - mmcls - INFO - Epoch [74][3500/5005]\tlr: 1.000e-03, eta: 3:13:11, time: 0.083, data_time: 0.005, memory: 1125, loss: 1.3724\n2023-01-04 00:48:10,455 - mmcls - INFO - Epoch [74][3600/5005]\tlr: 1.000e-03, eta: 3:13:02, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.4029\n2023-01-04 00:48:19,318 - mmcls - INFO - Epoch [74][3700/5005]\tlr: 1.000e-03, eta: 3:12:53, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4238\n2023-01-04 00:48:27,943 - mmcls - INFO - Epoch [74][3800/5005]\tlr: 1.000e-03, eta: 3:12:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3870\n2023-01-04 00:48:36,489 - mmcls - INFO - Epoch [74][3900/5005]\tlr: 1.000e-03, eta: 3:12:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4024\n2023-01-04 00:48:45,157 - mmcls - INFO - Epoch [74][4000/5005]\tlr: 1.000e-03, eta: 3:12:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3748\n2023-01-04 00:48:53,966 - mmcls - INFO - Epoch [74][4100/5005]\tlr: 1.000e-03, eta: 3:12:18, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.4155\n2023-01-04 00:49:02,447 - mmcls - INFO - Epoch [74][4200/5005]\tlr: 1.000e-03, eta: 3:12:09, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.3929\n2023-01-04 00:49:11,283 - mmcls - INFO - Epoch [74][4300/5005]\tlr: 1.000e-03, eta: 3:12:00, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.4015\n2023-01-04 00:49:20,038 - mmcls - INFO - Epoch [74][4400/5005]\tlr: 1.000e-03, eta: 3:11:51, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.3841\n2023-01-04 00:49:28,777 - mmcls - INFO - Epoch [74][4500/5005]\tlr: 1.000e-03, eta: 3:11:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4074\n2023-01-04 00:49:37,479 - mmcls - INFO - Epoch [74][4600/5005]\tlr: 1.000e-03, eta: 3:11:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4044\n2023-01-04 00:49:46,176 - mmcls - INFO - Epoch [74][4700/5005]\tlr: 1.000e-03, eta: 3:11:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4131\n2023-01-04 00:49:54,748 - mmcls - INFO - Epoch [74][4800/5005]\tlr: 1.000e-03, eta: 3:11:16, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4035\n2023-01-04 00:50:03,618 - mmcls - INFO - Epoch [74][4900/5005]\tlr: 1.000e-03, eta: 3:11:07, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3928\n2023-01-04 00:50:12,349 - mmcls - INFO - Epoch [74][5000/5005]\tlr: 1.000e-03, eta: 3:10:58, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3907\n2023-01-04 00:50:34,898 - mmcls - INFO - Epoch(val) [74][196]\taccuracy_top-1: 69.5300, accuracy_top-5: 89.0200\n2023-01-04 00:50:46,217 - mmcls - INFO - Epoch [75][100/5005]\tlr: 1.000e-03, eta: 3:10:49, time: 0.113, data_time: 0.043, memory: 1125, loss: 1.3996\n2023-01-04 00:50:55,021 - mmcls - INFO - Epoch [75][200/5005]\tlr: 1.000e-03, eta: 3:10:41, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3744\n2023-01-04 00:51:03,890 - mmcls - INFO - Epoch [75][300/5005]\tlr: 1.000e-03, eta: 3:10:32, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3827\n2023-01-04 00:51:12,692 - mmcls - INFO - Epoch [75][400/5005]\tlr: 1.000e-03, eta: 3:10:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3952\n2023-01-04 00:51:21,626 - mmcls - INFO - Epoch [75][500/5005]\tlr: 1.000e-03, eta: 3:10:14, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3981\n2023-01-04 00:51:30,294 - mmcls - INFO - Epoch [75][600/5005]\tlr: 1.000e-03, eta: 3:10:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4064\n2023-01-04 00:51:38,970 - mmcls - INFO - Epoch [75][700/5005]\tlr: 1.000e-03, eta: 3:09:57, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3584\n2023-01-04 00:51:47,614 - mmcls - INFO - Epoch [75][800/5005]\tlr: 1.000e-03, eta: 3:09:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4032\n2023-01-04 00:51:56,271 - mmcls - INFO - Epoch [75][900/5005]\tlr: 1.000e-03, eta: 3:09:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3904\n2023-01-04 00:52:04,834 - mmcls - INFO - Epoch [75][1000/5005]\tlr: 1.000e-03, eta: 3:09:30, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3913\n2023-01-04 00:52:13,611 - mmcls - INFO - Epoch [75][1100/5005]\tlr: 1.000e-03, eta: 3:09:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3983\n2023-01-04 00:52:22,202 - mmcls - INFO - Epoch [75][1200/5005]\tlr: 1.000e-03, eta: 3:09:12, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3900\n2023-01-04 00:52:31,215 - mmcls - INFO - Epoch [75][1300/5005]\tlr: 1.000e-03, eta: 3:09:04, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3981\n2023-01-04 00:52:39,953 - mmcls - INFO - Epoch [75][1400/5005]\tlr: 1.000e-03, eta: 3:08:55, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3891\n2023-01-04 00:52:48,783 - mmcls - INFO - Epoch [75][1500/5005]\tlr: 1.000e-03, eta: 3:08:46, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3876\n2023-01-04 00:52:57,310 - mmcls - INFO - Epoch [75][1600/5005]\tlr: 1.000e-03, eta: 3:08:37, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.3790\n2023-01-04 00:53:06,097 - mmcls - INFO - Epoch [75][1700/5005]\tlr: 1.000e-03, eta: 3:08:28, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3905\n2023-01-04 00:53:14,618 - mmcls - INFO - Epoch [75][1800/5005]\tlr: 1.000e-03, eta: 3:08:19, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3835\n2023-01-04 00:53:23,387 - mmcls - INFO - Epoch [75][1900/5005]\tlr: 1.000e-03, eta: 3:08:10, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4019\n2023-01-04 00:53:32,077 - mmcls - INFO - Epoch [75][2000/5005]\tlr: 1.000e-03, eta: 3:08:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4176\n2023-01-04 00:53:40,784 - mmcls - INFO - Epoch [75][2100/5005]\tlr: 1.000e-03, eta: 3:07:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3767\n2023-01-04 00:53:49,573 - mmcls - INFO - Epoch [75][2200/5005]\tlr: 1.000e-03, eta: 3:07:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3901\n2023-01-04 00:53:58,240 - mmcls - INFO - Epoch [75][2300/5005]\tlr: 1.000e-03, eta: 3:07:35, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3839\n2023-01-04 00:54:06,931 - mmcls - INFO - Epoch [75][2400/5005]\tlr: 1.000e-03, eta: 3:07:26, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3720\n2023-01-04 00:54:15,328 - mmcls - INFO - Epoch [75][2500/5005]\tlr: 1.000e-03, eta: 3:07:17, time: 0.084, data_time: 0.026, memory: 1125, loss: 1.4072\n2023-01-04 00:54:24,238 - mmcls - INFO - Epoch [75][2600/5005]\tlr: 1.000e-03, eta: 3:07:09, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4011\n2023-01-04 00:54:33,206 - mmcls - INFO - Epoch [75][2700/5005]\tlr: 1.000e-03, eta: 3:07:00, time: 0.090, data_time: 0.041, memory: 1125, loss: 1.4160\n2023-01-04 00:54:41,625 - mmcls - INFO - Epoch [75][2800/5005]\tlr: 1.000e-03, eta: 3:06:51, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.4059\n2023-01-04 00:54:50,290 - mmcls - INFO - Epoch [75][2900/5005]\tlr: 1.000e-03, eta: 3:06:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4014\n2023-01-04 00:54:59,241 - mmcls - INFO - Epoch [75][3000/5005]\tlr: 1.000e-03, eta: 3:06:33, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4011\n2023-01-04 00:55:08,245 - mmcls - INFO - Epoch [75][3100/5005]\tlr: 1.000e-03, eta: 3:06:25, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4028\n2023-01-04 00:55:16,967 - mmcls - INFO - Epoch [75][3200/5005]\tlr: 1.000e-03, eta: 3:06:16, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3858\n2023-01-04 00:55:25,512 - mmcls - INFO - Epoch [75][3300/5005]\tlr: 1.000e-03, eta: 3:06:07, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.3953\n2023-01-04 00:55:34,598 - mmcls - INFO - Epoch [75][3400/5005]\tlr: 1.000e-03, eta: 3:05:58, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3817\n2023-01-04 00:55:43,220 - mmcls - INFO - Epoch [75][3500/5005]\tlr: 1.000e-03, eta: 3:05:49, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3886\n2023-01-04 00:55:52,111 - mmcls - INFO - Epoch [75][3600/5005]\tlr: 1.000e-03, eta: 3:05:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3985\n2023-01-04 00:56:00,945 - mmcls - INFO - Epoch [75][3700/5005]\tlr: 1.000e-03, eta: 3:05:32, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4066\n2023-01-04 00:56:09,462 - mmcls - INFO - Epoch [75][3800/5005]\tlr: 1.000e-03, eta: 3:05:23, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3988\n2023-01-04 00:56:18,162 - mmcls - INFO - Epoch [75][3900/5005]\tlr: 1.000e-03, eta: 3:05:14, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3904\n2023-01-04 00:56:26,987 - mmcls - INFO - Epoch [75][4000/5005]\tlr: 1.000e-03, eta: 3:05:05, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.3905\n2023-01-04 00:56:35,871 - mmcls - INFO - Epoch [75][4100/5005]\tlr: 1.000e-03, eta: 3:04:56, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4010\n2023-01-04 00:56:44,590 - mmcls - INFO - Epoch [75][4200/5005]\tlr: 1.000e-03, eta: 3:04:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4099\n2023-01-04 00:56:53,546 - mmcls - INFO - Epoch [75][4300/5005]\tlr: 1.000e-03, eta: 3:04:39, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4000\n2023-01-04 00:57:01,846 - mmcls - INFO - Epoch [75][4400/5005]\tlr: 1.000e-03, eta: 3:04:30, time: 0.083, data_time: 0.033, memory: 1125, loss: 1.3866\n2023-01-04 00:57:10,526 - mmcls - INFO - Epoch [75][4500/5005]\tlr: 1.000e-03, eta: 3:04:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3975\n2023-01-04 00:57:19,426 - mmcls - INFO - Epoch [75][4600/5005]\tlr: 1.000e-03, eta: 3:04:12, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3797\n2023-01-04 00:57:28,226 - mmcls - INFO - Epoch [75][4700/5005]\tlr: 1.000e-03, eta: 3:04:03, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4004\n2023-01-04 00:57:37,185 - mmcls - INFO - Epoch [75][4800/5005]\tlr: 1.000e-03, eta: 3:03:55, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3881\n2023-01-04 00:57:45,586 - mmcls - INFO - Epoch [75][4900/5005]\tlr: 1.000e-03, eta: 3:03:46, time: 0.084, data_time: 0.032, memory: 1125, loss: 1.4026\n2023-01-04 00:57:54,264 - mmcls - INFO - Epoch [75][5000/5005]\tlr: 1.000e-03, eta: 3:03:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3744\n2023-01-04 00:58:16,782 - mmcls - INFO - Epoch(val) [75][196]\taccuracy_top-1: 69.5940, accuracy_top-5: 89.1280\n2023-01-04 00:58:27,778 - mmcls - INFO - Epoch [76][100/5005]\tlr: 1.000e-03, eta: 3:03:28, time: 0.110, data_time: 0.044, memory: 1125, loss: 1.3781\n2023-01-04 00:58:36,626 - mmcls - INFO - Epoch [76][200/5005]\tlr: 1.000e-03, eta: 3:03:19, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3736\n2023-01-04 00:58:45,125 - mmcls - INFO - Epoch [76][300/5005]\tlr: 1.000e-03, eta: 3:03:10, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.3996\n2023-01-04 00:58:53,773 - mmcls - INFO - Epoch [76][400/5005]\tlr: 1.000e-03, eta: 3:03:02, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3830\n2023-01-04 00:59:02,213 - mmcls - INFO - Epoch [76][500/5005]\tlr: 1.000e-03, eta: 3:02:53, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3811\n2023-01-04 00:59:10,820 - mmcls - INFO - Epoch [76][600/5005]\tlr: 1.000e-03, eta: 3:02:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4011\n2023-01-04 00:59:19,749 - mmcls - INFO - Epoch [76][700/5005]\tlr: 1.000e-03, eta: 3:02:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3906\n2023-01-04 00:59:28,184 - mmcls - INFO - Epoch [76][800/5005]\tlr: 1.000e-03, eta: 3:02:26, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.3668\n2023-01-04 00:59:37,035 - mmcls - INFO - Epoch [76][900/5005]\tlr: 1.000e-03, eta: 3:02:17, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3748\n2023-01-04 00:59:45,650 - mmcls - INFO - Epoch [76][1000/5005]\tlr: 1.000e-03, eta: 3:02:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3819\n2023-01-04 00:59:54,447 - mmcls - INFO - Epoch [76][1100/5005]\tlr: 1.000e-03, eta: 3:02:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4008\n2023-01-04 01:00:03,334 - mmcls - INFO - Epoch [76][1200/5005]\tlr: 1.000e-03, eta: 3:01:51, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4007\n2023-01-04 01:00:11,943 - mmcls - INFO - Epoch [76][1300/5005]\tlr: 1.000e-03, eta: 3:01:42, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4064\n2023-01-04 01:00:20,795 - mmcls - INFO - Epoch [76][1400/5005]\tlr: 1.000e-03, eta: 3:01:33, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3757\n2023-01-04 01:00:29,650 - mmcls - INFO - Epoch [76][1500/5005]\tlr: 1.000e-03, eta: 3:01:24, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3907\n2023-01-04 01:00:38,139 - mmcls - INFO - Epoch [76][1600/5005]\tlr: 1.000e-03, eta: 3:01:15, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3829\n2023-01-04 01:00:47,228 - mmcls - INFO - Epoch [76][1700/5005]\tlr: 1.000e-03, eta: 3:01:07, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3860\n2023-01-04 01:00:55,904 - mmcls - INFO - Epoch [76][1800/5005]\tlr: 1.000e-03, eta: 3:00:58, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3923\n2023-01-04 01:01:04,904 - mmcls - INFO - Epoch [76][1900/5005]\tlr: 1.000e-03, eta: 3:00:49, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4100\n2023-01-04 01:01:13,467 - mmcls - INFO - Epoch [76][2000/5005]\tlr: 1.000e-03, eta: 3:00:40, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3979\n2023-01-04 01:01:22,225 - mmcls - INFO - Epoch [76][2100/5005]\tlr: 1.000e-03, eta: 3:00:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4034\n2023-01-04 01:01:31,014 - mmcls - INFO - Epoch [76][2200/5005]\tlr: 1.000e-03, eta: 3:00:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3805\n2023-01-04 01:01:39,964 - mmcls - INFO - Epoch [76][2300/5005]\tlr: 1.000e-03, eta: 3:00:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3670\n2023-01-04 01:01:48,835 - mmcls - INFO - Epoch [76][2400/5005]\tlr: 1.000e-03, eta: 3:00:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3737\n2023-01-04 01:01:57,540 - mmcls - INFO - Epoch [76][2500/5005]\tlr: 1.000e-03, eta: 2:59:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3735\n2023-01-04 01:02:06,363 - mmcls - INFO - Epoch [76][2600/5005]\tlr: 1.000e-03, eta: 2:59:47, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3725\n2023-01-04 01:02:15,091 - mmcls - INFO - Epoch [76][2700/5005]\tlr: 1.000e-03, eta: 2:59:39, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3998\n2023-01-04 01:02:23,957 - mmcls - INFO - Epoch [76][2800/5005]\tlr: 1.000e-03, eta: 2:59:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3879\n2023-01-04 01:02:32,474 - mmcls - INFO - Epoch [76][2900/5005]\tlr: 1.000e-03, eta: 2:59:21, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4074\n2023-01-04 01:02:41,156 - mmcls - INFO - Epoch [76][3000/5005]\tlr: 1.000e-03, eta: 2:59:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.4001\n2023-01-04 01:02:50,104 - mmcls - INFO - Epoch [76][3100/5005]\tlr: 1.000e-03, eta: 2:59:03, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3917\n2023-01-04 01:02:58,855 - mmcls - INFO - Epoch [76][3200/5005]\tlr: 1.000e-03, eta: 2:58:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3923\n2023-01-04 01:03:07,517 - mmcls - INFO - Epoch [76][3300/5005]\tlr: 1.000e-03, eta: 2:58:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3945\n2023-01-04 01:03:16,209 - mmcls - INFO - Epoch [76][3400/5005]\tlr: 1.000e-03, eta: 2:58:37, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3895\n2023-01-04 01:03:25,000 - mmcls - INFO - Epoch [76][3500/5005]\tlr: 1.000e-03, eta: 2:58:28, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.4054\n2023-01-04 01:03:33,795 - mmcls - INFO - Epoch [76][3600/5005]\tlr: 1.000e-03, eta: 2:58:19, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3600\n2023-01-04 01:03:42,499 - mmcls - INFO - Epoch [76][3700/5005]\tlr: 1.000e-03, eta: 2:58:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3750\n2023-01-04 01:03:51,370 - mmcls - INFO - Epoch [76][3800/5005]\tlr: 1.000e-03, eta: 2:58:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3819\n2023-01-04 01:04:00,023 - mmcls - INFO - Epoch [76][3900/5005]\tlr: 1.000e-03, eta: 2:57:53, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3955\n2023-01-04 01:04:08,660 - mmcls - INFO - Epoch [76][4000/5005]\tlr: 1.000e-03, eta: 2:57:44, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.4033\n2023-01-04 01:04:17,333 - mmcls - INFO - Epoch [76][4100/5005]\tlr: 1.000e-03, eta: 2:57:35, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3842\n2023-01-04 01:04:26,266 - mmcls - INFO - Epoch [76][4200/5005]\tlr: 1.000e-03, eta: 2:57:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4029\n2023-01-04 01:04:35,060 - mmcls - INFO - Epoch [76][4300/5005]\tlr: 1.000e-03, eta: 2:57:17, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3926\n2023-01-04 01:04:43,607 - mmcls - INFO - Epoch [76][4400/5005]\tlr: 1.000e-03, eta: 2:57:09, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3674\n2023-01-04 01:04:52,341 - mmcls - INFO - Epoch [76][4500/5005]\tlr: 1.000e-03, eta: 2:57:00, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.4075\n2023-01-04 01:05:01,006 - mmcls - INFO - Epoch [76][4600/5005]\tlr: 1.000e-03, eta: 2:56:51, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3971\n2023-01-04 01:05:09,577 - mmcls - INFO - Epoch [76][4700/5005]\tlr: 1.000e-03, eta: 2:56:42, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.3836\n2023-01-04 01:05:18,584 - mmcls - INFO - Epoch [76][4800/5005]\tlr: 1.000e-03, eta: 2:56:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3908\n2023-01-04 01:05:27,205 - mmcls - INFO - Epoch [76][4900/5005]\tlr: 1.000e-03, eta: 2:56:24, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3994\n2023-01-04 01:05:35,851 - mmcls - INFO - Epoch [76][5000/5005]\tlr: 1.000e-03, eta: 2:56:16, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3761\n2023-01-04 01:05:58,658 - mmcls - INFO - Epoch(val) [76][196]\taccuracy_top-1: 69.3840, accuracy_top-5: 89.1060\n2023-01-04 01:06:10,138 - mmcls - INFO - Epoch [77][100/5005]\tlr: 1.000e-03, eta: 2:56:07, time: 0.115, data_time: 0.062, memory: 1125, loss: 1.3614\n2023-01-04 01:06:18,867 - mmcls - INFO - Epoch [77][200/5005]\tlr: 1.000e-03, eta: 2:55:58, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3716\n2023-01-04 01:06:27,742 - mmcls - INFO - Epoch [77][300/5005]\tlr: 1.000e-03, eta: 2:55:49, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3931\n2023-01-04 01:06:36,408 - mmcls - INFO - Epoch [77][400/5005]\tlr: 1.000e-03, eta: 2:55:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3771\n2023-01-04 01:06:45,101 - mmcls - INFO - Epoch [77][500/5005]\tlr: 1.000e-03, eta: 2:55:32, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3983\n2023-01-04 01:06:53,890 - mmcls - INFO - Epoch [77][600/5005]\tlr: 1.000e-03, eta: 2:55:23, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3559\n2023-01-04 01:07:02,480 - mmcls - INFO - Epoch [77][700/5005]\tlr: 1.000e-03, eta: 2:55:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3961\n2023-01-04 01:07:11,196 - mmcls - INFO - Epoch [77][800/5005]\tlr: 1.000e-03, eta: 2:55:05, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4010\n2023-01-04 01:07:19,976 - mmcls - INFO - Epoch [77][900/5005]\tlr: 1.000e-03, eta: 2:54:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3880\n2023-01-04 01:07:28,678 - mmcls - INFO - Epoch [77][1000/5005]\tlr: 1.000e-03, eta: 2:54:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3854\n2023-01-04 01:07:37,204 - mmcls - INFO - Epoch [77][1100/5005]\tlr: 1.000e-03, eta: 2:54:39, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3866\n2023-01-04 01:07:46,017 - mmcls - INFO - Epoch [77][1200/5005]\tlr: 1.000e-03, eta: 2:54:30, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3745\n2023-01-04 01:07:54,684 - mmcls - INFO - Epoch [77][1300/5005]\tlr: 1.000e-03, eta: 2:54:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3961\n2023-01-04 01:08:03,393 - mmcls - INFO - Epoch [77][1400/5005]\tlr: 1.000e-03, eta: 2:54:12, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4041\n2023-01-04 01:08:11,995 - mmcls - INFO - Epoch [77][1500/5005]\tlr: 1.000e-03, eta: 2:54:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3936\n2023-01-04 01:08:20,717 - mmcls - INFO - Epoch [77][1600/5005]\tlr: 1.000e-03, eta: 2:53:55, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3773\n2023-01-04 01:08:29,517 - mmcls - INFO - Epoch [77][1700/5005]\tlr: 1.000e-03, eta: 2:53:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3851\n2023-01-04 01:08:38,407 - mmcls - INFO - Epoch [77][1800/5005]\tlr: 1.000e-03, eta: 2:53:37, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.4049\n2023-01-04 01:08:46,969 - mmcls - INFO - Epoch [77][1900/5005]\tlr: 1.000e-03, eta: 2:53:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3732\n2023-01-04 01:08:55,605 - mmcls - INFO - Epoch [77][2000/5005]\tlr: 1.000e-03, eta: 2:53:19, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3848\n2023-01-04 01:09:04,377 - mmcls - INFO - Epoch [77][2100/5005]\tlr: 1.000e-03, eta: 2:53:10, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3839\n2023-01-04 01:09:13,080 - mmcls - INFO - Epoch [77][2200/5005]\tlr: 1.000e-03, eta: 2:53:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3979\n2023-01-04 01:09:22,005 - mmcls - INFO - Epoch [77][2300/5005]\tlr: 1.000e-03, eta: 2:52:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3879\n2023-01-04 01:09:30,609 - mmcls - INFO - Epoch [77][2400/5005]\tlr: 1.000e-03, eta: 2:52:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3756\n2023-01-04 01:09:39,082 - mmcls - INFO - Epoch [77][2500/5005]\tlr: 1.000e-03, eta: 2:52:35, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4166\n2023-01-04 01:09:47,821 - mmcls - INFO - Epoch [77][2600/5005]\tlr: 1.000e-03, eta: 2:52:26, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3751\n2023-01-04 01:09:56,632 - mmcls - INFO - Epoch [77][2700/5005]\tlr: 1.000e-03, eta: 2:52:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3853\n2023-01-04 01:10:05,793 - mmcls - INFO - Epoch [77][2800/5005]\tlr: 1.000e-03, eta: 2:52:09, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3829\n2023-01-04 01:10:14,385 - mmcls - INFO - Epoch [77][2900/5005]\tlr: 1.000e-03, eta: 2:52:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4069\n2023-01-04 01:10:23,519 - mmcls - INFO - Epoch [77][3000/5005]\tlr: 1.000e-03, eta: 2:51:51, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.3952\n2023-01-04 01:10:32,287 - mmcls - INFO - Epoch [77][3100/5005]\tlr: 1.000e-03, eta: 2:51:42, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3762\n2023-01-04 01:10:41,174 - mmcls - INFO - Epoch [77][3200/5005]\tlr: 1.000e-03, eta: 2:51:34, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4081\n2023-01-04 01:10:49,943 - mmcls - INFO - Epoch [77][3300/5005]\tlr: 1.000e-03, eta: 2:51:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3882\n2023-01-04 01:10:58,689 - mmcls - INFO - Epoch [77][3400/5005]\tlr: 1.000e-03, eta: 2:51:16, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.3716\n2023-01-04 01:11:07,226 - mmcls - INFO - Epoch [77][3500/5005]\tlr: 1.000e-03, eta: 2:51:07, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3999\n2023-01-04 01:11:15,787 - mmcls - INFO - Epoch [77][3600/5005]\tlr: 1.000e-03, eta: 2:50:58, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4059\n2023-01-04 01:11:24,406 - mmcls - INFO - Epoch [77][3700/5005]\tlr: 1.000e-03, eta: 2:50:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3952\n2023-01-04 01:11:33,364 - mmcls - INFO - Epoch [77][3800/5005]\tlr: 1.000e-03, eta: 2:50:40, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3865\n2023-01-04 01:11:41,983 - mmcls - INFO - Epoch [77][3900/5005]\tlr: 1.000e-03, eta: 2:50:32, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3702\n2023-01-04 01:11:50,923 - mmcls - INFO - Epoch [77][4000/5005]\tlr: 1.000e-03, eta: 2:50:23, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3785\n2023-01-04 01:11:59,812 - mmcls - INFO - Epoch [77][4100/5005]\tlr: 1.000e-03, eta: 2:50:14, time: 0.089, data_time: 0.052, memory: 1125, loss: 1.3922\n2023-01-04 01:12:08,703 - mmcls - INFO - Epoch [77][4200/5005]\tlr: 1.000e-03, eta: 2:50:05, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.4032\n2023-01-04 01:12:17,341 - mmcls - INFO - Epoch [77][4300/5005]\tlr: 1.000e-03, eta: 2:49:56, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3965\n2023-01-04 01:12:26,242 - mmcls - INFO - Epoch [77][4400/5005]\tlr: 1.000e-03, eta: 2:49:48, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.3798\n2023-01-04 01:12:34,757 - mmcls - INFO - Epoch [77][4500/5005]\tlr: 1.000e-03, eta: 2:49:39, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4060\n2023-01-04 01:12:43,360 - mmcls - INFO - Epoch [77][4600/5005]\tlr: 1.000e-03, eta: 2:49:30, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.3994\n2023-01-04 01:12:52,194 - mmcls - INFO - Epoch [77][4700/5005]\tlr: 1.000e-03, eta: 2:49:21, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3903\n2023-01-04 01:13:00,864 - mmcls - INFO - Epoch [77][4800/5005]\tlr: 1.000e-03, eta: 2:49:12, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3891\n2023-01-04 01:13:09,729 - mmcls - INFO - Epoch [77][4900/5005]\tlr: 1.000e-03, eta: 2:49:03, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3890\n2023-01-04 01:13:18,327 - mmcls - INFO - Epoch [77][5000/5005]\tlr: 1.000e-03, eta: 2:48:55, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3987\n2023-01-04 01:13:41,108 - mmcls - INFO - Epoch(val) [77][196]\taccuracy_top-1: 69.5880, accuracy_top-5: 89.1660\n2023-01-04 01:13:52,578 - mmcls - INFO - Epoch [78][100/5005]\tlr: 1.000e-03, eta: 2:48:46, time: 0.114, data_time: 0.056, memory: 1125, loss: 1.3618\n2023-01-04 01:14:01,005 - mmcls - INFO - Epoch [78][200/5005]\tlr: 1.000e-03, eta: 2:48:37, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.3848\n2023-01-04 01:14:09,733 - mmcls - INFO - Epoch [78][300/5005]\tlr: 1.000e-03, eta: 2:48:28, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3849\n2023-01-04 01:14:18,824 - mmcls - INFO - Epoch [78][400/5005]\tlr: 1.000e-03, eta: 2:48:20, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.3801\n2023-01-04 01:14:27,384 - mmcls - INFO - Epoch [78][500/5005]\tlr: 1.000e-03, eta: 2:48:11, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3837\n2023-01-04 01:14:35,847 - mmcls - INFO - Epoch [78][600/5005]\tlr: 1.000e-03, eta: 2:48:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3799\n2023-01-04 01:14:44,603 - mmcls - INFO - Epoch [78][700/5005]\tlr: 1.000e-03, eta: 2:47:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3717\n2023-01-04 01:14:53,326 - mmcls - INFO - Epoch [78][800/5005]\tlr: 1.000e-03, eta: 2:47:44, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3887\n2023-01-04 01:15:01,829 - mmcls - INFO - Epoch [78][900/5005]\tlr: 1.000e-03, eta: 2:47:35, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3949\n2023-01-04 01:15:10,588 - mmcls - INFO - Epoch [78][1000/5005]\tlr: 1.000e-03, eta: 2:47:26, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3986\n2023-01-04 01:15:19,369 - mmcls - INFO - Epoch [78][1100/5005]\tlr: 1.000e-03, eta: 2:47:18, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3770\n2023-01-04 01:15:27,796 - mmcls - INFO - Epoch [78][1200/5005]\tlr: 1.000e-03, eta: 2:47:09, time: 0.084, data_time: 0.019, memory: 1125, loss: 1.3627\n2023-01-04 01:15:36,631 - mmcls - INFO - Epoch [78][1300/5005]\tlr: 1.000e-03, eta: 2:47:00, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3965\n2023-01-04 01:15:45,457 - mmcls - INFO - Epoch [78][1400/5005]\tlr: 1.000e-03, eta: 2:46:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3790\n2023-01-04 01:15:54,312 - mmcls - INFO - Epoch [78][1500/5005]\tlr: 1.000e-03, eta: 2:46:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3850\n2023-01-04 01:16:02,994 - mmcls - INFO - Epoch [78][1600/5005]\tlr: 1.000e-03, eta: 2:46:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3760\n2023-01-04 01:16:11,571 - mmcls - INFO - Epoch [78][1700/5005]\tlr: 1.000e-03, eta: 2:46:25, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3763\n2023-01-04 01:16:20,236 - mmcls - INFO - Epoch [78][1800/5005]\tlr: 1.000e-03, eta: 2:46:16, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3735\n2023-01-04 01:16:28,960 - mmcls - INFO - Epoch [78][1900/5005]\tlr: 1.000e-03, eta: 2:46:07, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3867\n2023-01-04 01:16:38,081 - mmcls - INFO - Epoch [78][2000/5005]\tlr: 1.000e-03, eta: 2:45:58, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3948\n2023-01-04 01:16:46,749 - mmcls - INFO - Epoch [78][2100/5005]\tlr: 1.000e-03, eta: 2:45:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3626\n2023-01-04 01:16:55,473 - mmcls - INFO - Epoch [78][2200/5005]\tlr: 1.000e-03, eta: 2:45:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3920\n2023-01-04 01:17:04,340 - mmcls - INFO - Epoch [78][2300/5005]\tlr: 1.000e-03, eta: 2:45:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3889\n2023-01-04 01:17:13,058 - mmcls - INFO - Epoch [78][2400/5005]\tlr: 1.000e-03, eta: 2:45:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3797\n2023-01-04 01:17:21,893 - mmcls - INFO - Epoch [78][2500/5005]\tlr: 1.000e-03, eta: 2:45:14, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.4030\n2023-01-04 01:17:31,198 - mmcls - INFO - Epoch [78][2600/5005]\tlr: 1.000e-03, eta: 2:45:05, time: 0.093, data_time: 0.018, memory: 1125, loss: 1.3728\n2023-01-04 01:17:40,249 - mmcls - INFO - Epoch [78][2700/5005]\tlr: 1.000e-03, eta: 2:44:57, time: 0.091, data_time: 0.045, memory: 1125, loss: 1.4155\n2023-01-04 01:17:49,056 - mmcls - INFO - Epoch [78][2800/5005]\tlr: 1.000e-03, eta: 2:44:48, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3952\n2023-01-04 01:17:57,851 - mmcls - INFO - Epoch [78][2900/5005]\tlr: 1.000e-03, eta: 2:44:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3913\n2023-01-04 01:18:06,457 - mmcls - INFO - Epoch [78][3000/5005]\tlr: 1.000e-03, eta: 2:44:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4000\n2023-01-04 01:18:15,284 - mmcls - INFO - Epoch [78][3100/5005]\tlr: 1.000e-03, eta: 2:44:21, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3830\n2023-01-04 01:18:24,351 - mmcls - INFO - Epoch [78][3200/5005]\tlr: 1.000e-03, eta: 2:44:13, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3979\n2023-01-04 01:18:33,229 - mmcls - INFO - Epoch [78][3300/5005]\tlr: 1.000e-03, eta: 2:44:04, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3975\n2023-01-04 01:18:42,372 - mmcls - INFO - Epoch [78][3400/5005]\tlr: 1.000e-03, eta: 2:43:55, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3784\n2023-01-04 01:18:51,065 - mmcls - INFO - Epoch [78][3500/5005]\tlr: 1.000e-03, eta: 2:43:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3985\n2023-01-04 01:18:59,640 - mmcls - INFO - Epoch [78][3600/5005]\tlr: 1.000e-03, eta: 2:43:38, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3876\n2023-01-04 01:19:08,342 - mmcls - INFO - Epoch [78][3700/5005]\tlr: 1.000e-03, eta: 2:43:29, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4042\n2023-01-04 01:19:16,923 - mmcls - INFO - Epoch [78][3800/5005]\tlr: 1.000e-03, eta: 2:43:20, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3721\n2023-01-04 01:19:25,786 - mmcls - INFO - Epoch [78][3900/5005]\tlr: 1.000e-03, eta: 2:43:11, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3876\n2023-01-04 01:19:34,416 - mmcls - INFO - Epoch [78][4000/5005]\tlr: 1.000e-03, eta: 2:43:02, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3951\n2023-01-04 01:19:42,845 - mmcls - INFO - Epoch [78][4100/5005]\tlr: 1.000e-03, eta: 2:42:53, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.4003\n2023-01-04 01:19:51,467 - mmcls - INFO - Epoch [78][4200/5005]\tlr: 1.000e-03, eta: 2:42:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3781\n2023-01-04 01:20:00,208 - mmcls - INFO - Epoch [78][4300/5005]\tlr: 1.000e-03, eta: 2:42:36, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3701\n2023-01-04 01:20:08,923 - mmcls - INFO - Epoch [78][4400/5005]\tlr: 1.000e-03, eta: 2:42:27, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4029\n2023-01-04 01:20:17,488 - mmcls - INFO - Epoch [78][4500/5005]\tlr: 1.000e-03, eta: 2:42:18, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3878\n2023-01-04 01:20:26,241 - mmcls - INFO - Epoch [78][4600/5005]\tlr: 1.000e-03, eta: 2:42:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3825\n2023-01-04 01:20:34,845 - mmcls - INFO - Epoch [78][4700/5005]\tlr: 1.000e-03, eta: 2:42:00, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3735\n2023-01-04 01:20:43,466 - mmcls - INFO - Epoch [78][4800/5005]\tlr: 1.000e-03, eta: 2:41:51, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3898\n2023-01-04 01:20:52,525 - mmcls - INFO - Epoch [78][4900/5005]\tlr: 1.000e-03, eta: 2:41:43, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3773\n2023-01-04 01:21:01,294 - mmcls - INFO - Epoch [78][5000/5005]\tlr: 1.000e-03, eta: 2:41:34, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4091\n2023-01-04 01:21:23,848 - mmcls - INFO - Epoch(val) [78][196]\taccuracy_top-1: 69.6160, accuracy_top-5: 89.1220\n2023-01-04 01:21:35,117 - mmcls - INFO - Epoch [79][100/5005]\tlr: 1.000e-03, eta: 2:41:25, time: 0.112, data_time: 0.035, memory: 1125, loss: 1.3906\n2023-01-04 01:21:43,980 - mmcls - INFO - Epoch [79][200/5005]\tlr: 1.000e-03, eta: 2:41:16, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.3753\n2023-01-04 01:21:52,759 - mmcls - INFO - Epoch [79][300/5005]\tlr: 1.000e-03, eta: 2:41:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3841\n2023-01-04 01:22:01,709 - mmcls - INFO - Epoch [79][400/5005]\tlr: 1.000e-03, eta: 2:40:59, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3674\n2023-01-04 01:22:10,524 - mmcls - INFO - Epoch [79][500/5005]\tlr: 1.000e-03, eta: 2:40:50, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3897\n2023-01-04 01:22:19,526 - mmcls - INFO - Epoch [79][600/5005]\tlr: 1.000e-03, eta: 2:40:41, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.3786\n2023-01-04 01:22:28,292 - mmcls - INFO - Epoch [79][700/5005]\tlr: 1.000e-03, eta: 2:40:32, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3747\n2023-01-04 01:22:36,743 - mmcls - INFO - Epoch [79][800/5005]\tlr: 1.000e-03, eta: 2:40:24, time: 0.084, data_time: 0.026, memory: 1125, loss: 1.3784\n2023-01-04 01:22:45,565 - mmcls - INFO - Epoch [79][900/5005]\tlr: 1.000e-03, eta: 2:40:15, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3611\n2023-01-04 01:22:54,341 - mmcls - INFO - Epoch [79][1000/5005]\tlr: 1.000e-03, eta: 2:40:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3919\n2023-01-04 01:23:03,080 - mmcls - INFO - Epoch [79][1100/5005]\tlr: 1.000e-03, eta: 2:39:57, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3815\n2023-01-04 01:23:11,871 - mmcls - INFO - Epoch [79][1200/5005]\tlr: 1.000e-03, eta: 2:39:48, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3792\n2023-01-04 01:23:20,672 - mmcls - INFO - Epoch [79][1300/5005]\tlr: 1.000e-03, eta: 2:39:39, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3780\n2023-01-04 01:23:29,299 - mmcls - INFO - Epoch [79][1400/5005]\tlr: 1.000e-03, eta: 2:39:31, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3809\n2023-01-04 01:23:38,259 - mmcls - INFO - Epoch [79][1500/5005]\tlr: 1.000e-03, eta: 2:39:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3849\n2023-01-04 01:23:47,186 - mmcls - INFO - Epoch [79][1600/5005]\tlr: 1.000e-03, eta: 2:39:13, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3790\n2023-01-04 01:23:55,888 - mmcls - INFO - Epoch [79][1700/5005]\tlr: 1.000e-03, eta: 2:39:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3989\n2023-01-04 01:24:04,491 - mmcls - INFO - Epoch [79][1800/5005]\tlr: 1.000e-03, eta: 2:38:55, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3875\n2023-01-04 01:24:13,197 - mmcls - INFO - Epoch [79][1900/5005]\tlr: 1.000e-03, eta: 2:38:47, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3637\n2023-01-04 01:24:21,768 - mmcls - INFO - Epoch [79][2000/5005]\tlr: 1.000e-03, eta: 2:38:38, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3903\n2023-01-04 01:24:30,276 - mmcls - INFO - Epoch [79][2100/5005]\tlr: 1.000e-03, eta: 2:38:29, time: 0.085, data_time: 0.027, memory: 1125, loss: 1.4100\n2023-01-04 01:24:39,483 - mmcls - INFO - Epoch [79][2200/5005]\tlr: 1.000e-03, eta: 2:38:20, time: 0.092, data_time: 0.017, memory: 1125, loss: 1.3833\n2023-01-04 01:24:47,978 - mmcls - INFO - Epoch [79][2300/5005]\tlr: 1.000e-03, eta: 2:38:11, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3915\n2023-01-04 01:24:56,888 - mmcls - INFO - Epoch [79][2400/5005]\tlr: 1.000e-03, eta: 2:38:02, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.3733\n2023-01-04 01:25:05,460 - mmcls - INFO - Epoch [79][2500/5005]\tlr: 1.000e-03, eta: 2:37:54, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3822\n2023-01-04 01:25:14,456 - mmcls - INFO - Epoch [79][2600/5005]\tlr: 1.000e-03, eta: 2:37:45, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3896\n2023-01-04 01:25:23,207 - mmcls - INFO - Epoch [79][2700/5005]\tlr: 1.000e-03, eta: 2:37:36, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4042\n2023-01-04 01:25:31,841 - mmcls - INFO - Epoch [79][2800/5005]\tlr: 1.000e-03, eta: 2:37:27, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.3697\n2023-01-04 01:25:40,630 - mmcls - INFO - Epoch [79][2900/5005]\tlr: 1.000e-03, eta: 2:37:18, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3949\n2023-01-04 01:25:49,263 - mmcls - INFO - Epoch [79][3000/5005]\tlr: 1.000e-03, eta: 2:37:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3729\n2023-01-04 01:25:57,912 - mmcls - INFO - Epoch [79][3100/5005]\tlr: 1.000e-03, eta: 2:37:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3744\n2023-01-04 01:26:06,337 - mmcls - INFO - Epoch [79][3200/5005]\tlr: 1.000e-03, eta: 2:36:52, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3861\n2023-01-04 01:26:15,344 - mmcls - INFO - Epoch [79][3300/5005]\tlr: 1.000e-03, eta: 2:36:43, time: 0.090, data_time: 0.035, memory: 1125, loss: 1.3835\n2023-01-04 01:26:23,987 - mmcls - INFO - Epoch [79][3400/5005]\tlr: 1.000e-03, eta: 2:36:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3698\n2023-01-04 01:26:32,790 - mmcls - INFO - Epoch [79][3500/5005]\tlr: 1.000e-03, eta: 2:36:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3922\n2023-01-04 01:26:41,306 - mmcls - INFO - Epoch [79][3600/5005]\tlr: 1.000e-03, eta: 2:36:16, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4098\n2023-01-04 01:26:50,236 - mmcls - INFO - Epoch [79][3700/5005]\tlr: 1.000e-03, eta: 2:36:08, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4026\n2023-01-04 01:26:58,888 - mmcls - INFO - Epoch [79][3800/5005]\tlr: 1.000e-03, eta: 2:35:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3893\n2023-01-04 01:27:07,762 - mmcls - INFO - Epoch [79][3900/5005]\tlr: 1.000e-03, eta: 2:35:50, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3976\n2023-01-04 01:27:16,466 - mmcls - INFO - Epoch [79][4000/5005]\tlr: 1.000e-03, eta: 2:35:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3968\n2023-01-04 01:27:25,188 - mmcls - INFO - Epoch [79][4100/5005]\tlr: 1.000e-03, eta: 2:35:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3924\n2023-01-04 01:27:33,606 - mmcls - INFO - Epoch [79][4200/5005]\tlr: 1.000e-03, eta: 2:35:23, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.3955\n2023-01-04 01:27:42,732 - mmcls - INFO - Epoch [79][4300/5005]\tlr: 1.000e-03, eta: 2:35:15, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.3952\n2023-01-04 01:27:51,334 - mmcls - INFO - Epoch [79][4400/5005]\tlr: 1.000e-03, eta: 2:35:06, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3742\n2023-01-04 01:28:00,334 - mmcls - INFO - Epoch [79][4500/5005]\tlr: 1.000e-03, eta: 2:34:57, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3901\n2023-01-04 01:28:09,197 - mmcls - INFO - Epoch [79][4600/5005]\tlr: 1.000e-03, eta: 2:34:48, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3808\n2023-01-04 01:28:17,833 - mmcls - INFO - Epoch [79][4700/5005]\tlr: 1.000e-03, eta: 2:34:40, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.3788\n2023-01-04 01:28:26,450 - mmcls - INFO - Epoch [79][4800/5005]\tlr: 1.000e-03, eta: 2:34:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3681\n2023-01-04 01:28:35,598 - mmcls - INFO - Epoch [79][4900/5005]\tlr: 1.000e-03, eta: 2:34:22, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3964\n2023-01-04 01:28:44,515 - mmcls - INFO - Epoch [79][5000/5005]\tlr: 1.000e-03, eta: 2:34:13, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3873\n2023-01-04 01:29:07,125 - mmcls - INFO - Epoch(val) [79][196]\taccuracy_top-1: 69.5740, accuracy_top-5: 89.0760\n2023-01-04 01:29:18,192 - mmcls - INFO - Epoch [80][100/5005]\tlr: 1.000e-03, eta: 2:34:04, time: 0.110, data_time: 0.036, memory: 1125, loss: 1.3396\n2023-01-04 01:29:26,714 - mmcls - INFO - Epoch [80][200/5005]\tlr: 1.000e-03, eta: 2:33:56, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3831\n2023-01-04 01:29:35,243 - mmcls - INFO - Epoch [80][300/5005]\tlr: 1.000e-03, eta: 2:33:47, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.3728\n2023-01-04 01:29:43,818 - mmcls - INFO - Epoch [80][400/5005]\tlr: 1.000e-03, eta: 2:33:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3697\n2023-01-04 01:29:52,888 - mmcls - INFO - Epoch [80][500/5005]\tlr: 1.000e-03, eta: 2:33:29, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.3596\n2023-01-04 01:30:02,313 - mmcls - INFO - Epoch [80][600/5005]\tlr: 1.000e-03, eta: 2:33:20, time: 0.094, data_time: 0.049, memory: 1125, loss: 1.3694\n2023-01-04 01:30:10,927 - mmcls - INFO - Epoch [80][700/5005]\tlr: 1.000e-03, eta: 2:33:12, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3678\n2023-01-04 01:30:19,587 - mmcls - INFO - Epoch [80][800/5005]\tlr: 1.000e-03, eta: 2:33:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3750\n2023-01-04 01:30:28,300 - mmcls - INFO - Epoch [80][900/5005]\tlr: 1.000e-03, eta: 2:32:54, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3680\n2023-01-04 01:30:37,094 - mmcls - INFO - Epoch [80][1000/5005]\tlr: 1.000e-03, eta: 2:32:45, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3862\n2023-01-04 01:30:46,092 - mmcls - INFO - Epoch [80][1100/5005]\tlr: 1.000e-03, eta: 2:32:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3797\n2023-01-04 01:30:55,039 - mmcls - INFO - Epoch [80][1200/5005]\tlr: 1.000e-03, eta: 2:32:28, time: 0.089, data_time: 0.043, memory: 1125, loss: 1.3615\n2023-01-04 01:31:03,709 - mmcls - INFO - Epoch [80][1300/5005]\tlr: 1.000e-03, eta: 2:32:19, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3931\n2023-01-04 01:31:12,667 - mmcls - INFO - Epoch [80][1400/5005]\tlr: 1.000e-03, eta: 2:32:10, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3638\n2023-01-04 01:31:21,553 - mmcls - INFO - Epoch [80][1500/5005]\tlr: 1.000e-03, eta: 2:32:01, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3739\n2023-01-04 01:31:30,477 - mmcls - INFO - Epoch [80][1600/5005]\tlr: 1.000e-03, eta: 2:31:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3902\n2023-01-04 01:31:39,067 - mmcls - INFO - Epoch [80][1700/5005]\tlr: 1.000e-03, eta: 2:31:44, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3830\n2023-01-04 01:31:47,785 - mmcls - INFO - Epoch [80][1800/5005]\tlr: 1.000e-03, eta: 2:31:35, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3799\n2023-01-04 01:31:56,693 - mmcls - INFO - Epoch [80][1900/5005]\tlr: 1.000e-03, eta: 2:31:26, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.4019\n2023-01-04 01:32:05,538 - mmcls - INFO - Epoch [80][2000/5005]\tlr: 1.000e-03, eta: 2:31:17, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3786\n2023-01-04 01:32:14,251 - mmcls - INFO - Epoch [80][2100/5005]\tlr: 1.000e-03, eta: 2:31:08, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3702\n2023-01-04 01:32:22,936 - mmcls - INFO - Epoch [80][2200/5005]\tlr: 1.000e-03, eta: 2:30:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3769\n2023-01-04 01:32:31,796 - mmcls - INFO - Epoch [80][2300/5005]\tlr: 1.000e-03, eta: 2:30:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3570\n2023-01-04 01:32:40,759 - mmcls - INFO - Epoch [80][2400/5005]\tlr: 1.000e-03, eta: 2:30:42, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.3918\n2023-01-04 01:32:50,151 - mmcls - INFO - Epoch [80][2500/5005]\tlr: 1.000e-03, eta: 2:30:33, time: 0.094, data_time: 0.027, memory: 1125, loss: 1.3794\n2023-01-04 01:32:58,752 - mmcls - INFO - Epoch [80][2600/5005]\tlr: 1.000e-03, eta: 2:30:24, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3827\n2023-01-04 01:33:07,356 - mmcls - INFO - Epoch [80][2700/5005]\tlr: 1.000e-03, eta: 2:30:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3727\n2023-01-04 01:33:16,042 - mmcls - INFO - Epoch [80][2800/5005]\tlr: 1.000e-03, eta: 2:30:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3789\n2023-01-04 01:33:25,551 - mmcls - INFO - Epoch [80][2900/5005]\tlr: 1.000e-03, eta: 2:29:58, time: 0.095, data_time: 0.037, memory: 1125, loss: 1.3896\n2023-01-04 01:33:34,181 - mmcls - INFO - Epoch [80][3000/5005]\tlr: 1.000e-03, eta: 2:29:49, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3803\n2023-01-04 01:33:42,841 - mmcls - INFO - Epoch [80][3100/5005]\tlr: 1.000e-03, eta: 2:29:40, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3665\n2023-01-04 01:33:51,605 - mmcls - INFO - Epoch [80][3200/5005]\tlr: 1.000e-03, eta: 2:29:32, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3693\n2023-01-04 01:34:00,105 - mmcls - INFO - Epoch [80][3300/5005]\tlr: 1.000e-03, eta: 2:29:23, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3835\n2023-01-04 01:34:08,952 - mmcls - INFO - Epoch [80][3400/5005]\tlr: 1.000e-03, eta: 2:29:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3901\n2023-01-04 01:34:17,579 - mmcls - INFO - Epoch [80][3500/5005]\tlr: 1.000e-03, eta: 2:29:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3763\n2023-01-04 01:34:26,337 - mmcls - INFO - Epoch [80][3600/5005]\tlr: 1.000e-03, eta: 2:28:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3808\n2023-01-04 01:34:34,801 - mmcls - INFO - Epoch [80][3700/5005]\tlr: 1.000e-03, eta: 2:28:47, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3844\n2023-01-04 01:34:43,590 - mmcls - INFO - Epoch [80][3800/5005]\tlr: 1.000e-03, eta: 2:28:39, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3933\n2023-01-04 01:34:52,361 - mmcls - INFO - Epoch [80][3900/5005]\tlr: 1.000e-03, eta: 2:28:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3822\n2023-01-04 01:35:00,937 - mmcls - INFO - Epoch [80][4000/5005]\tlr: 1.000e-03, eta: 2:28:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4133\n2023-01-04 01:35:09,742 - mmcls - INFO - Epoch [80][4100/5005]\tlr: 1.000e-03, eta: 2:28:12, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.3725\n2023-01-04 01:35:18,450 - mmcls - INFO - Epoch [80][4200/5005]\tlr: 1.000e-03, eta: 2:28:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3751\n2023-01-04 01:35:27,501 - mmcls - INFO - Epoch [80][4300/5005]\tlr: 1.000e-03, eta: 2:27:55, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.3760\n2023-01-04 01:35:36,198 - mmcls - INFO - Epoch [80][4400/5005]\tlr: 1.000e-03, eta: 2:27:46, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3821\n2023-01-04 01:35:45,035 - mmcls - INFO - Epoch [80][4500/5005]\tlr: 1.000e-03, eta: 2:27:37, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4074\n2023-01-04 01:35:53,589 - mmcls - INFO - Epoch [80][4600/5005]\tlr: 1.000e-03, eta: 2:27:28, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3836\n2023-01-04 01:36:02,385 - mmcls - INFO - Epoch [80][4700/5005]\tlr: 1.000e-03, eta: 2:27:19, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.3909\n2023-01-04 01:36:11,211 - mmcls - INFO - Epoch [80][4800/5005]\tlr: 1.000e-03, eta: 2:27:10, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3972\n2023-01-04 01:36:19,850 - mmcls - INFO - Epoch [80][4900/5005]\tlr: 1.000e-03, eta: 2:27:02, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3867\n2023-01-04 01:36:28,551 - mmcls - INFO - Epoch [80][5000/5005]\tlr: 1.000e-03, eta: 2:26:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3867\n2023-01-04 01:36:51,125 - mmcls - INFO - Epoch(val) [80][196]\taccuracy_top-1: 69.6340, accuracy_top-5: 89.1060\n2023-01-04 01:37:02,095 - mmcls - INFO - Epoch [81][100/5005]\tlr: 1.000e-03, eta: 2:26:44, time: 0.109, data_time: 0.057, memory: 1125, loss: 1.3826\n2023-01-04 01:37:10,766 - mmcls - INFO - Epoch [81][200/5005]\tlr: 1.000e-03, eta: 2:26:35, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3789\n2023-01-04 01:37:19,462 - mmcls - INFO - Epoch [81][300/5005]\tlr: 1.000e-03, eta: 2:26:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3555\n2023-01-04 01:37:28,160 - mmcls - INFO - Epoch [81][400/5005]\tlr: 1.000e-03, eta: 2:26:17, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3728\n2023-01-04 01:37:36,757 - mmcls - INFO - Epoch [81][500/5005]\tlr: 1.000e-03, eta: 2:26:09, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3833\n2023-01-04 01:37:45,691 - mmcls - INFO - Epoch [81][600/5005]\tlr: 1.000e-03, eta: 2:26:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3493\n2023-01-04 01:37:54,509 - mmcls - INFO - Epoch [81][700/5005]\tlr: 1.000e-03, eta: 2:25:51, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3658\n2023-01-04 01:38:02,995 - mmcls - INFO - Epoch [81][800/5005]\tlr: 1.000e-03, eta: 2:25:42, time: 0.085, data_time: 0.029, memory: 1125, loss: 1.3569\n2023-01-04 01:38:12,126 - mmcls - INFO - Epoch [81][900/5005]\tlr: 1.000e-03, eta: 2:25:33, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3799\n2023-01-04 01:38:21,055 - mmcls - INFO - Epoch [81][1000/5005]\tlr: 1.000e-03, eta: 2:25:25, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3777\n2023-01-04 01:38:29,567 - mmcls - INFO - Epoch [81][1100/5005]\tlr: 1.000e-03, eta: 2:25:16, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3518\n2023-01-04 01:38:38,128 - mmcls - INFO - Epoch [81][1200/5005]\tlr: 1.000e-03, eta: 2:25:07, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3733\n2023-01-04 01:38:46,746 - mmcls - INFO - Epoch [81][1300/5005]\tlr: 1.000e-03, eta: 2:24:58, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3726\n2023-01-04 01:38:55,624 - mmcls - INFO - Epoch [81][1400/5005]\tlr: 1.000e-03, eta: 2:24:49, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3834\n2023-01-04 01:39:04,390 - mmcls - INFO - Epoch [81][1500/5005]\tlr: 1.000e-03, eta: 2:24:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3852\n2023-01-04 01:39:13,092 - mmcls - INFO - Epoch [81][1600/5005]\tlr: 1.000e-03, eta: 2:24:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4001\n2023-01-04 01:39:21,780 - mmcls - INFO - Epoch [81][1700/5005]\tlr: 1.000e-03, eta: 2:24:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3757\n2023-01-04 01:39:30,561 - mmcls - INFO - Epoch [81][1800/5005]\tlr: 1.000e-03, eta: 2:24:14, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3585\n2023-01-04 01:39:39,030 - mmcls - INFO - Epoch [81][1900/5005]\tlr: 1.000e-03, eta: 2:24:05, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3799\n2023-01-04 01:39:47,785 - mmcls - INFO - Epoch [81][2000/5005]\tlr: 1.000e-03, eta: 2:23:56, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3881\n2023-01-04 01:39:56,450 - mmcls - INFO - Epoch [81][2100/5005]\tlr: 1.000e-03, eta: 2:23:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3632\n2023-01-04 01:40:05,446 - mmcls - INFO - Epoch [81][2200/5005]\tlr: 1.000e-03, eta: 2:23:39, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3603\n2023-01-04 01:40:14,211 - mmcls - INFO - Epoch [81][2300/5005]\tlr: 1.000e-03, eta: 2:23:30, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3807\n2023-01-04 01:40:23,384 - mmcls - INFO - Epoch [81][2400/5005]\tlr: 1.000e-03, eta: 2:23:21, time: 0.092, data_time: 0.025, memory: 1125, loss: 1.3558\n2023-01-04 01:40:32,026 - mmcls - INFO - Epoch [81][2500/5005]\tlr: 1.000e-03, eta: 2:23:12, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3860\n2023-01-04 01:40:40,803 - mmcls - INFO - Epoch [81][2600/5005]\tlr: 1.000e-03, eta: 2:23:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3759\n2023-01-04 01:40:49,634 - mmcls - INFO - Epoch [81][2700/5005]\tlr: 1.000e-03, eta: 2:22:55, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3914\n2023-01-04 01:40:58,150 - mmcls - INFO - Epoch [81][2800/5005]\tlr: 1.000e-03, eta: 2:22:46, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.3830\n2023-01-04 01:41:07,081 - mmcls - INFO - Epoch [81][2900/5005]\tlr: 1.000e-03, eta: 2:22:37, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3773\n2023-01-04 01:41:15,847 - mmcls - INFO - Epoch [81][3000/5005]\tlr: 1.000e-03, eta: 2:22:28, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3727\n2023-01-04 01:41:24,643 - mmcls - INFO - Epoch [81][3100/5005]\tlr: 1.000e-03, eta: 2:22:19, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4089\n2023-01-04 01:41:33,225 - mmcls - INFO - Epoch [81][3200/5005]\tlr: 1.000e-03, eta: 2:22:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3739\n2023-01-04 01:41:41,878 - mmcls - INFO - Epoch [81][3300/5005]\tlr: 1.000e-03, eta: 2:22:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3940\n2023-01-04 01:41:50,768 - mmcls - INFO - Epoch [81][3400/5005]\tlr: 1.000e-03, eta: 2:21:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3874\n2023-01-04 01:41:59,746 - mmcls - INFO - Epoch [81][3500/5005]\tlr: 1.000e-03, eta: 2:21:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3669\n2023-01-04 01:42:08,365 - mmcls - INFO - Epoch [81][3600/5005]\tlr: 1.000e-03, eta: 2:21:35, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3751\n2023-01-04 01:42:17,217 - mmcls - INFO - Epoch [81][3700/5005]\tlr: 1.000e-03, eta: 2:21:27, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.3960\n2023-01-04 01:42:25,886 - mmcls - INFO - Epoch [81][3800/5005]\tlr: 1.000e-03, eta: 2:21:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3931\n2023-01-04 01:42:34,443 - mmcls - INFO - Epoch [81][3900/5005]\tlr: 1.000e-03, eta: 2:21:09, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.3774\n2023-01-04 01:42:43,357 - mmcls - INFO - Epoch [81][4000/5005]\tlr: 1.000e-03, eta: 2:21:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3590\n2023-01-04 01:42:51,894 - mmcls - INFO - Epoch [81][4100/5005]\tlr: 1.000e-03, eta: 2:20:51, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3669\n2023-01-04 01:43:00,647 - mmcls - INFO - Epoch [81][4200/5005]\tlr: 1.000e-03, eta: 2:20:42, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3934\n2023-01-04 01:43:09,384 - mmcls - INFO - Epoch [81][4300/5005]\tlr: 1.000e-03, eta: 2:20:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3904\n2023-01-04 01:43:18,502 - mmcls - INFO - Epoch [81][4400/5005]\tlr: 1.000e-03, eta: 2:20:25, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3877\n2023-01-04 01:43:27,018 - mmcls - INFO - Epoch [81][4500/5005]\tlr: 1.000e-03, eta: 2:20:16, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3802\n2023-01-04 01:43:35,750 - mmcls - INFO - Epoch [81][4600/5005]\tlr: 1.000e-03, eta: 2:20:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3665\n2023-01-04 01:43:44,945 - mmcls - INFO - Epoch [81][4700/5005]\tlr: 1.000e-03, eta: 2:19:58, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3805\n2023-01-04 01:43:53,528 - mmcls - INFO - Epoch [81][4800/5005]\tlr: 1.000e-03, eta: 2:19:50, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.4001\n2023-01-04 01:44:02,557 - mmcls - INFO - Epoch [81][4900/5005]\tlr: 1.000e-03, eta: 2:19:41, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3781\n2023-01-04 01:44:11,398 - mmcls - INFO - Epoch [81][5000/5005]\tlr: 1.000e-03, eta: 2:19:32, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3738\n2023-01-04 01:44:34,199 - mmcls - INFO - Epoch(val) [81][196]\taccuracy_top-1: 69.8320, accuracy_top-5: 89.2200\n2023-01-04 01:44:45,465 - mmcls - INFO - Epoch [82][100/5005]\tlr: 1.000e-03, eta: 2:19:23, time: 0.112, data_time: 0.044, memory: 1125, loss: 1.3682\n2023-01-04 01:44:53,993 - mmcls - INFO - Epoch [82][200/5005]\tlr: 1.000e-03, eta: 2:19:14, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3890\n2023-01-04 01:45:02,966 - mmcls - INFO - Epoch [82][300/5005]\tlr: 1.000e-03, eta: 2:19:06, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3592\n2023-01-04 01:45:11,694 - mmcls - INFO - Epoch [82][400/5005]\tlr: 1.000e-03, eta: 2:18:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3742\n2023-01-04 01:45:20,387 - mmcls - INFO - Epoch [82][500/5005]\tlr: 1.000e-03, eta: 2:18:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3682\n2023-01-04 01:45:29,204 - mmcls - INFO - Epoch [82][600/5005]\tlr: 1.000e-03, eta: 2:18:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3528\n2023-01-04 01:45:37,762 - mmcls - INFO - Epoch [82][700/5005]\tlr: 1.000e-03, eta: 2:18:30, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3794\n2023-01-04 01:45:46,367 - mmcls - INFO - Epoch [82][800/5005]\tlr: 1.000e-03, eta: 2:18:21, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3662\n2023-01-04 01:45:55,333 - mmcls - INFO - Epoch [82][900/5005]\tlr: 1.000e-03, eta: 2:18:13, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.3831\n2023-01-04 01:46:04,396 - mmcls - INFO - Epoch [82][1000/5005]\tlr: 1.000e-03, eta: 2:18:04, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3865\n2023-01-04 01:46:13,061 - mmcls - INFO - Epoch [82][1100/5005]\tlr: 1.000e-03, eta: 2:17:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3686\n2023-01-04 01:46:21,536 - mmcls - INFO - Epoch [82][1200/5005]\tlr: 1.000e-03, eta: 2:17:46, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3552\n2023-01-04 01:46:30,394 - mmcls - INFO - Epoch [82][1300/5005]\tlr: 1.000e-03, eta: 2:17:37, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3748\n2023-01-04 01:46:38,964 - mmcls - INFO - Epoch [82][1400/5005]\tlr: 1.000e-03, eta: 2:17:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3670\n2023-01-04 01:46:48,087 - mmcls - INFO - Epoch [82][1500/5005]\tlr: 1.000e-03, eta: 2:17:20, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3758\n2023-01-04 01:46:56,862 - mmcls - INFO - Epoch [82][1600/5005]\tlr: 1.000e-03, eta: 2:17:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3767\n2023-01-04 01:47:05,532 - mmcls - INFO - Epoch [82][1700/5005]\tlr: 1.000e-03, eta: 2:17:02, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3736\n2023-01-04 01:47:14,414 - mmcls - INFO - Epoch [82][1800/5005]\tlr: 1.000e-03, eta: 2:16:53, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3880\n2023-01-04 01:47:23,167 - mmcls - INFO - Epoch [82][1900/5005]\tlr: 1.000e-03, eta: 2:16:45, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3834\n2023-01-04 01:47:31,813 - mmcls - INFO - Epoch [82][2000/5005]\tlr: 1.000e-03, eta: 2:16:36, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3895\n2023-01-04 01:47:40,531 - mmcls - INFO - Epoch [82][2100/5005]\tlr: 1.000e-03, eta: 2:16:27, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3726\n2023-01-04 01:47:49,292 - mmcls - INFO - Epoch [82][2200/5005]\tlr: 1.000e-03, eta: 2:16:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3667\n2023-01-04 01:47:57,898 - mmcls - INFO - Epoch [82][2300/5005]\tlr: 1.000e-03, eta: 2:16:09, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3754\n2023-01-04 01:48:06,694 - mmcls - INFO - Epoch [82][2400/5005]\tlr: 1.000e-03, eta: 2:16:00, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3937\n2023-01-04 01:48:15,261 - mmcls - INFO - Epoch [82][2500/5005]\tlr: 1.000e-03, eta: 2:15:52, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3949\n2023-01-04 01:48:23,795 - mmcls - INFO - Epoch [82][2600/5005]\tlr: 1.000e-03, eta: 2:15:43, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3839\n2023-01-04 01:48:32,518 - mmcls - INFO - Epoch [82][2700/5005]\tlr: 1.000e-03, eta: 2:15:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3718\n2023-01-04 01:48:41,349 - mmcls - INFO - Epoch [82][2800/5005]\tlr: 1.000e-03, eta: 2:15:25, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.3766\n2023-01-04 01:48:50,152 - mmcls - INFO - Epoch [82][2900/5005]\tlr: 1.000e-03, eta: 2:15:16, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3735\n2023-01-04 01:48:59,153 - mmcls - INFO - Epoch [82][3000/5005]\tlr: 1.000e-03, eta: 2:15:08, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3784\n2023-01-04 01:49:07,812 - mmcls - INFO - Epoch [82][3100/5005]\tlr: 1.000e-03, eta: 2:14:59, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3676\n2023-01-04 01:49:16,841 - mmcls - INFO - Epoch [82][3200/5005]\tlr: 1.000e-03, eta: 2:14:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.3703\n2023-01-04 01:49:25,976 - mmcls - INFO - Epoch [82][3300/5005]\tlr: 1.000e-03, eta: 2:14:41, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.3774\n2023-01-04 01:49:34,741 - mmcls - INFO - Epoch [82][3400/5005]\tlr: 1.000e-03, eta: 2:14:32, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3960\n2023-01-04 01:49:43,527 - mmcls - INFO - Epoch [82][3500/5005]\tlr: 1.000e-03, eta: 2:14:24, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3781\n2023-01-04 01:49:52,048 - mmcls - INFO - Epoch [82][3600/5005]\tlr: 1.000e-03, eta: 2:14:15, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.4049\n2023-01-04 01:50:01,084 - mmcls - INFO - Epoch [82][3700/5005]\tlr: 1.000e-03, eta: 2:14:06, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3866\n2023-01-04 01:50:09,512 - mmcls - INFO - Epoch [82][3800/5005]\tlr: 1.000e-03, eta: 2:13:57, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.3671\n2023-01-04 01:50:18,157 - mmcls - INFO - Epoch [82][3900/5005]\tlr: 1.000e-03, eta: 2:13:48, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3683\n2023-01-04 01:50:27,284 - mmcls - INFO - Epoch [82][4000/5005]\tlr: 1.000e-03, eta: 2:13:40, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3739\n2023-01-04 01:50:35,983 - mmcls - INFO - Epoch [82][4100/5005]\tlr: 1.000e-03, eta: 2:13:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3902\n2023-01-04 01:50:44,770 - mmcls - INFO - Epoch [82][4200/5005]\tlr: 1.000e-03, eta: 2:13:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3825\n2023-01-04 01:50:53,593 - mmcls - INFO - Epoch [82][4300/5005]\tlr: 1.000e-03, eta: 2:13:13, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3858\n2023-01-04 01:51:02,396 - mmcls - INFO - Epoch [82][4400/5005]\tlr: 1.000e-03, eta: 2:13:04, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3743\n2023-01-04 01:51:10,934 - mmcls - INFO - Epoch [82][4500/5005]\tlr: 1.000e-03, eta: 2:12:55, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4040\n2023-01-04 01:51:19,831 - mmcls - INFO - Epoch [82][4600/5005]\tlr: 1.000e-03, eta: 2:12:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3724\n2023-01-04 01:51:28,666 - mmcls - INFO - Epoch [82][4700/5005]\tlr: 1.000e-03, eta: 2:12:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3669\n2023-01-04 01:51:37,582 - mmcls - INFO - Epoch [82][4800/5005]\tlr: 1.000e-03, eta: 2:12:29, time: 0.089, data_time: 0.038, memory: 1125, loss: 1.3727\n2023-01-04 01:51:46,385 - mmcls - INFO - Epoch [82][4900/5005]\tlr: 1.000e-03, eta: 2:12:20, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.3619\n2023-01-04 01:51:55,128 - mmcls - INFO - Epoch [82][5000/5005]\tlr: 1.000e-03, eta: 2:12:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3937\n2023-01-04 01:52:17,876 - mmcls - INFO - Epoch(val) [82][196]\taccuracy_top-1: 69.7920, accuracy_top-5: 89.2960\n2023-01-04 01:52:28,937 - mmcls - INFO - Epoch [83][100/5005]\tlr: 1.000e-03, eta: 2:12:03, time: 0.110, data_time: 0.047, memory: 1125, loss: 1.3740\n2023-01-04 01:52:38,026 - mmcls - INFO - Epoch [83][200/5005]\tlr: 1.000e-03, eta: 2:11:54, time: 0.091, data_time: 0.042, memory: 1125, loss: 1.3467\n2023-01-04 01:52:46,461 - mmcls - INFO - Epoch [83][300/5005]\tlr: 1.000e-03, eta: 2:11:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3642\n2023-01-04 01:52:55,517 - mmcls - INFO - Epoch [83][400/5005]\tlr: 1.000e-03, eta: 2:11:36, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3667\n2023-01-04 01:53:04,096 - mmcls - INFO - Epoch [83][500/5005]\tlr: 1.000e-03, eta: 2:11:27, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3390\n2023-01-04 01:53:13,055 - mmcls - INFO - Epoch [83][600/5005]\tlr: 1.000e-03, eta: 2:11:19, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.3509\n2023-01-04 01:53:22,117 - mmcls - INFO - Epoch [83][700/5005]\tlr: 1.000e-03, eta: 2:11:10, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3771\n2023-01-04 01:53:31,009 - mmcls - INFO - Epoch [83][800/5005]\tlr: 1.000e-03, eta: 2:11:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3732\n2023-01-04 01:53:39,895 - mmcls - INFO - Epoch [83][900/5005]\tlr: 1.000e-03, eta: 2:10:52, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3715\n2023-01-04 01:53:48,474 - mmcls - INFO - Epoch [83][1000/5005]\tlr: 1.000e-03, eta: 2:10:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3700\n2023-01-04 01:53:57,086 - mmcls - INFO - Epoch [83][1100/5005]\tlr: 1.000e-03, eta: 2:10:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3547\n2023-01-04 01:54:05,839 - mmcls - INFO - Epoch [83][1200/5005]\tlr: 1.000e-03, eta: 2:10:26, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3727\n2023-01-04 01:54:14,337 - mmcls - INFO - Epoch [83][1300/5005]\tlr: 1.000e-03, eta: 2:10:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3796\n2023-01-04 01:54:23,335 - mmcls - INFO - Epoch [83][1400/5005]\tlr: 1.000e-03, eta: 2:10:08, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3821\n2023-01-04 01:54:31,881 - mmcls - INFO - Epoch [83][1500/5005]\tlr: 1.000e-03, eta: 2:09:59, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3912\n2023-01-04 01:54:41,054 - mmcls - INFO - Epoch [83][1600/5005]\tlr: 1.000e-03, eta: 2:09:51, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.3843\n2023-01-04 01:54:49,803 - mmcls - INFO - Epoch [83][1700/5005]\tlr: 1.000e-03, eta: 2:09:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3596\n2023-01-04 01:54:58,342 - mmcls - INFO - Epoch [83][1800/5005]\tlr: 1.000e-03, eta: 2:09:33, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.3803\n2023-01-04 01:55:06,866 - mmcls - INFO - Epoch [83][1900/5005]\tlr: 1.000e-03, eta: 2:09:24, time: 0.085, data_time: 0.033, memory: 1125, loss: 1.3710\n2023-01-04 01:55:15,638 - mmcls - INFO - Epoch [83][2000/5005]\tlr: 1.000e-03, eta: 2:09:15, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3728\n2023-01-04 01:55:24,603 - mmcls - INFO - Epoch [83][2100/5005]\tlr: 1.000e-03, eta: 2:09:06, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3517\n2023-01-04 01:55:33,293 - mmcls - INFO - Epoch [83][2200/5005]\tlr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3705\n2023-01-04 01:55:42,121 - mmcls - INFO - Epoch [83][2300/5005]\tlr: 1.000e-03, eta: 2:08:49, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3662\n2023-01-04 01:55:51,157 - mmcls - INFO - Epoch [83][2400/5005]\tlr: 1.000e-03, eta: 2:08:40, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.3736\n2023-01-04 01:55:59,916 - mmcls - INFO - Epoch [83][2500/5005]\tlr: 1.000e-03, eta: 2:08:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3772\n2023-01-04 01:56:08,959 - mmcls - INFO - Epoch [83][2600/5005]\tlr: 1.000e-03, eta: 2:08:22, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.3926\n2023-01-04 01:56:17,782 - mmcls - INFO - Epoch [83][2700/5005]\tlr: 1.000e-03, eta: 2:08:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3669\n2023-01-04 01:56:26,877 - mmcls - INFO - Epoch [83][2800/5005]\tlr: 1.000e-03, eta: 2:08:05, time: 0.091, data_time: 0.027, memory: 1125, loss: 1.3604\n2023-01-04 01:56:35,461 - mmcls - INFO - Epoch [83][2900/5005]\tlr: 1.000e-03, eta: 2:07:56, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3554\n2023-01-04 01:56:44,138 - mmcls - INFO - Epoch [83][3000/5005]\tlr: 1.000e-03, eta: 2:07:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3793\n2023-01-04 01:56:53,018 - mmcls - INFO - Epoch [83][3100/5005]\tlr: 1.000e-03, eta: 2:07:38, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.3736\n2023-01-04 01:57:01,791 - mmcls - INFO - Epoch [83][3200/5005]\tlr: 1.000e-03, eta: 2:07:30, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3718\n2023-01-04 01:57:10,510 - mmcls - INFO - Epoch [83][3300/5005]\tlr: 1.000e-03, eta: 2:07:21, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3813\n2023-01-04 01:57:19,293 - mmcls - INFO - Epoch [83][3400/5005]\tlr: 1.000e-03, eta: 2:07:12, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3672\n2023-01-04 01:57:28,106 - mmcls - INFO - Epoch [83][3500/5005]\tlr: 1.000e-03, eta: 2:07:03, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3679\n2023-01-04 01:57:37,007 - mmcls - INFO - Epoch [83][3600/5005]\tlr: 1.000e-03, eta: 2:06:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3723\n2023-01-04 01:57:46,233 - mmcls - INFO - Epoch [83][3700/5005]\tlr: 1.000e-03, eta: 2:06:46, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3749\n2023-01-04 01:57:54,960 - mmcls - INFO - Epoch [83][3800/5005]\tlr: 1.000e-03, eta: 2:06:37, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3646\n2023-01-04 01:58:03,428 - mmcls - INFO - Epoch [83][3900/5005]\tlr: 1.000e-03, eta: 2:06:28, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3778\n2023-01-04 01:58:12,559 - mmcls - INFO - Epoch [83][4000/5005]\tlr: 1.000e-03, eta: 2:06:19, time: 0.091, data_time: 0.027, memory: 1125, loss: 1.3920\n2023-01-04 01:58:21,061 - mmcls - INFO - Epoch [83][4100/5005]\tlr: 1.000e-03, eta: 2:06:10, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3817\n2023-01-04 01:58:29,640 - mmcls - INFO - Epoch [83][4200/5005]\tlr: 1.000e-03, eta: 2:06:02, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3779\n2023-01-04 01:58:38,283 - mmcls - INFO - Epoch [83][4300/5005]\tlr: 1.000e-03, eta: 2:05:53, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3597\n2023-01-04 01:58:47,009 - mmcls - INFO - Epoch [83][4400/5005]\tlr: 1.000e-03, eta: 2:05:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3721\n2023-01-04 01:58:55,976 - mmcls - INFO - Epoch [83][4500/5005]\tlr: 1.000e-03, eta: 2:05:35, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3970\n2023-01-04 01:59:04,524 - mmcls - INFO - Epoch [83][4600/5005]\tlr: 1.000e-03, eta: 2:05:26, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3808\n2023-01-04 01:59:13,110 - mmcls - INFO - Epoch [83][4700/5005]\tlr: 1.000e-03, eta: 2:05:17, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3937\n2023-01-04 01:59:22,058 - mmcls - INFO - Epoch [83][4800/5005]\tlr: 1.000e-03, eta: 2:05:09, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3744\n2023-01-04 01:59:31,323 - mmcls - INFO - Epoch [83][4900/5005]\tlr: 1.000e-03, eta: 2:05:00, time: 0.093, data_time: 0.010, memory: 1125, loss: 1.3624\n2023-01-04 01:59:39,874 - mmcls - INFO - Epoch [83][5000/5005]\tlr: 1.000e-03, eta: 2:04:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3674\n2023-01-04 02:00:02,433 - mmcls - INFO - Epoch(val) [83][196]\taccuracy_top-1: 69.5120, accuracy_top-5: 89.2240\n2023-01-04 02:00:13,515 - mmcls - INFO - Epoch [84][100/5005]\tlr: 1.000e-03, eta: 2:04:42, time: 0.111, data_time: 0.063, memory: 1125, loss: 1.3772\n2023-01-04 02:00:22,163 - mmcls - INFO - Epoch [84][200/5005]\tlr: 1.000e-03, eta: 2:04:33, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3572\n2023-01-04 02:00:30,889 - mmcls - INFO - Epoch [84][300/5005]\tlr: 1.000e-03, eta: 2:04:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3786\n2023-01-04 02:00:39,965 - mmcls - INFO - Epoch [84][400/5005]\tlr: 1.000e-03, eta: 2:04:16, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3372\n2023-01-04 02:00:48,585 - mmcls - INFO - Epoch [84][500/5005]\tlr: 1.000e-03, eta: 2:04:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3498\n2023-01-04 02:00:57,637 - mmcls - INFO - Epoch [84][600/5005]\tlr: 1.000e-03, eta: 2:03:58, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3803\n2023-01-04 02:01:06,656 - mmcls - INFO - Epoch [84][700/5005]\tlr: 1.000e-03, eta: 2:03:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3715\n2023-01-04 02:01:15,088 - mmcls - INFO - Epoch [84][800/5005]\tlr: 1.000e-03, eta: 2:03:41, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.3513\n2023-01-04 02:01:24,089 - mmcls - INFO - Epoch [84][900/5005]\tlr: 1.000e-03, eta: 2:03:32, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.3522\n2023-01-04 02:01:32,978 - mmcls - INFO - Epoch [84][1000/5005]\tlr: 1.000e-03, eta: 2:03:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3734\n2023-01-04 02:01:41,805 - mmcls - INFO - Epoch [84][1100/5005]\tlr: 1.000e-03, eta: 2:03:14, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3899\n2023-01-04 02:01:51,064 - mmcls - INFO - Epoch [84][1200/5005]\tlr: 1.000e-03, eta: 2:03:05, time: 0.093, data_time: 0.024, memory: 1125, loss: 1.3676\n2023-01-04 02:01:59,525 - mmcls - INFO - Epoch [84][1300/5005]\tlr: 1.000e-03, eta: 2:02:57, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3909\n2023-01-04 02:02:08,313 - mmcls - INFO - Epoch [84][1400/5005]\tlr: 1.000e-03, eta: 2:02:48, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3723\n2023-01-04 02:02:17,089 - mmcls - INFO - Epoch [84][1500/5005]\tlr: 1.000e-03, eta: 2:02:39, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3681\n2023-01-04 02:02:25,656 - mmcls - INFO - Epoch [84][1600/5005]\tlr: 1.000e-03, eta: 2:02:30, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3780\n2023-01-04 02:02:34,477 - mmcls - INFO - Epoch [84][1700/5005]\tlr: 1.000e-03, eta: 2:02:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3557\n2023-01-04 02:02:42,949 - mmcls - INFO - Epoch [84][1800/5005]\tlr: 1.000e-03, eta: 2:02:12, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3606\n2023-01-04 02:02:51,670 - mmcls - INFO - Epoch [84][1900/5005]\tlr: 1.000e-03, eta: 2:02:04, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3728\n2023-01-04 02:03:00,323 - mmcls - INFO - Epoch [84][2000/5005]\tlr: 1.000e-03, eta: 2:01:55, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3594\n2023-01-04 02:03:09,120 - mmcls - INFO - Epoch [84][2100/5005]\tlr: 1.000e-03, eta: 2:01:46, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3765\n2023-01-04 02:03:17,692 - mmcls - INFO - Epoch [84][2200/5005]\tlr: 1.000e-03, eta: 2:01:37, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3645\n2023-01-04 02:03:26,157 - mmcls - INFO - Epoch [84][2300/5005]\tlr: 1.000e-03, eta: 2:01:28, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3812\n2023-01-04 02:03:34,687 - mmcls - INFO - Epoch [84][2400/5005]\tlr: 1.000e-03, eta: 2:01:19, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3614\n2023-01-04 02:03:43,461 - mmcls - INFO - Epoch [84][2500/5005]\tlr: 1.000e-03, eta: 2:01:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3798\n2023-01-04 02:03:52,337 - mmcls - INFO - Epoch [84][2600/5005]\tlr: 1.000e-03, eta: 2:01:02, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3773\n2023-01-04 02:04:01,505 - mmcls - INFO - Epoch [84][2700/5005]\tlr: 1.000e-03, eta: 2:00:53, time: 0.092, data_time: 0.021, memory: 1125, loss: 1.3749\n2023-01-04 02:04:10,212 - mmcls - INFO - Epoch [84][2800/5005]\tlr: 1.000e-03, eta: 2:00:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3899\n2023-01-04 02:04:18,794 - mmcls - INFO - Epoch [84][2900/5005]\tlr: 1.000e-03, eta: 2:00:35, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3615\n2023-01-04 02:04:27,463 - mmcls - INFO - Epoch [84][3000/5005]\tlr: 1.000e-03, eta: 2:00:27, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3628\n2023-01-04 02:04:36,054 - mmcls - INFO - Epoch [84][3100/5005]\tlr: 1.000e-03, eta: 2:00:18, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.3745\n2023-01-04 02:04:44,751 - mmcls - INFO - Epoch [84][3200/5005]\tlr: 1.000e-03, eta: 2:00:09, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3706\n2023-01-04 02:04:53,650 - mmcls - INFO - Epoch [84][3300/5005]\tlr: 1.000e-03, eta: 2:00:00, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3806\n2023-01-04 02:05:02,681 - mmcls - INFO - Epoch [84][3400/5005]\tlr: 1.000e-03, eta: 1:59:51, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3551\n2023-01-04 02:05:11,467 - mmcls - INFO - Epoch [84][3500/5005]\tlr: 1.000e-03, eta: 1:59:43, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3678\n2023-01-04 02:05:20,372 - mmcls - INFO - Epoch [84][3600/5005]\tlr: 1.000e-03, eta: 1:59:34, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3896\n2023-01-04 02:05:29,199 - mmcls - INFO - Epoch [84][3700/5005]\tlr: 1.000e-03, eta: 1:59:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3657\n2023-01-04 02:05:37,841 - mmcls - INFO - Epoch [84][3800/5005]\tlr: 1.000e-03, eta: 1:59:16, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3602\n2023-01-04 02:05:46,745 - mmcls - INFO - Epoch [84][3900/5005]\tlr: 1.000e-03, eta: 1:59:07, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.3691\n2023-01-04 02:05:55,613 - mmcls - INFO - Epoch [84][4000/5005]\tlr: 1.000e-03, eta: 1:58:59, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3942\n2023-01-04 02:06:04,637 - mmcls - INFO - Epoch [84][4100/5005]\tlr: 1.000e-03, eta: 1:58:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4027\n2023-01-04 02:06:13,561 - mmcls - INFO - Epoch [84][4200/5005]\tlr: 1.000e-03, eta: 1:58:41, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3826\n2023-01-04 02:06:22,355 - mmcls - INFO - Epoch [84][4300/5005]\tlr: 1.000e-03, eta: 1:58:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3785\n2023-01-04 02:06:31,220 - mmcls - INFO - Epoch [84][4400/5005]\tlr: 1.000e-03, eta: 1:58:23, time: 0.089, data_time: 0.036, memory: 1125, loss: 1.3536\n2023-01-04 02:06:40,021 - mmcls - INFO - Epoch [84][4500/5005]\tlr: 1.000e-03, eta: 1:58:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3823\n2023-01-04 02:06:48,799 - mmcls - INFO - Epoch [84][4600/5005]\tlr: 1.000e-03, eta: 1:58:06, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.3792\n2023-01-04 02:06:57,849 - mmcls - INFO - Epoch [84][4700/5005]\tlr: 1.000e-03, eta: 1:57:57, time: 0.091, data_time: 0.034, memory: 1125, loss: 1.3873\n2023-01-04 02:07:06,527 - mmcls - INFO - Epoch [84][4800/5005]\tlr: 1.000e-03, eta: 1:57:48, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3756\n2023-01-04 02:07:15,265 - mmcls - INFO - Epoch [84][4900/5005]\tlr: 1.000e-03, eta: 1:57:39, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3567\n2023-01-04 02:07:23,838 - mmcls - INFO - Epoch [84][5000/5005]\tlr: 1.000e-03, eta: 1:57:31, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3818\n2023-01-04 02:07:46,445 - mmcls - INFO - Epoch(val) [84][196]\taccuracy_top-1: 69.6820, accuracy_top-5: 89.2040\n2023-01-04 02:07:57,544 - mmcls - INFO - Epoch [85][100/5005]\tlr: 1.000e-03, eta: 1:57:22, time: 0.111, data_time: 0.036, memory: 1125, loss: 1.3756\n2023-01-04 02:08:06,266 - mmcls - INFO - Epoch [85][200/5005]\tlr: 1.000e-03, eta: 1:57:13, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3849\n2023-01-04 02:08:14,933 - mmcls - INFO - Epoch [85][300/5005]\tlr: 1.000e-03, eta: 1:57:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3854\n2023-01-04 02:08:23,527 - mmcls - INFO - Epoch [85][400/5005]\tlr: 1.000e-03, eta: 1:56:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3485\n2023-01-04 02:08:32,174 - mmcls - INFO - Epoch [85][500/5005]\tlr: 1.000e-03, eta: 1:56:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3816\n2023-01-04 02:08:40,902 - mmcls - INFO - Epoch [85][600/5005]\tlr: 1.000e-03, eta: 1:56:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3664\n2023-01-04 02:08:49,786 - mmcls - INFO - Epoch [85][700/5005]\tlr: 1.000e-03, eta: 1:56:29, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3704\n2023-01-04 02:08:58,494 - mmcls - INFO - Epoch [85][800/5005]\tlr: 1.000e-03, eta: 1:56:20, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3658\n2023-01-04 02:09:07,795 - mmcls - INFO - Epoch [85][900/5005]\tlr: 1.000e-03, eta: 1:56:11, time: 0.093, data_time: 0.006, memory: 1125, loss: 1.3642\n2023-01-04 02:09:16,359 - mmcls - INFO - Epoch [85][1000/5005]\tlr: 1.000e-03, eta: 1:56:02, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3600\n2023-01-04 02:09:25,110 - mmcls - INFO - Epoch [85][1100/5005]\tlr: 1.000e-03, eta: 1:55:53, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3490\n2023-01-04 02:09:33,740 - mmcls - INFO - Epoch [85][1200/5005]\tlr: 1.000e-03, eta: 1:55:45, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3532\n2023-01-04 02:09:42,333 - mmcls - INFO - Epoch [85][1300/5005]\tlr: 1.000e-03, eta: 1:55:36, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3719\n2023-01-04 02:09:51,107 - mmcls - INFO - Epoch [85][1400/5005]\tlr: 1.000e-03, eta: 1:55:27, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3842\n2023-01-04 02:09:59,516 - mmcls - INFO - Epoch [85][1500/5005]\tlr: 1.000e-03, eta: 1:55:18, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.3555\n2023-01-04 02:10:08,091 - mmcls - INFO - Epoch [85][1600/5005]\tlr: 1.000e-03, eta: 1:55:09, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3746\n2023-01-04 02:10:16,931 - mmcls - INFO - Epoch [85][1700/5005]\tlr: 1.000e-03, eta: 1:55:00, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3622\n2023-01-04 02:10:25,776 - mmcls - INFO - Epoch [85][1800/5005]\tlr: 1.000e-03, eta: 1:54:52, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3513\n2023-01-04 02:10:34,772 - mmcls - INFO - Epoch [85][1900/5005]\tlr: 1.000e-03, eta: 1:54:43, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.3735\n2023-01-04 02:10:43,934 - mmcls - INFO - Epoch [85][2000/5005]\tlr: 1.000e-03, eta: 1:54:34, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.3706\n2023-01-04 02:10:52,468 - mmcls - INFO - Epoch [85][2100/5005]\tlr: 1.000e-03, eta: 1:54:25, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3755\n2023-01-04 02:11:00,920 - mmcls - INFO - Epoch [85][2200/5005]\tlr: 1.000e-03, eta: 1:54:16, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3714\n2023-01-04 02:11:09,755 - mmcls - INFO - Epoch [85][2300/5005]\tlr: 1.000e-03, eta: 1:54:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3715\n2023-01-04 02:11:18,394 - mmcls - INFO - Epoch [85][2400/5005]\tlr: 1.000e-03, eta: 1:53:59, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3876\n2023-01-04 02:11:27,418 - mmcls - INFO - Epoch [85][2500/5005]\tlr: 1.000e-03, eta: 1:53:50, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.3441\n2023-01-04 02:11:36,221 - mmcls - INFO - Epoch [85][2600/5005]\tlr: 1.000e-03, eta: 1:53:41, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3649\n2023-01-04 02:11:45,231 - mmcls - INFO - Epoch [85][2700/5005]\tlr: 1.000e-03, eta: 1:53:32, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3689\n2023-01-04 02:11:54,041 - mmcls - INFO - Epoch [85][2800/5005]\tlr: 1.000e-03, eta: 1:53:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3686\n2023-01-04 02:12:02,940 - mmcls - INFO - Epoch [85][2900/5005]\tlr: 1.000e-03, eta: 1:53:15, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3589\n2023-01-04 02:12:11,635 - mmcls - INFO - Epoch [85][3000/5005]\tlr: 1.000e-03, eta: 1:53:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3606\n2023-01-04 02:12:20,214 - mmcls - INFO - Epoch [85][3100/5005]\tlr: 1.000e-03, eta: 1:52:57, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3735\n2023-01-04 02:12:28,933 - mmcls - INFO - Epoch [85][3200/5005]\tlr: 1.000e-03, eta: 1:52:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3499\n2023-01-04 02:12:37,820 - mmcls - INFO - Epoch [85][3300/5005]\tlr: 1.000e-03, eta: 1:52:40, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3804\n2023-01-04 02:12:46,814 - mmcls - INFO - Epoch [85][3400/5005]\tlr: 1.000e-03, eta: 1:52:31, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3893\n2023-01-04 02:12:55,422 - mmcls - INFO - Epoch [85][3500/5005]\tlr: 1.000e-03, eta: 1:52:22, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3749\n2023-01-04 02:13:04,104 - mmcls - INFO - Epoch [85][3600/5005]\tlr: 1.000e-03, eta: 1:52:13, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3659\n2023-01-04 02:13:12,868 - mmcls - INFO - Epoch [85][3700/5005]\tlr: 1.000e-03, eta: 1:52:04, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3759\n2023-01-04 02:13:21,779 - mmcls - INFO - Epoch [85][3800/5005]\tlr: 1.000e-03, eta: 1:51:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3778\n2023-01-04 02:13:30,514 - mmcls - INFO - Epoch [85][3900/5005]\tlr: 1.000e-03, eta: 1:51:47, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3625\n2023-01-04 02:13:39,079 - mmcls - INFO - Epoch [85][4000/5005]\tlr: 1.000e-03, eta: 1:51:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3823\n2023-01-04 02:13:47,844 - mmcls - INFO - Epoch [85][4100/5005]\tlr: 1.000e-03, eta: 1:51:29, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3761\n2023-01-04 02:13:56,375 - mmcls - INFO - Epoch [85][4200/5005]\tlr: 1.000e-03, eta: 1:51:20, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3667\n2023-01-04 02:14:05,060 - mmcls - INFO - Epoch [85][4300/5005]\tlr: 1.000e-03, eta: 1:51:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3847\n2023-01-04 02:14:13,996 - mmcls - INFO - Epoch [85][4400/5005]\tlr: 1.000e-03, eta: 1:51:03, time: 0.089, data_time: 0.034, memory: 1125, loss: 1.3698\n2023-01-04 02:14:22,797 - mmcls - INFO - Epoch [85][4500/5005]\tlr: 1.000e-03, eta: 1:50:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3395\n2023-01-04 02:14:31,577 - mmcls - INFO - Epoch [85][4600/5005]\tlr: 1.000e-03, eta: 1:50:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3671\n2023-01-04 02:14:40,368 - mmcls - INFO - Epoch [85][4700/5005]\tlr: 1.000e-03, eta: 1:50:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3658\n2023-01-04 02:14:49,023 - mmcls - INFO - Epoch [85][4800/5005]\tlr: 1.000e-03, eta: 1:50:27, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3790\n2023-01-04 02:14:57,546 - mmcls - INFO - Epoch [85][4900/5005]\tlr: 1.000e-03, eta: 1:50:19, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3783\n2023-01-04 02:15:06,334 - mmcls - INFO - Epoch [85][5000/5005]\tlr: 1.000e-03, eta: 1:50:10, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3637\n2023-01-04 02:15:28,963 - mmcls - INFO - Epoch(val) [85][196]\taccuracy_top-1: 69.7980, accuracy_top-5: 89.2500\n2023-01-04 02:15:40,120 - mmcls - INFO - Epoch [86][100/5005]\tlr: 1.000e-03, eta: 1:50:01, time: 0.111, data_time: 0.043, memory: 1125, loss: 1.3423\n2023-01-04 02:15:49,178 - mmcls - INFO - Epoch [86][200/5005]\tlr: 1.000e-03, eta: 1:49:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3424\n2023-01-04 02:15:57,971 - mmcls - INFO - Epoch [86][300/5005]\tlr: 1.000e-03, eta: 1:49:43, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3585\n2023-01-04 02:16:06,626 - mmcls - INFO - Epoch [86][400/5005]\tlr: 1.000e-03, eta: 1:49:34, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3684\n2023-01-04 02:16:15,495 - mmcls - INFO - Epoch [86][500/5005]\tlr: 1.000e-03, eta: 1:49:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3693\n2023-01-04 02:16:24,119 - mmcls - INFO - Epoch [86][600/5005]\tlr: 1.000e-03, eta: 1:49:17, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3675\n2023-01-04 02:16:33,195 - mmcls - INFO - Epoch [86][700/5005]\tlr: 1.000e-03, eta: 1:49:08, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3644\n2023-01-04 02:16:41,881 - mmcls - INFO - Epoch [86][800/5005]\tlr: 1.000e-03, eta: 1:48:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3865\n2023-01-04 02:16:50,319 - mmcls - INFO - Epoch [86][900/5005]\tlr: 1.000e-03, eta: 1:48:50, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3658\n2023-01-04 02:16:58,869 - mmcls - INFO - Epoch [86][1000/5005]\tlr: 1.000e-03, eta: 1:48:41, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3556\n2023-01-04 02:17:07,410 - mmcls - INFO - Epoch [86][1100/5005]\tlr: 1.000e-03, eta: 1:48:33, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3627\n2023-01-04 02:17:15,991 - mmcls - INFO - Epoch [86][1200/5005]\tlr: 1.000e-03, eta: 1:48:24, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3688\n2023-01-04 02:17:25,087 - mmcls - INFO - Epoch [86][1300/5005]\tlr: 1.000e-03, eta: 1:48:15, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3770\n2023-01-04 02:17:33,831 - mmcls - INFO - Epoch [86][1400/5005]\tlr: 1.000e-03, eta: 1:48:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3756\n2023-01-04 02:17:42,597 - mmcls - INFO - Epoch [86][1500/5005]\tlr: 1.000e-03, eta: 1:47:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3636\n2023-01-04 02:17:51,692 - mmcls - INFO - Epoch [86][1600/5005]\tlr: 1.000e-03, eta: 1:47:49, time: 0.091, data_time: 0.026, memory: 1125, loss: 1.3601\n2023-01-04 02:18:00,553 - mmcls - INFO - Epoch [86][1700/5005]\tlr: 1.000e-03, eta: 1:47:40, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3636\n2023-01-04 02:18:09,137 - mmcls - INFO - Epoch [86][1800/5005]\tlr: 1.000e-03, eta: 1:47:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3692\n2023-01-04 02:18:17,745 - mmcls - INFO - Epoch [86][1900/5005]\tlr: 1.000e-03, eta: 1:47:22, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3607\n2023-01-04 02:18:26,709 - mmcls - INFO - Epoch [86][2000/5005]\tlr: 1.000e-03, eta: 1:47:13, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3637\n2023-01-04 02:18:35,476 - mmcls - INFO - Epoch [86][2100/5005]\tlr: 1.000e-03, eta: 1:47:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3638\n2023-01-04 02:18:44,046 - mmcls - INFO - Epoch [86][2200/5005]\tlr: 1.000e-03, eta: 1:46:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3835\n2023-01-04 02:18:52,836 - mmcls - INFO - Epoch [86][2300/5005]\tlr: 1.000e-03, eta: 1:46:47, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.3647\n2023-01-04 02:19:01,423 - mmcls - INFO - Epoch [86][2400/5005]\tlr: 1.000e-03, eta: 1:46:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3691\n2023-01-04 02:19:10,142 - mmcls - INFO - Epoch [86][2500/5005]\tlr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.3750\n2023-01-04 02:19:19,097 - mmcls - INFO - Epoch [86][2600/5005]\tlr: 1.000e-03, eta: 1:46:20, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3780\n2023-01-04 02:19:27,666 - mmcls - INFO - Epoch [86][2700/5005]\tlr: 1.000e-03, eta: 1:46:12, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3690\n2023-01-04 02:19:36,439 - mmcls - INFO - Epoch [86][2800/5005]\tlr: 1.000e-03, eta: 1:46:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3705\n2023-01-04 02:19:45,089 - mmcls - INFO - Epoch [86][2900/5005]\tlr: 1.000e-03, eta: 1:45:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3846\n2023-01-04 02:19:54,134 - mmcls - INFO - Epoch [86][3000/5005]\tlr: 1.000e-03, eta: 1:45:45, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3804\n2023-01-04 02:20:02,805 - mmcls - INFO - Epoch [86][3100/5005]\tlr: 1.000e-03, eta: 1:45:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3843\n2023-01-04 02:20:11,439 - mmcls - INFO - Epoch [86][3200/5005]\tlr: 1.000e-03, eta: 1:45:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3511\n2023-01-04 02:20:20,545 - mmcls - INFO - Epoch [86][3300/5005]\tlr: 1.000e-03, eta: 1:45:19, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3734\n2023-01-04 02:20:29,432 - mmcls - INFO - Epoch [86][3400/5005]\tlr: 1.000e-03, eta: 1:45:10, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3595\n2023-01-04 02:20:37,983 - mmcls - INFO - Epoch [86][3500/5005]\tlr: 1.000e-03, eta: 1:45:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3674\n2023-01-04 02:20:46,605 - mmcls - INFO - Epoch [86][3600/5005]\tlr: 1.000e-03, eta: 1:44:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3650\n2023-01-04 02:20:55,416 - mmcls - INFO - Epoch [86][3700/5005]\tlr: 1.000e-03, eta: 1:44:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3715\n2023-01-04 02:21:04,040 - mmcls - INFO - Epoch [86][3800/5005]\tlr: 1.000e-03, eta: 1:44:35, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3698\n2023-01-04 02:21:13,053 - mmcls - INFO - Epoch [86][3900/5005]\tlr: 1.000e-03, eta: 1:44:26, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3630\n2023-01-04 02:21:21,897 - mmcls - INFO - Epoch [86][4000/5005]\tlr: 1.000e-03, eta: 1:44:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3623\n2023-01-04 02:21:30,812 - mmcls - INFO - Epoch [86][4100/5005]\tlr: 1.000e-03, eta: 1:44:08, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3827\n2023-01-04 02:21:39,726 - mmcls - INFO - Epoch [86][4200/5005]\tlr: 1.000e-03, eta: 1:44:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3594\n2023-01-04 02:21:48,591 - mmcls - INFO - Epoch [86][4300/5005]\tlr: 1.000e-03, eta: 1:43:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3986\n2023-01-04 02:21:57,418 - mmcls - INFO - Epoch [86][4400/5005]\tlr: 1.000e-03, eta: 1:43:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3721\n2023-01-04 02:22:06,376 - mmcls - INFO - Epoch [86][4500/5005]\tlr: 1.000e-03, eta: 1:43:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3322\n2023-01-04 02:22:14,934 - mmcls - INFO - Epoch [86][4600/5005]\tlr: 1.000e-03, eta: 1:43:24, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3681\n2023-01-04 02:22:24,030 - mmcls - INFO - Epoch [86][4700/5005]\tlr: 1.000e-03, eta: 1:43:16, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.3820\n2023-01-04 02:22:32,828 - mmcls - INFO - Epoch [86][4800/5005]\tlr: 1.000e-03, eta: 1:43:07, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3880\n2023-01-04 02:22:41,401 - mmcls - INFO - Epoch [86][4900/5005]\tlr: 1.000e-03, eta: 1:42:58, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3689\n2023-01-04 02:22:50,353 - mmcls - INFO - Epoch [86][5000/5005]\tlr: 1.000e-03, eta: 1:42:49, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3775\n2023-01-04 02:23:12,962 - mmcls - INFO - Epoch(val) [86][196]\taccuracy_top-1: 69.8360, accuracy_top-5: 89.2000\n2023-01-04 02:23:23,981 - mmcls - INFO - Epoch [87][100/5005]\tlr: 1.000e-03, eta: 1:42:40, time: 0.110, data_time: 0.032, memory: 1125, loss: 1.3689\n2023-01-04 02:23:32,706 - mmcls - INFO - Epoch [87][200/5005]\tlr: 1.000e-03, eta: 1:42:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3607\n2023-01-04 02:23:41,573 - mmcls - INFO - Epoch [87][300/5005]\tlr: 1.000e-03, eta: 1:42:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3574\n2023-01-04 02:23:50,345 - mmcls - INFO - Epoch [87][400/5005]\tlr: 1.000e-03, eta: 1:42:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3552\n2023-01-04 02:23:59,097 - mmcls - INFO - Epoch [87][500/5005]\tlr: 1.000e-03, eta: 1:42:05, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3297\n2023-01-04 02:24:07,715 - mmcls - INFO - Epoch [87][600/5005]\tlr: 1.000e-03, eta: 1:41:56, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3695\n2023-01-04 02:24:17,115 - mmcls - INFO - Epoch [87][700/5005]\tlr: 1.000e-03, eta: 1:41:47, time: 0.094, data_time: 0.008, memory: 1125, loss: 1.3477\n2023-01-04 02:24:25,758 - mmcls - INFO - Epoch [87][800/5005]\tlr: 1.000e-03, eta: 1:41:39, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3616\n2023-01-04 02:24:34,609 - mmcls - INFO - Epoch [87][900/5005]\tlr: 1.000e-03, eta: 1:41:30, time: 0.089, data_time: 0.042, memory: 1125, loss: 1.3479\n2023-01-04 02:24:43,330 - mmcls - INFO - Epoch [87][1000/5005]\tlr: 1.000e-03, eta: 1:41:21, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3695\n2023-01-04 02:24:52,325 - mmcls - INFO - Epoch [87][1100/5005]\tlr: 1.000e-03, eta: 1:41:12, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3887\n2023-01-04 02:25:00,936 - mmcls - INFO - Epoch [87][1200/5005]\tlr: 1.000e-03, eta: 1:41:03, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3655\n2023-01-04 02:25:09,721 - mmcls - INFO - Epoch [87][1300/5005]\tlr: 1.000e-03, eta: 1:40:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3786\n2023-01-04 02:25:18,275 - mmcls - INFO - Epoch [87][1400/5005]\tlr: 1.000e-03, eta: 1:40:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3490\n2023-01-04 02:25:26,914 - mmcls - INFO - Epoch [87][1500/5005]\tlr: 1.000e-03, eta: 1:40:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3487\n2023-01-04 02:25:35,530 - mmcls - INFO - Epoch [87][1600/5005]\tlr: 1.000e-03, eta: 1:40:28, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3437\n2023-01-04 02:25:44,133 - mmcls - INFO - Epoch [87][1700/5005]\tlr: 1.000e-03, eta: 1:40:19, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3574\n2023-01-04 02:25:52,913 - mmcls - INFO - Epoch [87][1800/5005]\tlr: 1.000e-03, eta: 1:40:10, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3582\n2023-01-04 02:26:02,144 - mmcls - INFO - Epoch [87][1900/5005]\tlr: 1.000e-03, eta: 1:40:02, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.3695\n2023-01-04 02:26:10,846 - mmcls - INFO - Epoch [87][2000/5005]\tlr: 1.000e-03, eta: 1:39:53, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3480\n2023-01-04 02:26:19,465 - mmcls - INFO - Epoch [87][2100/5005]\tlr: 1.000e-03, eta: 1:39:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3570\n2023-01-04 02:26:28,304 - mmcls - INFO - Epoch [87][2200/5005]\tlr: 1.000e-03, eta: 1:39:35, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3714\n2023-01-04 02:26:37,135 - mmcls - INFO - Epoch [87][2300/5005]\tlr: 1.000e-03, eta: 1:39:26, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3707\n2023-01-04 02:26:45,936 - mmcls - INFO - Epoch [87][2400/5005]\tlr: 1.000e-03, eta: 1:39:18, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3774\n2023-01-04 02:26:54,655 - mmcls - INFO - Epoch [87][2500/5005]\tlr: 1.000e-03, eta: 1:39:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3812\n2023-01-04 02:27:03,560 - mmcls - INFO - Epoch [87][2600/5005]\tlr: 1.000e-03, eta: 1:39:00, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3655\n2023-01-04 02:27:12,311 - mmcls - INFO - Epoch [87][2700/5005]\tlr: 1.000e-03, eta: 1:38:51, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3617\n2023-01-04 02:27:20,963 - mmcls - INFO - Epoch [87][2800/5005]\tlr: 1.000e-03, eta: 1:38:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3769\n2023-01-04 02:27:29,486 - mmcls - INFO - Epoch [87][2900/5005]\tlr: 1.000e-03, eta: 1:38:34, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3579\n2023-01-04 02:27:38,100 - mmcls - INFO - Epoch [87][3000/5005]\tlr: 1.000e-03, eta: 1:38:25, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3637\n2023-01-04 02:27:46,731 - mmcls - INFO - Epoch [87][3100/5005]\tlr: 1.000e-03, eta: 1:38:16, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.3800\n2023-01-04 02:27:55,349 - mmcls - INFO - Epoch [87][3200/5005]\tlr: 1.000e-03, eta: 1:38:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3629\n2023-01-04 02:28:03,829 - mmcls - INFO - Epoch [87][3300/5005]\tlr: 1.000e-03, eta: 1:37:58, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.3810\n2023-01-04 02:28:12,576 - mmcls - INFO - Epoch [87][3400/5005]\tlr: 1.000e-03, eta: 1:37:49, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3633\n2023-01-04 02:28:21,371 - mmcls - INFO - Epoch [87][3500/5005]\tlr: 1.000e-03, eta: 1:37:41, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3813\n2023-01-04 02:28:30,151 - mmcls - INFO - Epoch [87][3600/5005]\tlr: 1.000e-03, eta: 1:37:32, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3615\n2023-01-04 02:28:38,952 - mmcls - INFO - Epoch [87][3700/5005]\tlr: 1.000e-03, eta: 1:37:23, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3767\n2023-01-04 02:28:47,851 - mmcls - INFO - Epoch [87][3800/5005]\tlr: 1.000e-03, eta: 1:37:14, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3563\n2023-01-04 02:28:56,540 - mmcls - INFO - Epoch [87][3900/5005]\tlr: 1.000e-03, eta: 1:37:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3660\n2023-01-04 02:29:05,544 - mmcls - INFO - Epoch [87][4000/5005]\tlr: 1.000e-03, eta: 1:36:57, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3726\n2023-01-04 02:29:14,550 - mmcls - INFO - Epoch [87][4100/5005]\tlr: 1.000e-03, eta: 1:36:48, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.3553\n2023-01-04 02:29:23,456 - mmcls - INFO - Epoch [87][4200/5005]\tlr: 1.000e-03, eta: 1:36:39, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.3707\n2023-01-04 02:29:32,069 - mmcls - INFO - Epoch [87][4300/5005]\tlr: 1.000e-03, eta: 1:36:30, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3525\n2023-01-04 02:29:40,799 - mmcls - INFO - Epoch [87][4400/5005]\tlr: 1.000e-03, eta: 1:36:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3764\n2023-01-04 02:29:49,893 - mmcls - INFO - Epoch [87][4500/5005]\tlr: 1.000e-03, eta: 1:36:13, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3711\n2023-01-04 02:29:59,081 - mmcls - INFO - Epoch [87][4600/5005]\tlr: 1.000e-03, eta: 1:36:04, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3471\n2023-01-04 02:30:08,040 - mmcls - INFO - Epoch [87][4700/5005]\tlr: 1.000e-03, eta: 1:35:55, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.3734\n2023-01-04 02:30:16,947 - mmcls - INFO - Epoch [87][4800/5005]\tlr: 1.000e-03, eta: 1:35:46, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3744\n2023-01-04 02:30:25,770 - mmcls - INFO - Epoch [87][4900/5005]\tlr: 1.000e-03, eta: 1:35:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3825\n2023-01-04 02:30:34,272 - mmcls - INFO - Epoch [87][5000/5005]\tlr: 1.000e-03, eta: 1:35:29, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.3725\n2023-01-04 02:30:56,879 - mmcls - INFO - Epoch(val) [87][196]\taccuracy_top-1: 69.7200, accuracy_top-5: 89.1920\n2023-01-04 02:31:07,713 - mmcls - INFO - Epoch [88][100/5005]\tlr: 1.000e-03, eta: 1:35:20, time: 0.108, data_time: 0.056, memory: 1125, loss: 1.3610\n2023-01-04 02:31:16,169 - mmcls - INFO - Epoch [88][200/5005]\tlr: 1.000e-03, eta: 1:35:11, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3805\n2023-01-04 02:31:25,241 - mmcls - INFO - Epoch [88][300/5005]\tlr: 1.000e-03, eta: 1:35:02, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3512\n2023-01-04 02:31:33,729 - mmcls - INFO - Epoch [88][400/5005]\tlr: 1.000e-03, eta: 1:34:53, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3382\n2023-01-04 02:31:42,268 - mmcls - INFO - Epoch [88][500/5005]\tlr: 1.000e-03, eta: 1:34:44, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3568\n2023-01-04 02:31:50,973 - mmcls - INFO - Epoch [88][600/5005]\tlr: 1.000e-03, eta: 1:34:35, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3558\n2023-01-04 02:31:59,610 - mmcls - INFO - Epoch [88][700/5005]\tlr: 1.000e-03, eta: 1:34:27, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3631\n2023-01-04 02:32:08,258 - mmcls - INFO - Epoch [88][800/5005]\tlr: 1.000e-03, eta: 1:34:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3448\n2023-01-04 02:32:16,977 - mmcls - INFO - Epoch [88][900/5005]\tlr: 1.000e-03, eta: 1:34:09, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3453\n2023-01-04 02:32:25,635 - mmcls - INFO - Epoch [88][1000/5005]\tlr: 1.000e-03, eta: 1:34:00, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3513\n2023-01-04 02:32:34,854 - mmcls - INFO - Epoch [88][1100/5005]\tlr: 1.000e-03, eta: 1:33:51, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.3654\n2023-01-04 02:32:43,434 - mmcls - INFO - Epoch [88][1200/5005]\tlr: 1.000e-03, eta: 1:33:43, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3632\n2023-01-04 02:32:52,279 - mmcls - INFO - Epoch [88][1300/5005]\tlr: 1.000e-03, eta: 1:33:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3785\n2023-01-04 02:33:00,749 - mmcls - INFO - Epoch [88][1400/5005]\tlr: 1.000e-03, eta: 1:33:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3584\n2023-01-04 02:33:09,477 - mmcls - INFO - Epoch [88][1500/5005]\tlr: 1.000e-03, eta: 1:33:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3614\n2023-01-04 02:33:18,515 - mmcls - INFO - Epoch [88][1600/5005]\tlr: 1.000e-03, eta: 1:33:07, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3747\n2023-01-04 02:33:26,995 - mmcls - INFO - Epoch [88][1700/5005]\tlr: 1.000e-03, eta: 1:32:58, time: 0.085, data_time: 0.027, memory: 1125, loss: 1.3473\n2023-01-04 02:33:36,088 - mmcls - INFO - Epoch [88][1800/5005]\tlr: 1.000e-03, eta: 1:32:50, time: 0.091, data_time: 0.039, memory: 1125, loss: 1.3528\n2023-01-04 02:33:44,989 - mmcls - INFO - Epoch [88][1900/5005]\tlr: 1.000e-03, eta: 1:32:41, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3710\n2023-01-04 02:33:53,592 - mmcls - INFO - Epoch [88][2000/5005]\tlr: 1.000e-03, eta: 1:32:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3544\n2023-01-04 02:34:02,266 - mmcls - INFO - Epoch [88][2100/5005]\tlr: 1.000e-03, eta: 1:32:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3602\n2023-01-04 02:34:11,013 - mmcls - INFO - Epoch [88][2200/5005]\tlr: 1.000e-03, eta: 1:32:14, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3541\n2023-01-04 02:34:19,768 - mmcls - INFO - Epoch [88][2300/5005]\tlr: 1.000e-03, eta: 1:32:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3760\n2023-01-04 02:34:28,549 - mmcls - INFO - Epoch [88][2400/5005]\tlr: 1.000e-03, eta: 1:31:57, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3407\n2023-01-04 02:34:37,791 - mmcls - INFO - Epoch [88][2500/5005]\tlr: 1.000e-03, eta: 1:31:48, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3942\n2023-01-04 02:34:46,187 - mmcls - INFO - Epoch [88][2600/5005]\tlr: 1.000e-03, eta: 1:31:39, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3501\n2023-01-04 02:34:54,923 - mmcls - INFO - Epoch [88][2700/5005]\tlr: 1.000e-03, eta: 1:31:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3578\n2023-01-04 02:35:03,666 - mmcls - INFO - Epoch [88][2800/5005]\tlr: 1.000e-03, eta: 1:31:22, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3617\n2023-01-04 02:35:12,799 - mmcls - INFO - Epoch [88][2900/5005]\tlr: 1.000e-03, eta: 1:31:13, time: 0.091, data_time: 0.026, memory: 1125, loss: 1.3721\n2023-01-04 02:35:21,476 - mmcls - INFO - Epoch [88][3000/5005]\tlr: 1.000e-03, eta: 1:31:04, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3557\n2023-01-04 02:35:30,525 - mmcls - INFO - Epoch [88][3100/5005]\tlr: 1.000e-03, eta: 1:30:55, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3649\n2023-01-04 02:35:38,981 - mmcls - INFO - Epoch [88][3200/5005]\tlr: 1.000e-03, eta: 1:30:46, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3617\n2023-01-04 02:35:47,977 - mmcls - INFO - Epoch [88][3300/5005]\tlr: 1.000e-03, eta: 1:30:38, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3771\n2023-01-04 02:35:56,619 - mmcls - INFO - Epoch [88][3400/5005]\tlr: 1.000e-03, eta: 1:30:29, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3696\n2023-01-04 02:36:05,171 - mmcls - INFO - Epoch [88][3500/5005]\tlr: 1.000e-03, eta: 1:30:20, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3704\n2023-01-04 02:36:13,905 - mmcls - INFO - Epoch [88][3600/5005]\tlr: 1.000e-03, eta: 1:30:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3641\n2023-01-04 02:36:22,682 - mmcls - INFO - Epoch [88][3700/5005]\tlr: 1.000e-03, eta: 1:30:02, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.3815\n2023-01-04 02:36:31,580 - mmcls - INFO - Epoch [88][3800/5005]\tlr: 1.000e-03, eta: 1:29:54, time: 0.089, data_time: 0.050, memory: 1125, loss: 1.3651\n2023-01-04 02:36:40,700 - mmcls - INFO - Epoch [88][3900/5005]\tlr: 1.000e-03, eta: 1:29:45, time: 0.091, data_time: 0.025, memory: 1125, loss: 1.3450\n2023-01-04 02:36:49,673 - mmcls - INFO - Epoch [88][4000/5005]\tlr: 1.000e-03, eta: 1:29:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3540\n2023-01-04 02:36:58,363 - mmcls - INFO - Epoch [88][4100/5005]\tlr: 1.000e-03, eta: 1:29:27, time: 0.087, data_time: 0.034, memory: 1125, loss: 1.3710\n2023-01-04 02:37:07,099 - mmcls - INFO - Epoch [88][4200/5005]\tlr: 1.000e-03, eta: 1:29:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3688\n2023-01-04 02:37:15,923 - mmcls - INFO - Epoch [88][4300/5005]\tlr: 1.000e-03, eta: 1:29:10, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3702\n2023-01-04 02:37:24,773 - mmcls - INFO - Epoch [88][4400/5005]\tlr: 1.000e-03, eta: 1:29:01, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3610\n2023-01-04 02:37:33,528 - mmcls - INFO - Epoch [88][4500/5005]\tlr: 1.000e-03, eta: 1:28:52, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3775\n2023-01-04 02:37:42,117 - mmcls - INFO - Epoch [88][4600/5005]\tlr: 1.000e-03, eta: 1:28:43, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3702\n2023-01-04 02:37:51,194 - mmcls - INFO - Epoch [88][4700/5005]\tlr: 1.000e-03, eta: 1:28:34, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.3588\n2023-01-04 02:37:59,785 - mmcls - INFO - Epoch [88][4800/5005]\tlr: 1.000e-03, eta: 1:28:26, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3849\n2023-01-04 02:38:08,567 - mmcls - INFO - Epoch [88][4900/5005]\tlr: 1.000e-03, eta: 1:28:17, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3521\n2023-01-04 02:38:17,286 - mmcls - INFO - Epoch [88][5000/5005]\tlr: 1.000e-03, eta: 1:28:08, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3688\n2023-01-04 02:38:39,770 - mmcls - INFO - Epoch(val) [88][196]\taccuracy_top-1: 69.6920, accuracy_top-5: 89.1660\n2023-01-04 02:38:50,565 - mmcls - INFO - Epoch [89][100/5005]\tlr: 1.000e-03, eta: 1:27:59, time: 0.108, data_time: 0.031, memory: 1125, loss: 1.3466\n2023-01-04 02:38:59,286 - mmcls - INFO - Epoch [89][200/5005]\tlr: 1.000e-03, eta: 1:27:50, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3503\n2023-01-04 02:39:08,085 - mmcls - INFO - Epoch [89][300/5005]\tlr: 1.000e-03, eta: 1:27:41, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3440\n2023-01-04 02:39:16,885 - mmcls - INFO - Epoch [89][400/5005]\tlr: 1.000e-03, eta: 1:27:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3479\n2023-01-04 02:39:25,601 - mmcls - INFO - Epoch [89][500/5005]\tlr: 1.000e-03, eta: 1:27:24, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3553\n2023-01-04 02:39:34,467 - mmcls - INFO - Epoch [89][600/5005]\tlr: 1.000e-03, eta: 1:27:15, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3516\n2023-01-04 02:39:43,348 - mmcls - INFO - Epoch [89][700/5005]\tlr: 1.000e-03, eta: 1:27:06, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3621\n2023-01-04 02:39:52,109 - mmcls - INFO - Epoch [89][800/5005]\tlr: 1.000e-03, eta: 1:26:57, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3453\n2023-01-04 02:40:00,865 - mmcls - INFO - Epoch [89][900/5005]\tlr: 1.000e-03, eta: 1:26:48, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3580\n2023-01-04 02:40:09,278 - mmcls - INFO - Epoch [89][1000/5005]\tlr: 1.000e-03, eta: 1:26:40, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3514\n2023-01-04 02:40:17,778 - mmcls - INFO - Epoch [89][1100/5005]\tlr: 1.000e-03, eta: 1:26:31, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.3913\n2023-01-04 02:40:26,509 - mmcls - INFO - Epoch [89][1200/5005]\tlr: 1.000e-03, eta: 1:26:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3511\n2023-01-04 02:40:35,181 - mmcls - INFO - Epoch [89][1300/5005]\tlr: 1.000e-03, eta: 1:26:13, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3562\n2023-01-04 02:40:43,852 - mmcls - INFO - Epoch [89][1400/5005]\tlr: 1.000e-03, eta: 1:26:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3814\n2023-01-04 02:40:52,441 - mmcls - INFO - Epoch [89][1500/5005]\tlr: 1.000e-03, eta: 1:25:55, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.3513\n2023-01-04 02:41:01,182 - mmcls - INFO - Epoch [89][1600/5005]\tlr: 1.000e-03, eta: 1:25:47, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.3676\n2023-01-04 02:41:09,923 - mmcls - INFO - Epoch [89][1700/5005]\tlr: 1.000e-03, eta: 1:25:38, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3557\n2023-01-04 02:41:18,526 - mmcls - INFO - Epoch [89][1800/5005]\tlr: 1.000e-03, eta: 1:25:29, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3671\n2023-01-04 02:41:27,304 - mmcls - INFO - Epoch [89][1900/5005]\tlr: 1.000e-03, eta: 1:25:20, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3487\n2023-01-04 02:41:36,089 - mmcls - INFO - Epoch [89][2000/5005]\tlr: 1.000e-03, eta: 1:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3659\n2023-01-04 02:41:44,829 - mmcls - INFO - Epoch [89][2100/5005]\tlr: 1.000e-03, eta: 1:25:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3763\n2023-01-04 02:41:53,457 - mmcls - INFO - Epoch [89][2200/5005]\tlr: 1.000e-03, eta: 1:24:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3500\n2023-01-04 02:42:02,447 - mmcls - INFO - Epoch [89][2300/5005]\tlr: 1.000e-03, eta: 1:24:45, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3691\n2023-01-04 02:42:11,129 - mmcls - INFO - Epoch [89][2400/5005]\tlr: 1.000e-03, eta: 1:24:36, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3663\n2023-01-04 02:42:20,049 - mmcls - INFO - Epoch [89][2500/5005]\tlr: 1.000e-03, eta: 1:24:27, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3783\n2023-01-04 02:42:28,898 - mmcls - INFO - Epoch [89][2600/5005]\tlr: 1.000e-03, eta: 1:24:18, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3686\n2023-01-04 02:42:37,704 - mmcls - INFO - Epoch [89][2700/5005]\tlr: 1.000e-03, eta: 1:24:10, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.3645\n2023-01-04 02:42:46,381 - mmcls - INFO - Epoch [89][2800/5005]\tlr: 1.000e-03, eta: 1:24:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3747\n2023-01-04 02:42:55,113 - mmcls - INFO - Epoch [89][2900/5005]\tlr: 1.000e-03, eta: 1:23:52, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3635\n2023-01-04 02:43:04,122 - mmcls - INFO - Epoch [89][3000/5005]\tlr: 1.000e-03, eta: 1:23:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3712\n2023-01-04 02:43:13,009 - mmcls - INFO - Epoch [89][3100/5005]\tlr: 1.000e-03, eta: 1:23:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3485\n2023-01-04 02:43:21,849 - mmcls - INFO - Epoch [89][3200/5005]\tlr: 1.000e-03, eta: 1:23:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3721\n2023-01-04 02:43:30,299 - mmcls - INFO - Epoch [89][3300/5005]\tlr: 1.000e-03, eta: 1:23:17, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.3702\n2023-01-04 02:43:39,085 - mmcls - INFO - Epoch [89][3400/5005]\tlr: 1.000e-03, eta: 1:23:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3494\n2023-01-04 02:43:47,661 - mmcls - INFO - Epoch [89][3500/5005]\tlr: 1.000e-03, eta: 1:22:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3544\n2023-01-04 02:43:56,202 - mmcls - INFO - Epoch [89][3600/5005]\tlr: 1.000e-03, eta: 1:22:50, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3827\n2023-01-04 02:44:04,891 - mmcls - INFO - Epoch [89][3700/5005]\tlr: 1.000e-03, eta: 1:22:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3702\n2023-01-04 02:44:13,583 - mmcls - INFO - Epoch [89][3800/5005]\tlr: 1.000e-03, eta: 1:22:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3632\n2023-01-04 02:44:22,629 - mmcls - INFO - Epoch [89][3900/5005]\tlr: 1.000e-03, eta: 1:22:24, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3844\n2023-01-04 02:44:31,353 - mmcls - INFO - Epoch [89][4000/5005]\tlr: 1.000e-03, eta: 1:22:15, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3626\n2023-01-04 02:44:40,266 - mmcls - INFO - Epoch [89][4100/5005]\tlr: 1.000e-03, eta: 1:22:06, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3869\n2023-01-04 02:44:49,006 - mmcls - INFO - Epoch [89][4200/5005]\tlr: 1.000e-03, eta: 1:21:58, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3476\n2023-01-04 02:44:57,803 - mmcls - INFO - Epoch [89][4300/5005]\tlr: 1.000e-03, eta: 1:21:49, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3664\n2023-01-04 02:45:06,341 - mmcls - INFO - Epoch [89][4400/5005]\tlr: 1.000e-03, eta: 1:21:40, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3587\n2023-01-04 02:45:15,202 - mmcls - INFO - Epoch [89][4500/5005]\tlr: 1.000e-03, eta: 1:21:31, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3681\n2023-01-04 02:45:23,989 - mmcls - INFO - Epoch [89][4600/5005]\tlr: 1.000e-03, eta: 1:21:22, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3709\n2023-01-04 02:45:32,460 - mmcls - INFO - Epoch [89][4700/5005]\tlr: 1.000e-03, eta: 1:21:13, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.3434\n2023-01-04 02:45:41,045 - mmcls - INFO - Epoch [89][4800/5005]\tlr: 1.000e-03, eta: 1:21:05, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3637\n2023-01-04 02:45:50,068 - mmcls - INFO - Epoch [89][4900/5005]\tlr: 1.000e-03, eta: 1:20:56, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.3710\n2023-01-04 02:45:58,715 - mmcls - INFO - Epoch [89][5000/5005]\tlr: 1.000e-03, eta: 1:20:47, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3616\n2023-01-04 02:46:21,311 - mmcls - INFO - Epoch(val) [89][196]\taccuracy_top-1: 69.7160, accuracy_top-5: 89.2380\n2023-01-04 02:46:32,485 - mmcls - INFO - Epoch [90][100/5005]\tlr: 1.000e-03, eta: 1:20:38, time: 0.111, data_time: 0.074, memory: 1125, loss: 1.3506\n2023-01-04 02:46:41,011 - mmcls - INFO - Epoch [90][200/5005]\tlr: 1.000e-03, eta: 1:20:29, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3594\n2023-01-04 02:46:50,130 - mmcls - INFO - Epoch [90][300/5005]\tlr: 1.000e-03, eta: 1:20:20, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3448\n2023-01-04 02:46:58,566 - mmcls - INFO - Epoch [90][400/5005]\tlr: 1.000e-03, eta: 1:20:12, time: 0.084, data_time: 0.023, memory: 1125, loss: 1.3390\n2023-01-04 02:47:07,275 - mmcls - INFO - Epoch [90][500/5005]\tlr: 1.000e-03, eta: 1:20:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3497\n2023-01-04 02:47:15,821 - mmcls - INFO - Epoch [90][600/5005]\tlr: 1.000e-03, eta: 1:19:54, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3466\n2023-01-04 02:47:24,533 - mmcls - INFO - Epoch [90][700/5005]\tlr: 1.000e-03, eta: 1:19:45, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3620\n2023-01-04 02:47:33,599 - mmcls - INFO - Epoch [90][800/5005]\tlr: 1.000e-03, eta: 1:19:36, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.3328\n2023-01-04 02:47:42,553 - mmcls - INFO - Epoch [90][900/5005]\tlr: 1.000e-03, eta: 1:19:28, time: 0.090, data_time: 0.041, memory: 1125, loss: 1.3483\n2023-01-04 02:47:51,169 - mmcls - INFO - Epoch [90][1000/5005]\tlr: 1.000e-03, eta: 1:19:19, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.3641\n2023-01-04 02:47:59,807 - mmcls - INFO - Epoch [90][1100/5005]\tlr: 1.000e-03, eta: 1:19:10, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3598\n2023-01-04 02:48:08,653 - mmcls - INFO - Epoch [90][1200/5005]\tlr: 1.000e-03, eta: 1:19:01, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3683\n2023-01-04 02:48:17,259 - mmcls - INFO - Epoch [90][1300/5005]\tlr: 1.000e-03, eta: 1:18:52, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3414\n2023-01-04 02:48:26,392 - mmcls - INFO - Epoch [90][1400/5005]\tlr: 1.000e-03, eta: 1:18:43, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.3635\n2023-01-04 02:48:35,018 - mmcls - INFO - Epoch [90][1500/5005]\tlr: 1.000e-03, eta: 1:18:35, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3554\n2023-01-04 02:48:43,699 - mmcls - INFO - Epoch [90][1600/5005]\tlr: 1.000e-03, eta: 1:18:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3684\n2023-01-04 02:48:52,185 - mmcls - INFO - Epoch [90][1700/5005]\tlr: 1.000e-03, eta: 1:18:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3756\n2023-01-04 02:49:01,238 - mmcls - INFO - Epoch [90][1800/5005]\tlr: 1.000e-03, eta: 1:18:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3421\n2023-01-04 02:49:09,753 - mmcls - INFO - Epoch [90][1900/5005]\tlr: 1.000e-03, eta: 1:17:59, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3606\n2023-01-04 02:49:18,364 - mmcls - INFO - Epoch [90][2000/5005]\tlr: 1.000e-03, eta: 1:17:51, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3551\n2023-01-04 02:49:26,946 - mmcls - INFO - Epoch [90][2100/5005]\tlr: 1.000e-03, eta: 1:17:42, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3862\n2023-01-04 02:49:35,379 - mmcls - INFO - Epoch [90][2200/5005]\tlr: 1.000e-03, eta: 1:17:33, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.3724\n2023-01-04 02:49:44,135 - mmcls - INFO - Epoch [90][2300/5005]\tlr: 1.000e-03, eta: 1:17:24, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3622\n2023-01-04 02:49:52,841 - mmcls - INFO - Epoch [90][2400/5005]\tlr: 1.000e-03, eta: 1:17:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3312\n2023-01-04 02:50:01,528 - mmcls - INFO - Epoch [90][2500/5005]\tlr: 1.000e-03, eta: 1:17:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3387\n2023-01-04 02:50:10,450 - mmcls - INFO - Epoch [90][2600/5005]\tlr: 1.000e-03, eta: 1:16:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3426\n2023-01-04 02:50:19,265 - mmcls - INFO - Epoch [90][2700/5005]\tlr: 1.000e-03, eta: 1:16:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3745\n2023-01-04 02:50:27,941 - mmcls - INFO - Epoch [90][2800/5005]\tlr: 1.000e-03, eta: 1:16:40, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3321\n2023-01-04 02:50:36,747 - mmcls - INFO - Epoch [90][2900/5005]\tlr: 1.000e-03, eta: 1:16:31, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.3615\n2023-01-04 02:50:45,535 - mmcls - INFO - Epoch [90][3000/5005]\tlr: 1.000e-03, eta: 1:16:22, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.3410\n2023-01-04 02:50:54,241 - mmcls - INFO - Epoch [90][3100/5005]\tlr: 1.000e-03, eta: 1:16:14, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3512\n2023-01-04 02:51:03,088 - mmcls - INFO - Epoch [90][3200/5005]\tlr: 1.000e-03, eta: 1:16:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3544\n2023-01-04 02:51:11,994 - mmcls - INFO - Epoch [90][3300/5005]\tlr: 1.000e-03, eta: 1:15:56, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3777\n2023-01-04 02:51:20,918 - mmcls - INFO - Epoch [90][3400/5005]\tlr: 1.000e-03, eta: 1:15:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3654\n2023-01-04 02:51:29,579 - mmcls - INFO - Epoch [90][3500/5005]\tlr: 1.000e-03, eta: 1:15:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3617\n2023-01-04 02:51:38,278 - mmcls - INFO - Epoch [90][3600/5005]\tlr: 1.000e-03, eta: 1:15:30, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3781\n2023-01-04 02:51:47,306 - mmcls - INFO - Epoch [90][3700/5005]\tlr: 1.000e-03, eta: 1:15:21, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.3574\n2023-01-04 02:51:55,948 - mmcls - INFO - Epoch [90][3800/5005]\tlr: 1.000e-03, eta: 1:15:12, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3542\n2023-01-04 02:52:04,828 - mmcls - INFO - Epoch [90][3900/5005]\tlr: 1.000e-03, eta: 1:15:03, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.3650\n2023-01-04 02:52:13,397 - mmcls - INFO - Epoch [90][4000/5005]\tlr: 1.000e-03, eta: 1:14:54, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3532\n2023-01-04 02:52:21,880 - mmcls - INFO - Epoch [90][4100/5005]\tlr: 1.000e-03, eta: 1:14:45, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3476\n2023-01-04 02:52:30,592 - mmcls - INFO - Epoch [90][4200/5005]\tlr: 1.000e-03, eta: 1:14:37, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3771\n2023-01-04 02:52:39,276 - mmcls - INFO - Epoch [90][4300/5005]\tlr: 1.000e-03, eta: 1:14:28, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3811\n2023-01-04 02:52:47,870 - mmcls - INFO - Epoch [90][4400/5005]\tlr: 1.000e-03, eta: 1:14:19, time: 0.086, data_time: 0.039, memory: 1125, loss: 1.3683\n2023-01-04 02:52:56,922 - mmcls - INFO - Epoch [90][4500/5005]\tlr: 1.000e-03, eta: 1:14:10, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3696\n2023-01-04 02:53:05,502 - mmcls - INFO - Epoch [90][4600/5005]\tlr: 1.000e-03, eta: 1:14:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3616\n2023-01-04 02:53:13,987 - mmcls - INFO - Epoch [90][4700/5005]\tlr: 1.000e-03, eta: 1:13:53, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3836\n2023-01-04 02:53:22,455 - mmcls - INFO - Epoch [90][4800/5005]\tlr: 1.000e-03, eta: 1:13:44, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.3749\n2023-01-04 02:53:31,245 - mmcls - INFO - Epoch [90][4900/5005]\tlr: 1.000e-03, eta: 1:13:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3797\n2023-01-04 02:53:40,217 - mmcls - INFO - Epoch [90][5000/5005]\tlr: 1.000e-03, eta: 1:13:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3772\n2023-01-04 02:54:02,900 - mmcls - INFO - Epoch(val) [90][196]\taccuracy_top-1: 69.7560, accuracy_top-5: 89.0840\n2023-01-04 02:54:13,938 - mmcls - INFO - Epoch [91][100/5005]\tlr: 1.000e-04, eta: 1:13:17, time: 0.110, data_time: 0.051, memory: 1125, loss: 1.3510\n2023-01-04 02:54:22,537 - mmcls - INFO - Epoch [91][200/5005]\tlr: 1.000e-04, eta: 1:13:08, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3424\n2023-01-04 02:54:31,536 - mmcls - INFO - Epoch [91][300/5005]\tlr: 1.000e-04, eta: 1:13:00, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3293\n2023-01-04 02:54:40,720 - mmcls - INFO - Epoch [91][400/5005]\tlr: 1.000e-04, eta: 1:12:51, time: 0.092, data_time: 0.020, memory: 1125, loss: 1.3266\n2023-01-04 02:54:49,599 - mmcls - INFO - Epoch [91][500/5005]\tlr: 1.000e-04, eta: 1:12:42, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3406\n2023-01-04 02:54:58,241 - mmcls - INFO - Epoch [91][600/5005]\tlr: 1.000e-04, eta: 1:12:33, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3309\n2023-01-04 02:55:07,040 - mmcls - INFO - Epoch [91][700/5005]\tlr: 1.000e-04, eta: 1:12:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3285\n2023-01-04 02:55:15,682 - mmcls - INFO - Epoch [91][800/5005]\tlr: 1.000e-04, eta: 1:12:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3358\n2023-01-04 02:55:24,507 - mmcls - INFO - Epoch [91][900/5005]\tlr: 1.000e-04, eta: 1:12:07, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3338\n2023-01-04 02:55:33,050 - mmcls - INFO - Epoch [91][1000/5005]\tlr: 1.000e-04, eta: 1:11:58, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3547\n2023-01-04 02:55:42,159 - mmcls - INFO - Epoch [91][1100/5005]\tlr: 1.000e-04, eta: 1:11:49, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3172\n2023-01-04 02:55:50,605 - mmcls - INFO - Epoch [91][1200/5005]\tlr: 1.000e-04, eta: 1:11:40, time: 0.084, data_time: 0.020, memory: 1125, loss: 1.3313\n2023-01-04 02:55:59,432 - mmcls - INFO - Epoch [91][1300/5005]\tlr: 1.000e-04, eta: 1:11:31, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3241\n2023-01-04 02:56:08,937 - mmcls - INFO - Epoch [91][1400/5005]\tlr: 1.000e-04, eta: 1:11:23, time: 0.095, data_time: 0.016, memory: 1125, loss: 1.3192\n2023-01-04 02:56:17,926 - mmcls - INFO - Epoch [91][1500/5005]\tlr: 1.000e-04, eta: 1:11:14, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3029\n2023-01-04 02:56:26,738 - mmcls - INFO - Epoch [91][1600/5005]\tlr: 1.000e-04, eta: 1:11:05, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.3281\n2023-01-04 02:56:35,554 - mmcls - INFO - Epoch [91][1700/5005]\tlr: 1.000e-04, eta: 1:10:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3113\n2023-01-04 02:56:44,201 - mmcls - INFO - Epoch [91][1800/5005]\tlr: 1.000e-04, eta: 1:10:48, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3450\n2023-01-04 02:56:52,674 - mmcls - INFO - Epoch [91][1900/5005]\tlr: 1.000e-04, eta: 1:10:39, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3267\n2023-01-04 02:57:01,560 - mmcls - INFO - Epoch [91][2000/5005]\tlr: 1.000e-04, eta: 1:10:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3214\n2023-01-04 02:57:09,974 - mmcls - INFO - Epoch [91][2100/5005]\tlr: 1.000e-04, eta: 1:10:21, time: 0.084, data_time: 0.032, memory: 1125, loss: 1.3180\n2023-01-04 02:57:18,591 - mmcls - INFO - Epoch [91][2200/5005]\tlr: 1.000e-04, eta: 1:10:12, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3232\n2023-01-04 02:57:27,474 - mmcls - INFO - Epoch [91][2300/5005]\tlr: 1.000e-04, eta: 1:10:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3390\n2023-01-04 02:57:36,719 - mmcls - INFO - Epoch [91][2400/5005]\tlr: 1.000e-04, eta: 1:09:55, time: 0.093, data_time: 0.009, memory: 1125, loss: 1.3170\n2023-01-04 02:57:45,452 - mmcls - INFO - Epoch [91][2500/5005]\tlr: 1.000e-04, eta: 1:09:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3409\n2023-01-04 02:57:54,249 - mmcls - INFO - Epoch [91][2600/5005]\tlr: 1.000e-04, eta: 1:09:37, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3247\n2023-01-04 02:58:03,149 - mmcls - INFO - Epoch [91][2700/5005]\tlr: 1.000e-04, eta: 1:09:28, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3314\n2023-01-04 02:58:12,027 - mmcls - INFO - Epoch [91][2800/5005]\tlr: 1.000e-04, eta: 1:09:19, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3233\n2023-01-04 02:58:20,850 - mmcls - INFO - Epoch [91][2900/5005]\tlr: 1.000e-04, eta: 1:09:11, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3323\n2023-01-04 02:58:29,539 - mmcls - INFO - Epoch [91][3000/5005]\tlr: 1.000e-04, eta: 1:09:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3169\n2023-01-04 02:58:38,268 - mmcls - INFO - Epoch [91][3100/5005]\tlr: 1.000e-04, eta: 1:08:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3006\n2023-01-04 02:58:47,095 - mmcls - INFO - Epoch [91][3200/5005]\tlr: 1.000e-04, eta: 1:08:44, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3239\n2023-01-04 02:58:55,720 - mmcls - INFO - Epoch [91][3300/5005]\tlr: 1.000e-04, eta: 1:08:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3284\n2023-01-04 02:59:04,433 - mmcls - INFO - Epoch [91][3400/5005]\tlr: 1.000e-04, eta: 1:08:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3380\n2023-01-04 02:59:13,250 - mmcls - INFO - Epoch [91][3500/5005]\tlr: 1.000e-04, eta: 1:08:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3272\n2023-01-04 02:59:22,078 - mmcls - INFO - Epoch [91][3600/5005]\tlr: 1.000e-04, eta: 1:08:09, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.3140\n2023-01-04 02:59:30,825 - mmcls - INFO - Epoch [91][3700/5005]\tlr: 1.000e-04, eta: 1:08:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3304\n2023-01-04 02:59:39,606 - mmcls - INFO - Epoch [91][3800/5005]\tlr: 1.000e-04, eta: 1:07:51, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3227\n2023-01-04 02:59:48,191 - mmcls - INFO - Epoch [91][3900/5005]\tlr: 1.000e-04, eta: 1:07:43, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3203\n2023-01-04 02:59:56,938 - mmcls - INFO - Epoch [91][4000/5005]\tlr: 1.000e-04, eta: 1:07:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3438\n2023-01-04 03:00:05,470 - mmcls - INFO - Epoch [91][4100/5005]\tlr: 1.000e-04, eta: 1:07:25, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3362\n2023-01-04 03:00:14,283 - mmcls - INFO - Epoch [91][4200/5005]\tlr: 1.000e-04, eta: 1:07:16, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3201\n2023-01-04 03:00:22,841 - mmcls - INFO - Epoch [91][4300/5005]\tlr: 1.000e-04, eta: 1:07:07, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.3154\n2023-01-04 03:00:31,509 - mmcls - INFO - Epoch [91][4400/5005]\tlr: 1.000e-04, eta: 1:06:58, time: 0.087, data_time: 0.041, memory: 1125, loss: 1.3509\n2023-01-04 03:00:40,049 - mmcls - INFO - Epoch [91][4500/5005]\tlr: 1.000e-04, eta: 1:06:50, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.3148\n2023-01-04 03:00:48,843 - mmcls - INFO - Epoch [91][4600/5005]\tlr: 1.000e-04, eta: 1:06:41, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3299\n2023-01-04 03:00:57,679 - mmcls - INFO - Epoch [91][4700/5005]\tlr: 1.000e-04, eta: 1:06:32, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3433\n2023-01-04 03:01:06,531 - mmcls - INFO - Epoch [91][4800/5005]\tlr: 1.000e-04, eta: 1:06:23, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.3444\n2023-01-04 03:01:15,075 - mmcls - INFO - Epoch [91][4900/5005]\tlr: 1.000e-04, eta: 1:06:14, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.3432\n2023-01-04 03:01:23,854 - mmcls - INFO - Epoch [91][5000/5005]\tlr: 1.000e-04, eta: 1:06:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3234\n2023-01-04 03:01:46,438 - mmcls - INFO - Epoch(val) [91][196]\taccuracy_top-1: 70.1380, accuracy_top-5: 89.3860\n2023-01-04 03:01:57,597 - mmcls - INFO - Epoch [92][100/5005]\tlr: 1.000e-04, eta: 1:05:57, time: 0.111, data_time: 0.048, memory: 1125, loss: 1.2971\n2023-01-04 03:02:06,576 - mmcls - INFO - Epoch [92][200/5005]\tlr: 1.000e-04, eta: 1:05:48, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.3255\n2023-01-04 03:02:15,450 - mmcls - INFO - Epoch [92][300/5005]\tlr: 1.000e-04, eta: 1:05:39, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.2997\n2023-01-04 03:02:24,217 - mmcls - INFO - Epoch [92][400/5005]\tlr: 1.000e-04, eta: 1:05:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3173\n2023-01-04 03:02:33,103 - mmcls - INFO - Epoch [92][500/5005]\tlr: 1.000e-04, eta: 1:05:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3423\n2023-01-04 03:02:41,845 - mmcls - INFO - Epoch [92][600/5005]\tlr: 1.000e-04, eta: 1:05:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3260\n2023-01-04 03:02:50,740 - mmcls - INFO - Epoch [92][700/5005]\tlr: 1.000e-04, eta: 1:05:04, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3182\n2023-01-04 03:02:59,840 - mmcls - INFO - Epoch [92][800/5005]\tlr: 1.000e-04, eta: 1:04:55, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3407\n2023-01-04 03:03:08,669 - mmcls - INFO - Epoch [92][900/5005]\tlr: 1.000e-04, eta: 1:04:46, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3268\n2023-01-04 03:03:17,349 - mmcls - INFO - Epoch [92][1000/5005]\tlr: 1.000e-04, eta: 1:04:37, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3153\n2023-01-04 03:03:26,138 - mmcls - INFO - Epoch [92][1100/5005]\tlr: 1.000e-04, eta: 1:04:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3216\n2023-01-04 03:03:34,819 - mmcls - INFO - Epoch [92][1200/5005]\tlr: 1.000e-04, eta: 1:04:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3233\n2023-01-04 03:03:44,028 - mmcls - INFO - Epoch [92][1300/5005]\tlr: 1.000e-04, eta: 1:04:11, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3332\n2023-01-04 03:03:52,692 - mmcls - INFO - Epoch [92][1400/5005]\tlr: 1.000e-04, eta: 1:04:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3392\n2023-01-04 03:04:01,266 - mmcls - INFO - Epoch [92][1500/5005]\tlr: 1.000e-04, eta: 1:03:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3152\n2023-01-04 03:04:10,062 - mmcls - INFO - Epoch [92][1600/5005]\tlr: 1.000e-04, eta: 1:03:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3113\n2023-01-04 03:04:18,716 - mmcls - INFO - Epoch [92][1700/5005]\tlr: 1.000e-04, eta: 1:03:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3237\n2023-01-04 03:04:27,609 - mmcls - INFO - Epoch [92][1800/5005]\tlr: 1.000e-04, eta: 1:03:27, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3379\n2023-01-04 03:04:36,177 - mmcls - INFO - Epoch [92][1900/5005]\tlr: 1.000e-04, eta: 1:03:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3245\n2023-01-04 03:04:44,874 - mmcls - INFO - Epoch [92][2000/5005]\tlr: 1.000e-04, eta: 1:03:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3343\n2023-01-04 03:04:53,647 - mmcls - INFO - Epoch [92][2100/5005]\tlr: 1.000e-04, eta: 1:03:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3253\n2023-01-04 03:05:02,353 - mmcls - INFO - Epoch [92][2200/5005]\tlr: 1.000e-04, eta: 1:02:52, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3252\n2023-01-04 03:05:11,259 - mmcls - INFO - Epoch [92][2300/5005]\tlr: 1.000e-04, eta: 1:02:43, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3316\n2023-01-04 03:05:20,087 - mmcls - INFO - Epoch [92][2400/5005]\tlr: 1.000e-04, eta: 1:02:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3330\n2023-01-04 03:05:28,912 - mmcls - INFO - Epoch [92][2500/5005]\tlr: 1.000e-04, eta: 1:02:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3195\n2023-01-04 03:05:37,655 - mmcls - INFO - Epoch [92][2600/5005]\tlr: 1.000e-04, eta: 1:02:16, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3299\n2023-01-04 03:05:46,681 - mmcls - INFO - Epoch [92][2700/5005]\tlr: 1.000e-04, eta: 1:02:08, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3209\n2023-01-04 03:05:55,124 - mmcls - INFO - Epoch [92][2800/5005]\tlr: 1.000e-04, eta: 1:01:59, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.3187\n2023-01-04 03:06:04,155 - mmcls - INFO - Epoch [92][2900/5005]\tlr: 1.000e-04, eta: 1:01:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3245\n2023-01-04 03:06:12,825 - mmcls - INFO - Epoch [92][3000/5005]\tlr: 1.000e-04, eta: 1:01:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3269\n2023-01-04 03:06:21,653 - mmcls - INFO - Epoch [92][3100/5005]\tlr: 1.000e-04, eta: 1:01:32, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3561\n2023-01-04 03:06:30,301 - mmcls - INFO - Epoch [92][3200/5005]\tlr: 1.000e-04, eta: 1:01:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3170\n2023-01-04 03:06:39,140 - mmcls - INFO - Epoch [92][3300/5005]\tlr: 1.000e-04, eta: 1:01:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3362\n2023-01-04 03:06:47,780 - mmcls - INFO - Epoch [92][3400/5005]\tlr: 1.000e-04, eta: 1:01:06, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3261\n2023-01-04 03:06:56,328 - mmcls - INFO - Epoch [92][3500/5005]\tlr: 1.000e-04, eta: 1:00:57, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.3147\n2023-01-04 03:07:04,993 - mmcls - INFO - Epoch [92][3600/5005]\tlr: 1.000e-04, eta: 1:00:48, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.3198\n2023-01-04 03:07:13,804 - mmcls - INFO - Epoch [92][3700/5005]\tlr: 1.000e-04, eta: 1:00:39, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3242\n2023-01-04 03:07:22,435 - mmcls - INFO - Epoch [92][3800/5005]\tlr: 1.000e-04, eta: 1:00:31, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3243\n2023-01-04 03:07:31,153 - mmcls - INFO - Epoch [92][3900/5005]\tlr: 1.000e-04, eta: 1:00:22, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3364\n2023-01-04 03:07:40,063 - mmcls - INFO - Epoch [92][4000/5005]\tlr: 1.000e-04, eta: 1:00:13, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.3266\n2023-01-04 03:07:48,900 - mmcls - INFO - Epoch [92][4100/5005]\tlr: 1.000e-04, eta: 1:00:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3095\n2023-01-04 03:07:57,462 - mmcls - INFO - Epoch [92][4200/5005]\tlr: 1.000e-04, eta: 0:59:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3052\n2023-01-04 03:08:06,599 - mmcls - INFO - Epoch [92][4300/5005]\tlr: 1.000e-04, eta: 0:59:47, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3056\n2023-01-04 03:08:15,166 - mmcls - INFO - Epoch [92][4400/5005]\tlr: 1.000e-04, eta: 0:59:38, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3033\n2023-01-04 03:08:23,750 - mmcls - INFO - Epoch [92][4500/5005]\tlr: 1.000e-04, eta: 0:59:29, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3161\n2023-01-04 03:08:32,500 - mmcls - INFO - Epoch [92][4600/5005]\tlr: 1.000e-04, eta: 0:59:20, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3317\n2023-01-04 03:08:41,218 - mmcls - INFO - Epoch [92][4700/5005]\tlr: 1.000e-04, eta: 0:59:11, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3352\n2023-01-04 03:08:49,808 - mmcls - INFO - Epoch [92][4800/5005]\tlr: 1.000e-04, eta: 0:59:03, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3214\n2023-01-04 03:08:58,468 - mmcls - INFO - Epoch [92][4900/5005]\tlr: 1.000e-04, eta: 0:58:54, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3271\n2023-01-04 03:09:07,632 - mmcls - INFO - Epoch [92][5000/5005]\tlr: 1.000e-04, eta: 0:58:45, time: 0.092, data_time: 0.027, memory: 1125, loss: 1.3280\n2023-01-04 03:09:30,223 - mmcls - INFO - Epoch(val) [92][196]\taccuracy_top-1: 70.0680, accuracy_top-5: 89.4160\n2023-01-04 03:09:41,318 - mmcls - INFO - Epoch [93][100/5005]\tlr: 1.000e-04, eta: 0:58:36, time: 0.111, data_time: 0.041, memory: 1125, loss: 1.3132\n2023-01-04 03:09:50,336 - mmcls - INFO - Epoch [93][200/5005]\tlr: 1.000e-04, eta: 0:58:27, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.3005\n2023-01-04 03:09:59,003 - mmcls - INFO - Epoch [93][300/5005]\tlr: 1.000e-04, eta: 0:58:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3149\n2023-01-04 03:10:08,086 - mmcls - INFO - Epoch [93][400/5005]\tlr: 1.000e-04, eta: 0:58:10, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3235\n2023-01-04 03:10:16,672 - mmcls - INFO - Epoch [93][500/5005]\tlr: 1.000e-04, eta: 0:58:01, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3096\n2023-01-04 03:10:25,453 - mmcls - INFO - Epoch [93][600/5005]\tlr: 1.000e-04, eta: 0:57:52, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3417\n2023-01-04 03:10:34,304 - mmcls - INFO - Epoch [93][700/5005]\tlr: 1.000e-04, eta: 0:57:43, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3020\n2023-01-04 03:10:42,967 - mmcls - INFO - Epoch [93][800/5005]\tlr: 1.000e-04, eta: 0:57:34, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3156\n2023-01-04 03:10:52,034 - mmcls - INFO - Epoch [93][900/5005]\tlr: 1.000e-04, eta: 0:57:25, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3295\n2023-01-04 03:11:01,088 - mmcls - INFO - Epoch [93][1000/5005]\tlr: 1.000e-04, eta: 0:57:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3388\n2023-01-04 03:11:09,564 - mmcls - INFO - Epoch [93][1100/5005]\tlr: 1.000e-04, eta: 0:57:08, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3096\n2023-01-04 03:11:18,605 - mmcls - INFO - Epoch [93][1200/5005]\tlr: 1.000e-04, eta: 0:56:59, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3107\n2023-01-04 03:11:27,166 - mmcls - INFO - Epoch [93][1300/5005]\tlr: 1.000e-04, eta: 0:56:50, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3178\n2023-01-04 03:11:35,667 - mmcls - INFO - Epoch [93][1400/5005]\tlr: 1.000e-04, eta: 0:56:41, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3091\n2023-01-04 03:11:44,639 - mmcls - INFO - Epoch [93][1500/5005]\tlr: 1.000e-04, eta: 0:56:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3383\n2023-01-04 03:11:53,476 - mmcls - INFO - Epoch [93][1600/5005]\tlr: 1.000e-04, eta: 0:56:24, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3191\n2023-01-04 03:12:02,271 - mmcls - INFO - Epoch [93][1700/5005]\tlr: 1.000e-04, eta: 0:56:15, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3215\n2023-01-04 03:12:10,869 - mmcls - INFO - Epoch [93][1800/5005]\tlr: 1.000e-04, eta: 0:56:06, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3321\n2023-01-04 03:12:20,002 - mmcls - INFO - Epoch [93][1900/5005]\tlr: 1.000e-04, eta: 0:55:57, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3220\n2023-01-04 03:12:28,874 - mmcls - INFO - Epoch [93][2000/5005]\tlr: 1.000e-04, eta: 0:55:49, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.3042\n2023-01-04 03:12:37,732 - mmcls - INFO - Epoch [93][2100/5005]\tlr: 1.000e-04, eta: 0:55:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3193\n2023-01-04 03:12:46,700 - mmcls - INFO - Epoch [93][2200/5005]\tlr: 1.000e-04, eta: 0:55:31, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.3280\n2023-01-04 03:12:55,692 - mmcls - INFO - Epoch [93][2300/5005]\tlr: 1.000e-04, eta: 0:55:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3081\n2023-01-04 03:13:04,288 - mmcls - INFO - Epoch [93][2400/5005]\tlr: 1.000e-04, eta: 0:55:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3219\n2023-01-04 03:13:13,001 - mmcls - INFO - Epoch [93][2500/5005]\tlr: 1.000e-04, eta: 0:55:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3314\n2023-01-04 03:13:21,972 - mmcls - INFO - Epoch [93][2600/5005]\tlr: 1.000e-04, eta: 0:54:56, time: 0.090, data_time: 0.025, memory: 1125, loss: 1.3324\n2023-01-04 03:13:30,687 - mmcls - INFO - Epoch [93][2700/5005]\tlr: 1.000e-04, eta: 0:54:47, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3209\n2023-01-04 03:13:39,482 - mmcls - INFO - Epoch [93][2800/5005]\tlr: 1.000e-04, eta: 0:54:38, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3066\n2023-01-04 03:13:48,210 - mmcls - INFO - Epoch [93][2900/5005]\tlr: 1.000e-04, eta: 0:54:29, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3119\n2023-01-04 03:13:57,172 - mmcls - INFO - Epoch [93][3000/5005]\tlr: 1.000e-04, eta: 0:54:21, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.3107\n2023-01-04 03:14:05,693 - mmcls - INFO - Epoch [93][3100/5005]\tlr: 1.000e-04, eta: 0:54:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3139\n2023-01-04 03:14:14,314 - mmcls - INFO - Epoch [93][3200/5005]\tlr: 1.000e-04, eta: 0:54:03, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3345\n2023-01-04 03:14:23,332 - mmcls - INFO - Epoch [93][3300/5005]\tlr: 1.000e-04, eta: 0:53:54, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.2898\n2023-01-04 03:14:31,937 - mmcls - INFO - Epoch [93][3400/5005]\tlr: 1.000e-04, eta: 0:53:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3101\n2023-01-04 03:14:40,654 - mmcls - INFO - Epoch [93][3500/5005]\tlr: 1.000e-04, eta: 0:53:37, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.3291\n2023-01-04 03:14:49,462 - mmcls - INFO - Epoch [93][3600/5005]\tlr: 1.000e-04, eta: 0:53:28, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3123\n2023-01-04 03:14:58,199 - mmcls - INFO - Epoch [93][3700/5005]\tlr: 1.000e-04, eta: 0:53:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.2991\n2023-01-04 03:15:06,960 - mmcls - INFO - Epoch [93][3800/5005]\tlr: 1.000e-04, eta: 0:53:10, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3182\n2023-01-04 03:15:16,175 - mmcls - INFO - Epoch [93][3900/5005]\tlr: 1.000e-04, eta: 0:53:01, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3127\n2023-01-04 03:15:25,102 - mmcls - INFO - Epoch [93][4000/5005]\tlr: 1.000e-04, eta: 0:52:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3141\n2023-01-04 03:15:33,736 - mmcls - INFO - Epoch [93][4100/5005]\tlr: 1.000e-04, eta: 0:52:44, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3274\n2023-01-04 03:15:42,461 - mmcls - INFO - Epoch [93][4200/5005]\tlr: 1.000e-04, eta: 0:52:35, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3055\n2023-01-04 03:15:51,418 - mmcls - INFO - Epoch [93][4300/5005]\tlr: 1.000e-04, eta: 0:52:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3347\n2023-01-04 03:16:00,181 - mmcls - INFO - Epoch [93][4400/5005]\tlr: 1.000e-04, eta: 0:52:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3279\n2023-01-04 03:16:08,624 - mmcls - INFO - Epoch [93][4500/5005]\tlr: 1.000e-04, eta: 0:52:09, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3232\n2023-01-04 03:16:17,257 - mmcls - INFO - Epoch [93][4600/5005]\tlr: 1.000e-04, eta: 0:52:00, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3190\n2023-01-04 03:16:26,345 - mmcls - INFO - Epoch [93][4700/5005]\tlr: 1.000e-04, eta: 0:51:51, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.3331\n2023-01-04 03:16:34,924 - mmcls - INFO - Epoch [93][4800/5005]\tlr: 1.000e-04, eta: 0:51:42, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.2987\n2023-01-04 03:16:43,412 - mmcls - INFO - Epoch [93][4900/5005]\tlr: 1.000e-04, eta: 0:51:33, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3301\n2023-01-04 03:16:52,241 - mmcls - INFO - Epoch [93][5000/5005]\tlr: 1.000e-04, eta: 0:51:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3155\n2023-01-04 03:17:14,869 - mmcls - INFO - Epoch(val) [93][196]\taccuracy_top-1: 70.3200, accuracy_top-5: 89.3660\n2023-01-04 03:17:26,103 - mmcls - INFO - Epoch [94][100/5005]\tlr: 1.000e-04, eta: 0:51:15, time: 0.112, data_time: 0.041, memory: 1125, loss: 1.3307\n2023-01-04 03:17:34,787 - mmcls - INFO - Epoch [94][200/5005]\tlr: 1.000e-04, eta: 0:51:07, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.3304\n2023-01-04 03:17:43,781 - mmcls - INFO - Epoch [94][300/5005]\tlr: 1.000e-04, eta: 0:50:58, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3269\n2023-01-04 03:17:52,859 - mmcls - INFO - Epoch [94][400/5005]\tlr: 1.000e-04, eta: 0:50:49, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3200\n2023-01-04 03:18:01,712 - mmcls - INFO - Epoch [94][500/5005]\tlr: 1.000e-04, eta: 0:50:40, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3013\n2023-01-04 03:18:10,274 - mmcls - INFO - Epoch [94][600/5005]\tlr: 1.000e-04, eta: 0:50:31, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3277\n2023-01-04 03:18:18,974 - mmcls - INFO - Epoch [94][700/5005]\tlr: 1.000e-04, eta: 0:50:23, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3327\n2023-01-04 03:18:27,751 - mmcls - INFO - Epoch [94][800/5005]\tlr: 1.000e-04, eta: 0:50:14, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3131\n2023-01-04 03:18:36,244 - mmcls - INFO - Epoch [94][900/5005]\tlr: 1.000e-04, eta: 0:50:05, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.2925\n2023-01-04 03:18:44,970 - mmcls - INFO - Epoch [94][1000/5005]\tlr: 1.000e-04, eta: 0:49:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3299\n2023-01-04 03:18:53,658 - mmcls - INFO - Epoch [94][1100/5005]\tlr: 1.000e-04, eta: 0:49:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3124\n2023-01-04 03:19:02,681 - mmcls - INFO - Epoch [94][1200/5005]\tlr: 1.000e-04, eta: 0:49:38, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.3173\n2023-01-04 03:19:11,280 - mmcls - INFO - Epoch [94][1300/5005]\tlr: 1.000e-04, eta: 0:49:30, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3418\n2023-01-04 03:19:19,993 - mmcls - INFO - Epoch [94][1400/5005]\tlr: 1.000e-04, eta: 0:49:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3191\n2023-01-04 03:19:29,157 - mmcls - INFO - Epoch [94][1500/5005]\tlr: 1.000e-04, eta: 0:49:12, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.3227\n2023-01-04 03:19:37,670 - mmcls - INFO - Epoch [94][1600/5005]\tlr: 1.000e-04, eta: 0:49:03, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.3172\n2023-01-04 03:19:46,522 - mmcls - INFO - Epoch [94][1700/5005]\tlr: 1.000e-04, eta: 0:48:54, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3326\n2023-01-04 03:19:55,318 - mmcls - INFO - Epoch [94][1800/5005]\tlr: 1.000e-04, eta: 0:48:46, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3126\n2023-01-04 03:20:04,321 - mmcls - INFO - Epoch [94][1900/5005]\tlr: 1.000e-04, eta: 0:48:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3258\n2023-01-04 03:20:13,422 - mmcls - INFO - Epoch [94][2000/5005]\tlr: 1.000e-04, eta: 0:48:28, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3232\n2023-01-04 03:20:22,253 - mmcls - INFO - Epoch [94][2100/5005]\tlr: 1.000e-04, eta: 0:48:19, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3343\n2023-01-04 03:20:31,365 - mmcls - INFO - Epoch [94][2200/5005]\tlr: 1.000e-04, eta: 0:48:10, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.3187\n2023-01-04 03:20:39,985 - mmcls - INFO - Epoch [94][2300/5005]\tlr: 1.000e-04, eta: 0:48:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3116\n2023-01-04 03:20:48,659 - mmcls - INFO - Epoch [94][2400/5005]\tlr: 1.000e-04, eta: 0:47:53, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3109\n2023-01-04 03:20:57,313 - mmcls - INFO - Epoch [94][2500/5005]\tlr: 1.000e-04, eta: 0:47:44, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3154\n2023-01-04 03:21:05,919 - mmcls - INFO - Epoch [94][2600/5005]\tlr: 1.000e-04, eta: 0:47:35, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3086\n2023-01-04 03:21:14,525 - mmcls - INFO - Epoch [94][2700/5005]\tlr: 1.000e-04, eta: 0:47:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3059\n2023-01-04 03:21:23,246 - mmcls - INFO - Epoch [94][2800/5005]\tlr: 1.000e-04, eta: 0:47:18, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3268\n2023-01-04 03:21:32,167 - mmcls - INFO - Epoch [94][2900/5005]\tlr: 1.000e-04, eta: 0:47:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3115\n2023-01-04 03:21:40,696 - mmcls - INFO - Epoch [94][3000/5005]\tlr: 1.000e-04, eta: 0:47:00, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3130\n2023-01-04 03:21:49,572 - mmcls - INFO - Epoch [94][3100/5005]\tlr: 1.000e-04, eta: 0:46:51, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3196\n2023-01-04 03:21:58,318 - mmcls - INFO - Epoch [94][3200/5005]\tlr: 1.000e-04, eta: 0:46:42, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.3128\n2023-01-04 03:22:07,252 - mmcls - INFO - Epoch [94][3300/5005]\tlr: 1.000e-04, eta: 0:46:34, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3040\n2023-01-04 03:22:16,383 - mmcls - INFO - Epoch [94][3400/5005]\tlr: 1.000e-04, eta: 0:46:25, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.3170\n2023-01-04 03:22:24,935 - mmcls - INFO - Epoch [94][3500/5005]\tlr: 1.000e-04, eta: 0:46:16, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3051\n2023-01-04 03:22:33,849 - mmcls - INFO - Epoch [94][3600/5005]\tlr: 1.000e-04, eta: 0:46:07, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3029\n2023-01-04 03:22:42,804 - mmcls - INFO - Epoch [94][3700/5005]\tlr: 1.000e-04, eta: 0:45:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3327\n2023-01-04 03:22:51,695 - mmcls - INFO - Epoch [94][3800/5005]\tlr: 1.000e-04, eta: 0:45:50, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.3233\n2023-01-04 03:23:00,214 - mmcls - INFO - Epoch [94][3900/5005]\tlr: 1.000e-04, eta: 0:45:41, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.3275\n2023-01-04 03:23:08,953 - mmcls - INFO - Epoch [94][4000/5005]\tlr: 1.000e-04, eta: 0:45:32, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3181\n2023-01-04 03:23:17,445 - mmcls - INFO - Epoch [94][4100/5005]\tlr: 1.000e-04, eta: 0:45:23, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3105\n2023-01-04 03:23:26,274 - mmcls - INFO - Epoch [94][4200/5005]\tlr: 1.000e-04, eta: 0:45:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3103\n2023-01-04 03:23:35,018 - mmcls - INFO - Epoch [94][4300/5005]\tlr: 1.000e-04, eta: 0:45:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3147\n2023-01-04 03:23:43,629 - mmcls - INFO - Epoch [94][4400/5005]\tlr: 1.000e-04, eta: 0:44:57, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3229\n2023-01-04 03:23:52,410 - mmcls - INFO - Epoch [94][4500/5005]\tlr: 1.000e-04, eta: 0:44:48, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3232\n2023-01-04 03:24:00,837 - mmcls - INFO - Epoch [94][4600/5005]\tlr: 1.000e-04, eta: 0:44:39, time: 0.084, data_time: 0.022, memory: 1125, loss: 1.3219\n2023-01-04 03:24:09,872 - mmcls - INFO - Epoch [94][4700/5005]\tlr: 1.000e-04, eta: 0:44:30, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3191\n2023-01-04 03:24:18,509 - mmcls - INFO - Epoch [94][4800/5005]\tlr: 1.000e-04, eta: 0:44:21, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3067\n2023-01-04 03:24:27,045 - mmcls - INFO - Epoch [94][4900/5005]\tlr: 1.000e-04, eta: 0:44:13, time: 0.085, data_time: 0.041, memory: 1125, loss: 1.2878\n2023-01-04 03:24:35,909 - mmcls - INFO - Epoch [94][5000/5005]\tlr: 1.000e-04, eta: 0:44:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3370\n2023-01-04 03:24:58,678 - mmcls - INFO - Epoch(val) [94][196]\taccuracy_top-1: 70.0600, accuracy_top-5: 89.4140\n2023-01-04 03:25:09,618 - mmcls - INFO - Epoch [95][100/5005]\tlr: 1.000e-04, eta: 0:43:55, time: 0.109, data_time: 0.039, memory: 1125, loss: 1.2971\n2023-01-04 03:25:18,270 - mmcls - INFO - Epoch [95][200/5005]\tlr: 1.000e-04, eta: 0:43:46, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.3198\n2023-01-04 03:25:26,880 - mmcls - INFO - Epoch [95][300/5005]\tlr: 1.000e-04, eta: 0:43:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3186\n2023-01-04 03:25:35,728 - mmcls - INFO - Epoch [95][400/5005]\tlr: 1.000e-04, eta: 0:43:28, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3227\n2023-01-04 03:25:44,777 - mmcls - INFO - Epoch [95][500/5005]\tlr: 1.000e-04, eta: 0:43:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3291\n2023-01-04 03:25:53,990 - mmcls - INFO - Epoch [95][600/5005]\tlr: 1.000e-04, eta: 0:43:11, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3022\n2023-01-04 03:26:02,773 - mmcls - INFO - Epoch [95][700/5005]\tlr: 1.000e-04, eta: 0:43:02, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3215\n2023-01-04 03:26:11,232 - mmcls - INFO - Epoch [95][800/5005]\tlr: 1.000e-04, eta: 0:42:53, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3277\n2023-01-04 03:26:20,249 - mmcls - INFO - Epoch [95][900/5005]\tlr: 1.000e-04, eta: 0:42:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3216\n2023-01-04 03:26:29,058 - mmcls - INFO - Epoch [95][1000/5005]\tlr: 1.000e-04, eta: 0:42:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.2941\n2023-01-04 03:26:37,866 - mmcls - INFO - Epoch [95][1100/5005]\tlr: 1.000e-04, eta: 0:42:27, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3140\n2023-01-04 03:26:46,616 - mmcls - INFO - Epoch [95][1200/5005]\tlr: 1.000e-04, eta: 0:42:18, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3142\n2023-01-04 03:26:55,564 - mmcls - INFO - Epoch [95][1300/5005]\tlr: 1.000e-04, eta: 0:42:09, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3274\n2023-01-04 03:27:04,266 - mmcls - INFO - Epoch [95][1400/5005]\tlr: 1.000e-04, eta: 0:42:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3113\n2023-01-04 03:27:13,030 - mmcls - INFO - Epoch [95][1500/5005]\tlr: 1.000e-04, eta: 0:41:51, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3055\n2023-01-04 03:27:21,916 - mmcls - INFO - Epoch [95][1600/5005]\tlr: 1.000e-04, eta: 0:41:43, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3085\n2023-01-04 03:27:30,780 - mmcls - INFO - Epoch [95][1700/5005]\tlr: 1.000e-04, eta: 0:41:34, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3147\n2023-01-04 03:27:39,438 - mmcls - INFO - Epoch [95][1800/5005]\tlr: 1.000e-04, eta: 0:41:25, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3128\n2023-01-04 03:27:48,470 - mmcls - INFO - Epoch [95][1900/5005]\tlr: 1.000e-04, eta: 0:41:16, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.3348\n2023-01-04 03:27:57,118 - mmcls - INFO - Epoch [95][2000/5005]\tlr: 1.000e-04, eta: 0:41:07, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3016\n2023-01-04 03:28:06,163 - mmcls - INFO - Epoch [95][2100/5005]\tlr: 1.000e-04, eta: 0:40:59, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3270\n2023-01-04 03:28:15,161 - mmcls - INFO - Epoch [95][2200/5005]\tlr: 1.000e-04, eta: 0:40:50, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3252\n2023-01-04 03:28:24,218 - mmcls - INFO - Epoch [95][2300/5005]\tlr: 1.000e-04, eta: 0:40:41, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3251\n2023-01-04 03:28:32,732 - mmcls - INFO - Epoch [95][2400/5005]\tlr: 1.000e-04, eta: 0:40:32, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3059\n2023-01-04 03:28:41,319 - mmcls - INFO - Epoch [95][2500/5005]\tlr: 1.000e-04, eta: 0:40:23, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3206\n2023-01-04 03:28:50,135 - mmcls - INFO - Epoch [95][2600/5005]\tlr: 1.000e-04, eta: 0:40:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3067\n2023-01-04 03:28:58,899 - mmcls - INFO - Epoch [95][2700/5005]\tlr: 1.000e-04, eta: 0:40:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3265\n2023-01-04 03:29:07,865 - mmcls - INFO - Epoch [95][2800/5005]\tlr: 1.000e-04, eta: 0:39:57, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.3115\n2023-01-04 03:29:16,632 - mmcls - INFO - Epoch [95][2900/5005]\tlr: 1.000e-04, eta: 0:39:48, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.2951\n2023-01-04 03:29:25,309 - mmcls - INFO - Epoch [95][3000/5005]\tlr: 1.000e-04, eta: 0:39:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3029\n2023-01-04 03:29:33,938 - mmcls - INFO - Epoch [95][3100/5005]\tlr: 1.000e-04, eta: 0:39:31, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3174\n2023-01-04 03:29:42,916 - mmcls - INFO - Epoch [95][3200/5005]\tlr: 1.000e-04, eta: 0:39:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3494\n2023-01-04 03:29:51,704 - mmcls - INFO - Epoch [95][3300/5005]\tlr: 1.000e-04, eta: 0:39:13, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3069\n2023-01-04 03:30:00,433 - mmcls - INFO - Epoch [95][3400/5005]\tlr: 1.000e-04, eta: 0:39:04, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3113\n2023-01-04 03:30:09,165 - mmcls - INFO - Epoch [95][3500/5005]\tlr: 1.000e-04, eta: 0:38:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3417\n2023-01-04 03:30:17,878 - mmcls - INFO - Epoch [95][3600/5005]\tlr: 1.000e-04, eta: 0:38:47, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3436\n2023-01-04 03:30:26,879 - mmcls - INFO - Epoch [95][3700/5005]\tlr: 1.000e-04, eta: 0:38:38, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3099\n2023-01-04 03:30:35,657 - mmcls - INFO - Epoch [95][3800/5005]\tlr: 1.000e-04, eta: 0:38:29, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3080\n2023-01-04 03:30:44,523 - mmcls - INFO - Epoch [95][3900/5005]\tlr: 1.000e-04, eta: 0:38:20, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.3202\n2023-01-04 03:30:53,173 - mmcls - INFO - Epoch [95][4000/5005]\tlr: 1.000e-04, eta: 0:38:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3103\n2023-01-04 03:31:01,857 - mmcls - INFO - Epoch [95][4100/5005]\tlr: 1.000e-04, eta: 0:38:03, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.3166\n2023-01-04 03:31:10,488 - mmcls - INFO - Epoch [95][4200/5005]\tlr: 1.000e-04, eta: 0:37:54, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3104\n2023-01-04 03:31:19,411 - mmcls - INFO - Epoch [95][4300/5005]\tlr: 1.000e-04, eta: 0:37:45, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3022\n2023-01-04 03:31:28,404 - mmcls - INFO - Epoch [95][4400/5005]\tlr: 1.000e-04, eta: 0:37:36, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3243\n2023-01-04 03:31:37,245 - mmcls - INFO - Epoch [95][4500/5005]\tlr: 1.000e-04, eta: 0:37:27, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3174\n2023-01-04 03:31:45,845 - mmcls - INFO - Epoch [95][4600/5005]\tlr: 1.000e-04, eta: 0:37:18, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3158\n2023-01-04 03:31:54,486 - mmcls - INFO - Epoch [95][4700/5005]\tlr: 1.000e-04, eta: 0:37:10, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3238\n2023-01-04 03:32:03,192 - mmcls - INFO - Epoch [95][4800/5005]\tlr: 1.000e-04, eta: 0:37:01, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.3085\n2023-01-04 03:32:12,082 - mmcls - INFO - Epoch [95][4900/5005]\tlr: 1.000e-04, eta: 0:36:52, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3127\n2023-01-04 03:32:20,912 - mmcls - INFO - Epoch [95][5000/5005]\tlr: 1.000e-04, eta: 0:36:43, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.2926\n2023-01-04 03:32:43,596 - mmcls - INFO - Epoch(val) [95][196]\taccuracy_top-1: 70.1480, accuracy_top-5: 89.4080\n2023-01-04 03:32:54,669 - mmcls - INFO - Epoch [96][100/5005]\tlr: 1.000e-04, eta: 0:36:34, time: 0.111, data_time: 0.062, memory: 1125, loss: 1.3236\n2023-01-04 03:33:03,493 - mmcls - INFO - Epoch [96][200/5005]\tlr: 1.000e-04, eta: 0:36:25, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3024\n2023-01-04 03:33:12,423 - mmcls - INFO - Epoch [96][300/5005]\tlr: 1.000e-04, eta: 0:36:17, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3098\n2023-01-04 03:33:21,289 - mmcls - INFO - Epoch [96][400/5005]\tlr: 1.000e-04, eta: 0:36:08, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3145\n2023-01-04 03:33:30,059 - mmcls - INFO - Epoch [96][500/5005]\tlr: 1.000e-04, eta: 0:35:59, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3057\n2023-01-04 03:33:38,709 - mmcls - INFO - Epoch [96][600/5005]\tlr: 1.000e-04, eta: 0:35:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3189\n2023-01-04 03:33:47,623 - mmcls - INFO - Epoch [96][700/5005]\tlr: 1.000e-04, eta: 0:35:41, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3328\n2023-01-04 03:33:55,993 - mmcls - INFO - Epoch [96][800/5005]\tlr: 1.000e-04, eta: 0:35:32, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.3275\n2023-01-04 03:34:05,062 - mmcls - INFO - Epoch [96][900/5005]\tlr: 1.000e-04, eta: 0:35:24, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3017\n2023-01-04 03:34:13,818 - mmcls - INFO - Epoch [96][1000/5005]\tlr: 1.000e-04, eta: 0:35:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3121\n2023-01-04 03:34:22,399 - mmcls - INFO - Epoch [96][1100/5005]\tlr: 1.000e-04, eta: 0:35:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3216\n2023-01-04 03:34:31,022 - mmcls - INFO - Epoch [96][1200/5005]\tlr: 1.000e-04, eta: 0:34:57, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3031\n2023-01-04 03:34:39,869 - mmcls - INFO - Epoch [96][1300/5005]\tlr: 1.000e-04, eta: 0:34:48, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.3314\n2023-01-04 03:34:48,749 - mmcls - INFO - Epoch [96][1400/5005]\tlr: 1.000e-04, eta: 0:34:40, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.2931\n2023-01-04 03:34:57,907 - mmcls - INFO - Epoch [96][1500/5005]\tlr: 1.000e-04, eta: 0:34:31, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3016\n2023-01-04 03:35:06,449 - mmcls - INFO - Epoch [96][1600/5005]\tlr: 1.000e-04, eta: 0:34:22, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3149\n2023-01-04 03:35:15,182 - mmcls - INFO - Epoch [96][1700/5005]\tlr: 1.000e-04, eta: 0:34:13, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3292\n2023-01-04 03:35:23,977 - mmcls - INFO - Epoch [96][1800/5005]\tlr: 1.000e-04, eta: 0:34:04, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3114\n2023-01-04 03:35:32,820 - mmcls - INFO - Epoch [96][1900/5005]\tlr: 1.000e-04, eta: 0:33:56, time: 0.088, data_time: 0.044, memory: 1125, loss: 1.3042\n2023-01-04 03:35:41,601 - mmcls - INFO - Epoch [96][2000/5005]\tlr: 1.000e-04, eta: 0:33:47, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3275\n2023-01-04 03:35:50,427 - mmcls - INFO - Epoch [96][2100/5005]\tlr: 1.000e-04, eta: 0:33:38, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3071\n2023-01-04 03:35:59,257 - mmcls - INFO - Epoch [96][2200/5005]\tlr: 1.000e-04, eta: 0:33:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3152\n2023-01-04 03:36:07,822 - mmcls - INFO - Epoch [96][2300/5005]\tlr: 1.000e-04, eta: 0:33:20, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3099\n2023-01-04 03:36:16,768 - mmcls - INFO - Epoch [96][2400/5005]\tlr: 1.000e-04, eta: 0:33:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3240\n2023-01-04 03:36:25,470 - mmcls - INFO - Epoch [96][2500/5005]\tlr: 1.000e-04, eta: 0:33:03, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3236\n2023-01-04 03:36:34,061 - mmcls - INFO - Epoch [96][2600/5005]\tlr: 1.000e-04, eta: 0:32:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3188\n2023-01-04 03:36:42,819 - mmcls - INFO - Epoch [96][2700/5005]\tlr: 1.000e-04, eta: 0:32:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3125\n2023-01-04 03:36:51,548 - mmcls - INFO - Epoch [96][2800/5005]\tlr: 1.000e-04, eta: 0:32:36, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3398\n2023-01-04 03:37:00,280 - mmcls - INFO - Epoch [96][2900/5005]\tlr: 1.000e-04, eta: 0:32:28, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3142\n2023-01-04 03:37:08,843 - mmcls - INFO - Epoch [96][3000/5005]\tlr: 1.000e-04, eta: 0:32:19, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3246\n2023-01-04 03:37:17,529 - mmcls - INFO - Epoch [96][3100/5005]\tlr: 1.000e-04, eta: 0:32:10, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.3190\n2023-01-04 03:37:26,478 - mmcls - INFO - Epoch [96][3200/5005]\tlr: 1.000e-04, eta: 0:32:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3162\n2023-01-04 03:37:35,273 - mmcls - INFO - Epoch [96][3300/5005]\tlr: 1.000e-04, eta: 0:31:52, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.2933\n2023-01-04 03:37:43,921 - mmcls - INFO - Epoch [96][3400/5005]\tlr: 1.000e-04, eta: 0:31:43, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.2943\n2023-01-04 03:37:52,450 - mmcls - INFO - Epoch [96][3500/5005]\tlr: 1.000e-04, eta: 0:31:35, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.2970\n2023-01-04 03:38:01,177 - mmcls - INFO - Epoch [96][3600/5005]\tlr: 1.000e-04, eta: 0:31:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3352\n2023-01-04 03:38:09,981 - mmcls - INFO - Epoch [96][3700/5005]\tlr: 1.000e-04, eta: 0:31:17, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3126\n2023-01-04 03:38:18,903 - mmcls - INFO - Epoch [96][3800/5005]\tlr: 1.000e-04, eta: 0:31:08, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3294\n2023-01-04 03:38:27,962 - mmcls - INFO - Epoch [96][3900/5005]\tlr: 1.000e-04, eta: 0:30:59, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.3093\n2023-01-04 03:38:36,537 - mmcls - INFO - Epoch [96][4000/5005]\tlr: 1.000e-04, eta: 0:30:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3001\n2023-01-04 03:38:45,447 - mmcls - INFO - Epoch [96][4100/5005]\tlr: 1.000e-04, eta: 0:30:42, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.3141\n2023-01-04 03:38:54,118 - mmcls - INFO - Epoch [96][4200/5005]\tlr: 1.000e-04, eta: 0:30:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3061\n2023-01-04 03:39:02,830 - mmcls - INFO - Epoch [96][4300/5005]\tlr: 1.000e-04, eta: 0:30:24, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3098\n2023-01-04 03:39:11,508 - mmcls - INFO - Epoch [96][4400/5005]\tlr: 1.000e-04, eta: 0:30:15, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3219\n2023-01-04 03:39:20,243 - mmcls - INFO - Epoch [96][4500/5005]\tlr: 1.000e-04, eta: 0:30:07, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3247\n2023-01-04 03:39:29,189 - mmcls - INFO - Epoch [96][4600/5005]\tlr: 1.000e-04, eta: 0:29:58, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3319\n2023-01-04 03:39:37,978 - mmcls - INFO - Epoch [96][4700/5005]\tlr: 1.000e-04, eta: 0:29:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3008\n2023-01-04 03:39:46,816 - mmcls - INFO - Epoch [96][4800/5005]\tlr: 1.000e-04, eta: 0:29:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3218\n2023-01-04 03:39:55,353 - mmcls - INFO - Epoch [96][4900/5005]\tlr: 1.000e-04, eta: 0:29:31, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.3172\n2023-01-04 03:40:03,822 - mmcls - INFO - Epoch [96][5000/5005]\tlr: 1.000e-04, eta: 0:29:23, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3270\n2023-01-04 03:40:26,593 - mmcls - INFO - Epoch(val) [96][196]\taccuracy_top-1: 70.3640, accuracy_top-5: 89.4200\n2023-01-04 03:40:37,682 - mmcls - INFO - Epoch [97][100/5005]\tlr: 1.000e-04, eta: 0:29:13, time: 0.111, data_time: 0.040, memory: 1125, loss: 1.3079\n2023-01-04 03:40:46,207 - mmcls - INFO - Epoch [97][200/5005]\tlr: 1.000e-04, eta: 0:29:05, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3038\n2023-01-04 03:40:55,093 - mmcls - INFO - Epoch [97][300/5005]\tlr: 1.000e-04, eta: 0:28:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3043\n2023-01-04 03:41:03,734 - mmcls - INFO - Epoch [97][400/5005]\tlr: 1.000e-04, eta: 0:28:47, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.3121\n2023-01-04 03:41:12,579 - mmcls - INFO - Epoch [97][500/5005]\tlr: 1.000e-04, eta: 0:28:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3199\n2023-01-04 03:41:21,580 - mmcls - INFO - Epoch [97][600/5005]\tlr: 1.000e-04, eta: 0:28:29, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3453\n2023-01-04 03:41:30,320 - mmcls - INFO - Epoch [97][700/5005]\tlr: 1.000e-04, eta: 0:28:21, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3147\n2023-01-04 03:41:39,368 - mmcls - INFO - Epoch [97][800/5005]\tlr: 1.000e-04, eta: 0:28:12, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.3293\n2023-01-04 03:41:48,479 - mmcls - INFO - Epoch [97][900/5005]\tlr: 1.000e-04, eta: 0:28:03, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.3184\n2023-01-04 03:41:57,262 - mmcls - INFO - Epoch [97][1000/5005]\tlr: 1.000e-04, eta: 0:27:54, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3088\n2023-01-04 03:42:05,798 - mmcls - INFO - Epoch [97][1100/5005]\tlr: 1.000e-04, eta: 0:27:45, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.2995\n2023-01-04 03:42:14,515 - mmcls - INFO - Epoch [97][1200/5005]\tlr: 1.000e-04, eta: 0:27:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3102\n2023-01-04 03:42:23,516 - mmcls - INFO - Epoch [97][1300/5005]\tlr: 1.000e-04, eta: 0:27:28, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.2833\n2023-01-04 03:42:32,315 - mmcls - INFO - Epoch [97][1400/5005]\tlr: 1.000e-04, eta: 0:27:19, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3083\n2023-01-04 03:42:41,279 - mmcls - INFO - Epoch [97][1500/5005]\tlr: 1.000e-04, eta: 0:27:10, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3353\n2023-01-04 03:42:50,151 - mmcls - INFO - Epoch [97][1600/5005]\tlr: 1.000e-04, eta: 0:27:01, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3135\n2023-01-04 03:42:58,696 - mmcls - INFO - Epoch [97][1700/5005]\tlr: 1.000e-04, eta: 0:26:53, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3156\n2023-01-04 03:43:07,588 - mmcls - INFO - Epoch [97][1800/5005]\tlr: 1.000e-04, eta: 0:26:44, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3174\n2023-01-04 03:43:16,283 - mmcls - INFO - Epoch [97][1900/5005]\tlr: 1.000e-04, eta: 0:26:35, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3071\n2023-01-04 03:43:25,078 - mmcls - INFO - Epoch [97][2000/5005]\tlr: 1.000e-04, eta: 0:26:26, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3066\n2023-01-04 03:43:33,818 - mmcls - INFO - Epoch [97][2100/5005]\tlr: 1.000e-04, eta: 0:26:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3034\n2023-01-04 03:43:42,714 - mmcls - INFO - Epoch [97][2200/5005]\tlr: 1.000e-04, eta: 0:26:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3228\n2023-01-04 03:43:51,404 - mmcls - INFO - Epoch [97][2300/5005]\tlr: 1.000e-04, eta: 0:26:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3241\n2023-01-04 03:43:59,961 - mmcls - INFO - Epoch [97][2400/5005]\tlr: 1.000e-04, eta: 0:25:51, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3254\n2023-01-04 03:44:08,523 - mmcls - INFO - Epoch [97][2500/5005]\tlr: 1.000e-04, eta: 0:25:42, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3031\n2023-01-04 03:44:17,043 - mmcls - INFO - Epoch [97][2600/5005]\tlr: 1.000e-04, eta: 0:25:33, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3109\n2023-01-04 03:44:25,975 - mmcls - INFO - Epoch [97][2700/5005]\tlr: 1.000e-04, eta: 0:25:24, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3161\n2023-01-04 03:44:34,988 - mmcls - INFO - Epoch [97][2800/5005]\tlr: 1.000e-04, eta: 0:25:16, time: 0.090, data_time: 0.031, memory: 1125, loss: 1.3278\n2023-01-04 03:44:43,743 - mmcls - INFO - Epoch [97][2900/5005]\tlr: 1.000e-04, eta: 0:25:07, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3170\n2023-01-04 03:44:52,349 - mmcls - INFO - Epoch [97][3000/5005]\tlr: 1.000e-04, eta: 0:24:58, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3025\n2023-01-04 03:45:01,202 - mmcls - INFO - Epoch [97][3100/5005]\tlr: 1.000e-04, eta: 0:24:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3131\n2023-01-04 03:45:09,853 - mmcls - INFO - Epoch [97][3200/5005]\tlr: 1.000e-04, eta: 0:24:40, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3149\n2023-01-04 03:45:18,630 - mmcls - INFO - Epoch [97][3300/5005]\tlr: 1.000e-04, eta: 0:24:32, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3024\n2023-01-04 03:45:27,190 - mmcls - INFO - Epoch [97][3400/5005]\tlr: 1.000e-04, eta: 0:24:23, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.3076\n2023-01-04 03:45:35,969 - mmcls - INFO - Epoch [97][3500/5005]\tlr: 1.000e-04, eta: 0:24:14, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3105\n2023-01-04 03:45:44,709 - mmcls - INFO - Epoch [97][3600/5005]\tlr: 1.000e-04, eta: 0:24:05, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3174\n2023-01-04 03:45:53,357 - mmcls - INFO - Epoch [97][3700/5005]\tlr: 1.000e-04, eta: 0:23:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3116\n2023-01-04 03:46:02,041 - mmcls - INFO - Epoch [97][3800/5005]\tlr: 1.000e-04, eta: 0:23:48, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3090\n2023-01-04 03:46:11,120 - mmcls - INFO - Epoch [97][3900/5005]\tlr: 1.000e-04, eta: 0:23:39, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.3099\n2023-01-04 03:46:19,848 - mmcls - INFO - Epoch [97][4000/5005]\tlr: 1.000e-04, eta: 0:23:30, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3060\n2023-01-04 03:46:28,544 - mmcls - INFO - Epoch [97][4100/5005]\tlr: 1.000e-04, eta: 0:23:21, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.3108\n2023-01-04 03:46:37,255 - mmcls - INFO - Epoch [97][4200/5005]\tlr: 1.000e-04, eta: 0:23:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3119\n2023-01-04 03:46:46,131 - mmcls - INFO - Epoch [97][4300/5005]\tlr: 1.000e-04, eta: 0:23:04, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.3062\n2023-01-04 03:46:54,878 - mmcls - INFO - Epoch [97][4400/5005]\tlr: 1.000e-04, eta: 0:22:55, time: 0.088, data_time: 0.048, memory: 1125, loss: 1.3349\n2023-01-04 03:47:03,598 - mmcls - INFO - Epoch [97][4500/5005]\tlr: 1.000e-04, eta: 0:22:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3182\n2023-01-04 03:47:12,744 - mmcls - INFO - Epoch [97][4600/5005]\tlr: 1.000e-04, eta: 0:22:37, time: 0.092, data_time: 0.003, memory: 1125, loss: 1.3235\n2023-01-04 03:47:21,907 - mmcls - INFO - Epoch [97][4700/5005]\tlr: 1.000e-04, eta: 0:22:28, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.3127\n2023-01-04 03:47:30,614 - mmcls - INFO - Epoch [97][4800/5005]\tlr: 1.000e-04, eta: 0:22:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3220\n2023-01-04 03:47:39,387 - mmcls - INFO - Epoch [97][4900/5005]\tlr: 1.000e-04, eta: 0:22:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3129\n2023-01-04 03:47:47,942 - mmcls - INFO - Epoch [97][5000/5005]\tlr: 1.000e-04, eta: 0:22:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3114\n2023-01-04 03:48:10,721 - mmcls - INFO - Epoch(val) [97][196]\taccuracy_top-1: 70.3740, accuracy_top-5: 89.4220\n2023-01-04 03:48:21,714 - mmcls - INFO - Epoch [98][100/5005]\tlr: 1.000e-04, eta: 0:21:53, time: 0.110, data_time: 0.034, memory: 1125, loss: 1.3191\n2023-01-04 03:48:30,193 - mmcls - INFO - Epoch [98][200/5005]\tlr: 1.000e-04, eta: 0:21:44, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3256\n2023-01-04 03:48:39,059 - mmcls - INFO - Epoch [98][300/5005]\tlr: 1.000e-04, eta: 0:21:35, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.2860\n2023-01-04 03:48:47,943 - mmcls - INFO - Epoch [98][400/5005]\tlr: 1.000e-04, eta: 0:21:26, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.2990\n2023-01-04 03:48:56,552 - mmcls - INFO - Epoch [98][500/5005]\tlr: 1.000e-04, eta: 0:21:18, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.3311\n2023-01-04 03:49:05,442 - mmcls - INFO - Epoch [98][600/5005]\tlr: 1.000e-04, eta: 0:21:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3124\n2023-01-04 03:49:14,335 - mmcls - INFO - Epoch [98][700/5005]\tlr: 1.000e-04, eta: 0:21:00, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3199\n2023-01-04 03:49:22,922 - mmcls - INFO - Epoch [98][800/5005]\tlr: 1.000e-04, eta: 0:20:51, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3251\n2023-01-04 03:49:31,541 - mmcls - INFO - Epoch [98][900/5005]\tlr: 1.000e-04, eta: 0:20:42, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3448\n2023-01-04 03:49:40,278 - mmcls - INFO - Epoch [98][1000/5005]\tlr: 1.000e-04, eta: 0:20:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3154\n2023-01-04 03:49:49,213 - mmcls - INFO - Epoch [98][1100/5005]\tlr: 1.000e-04, eta: 0:20:25, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3177\n2023-01-04 03:49:57,963 - mmcls - INFO - Epoch [98][1200/5005]\tlr: 1.000e-04, eta: 0:20:16, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3185\n2023-01-04 03:50:06,617 - mmcls - INFO - Epoch [98][1300/5005]\tlr: 1.000e-04, eta: 0:20:07, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3223\n2023-01-04 03:50:15,343 - mmcls - INFO - Epoch [98][1400/5005]\tlr: 1.000e-04, eta: 0:19:58, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3192\n2023-01-04 03:50:24,585 - mmcls - INFO - Epoch [98][1500/5005]\tlr: 1.000e-04, eta: 0:19:49, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3167\n2023-01-04 03:50:33,296 - mmcls - INFO - Epoch [98][1600/5005]\tlr: 1.000e-04, eta: 0:19:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3330\n2023-01-04 03:50:42,101 - mmcls - INFO - Epoch [98][1700/5005]\tlr: 1.000e-04, eta: 0:19:32, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3220\n2023-01-04 03:50:50,840 - mmcls - INFO - Epoch [98][1800/5005]\tlr: 1.000e-04, eta: 0:19:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3069\n2023-01-04 03:50:59,702 - mmcls - INFO - Epoch [98][1900/5005]\tlr: 1.000e-04, eta: 0:19:14, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3213\n2023-01-04 03:51:08,374 - mmcls - INFO - Epoch [98][2000/5005]\tlr: 1.000e-04, eta: 0:19:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3298\n2023-01-04 03:51:17,474 - mmcls - INFO - Epoch [98][2100/5005]\tlr: 1.000e-04, eta: 0:18:57, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.3285\n2023-01-04 03:51:26,169 - mmcls - INFO - Epoch [98][2200/5005]\tlr: 1.000e-04, eta: 0:18:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3069\n2023-01-04 03:51:34,719 - mmcls - INFO - Epoch [98][2300/5005]\tlr: 1.000e-04, eta: 0:18:39, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3298\n2023-01-04 03:51:43,688 - mmcls - INFO - Epoch [98][2400/5005]\tlr: 1.000e-04, eta: 0:18:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3000\n2023-01-04 03:51:52,684 - mmcls - INFO - Epoch [98][2500/5005]\tlr: 1.000e-04, eta: 0:18:21, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3144\n2023-01-04 03:52:01,147 - mmcls - INFO - Epoch [98][2600/5005]\tlr: 1.000e-04, eta: 0:18:13, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3214\n2023-01-04 03:52:09,883 - mmcls - INFO - Epoch [98][2700/5005]\tlr: 1.000e-04, eta: 0:18:04, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3286\n2023-01-04 03:52:18,534 - mmcls - INFO - Epoch [98][2800/5005]\tlr: 1.000e-04, eta: 0:17:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3061\n2023-01-04 03:52:27,239 - mmcls - INFO - Epoch [98][2900/5005]\tlr: 1.000e-04, eta: 0:17:46, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.2946\n2023-01-04 03:52:36,026 - mmcls - INFO - Epoch [98][3000/5005]\tlr: 1.000e-04, eta: 0:17:37, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.2995\n2023-01-04 03:52:44,934 - mmcls - INFO - Epoch [98][3100/5005]\tlr: 1.000e-04, eta: 0:17:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3112\n2023-01-04 03:52:54,089 - mmcls - INFO - Epoch [98][3200/5005]\tlr: 1.000e-04, eta: 0:17:20, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3088\n2023-01-04 03:53:02,901 - mmcls - INFO - Epoch [98][3300/5005]\tlr: 1.000e-04, eta: 0:17:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3210\n2023-01-04 03:53:11,852 - mmcls - INFO - Epoch [98][3400/5005]\tlr: 1.000e-04, eta: 0:17:02, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3128\n2023-01-04 03:53:20,714 - mmcls - INFO - Epoch [98][3500/5005]\tlr: 1.000e-04, eta: 0:16:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3221\n2023-01-04 03:53:29,264 - mmcls - INFO - Epoch [98][3600/5005]\tlr: 1.000e-04, eta: 0:16:45, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3337\n2023-01-04 03:53:38,079 - mmcls - INFO - Epoch [98][3700/5005]\tlr: 1.000e-04, eta: 0:16:36, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3198\n2023-01-04 03:53:46,922 - mmcls - INFO - Epoch [98][3800/5005]\tlr: 1.000e-04, eta: 0:16:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3184\n2023-01-04 03:53:55,746 - mmcls - INFO - Epoch [98][3900/5005]\tlr: 1.000e-04, eta: 0:16:18, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.2959\n2023-01-04 03:54:04,396 - mmcls - INFO - Epoch [98][4000/5005]\tlr: 1.000e-04, eta: 0:16:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3212\n2023-01-04 03:54:13,181 - mmcls - INFO - Epoch [98][4100/5005]\tlr: 1.000e-04, eta: 0:16:01, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3148\n2023-01-04 03:54:21,833 - mmcls - INFO - Epoch [98][4200/5005]\tlr: 1.000e-04, eta: 0:15:52, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.3248\n2023-01-04 03:54:30,834 - mmcls - INFO - Epoch [98][4300/5005]\tlr: 1.000e-04, eta: 0:15:43, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3036\n2023-01-04 03:54:39,508 - mmcls - INFO - Epoch [98][4400/5005]\tlr: 1.000e-04, eta: 0:15:34, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.2864\n2023-01-04 03:54:48,068 - mmcls - INFO - Epoch [98][4500/5005]\tlr: 1.000e-04, eta: 0:15:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3143\n2023-01-04 03:54:56,702 - mmcls - INFO - Epoch [98][4600/5005]\tlr: 1.000e-04, eta: 0:15:16, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3098\n2023-01-04 03:55:05,342 - mmcls - INFO - Epoch [98][4700/5005]\tlr: 1.000e-04, eta: 0:15:08, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3256\n2023-01-04 03:55:14,290 - mmcls - INFO - Epoch [98][4800/5005]\tlr: 1.000e-04, eta: 0:14:59, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.3095\n2023-01-04 03:55:23,137 - mmcls - INFO - Epoch [98][4900/5005]\tlr: 1.000e-04, eta: 0:14:50, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3024\n2023-01-04 03:55:32,249 - mmcls - INFO - Epoch [98][5000/5005]\tlr: 1.000e-04, eta: 0:14:41, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.3063\n2023-01-04 03:55:54,750 - mmcls - INFO - Epoch(val) [98][196]\taccuracy_top-1: 70.2920, accuracy_top-5: 89.5560\n2023-01-04 03:56:05,695 - mmcls - INFO - Epoch [99][100/5005]\tlr: 1.000e-04, eta: 0:14:32, time: 0.109, data_time: 0.057, memory: 1125, loss: 1.3110\n2023-01-04 03:56:14,703 - mmcls - INFO - Epoch [99][200/5005]\tlr: 1.000e-04, eta: 0:14:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3082\n2023-01-04 03:56:23,558 - mmcls - INFO - Epoch [99][300/5005]\tlr: 1.000e-04, eta: 0:14:14, time: 0.089, data_time: 0.049, memory: 1125, loss: 1.2983\n2023-01-04 03:56:32,038 - mmcls - INFO - Epoch [99][400/5005]\tlr: 1.000e-04, eta: 0:14:06, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3155\n2023-01-04 03:56:41,152 - mmcls - INFO - Epoch [99][500/5005]\tlr: 1.000e-04, eta: 0:13:57, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.2977\n2023-01-04 03:56:49,795 - mmcls - INFO - Epoch [99][600/5005]\tlr: 1.000e-04, eta: 0:13:48, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3222\n2023-01-04 03:56:58,534 - mmcls - INFO - Epoch [99][700/5005]\tlr: 1.000e-04, eta: 0:13:39, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3157\n2023-01-04 03:57:07,147 - mmcls - INFO - Epoch [99][800/5005]\tlr: 1.000e-04, eta: 0:13:30, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3071\n2023-01-04 03:57:16,131 - mmcls - INFO - Epoch [99][900/5005]\tlr: 1.000e-04, eta: 0:13:22, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3254\n2023-01-04 03:57:24,958 - mmcls - INFO - Epoch [99][1000/5005]\tlr: 1.000e-04, eta: 0:13:13, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3324\n2023-01-04 03:57:33,573 - mmcls - INFO - Epoch [99][1100/5005]\tlr: 1.000e-04, eta: 0:13:04, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3013\n2023-01-04 03:57:42,248 - mmcls - INFO - Epoch [99][1200/5005]\tlr: 1.000e-04, eta: 0:12:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3075\n2023-01-04 03:57:51,036 - mmcls - INFO - Epoch [99][1300/5005]\tlr: 1.000e-04, eta: 0:12:46, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3109\n2023-01-04 03:57:59,649 - mmcls - INFO - Epoch [99][1400/5005]\tlr: 1.000e-04, eta: 0:12:38, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3282\n2023-01-04 03:58:08,517 - mmcls - INFO - Epoch [99][1500/5005]\tlr: 1.000e-04, eta: 0:12:29, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3000\n2023-01-04 03:58:17,105 - mmcls - INFO - Epoch [99][1600/5005]\tlr: 1.000e-04, eta: 0:12:20, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.2938\n2023-01-04 03:58:25,610 - mmcls - INFO - Epoch [99][1700/5005]\tlr: 1.000e-04, eta: 0:12:11, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3271\n2023-01-04 03:58:34,408 - mmcls - INFO - Epoch [99][1800/5005]\tlr: 1.000e-04, eta: 0:12:02, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3290\n2023-01-04 03:58:43,224 - mmcls - INFO - Epoch [99][1900/5005]\tlr: 1.000e-04, eta: 0:11:54, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3112\n2023-01-04 03:58:51,961 - mmcls - INFO - Epoch [99][2000/5005]\tlr: 1.000e-04, eta: 0:11:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3024\n2023-01-04 03:59:00,849 - mmcls - INFO - Epoch [99][2100/5005]\tlr: 1.000e-04, eta: 0:11:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3154\n2023-01-04 03:59:09,486 - mmcls - INFO - Epoch [99][2200/5005]\tlr: 1.000e-04, eta: 0:11:27, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3343\n2023-01-04 03:59:18,680 - mmcls - INFO - Epoch [99][2300/5005]\tlr: 1.000e-04, eta: 0:11:18, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.2991\n2023-01-04 03:59:27,636 - mmcls - INFO - Epoch [99][2400/5005]\tlr: 1.000e-04, eta: 0:11:10, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3011\n2023-01-04 03:59:36,361 - mmcls - INFO - Epoch [99][2500/5005]\tlr: 1.000e-04, eta: 0:11:01, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.2918\n2023-01-04 03:59:44,930 - mmcls - INFO - Epoch [99][2600/5005]\tlr: 1.000e-04, eta: 0:10:52, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.3157\n2023-01-04 03:59:53,525 - mmcls - INFO - Epoch [99][2700/5005]\tlr: 1.000e-04, eta: 0:10:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.2980\n2023-01-04 04:00:02,018 - mmcls - INFO - Epoch [99][2800/5005]\tlr: 1.000e-04, eta: 0:10:34, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3135\n2023-01-04 04:00:10,677 - mmcls - INFO - Epoch [99][2900/5005]\tlr: 1.000e-04, eta: 0:10:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3206\n2023-01-04 04:00:19,666 - mmcls - INFO - Epoch [99][3000/5005]\tlr: 1.000e-04, eta: 0:10:17, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3065\n2023-01-04 04:00:28,240 - mmcls - INFO - Epoch [99][3100/5005]\tlr: 1.000e-04, eta: 0:10:08, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.2967\n2023-01-04 04:00:36,866 - mmcls - INFO - Epoch [99][3200/5005]\tlr: 1.000e-04, eta: 0:09:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3097\n2023-01-04 04:00:45,855 - mmcls - INFO - Epoch [99][3300/5005]\tlr: 1.000e-04, eta: 0:09:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3111\n2023-01-04 04:00:54,617 - mmcls - INFO - Epoch [99][3400/5005]\tlr: 1.000e-04, eta: 0:09:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3027\n2023-01-04 04:01:03,631 - mmcls - INFO - Epoch [99][3500/5005]\tlr: 1.000e-04, eta: 0:09:33, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.2976\n2023-01-04 04:01:12,356 - mmcls - INFO - Epoch [99][3600/5005]\tlr: 1.000e-04, eta: 0:09:24, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.2958\n2023-01-04 04:01:21,245 - mmcls - INFO - Epoch [99][3700/5005]\tlr: 1.000e-04, eta: 0:09:15, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3008\n2023-01-04 04:01:29,887 - mmcls - INFO - Epoch [99][3800/5005]\tlr: 1.000e-04, eta: 0:09:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3126\n2023-01-04 04:01:38,340 - mmcls - INFO - Epoch [99][3900/5005]\tlr: 1.000e-04, eta: 0:08:57, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3214\n2023-01-04 04:01:46,972 - mmcls - INFO - Epoch [99][4000/5005]\tlr: 1.000e-04, eta: 0:08:49, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3170\n2023-01-04 04:01:56,075 - mmcls - INFO - Epoch [99][4100/5005]\tlr: 1.000e-04, eta: 0:08:40, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.2985\n2023-01-04 04:02:05,266 - mmcls - INFO - Epoch [99][4200/5005]\tlr: 1.000e-04, eta: 0:08:31, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3022\n2023-01-04 04:02:13,673 - mmcls - INFO - Epoch [99][4300/5005]\tlr: 1.000e-04, eta: 0:08:22, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.3136\n2023-01-04 04:02:22,290 - mmcls - INFO - Epoch [99][4400/5005]\tlr: 1.000e-04, eta: 0:08:13, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3182\n2023-01-04 04:02:30,957 - mmcls - INFO - Epoch [99][4500/5005]\tlr: 1.000e-04, eta: 0:08:05, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3030\n2023-01-04 04:02:40,179 - mmcls - INFO - Epoch [99][4600/5005]\tlr: 1.000e-04, eta: 0:07:56, time: 0.092, data_time: 0.024, memory: 1125, loss: 1.3071\n2023-01-04 04:02:48,826 - mmcls - INFO - Epoch [99][4700/5005]\tlr: 1.000e-04, eta: 0:07:47, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.3198\n2023-01-04 04:02:57,669 - mmcls - INFO - Epoch [99][4800/5005]\tlr: 1.000e-04, eta: 0:07:38, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.2971\n2023-01-04 04:03:06,619 - mmcls - INFO - Epoch [99][4900/5005]\tlr: 1.000e-04, eta: 0:07:29, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3291\n2023-01-04 04:03:15,403 - mmcls - INFO - Epoch [99][5000/5005]\tlr: 1.000e-04, eta: 0:07:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3440\n2023-01-04 04:03:38,077 - mmcls - INFO - Epoch(val) [99][196]\taccuracy_top-1: 70.2240, accuracy_top-5: 89.5020\n2023-01-04 04:03:48,922 - mmcls - INFO - Epoch [100][100/5005]\tlr: 1.000e-04, eta: 0:07:11, time: 0.108, data_time: 0.038, memory: 1125, loss: 1.3140\n2023-01-04 04:03:57,726 - mmcls - INFO - Epoch [100][200/5005]\tlr: 1.000e-04, eta: 0:07:03, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.2898\n2023-01-04 04:04:06,536 - mmcls - INFO - Epoch [100][300/5005]\tlr: 1.000e-04, eta: 0:06:54, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.2998\n2023-01-04 04:04:15,457 - mmcls - INFO - Epoch [100][400/5005]\tlr: 1.000e-04, eta: 0:06:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3280\n2023-01-04 04:04:24,328 - mmcls - INFO - Epoch [100][500/5005]\tlr: 1.000e-04, eta: 0:06:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3081\n2023-01-04 04:04:33,270 - mmcls - INFO - Epoch [100][600/5005]\tlr: 1.000e-04, eta: 0:06:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3038\n2023-01-04 04:04:41,994 - mmcls - INFO - Epoch [100][700/5005]\tlr: 1.000e-04, eta: 0:06:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.2925\n2023-01-04 04:04:50,631 - mmcls - INFO - Epoch [100][800/5005]\tlr: 1.000e-04, eta: 0:06:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3319\n2023-01-04 04:04:59,581 - mmcls - INFO - Epoch [100][900/5005]\tlr: 1.000e-04, eta: 0:06:01, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3279\n2023-01-04 04:05:08,610 - mmcls - INFO - Epoch [100][1000/5005]\tlr: 1.000e-04, eta: 0:05:52, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3137\n2023-01-04 04:05:17,396 - mmcls - INFO - Epoch [100][1100/5005]\tlr: 1.000e-04, eta: 0:05:43, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3020\n2023-01-04 04:05:26,058 - mmcls - INFO - Epoch [100][1200/5005]\tlr: 1.000e-04, eta: 0:05:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3006\n2023-01-04 04:05:34,794 - mmcls - INFO - Epoch [100][1300/5005]\tlr: 1.000e-04, eta: 0:05:26, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3252\n2023-01-04 04:05:43,710 - mmcls - INFO - Epoch [100][1400/5005]\tlr: 1.000e-04, eta: 0:05:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3150\n2023-01-04 04:05:52,432 - mmcls - INFO - Epoch [100][1500/5005]\tlr: 1.000e-04, eta: 0:05:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3282\n2023-01-04 04:06:01,141 - mmcls - INFO - Epoch [100][1600/5005]\tlr: 1.000e-04, eta: 0:04:59, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3120\n2023-01-04 04:06:09,867 - mmcls - INFO - Epoch [100][1700/5005]\tlr: 1.000e-04, eta: 0:04:51, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3113\n2023-01-04 04:06:18,518 - mmcls - INFO - Epoch [100][1800/5005]\tlr: 1.000e-04, eta: 0:04:42, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.2839\n2023-01-04 04:06:27,305 - mmcls - INFO - Epoch [100][1900/5005]\tlr: 1.000e-04, eta: 0:04:33, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3131\n2023-01-04 04:06:36,029 - mmcls - INFO - Epoch [100][2000/5005]\tlr: 1.000e-04, eta: 0:04:24, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3003\n2023-01-04 04:06:44,635 - mmcls - INFO - Epoch [100][2100/5005]\tlr: 1.000e-04, eta: 0:04:15, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3139\n2023-01-04 04:06:53,376 - mmcls - INFO - Epoch [100][2200/5005]\tlr: 1.000e-04, eta: 0:04:06, time: 0.087, data_time: 0.042, memory: 1125, loss: 1.3018\n2023-01-04 04:07:02,500 - mmcls - INFO - Epoch [100][2300/5005]\tlr: 1.000e-04, eta: 0:03:58, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.3160\n2023-01-04 04:07:11,055 - mmcls - INFO - Epoch [100][2400/5005]\tlr: 1.000e-04, eta: 0:03:49, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.2964\n2023-01-04 04:07:19,759 - mmcls - INFO - Epoch [100][2500/5005]\tlr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3090\n2023-01-04 04:07:28,380 - mmcls - INFO - Epoch [100][2600/5005]\tlr: 1.000e-04, eta: 0:03:31, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3246\n2023-01-04 04:07:37,014 - mmcls - INFO - Epoch [100][2700/5005]\tlr: 1.000e-04, eta: 0:03:22, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.3149\n2023-01-04 04:07:45,992 - mmcls - INFO - Epoch [100][2800/5005]\tlr: 1.000e-04, eta: 0:03:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3204\n2023-01-04 04:07:54,938 - mmcls - INFO - Epoch [100][2900/5005]\tlr: 1.000e-04, eta: 0:03:05, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3128\n2023-01-04 04:08:03,708 - mmcls - INFO - Epoch [100][3000/5005]\tlr: 1.000e-04, eta: 0:02:56, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3033\n2023-01-04 04:08:12,213 - mmcls - INFO - Epoch [100][3100/5005]\tlr: 1.000e-04, eta: 0:02:47, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3194\n2023-01-04 04:08:20,934 - mmcls - INFO - Epoch [100][3200/5005]\tlr: 1.000e-04, eta: 0:02:38, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3229\n2023-01-04 04:08:29,801 - mmcls - INFO - Epoch [100][3300/5005]\tlr: 1.000e-04, eta: 0:02:30, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3191\n2023-01-04 04:08:38,542 - mmcls - INFO - Epoch [100][3400/5005]\tlr: 1.000e-04, eta: 0:02:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.2966\n2023-01-04 04:08:47,417 - mmcls - INFO - Epoch [100][3500/5005]\tlr: 1.000e-04, eta: 0:02:12, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3205\n2023-01-04 04:08:56,430 - mmcls - INFO - Epoch [100][3600/5005]\tlr: 1.000e-04, eta: 0:02:03, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.3105\n2023-01-04 04:09:05,169 - mmcls - INFO - Epoch [100][3700/5005]\tlr: 1.000e-04, eta: 0:01:54, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.2810\n2023-01-04 04:09:13,994 - mmcls - INFO - Epoch [100][3800/5005]\tlr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3143\n2023-01-04 04:09:22,890 - mmcls - INFO - Epoch [100][3900/5005]\tlr: 1.000e-04, eta: 0:01:37, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.2995\n2023-01-04 04:09:31,669 - mmcls - INFO - Epoch [100][4000/5005]\tlr: 1.000e-04, eta: 0:01:28, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3128\n2023-01-04 04:09:40,309 - mmcls - INFO - Epoch [100][4100/5005]\tlr: 1.000e-04, eta: 0:01:19, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3171\n2023-01-04 04:09:48,928 - mmcls - INFO - Epoch [100][4200/5005]\tlr: 1.000e-04, eta: 0:01:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3298\n2023-01-04 04:09:57,798 - mmcls - INFO - Epoch [100][4300/5005]\tlr: 1.000e-04, eta: 0:01:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3102\n2023-01-04 04:10:06,335 - mmcls - INFO - Epoch [100][4400/5005]\tlr: 1.000e-04, eta: 0:00:53, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3299\n2023-01-04 04:10:15,182 - mmcls - INFO - Epoch [100][4500/5005]\tlr: 1.000e-04, eta: 0:00:44, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3240\n2023-01-04 04:10:24,122 - mmcls - INFO - Epoch [100][4600/5005]\tlr: 1.000e-04, eta: 0:00:35, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3216\n2023-01-04 04:10:32,844 - mmcls - INFO - Epoch [100][4700/5005]\tlr: 1.000e-04, eta: 0:00:26, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3141\n2023-01-04 04:10:41,786 - mmcls - INFO - Epoch [100][4800/5005]\tlr: 1.000e-04, eta: 0:00:18, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.3268\n2023-01-04 04:10:50,222 - mmcls - INFO - Epoch [100][4900/5005]\tlr: 1.000e-04, eta: 0:00:09, time: 0.084, data_time: 0.015, memory: 1125, loss: 1.3135\n2023-01-04 04:10:59,074 - mmcls - INFO - Epoch [100][5000/5005]\tlr: 1.000e-04, eta: 0:00:00, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3223\n2023-01-04 04:10:59,463 - mmcls - INFO - Saving checkpoint at 100 epochs\n2023-01-04 04:11:21,750 - mmcls - INFO - Epoch(val) [100][196]\taccuracy_top-1: 70.3500, accuracy_top-5: 89.4340\n"
  },
  {
    "path": "classification-NO-activation-function/logs/resnet_norelu.log",
    "content": "2023-01-04 15:10:41,671 - mmcls - INFO - Environment info:\n------------------------------------------------------------\nsys.platform: linux\nPython: 3.6.12 |Anaconda, Inc.| (default, Sep  8 2020, 23:10:56) [GCC 7.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 10.2, V10.2.8\nGCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0\nPyTorch: 1.9.0+cu102\nPyTorch compiling details: PyTorch built with:\n  - GCC 7.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 10.2\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70\n  - CuDNN 7.6.5\n  - Magma 2.5.2\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n\nTorchVision: 0.10.0+cu102\nOpenCV: 4.5.5\nMMCV: 1.5.1\nMMCV Compiler: GCC 7.5\nMMCV CUDA Compiler: 10.2\nMMClassification: 0.24.0+91b85bb\n------------------------------------------------------------\n\n2023-01-04 15:10:41,672 - mmcls - INFO - Distributed training: True\n2023-01-04 15:10:41,805 - mmcls - INFO - Config:\nmodel = dict(\n    type='ImageClassifier',\n    backbone=dict(\n        type='ResNet',\n        depth=18,\n        num_stages=4,\n        out_indices=(3, ),\n        style='pytorch'),\n    neck=dict(type='GlobalAveragePooling'),\n    head=dict(\n        type='LinearClsHead',\n        num_classes=1000,\n        in_channels=512,\n        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n        topk=(1, 5)))\ndataset_type = 'ImageNet'\nimg_norm_cfg = dict(\n    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)\ntrain_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='RandomResizedCrop', size=224),\n    dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='ToTensor', keys=['gt_label']),\n    dict(type='Collect', keys=['img', 'gt_label'])\n]\ntest_pipeline = [\n    dict(type='LoadImageFromFile'),\n    dict(type='Resize', size=(256, -1)),\n    dict(type='CenterCrop', crop_size=224),\n    dict(\n        type='Normalize',\n        mean=[123.675, 116.28, 103.53],\n        std=[58.395, 57.12, 57.375],\n        to_rgb=True),\n    dict(type='ImageToTensor', keys=['img']),\n    dict(type='Collect', keys=['img'])\n]\ndata = dict(\n    samples_per_gpu=32,\n    workers_per_gpu=2,\n    train=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/train',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='RandomResizedCrop', size=224),\n            dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='ToTensor', keys=['gt_label']),\n            dict(type='Collect', keys=['img', 'gt_label'])\n        ]),\n    val=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]),\n    test=dict(\n        type='ImageNet',\n        data_prefix='data/imagenet/val',\n        ann_file='data/imagenet/meta/val.txt',\n        pipeline=[\n            dict(type='LoadImageFromFile'),\n            dict(type='Resize', size=(256, -1)),\n            dict(type='CenterCrop', crop_size=224),\n            dict(\n                type='Normalize',\n                mean=[123.675, 116.28, 103.53],\n                std=[58.395, 57.12, 57.375],\n                to_rgb=True),\n            dict(type='ImageToTensor', keys=['img']),\n            dict(type='Collect', keys=['img'])\n        ]))\nevaluation = dict(interval=1, metric='accuracy')\noptimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)\noptimizer_config = dict(grad_clip=None)\nlr_config = dict(policy='step', step=[30, 60, 90])\nrunner = dict(type='EpochBasedRunner', max_epochs=100)\ncheckpoint_config = dict(interval=50)\nlog_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\nwork_dir = './work_dirs/resnet18_8xb32_in1k_pi'\ngpu_ids = range(0, 8)\n\n2023-01-04 15:10:47,234 - mmcls - INFO - Set random seed to 57587029, deterministic: False\n2023-01-04 15:10:47,418 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}]\n2023-01-04 15:10:47,501 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\nName of parameter - Initialization information\n\nbackbone.conv1.weight - torch.Size([64, 3, 7, 7]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.0.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.0.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn1.weight - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.bn1.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer1.1.bn2.weight - torch.Size([64]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer1.1.bn2.bias - torch.Size([64]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.0.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.0.downsample.1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.0.downsample.1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn1.weight - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.bn1.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer2.1.bn2.weight - torch.Size([128]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer2.1.bn2.bias - torch.Size([128]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.0.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.0.downsample.1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.0.downsample.1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn1.weight - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.bn1.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer3.1.bn2.weight - torch.Size([256]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer3.1.bn2.bias - torch.Size([256]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.0.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.0.downsample.1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.0.downsample.1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn1.weight - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.bn1.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nbackbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): \nKaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 \n\nbackbone.layer4.1.bn2.weight - torch.Size([512]): \nInitialized by user-defined `init_weights` in ResNet  \n\nbackbone.layer4.1.bn2.bias - torch.Size([512]): \nThe value is the same before and after calling `init_weights` of ImageClassifier  \n\nhead.fc.weight - torch.Size([1000, 512]): \nNormalInit: mean=0, std=0.01, bias=0 \n\nhead.fc.bias - torch.Size([1000]): \nNormalInit: mean=0, std=0.01, bias=0 \n2023-01-04 15:11:16,442 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi\n2023-01-04 15:11:16,442 - mmcls - INFO - Hooks will be executed in the following order:\nbefore_run:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_epoch:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_train_iter:\n(VERY_HIGH   ) StepLrUpdaterHook                  \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n -------------------- \nafter_train_iter:\n(ABOVE_NORMAL) DistOptimizerHook                  \n(NORMAL      ) CheckpointHook                     \n(LOW         ) IterTimerHook                      \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_train_epoch:\n(NORMAL      ) CheckpointHook                     \n(LOW         ) DistEvalHook                       \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_epoch:\n(NORMAL      ) DistSamplerSeedHook                \n(LOW         ) IterTimerHook                      \n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nbefore_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_iter:\n(LOW         ) IterTimerHook                      \n -------------------- \nafter_val_epoch:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \nafter_run:\n(VERY_LOW    ) TextLoggerHook                     \n -------------------- \n2023-01-04 15:11:16,443 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs\n2023-01-04 15:11:16,443 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend.\n2023-01-04 15:11:28,577 - mmcls - INFO - Epoch [1][100/5005]\tlr: 1.000e-01, eta: 16:50:32, time: 0.121, data_time: 0.029, memory: 928, loss: 6.7395\n2023-01-04 15:11:37,438 - mmcls - INFO - Epoch [1][200/5005]\tlr: 1.000e-01, eta: 14:34:46, time: 0.089, data_time: 0.008, memory: 928, loss: 6.5939\n2023-01-04 15:11:46,354 - mmcls - INFO - Epoch [1][300/5005]\tlr: 1.000e-01, eta: 13:50:17, time: 0.089, data_time: 0.001, memory: 928, loss: 6.4922\n2023-01-04 15:11:55,291 - mmcls - INFO - Epoch [1][400/5005]\tlr: 1.000e-01, eta: 13:28:56, time: 0.089, data_time: 0.001, memory: 928, loss: 6.4055\n2023-01-04 15:12:03,819 - mmcls - INFO - Epoch [1][500/5005]\tlr: 1.000e-01, eta: 13:09:03, time: 0.085, data_time: 0.004, memory: 928, loss: 6.3595\n2023-01-04 15:12:12,514 - mmcls - INFO - Epoch [1][600/5005]\tlr: 1.000e-01, eta: 12:58:19, time: 0.087, data_time: 0.007, memory: 928, loss: 6.3061\n2023-01-04 15:12:21,388 - mmcls - INFO - Epoch [1][700/5005]\tlr: 1.000e-01, eta: 12:52:24, time: 0.089, data_time: 0.011, memory: 928, loss: 6.2641\n2023-01-04 15:12:30,359 - mmcls - INFO - Epoch [1][800/5005]\tlr: 1.000e-01, eta: 12:49:11, time: 0.090, data_time: 0.001, memory: 928, loss: 6.2340\n2023-01-04 15:12:39,119 - mmcls - INFO - Epoch [1][900/5005]\tlr: 1.000e-01, eta: 12:44:38, time: 0.088, data_time: 0.002, memory: 928, loss: 6.2221\n2023-01-04 15:12:47,860 - mmcls - INFO - Epoch [1][1000/5005]\tlr: 1.000e-01, eta: 12:40:47, time: 0.087, data_time: 0.013, memory: 928, loss: 6.1993\n2023-01-04 15:12:56,925 - mmcls - INFO - Epoch [1][1100/5005]\tlr: 1.000e-01, eta: 12:40:05, time: 0.091, data_time: 0.016, memory: 928, loss: 6.1824\n2023-01-04 15:13:06,178 - mmcls - INFO - Epoch [1][1200/5005]\tlr: 1.000e-01, eta: 12:40:45, time: 0.092, data_time: 0.001, memory: 928, loss: 6.1719\n2023-01-04 15:13:15,013 - mmcls - INFO - Epoch [1][1300/5005]\tlr: 1.000e-01, eta: 12:38:40, time: 0.088, data_time: 0.001, memory: 928, loss: 6.1480\n2023-01-04 15:13:23,774 - mmcls - INFO - Epoch [1][1400/5005]\tlr: 1.000e-01, eta: 12:36:25, time: 0.088, data_time: 0.012, memory: 928, loss: 6.1345\n2023-01-04 15:13:32,475 - mmcls - INFO - Epoch [1][1500/5005]\tlr: 1.000e-01, eta: 12:34:02, time: 0.087, data_time: 0.002, memory: 928, loss: 6.1187\n2023-01-04 15:13:41,135 - mmcls - INFO - Epoch [1][1600/5005]\tlr: 1.000e-01, eta: 12:31:47, time: 0.087, data_time: 0.003, memory: 928, loss: 6.1191\n2023-01-04 15:13:50,066 - mmcls - INFO - Epoch [1][1700/5005]\tlr: 1.000e-01, eta: 12:31:03, time: 0.089, data_time: 0.004, memory: 928, loss: 6.0862\n2023-01-04 15:13:59,101 - mmcls - INFO - Epoch [1][1800/5005]\tlr: 1.000e-01, eta: 12:30:57, time: 0.090, data_time: 0.001, memory: 928, loss: 6.0892\n2023-01-04 15:14:08,038 - mmcls - INFO - Epoch [1][1900/5005]\tlr: 1.000e-01, eta: 12:30:20, time: 0.089, data_time: 0.015, memory: 928, loss: 6.0618\n2023-01-04 15:14:16,852 - mmcls - INFO - Epoch [1][2000/5005]\tlr: 1.000e-01, eta: 12:29:17, time: 0.088, data_time: 0.013, memory: 928, loss: 6.0565\n2023-01-04 15:14:25,510 - mmcls - INFO - Epoch [1][2100/5005]\tlr: 1.000e-01, eta: 12:27:44, time: 0.087, data_time: 0.010, memory: 928, loss: 6.0436\n2023-01-04 15:14:34,195 - mmcls - INFO - Epoch [1][2200/5005]\tlr: 1.000e-01, eta: 12:26:22, time: 0.087, data_time: 0.007, memory: 928, loss: 6.0503\n2023-01-04 15:14:42,714 - mmcls - INFO - Epoch [1][2300/5005]\tlr: 1.000e-01, eta: 12:24:28, time: 0.085, data_time: 0.005, memory: 928, loss: 6.0324\n2023-01-04 15:14:51,311 - mmcls - INFO - Epoch [1][2400/5005]\tlr: 1.000e-01, eta: 12:23:05, time: 0.086, data_time: 0.008, memory: 928, loss: 6.0265\n2023-01-04 15:15:00,046 - mmcls - INFO - Epoch [1][2500/5005]\tlr: 1.000e-01, eta: 12:22:12, time: 0.087, data_time: 0.010, memory: 928, loss: 6.0058\n2023-01-04 15:15:08,837 - mmcls - INFO - Epoch [1][2600/5005]\tlr: 1.000e-01, eta: 12:21:34, time: 0.088, data_time: 0.006, memory: 928, loss: 5.9983\n2023-01-04 15:15:17,811 - mmcls - INFO - Epoch [1][2700/5005]\tlr: 1.000e-01, eta: 12:21:33, time: 0.090, data_time: 0.030, memory: 928, loss: 5.9877\n2023-01-04 15:15:26,828 - mmcls - INFO - Epoch [1][2800/5005]\tlr: 1.000e-01, eta: 12:21:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.9932\n2023-01-04 15:15:35,665 - mmcls - INFO - Epoch [1][2900/5005]\tlr: 1.000e-01, eta: 12:21:10, time: 0.088, data_time: 0.011, memory: 928, loss: 5.9754\n2023-01-04 15:15:44,374 - mmcls - INFO - Epoch [1][3000/5005]\tlr: 1.000e-01, eta: 12:20:23, time: 0.087, data_time: 0.013, memory: 928, loss: 5.9695\n2023-01-04 15:15:53,221 - mmcls - INFO - Epoch [1][3100/5005]\tlr: 1.000e-01, eta: 12:20:01, time: 0.088, data_time: 0.020, memory: 928, loss: 5.9515\n2023-01-04 15:16:02,227 - mmcls - INFO - Epoch [1][3200/5005]\tlr: 1.000e-01, eta: 12:20:06, time: 0.090, data_time: 0.006, memory: 928, loss: 5.9519\n2023-01-04 15:16:11,200 - mmcls - INFO - Epoch [1][3300/5005]\tlr: 1.000e-01, eta: 12:20:03, time: 0.090, data_time: 0.011, memory: 928, loss: 5.9541\n2023-01-04 15:16:19,876 - mmcls - INFO - Epoch [1][3400/5005]\tlr: 1.000e-01, eta: 12:19:15, time: 0.087, data_time: 0.005, memory: 928, loss: 5.9269\n2023-01-04 15:16:28,875 - mmcls - INFO - Epoch [1][3500/5005]\tlr: 1.000e-01, eta: 12:19:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.9199\n2023-01-04 15:16:37,873 - mmcls - INFO - Epoch [1][3600/5005]\tlr: 1.000e-01, eta: 12:19:18, time: 0.090, data_time: 0.027, memory: 928, loss: 5.9149\n2023-01-04 15:16:46,610 - mmcls - INFO - Epoch [1][3700/5005]\tlr: 1.000e-01, eta: 12:18:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.9133\n2023-01-04 15:16:55,436 - mmcls - INFO - Epoch [1][3800/5005]\tlr: 1.000e-01, eta: 12:18:22, time: 0.088, data_time: 0.006, memory: 928, loss: 5.9081\n2023-01-04 15:17:04,191 - mmcls - INFO - Epoch [1][3900/5005]\tlr: 1.000e-01, eta: 12:17:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.9085\n2023-01-04 15:17:13,064 - mmcls - INFO - Epoch [1][4000/5005]\tlr: 1.000e-01, eta: 12:17:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.8858\n2023-01-04 15:17:22,085 - mmcls - INFO - Epoch [1][4100/5005]\tlr: 1.000e-01, eta: 12:17:43, time: 0.090, data_time: 0.011, memory: 928, loss: 5.8997\n2023-01-04 15:17:31,336 - mmcls - INFO - Epoch [1][4200/5005]\tlr: 1.000e-01, eta: 12:18:13, time: 0.092, data_time: 0.007, memory: 928, loss: 5.8978\n2023-01-04 15:17:40,322 - mmcls - INFO - Epoch [1][4300/5005]\tlr: 1.000e-01, eta: 12:18:12, time: 0.090, data_time: 0.005, memory: 928, loss: 5.8896\n2023-01-04 15:17:49,104 - mmcls - INFO - Epoch [1][4400/5005]\tlr: 1.000e-01, eta: 12:17:46, time: 0.088, data_time: 0.015, memory: 928, loss: 5.8744\n2023-01-04 15:17:57,894 - mmcls - INFO - Epoch [1][4500/5005]\tlr: 1.000e-01, eta: 12:17:21, time: 0.088, data_time: 0.012, memory: 928, loss: 5.8636\n2023-01-04 15:18:07,056 - mmcls - INFO - Epoch [1][4600/5005]\tlr: 1.000e-01, eta: 12:17:38, time: 0.092, data_time: 0.022, memory: 928, loss: 5.8582\n2023-01-04 15:18:15,891 - mmcls - INFO - Epoch [1][4700/5005]\tlr: 1.000e-01, eta: 12:17:20, time: 0.088, data_time: 0.021, memory: 928, loss: 5.8447\n2023-01-04 15:18:24,651 - mmcls - INFO - Epoch [1][4800/5005]\tlr: 1.000e-01, eta: 12:16:54, time: 0.088, data_time: 0.032, memory: 928, loss: 5.8417\n2023-01-04 15:18:33,359 - mmcls - INFO - Epoch [1][4900/5005]\tlr: 1.000e-01, eta: 12:16:23, time: 0.087, data_time: 0.004, memory: 928, loss: 5.8609\n2023-01-04 15:18:42,617 - mmcls - INFO - Epoch [1][5000/5005]\tlr: 1.000e-01, eta: 12:16:49, time: 0.093, data_time: 0.001, memory: 928, loss: 5.8492\n2023-01-04 15:19:06,421 - mmcls - INFO - Epoch(val) [1][196]\taccuracy_top-1: 4.3440, accuracy_top-5: 12.6540\n2023-01-04 15:19:17,483 - mmcls - INFO - Epoch [2][100/5005]\tlr: 1.000e-01, eta: 12:19:23, time: 0.110, data_time: 0.036, memory: 928, loss: 5.8502\n2023-01-04 15:19:26,285 - mmcls - INFO - Epoch [2][200/5005]\tlr: 1.000e-01, eta: 12:18:58, time: 0.088, data_time: 0.023, memory: 928, loss: 5.8321\n2023-01-04 15:19:35,413 - mmcls - INFO - Epoch [2][300/5005]\tlr: 1.000e-01, eta: 12:19:06, time: 0.091, data_time: 0.003, memory: 928, loss: 5.8442\n2023-01-04 15:19:44,401 - mmcls - INFO - Epoch [2][400/5005]\tlr: 1.000e-01, eta: 12:19:00, time: 0.090, data_time: 0.009, memory: 928, loss: 5.8364\n2023-01-04 15:19:52,840 - mmcls - INFO - Epoch [2][500/5005]\tlr: 1.000e-01, eta: 12:18:05, time: 0.084, data_time: 0.005, memory: 928, loss: 5.8206\n2023-01-04 15:20:01,758 - mmcls - INFO - Epoch [2][600/5005]\tlr: 1.000e-01, eta: 12:17:52, time: 0.089, data_time: 0.002, memory: 928, loss: 5.8263\n2023-01-04 15:20:10,374 - mmcls - INFO - Epoch [2][700/5005]\tlr: 1.000e-01, eta: 12:17:15, time: 0.086, data_time: 0.021, memory: 928, loss: 5.8219\n2023-01-04 15:20:19,328 - mmcls - INFO - Epoch [2][800/5005]\tlr: 1.000e-01, eta: 12:17:08, time: 0.090, data_time: 0.013, memory: 928, loss: 5.8029\n2023-01-04 15:20:28,060 - mmcls - INFO - Epoch [2][900/5005]\tlr: 1.000e-01, eta: 12:16:42, time: 0.087, data_time: 0.002, memory: 928, loss: 5.8130\n2023-01-04 15:20:36,800 - mmcls - INFO - Epoch [2][1000/5005]\tlr: 1.000e-01, eta: 12:16:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.7985\n2023-01-04 15:20:45,819 - mmcls - INFO - Epoch [2][1100/5005]\tlr: 1.000e-01, eta: 12:16:14, time: 0.090, data_time: 0.001, memory: 928, loss: 5.8009\n2023-01-04 15:20:54,440 - mmcls - INFO - Epoch [2][1200/5005]\tlr: 1.000e-01, eta: 12:15:39, time: 0.086, data_time: 0.002, memory: 928, loss: 5.7843\n2023-01-04 15:21:03,070 - mmcls - INFO - Epoch [2][1300/5005]\tlr: 1.000e-01, eta: 12:15:07, time: 0.086, data_time: 0.003, memory: 928, loss: 5.8084\n2023-01-04 15:21:11,589 - mmcls - INFO - Epoch [2][1400/5005]\tlr: 1.000e-01, eta: 12:14:27, time: 0.085, data_time: 0.002, memory: 928, loss: 5.7997\n2023-01-04 15:21:20,588 - mmcls - INFO - Epoch [2][1500/5005]\tlr: 1.000e-01, eta: 12:14:24, time: 0.090, data_time: 0.002, memory: 928, loss: 5.7827\n2023-01-04 15:21:29,393 - mmcls - INFO - Epoch [2][1600/5005]\tlr: 1.000e-01, eta: 12:14:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.7832\n2023-01-04 15:21:38,145 - mmcls - INFO - Epoch [2][1700/5005]\tlr: 1.000e-01, eta: 12:13:46, time: 0.088, data_time: 0.009, memory: 928, loss: 5.7894\n2023-01-04 15:21:47,026 - mmcls - INFO - Epoch [2][1800/5005]\tlr: 1.000e-01, eta: 12:13:34, time: 0.089, data_time: 0.014, memory: 928, loss: 5.7914\n2023-01-04 15:21:55,907 - mmcls - INFO - Epoch [2][1900/5005]\tlr: 1.000e-01, eta: 12:13:22, time: 0.089, data_time: 0.004, memory: 928, loss: 5.7744\n2023-01-04 15:22:04,477 - mmcls - INFO - Epoch [2][2000/5005]\tlr: 1.000e-01, eta: 12:12:49, time: 0.086, data_time: 0.004, memory: 928, loss: 5.8033\n2023-01-04 15:22:13,571 - mmcls - INFO - Epoch [2][2100/5005]\tlr: 1.000e-01, eta: 12:12:53, time: 0.091, data_time: 0.001, memory: 928, loss: 5.7764\n2023-01-04 15:22:22,093 - mmcls - INFO - Epoch [2][2200/5005]\tlr: 1.000e-01, eta: 12:12:17, time: 0.085, data_time: 0.002, memory: 928, loss: 5.7867\n2023-01-04 15:22:31,080 - mmcls - INFO - Epoch [2][2300/5005]\tlr: 1.000e-01, eta: 12:12:14, time: 0.090, data_time: 0.018, memory: 928, loss: 5.7649\n2023-01-04 15:22:39,731 - mmcls - INFO - Epoch [2][2400/5005]\tlr: 1.000e-01, eta: 12:11:48, time: 0.087, data_time: 0.002, memory: 928, loss: 5.7729\n2023-01-04 15:22:48,451 - mmcls - INFO - Epoch [2][2500/5005]\tlr: 1.000e-01, eta: 12:11:26, time: 0.087, data_time: 0.005, memory: 928, loss: 5.7640\n2023-01-04 15:22:57,425 - mmcls - INFO - Epoch [2][2600/5005]\tlr: 1.000e-01, eta: 12:11:22, time: 0.090, data_time: 0.006, memory: 928, loss: 5.7520\n2023-01-04 15:23:06,332 - mmcls - INFO - Epoch [2][2700/5005]\tlr: 1.000e-01, eta: 12:11:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.7593\n2023-01-04 15:23:15,641 - mmcls - INFO - Epoch [2][2800/5005]\tlr: 1.000e-01, eta: 12:11:29, time: 0.093, data_time: 0.010, memory: 928, loss: 5.7615\n2023-01-04 15:23:24,443 - mmcls - INFO - Epoch [2][2900/5005]\tlr: 1.000e-01, eta: 12:11:14, time: 0.088, data_time: 0.011, memory: 928, loss: 5.7453\n2023-01-04 15:23:33,255 - mmcls - INFO - Epoch [2][3000/5005]\tlr: 1.000e-01, eta: 12:11:00, time: 0.088, data_time: 0.004, memory: 928, loss: 5.7471\n2023-01-04 15:23:42,247 - mmcls - INFO - Epoch [2][3100/5005]\tlr: 1.000e-01, eta: 12:10:55, time: 0.090, data_time: 0.002, memory: 928, loss: 5.7398\n2023-01-04 15:23:51,136 - mmcls - INFO - Epoch [2][3200/5005]\tlr: 1.000e-01, eta: 12:10:46, time: 0.089, data_time: 0.006, memory: 928, loss: 5.7542\n2023-01-04 15:24:00,006 - mmcls - INFO - Epoch [2][3300/5005]\tlr: 1.000e-01, eta: 12:10:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.7390\n2023-01-04 15:24:08,841 - mmcls - INFO - Epoch [2][3400/5005]\tlr: 1.000e-01, eta: 12:10:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.7325\n2023-01-04 15:24:17,701 - mmcls - INFO - Epoch [2][3500/5005]\tlr: 1.000e-01, eta: 12:10:10, time: 0.089, data_time: 0.001, memory: 928, loss: 5.7742\n2023-01-04 15:24:26,607 - mmcls - INFO - Epoch [2][3600/5005]\tlr: 1.000e-01, eta: 12:10:01, time: 0.089, data_time: 0.008, memory: 928, loss: 5.7344\n2023-01-04 15:24:35,492 - mmcls - INFO - Epoch [2][3700/5005]\tlr: 1.000e-01, eta: 12:09:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.7395\n2023-01-04 15:24:44,405 - mmcls - INFO - Epoch [2][3800/5005]\tlr: 1.000e-01, eta: 12:09:43, time: 0.089, data_time: 0.007, memory: 928, loss: 5.7364\n2023-01-04 15:24:53,174 - mmcls - INFO - Epoch [2][3900/5005]\tlr: 1.000e-01, eta: 12:09:26, time: 0.088, data_time: 0.003, memory: 928, loss: 5.7253\n2023-01-04 15:25:02,119 - mmcls - INFO - Epoch [2][4000/5005]\tlr: 1.000e-01, eta: 12:09:19, time: 0.089, data_time: 0.002, memory: 928, loss: 5.7247\n2023-01-04 15:25:11,233 - mmcls - INFO - Epoch [2][4100/5005]\tlr: 1.000e-01, eta: 12:09:22, time: 0.091, data_time: 0.002, memory: 928, loss: 5.7267\n2023-01-04 15:25:20,017 - mmcls - INFO - Epoch [2][4200/5005]\tlr: 1.000e-01, eta: 12:09:07, time: 0.088, data_time: 0.010, memory: 928, loss: 5.7235\n2023-01-04 15:25:28,624 - mmcls - INFO - Epoch [2][4300/5005]\tlr: 1.000e-01, eta: 12:08:42, time: 0.086, data_time: 0.004, memory: 928, loss: 5.7428\n2023-01-04 15:25:37,240 - mmcls - INFO - Epoch [2][4400/5005]\tlr: 1.000e-01, eta: 12:08:18, time: 0.086, data_time: 0.007, memory: 928, loss: 5.7095\n2023-01-04 15:25:45,789 - mmcls - INFO - Epoch [2][4500/5005]\tlr: 1.000e-01, eta: 12:07:51, time: 0.085, data_time: 0.003, memory: 928, loss: 5.7190\n2023-01-04 15:25:54,692 - mmcls - INFO - Epoch [2][4600/5005]\tlr: 1.000e-01, eta: 12:07:42, time: 0.089, data_time: 0.008, memory: 928, loss: 5.7414\n2023-01-04 15:26:03,872 - mmcls - INFO - Epoch [2][4700/5005]\tlr: 1.000e-01, eta: 12:07:48, time: 0.092, data_time: 0.029, memory: 928, loss: 5.7176\n2023-01-04 15:26:12,597 - mmcls - INFO - Epoch [2][4800/5005]\tlr: 1.000e-01, eta: 12:07:30, time: 0.087, data_time: 0.009, memory: 928, loss: 5.6865\n2023-01-04 15:26:21,352 - mmcls - INFO - Epoch [2][4900/5005]\tlr: 1.000e-01, eta: 12:07:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.7138\n2023-01-04 15:26:30,093 - mmcls - INFO - Epoch [2][5000/5005]\tlr: 1.000e-01, eta: 12:06:58, time: 0.087, data_time: 0.005, memory: 928, loss: 5.7016\n2023-01-04 15:26:53,136 - mmcls - INFO - Epoch(val) [2][196]\taccuracy_top-1: 3.8800, accuracy_top-5: 10.9480\n2023-01-04 15:27:04,260 - mmcls - INFO - Epoch [3][100/5005]\tlr: 1.000e-01, eta: 12:08:14, time: 0.111, data_time: 0.036, memory: 928, loss: 5.7093\n2023-01-04 15:27:13,004 - mmcls - INFO - Epoch [3][200/5005]\tlr: 1.000e-01, eta: 12:07:57, time: 0.087, data_time: 0.016, memory: 928, loss: 5.7154\n2023-01-04 15:27:21,823 - mmcls - INFO - Epoch [3][300/5005]\tlr: 1.000e-01, eta: 12:07:44, time: 0.088, data_time: 0.003, memory: 928, loss: 5.7044\n2023-01-04 15:27:30,613 - mmcls - INFO - Epoch [3][400/5005]\tlr: 1.000e-01, eta: 12:07:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6961\n2023-01-04 15:27:39,513 - mmcls - INFO - Epoch [3][500/5005]\tlr: 1.000e-01, eta: 12:07:20, time: 0.089, data_time: 0.020, memory: 928, loss: 5.6850\n2023-01-04 15:27:48,728 - mmcls - INFO - Epoch [3][600/5005]\tlr: 1.000e-01, eta: 12:07:26, time: 0.092, data_time: 0.011, memory: 928, loss: 5.7082\n2023-01-04 15:27:57,172 - mmcls - INFO - Epoch [3][700/5005]\tlr: 1.000e-01, eta: 12:06:55, time: 0.084, data_time: 0.002, memory: 928, loss: 5.6740\n2023-01-04 15:28:05,944 - mmcls - INFO - Epoch [3][800/5005]\tlr: 1.000e-01, eta: 12:06:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.6898\n2023-01-04 15:28:14,598 - mmcls - INFO - Epoch [3][900/5005]\tlr: 1.000e-01, eta: 12:06:21, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6928\n2023-01-04 15:28:23,612 - mmcls - INFO - Epoch [3][1000/5005]\tlr: 1.000e-01, eta: 12:06:17, time: 0.090, data_time: 0.001, memory: 928, loss: 5.7001\n2023-01-04 15:28:32,850 - mmcls - INFO - Epoch [3][1100/5005]\tlr: 1.000e-01, eta: 12:06:22, time: 0.092, data_time: 0.002, memory: 928, loss: 5.6799\n2023-01-04 15:28:41,443 - mmcls - INFO - Epoch [3][1200/5005]\tlr: 1.000e-01, eta: 12:06:00, time: 0.086, data_time: 0.004, memory: 928, loss: 5.6823\n2023-01-04 15:28:50,071 - mmcls - INFO - Epoch [3][1300/5005]\tlr: 1.000e-01, eta: 12:05:39, time: 0.086, data_time: 0.029, memory: 928, loss: 5.6929\n2023-01-04 15:28:58,786 - mmcls - INFO - Epoch [3][1400/5005]\tlr: 1.000e-01, eta: 12:05:22, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6876\n2023-01-04 15:29:07,821 - mmcls - INFO - Epoch [3][1500/5005]\tlr: 1.000e-01, eta: 12:05:19, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6964\n2023-01-04 15:29:16,406 - mmcls - INFO - Epoch [3][1600/5005]\tlr: 1.000e-01, eta: 12:04:56, time: 0.086, data_time: 0.003, memory: 928, loss: 5.6826\n2023-01-04 15:29:25,558 - mmcls - INFO - Epoch [3][1700/5005]\tlr: 1.000e-01, eta: 12:04:58, time: 0.091, data_time: 0.001, memory: 928, loss: 5.6936\n2023-01-04 15:29:34,478 - mmcls - INFO - Epoch [3][1800/5005]\tlr: 1.000e-01, eta: 12:04:50, time: 0.089, data_time: 0.008, memory: 928, loss: 5.6836\n2023-01-04 15:29:43,151 - mmcls - INFO - Epoch [3][1900/5005]\tlr: 1.000e-01, eta: 12:04:32, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6790\n2023-01-04 15:29:51,886 - mmcls - INFO - Epoch [3][2000/5005]\tlr: 1.000e-01, eta: 12:04:16, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6979\n2023-01-04 15:30:01,175 - mmcls - INFO - Epoch [3][2100/5005]\tlr: 1.000e-01, eta: 12:04:23, time: 0.093, data_time: 0.001, memory: 928, loss: 5.6845\n2023-01-04 15:30:10,119 - mmcls - INFO - Epoch [3][2200/5005]\tlr: 1.000e-01, eta: 12:04:16, time: 0.089, data_time: 0.004, memory: 928, loss: 5.6751\n2023-01-04 15:30:18,997 - mmcls - INFO - Epoch [3][2300/5005]\tlr: 1.000e-01, eta: 12:04:06, time: 0.089, data_time: 0.001, memory: 928, loss: 5.6690\n2023-01-04 15:30:27,741 - mmcls - INFO - Epoch [3][2400/5005]\tlr: 1.000e-01, eta: 12:03:51, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6571\n2023-01-04 15:30:36,733 - mmcls - INFO - Epoch [3][2500/5005]\tlr: 1.000e-01, eta: 12:03:46, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6731\n2023-01-04 15:30:45,477 - mmcls - INFO - Epoch [3][2600/5005]\tlr: 1.000e-01, eta: 12:03:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.6569\n2023-01-04 15:30:54,312 - mmcls - INFO - Epoch [3][2700/5005]\tlr: 1.000e-01, eta: 12:03:20, time: 0.088, data_time: 0.016, memory: 928, loss: 5.6613\n2023-01-04 15:31:03,154 - mmcls - INFO - Epoch [3][2800/5005]\tlr: 1.000e-01, eta: 12:03:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.6653\n2023-01-04 15:31:12,151 - mmcls - INFO - Epoch [3][2900/5005]\tlr: 1.000e-01, eta: 12:03:03, time: 0.090, data_time: 0.001, memory: 928, loss: 5.6701\n2023-01-04 15:31:20,895 - mmcls - INFO - Epoch [3][3000/5005]\tlr: 1.000e-01, eta: 12:02:49, time: 0.088, data_time: 0.015, memory: 928, loss: 5.6756\n2023-01-04 15:31:29,623 - mmcls - INFO - Epoch [3][3100/5005]\tlr: 1.000e-01, eta: 12:02:34, time: 0.087, data_time: 0.013, memory: 928, loss: 5.6387\n2023-01-04 15:31:38,582 - mmcls - INFO - Epoch [3][3200/5005]\tlr: 1.000e-01, eta: 12:02:27, time: 0.090, data_time: 0.020, memory: 928, loss: 5.6532\n2023-01-04 15:31:47,442 - mmcls - INFO - Epoch [3][3300/5005]\tlr: 1.000e-01, eta: 12:02:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.6573\n2023-01-04 15:31:55,890 - mmcls - INFO - Epoch [3][3400/5005]\tlr: 1.000e-01, eta: 12:01:52, time: 0.084, data_time: 0.003, memory: 928, loss: 5.6567\n2023-01-04 15:32:04,379 - mmcls - INFO - Epoch [3][3500/5005]\tlr: 1.000e-01, eta: 12:01:28, time: 0.085, data_time: 0.004, memory: 928, loss: 5.6452\n2023-01-04 15:32:12,933 - mmcls - INFO - Epoch [3][3600/5005]\tlr: 1.000e-01, eta: 12:01:07, time: 0.086, data_time: 0.008, memory: 928, loss: 5.6496\n2023-01-04 15:32:21,597 - mmcls - INFO - Epoch [3][3700/5005]\tlr: 1.000e-01, eta: 12:00:50, time: 0.087, data_time: 0.009, memory: 928, loss: 5.6638\n2023-01-04 15:32:30,232 - mmcls - INFO - Epoch [3][3800/5005]\tlr: 1.000e-01, eta: 12:00:33, time: 0.086, data_time: 0.015, memory: 928, loss: 5.6494\n2023-01-04 15:32:39,081 - mmcls - INFO - Epoch [3][3900/5005]\tlr: 1.000e-01, eta: 12:00:23, time: 0.089, data_time: 0.012, memory: 928, loss: 5.6207\n2023-01-04 15:32:47,755 - mmcls - INFO - Epoch [3][4000/5005]\tlr: 1.000e-01, eta: 12:00:07, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6475\n2023-01-04 15:32:56,417 - mmcls - INFO - Epoch [3][4100/5005]\tlr: 1.000e-01, eta: 11:59:50, time: 0.087, data_time: 0.021, memory: 928, loss: 5.6279\n2023-01-04 15:33:05,386 - mmcls - INFO - Epoch [3][4200/5005]\tlr: 1.000e-01, eta: 11:59:44, time: 0.090, data_time: 0.036, memory: 928, loss: 5.6414\n2023-01-04 15:33:14,256 - mmcls - INFO - Epoch [3][4300/5005]\tlr: 1.000e-01, eta: 11:59:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.6510\n2023-01-04 15:33:23,232 - mmcls - INFO - Epoch [3][4400/5005]\tlr: 1.000e-01, eta: 11:59:29, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6503\n2023-01-04 15:33:32,370 - mmcls - INFO - Epoch [3][4500/5005]\tlr: 1.000e-01, eta: 11:59:29, time: 0.091, data_time: 0.018, memory: 928, loss: 5.6469\n2023-01-04 15:33:41,321 - mmcls - INFO - Epoch [3][4600/5005]\tlr: 1.000e-01, eta: 11:59:22, time: 0.090, data_time: 0.011, memory: 928, loss: 5.6476\n2023-01-04 15:33:50,030 - mmcls - INFO - Epoch [3][4700/5005]\tlr: 1.000e-01, eta: 11:59:07, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6375\n2023-01-04 15:33:58,848 - mmcls - INFO - Epoch [3][4800/5005]\tlr: 1.000e-01, eta: 11:58:57, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6186\n2023-01-04 15:34:07,464 - mmcls - INFO - Epoch [3][4900/5005]\tlr: 1.000e-01, eta: 11:58:39, time: 0.086, data_time: 0.001, memory: 928, loss: 5.6271\n2023-01-04 15:34:16,578 - mmcls - INFO - Epoch [3][5000/5005]\tlr: 1.000e-01, eta: 11:58:38, time: 0.091, data_time: 0.001, memory: 928, loss: 5.6248\n2023-01-04 15:34:39,382 - mmcls - INFO - Epoch(val) [3][196]\taccuracy_top-1: 4.4440, accuracy_top-5: 12.2240\n2023-01-04 15:34:50,460 - mmcls - INFO - Epoch [4][100/5005]\tlr: 1.000e-01, eta: 11:59:24, time: 0.111, data_time: 0.037, memory: 928, loss: 5.6246\n2023-01-04 15:34:59,211 - mmcls - INFO - Epoch [4][200/5005]\tlr: 1.000e-01, eta: 11:59:11, time: 0.088, data_time: 0.007, memory: 928, loss: 5.6257\n2023-01-04 15:35:07,959 - mmcls - INFO - Epoch [4][300/5005]\tlr: 1.000e-01, eta: 11:58:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6128\n2023-01-04 15:35:16,585 - mmcls - INFO - Epoch [4][400/5005]\tlr: 1.000e-01, eta: 11:58:40, time: 0.086, data_time: 0.003, memory: 928, loss: 5.6279\n2023-01-04 15:35:25,687 - mmcls - INFO - Epoch [4][500/5005]\tlr: 1.000e-01, eta: 11:58:37, time: 0.091, data_time: 0.015, memory: 928, loss: 5.6190\n2023-01-04 15:35:34,642 - mmcls - INFO - Epoch [4][600/5005]\tlr: 1.000e-01, eta: 11:58:30, time: 0.089, data_time: 0.014, memory: 928, loss: 5.6253\n2023-01-04 15:35:43,424 - mmcls - INFO - Epoch [4][700/5005]\tlr: 1.000e-01, eta: 11:58:18, time: 0.088, data_time: 0.008, memory: 928, loss: 5.6263\n2023-01-04 15:35:52,197 - mmcls - INFO - Epoch [4][800/5005]\tlr: 1.000e-01, eta: 11:58:06, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6077\n2023-01-04 15:36:00,885 - mmcls - INFO - Epoch [4][900/5005]\tlr: 1.000e-01, eta: 11:57:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6368\n2023-01-04 15:36:09,333 - mmcls - INFO - Epoch [4][1000/5005]\tlr: 1.000e-01, eta: 11:57:28, time: 0.084, data_time: 0.003, memory: 928, loss: 5.6131\n2023-01-04 15:36:18,093 - mmcls - INFO - Epoch [4][1100/5005]\tlr: 1.000e-01, eta: 11:57:16, time: 0.088, data_time: 0.023, memory: 928, loss: 5.6194\n2023-01-04 15:36:26,952 - mmcls - INFO - Epoch [4][1200/5005]\tlr: 1.000e-01, eta: 11:57:06, time: 0.089, data_time: 0.036, memory: 928, loss: 5.6230\n2023-01-04 15:36:35,580 - mmcls - INFO - Epoch [4][1300/5005]\tlr: 1.000e-01, eta: 11:56:50, time: 0.086, data_time: 0.007, memory: 928, loss: 5.6289\n2023-01-04 15:36:44,384 - mmcls - INFO - Epoch [4][1400/5005]\tlr: 1.000e-01, eta: 11:56:38, time: 0.088, data_time: 0.005, memory: 928, loss: 5.6068\n2023-01-04 15:36:52,908 - mmcls - INFO - Epoch [4][1500/5005]\tlr: 1.000e-01, eta: 11:56:19, time: 0.085, data_time: 0.011, memory: 928, loss: 5.6164\n2023-01-04 15:37:01,710 - mmcls - INFO - Epoch [4][1600/5005]\tlr: 1.000e-01, eta: 11:56:08, time: 0.088, data_time: 0.031, memory: 928, loss: 5.6069\n2023-01-04 15:37:10,320 - mmcls - INFO - Epoch [4][1700/5005]\tlr: 1.000e-01, eta: 11:55:51, time: 0.086, data_time: 0.020, memory: 928, loss: 5.6210\n2023-01-04 15:37:18,878 - mmcls - INFO - Epoch [4][1800/5005]\tlr: 1.000e-01, eta: 11:55:33, time: 0.086, data_time: 0.007, memory: 928, loss: 5.6131\n2023-01-04 15:37:27,860 - mmcls - INFO - Epoch [4][1900/5005]\tlr: 1.000e-01, eta: 11:55:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5916\n2023-01-04 15:37:36,525 - mmcls - INFO - Epoch [4][2000/5005]\tlr: 1.000e-01, eta: 11:55:12, time: 0.087, data_time: 0.012, memory: 928, loss: 5.6198\n2023-01-04 15:37:45,092 - mmcls - INFO - Epoch [4][2100/5005]\tlr: 1.000e-01, eta: 11:54:55, time: 0.086, data_time: 0.001, memory: 928, loss: 5.6211\n2023-01-04 15:37:54,098 - mmcls - INFO - Epoch [4][2200/5005]\tlr: 1.000e-01, eta: 11:54:49, time: 0.090, data_time: 0.001, memory: 928, loss: 5.6006\n2023-01-04 15:38:03,053 - mmcls - INFO - Epoch [4][2300/5005]\tlr: 1.000e-01, eta: 11:54:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.6115\n2023-01-04 15:38:11,795 - mmcls - INFO - Epoch [4][2400/5005]\tlr: 1.000e-01, eta: 11:54:30, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5960\n2023-01-04 15:38:20,819 - mmcls - INFO - Epoch [4][2500/5005]\tlr: 1.000e-01, eta: 11:54:25, time: 0.090, data_time: 0.003, memory: 928, loss: 5.5862\n2023-01-04 15:38:29,590 - mmcls - INFO - Epoch [4][2600/5005]\tlr: 1.000e-01, eta: 11:54:14, time: 0.088, data_time: 0.022, memory: 928, loss: 5.6165\n2023-01-04 15:38:38,240 - mmcls - INFO - Epoch [4][2700/5005]\tlr: 1.000e-01, eta: 11:53:59, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5764\n2023-01-04 15:38:47,242 - mmcls - INFO - Epoch [4][2800/5005]\tlr: 1.000e-01, eta: 11:53:53, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6036\n2023-01-04 15:38:55,951 - mmcls - INFO - Epoch [4][2900/5005]\tlr: 1.000e-01, eta: 11:53:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5876\n2023-01-04 15:39:04,829 - mmcls - INFO - Epoch [4][3000/5005]\tlr: 1.000e-01, eta: 11:53:31, time: 0.089, data_time: 0.011, memory: 928, loss: 5.6112\n2023-01-04 15:39:13,574 - mmcls - INFO - Epoch [4][3100/5005]\tlr: 1.000e-01, eta: 11:53:19, time: 0.087, data_time: 0.021, memory: 928, loss: 5.6140\n2023-01-04 15:39:22,593 - mmcls - INFO - Epoch [4][3200/5005]\tlr: 1.000e-01, eta: 11:53:14, time: 0.090, data_time: 0.036, memory: 928, loss: 5.5781\n2023-01-04 15:39:31,499 - mmcls - INFO - Epoch [4][3300/5005]\tlr: 1.000e-01, eta: 11:53:06, time: 0.089, data_time: 0.005, memory: 928, loss: 5.5856\n2023-01-04 15:39:40,054 - mmcls - INFO - Epoch [4][3400/5005]\tlr: 1.000e-01, eta: 11:52:48, time: 0.085, data_time: 0.009, memory: 928, loss: 5.5865\n2023-01-04 15:39:48,970 - mmcls - INFO - Epoch [4][3500/5005]\tlr: 1.000e-01, eta: 11:52:41, time: 0.089, data_time: 0.012, memory: 928, loss: 5.5918\n2023-01-04 15:39:57,854 - mmcls - INFO - Epoch [4][3600/5005]\tlr: 1.000e-01, eta: 11:52:32, time: 0.089, data_time: 0.003, memory: 928, loss: 5.6019\n2023-01-04 15:40:06,702 - mmcls - INFO - Epoch [4][3700/5005]\tlr: 1.000e-01, eta: 11:52:22, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5907\n2023-01-04 15:40:15,437 - mmcls - INFO - Epoch [4][3800/5005]\tlr: 1.000e-01, eta: 11:52:10, time: 0.087, data_time: 0.004, memory: 928, loss: 5.6012\n2023-01-04 15:40:24,121 - mmcls - INFO - Epoch [4][3900/5005]\tlr: 1.000e-01, eta: 11:51:57, time: 0.087, data_time: 0.026, memory: 928, loss: 5.6020\n2023-01-04 15:40:32,941 - mmcls - INFO - Epoch [4][4000/5005]\tlr: 1.000e-01, eta: 11:51:46, time: 0.088, data_time: 0.014, memory: 928, loss: 5.5683\n2023-01-04 15:40:41,773 - mmcls - INFO - Epoch [4][4100/5005]\tlr: 1.000e-01, eta: 11:51:37, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5944\n2023-01-04 15:40:50,202 - mmcls - INFO - Epoch [4][4200/5005]\tlr: 1.000e-01, eta: 11:51:17, time: 0.084, data_time: 0.027, memory: 928, loss: 5.5743\n2023-01-04 15:40:59,202 - mmcls - INFO - Epoch [4][4300/5005]\tlr: 1.000e-01, eta: 11:51:11, time: 0.090, data_time: 0.010, memory: 928, loss: 5.5973\n2023-01-04 15:41:07,882 - mmcls - INFO - Epoch [4][4400/5005]\tlr: 1.000e-01, eta: 11:50:58, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5979\n2023-01-04 15:41:16,727 - mmcls - INFO - Epoch [4][4500/5005]\tlr: 1.000e-01, eta: 11:50:48, time: 0.088, data_time: 0.002, memory: 928, loss: 5.6079\n2023-01-04 15:41:25,267 - mmcls - INFO - Epoch [4][4600/5005]\tlr: 1.000e-01, eta: 11:50:31, time: 0.085, data_time: 0.010, memory: 928, loss: 5.5701\n2023-01-04 15:41:34,160 - mmcls - INFO - Epoch [4][4700/5005]\tlr: 1.000e-01, eta: 11:50:23, time: 0.089, data_time: 0.017, memory: 928, loss: 5.5825\n2023-01-04 15:41:43,284 - mmcls - INFO - Epoch [4][4800/5005]\tlr: 1.000e-01, eta: 11:50:20, time: 0.091, data_time: 0.016, memory: 928, loss: 5.5981\n2023-01-04 15:41:52,145 - mmcls - INFO - Epoch [4][4900/5005]\tlr: 1.000e-01, eta: 11:50:11, time: 0.089, data_time: 0.015, memory: 928, loss: 5.5770\n2023-01-04 15:42:00,771 - mmcls - INFO - Epoch [4][5000/5005]\tlr: 1.000e-01, eta: 11:49:56, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5755\n2023-01-04 15:42:23,648 - mmcls - INFO - Epoch(val) [4][196]\taccuracy_top-1: 4.4920, accuracy_top-5: 12.2320\n2023-01-04 15:42:34,620 - mmcls - INFO - Epoch [5][100/5005]\tlr: 1.000e-01, eta: 11:50:26, time: 0.110, data_time: 0.034, memory: 928, loss: 5.5445\n2023-01-04 15:42:43,617 - mmcls - INFO - Epoch [5][200/5005]\tlr: 1.000e-01, eta: 11:50:21, time: 0.090, data_time: 0.004, memory: 928, loss: 5.5816\n2023-01-04 15:42:52,108 - mmcls - INFO - Epoch [5][300/5005]\tlr: 1.000e-01, eta: 11:50:02, time: 0.085, data_time: 0.021, memory: 928, loss: 5.5756\n2023-01-04 15:43:00,842 - mmcls - INFO - Epoch [5][400/5005]\tlr: 1.000e-01, eta: 11:49:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5566\n2023-01-04 15:43:09,441 - mmcls - INFO - Epoch [5][500/5005]\tlr: 1.000e-01, eta: 11:49:35, time: 0.086, data_time: 0.020, memory: 928, loss: 5.5906\n2023-01-04 15:43:18,113 - mmcls - INFO - Epoch [5][600/5005]\tlr: 1.000e-01, eta: 11:49:21, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5652\n2023-01-04 15:43:27,139 - mmcls - INFO - Epoch [5][700/5005]\tlr: 1.000e-01, eta: 11:49:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5767\n2023-01-04 15:43:35,879 - mmcls - INFO - Epoch [5][800/5005]\tlr: 1.000e-01, eta: 11:49:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5873\n2023-01-04 15:43:44,875 - mmcls - INFO - Epoch [5][900/5005]\tlr: 1.000e-01, eta: 11:48:58, time: 0.090, data_time: 0.005, memory: 928, loss: 5.6030\n2023-01-04 15:43:53,642 - mmcls - INFO - Epoch [5][1000/5005]\tlr: 1.000e-01, eta: 11:48:47, time: 0.088, data_time: 0.006, memory: 928, loss: 5.5736\n2023-01-04 15:44:02,513 - mmcls - INFO - Epoch [5][1100/5005]\tlr: 1.000e-01, eta: 11:48:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.5753\n2023-01-04 15:44:11,318 - mmcls - INFO - Epoch [5][1200/5005]\tlr: 1.000e-01, eta: 11:48:28, time: 0.088, data_time: 0.020, memory: 928, loss: 5.5835\n2023-01-04 15:44:20,233 - mmcls - INFO - Epoch [5][1300/5005]\tlr: 1.000e-01, eta: 11:48:20, time: 0.089, data_time: 0.007, memory: 928, loss: 5.5906\n2023-01-04 15:44:28,914 - mmcls - INFO - Epoch [5][1400/5005]\tlr: 1.000e-01, eta: 11:48:07, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5450\n2023-01-04 15:44:37,475 - mmcls - INFO - Epoch [5][1500/5005]\tlr: 1.000e-01, eta: 11:47:51, time: 0.086, data_time: 0.012, memory: 928, loss: 5.5944\n2023-01-04 15:44:46,328 - mmcls - INFO - Epoch [5][1600/5005]\tlr: 1.000e-01, eta: 11:47:42, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5683\n2023-01-04 15:44:55,442 - mmcls - INFO - Epoch [5][1700/5005]\tlr: 1.000e-01, eta: 11:47:39, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5950\n2023-01-04 15:45:04,139 - mmcls - INFO - Epoch [5][1800/5005]\tlr: 1.000e-01, eta: 11:47:26, time: 0.087, data_time: 0.028, memory: 928, loss: 5.5673\n2023-01-04 15:45:12,857 - mmcls - INFO - Epoch [5][1900/5005]\tlr: 1.000e-01, eta: 11:47:14, time: 0.087, data_time: 0.023, memory: 928, loss: 5.5755\n2023-01-04 15:45:21,634 - mmcls - INFO - Epoch [5][2000/5005]\tlr: 1.000e-01, eta: 11:47:03, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5715\n2023-01-04 15:45:30,525 - mmcls - INFO - Epoch [5][2100/5005]\tlr: 1.000e-01, eta: 11:46:54, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5445\n2023-01-04 15:45:39,204 - mmcls - INFO - Epoch [5][2200/5005]\tlr: 1.000e-01, eta: 11:46:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.5634\n2023-01-04 15:45:48,282 - mmcls - INFO - Epoch [5][2300/5005]\tlr: 1.000e-01, eta: 11:46:37, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5590\n2023-01-04 15:45:56,893 - mmcls - INFO - Epoch [5][2400/5005]\tlr: 1.000e-01, eta: 11:46:23, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5490\n2023-01-04 15:46:05,588 - mmcls - INFO - Epoch [5][2500/5005]\tlr: 1.000e-01, eta: 11:46:10, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5632\n2023-01-04 15:46:14,115 - mmcls - INFO - Epoch [5][2600/5005]\tlr: 1.000e-01, eta: 11:45:55, time: 0.085, data_time: 0.004, memory: 928, loss: 5.5678\n2023-01-04 15:46:22,718 - mmcls - INFO - Epoch [5][2700/5005]\tlr: 1.000e-01, eta: 11:45:40, time: 0.086, data_time: 0.010, memory: 928, loss: 5.5709\n2023-01-04 15:46:31,503 - mmcls - INFO - Epoch [5][2800/5005]\tlr: 1.000e-01, eta: 11:45:30, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5702\n2023-01-04 15:46:40,223 - mmcls - INFO - Epoch [5][2900/5005]\tlr: 1.000e-01, eta: 11:45:18, time: 0.087, data_time: 0.016, memory: 928, loss: 5.5759\n2023-01-04 15:46:49,257 - mmcls - INFO - Epoch [5][3000/5005]\tlr: 1.000e-01, eta: 11:45:12, time: 0.090, data_time: 0.012, memory: 928, loss: 5.5600\n2023-01-04 15:46:58,108 - mmcls - INFO - Epoch [5][3100/5005]\tlr: 1.000e-01, eta: 11:45:03, time: 0.089, data_time: 0.014, memory: 928, loss: 5.5545\n2023-01-04 15:47:06,988 - mmcls - INFO - Epoch [5][3200/5005]\tlr: 1.000e-01, eta: 11:44:55, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5517\n2023-01-04 15:47:15,671 - mmcls - INFO - Epoch [5][3300/5005]\tlr: 1.000e-01, eta: 11:44:42, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5694\n2023-01-04 15:47:24,338 - mmcls - INFO - Epoch [5][3400/5005]\tlr: 1.000e-01, eta: 11:44:29, time: 0.087, data_time: 0.010, memory: 928, loss: 5.5649\n2023-01-04 15:47:33,138 - mmcls - INFO - Epoch [5][3500/5005]\tlr: 1.000e-01, eta: 11:44:19, time: 0.088, data_time: 0.035, memory: 928, loss: 5.5629\n2023-01-04 15:47:41,897 - mmcls - INFO - Epoch [5][3600/5005]\tlr: 1.000e-01, eta: 11:44:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.5596\n2023-01-04 15:47:50,683 - mmcls - INFO - Epoch [5][3700/5005]\tlr: 1.000e-01, eta: 11:43:58, time: 0.088, data_time: 0.006, memory: 928, loss: 5.5712\n2023-01-04 15:47:59,509 - mmcls - INFO - Epoch [5][3800/5005]\tlr: 1.000e-01, eta: 11:43:49, time: 0.088, data_time: 0.023, memory: 928, loss: 5.5817\n2023-01-04 15:48:08,262 - mmcls - INFO - Epoch [5][3900/5005]\tlr: 1.000e-01, eta: 11:43:38, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5616\n2023-01-04 15:48:16,948 - mmcls - INFO - Epoch [5][4000/5005]\tlr: 1.000e-01, eta: 11:43:25, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5775\n2023-01-04 15:48:25,815 - mmcls - INFO - Epoch [5][4100/5005]\tlr: 1.000e-01, eta: 11:43:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5547\n2023-01-04 15:48:34,502 - mmcls - INFO - Epoch [5][4200/5005]\tlr: 1.000e-01, eta: 11:43:04, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5539\n2023-01-04 15:48:43,191 - mmcls - INFO - Epoch [5][4300/5005]\tlr: 1.000e-01, eta: 11:42:52, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5635\n2023-01-04 15:48:52,731 - mmcls - INFO - Epoch [5][4400/5005]\tlr: 1.000e-01, eta: 11:42:57, time: 0.095, data_time: 0.001, memory: 928, loss: 5.5640\n2023-01-04 15:49:01,459 - mmcls - INFO - Epoch [5][4500/5005]\tlr: 1.000e-01, eta: 11:42:45, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5749\n2023-01-04 15:49:10,288 - mmcls - INFO - Epoch [5][4600/5005]\tlr: 1.000e-01, eta: 11:42:36, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5575\n2023-01-04 15:49:19,137 - mmcls - INFO - Epoch [5][4700/5005]\tlr: 1.000e-01, eta: 11:42:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5526\n2023-01-04 15:49:27,736 - mmcls - INFO - Epoch [5][4800/5005]\tlr: 1.000e-01, eta: 11:42:13, time: 0.086, data_time: 0.038, memory: 928, loss: 5.5670\n2023-01-04 15:49:36,448 - mmcls - INFO - Epoch [5][4900/5005]\tlr: 1.000e-01, eta: 11:42:01, time: 0.087, data_time: 0.012, memory: 928, loss: 5.5617\n2023-01-04 15:49:45,056 - mmcls - INFO - Epoch [5][5000/5005]\tlr: 1.000e-01, eta: 11:41:48, time: 0.086, data_time: 0.011, memory: 928, loss: 5.5594\n2023-01-04 15:50:07,998 - mmcls - INFO - Epoch(val) [5][196]\taccuracy_top-1: 4.8740, accuracy_top-5: 13.0580\n2023-01-04 15:50:18,983 - mmcls - INFO - Epoch [6][100/5005]\tlr: 1.000e-01, eta: 11:42:10, time: 0.110, data_time: 0.039, memory: 928, loss: 5.5321\n2023-01-04 15:50:27,762 - mmcls - INFO - Epoch [6][200/5005]\tlr: 1.000e-01, eta: 11:41:59, time: 0.088, data_time: 0.029, memory: 928, loss: 5.5453\n2023-01-04 15:50:36,669 - mmcls - INFO - Epoch [6][300/5005]\tlr: 1.000e-01, eta: 11:41:51, time: 0.089, data_time: 0.018, memory: 928, loss: 5.5407\n2023-01-04 15:50:45,615 - mmcls - INFO - Epoch [6][400/5005]\tlr: 1.000e-01, eta: 11:41:44, time: 0.089, data_time: 0.017, memory: 928, loss: 5.5559\n2023-01-04 15:50:54,711 - mmcls - INFO - Epoch [6][500/5005]\tlr: 1.000e-01, eta: 11:41:40, time: 0.091, data_time: 0.005, memory: 928, loss: 5.5408\n2023-01-04 15:51:03,570 - mmcls - INFO - Epoch [6][600/5005]\tlr: 1.000e-01, eta: 11:41:31, time: 0.089, data_time: 0.025, memory: 928, loss: 5.5412\n2023-01-04 15:51:12,363 - mmcls - INFO - Epoch [6][700/5005]\tlr: 1.000e-01, eta: 11:41:20, time: 0.088, data_time: 0.029, memory: 928, loss: 5.5317\n2023-01-04 15:51:20,934 - mmcls - INFO - Epoch [6][800/5005]\tlr: 1.000e-01, eta: 11:41:06, time: 0.086, data_time: 0.013, memory: 928, loss: 5.5731\n2023-01-04 15:51:29,540 - mmcls - INFO - Epoch [6][900/5005]\tlr: 1.000e-01, eta: 11:40:52, time: 0.086, data_time: 0.010, memory: 928, loss: 5.5349\n2023-01-04 15:51:38,362 - mmcls - INFO - Epoch [6][1000/5005]\tlr: 1.000e-01, eta: 11:40:43, time: 0.088, data_time: 0.015, memory: 928, loss: 5.5558\n2023-01-04 15:51:47,096 - mmcls - INFO - Epoch [6][1100/5005]\tlr: 1.000e-01, eta: 11:40:32, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5396\n2023-01-04 15:51:56,206 - mmcls - INFO - Epoch [6][1200/5005]\tlr: 1.000e-01, eta: 11:40:28, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5514\n2023-01-04 15:52:05,149 - mmcls - INFO - Epoch [6][1300/5005]\tlr: 1.000e-01, eta: 11:40:20, time: 0.089, data_time: 0.003, memory: 928, loss: 5.5691\n2023-01-04 15:52:13,806 - mmcls - INFO - Epoch [6][1400/5005]\tlr: 1.000e-01, eta: 11:40:07, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5618\n2023-01-04 15:52:22,453 - mmcls - INFO - Epoch [6][1500/5005]\tlr: 1.000e-01, eta: 11:39:55, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5474\n2023-01-04 15:52:31,561 - mmcls - INFO - Epoch [6][1600/5005]\tlr: 1.000e-01, eta: 11:39:50, time: 0.091, data_time: 0.003, memory: 928, loss: 5.5566\n2023-01-04 15:52:40,610 - mmcls - INFO - Epoch [6][1700/5005]\tlr: 1.000e-01, eta: 11:39:45, time: 0.090, data_time: 0.011, memory: 928, loss: 5.5647\n2023-01-04 15:52:49,263 - mmcls - INFO - Epoch [6][1800/5005]\tlr: 1.000e-01, eta: 11:39:32, time: 0.086, data_time: 0.019, memory: 928, loss: 5.5452\n2023-01-04 15:52:57,996 - mmcls - INFO - Epoch [6][1900/5005]\tlr: 1.000e-01, eta: 11:39:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5615\n2023-01-04 15:53:06,815 - mmcls - INFO - Epoch [6][2000/5005]\tlr: 1.000e-01, eta: 11:39:11, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5510\n2023-01-04 15:53:15,461 - mmcls - INFO - Epoch [6][2100/5005]\tlr: 1.000e-01, eta: 11:38:59, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5328\n2023-01-04 15:53:24,339 - mmcls - INFO - Epoch [6][2200/5005]\tlr: 1.000e-01, eta: 11:38:50, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5698\n2023-01-04 15:53:32,936 - mmcls - INFO - Epoch [6][2300/5005]\tlr: 1.000e-01, eta: 11:38:37, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5476\n2023-01-04 15:53:41,717 - mmcls - INFO - Epoch [6][2400/5005]\tlr: 1.000e-01, eta: 11:38:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5640\n2023-01-04 15:53:50,388 - mmcls - INFO - Epoch [6][2500/5005]\tlr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.016, memory: 928, loss: 5.5383\n2023-01-04 15:53:59,187 - mmcls - INFO - Epoch [6][2600/5005]\tlr: 1.000e-01, eta: 11:38:05, time: 0.088, data_time: 0.013, memory: 928, loss: 5.5434\n2023-01-04 15:54:07,784 - mmcls - INFO - Epoch [6][2700/5005]\tlr: 1.000e-01, eta: 11:37:51, time: 0.086, data_time: 0.009, memory: 928, loss: 5.5610\n2023-01-04 15:54:16,510 - mmcls - INFO - Epoch [6][2800/5005]\tlr: 1.000e-01, eta: 11:37:40, time: 0.087, data_time: 0.017, memory: 928, loss: 5.5687\n2023-01-04 15:54:25,254 - mmcls - INFO - Epoch [6][2900/5005]\tlr: 1.000e-01, eta: 11:37:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5351\n2023-01-04 15:54:34,216 - mmcls - INFO - Epoch [6][3000/5005]\tlr: 1.000e-01, eta: 11:37:22, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5410\n2023-01-04 15:54:43,010 - mmcls - INFO - Epoch [6][3100/5005]\tlr: 1.000e-01, eta: 11:37:13, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5448\n2023-01-04 15:54:52,016 - mmcls - INFO - Epoch [6][3200/5005]\tlr: 1.000e-01, eta: 11:37:06, time: 0.090, data_time: 0.006, memory: 928, loss: 5.5557\n2023-01-04 15:55:00,625 - mmcls - INFO - Epoch [6][3300/5005]\tlr: 1.000e-01, eta: 11:36:53, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5586\n2023-01-04 15:55:09,194 - mmcls - INFO - Epoch [6][3400/5005]\tlr: 1.000e-01, eta: 11:36:40, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5429\n2023-01-04 15:55:18,108 - mmcls - INFO - Epoch [6][3500/5005]\tlr: 1.000e-01, eta: 11:36:32, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5361\n2023-01-04 15:55:26,707 - mmcls - INFO - Epoch [6][3600/5005]\tlr: 1.000e-01, eta: 11:36:19, time: 0.086, data_time: 0.013, memory: 928, loss: 5.5446\n2023-01-04 15:55:35,415 - mmcls - INFO - Epoch [6][3700/5005]\tlr: 1.000e-01, eta: 11:36:07, time: 0.087, data_time: 0.021, memory: 928, loss: 5.5298\n2023-01-04 15:55:44,107 - mmcls - INFO - Epoch [6][3800/5005]\tlr: 1.000e-01, eta: 11:35:56, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5455\n2023-01-04 15:55:53,077 - mmcls - INFO - Epoch [6][3900/5005]\tlr: 1.000e-01, eta: 11:35:49, time: 0.090, data_time: 0.028, memory: 928, loss: 5.5389\n2023-01-04 15:56:01,904 - mmcls - INFO - Epoch [6][4000/5005]\tlr: 1.000e-01, eta: 11:35:39, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5561\n2023-01-04 15:56:10,556 - mmcls - INFO - Epoch [6][4100/5005]\tlr: 1.000e-01, eta: 11:35:27, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5222\n2023-01-04 15:56:19,254 - mmcls - INFO - Epoch [6][4200/5005]\tlr: 1.000e-01, eta: 11:35:16, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5518\n2023-01-04 15:56:28,191 - mmcls - INFO - Epoch [6][4300/5005]\tlr: 1.000e-01, eta: 11:35:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5510\n2023-01-04 15:56:37,106 - mmcls - INFO - Epoch [6][4400/5005]\tlr: 1.000e-01, eta: 11:35:01, time: 0.089, data_time: 0.009, memory: 928, loss: 5.5429\n2023-01-04 15:56:45,873 - mmcls - INFO - Epoch [6][4500/5005]\tlr: 1.000e-01, eta: 11:34:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5268\n2023-01-04 15:56:54,673 - mmcls - INFO - Epoch [6][4600/5005]\tlr: 1.000e-01, eta: 11:34:41, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5579\n2023-01-04 15:57:03,176 - mmcls - INFO - Epoch [6][4700/5005]\tlr: 1.000e-01, eta: 11:34:26, time: 0.085, data_time: 0.011, memory: 928, loss: 5.5650\n2023-01-04 15:57:12,049 - mmcls - INFO - Epoch [6][4800/5005]\tlr: 1.000e-01, eta: 11:34:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5482\n2023-01-04 15:57:20,662 - mmcls - INFO - Epoch [6][4900/5005]\tlr: 1.000e-01, eta: 11:34:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5277\n2023-01-04 15:57:29,258 - mmcls - INFO - Epoch [6][5000/5005]\tlr: 1.000e-01, eta: 11:33:52, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5478\n2023-01-04 15:57:51,991 - mmcls - INFO - Epoch(val) [6][196]\taccuracy_top-1: 5.0180, accuracy_top-5: 13.8500\n2023-01-04 15:58:02,968 - mmcls - INFO - Epoch [7][100/5005]\tlr: 1.000e-01, eta: 11:34:09, time: 0.110, data_time: 0.053, memory: 928, loss: 5.5271\n2023-01-04 15:58:11,505 - mmcls - INFO - Epoch [7][200/5005]\tlr: 1.000e-01, eta: 11:33:55, time: 0.085, data_time: 0.027, memory: 928, loss: 5.5204\n2023-01-04 15:58:20,096 - mmcls - INFO - Epoch [7][300/5005]\tlr: 1.000e-01, eta: 11:33:42, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5369\n2023-01-04 15:58:28,703 - mmcls - INFO - Epoch [7][400/5005]\tlr: 1.000e-01, eta: 11:33:29, time: 0.086, data_time: 0.007, memory: 928, loss: 5.5453\n2023-01-04 15:58:37,707 - mmcls - INFO - Epoch [7][500/5005]\tlr: 1.000e-01, eta: 11:33:23, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5333\n2023-01-04 15:58:46,288 - mmcls - INFO - Epoch [7][600/5005]\tlr: 1.000e-01, eta: 11:33:10, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5436\n2023-01-04 15:58:55,462 - mmcls - INFO - Epoch [7][700/5005]\tlr: 1.000e-01, eta: 11:33:06, time: 0.092, data_time: 0.009, memory: 928, loss: 5.5479\n2023-01-04 15:59:04,242 - mmcls - INFO - Epoch [7][800/5005]\tlr: 1.000e-01, eta: 11:32:56, time: 0.088, data_time: 0.027, memory: 928, loss: 5.5190\n2023-01-04 15:59:13,060 - mmcls - INFO - Epoch [7][900/5005]\tlr: 1.000e-01, eta: 11:32:47, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5464\n2023-01-04 15:59:21,847 - mmcls - INFO - Epoch [7][1000/5005]\tlr: 1.000e-01, eta: 11:32:37, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5345\n2023-01-04 15:59:30,829 - mmcls - INFO - Epoch [7][1100/5005]\tlr: 1.000e-01, eta: 11:32:30, time: 0.090, data_time: 0.005, memory: 928, loss: 5.5489\n2023-01-04 15:59:40,129 - mmcls - INFO - Epoch [7][1200/5005]\tlr: 1.000e-01, eta: 11:32:28, time: 0.093, data_time: 0.005, memory: 928, loss: 5.5482\n2023-01-04 15:59:48,785 - mmcls - INFO - Epoch [7][1300/5005]\tlr: 1.000e-01, eta: 11:32:16, time: 0.087, data_time: 0.010, memory: 928, loss: 5.5235\n2023-01-04 15:59:57,282 - mmcls - INFO - Epoch [7][1400/5005]\tlr: 1.000e-01, eta: 11:32:02, time: 0.085, data_time: 0.029, memory: 928, loss: 5.5083\n2023-01-04 16:00:06,095 - mmcls - INFO - Epoch [7][1500/5005]\tlr: 1.000e-01, eta: 11:31:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5283\n2023-01-04 16:00:14,791 - mmcls - INFO - Epoch [7][1600/5005]\tlr: 1.000e-01, eta: 11:31:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5413\n2023-01-04 16:00:23,905 - mmcls - INFO - Epoch [7][1700/5005]\tlr: 1.000e-01, eta: 11:31:36, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5216\n2023-01-04 16:00:32,730 - mmcls - INFO - Epoch [7][1800/5005]\tlr: 1.000e-01, eta: 11:31:27, time: 0.088, data_time: 0.015, memory: 928, loss: 5.5251\n2023-01-04 16:00:41,463 - mmcls - INFO - Epoch [7][1900/5005]\tlr: 1.000e-01, eta: 11:31:16, time: 0.087, data_time: 0.032, memory: 928, loss: 5.5480\n2023-01-04 16:00:50,206 - mmcls - INFO - Epoch [7][2000/5005]\tlr: 1.000e-01, eta: 11:31:06, time: 0.087, data_time: 0.024, memory: 928, loss: 5.5417\n2023-01-04 16:00:59,039 - mmcls - INFO - Epoch [7][2100/5005]\tlr: 1.000e-01, eta: 11:30:57, time: 0.088, data_time: 0.033, memory: 928, loss: 5.5372\n2023-01-04 16:01:07,691 - mmcls - INFO - Epoch [7][2200/5005]\tlr: 1.000e-01, eta: 11:30:45, time: 0.087, data_time: 0.038, memory: 928, loss: 5.5441\n2023-01-04 16:01:16,618 - mmcls - INFO - Epoch [7][2300/5005]\tlr: 1.000e-01, eta: 11:30:37, time: 0.089, data_time: 0.015, memory: 928, loss: 5.5248\n2023-01-04 16:01:25,247 - mmcls - INFO - Epoch [7][2400/5005]\tlr: 1.000e-01, eta: 11:30:25, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5319\n2023-01-04 16:01:33,783 - mmcls - INFO - Epoch [7][2500/5005]\tlr: 1.000e-01, eta: 11:30:12, time: 0.085, data_time: 0.002, memory: 928, loss: 5.5299\n2023-01-04 16:01:42,394 - mmcls - INFO - Epoch [7][2600/5005]\tlr: 1.000e-01, eta: 11:30:00, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5376\n2023-01-04 16:01:51,024 - mmcls - INFO - Epoch [7][2700/5005]\tlr: 1.000e-01, eta: 11:29:47, time: 0.086, data_time: 0.008, memory: 928, loss: 5.5230\n2023-01-04 16:02:00,000 - mmcls - INFO - Epoch [7][2800/5005]\tlr: 1.000e-01, eta: 11:29:41, time: 0.090, data_time: 0.012, memory: 928, loss: 5.5456\n2023-01-04 16:02:08,582 - mmcls - INFO - Epoch [7][2900/5005]\tlr: 1.000e-01, eta: 11:29:28, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5389\n2023-01-04 16:02:17,287 - mmcls - INFO - Epoch [7][3000/5005]\tlr: 1.000e-01, eta: 11:29:17, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5447\n2023-01-04 16:02:26,192 - mmcls - INFO - Epoch [7][3100/5005]\tlr: 1.000e-01, eta: 11:29:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5380\n2023-01-04 16:02:35,307 - mmcls - INFO - Epoch [7][3200/5005]\tlr: 1.000e-01, eta: 11:29:04, time: 0.091, data_time: 0.018, memory: 928, loss: 5.5394\n2023-01-04 16:02:43,946 - mmcls - INFO - Epoch [7][3300/5005]\tlr: 1.000e-01, eta: 11:28:52, time: 0.086, data_time: 0.024, memory: 928, loss: 5.5278\n2023-01-04 16:02:52,478 - mmcls - INFO - Epoch [7][3400/5005]\tlr: 1.000e-01, eta: 11:28:39, time: 0.085, data_time: 0.019, memory: 928, loss: 5.5293\n2023-01-04 16:03:01,266 - mmcls - INFO - Epoch [7][3500/5005]\tlr: 1.000e-01, eta: 11:28:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5108\n2023-01-04 16:03:10,299 - mmcls - INFO - Epoch [7][3600/5005]\tlr: 1.000e-01, eta: 11:28:23, time: 0.090, data_time: 0.018, memory: 928, loss: 5.5438\n2023-01-04 16:03:19,073 - mmcls - INFO - Epoch [7][3700/5005]\tlr: 1.000e-01, eta: 11:28:13, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5491\n2023-01-04 16:03:27,809 - mmcls - INFO - Epoch [7][3800/5005]\tlr: 1.000e-01, eta: 11:28:03, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5268\n2023-01-04 16:03:36,434 - mmcls - INFO - Epoch [7][3900/5005]\tlr: 1.000e-01, eta: 11:27:51, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5301\n2023-01-04 16:03:45,267 - mmcls - INFO - Epoch [7][4000/5005]\tlr: 1.000e-01, eta: 11:27:42, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5283\n2023-01-04 16:03:53,899 - mmcls - INFO - Epoch [7][4100/5005]\tlr: 1.000e-01, eta: 11:27:30, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5607\n2023-01-04 16:04:02,592 - mmcls - INFO - Epoch [7][4200/5005]\tlr: 1.000e-01, eta: 11:27:19, time: 0.087, data_time: 0.041, memory: 928, loss: 5.5143\n2023-01-04 16:04:11,604 - mmcls - INFO - Epoch [7][4300/5005]\tlr: 1.000e-01, eta: 11:27:12, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5344\n2023-01-04 16:04:20,256 - mmcls - INFO - Epoch [7][4400/5005]\tlr: 1.000e-01, eta: 11:27:01, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5216\n2023-01-04 16:04:28,994 - mmcls - INFO - Epoch [7][4500/5005]\tlr: 1.000e-01, eta: 11:26:51, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5245\n2023-01-04 16:04:37,956 - mmcls - INFO - Epoch [7][4600/5005]\tlr: 1.000e-01, eta: 11:26:44, time: 0.090, data_time: 0.007, memory: 928, loss: 5.5241\n2023-01-04 16:04:46,549 - mmcls - INFO - Epoch [7][4700/5005]\tlr: 1.000e-01, eta: 11:26:31, time: 0.086, data_time: 0.009, memory: 928, loss: 5.5235\n2023-01-04 16:04:55,226 - mmcls - INFO - Epoch [7][4800/5005]\tlr: 1.000e-01, eta: 11:26:20, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5325\n2023-01-04 16:05:03,758 - mmcls - INFO - Epoch [7][4900/5005]\tlr: 1.000e-01, eta: 11:26:07, time: 0.085, data_time: 0.020, memory: 928, loss: 5.5105\n2023-01-04 16:05:12,448 - mmcls - INFO - Epoch [7][5000/5005]\tlr: 1.000e-01, eta: 11:25:56, time: 0.087, data_time: 0.018, memory: 928, loss: 5.5349\n2023-01-04 16:05:35,287 - mmcls - INFO - Epoch(val) [7][196]\taccuracy_top-1: 5.2260, accuracy_top-5: 13.9840\n2023-01-04 16:05:46,090 - mmcls - INFO - Epoch [8][100/5005]\tlr: 1.000e-01, eta: 11:26:07, time: 0.108, data_time: 0.036, memory: 928, loss: 5.5068\n2023-01-04 16:05:54,774 - mmcls - INFO - Epoch [8][200/5005]\tlr: 1.000e-01, eta: 11:25:56, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4920\n2023-01-04 16:06:03,692 - mmcls - INFO - Epoch [8][300/5005]\tlr: 1.000e-01, eta: 11:25:48, time: 0.089, data_time: 0.019, memory: 928, loss: 5.5275\n2023-01-04 16:06:12,519 - mmcls - INFO - Epoch [8][400/5005]\tlr: 1.000e-01, eta: 11:25:39, time: 0.088, data_time: 0.025, memory: 928, loss: 5.5130\n2023-01-04 16:06:21,326 - mmcls - INFO - Epoch [8][500/5005]\tlr: 1.000e-01, eta: 11:25:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5202\n2023-01-04 16:06:30,085 - mmcls - INFO - Epoch [8][600/5005]\tlr: 1.000e-01, eta: 11:25:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5053\n2023-01-04 16:06:38,740 - mmcls - INFO - Epoch [8][700/5005]\tlr: 1.000e-01, eta: 11:25:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5150\n2023-01-04 16:06:47,708 - mmcls - INFO - Epoch [8][800/5005]\tlr: 1.000e-01, eta: 11:25:01, time: 0.090, data_time: 0.028, memory: 928, loss: 5.5082\n2023-01-04 16:06:56,467 - mmcls - INFO - Epoch [8][900/5005]\tlr: 1.000e-01, eta: 11:24:51, time: 0.088, data_time: 0.010, memory: 928, loss: 5.5100\n2023-01-04 16:07:05,303 - mmcls - INFO - Epoch [8][1000/5005]\tlr: 1.000e-01, eta: 11:24:42, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5074\n2023-01-04 16:07:13,927 - mmcls - INFO - Epoch [8][1100/5005]\tlr: 1.000e-01, eta: 11:24:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5147\n2023-01-04 16:07:23,013 - mmcls - INFO - Epoch [8][1200/5005]\tlr: 1.000e-01, eta: 11:24:24, time: 0.091, data_time: 0.004, memory: 928, loss: 5.5154\n2023-01-04 16:07:31,548 - mmcls - INFO - Epoch [8][1300/5005]\tlr: 1.000e-01, eta: 11:24:11, time: 0.085, data_time: 0.023, memory: 928, loss: 5.5310\n2023-01-04 16:07:40,333 - mmcls - INFO - Epoch [8][1400/5005]\tlr: 1.000e-01, eta: 11:24:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5155\n2023-01-04 16:07:49,495 - mmcls - INFO - Epoch [8][1500/5005]\tlr: 1.000e-01, eta: 11:23:57, time: 0.092, data_time: 0.001, memory: 928, loss: 5.5304\n2023-01-04 16:07:58,696 - mmcls - INFO - Epoch [8][1600/5005]\tlr: 1.000e-01, eta: 11:23:53, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4967\n2023-01-04 16:08:07,491 - mmcls - INFO - Epoch [8][1700/5005]\tlr: 1.000e-01, eta: 11:23:43, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5164\n2023-01-04 16:08:16,037 - mmcls - INFO - Epoch [8][1800/5005]\tlr: 1.000e-01, eta: 11:23:31, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4979\n2023-01-04 16:08:24,484 - mmcls - INFO - Epoch [8][1900/5005]\tlr: 1.000e-01, eta: 11:23:17, time: 0.084, data_time: 0.011, memory: 928, loss: 5.5027\n2023-01-04 16:08:33,196 - mmcls - INFO - Epoch [8][2000/5005]\tlr: 1.000e-01, eta: 11:23:06, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5108\n2023-01-04 16:08:42,201 - mmcls - INFO - Epoch [8][2100/5005]\tlr: 1.000e-01, eta: 11:22:59, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5092\n2023-01-04 16:08:50,786 - mmcls - INFO - Epoch [8][2200/5005]\tlr: 1.000e-01, eta: 11:22:47, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5332\n2023-01-04 16:08:59,624 - mmcls - INFO - Epoch [8][2300/5005]\tlr: 1.000e-01, eta: 11:22:38, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5130\n2023-01-04 16:09:08,176 - mmcls - INFO - Epoch [8][2400/5005]\tlr: 1.000e-01, eta: 11:22:26, time: 0.085, data_time: 0.007, memory: 928, loss: 5.5465\n2023-01-04 16:09:16,689 - mmcls - INFO - Epoch [8][2500/5005]\tlr: 1.000e-01, eta: 11:22:13, time: 0.085, data_time: 0.011, memory: 928, loss: 5.5102\n2023-01-04 16:09:25,317 - mmcls - INFO - Epoch [8][2600/5005]\tlr: 1.000e-01, eta: 11:22:02, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5155\n2023-01-04 16:09:34,046 - mmcls - INFO - Epoch [8][2700/5005]\tlr: 1.000e-01, eta: 11:21:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5358\n2023-01-04 16:09:43,164 - mmcls - INFO - Epoch [8][2800/5005]\tlr: 1.000e-01, eta: 11:21:46, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5255\n2023-01-04 16:09:52,148 - mmcls - INFO - Epoch [8][2900/5005]\tlr: 1.000e-01, eta: 11:21:39, time: 0.090, data_time: 0.007, memory: 928, loss: 5.5057\n2023-01-04 16:10:01,213 - mmcls - INFO - Epoch [8][3000/5005]\tlr: 1.000e-01, eta: 11:21:33, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5104\n2023-01-04 16:10:10,378 - mmcls - INFO - Epoch [8][3100/5005]\tlr: 1.000e-01, eta: 11:21:28, time: 0.092, data_time: 0.001, memory: 928, loss: 5.5097\n2023-01-04 16:10:19,189 - mmcls - INFO - Epoch [8][3200/5005]\tlr: 1.000e-01, eta: 11:21:18, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4925\n2023-01-04 16:10:28,034 - mmcls - INFO - Epoch [8][3300/5005]\tlr: 1.000e-01, eta: 11:21:10, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5049\n2023-01-04 16:10:37,569 - mmcls - INFO - Epoch [8][3400/5005]\tlr: 1.000e-01, eta: 11:21:09, time: 0.095, data_time: 0.015, memory: 928, loss: 5.5025\n2023-01-04 16:10:46,101 - mmcls - INFO - Epoch [8][3500/5005]\tlr: 1.000e-01, eta: 11:20:56, time: 0.085, data_time: 0.023, memory: 928, loss: 5.5290\n2023-01-04 16:10:54,946 - mmcls - INFO - Epoch [8][3600/5005]\tlr: 1.000e-01, eta: 11:20:48, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5046\n2023-01-04 16:11:03,636 - mmcls - INFO - Epoch [8][3700/5005]\tlr: 1.000e-01, eta: 11:20:37, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5008\n2023-01-04 16:11:12,479 - mmcls - INFO - Epoch [8][3800/5005]\tlr: 1.000e-01, eta: 11:20:28, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5178\n2023-01-04 16:11:21,455 - mmcls - INFO - Epoch [8][3900/5005]\tlr: 1.000e-01, eta: 11:20:21, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5243\n2023-01-04 16:11:30,132 - mmcls - INFO - Epoch [8][4000/5005]\tlr: 1.000e-01, eta: 11:20:10, time: 0.087, data_time: 0.022, memory: 928, loss: 5.5198\n2023-01-04 16:11:38,829 - mmcls - INFO - Epoch [8][4100/5005]\tlr: 1.000e-01, eta: 11:19:59, time: 0.087, data_time: 0.027, memory: 928, loss: 5.5073\n2023-01-04 16:11:47,493 - mmcls - INFO - Epoch [8][4200/5005]\tlr: 1.000e-01, eta: 11:19:48, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4995\n2023-01-04 16:11:56,201 - mmcls - INFO - Epoch [8][4300/5005]\tlr: 1.000e-01, eta: 11:19:38, time: 0.087, data_time: 0.000, memory: 928, loss: 5.5109\n2023-01-04 16:12:04,932 - mmcls - INFO - Epoch [8][4400/5005]\tlr: 1.000e-01, eta: 11:19:28, time: 0.087, data_time: 0.017, memory: 928, loss: 5.5215\n2023-01-04 16:12:14,003 - mmcls - INFO - Epoch [8][4500/5005]\tlr: 1.000e-01, eta: 11:19:22, time: 0.091, data_time: 0.004, memory: 928, loss: 5.4796\n2023-01-04 16:12:22,843 - mmcls - INFO - Epoch [8][4600/5005]\tlr: 1.000e-01, eta: 11:19:13, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5089\n2023-01-04 16:12:31,732 - mmcls - INFO - Epoch [8][4700/5005]\tlr: 1.000e-01, eta: 11:19:04, time: 0.089, data_time: 0.022, memory: 928, loss: 5.5003\n2023-01-04 16:12:40,448 - mmcls - INFO - Epoch [8][4800/5005]\tlr: 1.000e-01, eta: 11:18:54, time: 0.087, data_time: 0.033, memory: 928, loss: 5.4959\n2023-01-04 16:12:49,096 - mmcls - INFO - Epoch [8][4900/5005]\tlr: 1.000e-01, eta: 11:18:43, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5141\n2023-01-04 16:12:57,971 - mmcls - INFO - Epoch [8][5000/5005]\tlr: 1.000e-01, eta: 11:18:35, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5141\n2023-01-04 16:13:20,733 - mmcls - INFO - Epoch(val) [8][196]\taccuracy_top-1: 4.3560, accuracy_top-5: 11.5340\n2023-01-04 16:13:31,533 - mmcls - INFO - Epoch [9][100/5005]\tlr: 1.000e-01, eta: 11:18:42, time: 0.108, data_time: 0.038, memory: 928, loss: 5.4933\n2023-01-04 16:13:40,268 - mmcls - INFO - Epoch [9][200/5005]\tlr: 1.000e-01, eta: 11:18:32, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4906\n2023-01-04 16:13:49,124 - mmcls - INFO - Epoch [9][300/5005]\tlr: 1.000e-01, eta: 11:18:23, time: 0.088, data_time: 0.010, memory: 928, loss: 5.5069\n2023-01-04 16:13:58,089 - mmcls - INFO - Epoch [9][400/5005]\tlr: 1.000e-01, eta: 11:18:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5201\n2023-01-04 16:14:06,896 - mmcls - INFO - Epoch [9][500/5005]\tlr: 1.000e-01, eta: 11:18:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5101\n2023-01-04 16:14:15,922 - mmcls - INFO - Epoch [9][600/5005]\tlr: 1.000e-01, eta: 11:18:00, time: 0.090, data_time: 0.006, memory: 928, loss: 5.5189\n2023-01-04 16:14:24,796 - mmcls - INFO - Epoch [9][700/5005]\tlr: 1.000e-01, eta: 11:17:51, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5088\n2023-01-04 16:14:33,735 - mmcls - INFO - Epoch [9][800/5005]\tlr: 1.000e-01, eta: 11:17:43, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4956\n2023-01-04 16:14:42,289 - mmcls - INFO - Epoch [9][900/5005]\tlr: 1.000e-01, eta: 11:17:31, time: 0.086, data_time: 0.007, memory: 928, loss: 5.5224\n2023-01-04 16:14:50,865 - mmcls - INFO - Epoch [9][1000/5005]\tlr: 1.000e-01, eta: 11:17:20, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4918\n2023-01-04 16:14:59,491 - mmcls - INFO - Epoch [9][1100/5005]\tlr: 1.000e-01, eta: 11:17:08, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5012\n2023-01-04 16:15:08,308 - mmcls - INFO - Epoch [9][1200/5005]\tlr: 1.000e-01, eta: 11:16:59, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5171\n2023-01-04 16:15:16,968 - mmcls - INFO - Epoch [9][1300/5005]\tlr: 1.000e-01, eta: 11:16:48, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5212\n2023-01-04 16:15:25,637 - mmcls - INFO - Epoch [9][1400/5005]\tlr: 1.000e-01, eta: 11:16:37, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4957\n2023-01-04 16:15:34,216 - mmcls - INFO - Epoch [9][1500/5005]\tlr: 1.000e-01, eta: 11:16:26, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4965\n2023-01-04 16:15:43,059 - mmcls - INFO - Epoch [9][1600/5005]\tlr: 1.000e-01, eta: 11:16:17, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4945\n2023-01-04 16:15:51,638 - mmcls - INFO - Epoch [9][1700/5005]\tlr: 1.000e-01, eta: 11:16:05, time: 0.086, data_time: 0.026, memory: 928, loss: 5.5062\n2023-01-04 16:16:00,513 - mmcls - INFO - Epoch [9][1800/5005]\tlr: 1.000e-01, eta: 11:15:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5131\n2023-01-04 16:16:09,253 - mmcls - INFO - Epoch [9][1900/5005]\tlr: 1.000e-01, eta: 11:15:46, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5066\n2023-01-04 16:16:18,178 - mmcls - INFO - Epoch [9][2000/5005]\tlr: 1.000e-01, eta: 11:15:39, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5045\n2023-01-04 16:16:26,986 - mmcls - INFO - Epoch [9][2100/5005]\tlr: 1.000e-01, eta: 11:15:29, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5028\n2023-01-04 16:16:35,632 - mmcls - INFO - Epoch [9][2200/5005]\tlr: 1.000e-01, eta: 11:15:18, time: 0.086, data_time: 0.020, memory: 928, loss: 5.4819\n2023-01-04 16:16:44,779 - mmcls - INFO - Epoch [9][2300/5005]\tlr: 1.000e-01, eta: 11:15:13, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4933\n2023-01-04 16:16:53,830 - mmcls - INFO - Epoch [9][2400/5005]\tlr: 1.000e-01, eta: 11:15:06, time: 0.090, data_time: 0.014, memory: 928, loss: 5.5109\n2023-01-04 16:17:02,640 - mmcls - INFO - Epoch [9][2500/5005]\tlr: 1.000e-01, eta: 11:14:57, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5040\n2023-01-04 16:17:11,155 - mmcls - INFO - Epoch [9][2600/5005]\tlr: 1.000e-01, eta: 11:14:44, time: 0.085, data_time: 0.007, memory: 928, loss: 5.4885\n2023-01-04 16:17:19,953 - mmcls - INFO - Epoch [9][2700/5005]\tlr: 1.000e-01, eta: 11:14:35, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4959\n2023-01-04 16:17:28,595 - mmcls - INFO - Epoch [9][2800/5005]\tlr: 1.000e-01, eta: 11:14:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4996\n2023-01-04 16:17:37,074 - mmcls - INFO - Epoch [9][2900/5005]\tlr: 1.000e-01, eta: 11:14:12, time: 0.085, data_time: 0.001, memory: 928, loss: 5.5103\n2023-01-04 16:17:45,842 - mmcls - INFO - Epoch [9][3000/5005]\tlr: 1.000e-01, eta: 11:14:02, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4868\n2023-01-04 16:17:54,910 - mmcls - INFO - Epoch [9][3100/5005]\tlr: 1.000e-01, eta: 11:13:55, time: 0.091, data_time: 0.003, memory: 928, loss: 5.5028\n2023-01-04 16:18:03,608 - mmcls - INFO - Epoch [9][3200/5005]\tlr: 1.000e-01, eta: 11:13:45, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5043\n2023-01-04 16:18:12,468 - mmcls - INFO - Epoch [9][3300/5005]\tlr: 1.000e-01, eta: 11:13:36, time: 0.089, data_time: 0.010, memory: 928, loss: 5.5033\n2023-01-04 16:18:21,169 - mmcls - INFO - Epoch [9][3400/5005]\tlr: 1.000e-01, eta: 11:13:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4996\n2023-01-04 16:18:29,777 - mmcls - INFO - Epoch [9][3500/5005]\tlr: 1.000e-01, eta: 11:13:15, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4917\n2023-01-04 16:18:38,352 - mmcls - INFO - Epoch [9][3600/5005]\tlr: 1.000e-01, eta: 11:13:03, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5011\n2023-01-04 16:18:46,931 - mmcls - INFO - Epoch [9][3700/5005]\tlr: 1.000e-01, eta: 11:12:52, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4821\n2023-01-04 16:18:55,480 - mmcls - INFO - Epoch [9][3800/5005]\tlr: 1.000e-01, eta: 11:12:40, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4936\n2023-01-04 16:19:04,071 - mmcls - INFO - Epoch [9][3900/5005]\tlr: 1.000e-01, eta: 11:12:28, time: 0.086, data_time: 0.026, memory: 928, loss: 5.5162\n2023-01-04 16:19:12,742 - mmcls - INFO - Epoch [9][4000/5005]\tlr: 1.000e-01, eta: 11:12:18, time: 0.087, data_time: 0.025, memory: 928, loss: 5.4909\n2023-01-04 16:19:21,539 - mmcls - INFO - Epoch [9][4100/5005]\tlr: 1.000e-01, eta: 11:12:09, time: 0.088, data_time: 0.013, memory: 928, loss: 5.5033\n2023-01-04 16:19:30,257 - mmcls - INFO - Epoch [9][4200/5005]\tlr: 1.000e-01, eta: 11:11:58, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4897\n2023-01-04 16:19:39,085 - mmcls - INFO - Epoch [9][4300/5005]\tlr: 1.000e-01, eta: 11:11:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5055\n2023-01-04 16:19:47,852 - mmcls - INFO - Epoch [9][4400/5005]\tlr: 1.000e-01, eta: 11:11:40, time: 0.088, data_time: 0.018, memory: 928, loss: 5.4916\n2023-01-04 16:19:56,421 - mmcls - INFO - Epoch [9][4500/5005]\tlr: 1.000e-01, eta: 11:11:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5089\n2023-01-04 16:20:05,257 - mmcls - INFO - Epoch [9][4600/5005]\tlr: 1.000e-01, eta: 11:11:20, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4860\n2023-01-04 16:20:14,243 - mmcls - INFO - Epoch [9][4700/5005]\tlr: 1.000e-01, eta: 11:11:12, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4789\n2023-01-04 16:20:22,972 - mmcls - INFO - Epoch [9][4800/5005]\tlr: 1.000e-01, eta: 11:11:02, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5026\n2023-01-04 16:20:31,510 - mmcls - INFO - Epoch [9][4900/5005]\tlr: 1.000e-01, eta: 11:10:50, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4983\n2023-01-04 16:20:40,118 - mmcls - INFO - Epoch [9][5000/5005]\tlr: 1.000e-01, eta: 11:10:39, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4873\n2023-01-04 16:21:03,154 - mmcls - INFO - Epoch(val) [9][196]\taccuracy_top-1: 5.4760, accuracy_top-5: 14.2380\n2023-01-04 16:21:13,805 - mmcls - INFO - Epoch [10][100/5005]\tlr: 1.000e-01, eta: 11:10:44, time: 0.106, data_time: 0.047, memory: 928, loss: 5.4892\n2023-01-04 16:21:22,661 - mmcls - INFO - Epoch [10][200/5005]\tlr: 1.000e-01, eta: 11:10:35, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4816\n2023-01-04 16:21:31,695 - mmcls - INFO - Epoch [10][300/5005]\tlr: 1.000e-01, eta: 11:10:28, time: 0.090, data_time: 0.010, memory: 928, loss: 5.5092\n2023-01-04 16:21:40,519 - mmcls - INFO - Epoch [10][400/5005]\tlr: 1.000e-01, eta: 11:10:19, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4933\n2023-01-04 16:21:49,085 - mmcls - INFO - Epoch [10][500/5005]\tlr: 1.000e-01, eta: 11:10:07, time: 0.086, data_time: 0.014, memory: 928, loss: 5.4933\n2023-01-04 16:21:58,306 - mmcls - INFO - Epoch [10][600/5005]\tlr: 1.000e-01, eta: 11:10:02, time: 0.092, data_time: 0.008, memory: 928, loss: 5.4956\n2023-01-04 16:22:07,171 - mmcls - INFO - Epoch [10][700/5005]\tlr: 1.000e-01, eta: 11:09:54, time: 0.089, data_time: 0.015, memory: 928, loss: 5.4848\n2023-01-04 16:22:16,519 - mmcls - INFO - Epoch [10][800/5005]\tlr: 1.000e-01, eta: 11:09:50, time: 0.093, data_time: 0.051, memory: 928, loss: 5.4896\n2023-01-04 16:22:25,315 - mmcls - INFO - Epoch [10][900/5005]\tlr: 1.000e-01, eta: 11:09:41, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4912\n2023-01-04 16:22:34,583 - mmcls - INFO - Epoch [10][1000/5005]\tlr: 1.000e-01, eta: 11:09:36, time: 0.093, data_time: 0.009, memory: 928, loss: 5.4867\n2023-01-04 16:22:43,348 - mmcls - INFO - Epoch [10][1100/5005]\tlr: 1.000e-01, eta: 11:09:27, time: 0.088, data_time: 0.011, memory: 928, loss: 5.5022\n2023-01-04 16:22:52,176 - mmcls - INFO - Epoch [10][1200/5005]\tlr: 1.000e-01, eta: 11:09:18, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5066\n2023-01-04 16:23:01,001 - mmcls - INFO - Epoch [10][1300/5005]\tlr: 1.000e-01, eta: 11:09:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4945\n2023-01-04 16:23:09,727 - mmcls - INFO - Epoch [10][1400/5005]\tlr: 1.000e-01, eta: 11:08:59, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5075\n2023-01-04 16:23:18,438 - mmcls - INFO - Epoch [10][1500/5005]\tlr: 1.000e-01, eta: 11:08:48, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4796\n2023-01-04 16:23:27,002 - mmcls - INFO - Epoch [10][1600/5005]\tlr: 1.000e-01, eta: 11:08:37, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4862\n2023-01-04 16:23:35,846 - mmcls - INFO - Epoch [10][1700/5005]\tlr: 1.000e-01, eta: 11:08:28, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5046\n2023-01-04 16:23:44,633 - mmcls - INFO - Epoch [10][1800/5005]\tlr: 1.000e-01, eta: 11:08:19, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4880\n2023-01-04 16:23:53,852 - mmcls - INFO - Epoch [10][1900/5005]\tlr: 1.000e-01, eta: 11:08:14, time: 0.092, data_time: 0.017, memory: 928, loss: 5.4943\n2023-01-04 16:24:02,440 - mmcls - INFO - Epoch [10][2000/5005]\tlr: 1.000e-01, eta: 11:08:02, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4789\n2023-01-04 16:24:11,101 - mmcls - INFO - Epoch [10][2100/5005]\tlr: 1.000e-01, eta: 11:07:52, time: 0.086, data_time: 0.000, memory: 928, loss: 5.4966\n2023-01-04 16:24:19,761 - mmcls - INFO - Epoch [10][2200/5005]\tlr: 1.000e-01, eta: 11:07:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4897\n2023-01-04 16:24:28,629 - mmcls - INFO - Epoch [10][2300/5005]\tlr: 1.000e-01, eta: 11:07:33, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4843\n2023-01-04 16:24:37,090 - mmcls - INFO - Epoch [10][2400/5005]\tlr: 1.000e-01, eta: 11:07:20, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4994\n2023-01-04 16:24:46,012 - mmcls - INFO - Epoch [10][2500/5005]\tlr: 1.000e-01, eta: 11:07:12, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4778\n2023-01-04 16:24:55,008 - mmcls - INFO - Epoch [10][2600/5005]\tlr: 1.000e-01, eta: 11:07:05, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4847\n2023-01-04 16:25:03,683 - mmcls - INFO - Epoch [10][2700/5005]\tlr: 1.000e-01, eta: 11:06:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5137\n2023-01-04 16:25:12,717 - mmcls - INFO - Epoch [10][2800/5005]\tlr: 1.000e-01, eta: 11:06:47, time: 0.090, data_time: 0.007, memory: 928, loss: 5.4974\n2023-01-04 16:25:21,869 - mmcls - INFO - Epoch [10][2900/5005]\tlr: 1.000e-01, eta: 11:06:41, time: 0.091, data_time: 0.005, memory: 928, loss: 5.5060\n2023-01-04 16:25:30,769 - mmcls - INFO - Epoch [10][3000/5005]\tlr: 1.000e-01, eta: 11:06:33, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4960\n2023-01-04 16:25:39,381 - mmcls - INFO - Epoch [10][3100/5005]\tlr: 1.000e-01, eta: 11:06:22, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5101\n2023-01-04 16:25:47,783 - mmcls - INFO - Epoch [10][3200/5005]\tlr: 1.000e-01, eta: 11:06:09, time: 0.084, data_time: 0.008, memory: 928, loss: 5.4898\n2023-01-04 16:25:56,419 - mmcls - INFO - Epoch [10][3300/5005]\tlr: 1.000e-01, eta: 11:05:59, time: 0.086, data_time: 0.012, memory: 928, loss: 5.4956\n2023-01-04 16:26:05,125 - mmcls - INFO - Epoch [10][3400/5005]\tlr: 1.000e-01, eta: 11:05:49, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4856\n2023-01-04 16:26:13,920 - mmcls - INFO - Epoch [10][3500/5005]\tlr: 1.000e-01, eta: 11:05:39, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4836\n2023-01-04 16:26:22,487 - mmcls - INFO - Epoch [10][3600/5005]\tlr: 1.000e-01, eta: 11:05:28, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4960\n2023-01-04 16:26:31,216 - mmcls - INFO - Epoch [10][3700/5005]\tlr: 1.000e-01, eta: 11:05:18, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4866\n2023-01-04 16:26:39,883 - mmcls - INFO - Epoch [10][3800/5005]\tlr: 1.000e-01, eta: 11:05:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4965\n2023-01-04 16:26:49,207 - mmcls - INFO - Epoch [10][3900/5005]\tlr: 1.000e-01, eta: 11:05:03, time: 0.093, data_time: 0.010, memory: 928, loss: 5.4838\n2023-01-04 16:26:57,690 - mmcls - INFO - Epoch [10][4000/5005]\tlr: 1.000e-01, eta: 11:04:51, time: 0.085, data_time: 0.001, memory: 928, loss: 5.5013\n2023-01-04 16:27:06,542 - mmcls - INFO - Epoch [10][4100/5005]\tlr: 1.000e-01, eta: 11:04:42, time: 0.088, data_time: 0.026, memory: 928, loss: 5.4950\n2023-01-04 16:27:15,586 - mmcls - INFO - Epoch [10][4200/5005]\tlr: 1.000e-01, eta: 11:04:36, time: 0.091, data_time: 0.015, memory: 928, loss: 5.4903\n2023-01-04 16:27:24,401 - mmcls - INFO - Epoch [10][4300/5005]\tlr: 1.000e-01, eta: 11:04:27, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4941\n2023-01-04 16:27:33,334 - mmcls - INFO - Epoch [10][4400/5005]\tlr: 1.000e-01, eta: 11:04:19, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4755\n2023-01-04 16:27:41,956 - mmcls - INFO - Epoch [10][4500/5005]\tlr: 1.000e-01, eta: 11:04:08, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4892\n2023-01-04 16:27:50,764 - mmcls - INFO - Epoch [10][4600/5005]\tlr: 1.000e-01, eta: 11:03:59, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4877\n2023-01-04 16:27:59,547 - mmcls - INFO - Epoch [10][4700/5005]\tlr: 1.000e-01, eta: 11:03:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4911\n2023-01-04 16:28:08,180 - mmcls - INFO - Epoch [10][4800/5005]\tlr: 1.000e-01, eta: 11:03:39, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5009\n2023-01-04 16:28:17,159 - mmcls - INFO - Epoch [10][4900/5005]\tlr: 1.000e-01, eta: 11:03:31, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4969\n2023-01-04 16:28:25,846 - mmcls - INFO - Epoch [10][5000/5005]\tlr: 1.000e-01, eta: 11:03:21, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4930\n2023-01-04 16:28:48,666 - mmcls - INFO - Epoch(val) [10][196]\taccuracy_top-1: 4.9860, accuracy_top-5: 13.8700\n2023-01-04 16:28:59,782 - mmcls - INFO - Epoch [11][100/5005]\tlr: 1.000e-01, eta: 11:03:28, time: 0.111, data_time: 0.047, memory: 928, loss: 5.4736\n2023-01-04 16:29:08,522 - mmcls - INFO - Epoch [11][200/5005]\tlr: 1.000e-01, eta: 11:03:18, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4741\n2023-01-04 16:29:17,482 - mmcls - INFO - Epoch [11][300/5005]\tlr: 1.000e-01, eta: 11:03:10, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4817\n2023-01-04 16:29:26,148 - mmcls - INFO - Epoch [11][400/5005]\tlr: 1.000e-01, eta: 11:03:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4735\n2023-01-04 16:29:34,887 - mmcls - INFO - Epoch [11][500/5005]\tlr: 1.000e-01, eta: 11:02:50, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4796\n2023-01-04 16:29:43,742 - mmcls - INFO - Epoch [11][600/5005]\tlr: 1.000e-01, eta: 11:02:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4937\n2023-01-04 16:29:52,509 - mmcls - INFO - Epoch [11][700/5005]\tlr: 1.000e-01, eta: 11:02:32, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4874\n2023-01-04 16:30:00,971 - mmcls - INFO - Epoch [11][800/5005]\tlr: 1.000e-01, eta: 11:02:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.4927\n2023-01-04 16:30:09,524 - mmcls - INFO - Epoch [11][900/5005]\tlr: 1.000e-01, eta: 11:02:09, time: 0.085, data_time: 0.006, memory: 928, loss: 5.4756\n2023-01-04 16:30:18,447 - mmcls - INFO - Epoch [11][1000/5005]\tlr: 1.000e-01, eta: 11:02:00, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4831\n2023-01-04 16:30:27,100 - mmcls - INFO - Epoch [11][1100/5005]\tlr: 1.000e-01, eta: 11:01:50, time: 0.087, data_time: 0.018, memory: 928, loss: 5.4853\n2023-01-04 16:30:35,847 - mmcls - INFO - Epoch [11][1200/5005]\tlr: 1.000e-01, eta: 11:01:40, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4915\n2023-01-04 16:30:44,914 - mmcls - INFO - Epoch [11][1300/5005]\tlr: 1.000e-01, eta: 11:01:34, time: 0.091, data_time: 0.022, memory: 928, loss: 5.4899\n2023-01-04 16:30:53,455 - mmcls - INFO - Epoch [11][1400/5005]\tlr: 1.000e-01, eta: 11:01:22, time: 0.085, data_time: 0.006, memory: 928, loss: 5.5138\n2023-01-04 16:31:01,993 - mmcls - INFO - Epoch [11][1500/5005]\tlr: 1.000e-01, eta: 11:01:11, time: 0.085, data_time: 0.002, memory: 928, loss: 5.5029\n2023-01-04 16:31:10,853 - mmcls - INFO - Epoch [11][1600/5005]\tlr: 1.000e-01, eta: 11:01:02, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4653\n2023-01-04 16:31:19,842 - mmcls - INFO - Epoch [11][1700/5005]\tlr: 1.000e-01, eta: 11:00:55, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4802\n2023-01-04 16:31:28,314 - mmcls - INFO - Epoch [11][1800/5005]\tlr: 1.000e-01, eta: 11:00:42, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4786\n2023-01-04 16:31:37,087 - mmcls - INFO - Epoch [11][1900/5005]\tlr: 1.000e-01, eta: 11:00:33, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4748\n2023-01-04 16:31:45,974 - mmcls - INFO - Epoch [11][2000/5005]\tlr: 1.000e-01, eta: 11:00:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4678\n2023-01-04 16:31:54,931 - mmcls - INFO - Epoch [11][2100/5005]\tlr: 1.000e-01, eta: 11:00:17, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4970\n2023-01-04 16:32:03,707 - mmcls - INFO - Epoch [11][2200/5005]\tlr: 1.000e-01, eta: 11:00:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4881\n2023-01-04 16:32:12,371 - mmcls - INFO - Epoch [11][2300/5005]\tlr: 1.000e-01, eta: 10:59:57, time: 0.087, data_time: 0.022, memory: 928, loss: 5.4745\n2023-01-04 16:32:21,016 - mmcls - INFO - Epoch [11][2400/5005]\tlr: 1.000e-01, eta: 10:59:47, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4892\n2023-01-04 16:32:29,493 - mmcls - INFO - Epoch [11][2500/5005]\tlr: 1.000e-01, eta: 10:59:35, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4770\n2023-01-04 16:32:38,606 - mmcls - INFO - Epoch [11][2600/5005]\tlr: 1.000e-01, eta: 10:59:28, time: 0.091, data_time: 0.015, memory: 928, loss: 5.4920\n2023-01-04 16:32:47,298 - mmcls - INFO - Epoch [11][2700/5005]\tlr: 1.000e-01, eta: 10:59:18, time: 0.087, data_time: 0.017, memory: 928, loss: 5.4810\n2023-01-04 16:32:56,138 - mmcls - INFO - Epoch [11][2800/5005]\tlr: 1.000e-01, eta: 10:59:10, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4988\n2023-01-04 16:33:04,780 - mmcls - INFO - Epoch [11][2900/5005]\tlr: 1.000e-01, eta: 10:58:59, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4898\n2023-01-04 16:33:13,505 - mmcls - INFO - Epoch [11][3000/5005]\tlr: 1.000e-01, eta: 10:58:49, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4764\n2023-01-04 16:33:22,210 - mmcls - INFO - Epoch [11][3100/5005]\tlr: 1.000e-01, eta: 10:58:39, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5079\n2023-01-04 16:33:31,754 - mmcls - INFO - Epoch [11][3200/5005]\tlr: 1.000e-01, eta: 10:58:36, time: 0.095, data_time: 0.025, memory: 928, loss: 5.4646\n2023-01-04 16:33:40,624 - mmcls - INFO - Epoch [11][3300/5005]\tlr: 1.000e-01, eta: 10:58:28, time: 0.089, data_time: 0.014, memory: 928, loss: 5.4742\n2023-01-04 16:33:49,485 - mmcls - INFO - Epoch [11][3400/5005]\tlr: 1.000e-01, eta: 10:58:19, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4943\n2023-01-04 16:33:58,357 - mmcls - INFO - Epoch [11][3500/5005]\tlr: 1.000e-01, eta: 10:58:11, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4714\n2023-01-04 16:34:07,307 - mmcls - INFO - Epoch [11][3600/5005]\tlr: 1.000e-01, eta: 10:58:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.5019\n2023-01-04 16:34:15,843 - mmcls - INFO - Epoch [11][3700/5005]\tlr: 1.000e-01, eta: 10:57:52, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4737\n2023-01-04 16:34:24,588 - mmcls - INFO - Epoch [11][3800/5005]\tlr: 1.000e-01, eta: 10:57:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4740\n2023-01-04 16:34:33,459 - mmcls - INFO - Epoch [11][3900/5005]\tlr: 1.000e-01, eta: 10:57:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4782\n2023-01-04 16:34:42,234 - mmcls - INFO - Epoch [11][4000/5005]\tlr: 1.000e-01, eta: 10:57:24, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4629\n2023-01-04 16:34:51,084 - mmcls - INFO - Epoch [11][4100/5005]\tlr: 1.000e-01, eta: 10:57:15, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4813\n2023-01-04 16:35:00,084 - mmcls - INFO - Epoch [11][4200/5005]\tlr: 1.000e-01, eta: 10:57:08, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4766\n2023-01-04 16:35:09,011 - mmcls - INFO - Epoch [11][4300/5005]\tlr: 1.000e-01, eta: 10:57:00, time: 0.089, data_time: 0.021, memory: 928, loss: 5.5054\n2023-01-04 16:35:17,782 - mmcls - INFO - Epoch [11][4400/5005]\tlr: 1.000e-01, eta: 10:56:50, time: 0.088, data_time: 0.024, memory: 928, loss: 5.4984\n2023-01-04 16:35:26,683 - mmcls - INFO - Epoch [11][4500/5005]\tlr: 1.000e-01, eta: 10:56:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4888\n2023-01-04 16:35:35,462 - mmcls - INFO - Epoch [11][4600/5005]\tlr: 1.000e-01, eta: 10:56:33, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4758\n2023-01-04 16:35:44,008 - mmcls - INFO - Epoch [11][4700/5005]\tlr: 1.000e-01, eta: 10:56:22, time: 0.085, data_time: 0.022, memory: 928, loss: 5.4875\n2023-01-04 16:35:52,490 - mmcls - INFO - Epoch [11][4800/5005]\tlr: 1.000e-01, eta: 10:56:10, time: 0.085, data_time: 0.034, memory: 928, loss: 5.4937\n2023-01-04 16:36:01,284 - mmcls - INFO - Epoch [11][4900/5005]\tlr: 1.000e-01, eta: 10:56:01, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4789\n2023-01-04 16:36:09,944 - mmcls - INFO - Epoch [11][5000/5005]\tlr: 1.000e-01, eta: 10:55:50, time: 0.087, data_time: 0.023, memory: 928, loss: 5.4789\n2023-01-04 16:36:32,777 - mmcls - INFO - Epoch(val) [11][196]\taccuracy_top-1: 4.9460, accuracy_top-5: 13.6020\n2023-01-04 16:36:43,744 - mmcls - INFO - Epoch [12][100/5005]\tlr: 1.000e-01, eta: 10:55:55, time: 0.109, data_time: 0.033, memory: 928, loss: 5.4754\n2023-01-04 16:36:52,900 - mmcls - INFO - Epoch [12][200/5005]\tlr: 1.000e-01, eta: 10:55:48, time: 0.092, data_time: 0.008, memory: 928, loss: 5.4709\n2023-01-04 16:37:01,536 - mmcls - INFO - Epoch [12][300/5005]\tlr: 1.000e-01, eta: 10:55:38, time: 0.086, data_time: 0.018, memory: 928, loss: 5.5063\n2023-01-04 16:37:10,693 - mmcls - INFO - Epoch [12][400/5005]\tlr: 1.000e-01, eta: 10:55:32, time: 0.092, data_time: 0.016, memory: 928, loss: 5.4586\n2023-01-04 16:37:19,469 - mmcls - INFO - Epoch [12][500/5005]\tlr: 1.000e-01, eta: 10:55:22, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4919\n2023-01-04 16:37:28,112 - mmcls - INFO - Epoch [12][600/5005]\tlr: 1.000e-01, eta: 10:55:12, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5011\n2023-01-04 16:37:37,354 - mmcls - INFO - Epoch [12][700/5005]\tlr: 1.000e-01, eta: 10:55:06, time: 0.092, data_time: 0.025, memory: 928, loss: 5.4811\n2023-01-04 16:37:46,238 - mmcls - INFO - Epoch [12][800/5005]\tlr: 1.000e-01, eta: 10:54:58, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4709\n2023-01-04 16:37:55,055 - mmcls - INFO - Epoch [12][900/5005]\tlr: 1.000e-01, eta: 10:54:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4863\n2023-01-04 16:38:03,878 - mmcls - INFO - Epoch [12][1000/5005]\tlr: 1.000e-01, eta: 10:54:40, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4703\n2023-01-04 16:38:12,705 - mmcls - INFO - Epoch [12][1100/5005]\tlr: 1.000e-01, eta: 10:54:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5052\n2023-01-04 16:38:21,509 - mmcls - INFO - Epoch [12][1200/5005]\tlr: 1.000e-01, eta: 10:54:22, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4770\n2023-01-04 16:38:30,010 - mmcls - INFO - Epoch [12][1300/5005]\tlr: 1.000e-01, eta: 10:54:10, time: 0.085, data_time: 0.009, memory: 928, loss: 5.4807\n2023-01-04 16:38:38,634 - mmcls - INFO - Epoch [12][1400/5005]\tlr: 1.000e-01, eta: 10:54:00, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4720\n2023-01-04 16:38:47,519 - mmcls - INFO - Epoch [12][1500/5005]\tlr: 1.000e-01, eta: 10:53:51, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4904\n2023-01-04 16:38:56,632 - mmcls - INFO - Epoch [12][1600/5005]\tlr: 1.000e-01, eta: 10:53:45, time: 0.091, data_time: 0.003, memory: 928, loss: 5.4735\n2023-01-04 16:39:05,339 - mmcls - INFO - Epoch [12][1700/5005]\tlr: 1.000e-01, eta: 10:53:35, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4839\n2023-01-04 16:39:14,020 - mmcls - INFO - Epoch [12][1800/5005]\tlr: 1.000e-01, eta: 10:53:25, time: 0.087, data_time: 0.013, memory: 928, loss: 5.4666\n2023-01-04 16:39:22,641 - mmcls - INFO - Epoch [12][1900/5005]\tlr: 1.000e-01, eta: 10:53:14, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4880\n2023-01-04 16:39:31,588 - mmcls - INFO - Epoch [12][2000/5005]\tlr: 1.000e-01, eta: 10:53:06, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4730\n2023-01-04 16:39:40,200 - mmcls - INFO - Epoch [12][2100/5005]\tlr: 1.000e-01, eta: 10:52:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5025\n2023-01-04 16:39:48,684 - mmcls - INFO - Epoch [12][2200/5005]\tlr: 1.000e-01, eta: 10:52:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4758\n2023-01-04 16:39:57,935 - mmcls - INFO - Epoch [12][2300/5005]\tlr: 1.000e-01, eta: 10:52:38, time: 0.093, data_time: 0.002, memory: 928, loss: 5.4495\n2023-01-04 16:40:06,580 - mmcls - INFO - Epoch [12][2400/5005]\tlr: 1.000e-01, eta: 10:52:28, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4871\n2023-01-04 16:40:15,352 - mmcls - INFO - Epoch [12][2500/5005]\tlr: 1.000e-01, eta: 10:52:19, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4866\n2023-01-04 16:40:24,300 - mmcls - INFO - Epoch [12][2600/5005]\tlr: 1.000e-01, eta: 10:52:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4572\n2023-01-04 16:40:33,072 - mmcls - INFO - Epoch [12][2700/5005]\tlr: 1.000e-01, eta: 10:52:01, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4807\n2023-01-04 16:40:41,849 - mmcls - INFO - Epoch [12][2800/5005]\tlr: 1.000e-01, eta: 10:51:52, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4807\n2023-01-04 16:40:50,418 - mmcls - INFO - Epoch [12][2900/5005]\tlr: 1.000e-01, eta: 10:51:41, time: 0.086, data_time: 0.026, memory: 928, loss: 5.4630\n2023-01-04 16:40:59,282 - mmcls - INFO - Epoch [12][3000/5005]\tlr: 1.000e-01, eta: 10:51:33, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4950\n2023-01-04 16:41:08,262 - mmcls - INFO - Epoch [12][3100/5005]\tlr: 1.000e-01, eta: 10:51:25, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4545\n2023-01-04 16:41:17,147 - mmcls - INFO - Epoch [12][3200/5005]\tlr: 1.000e-01, eta: 10:51:16, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4953\n2023-01-04 16:41:25,749 - mmcls - INFO - Epoch [12][3300/5005]\tlr: 1.000e-01, eta: 10:51:06, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4720\n2023-01-04 16:41:34,600 - mmcls - INFO - Epoch [12][3400/5005]\tlr: 1.000e-01, eta: 10:50:57, time: 0.089, data_time: 0.007, memory: 928, loss: 5.4839\n2023-01-04 16:41:43,276 - mmcls - INFO - Epoch [12][3500/5005]\tlr: 1.000e-01, eta: 10:50:47, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4878\n2023-01-04 16:41:52,264 - mmcls - INFO - Epoch [12][3600/5005]\tlr: 1.000e-01, eta: 10:50:39, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4810\n2023-01-04 16:42:01,269 - mmcls - INFO - Epoch [12][3700/5005]\tlr: 1.000e-01, eta: 10:50:32, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4556\n2023-01-04 16:42:10,255 - mmcls - INFO - Epoch [12][3800/5005]\tlr: 1.000e-01, eta: 10:50:24, time: 0.090, data_time: 0.015, memory: 928, loss: 5.4547\n2023-01-04 16:42:18,907 - mmcls - INFO - Epoch [12][3900/5005]\tlr: 1.000e-01, eta: 10:50:14, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4968\n2023-01-04 16:42:27,706 - mmcls - INFO - Epoch [12][4000/5005]\tlr: 1.000e-01, eta: 10:50:05, time: 0.088, data_time: 0.020, memory: 928, loss: 5.4822\n2023-01-04 16:42:36,557 - mmcls - INFO - Epoch [12][4100/5005]\tlr: 1.000e-01, eta: 10:49:56, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4798\n2023-01-04 16:42:45,184 - mmcls - INFO - Epoch [12][4200/5005]\tlr: 1.000e-01, eta: 10:49:45, time: 0.086, data_time: 0.028, memory: 928, loss: 5.4709\n2023-01-04 16:42:53,717 - mmcls - INFO - Epoch [12][4300/5005]\tlr: 1.000e-01, eta: 10:49:34, time: 0.085, data_time: 0.011, memory: 928, loss: 5.4970\n2023-01-04 16:43:02,666 - mmcls - INFO - Epoch [12][4400/5005]\tlr: 1.000e-01, eta: 10:49:26, time: 0.090, data_time: 0.014, memory: 928, loss: 5.4656\n2023-01-04 16:43:11,300 - mmcls - INFO - Epoch [12][4500/5005]\tlr: 1.000e-01, eta: 10:49:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4746\n2023-01-04 16:43:20,527 - mmcls - INFO - Epoch [12][4600/5005]\tlr: 1.000e-01, eta: 10:49:10, time: 0.092, data_time: 0.000, memory: 928, loss: 5.4741\n2023-01-04 16:43:29,148 - mmcls - INFO - Epoch [12][4700/5005]\tlr: 1.000e-01, eta: 10:49:00, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4910\n2023-01-04 16:43:38,090 - mmcls - INFO - Epoch [12][4800/5005]\tlr: 1.000e-01, eta: 10:48:52, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4838\n2023-01-04 16:43:46,985 - mmcls - INFO - Epoch [12][4900/5005]\tlr: 1.000e-01, eta: 10:48:43, time: 0.089, data_time: 0.020, memory: 928, loss: 5.4702\n2023-01-04 16:43:55,747 - mmcls - INFO - Epoch [12][5000/5005]\tlr: 1.000e-01, eta: 10:48:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4712\n2023-01-04 16:44:18,512 - mmcls - INFO - Epoch(val) [12][196]\taccuracy_top-1: 5.0720, accuracy_top-5: 13.6000\n2023-01-04 16:44:29,349 - mmcls - INFO - Epoch [13][100/5005]\tlr: 1.000e-01, eta: 10:48:36, time: 0.108, data_time: 0.033, memory: 928, loss: 5.4794\n2023-01-04 16:44:38,170 - mmcls - INFO - Epoch [13][200/5005]\tlr: 1.000e-01, eta: 10:48:27, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4801\n2023-01-04 16:44:47,086 - mmcls - INFO - Epoch [13][300/5005]\tlr: 1.000e-01, eta: 10:48:19, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4712\n2023-01-04 16:44:55,988 - mmcls - INFO - Epoch [13][400/5005]\tlr: 1.000e-01, eta: 10:48:10, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4651\n2023-01-04 16:45:04,717 - mmcls - INFO - Epoch [13][500/5005]\tlr: 1.000e-01, eta: 10:48:01, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4880\n2023-01-04 16:45:13,410 - mmcls - INFO - Epoch [13][600/5005]\tlr: 1.000e-01, eta: 10:47:51, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4742\n2023-01-04 16:45:22,122 - mmcls - INFO - Epoch [13][700/5005]\tlr: 1.000e-01, eta: 10:47:41, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4556\n2023-01-04 16:45:30,865 - mmcls - INFO - Epoch [13][800/5005]\tlr: 1.000e-01, eta: 10:47:31, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4719\n2023-01-04 16:45:39,819 - mmcls - INFO - Epoch [13][900/5005]\tlr: 1.000e-01, eta: 10:47:23, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4744\n2023-01-04 16:45:48,338 - mmcls - INFO - Epoch [13][1000/5005]\tlr: 1.000e-01, eta: 10:47:12, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4761\n2023-01-04 16:45:57,650 - mmcls - INFO - Epoch [13][1100/5005]\tlr: 1.000e-01, eta: 10:47:07, time: 0.093, data_time: 0.001, memory: 928, loss: 5.4679\n2023-01-04 16:46:06,370 - mmcls - INFO - Epoch [13][1200/5005]\tlr: 1.000e-01, eta: 10:46:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4570\n2023-01-04 16:46:15,199 - mmcls - INFO - Epoch [13][1300/5005]\tlr: 1.000e-01, eta: 10:46:48, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4730\n2023-01-04 16:46:24,085 - mmcls - INFO - Epoch [13][1400/5005]\tlr: 1.000e-01, eta: 10:46:40, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4686\n2023-01-04 16:46:32,737 - mmcls - INFO - Epoch [13][1500/5005]\tlr: 1.000e-01, eta: 10:46:30, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4755\n2023-01-04 16:46:41,814 - mmcls - INFO - Epoch [13][1600/5005]\tlr: 1.000e-01, eta: 10:46:22, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4679\n2023-01-04 16:46:50,640 - mmcls - INFO - Epoch [13][1700/5005]\tlr: 1.000e-01, eta: 10:46:14, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4741\n2023-01-04 16:46:59,598 - mmcls - INFO - Epoch [13][1800/5005]\tlr: 1.000e-01, eta: 10:46:06, time: 0.090, data_time: 0.016, memory: 928, loss: 5.4692\n2023-01-04 16:47:08,372 - mmcls - INFO - Epoch [13][1900/5005]\tlr: 1.000e-01, eta: 10:45:56, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4574\n2023-01-04 16:47:17,064 - mmcls - INFO - Epoch [13][2000/5005]\tlr: 1.000e-01, eta: 10:45:46, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4816\n2023-01-04 16:47:25,577 - mmcls - INFO - Epoch [13][2100/5005]\tlr: 1.000e-01, eta: 10:45:35, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4834\n2023-01-04 16:47:34,546 - mmcls - INFO - Epoch [13][2200/5005]\tlr: 1.000e-01, eta: 10:45:27, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4662\n2023-01-04 16:47:43,456 - mmcls - INFO - Epoch [13][2300/5005]\tlr: 1.000e-01, eta: 10:45:19, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4783\n2023-01-04 16:47:52,392 - mmcls - INFO - Epoch [13][2400/5005]\tlr: 1.000e-01, eta: 10:45:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4697\n2023-01-04 16:48:01,658 - mmcls - INFO - Epoch [13][2500/5005]\tlr: 1.000e-01, eta: 10:45:05, time: 0.093, data_time: 0.000, memory: 928, loss: 5.4781\n2023-01-04 16:48:10,328 - mmcls - INFO - Epoch [13][2600/5005]\tlr: 1.000e-01, eta: 10:44:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4612\n2023-01-04 16:48:19,124 - mmcls - INFO - Epoch [13][2700/5005]\tlr: 1.000e-01, eta: 10:44:46, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4707\n2023-01-04 16:48:27,765 - mmcls - INFO - Epoch [13][2800/5005]\tlr: 1.000e-01, eta: 10:44:36, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4793\n2023-01-04 16:48:36,514 - mmcls - INFO - Epoch [13][2900/5005]\tlr: 1.000e-01, eta: 10:44:26, time: 0.087, data_time: 0.036, memory: 928, loss: 5.4702\n2023-01-04 16:48:45,120 - mmcls - INFO - Epoch [13][3000/5005]\tlr: 1.000e-01, eta: 10:44:16, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4767\n2023-01-04 16:48:54,310 - mmcls - INFO - Epoch [13][3100/5005]\tlr: 1.000e-01, eta: 10:44:09, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4640\n2023-01-04 16:49:03,374 - mmcls - INFO - Epoch [13][3200/5005]\tlr: 1.000e-01, eta: 10:44:02, time: 0.091, data_time: 0.003, memory: 928, loss: 5.4561\n2023-01-04 16:49:11,952 - mmcls - INFO - Epoch [13][3300/5005]\tlr: 1.000e-01, eta: 10:43:51, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4796\n2023-01-04 16:49:20,626 - mmcls - INFO - Epoch [13][3400/5005]\tlr: 1.000e-01, eta: 10:43:41, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4783\n2023-01-04 16:49:29,532 - mmcls - INFO - Epoch [13][3500/5005]\tlr: 1.000e-01, eta: 10:43:33, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4809\n2023-01-04 16:49:38,394 - mmcls - INFO - Epoch [13][3600/5005]\tlr: 1.000e-01, eta: 10:43:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4687\n2023-01-04 16:49:47,179 - mmcls - INFO - Epoch [13][3700/5005]\tlr: 1.000e-01, eta: 10:43:15, time: 0.088, data_time: 0.025, memory: 928, loss: 5.4720\n2023-01-04 16:49:55,841 - mmcls - INFO - Epoch [13][3800/5005]\tlr: 1.000e-01, eta: 10:43:05, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4595\n2023-01-04 16:50:04,540 - mmcls - INFO - Epoch [13][3900/5005]\tlr: 1.000e-01, eta: 10:42:55, time: 0.087, data_time: 0.026, memory: 928, loss: 5.4654\n2023-01-04 16:50:13,425 - mmcls - INFO - Epoch [13][4000/5005]\tlr: 1.000e-01, eta: 10:42:47, time: 0.089, data_time: 0.024, memory: 928, loss: 5.4599\n2023-01-04 16:50:22,220 - mmcls - INFO - Epoch [13][4100/5005]\tlr: 1.000e-01, eta: 10:42:38, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4730\n2023-01-04 16:50:31,182 - mmcls - INFO - Epoch [13][4200/5005]\tlr: 1.000e-01, eta: 10:42:30, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4592\n2023-01-04 16:50:39,992 - mmcls - INFO - Epoch [13][4300/5005]\tlr: 1.000e-01, eta: 10:42:21, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4565\n2023-01-04 16:50:48,590 - mmcls - INFO - Epoch [13][4400/5005]\tlr: 1.000e-01, eta: 10:42:10, time: 0.086, data_time: 0.020, memory: 928, loss: 5.4668\n2023-01-04 16:50:57,448 - mmcls - INFO - Epoch [13][4500/5005]\tlr: 1.000e-01, eta: 10:42:02, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4609\n2023-01-04 16:51:06,342 - mmcls - INFO - Epoch [13][4600/5005]\tlr: 1.000e-01, eta: 10:41:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4629\n2023-01-04 16:51:15,198 - mmcls - INFO - Epoch [13][4700/5005]\tlr: 1.000e-01, eta: 10:41:44, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4402\n2023-01-04 16:51:24,114 - mmcls - INFO - Epoch [13][4800/5005]\tlr: 1.000e-01, eta: 10:41:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4613\n2023-01-04 16:51:33,313 - mmcls - INFO - Epoch [13][4900/5005]\tlr: 1.000e-01, eta: 10:41:30, time: 0.092, data_time: 0.014, memory: 928, loss: 5.4737\n2023-01-04 16:51:42,144 - mmcls - INFO - Epoch [13][5000/5005]\tlr: 1.000e-01, eta: 10:41:21, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4471\n2023-01-04 16:52:05,233 - mmcls - INFO - Epoch(val) [13][196]\taccuracy_top-1: 5.6820, accuracy_top-5: 15.3040\n2023-01-04 16:52:16,084 - mmcls - INFO - Epoch [14][100/5005]\tlr: 1.000e-01, eta: 10:41:22, time: 0.108, data_time: 0.036, memory: 928, loss: 5.4477\n2023-01-04 16:52:24,946 - mmcls - INFO - Epoch [14][200/5005]\tlr: 1.000e-01, eta: 10:41:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4706\n2023-01-04 16:52:33,769 - mmcls - INFO - Epoch [14][300/5005]\tlr: 1.000e-01, eta: 10:41:04, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4454\n2023-01-04 16:52:42,424 - mmcls - INFO - Epoch [14][400/5005]\tlr: 1.000e-01, eta: 10:40:54, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4755\n2023-01-04 16:52:51,190 - mmcls - INFO - Epoch [14][500/5005]\tlr: 1.000e-01, eta: 10:40:45, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4380\n2023-01-04 16:52:59,809 - mmcls - INFO - Epoch [14][600/5005]\tlr: 1.000e-01, eta: 10:40:35, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4793\n2023-01-04 16:53:08,524 - mmcls - INFO - Epoch [14][700/5005]\tlr: 1.000e-01, eta: 10:40:25, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4609\n2023-01-04 16:53:17,186 - mmcls - INFO - Epoch [14][800/5005]\tlr: 1.000e-01, eta: 10:40:15, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4732\n2023-01-04 16:53:26,257 - mmcls - INFO - Epoch [14][900/5005]\tlr: 1.000e-01, eta: 10:40:07, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4596\n2023-01-04 16:53:35,085 - mmcls - INFO - Epoch [14][1000/5005]\tlr: 1.000e-01, eta: 10:39:59, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4603\n2023-01-04 16:53:43,926 - mmcls - INFO - Epoch [14][1100/5005]\tlr: 1.000e-01, eta: 10:39:50, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4581\n2023-01-04 16:53:52,676 - mmcls - INFO - Epoch [14][1200/5005]\tlr: 1.000e-01, eta: 10:39:40, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4553\n2023-01-04 16:54:01,447 - mmcls - INFO - Epoch [14][1300/5005]\tlr: 1.000e-01, eta: 10:39:31, time: 0.088, data_time: 0.031, memory: 928, loss: 5.4583\n2023-01-04 16:54:10,283 - mmcls - INFO - Epoch [14][1400/5005]\tlr: 1.000e-01, eta: 10:39:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4431\n2023-01-04 16:54:19,026 - mmcls - INFO - Epoch [14][1500/5005]\tlr: 1.000e-01, eta: 10:39:13, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4582\n2023-01-04 16:54:27,452 - mmcls - INFO - Epoch [14][1600/5005]\tlr: 1.000e-01, eta: 10:39:01, time: 0.084, data_time: 0.004, memory: 928, loss: 5.4601\n2023-01-04 16:54:36,260 - mmcls - INFO - Epoch [14][1700/5005]\tlr: 1.000e-01, eta: 10:38:52, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4519\n2023-01-04 16:54:45,122 - mmcls - INFO - Epoch [14][1800/5005]\tlr: 1.000e-01, eta: 10:38:43, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4606\n2023-01-04 16:54:53,850 - mmcls - INFO - Epoch [14][1900/5005]\tlr: 1.000e-01, eta: 10:38:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4505\n2023-01-04 16:55:02,802 - mmcls - INFO - Epoch [14][2000/5005]\tlr: 1.000e-01, eta: 10:38:26, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4462\n2023-01-04 16:55:11,737 - mmcls - INFO - Epoch [14][2100/5005]\tlr: 1.000e-01, eta: 10:38:18, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4559\n2023-01-04 16:55:20,715 - mmcls - INFO - Epoch [14][2200/5005]\tlr: 1.000e-01, eta: 10:38:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4750\n2023-01-04 16:55:29,356 - mmcls - INFO - Epoch [14][2300/5005]\tlr: 1.000e-01, eta: 10:38:00, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4562\n2023-01-04 16:55:38,052 - mmcls - INFO - Epoch [14][2400/5005]\tlr: 1.000e-01, eta: 10:37:50, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4808\n2023-01-04 16:55:46,809 - mmcls - INFO - Epoch [14][2500/5005]\tlr: 1.000e-01, eta: 10:37:40, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4634\n2023-01-04 16:55:55,474 - mmcls - INFO - Epoch [14][2600/5005]\tlr: 1.000e-01, eta: 10:37:30, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4609\n2023-01-04 16:56:04,219 - mmcls - INFO - Epoch [14][2700/5005]\tlr: 1.000e-01, eta: 10:37:21, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4471\n2023-01-04 16:56:13,096 - mmcls - INFO - Epoch [14][2800/5005]\tlr: 1.000e-01, eta: 10:37:12, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4598\n2023-01-04 16:56:21,913 - mmcls - INFO - Epoch [14][2900/5005]\tlr: 1.000e-01, eta: 10:37:03, time: 0.088, data_time: 0.035, memory: 928, loss: 5.4432\n2023-01-04 16:56:30,711 - mmcls - INFO - Epoch [14][3000/5005]\tlr: 1.000e-01, eta: 10:36:54, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4434\n2023-01-04 16:56:39,463 - mmcls - INFO - Epoch [14][3100/5005]\tlr: 1.000e-01, eta: 10:36:45, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4886\n2023-01-04 16:56:48,355 - mmcls - INFO - Epoch [14][3200/5005]\tlr: 1.000e-01, eta: 10:36:36, time: 0.089, data_time: 0.018, memory: 928, loss: 5.4571\n2023-01-04 16:56:57,095 - mmcls - INFO - Epoch [14][3300/5005]\tlr: 1.000e-01, eta: 10:36:27, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4347\n2023-01-04 16:57:05,741 - mmcls - INFO - Epoch [14][3400/5005]\tlr: 1.000e-01, eta: 10:36:17, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4381\n2023-01-04 16:57:14,346 - mmcls - INFO - Epoch [14][3500/5005]\tlr: 1.000e-01, eta: 10:36:07, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4510\n2023-01-04 16:57:23,108 - mmcls - INFO - Epoch [14][3600/5005]\tlr: 1.000e-01, eta: 10:35:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4418\n2023-01-04 16:57:31,862 - mmcls - INFO - Epoch [14][3700/5005]\tlr: 1.000e-01, eta: 10:35:48, time: 0.087, data_time: 0.000, memory: 928, loss: 5.4714\n2023-01-04 16:57:40,340 - mmcls - INFO - Epoch [14][3800/5005]\tlr: 1.000e-01, eta: 10:35:37, time: 0.085, data_time: 0.015, memory: 928, loss: 5.4627\n2023-01-04 16:57:48,966 - mmcls - INFO - Epoch [14][3900/5005]\tlr: 1.000e-01, eta: 10:35:27, time: 0.086, data_time: 0.014, memory: 928, loss: 5.4438\n2023-01-04 16:57:57,883 - mmcls - INFO - Epoch [14][4000/5005]\tlr: 1.000e-01, eta: 10:35:19, time: 0.089, data_time: 0.029, memory: 928, loss: 5.4413\n2023-01-04 16:58:06,296 - mmcls - INFO - Epoch [14][4100/5005]\tlr: 1.000e-01, eta: 10:35:07, time: 0.084, data_time: 0.005, memory: 928, loss: 5.4352\n2023-01-04 16:58:15,363 - mmcls - INFO - Epoch [14][4200/5005]\tlr: 1.000e-01, eta: 10:35:00, time: 0.091, data_time: 0.022, memory: 928, loss: 5.4648\n2023-01-04 16:58:24,227 - mmcls - INFO - Epoch [14][4300/5005]\tlr: 1.000e-01, eta: 10:34:51, time: 0.089, data_time: 0.012, memory: 928, loss: 5.4499\n2023-01-04 16:58:33,181 - mmcls - INFO - Epoch [14][4400/5005]\tlr: 1.000e-01, eta: 10:34:43, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4432\n2023-01-04 16:58:42,021 - mmcls - INFO - Epoch [14][4500/5005]\tlr: 1.000e-01, eta: 10:34:34, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4679\n2023-01-04 16:58:50,888 - mmcls - INFO - Epoch [14][4600/5005]\tlr: 1.000e-01, eta: 10:34:25, time: 0.089, data_time: 0.022, memory: 928, loss: 5.4521\n2023-01-04 16:59:00,141 - mmcls - INFO - Epoch [14][4700/5005]\tlr: 1.000e-01, eta: 10:34:19, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4391\n2023-01-04 16:59:08,860 - mmcls - INFO - Epoch [14][4800/5005]\tlr: 1.000e-01, eta: 10:34:10, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4667\n2023-01-04 16:59:17,402 - mmcls - INFO - Epoch [14][4900/5005]\tlr: 1.000e-01, eta: 10:33:59, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4459\n2023-01-04 16:59:26,207 - mmcls - INFO - Epoch [14][5000/5005]\tlr: 1.000e-01, eta: 10:33:50, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4661\n2023-01-04 16:59:49,143 - mmcls - INFO - Epoch(val) [14][196]\taccuracy_top-1: 4.6260, accuracy_top-5: 12.9140\n2023-01-04 17:00:00,035 - mmcls - INFO - Epoch [15][100/5005]\tlr: 1.000e-01, eta: 10:33:50, time: 0.109, data_time: 0.046, memory: 928, loss: 5.4610\n2023-01-04 17:00:08,784 - mmcls - INFO - Epoch [15][200/5005]\tlr: 1.000e-01, eta: 10:33:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4600\n2023-01-04 17:00:17,818 - mmcls - INFO - Epoch [15][300/5005]\tlr: 1.000e-01, eta: 10:33:33, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4366\n2023-01-04 17:00:26,781 - mmcls - INFO - Epoch [15][400/5005]\tlr: 1.000e-01, eta: 10:33:25, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4419\n2023-01-04 17:00:35,700 - mmcls - INFO - Epoch [15][500/5005]\tlr: 1.000e-01, eta: 10:33:17, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4331\n2023-01-04 17:00:44,553 - mmcls - INFO - Epoch [15][600/5005]\tlr: 1.000e-01, eta: 10:33:08, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4626\n2023-01-04 17:00:53,048 - mmcls - INFO - Epoch [15][700/5005]\tlr: 1.000e-01, eta: 10:32:57, time: 0.085, data_time: 0.027, memory: 928, loss: 5.4116\n2023-01-04 17:01:01,732 - mmcls - INFO - Epoch [15][800/5005]\tlr: 1.000e-01, eta: 10:32:47, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4353\n2023-01-04 17:01:10,294 - mmcls - INFO - Epoch [15][900/5005]\tlr: 1.000e-01, eta: 10:32:37, time: 0.085, data_time: 0.017, memory: 928, loss: 5.4550\n2023-01-04 17:01:18,878 - mmcls - INFO - Epoch [15][1000/5005]\tlr: 1.000e-01, eta: 10:32:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4353\n2023-01-04 17:01:27,666 - mmcls - INFO - Epoch [15][1100/5005]\tlr: 1.000e-01, eta: 10:32:17, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4433\n2023-01-04 17:01:36,547 - mmcls - INFO - Epoch [15][1200/5005]\tlr: 1.000e-01, eta: 10:32:09, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4246\n2023-01-04 17:01:45,143 - mmcls - INFO - Epoch [15][1300/5005]\tlr: 1.000e-01, eta: 10:31:59, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4296\n2023-01-04 17:01:53,700 - mmcls - INFO - Epoch [15][1400/5005]\tlr: 1.000e-01, eta: 10:31:48, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4547\n2023-01-04 17:02:02,454 - mmcls - INFO - Epoch [15][1500/5005]\tlr: 1.000e-01, eta: 10:31:39, time: 0.087, data_time: 0.029, memory: 928, loss: 5.4646\n2023-01-04 17:02:11,040 - mmcls - INFO - Epoch [15][1600/5005]\tlr: 1.000e-01, eta: 10:31:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4530\n2023-01-04 17:02:19,793 - mmcls - INFO - Epoch [15][1700/5005]\tlr: 1.000e-01, eta: 10:31:19, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4559\n2023-01-04 17:02:28,621 - mmcls - INFO - Epoch [15][1800/5005]\tlr: 1.000e-01, eta: 10:31:10, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4558\n2023-01-04 17:02:37,338 - mmcls - INFO - Epoch [15][1900/5005]\tlr: 1.000e-01, eta: 10:31:01, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4509\n2023-01-04 17:02:45,954 - mmcls - INFO - Epoch [15][2000/5005]\tlr: 1.000e-01, eta: 10:30:50, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4459\n2023-01-04 17:02:54,686 - mmcls - INFO - Epoch [15][2100/5005]\tlr: 1.000e-01, eta: 10:30:41, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4436\n2023-01-04 17:03:03,471 - mmcls - INFO - Epoch [15][2200/5005]\tlr: 1.000e-01, eta: 10:30:32, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4521\n2023-01-04 17:03:12,516 - mmcls - INFO - Epoch [15][2300/5005]\tlr: 1.000e-01, eta: 10:30:24, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4407\n2023-01-04 17:03:21,322 - mmcls - INFO - Epoch [15][2400/5005]\tlr: 1.000e-01, eta: 10:30:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4491\n2023-01-04 17:03:30,101 - mmcls - INFO - Epoch [15][2500/5005]\tlr: 1.000e-01, eta: 10:30:06, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4427\n2023-01-04 17:03:38,709 - mmcls - INFO - Epoch [15][2600/5005]\tlr: 1.000e-01, eta: 10:29:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4457\n2023-01-04 17:03:47,429 - mmcls - INFO - Epoch [15][2700/5005]\tlr: 1.000e-01, eta: 10:29:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4402\n2023-01-04 17:03:55,944 - mmcls - INFO - Epoch [15][2800/5005]\tlr: 1.000e-01, eta: 10:29:36, time: 0.085, data_time: 0.012, memory: 928, loss: 5.4402\n2023-01-04 17:04:04,767 - mmcls - INFO - Epoch [15][2900/5005]\tlr: 1.000e-01, eta: 10:29:27, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4499\n2023-01-04 17:04:13,585 - mmcls - INFO - Epoch [15][3000/5005]\tlr: 1.000e-01, eta: 10:29:18, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4475\n2023-01-04 17:04:22,444 - mmcls - INFO - Epoch [15][3100/5005]\tlr: 1.000e-01, eta: 10:29:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4572\n2023-01-04 17:04:31,026 - mmcls - INFO - Epoch [15][3200/5005]\tlr: 1.000e-01, eta: 10:28:59, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4549\n2023-01-04 17:04:39,791 - mmcls - INFO - Epoch [15][3300/5005]\tlr: 1.000e-01, eta: 10:28:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4420\n2023-01-04 17:04:48,471 - mmcls - INFO - Epoch [15][3400/5005]\tlr: 1.000e-01, eta: 10:28:40, time: 0.087, data_time: 0.025, memory: 928, loss: 5.4633\n2023-01-04 17:04:57,681 - mmcls - INFO - Epoch [15][3500/5005]\tlr: 1.000e-01, eta: 10:28:33, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4244\n2023-01-04 17:05:06,578 - mmcls - INFO - Epoch [15][3600/5005]\tlr: 1.000e-01, eta: 10:28:25, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4626\n2023-01-04 17:05:15,459 - mmcls - INFO - Epoch [15][3700/5005]\tlr: 1.000e-01, eta: 10:28:16, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4681\n2023-01-04 17:05:24,530 - mmcls - INFO - Epoch [15][3800/5005]\tlr: 1.000e-01, eta: 10:28:09, time: 0.091, data_time: 0.011, memory: 928, loss: 5.4483\n2023-01-04 17:05:33,889 - mmcls - INFO - Epoch [15][3900/5005]\tlr: 1.000e-01, eta: 10:28:03, time: 0.094, data_time: 0.018, memory: 928, loss: 5.4285\n2023-01-04 17:05:42,627 - mmcls - INFO - Epoch [15][4000/5005]\tlr: 1.000e-01, eta: 10:27:54, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4577\n2023-01-04 17:05:51,375 - mmcls - INFO - Epoch [15][4100/5005]\tlr: 1.000e-01, eta: 10:27:44, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4411\n2023-01-04 17:06:00,125 - mmcls - INFO - Epoch [15][4200/5005]\tlr: 1.000e-01, eta: 10:27:35, time: 0.088, data_time: 0.033, memory: 928, loss: 5.4635\n2023-01-04 17:06:08,921 - mmcls - INFO - Epoch [15][4300/5005]\tlr: 1.000e-01, eta: 10:27:26, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4247\n2023-01-04 17:06:17,554 - mmcls - INFO - Epoch [15][4400/5005]\tlr: 1.000e-01, eta: 10:27:16, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4541\n2023-01-04 17:06:26,536 - mmcls - INFO - Epoch [15][4500/5005]\tlr: 1.000e-01, eta: 10:27:08, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4298\n2023-01-04 17:06:34,915 - mmcls - INFO - Epoch [15][4600/5005]\tlr: 1.000e-01, eta: 10:26:56, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4456\n2023-01-04 17:06:43,692 - mmcls - INFO - Epoch [15][4700/5005]\tlr: 1.000e-01, eta: 10:26:47, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4531\n2023-01-04 17:06:52,412 - mmcls - INFO - Epoch [15][4800/5005]\tlr: 1.000e-01, eta: 10:26:38, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4220\n2023-01-04 17:07:01,443 - mmcls - INFO - Epoch [15][4900/5005]\tlr: 1.000e-01, eta: 10:26:30, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4558\n2023-01-04 17:07:10,151 - mmcls - INFO - Epoch [15][5000/5005]\tlr: 1.000e-01, eta: 10:26:20, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4622\n2023-01-04 17:07:32,960 - mmcls - INFO - Epoch(val) [15][196]\taccuracy_top-1: 4.0540, accuracy_top-5: 11.2260\n2023-01-04 17:07:44,065 - mmcls - INFO - Epoch [16][100/5005]\tlr: 1.000e-01, eta: 10:26:21, time: 0.111, data_time: 0.030, memory: 928, loss: 5.4478\n2023-01-04 17:07:52,593 - mmcls - INFO - Epoch [16][200/5005]\tlr: 1.000e-01, eta: 10:26:11, time: 0.085, data_time: 0.020, memory: 928, loss: 5.4344\n2023-01-04 17:08:01,576 - mmcls - INFO - Epoch [16][300/5005]\tlr: 1.000e-01, eta: 10:26:03, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4233\n2023-01-04 17:08:10,421 - mmcls - INFO - Epoch [16][400/5005]\tlr: 1.000e-01, eta: 10:25:54, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4376\n2023-01-04 17:08:19,136 - mmcls - INFO - Epoch [16][500/5005]\tlr: 1.000e-01, eta: 10:25:45, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4317\n2023-01-04 17:08:27,735 - mmcls - INFO - Epoch [16][600/5005]\tlr: 1.000e-01, eta: 10:25:34, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4172\n2023-01-04 17:08:36,615 - mmcls - INFO - Epoch [16][700/5005]\tlr: 1.000e-01, eta: 10:25:26, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4202\n2023-01-04 17:08:45,423 - mmcls - INFO - Epoch [16][800/5005]\tlr: 1.000e-01, eta: 10:25:17, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4442\n2023-01-04 17:08:54,410 - mmcls - INFO - Epoch [16][900/5005]\tlr: 1.000e-01, eta: 10:25:09, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4391\n2023-01-04 17:09:02,989 - mmcls - INFO - Epoch [16][1000/5005]\tlr: 1.000e-01, eta: 10:24:59, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4480\n2023-01-04 17:09:11,947 - mmcls - INFO - Epoch [16][1100/5005]\tlr: 1.000e-01, eta: 10:24:50, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4493\n2023-01-04 17:09:20,605 - mmcls - INFO - Epoch [16][1200/5005]\tlr: 1.000e-01, eta: 10:24:40, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4283\n2023-01-04 17:09:29,287 - mmcls - INFO - Epoch [16][1300/5005]\tlr: 1.000e-01, eta: 10:24:31, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4589\n2023-01-04 17:09:37,876 - mmcls - INFO - Epoch [16][1400/5005]\tlr: 1.000e-01, eta: 10:24:21, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4472\n2023-01-04 17:09:46,620 - mmcls - INFO - Epoch [16][1500/5005]\tlr: 1.000e-01, eta: 10:24:11, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4393\n2023-01-04 17:09:55,479 - mmcls - INFO - Epoch [16][1600/5005]\tlr: 1.000e-01, eta: 10:24:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4464\n2023-01-04 17:10:04,096 - mmcls - INFO - Epoch [16][1700/5005]\tlr: 1.000e-01, eta: 10:23:53, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4361\n2023-01-04 17:10:12,781 - mmcls - INFO - Epoch [16][1800/5005]\tlr: 1.000e-01, eta: 10:23:43, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4403\n2023-01-04 17:10:21,357 - mmcls - INFO - Epoch [16][1900/5005]\tlr: 1.000e-01, eta: 10:23:33, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4352\n2023-01-04 17:10:29,965 - mmcls - INFO - Epoch [16][2000/5005]\tlr: 1.000e-01, eta: 10:23:23, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4461\n2023-01-04 17:10:38,683 - mmcls - INFO - Epoch [16][2100/5005]\tlr: 1.000e-01, eta: 10:23:13, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4491\n2023-01-04 17:10:47,412 - mmcls - INFO - Epoch [16][2200/5005]\tlr: 1.000e-01, eta: 10:23:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4294\n2023-01-04 17:10:56,144 - mmcls - INFO - Epoch [16][2300/5005]\tlr: 1.000e-01, eta: 10:22:54, time: 0.087, data_time: 0.028, memory: 928, loss: 5.4525\n2023-01-04 17:11:05,010 - mmcls - INFO - Epoch [16][2400/5005]\tlr: 1.000e-01, eta: 10:22:46, time: 0.089, data_time: 0.035, memory: 928, loss: 5.4270\n2023-01-04 17:11:13,910 - mmcls - INFO - Epoch [16][2500/5005]\tlr: 1.000e-01, eta: 10:22:37, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4436\n2023-01-04 17:11:22,547 - mmcls - INFO - Epoch [16][2600/5005]\tlr: 1.000e-01, eta: 10:22:27, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4355\n2023-01-04 17:11:31,137 - mmcls - INFO - Epoch [16][2700/5005]\tlr: 1.000e-01, eta: 10:22:17, time: 0.086, data_time: 0.028, memory: 928, loss: 5.4536\n2023-01-04 17:11:40,392 - mmcls - INFO - Epoch [16][2800/5005]\tlr: 1.000e-01, eta: 10:22:11, time: 0.093, data_time: 0.007, memory: 928, loss: 5.4440\n2023-01-04 17:11:48,953 - mmcls - INFO - Epoch [16][2900/5005]\tlr: 1.000e-01, eta: 10:22:00, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4350\n2023-01-04 17:11:57,949 - mmcls - INFO - Epoch [16][3000/5005]\tlr: 1.000e-01, eta: 10:21:52, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4176\n2023-01-04 17:12:06,969 - mmcls - INFO - Epoch [16][3100/5005]\tlr: 1.000e-01, eta: 10:21:44, time: 0.090, data_time: 0.010, memory: 928, loss: 5.4527\n2023-01-04 17:12:15,622 - mmcls - INFO - Epoch [16][3200/5005]\tlr: 1.000e-01, eta: 10:21:35, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4329\n2023-01-04 17:12:24,188 - mmcls - INFO - Epoch [16][3300/5005]\tlr: 1.000e-01, eta: 10:21:24, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4363\n2023-01-04 17:12:33,019 - mmcls - INFO - Epoch [16][3400/5005]\tlr: 1.000e-01, eta: 10:21:15, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4428\n2023-01-04 17:12:41,646 - mmcls - INFO - Epoch [16][3500/5005]\tlr: 1.000e-01, eta: 10:21:06, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4397\n2023-01-04 17:12:50,567 - mmcls - INFO - Epoch [16][3600/5005]\tlr: 1.000e-01, eta: 10:20:57, time: 0.089, data_time: 0.023, memory: 928, loss: 5.4439\n2023-01-04 17:12:59,557 - mmcls - INFO - Epoch [16][3700/5005]\tlr: 1.000e-01, eta: 10:20:49, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4443\n2023-01-04 17:13:08,115 - mmcls - INFO - Epoch [16][3800/5005]\tlr: 1.000e-01, eta: 10:20:39, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4334\n2023-01-04 17:13:16,709 - mmcls - INFO - Epoch [16][3900/5005]\tlr: 1.000e-01, eta: 10:20:29, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4448\n2023-01-04 17:13:25,462 - mmcls - INFO - Epoch [16][4000/5005]\tlr: 1.000e-01, eta: 10:20:20, time: 0.087, data_time: 0.039, memory: 928, loss: 5.4382\n2023-01-04 17:13:34,458 - mmcls - INFO - Epoch [16][4100/5005]\tlr: 1.000e-01, eta: 10:20:12, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4535\n2023-01-04 17:13:43,050 - mmcls - INFO - Epoch [16][4200/5005]\tlr: 1.000e-01, eta: 10:20:01, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4632\n2023-01-04 17:13:51,743 - mmcls - INFO - Epoch [16][4300/5005]\tlr: 1.000e-01, eta: 10:19:52, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4453\n2023-01-04 17:14:00,921 - mmcls - INFO - Epoch [16][4400/5005]\tlr: 1.000e-01, eta: 10:19:45, time: 0.092, data_time: 0.024, memory: 928, loss: 5.4406\n2023-01-04 17:14:09,795 - mmcls - INFO - Epoch [16][4500/5005]\tlr: 1.000e-01, eta: 10:19:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4429\n2023-01-04 17:14:18,638 - mmcls - INFO - Epoch [16][4600/5005]\tlr: 1.000e-01, eta: 10:19:28, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4349\n2023-01-04 17:14:27,111 - mmcls - INFO - Epoch [16][4700/5005]\tlr: 1.000e-01, eta: 10:19:17, time: 0.085, data_time: 0.009, memory: 928, loss: 5.4577\n2023-01-04 17:14:36,113 - mmcls - INFO - Epoch [16][4800/5005]\tlr: 1.000e-01, eta: 10:19:09, time: 0.090, data_time: 0.020, memory: 928, loss: 5.4570\n2023-01-04 17:14:44,867 - mmcls - INFO - Epoch [16][4900/5005]\tlr: 1.000e-01, eta: 10:19:00, time: 0.088, data_time: 0.022, memory: 928, loss: 5.4394\n2023-01-04 17:14:53,434 - mmcls - INFO - Epoch [16][5000/5005]\tlr: 1.000e-01, eta: 10:18:49, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4362\n2023-01-04 17:15:16,371 - mmcls - INFO - Epoch(val) [16][196]\taccuracy_top-1: 5.0260, accuracy_top-5: 13.1060\n2023-01-04 17:15:27,468 - mmcls - INFO - Epoch [17][100/5005]\tlr: 1.000e-01, eta: 10:18:49, time: 0.111, data_time: 0.037, memory: 928, loss: 5.4307\n2023-01-04 17:15:36,197 - mmcls - INFO - Epoch [17][200/5005]\tlr: 1.000e-01, eta: 10:18:40, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4305\n2023-01-04 17:15:45,024 - mmcls - INFO - Epoch [17][300/5005]\tlr: 1.000e-01, eta: 10:18:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4424\n2023-01-04 17:15:53,704 - mmcls - INFO - Epoch [17][400/5005]\tlr: 1.000e-01, eta: 10:18:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4353\n2023-01-04 17:16:02,345 - mmcls - INFO - Epoch [17][500/5005]\tlr: 1.000e-01, eta: 10:18:12, time: 0.086, data_time: 0.021, memory: 928, loss: 5.4092\n2023-01-04 17:16:11,140 - mmcls - INFO - Epoch [17][600/5005]\tlr: 1.000e-01, eta: 10:18:03, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4438\n2023-01-04 17:16:19,841 - mmcls - INFO - Epoch [17][700/5005]\tlr: 1.000e-01, eta: 10:17:53, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4325\n2023-01-04 17:16:28,322 - mmcls - INFO - Epoch [17][800/5005]\tlr: 1.000e-01, eta: 10:17:43, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4338\n2023-01-04 17:16:36,930 - mmcls - INFO - Epoch [17][900/5005]\tlr: 1.000e-01, eta: 10:17:33, time: 0.086, data_time: 0.027, memory: 928, loss: 5.4309\n2023-01-04 17:16:45,341 - mmcls - INFO - Epoch [17][1000/5005]\tlr: 1.000e-01, eta: 10:17:22, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4490\n2023-01-04 17:16:53,898 - mmcls - INFO - Epoch [17][1100/5005]\tlr: 1.000e-01, eta: 10:17:11, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4271\n2023-01-04 17:17:02,535 - mmcls - INFO - Epoch [17][1200/5005]\tlr: 1.000e-01, eta: 10:17:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4294\n2023-01-04 17:17:11,331 - mmcls - INFO - Epoch [17][1300/5005]\tlr: 1.000e-01, eta: 10:16:52, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4226\n2023-01-04 17:17:20,039 - mmcls - INFO - Epoch [17][1400/5005]\tlr: 1.000e-01, eta: 10:16:43, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4521\n2023-01-04 17:17:28,802 - mmcls - INFO - Epoch [17][1500/5005]\tlr: 1.000e-01, eta: 10:16:34, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4264\n2023-01-04 17:17:37,789 - mmcls - INFO - Epoch [17][1600/5005]\tlr: 1.000e-01, eta: 10:16:26, time: 0.090, data_time: 0.011, memory: 928, loss: 5.4250\n2023-01-04 17:17:46,714 - mmcls - INFO - Epoch [17][1700/5005]\tlr: 1.000e-01, eta: 10:16:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4281\n2023-01-04 17:17:55,379 - mmcls - INFO - Epoch [17][1800/5005]\tlr: 1.000e-01, eta: 10:16:08, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4456\n2023-01-04 17:18:04,310 - mmcls - INFO - Epoch [17][1900/5005]\tlr: 1.000e-01, eta: 10:15:59, time: 0.089, data_time: 0.015, memory: 928, loss: 5.4278\n2023-01-04 17:18:13,312 - mmcls - INFO - Epoch [17][2000/5005]\tlr: 1.000e-01, eta: 10:15:51, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4256\n2023-01-04 17:18:21,985 - mmcls - INFO - Epoch [17][2100/5005]\tlr: 1.000e-01, eta: 10:15:42, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4241\n2023-01-04 17:18:30,876 - mmcls - INFO - Epoch [17][2200/5005]\tlr: 1.000e-01, eta: 10:15:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4287\n2023-01-04 17:18:39,951 - mmcls - INFO - Epoch [17][2300/5005]\tlr: 1.000e-01, eta: 10:15:26, time: 0.091, data_time: 0.025, memory: 928, loss: 5.4458\n2023-01-04 17:18:48,627 - mmcls - INFO - Epoch [17][2400/5005]\tlr: 1.000e-01, eta: 10:15:16, time: 0.087, data_time: 0.029, memory: 928, loss: 5.4227\n2023-01-04 17:18:57,360 - mmcls - INFO - Epoch [17][2500/5005]\tlr: 1.000e-01, eta: 10:15:07, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4416\n2023-01-04 17:19:06,366 - mmcls - INFO - Epoch [17][2600/5005]\tlr: 1.000e-01, eta: 10:14:59, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4434\n2023-01-04 17:19:15,162 - mmcls - INFO - Epoch [17][2700/5005]\tlr: 1.000e-01, eta: 10:14:50, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4162\n2023-01-04 17:19:23,708 - mmcls - INFO - Epoch [17][2800/5005]\tlr: 1.000e-01, eta: 10:14:39, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4270\n2023-01-04 17:19:32,606 - mmcls - INFO - Epoch [17][2900/5005]\tlr: 1.000e-01, eta: 10:14:31, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4502\n2023-01-04 17:19:41,453 - mmcls - INFO - Epoch [17][3000/5005]\tlr: 1.000e-01, eta: 10:14:22, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4284\n2023-01-04 17:19:50,249 - mmcls - INFO - Epoch [17][3100/5005]\tlr: 1.000e-01, eta: 10:14:13, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4241\n2023-01-04 17:19:58,928 - mmcls - INFO - Epoch [17][3200/5005]\tlr: 1.000e-01, eta: 10:14:04, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4367\n2023-01-04 17:20:07,868 - mmcls - INFO - Epoch [17][3300/5005]\tlr: 1.000e-01, eta: 10:13:55, time: 0.089, data_time: 0.008, memory: 928, loss: 5.4307\n2023-01-04 17:20:16,624 - mmcls - INFO - Epoch [17][3400/5005]\tlr: 1.000e-01, eta: 10:13:46, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4315\n2023-01-04 17:20:25,338 - mmcls - INFO - Epoch [17][3500/5005]\tlr: 1.000e-01, eta: 10:13:37, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4385\n2023-01-04 17:20:34,052 - mmcls - INFO - Epoch [17][3600/5005]\tlr: 1.000e-01, eta: 10:13:27, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4197\n2023-01-04 17:20:42,787 - mmcls - INFO - Epoch [17][3700/5005]\tlr: 1.000e-01, eta: 10:13:18, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4414\n2023-01-04 17:20:51,763 - mmcls - INFO - Epoch [17][3800/5005]\tlr: 1.000e-01, eta: 10:13:10, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4039\n2023-01-04 17:21:00,570 - mmcls - INFO - Epoch [17][3900/5005]\tlr: 1.000e-01, eta: 10:13:01, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4335\n2023-01-04 17:21:09,355 - mmcls - INFO - Epoch [17][4000/5005]\tlr: 1.000e-01, eta: 10:12:52, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4029\n2023-01-04 17:21:17,735 - mmcls - INFO - Epoch [17][4100/5005]\tlr: 1.000e-01, eta: 10:12:41, time: 0.084, data_time: 0.015, memory: 928, loss: 5.4271\n2023-01-04 17:21:26,547 - mmcls - INFO - Epoch [17][4200/5005]\tlr: 1.000e-01, eta: 10:12:32, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4180\n2023-01-04 17:21:35,393 - mmcls - INFO - Epoch [17][4300/5005]\tlr: 1.000e-01, eta: 10:12:23, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4312\n2023-01-04 17:21:44,204 - mmcls - INFO - Epoch [17][4400/5005]\tlr: 1.000e-01, eta: 10:12:14, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4408\n2023-01-04 17:21:53,105 - mmcls - INFO - Epoch [17][4500/5005]\tlr: 1.000e-01, eta: 10:12:06, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4263\n2023-01-04 17:22:01,782 - mmcls - INFO - Epoch [17][4600/5005]\tlr: 1.000e-01, eta: 10:11:56, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4462\n2023-01-04 17:22:10,546 - mmcls - INFO - Epoch [17][4700/5005]\tlr: 1.000e-01, eta: 10:11:47, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4375\n2023-01-04 17:22:19,411 - mmcls - INFO - Epoch [17][4800/5005]\tlr: 1.000e-01, eta: 10:11:38, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4414\n2023-01-04 17:22:28,190 - mmcls - INFO - Epoch [17][4900/5005]\tlr: 1.000e-01, eta: 10:11:29, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4482\n2023-01-04 17:22:36,728 - mmcls - INFO - Epoch [17][5000/5005]\tlr: 1.000e-01, eta: 10:11:19, time: 0.085, data_time: 0.008, memory: 928, loss: 5.4294\n2023-01-04 17:22:59,574 - mmcls - INFO - Epoch(val) [17][196]\taccuracy_top-1: 3.4120, accuracy_top-5: 9.6580\n2023-01-04 17:23:10,519 - mmcls - INFO - Epoch [18][100/5005]\tlr: 1.000e-01, eta: 10:11:18, time: 0.109, data_time: 0.038, memory: 928, loss: 5.4124\n2023-01-04 17:23:19,667 - mmcls - INFO - Epoch [18][200/5005]\tlr: 1.000e-01, eta: 10:11:10, time: 0.091, data_time: 0.022, memory: 928, loss: 5.3969\n2023-01-04 17:23:28,242 - mmcls - INFO - Epoch [18][300/5005]\tlr: 1.000e-01, eta: 10:11:00, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4158\n2023-01-04 17:23:36,841 - mmcls - INFO - Epoch [18][400/5005]\tlr: 1.000e-01, eta: 10:10:50, time: 0.086, data_time: 0.021, memory: 928, loss: 5.4150\n2023-01-04 17:23:45,941 - mmcls - INFO - Epoch [18][500/5005]\tlr: 1.000e-01, eta: 10:10:43, time: 0.091, data_time: 0.008, memory: 928, loss: 5.4051\n2023-01-04 17:23:54,419 - mmcls - INFO - Epoch [18][600/5005]\tlr: 1.000e-01, eta: 10:10:32, time: 0.085, data_time: 0.012, memory: 928, loss: 5.4142\n2023-01-04 17:24:03,244 - mmcls - INFO - Epoch [18][700/5005]\tlr: 1.000e-01, eta: 10:10:23, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4255\n2023-01-04 17:24:12,262 - mmcls - INFO - Epoch [18][800/5005]\tlr: 1.000e-01, eta: 10:10:15, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4414\n2023-01-04 17:24:20,864 - mmcls - INFO - Epoch [18][900/5005]\tlr: 1.000e-01, eta: 10:10:06, time: 0.086, data_time: 0.036, memory: 928, loss: 5.4127\n2023-01-04 17:24:29,512 - mmcls - INFO - Epoch [18][1000/5005]\tlr: 1.000e-01, eta: 10:09:56, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4210\n2023-01-04 17:24:38,336 - mmcls - INFO - Epoch [18][1100/5005]\tlr: 1.000e-01, eta: 10:09:47, time: 0.088, data_time: 0.023, memory: 928, loss: 5.4287\n2023-01-04 17:24:46,906 - mmcls - INFO - Epoch [18][1200/5005]\tlr: 1.000e-01, eta: 10:09:37, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4071\n2023-01-04 17:24:55,634 - mmcls - INFO - Epoch [18][1300/5005]\tlr: 1.000e-01, eta: 10:09:28, time: 0.087, data_time: 0.036, memory: 928, loss: 5.4378\n2023-01-04 17:25:04,347 - mmcls - INFO - Epoch [18][1400/5005]\tlr: 1.000e-01, eta: 10:09:18, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4100\n2023-01-04 17:25:12,861 - mmcls - INFO - Epoch [18][1500/5005]\tlr: 1.000e-01, eta: 10:09:08, time: 0.085, data_time: 0.033, memory: 928, loss: 5.4105\n2023-01-04 17:25:21,846 - mmcls - INFO - Epoch [18][1600/5005]\tlr: 1.000e-01, eta: 10:09:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.4160\n2023-01-04 17:25:30,364 - mmcls - INFO - Epoch [18][1700/5005]\tlr: 1.000e-01, eta: 10:08:49, time: 0.085, data_time: 0.022, memory: 928, loss: 5.4088\n2023-01-04 17:25:39,259 - mmcls - INFO - Epoch [18][1800/5005]\tlr: 1.000e-01, eta: 10:08:41, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4308\n2023-01-04 17:25:47,920 - mmcls - INFO - Epoch [18][1900/5005]\tlr: 1.000e-01, eta: 10:08:31, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4054\n2023-01-04 17:25:57,075 - mmcls - INFO - Epoch [18][2000/5005]\tlr: 1.000e-01, eta: 10:08:24, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4284\n2023-01-04 17:26:05,558 - mmcls - INFO - Epoch [18][2100/5005]\tlr: 1.000e-01, eta: 10:08:13, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3968\n2023-01-04 17:26:14,512 - mmcls - INFO - Epoch [18][2200/5005]\tlr: 1.000e-01, eta: 10:08:05, time: 0.090, data_time: 0.006, memory: 928, loss: 5.4295\n2023-01-04 17:26:23,257 - mmcls - INFO - Epoch [18][2300/5005]\tlr: 1.000e-01, eta: 10:07:56, time: 0.087, data_time: 0.018, memory: 928, loss: 5.4210\n2023-01-04 17:26:32,135 - mmcls - INFO - Epoch [18][2400/5005]\tlr: 1.000e-01, eta: 10:07:47, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4334\n2023-01-04 17:26:40,785 - mmcls - INFO - Epoch [18][2500/5005]\tlr: 1.000e-01, eta: 10:07:38, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4201\n2023-01-04 17:26:49,646 - mmcls - INFO - Epoch [18][2600/5005]\tlr: 1.000e-01, eta: 10:07:29, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4212\n2023-01-04 17:26:58,427 - mmcls - INFO - Epoch [18][2700/5005]\tlr: 1.000e-01, eta: 10:07:20, time: 0.088, data_time: 0.022, memory: 928, loss: 5.4318\n2023-01-04 17:27:06,935 - mmcls - INFO - Epoch [18][2800/5005]\tlr: 1.000e-01, eta: 10:07:10, time: 0.085, data_time: 0.006, memory: 928, loss: 5.4353\n2023-01-04 17:27:15,637 - mmcls - INFO - Epoch [18][2900/5005]\tlr: 1.000e-01, eta: 10:07:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4306\n2023-01-04 17:27:24,262 - mmcls - INFO - Epoch [18][3000/5005]\tlr: 1.000e-01, eta: 10:06:50, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4131\n2023-01-04 17:27:32,889 - mmcls - INFO - Epoch [18][3100/5005]\tlr: 1.000e-01, eta: 10:06:41, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4190\n2023-01-04 17:27:41,519 - mmcls - INFO - Epoch [18][3200/5005]\tlr: 1.000e-01, eta: 10:06:31, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4493\n2023-01-04 17:27:50,389 - mmcls - INFO - Epoch [18][3300/5005]\tlr: 1.000e-01, eta: 10:06:22, time: 0.089, data_time: 0.018, memory: 928, loss: 5.4342\n2023-01-04 17:27:59,000 - mmcls - INFO - Epoch [18][3400/5005]\tlr: 1.000e-01, eta: 10:06:12, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4256\n2023-01-04 17:28:07,392 - mmcls - INFO - Epoch [18][3500/5005]\tlr: 1.000e-01, eta: 10:06:01, time: 0.084, data_time: 0.005, memory: 928, loss: 5.4051\n2023-01-04 17:28:16,045 - mmcls - INFO - Epoch [18][3600/5005]\tlr: 1.000e-01, eta: 10:05:52, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4255\n2023-01-04 17:28:25,111 - mmcls - INFO - Epoch [18][3700/5005]\tlr: 1.000e-01, eta: 10:05:44, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4245\n2023-01-04 17:28:34,046 - mmcls - INFO - Epoch [18][3800/5005]\tlr: 1.000e-01, eta: 10:05:36, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4247\n2023-01-04 17:28:43,009 - mmcls - INFO - Epoch [18][3900/5005]\tlr: 1.000e-01, eta: 10:05:28, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4333\n2023-01-04 17:28:51,991 - mmcls - INFO - Epoch [18][4000/5005]\tlr: 1.000e-01, eta: 10:05:19, time: 0.090, data_time: 0.018, memory: 928, loss: 5.4313\n2023-01-04 17:29:00,632 - mmcls - INFO - Epoch [18][4100/5005]\tlr: 1.000e-01, eta: 10:05:10, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4383\n2023-01-04 17:29:09,262 - mmcls - INFO - Epoch [18][4200/5005]\tlr: 1.000e-01, eta: 10:05:00, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4485\n2023-01-04 17:29:18,276 - mmcls - INFO - Epoch [18][4300/5005]\tlr: 1.000e-01, eta: 10:04:52, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4252\n2023-01-04 17:29:27,208 - mmcls - INFO - Epoch [18][4400/5005]\tlr: 1.000e-01, eta: 10:04:44, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4192\n2023-01-04 17:29:35,759 - mmcls - INFO - Epoch [18][4500/5005]\tlr: 1.000e-01, eta: 10:04:34, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4252\n2023-01-04 17:29:44,635 - mmcls - INFO - Epoch [18][4600/5005]\tlr: 1.000e-01, eta: 10:04:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4164\n2023-01-04 17:29:53,188 - mmcls - INFO - Epoch [18][4700/5005]\tlr: 1.000e-01, eta: 10:04:15, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4199\n2023-01-04 17:30:01,812 - mmcls - INFO - Epoch [18][4800/5005]\tlr: 1.000e-01, eta: 10:04:05, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4470\n2023-01-04 17:30:10,590 - mmcls - INFO - Epoch [18][4900/5005]\tlr: 1.000e-01, eta: 10:03:56, time: 0.088, data_time: 0.021, memory: 928, loss: 5.4553\n2023-01-04 17:30:19,330 - mmcls - INFO - Epoch [18][5000/5005]\tlr: 1.000e-01, eta: 10:03:47, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4135\n2023-01-04 17:30:42,329 - mmcls - INFO - Epoch(val) [18][196]\taccuracy_top-1: 4.3120, accuracy_top-5: 11.6280\n2023-01-04 17:30:53,313 - mmcls - INFO - Epoch [19][100/5005]\tlr: 1.000e-01, eta: 10:03:45, time: 0.110, data_time: 0.031, memory: 928, loss: 5.3949\n2023-01-04 17:31:01,944 - mmcls - INFO - Epoch [19][200/5005]\tlr: 1.000e-01, eta: 10:03:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4001\n2023-01-04 17:31:10,793 - mmcls - INFO - Epoch [19][300/5005]\tlr: 1.000e-01, eta: 10:03:27, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4193\n2023-01-04 17:31:19,667 - mmcls - INFO - Epoch [19][400/5005]\tlr: 1.000e-01, eta: 10:03:18, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4204\n2023-01-04 17:31:28,225 - mmcls - INFO - Epoch [19][500/5005]\tlr: 1.000e-01, eta: 10:03:08, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4272\n2023-01-04 17:31:37,170 - mmcls - INFO - Epoch [19][600/5005]\tlr: 1.000e-01, eta: 10:03:00, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4313\n2023-01-04 17:31:45,789 - mmcls - INFO - Epoch [19][700/5005]\tlr: 1.000e-01, eta: 10:02:50, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4097\n2023-01-04 17:31:54,757 - mmcls - INFO - Epoch [19][800/5005]\tlr: 1.000e-01, eta: 10:02:42, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4305\n2023-01-04 17:32:03,614 - mmcls - INFO - Epoch [19][900/5005]\tlr: 1.000e-01, eta: 10:02:33, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4199\n2023-01-04 17:32:12,776 - mmcls - INFO - Epoch [19][1000/5005]\tlr: 1.000e-01, eta: 10:02:26, time: 0.092, data_time: 0.003, memory: 928, loss: 5.4160\n2023-01-04 17:32:21,424 - mmcls - INFO - Epoch [19][1100/5005]\tlr: 1.000e-01, eta: 10:02:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4304\n2023-01-04 17:32:30,193 - mmcls - INFO - Epoch [19][1200/5005]\tlr: 1.000e-01, eta: 10:02:07, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4251\n2023-01-04 17:32:38,857 - mmcls - INFO - Epoch [19][1300/5005]\tlr: 1.000e-01, eta: 10:01:58, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4202\n2023-01-04 17:32:47,822 - mmcls - INFO - Epoch [19][1400/5005]\tlr: 1.000e-01, eta: 10:01:49, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4049\n2023-01-04 17:32:56,587 - mmcls - INFO - Epoch [19][1500/5005]\tlr: 1.000e-01, eta: 10:01:40, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4344\n2023-01-04 17:33:05,238 - mmcls - INFO - Epoch [19][1600/5005]\tlr: 1.000e-01, eta: 10:01:31, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4315\n2023-01-04 17:33:14,521 - mmcls - INFO - Epoch [19][1700/5005]\tlr: 1.000e-01, eta: 10:01:24, time: 0.093, data_time: 0.002, memory: 928, loss: 5.4169\n2023-01-04 17:33:23,214 - mmcls - INFO - Epoch [19][1800/5005]\tlr: 1.000e-01, eta: 10:01:14, time: 0.087, data_time: 0.023, memory: 928, loss: 5.3944\n2023-01-04 17:33:31,807 - mmcls - INFO - Epoch [19][1900/5005]\tlr: 1.000e-01, eta: 10:01:04, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4096\n2023-01-04 17:33:40,499 - mmcls - INFO - Epoch [19][2000/5005]\tlr: 1.000e-01, eta: 10:00:55, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4061\n2023-01-04 17:33:49,185 - mmcls - INFO - Epoch [19][2100/5005]\tlr: 1.000e-01, eta: 10:00:46, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4263\n2023-01-04 17:33:57,827 - mmcls - INFO - Epoch [19][2200/5005]\tlr: 1.000e-01, eta: 10:00:36, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4136\n2023-01-04 17:34:06,758 - mmcls - INFO - Epoch [19][2300/5005]\tlr: 1.000e-01, eta: 10:00:27, time: 0.089, data_time: 0.012, memory: 928, loss: 5.4207\n2023-01-04 17:34:15,362 - mmcls - INFO - Epoch [19][2400/5005]\tlr: 1.000e-01, eta: 10:00:18, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4384\n2023-01-04 17:34:23,960 - mmcls - INFO - Epoch [19][2500/5005]\tlr: 1.000e-01, eta: 10:00:08, time: 0.086, data_time: 0.026, memory: 928, loss: 5.4159\n2023-01-04 17:34:32,622 - mmcls - INFO - Epoch [19][2600/5005]\tlr: 1.000e-01, eta: 9:59:58, time: 0.087, data_time: 0.017, memory: 928, loss: 5.4043\n2023-01-04 17:34:41,552 - mmcls - INFO - Epoch [19][2700/5005]\tlr: 1.000e-01, eta: 9:59:50, time: 0.089, data_time: 0.008, memory: 928, loss: 5.4216\n2023-01-04 17:34:50,605 - mmcls - INFO - Epoch [19][2800/5005]\tlr: 1.000e-01, eta: 9:59:42, time: 0.091, data_time: 0.006, memory: 928, loss: 5.4033\n2023-01-04 17:34:59,742 - mmcls - INFO - Epoch [19][2900/5005]\tlr: 1.000e-01, eta: 9:59:35, time: 0.091, data_time: 0.005, memory: 928, loss: 5.4064\n2023-01-04 17:35:08,355 - mmcls - INFO - Epoch [19][3000/5005]\tlr: 1.000e-01, eta: 9:59:25, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4340\n2023-01-04 17:35:17,051 - mmcls - INFO - Epoch [19][3100/5005]\tlr: 1.000e-01, eta: 9:59:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4124\n2023-01-04 17:35:25,873 - mmcls - INFO - Epoch [19][3200/5005]\tlr: 1.000e-01, eta: 9:59:07, time: 0.088, data_time: 0.021, memory: 928, loss: 5.4230\n2023-01-04 17:35:34,506 - mmcls - INFO - Epoch [19][3300/5005]\tlr: 1.000e-01, eta: 9:58:57, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4108\n2023-01-04 17:35:43,360 - mmcls - INFO - Epoch [19][3400/5005]\tlr: 1.000e-01, eta: 9:58:48, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4201\n2023-01-04 17:35:52,201 - mmcls - INFO - Epoch [19][3500/5005]\tlr: 1.000e-01, eta: 9:58:39, time: 0.089, data_time: 0.040, memory: 928, loss: 5.4349\n2023-01-04 17:36:00,896 - mmcls - INFO - Epoch [19][3600/5005]\tlr: 1.000e-01, eta: 9:58:30, time: 0.087, data_time: 0.021, memory: 928, loss: 5.4094\n2023-01-04 17:36:10,074 - mmcls - INFO - Epoch [19][3700/5005]\tlr: 1.000e-01, eta: 9:58:23, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4115\n2023-01-04 17:36:18,793 - mmcls - INFO - Epoch [19][3800/5005]\tlr: 1.000e-01, eta: 9:58:13, time: 0.087, data_time: 0.027, memory: 928, loss: 5.4314\n2023-01-04 17:36:27,383 - mmcls - INFO - Epoch [19][3900/5005]\tlr: 1.000e-01, eta: 9:58:03, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3906\n2023-01-04 17:36:36,213 - mmcls - INFO - Epoch [19][4000/5005]\tlr: 1.000e-01, eta: 9:57:55, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4318\n2023-01-04 17:36:44,791 - mmcls - INFO - Epoch [19][4100/5005]\tlr: 1.000e-01, eta: 9:57:45, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4027\n2023-01-04 17:36:53,395 - mmcls - INFO - Epoch [19][4200/5005]\tlr: 1.000e-01, eta: 9:57:35, time: 0.086, data_time: 0.041, memory: 928, loss: 5.4209\n2023-01-04 17:37:02,393 - mmcls - INFO - Epoch [19][4300/5005]\tlr: 1.000e-01, eta: 9:57:27, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4200\n2023-01-04 17:37:11,381 - mmcls - INFO - Epoch [19][4400/5005]\tlr: 1.000e-01, eta: 9:57:19, time: 0.090, data_time: 0.014, memory: 928, loss: 5.4052\n2023-01-04 17:37:19,920 - mmcls - INFO - Epoch [19][4500/5005]\tlr: 1.000e-01, eta: 9:57:09, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4307\n2023-01-04 17:37:28,597 - mmcls - INFO - Epoch [19][4600/5005]\tlr: 1.000e-01, eta: 9:56:59, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4190\n2023-01-04 17:37:37,314 - mmcls - INFO - Epoch [19][4700/5005]\tlr: 1.000e-01, eta: 9:56:50, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4248\n2023-01-04 17:37:46,173 - mmcls - INFO - Epoch [19][4800/5005]\tlr: 1.000e-01, eta: 9:56:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3972\n2023-01-04 17:37:55,151 - mmcls - INFO - Epoch [19][4900/5005]\tlr: 1.000e-01, eta: 9:56:33, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3882\n2023-01-04 17:38:03,975 - mmcls - INFO - Epoch [19][5000/5005]\tlr: 1.000e-01, eta: 9:56:24, time: 0.088, data_time: 0.034, memory: 928, loss: 5.4117\n2023-01-04 17:38:26,697 - mmcls - INFO - Epoch(val) [19][196]\taccuracy_top-1: 4.0220, accuracy_top-5: 11.6040\n2023-01-04 17:38:38,004 - mmcls - INFO - Epoch [20][100/5005]\tlr: 1.000e-01, eta: 9:56:24, time: 0.113, data_time: 0.054, memory: 928, loss: 5.3870\n2023-01-04 17:38:47,035 - mmcls - INFO - Epoch [20][200/5005]\tlr: 1.000e-01, eta: 9:56:16, time: 0.090, data_time: 0.010, memory: 928, loss: 5.4100\n2023-01-04 17:38:55,780 - mmcls - INFO - Epoch [20][300/5005]\tlr: 1.000e-01, eta: 9:56:06, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4041\n2023-01-04 17:39:04,999 - mmcls - INFO - Epoch [20][400/5005]\tlr: 1.000e-01, eta: 9:55:59, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4055\n2023-01-04 17:39:13,935 - mmcls - INFO - Epoch [20][500/5005]\tlr: 1.000e-01, eta: 9:55:51, time: 0.089, data_time: 0.024, memory: 928, loss: 5.4023\n2023-01-04 17:39:22,731 - mmcls - INFO - Epoch [20][600/5005]\tlr: 1.000e-01, eta: 9:55:42, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4158\n2023-01-04 17:39:31,511 - mmcls - INFO - Epoch [20][700/5005]\tlr: 1.000e-01, eta: 9:55:33, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4027\n2023-01-04 17:39:40,158 - mmcls - INFO - Epoch [20][800/5005]\tlr: 1.000e-01, eta: 9:55:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4122\n2023-01-04 17:39:48,880 - mmcls - INFO - Epoch [20][900/5005]\tlr: 1.000e-01, eta: 9:55:14, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4438\n2023-01-04 17:39:57,800 - mmcls - INFO - Epoch [20][1000/5005]\tlr: 1.000e-01, eta: 9:55:05, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4002\n2023-01-04 17:40:06,498 - mmcls - INFO - Epoch [20][1100/5005]\tlr: 1.000e-01, eta: 9:54:56, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4198\n2023-01-04 17:40:15,206 - mmcls - INFO - Epoch [20][1200/5005]\tlr: 1.000e-01, eta: 9:54:47, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4035\n2023-01-04 17:40:23,817 - mmcls - INFO - Epoch [20][1300/5005]\tlr: 1.000e-01, eta: 9:54:37, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4313\n2023-01-04 17:40:32,612 - mmcls - INFO - Epoch [20][1400/5005]\tlr: 1.000e-01, eta: 9:54:28, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3972\n2023-01-04 17:40:41,684 - mmcls - INFO - Epoch [20][1500/5005]\tlr: 1.000e-01, eta: 9:54:20, time: 0.091, data_time: 0.002, memory: 928, loss: 5.3954\n2023-01-04 17:40:50,469 - mmcls - INFO - Epoch [20][1600/5005]\tlr: 1.000e-01, eta: 9:54:11, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4200\n2023-01-04 17:40:59,003 - mmcls - INFO - Epoch [20][1700/5005]\tlr: 1.000e-01, eta: 9:54:01, time: 0.085, data_time: 0.007, memory: 928, loss: 5.4062\n2023-01-04 17:41:07,751 - mmcls - INFO - Epoch [20][1800/5005]\tlr: 1.000e-01, eta: 9:53:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4109\n2023-01-04 17:41:16,293 - mmcls - INFO - Epoch [20][1900/5005]\tlr: 1.000e-01, eta: 9:53:42, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3977\n2023-01-04 17:41:25,075 - mmcls - INFO - Epoch [20][2000/5005]\tlr: 1.000e-01, eta: 9:53:33, time: 0.088, data_time: 0.020, memory: 928, loss: 5.4140\n2023-01-04 17:41:33,690 - mmcls - INFO - Epoch [20][2100/5005]\tlr: 1.000e-01, eta: 9:53:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4007\n2023-01-04 17:41:42,326 - mmcls - INFO - Epoch [20][2200/5005]\tlr: 1.000e-01, eta: 9:53:14, time: 0.086, data_time: 0.012, memory: 928, loss: 5.4019\n2023-01-04 17:41:50,888 - mmcls - INFO - Epoch [20][2300/5005]\tlr: 1.000e-01, eta: 9:53:04, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4123\n2023-01-04 17:41:59,723 - mmcls - INFO - Epoch [20][2400/5005]\tlr: 1.000e-01, eta: 9:52:55, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4128\n2023-01-04 17:42:08,342 - mmcls - INFO - Epoch [20][2500/5005]\tlr: 1.000e-01, eta: 9:52:45, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4208\n2023-01-04 17:42:17,130 - mmcls - INFO - Epoch [20][2600/5005]\tlr: 1.000e-01, eta: 9:52:36, time: 0.088, data_time: 0.010, memory: 928, loss: 5.3847\n2023-01-04 17:42:25,829 - mmcls - INFO - Epoch [20][2700/5005]\tlr: 1.000e-01, eta: 9:52:27, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4087\n2023-01-04 17:42:34,470 - mmcls - INFO - Epoch [20][2800/5005]\tlr: 1.000e-01, eta: 9:52:17, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3991\n2023-01-04 17:42:43,374 - mmcls - INFO - Epoch [20][2900/5005]\tlr: 1.000e-01, eta: 9:52:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4102\n2023-01-04 17:42:52,007 - mmcls - INFO - Epoch [20][3000/5005]\tlr: 1.000e-01, eta: 9:51:59, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4080\n2023-01-04 17:43:00,598 - mmcls - INFO - Epoch [20][3100/5005]\tlr: 1.000e-01, eta: 9:51:49, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3994\n2023-01-04 17:43:09,006 - mmcls - INFO - Epoch [20][3200/5005]\tlr: 1.000e-01, eta: 9:51:39, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4201\n2023-01-04 17:43:17,815 - mmcls - INFO - Epoch [20][3300/5005]\tlr: 1.000e-01, eta: 9:51:30, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4153\n2023-01-04 17:43:26,257 - mmcls - INFO - Epoch [20][3400/5005]\tlr: 1.000e-01, eta: 9:51:19, time: 0.084, data_time: 0.009, memory: 928, loss: 5.4277\n2023-01-04 17:43:35,285 - mmcls - INFO - Epoch [20][3500/5005]\tlr: 1.000e-01, eta: 9:51:11, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4269\n2023-01-04 17:43:43,963 - mmcls - INFO - Epoch [20][3600/5005]\tlr: 1.000e-01, eta: 9:51:02, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4038\n2023-01-04 17:43:52,927 - mmcls - INFO - Epoch [20][3700/5005]\tlr: 1.000e-01, eta: 9:50:54, time: 0.090, data_time: 0.044, memory: 928, loss: 5.4139\n2023-01-04 17:44:01,591 - mmcls - INFO - Epoch [20][3800/5005]\tlr: 1.000e-01, eta: 9:50:44, time: 0.087, data_time: 0.050, memory: 928, loss: 5.4279\n2023-01-04 17:44:10,254 - mmcls - INFO - Epoch [20][3900/5005]\tlr: 1.000e-01, eta: 9:50:35, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4065\n2023-01-04 17:44:19,263 - mmcls - INFO - Epoch [20][4000/5005]\tlr: 1.000e-01, eta: 9:50:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3880\n2023-01-04 17:44:27,926 - mmcls - INFO - Epoch [20][4100/5005]\tlr: 1.000e-01, eta: 9:50:17, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4228\n2023-01-04 17:44:36,760 - mmcls - INFO - Epoch [20][4200/5005]\tlr: 1.000e-01, eta: 9:50:08, time: 0.088, data_time: 0.029, memory: 928, loss: 5.3988\n2023-01-04 17:44:45,573 - mmcls - INFO - Epoch [20][4300/5005]\tlr: 1.000e-01, eta: 9:49:59, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4199\n2023-01-04 17:44:54,344 - mmcls - INFO - Epoch [20][4400/5005]\tlr: 1.000e-01, eta: 9:49:50, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4062\n2023-01-04 17:45:03,390 - mmcls - INFO - Epoch [20][4500/5005]\tlr: 1.000e-01, eta: 9:49:42, time: 0.090, data_time: 0.006, memory: 928, loss: 5.3964\n2023-01-04 17:45:11,882 - mmcls - INFO - Epoch [20][4600/5005]\tlr: 1.000e-01, eta: 9:49:32, time: 0.085, data_time: 0.008, memory: 928, loss: 5.4383\n2023-01-04 17:45:20,507 - mmcls - INFO - Epoch [20][4700/5005]\tlr: 1.000e-01, eta: 9:49:23, time: 0.086, data_time: 0.024, memory: 928, loss: 5.3867\n2023-01-04 17:45:29,754 - mmcls - INFO - Epoch [20][4800/5005]\tlr: 1.000e-01, eta: 9:49:16, time: 0.092, data_time: 0.041, memory: 928, loss: 5.3925\n2023-01-04 17:45:38,389 - mmcls - INFO - Epoch [20][4900/5005]\tlr: 1.000e-01, eta: 9:49:06, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4118\n2023-01-04 17:45:46,936 - mmcls - INFO - Epoch [20][5000/5005]\tlr: 1.000e-01, eta: 9:48:56, time: 0.085, data_time: 0.014, memory: 928, loss: 5.4140\n2023-01-04 17:46:09,752 - mmcls - INFO - Epoch(val) [20][196]\taccuracy_top-1: 4.1680, accuracy_top-5: 11.5700\n2023-01-04 17:46:20,951 - mmcls - INFO - Epoch [21][100/5005]\tlr: 1.000e-01, eta: 9:48:54, time: 0.112, data_time: 0.050, memory: 928, loss: 5.4228\n2023-01-04 17:46:29,981 - mmcls - INFO - Epoch [21][200/5005]\tlr: 1.000e-01, eta: 9:48:46, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4097\n2023-01-04 17:46:38,702 - mmcls - INFO - Epoch [21][300/5005]\tlr: 1.000e-01, eta: 9:48:37, time: 0.087, data_time: 0.013, memory: 928, loss: 5.4139\n2023-01-04 17:46:47,326 - mmcls - INFO - Epoch [21][400/5005]\tlr: 1.000e-01, eta: 9:48:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3856\n2023-01-04 17:46:55,978 - mmcls - INFO - Epoch [21][500/5005]\tlr: 1.000e-01, eta: 9:48:18, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4133\n2023-01-04 17:47:04,454 - mmcls - INFO - Epoch [21][600/5005]\tlr: 1.000e-01, eta: 9:48:08, time: 0.085, data_time: 0.011, memory: 928, loss: 5.3976\n2023-01-04 17:47:13,354 - mmcls - INFO - Epoch [21][700/5005]\tlr: 1.000e-01, eta: 9:47:59, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3748\n2023-01-04 17:47:22,260 - mmcls - INFO - Epoch [21][800/5005]\tlr: 1.000e-01, eta: 9:47:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4011\n2023-01-04 17:47:30,872 - mmcls - INFO - Epoch [21][900/5005]\tlr: 1.000e-01, eta: 9:47:41, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4020\n2023-01-04 17:47:39,649 - mmcls - INFO - Epoch [21][1000/5005]\tlr: 1.000e-01, eta: 9:47:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3863\n2023-01-04 17:47:48,329 - mmcls - INFO - Epoch [21][1100/5005]\tlr: 1.000e-01, eta: 9:47:23, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4044\n2023-01-04 17:47:56,851 - mmcls - INFO - Epoch [21][1200/5005]\tlr: 1.000e-01, eta: 9:47:12, time: 0.085, data_time: 0.026, memory: 928, loss: 5.4016\n2023-01-04 17:48:05,755 - mmcls - INFO - Epoch [21][1300/5005]\tlr: 1.000e-01, eta: 9:47:04, time: 0.089, data_time: 0.011, memory: 928, loss: 5.3953\n2023-01-04 17:48:14,482 - mmcls - INFO - Epoch [21][1400/5005]\tlr: 1.000e-01, eta: 9:46:55, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3955\n2023-01-04 17:48:23,185 - mmcls - INFO - Epoch [21][1500/5005]\tlr: 1.000e-01, eta: 9:46:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4069\n2023-01-04 17:48:32,530 - mmcls - INFO - Epoch [21][1600/5005]\tlr: 1.000e-01, eta: 9:46:39, time: 0.093, data_time: 0.019, memory: 928, loss: 5.3945\n2023-01-04 17:48:41,349 - mmcls - INFO - Epoch [21][1700/5005]\tlr: 1.000e-01, eta: 9:46:30, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4015\n2023-01-04 17:48:50,173 - mmcls - INFO - Epoch [21][1800/5005]\tlr: 1.000e-01, eta: 9:46:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4094\n2023-01-04 17:48:59,237 - mmcls - INFO - Epoch [21][1900/5005]\tlr: 1.000e-01, eta: 9:46:13, time: 0.091, data_time: 0.020, memory: 928, loss: 5.3913\n2023-01-04 17:49:08,001 - mmcls - INFO - Epoch [21][2000/5005]\tlr: 1.000e-01, eta: 9:46:04, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4035\n2023-01-04 17:49:17,335 - mmcls - INFO - Epoch [21][2100/5005]\tlr: 1.000e-01, eta: 9:45:57, time: 0.093, data_time: 0.001, memory: 928, loss: 5.4191\n2023-01-04 17:49:26,340 - mmcls - INFO - Epoch [21][2200/5005]\tlr: 1.000e-01, eta: 9:45:49, time: 0.090, data_time: 0.007, memory: 928, loss: 5.4136\n2023-01-04 17:49:35,377 - mmcls - INFO - Epoch [21][2300/5005]\tlr: 1.000e-01, eta: 9:45:41, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4130\n2023-01-04 17:49:44,131 - mmcls - INFO - Epoch [21][2400/5005]\tlr: 1.000e-01, eta: 9:45:32, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4162\n2023-01-04 17:49:53,204 - mmcls - INFO - Epoch [21][2500/5005]\tlr: 1.000e-01, eta: 9:45:24, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4239\n2023-01-04 17:50:02,067 - mmcls - INFO - Epoch [21][2600/5005]\tlr: 1.000e-01, eta: 9:45:15, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4158\n2023-01-04 17:50:10,939 - mmcls - INFO - Epoch [21][2700/5005]\tlr: 1.000e-01, eta: 9:45:07, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4082\n2023-01-04 17:50:19,818 - mmcls - INFO - Epoch [21][2800/5005]\tlr: 1.000e-01, eta: 9:44:58, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3946\n2023-01-04 17:50:28,606 - mmcls - INFO - Epoch [21][2900/5005]\tlr: 1.000e-01, eta: 9:44:49, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4215\n2023-01-04 17:50:37,279 - mmcls - INFO - Epoch [21][3000/5005]\tlr: 1.000e-01, eta: 9:44:40, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4123\n2023-01-04 17:50:45,971 - mmcls - INFO - Epoch [21][3100/5005]\tlr: 1.000e-01, eta: 9:44:30, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4164\n2023-01-04 17:50:54,734 - mmcls - INFO - Epoch [21][3200/5005]\tlr: 1.000e-01, eta: 9:44:21, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4097\n2023-01-04 17:51:03,498 - mmcls - INFO - Epoch [21][3300/5005]\tlr: 1.000e-01, eta: 9:44:12, time: 0.088, data_time: 0.037, memory: 928, loss: 5.3949\n2023-01-04 17:51:12,542 - mmcls - INFO - Epoch [21][3400/5005]\tlr: 1.000e-01, eta: 9:44:04, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4138\n2023-01-04 17:51:21,298 - mmcls - INFO - Epoch [21][3500/5005]\tlr: 1.000e-01, eta: 9:43:55, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4118\n2023-01-04 17:51:30,121 - mmcls - INFO - Epoch [21][3600/5005]\tlr: 1.000e-01, eta: 9:43:46, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4264\n2023-01-04 17:51:39,094 - mmcls - INFO - Epoch [21][3700/5005]\tlr: 1.000e-01, eta: 9:43:38, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3909\n2023-01-04 17:51:47,501 - mmcls - INFO - Epoch [21][3800/5005]\tlr: 1.000e-01, eta: 9:43:27, time: 0.084, data_time: 0.012, memory: 928, loss: 5.4037\n2023-01-04 17:51:56,163 - mmcls - INFO - Epoch [21][3900/5005]\tlr: 1.000e-01, eta: 9:43:18, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4069\n2023-01-04 17:52:04,924 - mmcls - INFO - Epoch [21][4000/5005]\tlr: 1.000e-01, eta: 9:43:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4004\n2023-01-04 17:52:13,865 - mmcls - INFO - Epoch [21][4100/5005]\tlr: 1.000e-01, eta: 9:43:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4005\n2023-01-04 17:52:22,497 - mmcls - INFO - Epoch [21][4200/5005]\tlr: 1.000e-01, eta: 9:42:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3988\n2023-01-04 17:52:31,912 - mmcls - INFO - Epoch [21][4300/5005]\tlr: 1.000e-01, eta: 9:42:44, time: 0.094, data_time: 0.011, memory: 928, loss: 5.3853\n2023-01-04 17:52:40,603 - mmcls - INFO - Epoch [21][4400/5005]\tlr: 1.000e-01, eta: 9:42:35, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4272\n2023-01-04 17:52:49,518 - mmcls - INFO - Epoch [21][4500/5005]\tlr: 1.000e-01, eta: 9:42:26, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4047\n2023-01-04 17:52:58,082 - mmcls - INFO - Epoch [21][4600/5005]\tlr: 1.000e-01, eta: 9:42:17, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4148\n2023-01-04 17:53:06,654 - mmcls - INFO - Epoch [21][4700/5005]\tlr: 1.000e-01, eta: 9:42:07, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4008\n2023-01-04 17:53:15,344 - mmcls - INFO - Epoch [21][4800/5005]\tlr: 1.000e-01, eta: 9:41:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4011\n2023-01-04 17:53:24,079 - mmcls - INFO - Epoch [21][4900/5005]\tlr: 1.000e-01, eta: 9:41:48, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4003\n2023-01-04 17:53:32,987 - mmcls - INFO - Epoch [21][5000/5005]\tlr: 1.000e-01, eta: 9:41:40, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3946\n2023-01-04 17:53:56,002 - mmcls - INFO - Epoch(val) [21][196]\taccuracy_top-1: 4.7680, accuracy_top-5: 13.3460\n2023-01-04 17:54:06,819 - mmcls - INFO - Epoch [22][100/5005]\tlr: 1.000e-01, eta: 9:41:36, time: 0.108, data_time: 0.060, memory: 928, loss: 5.3842\n2023-01-04 17:54:15,761 - mmcls - INFO - Epoch [22][200/5005]\tlr: 1.000e-01, eta: 9:41:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3735\n2023-01-04 17:54:24,507 - mmcls - INFO - Epoch [22][300/5005]\tlr: 1.000e-01, eta: 9:41:19, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4167\n2023-01-04 17:54:33,101 - mmcls - INFO - Epoch [22][400/5005]\tlr: 1.000e-01, eta: 9:41:09, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4007\n2023-01-04 17:54:41,952 - mmcls - INFO - Epoch [22][500/5005]\tlr: 1.000e-01, eta: 9:41:00, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3897\n2023-01-04 17:54:50,621 - mmcls - INFO - Epoch [22][600/5005]\tlr: 1.000e-01, eta: 9:40:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3992\n2023-01-04 17:54:59,520 - mmcls - INFO - Epoch [22][700/5005]\tlr: 1.000e-01, eta: 9:40:42, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4174\n2023-01-04 17:55:08,271 - mmcls - INFO - Epoch [22][800/5005]\tlr: 1.000e-01, eta: 9:40:33, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4196\n2023-01-04 17:55:17,078 - mmcls - INFO - Epoch [22][900/5005]\tlr: 1.000e-01, eta: 9:40:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3956\n2023-01-04 17:55:25,488 - mmcls - INFO - Epoch [22][1000/5005]\tlr: 1.000e-01, eta: 9:40:14, time: 0.084, data_time: 0.008, memory: 928, loss: 5.4267\n2023-01-04 17:55:34,277 - mmcls - INFO - Epoch [22][1100/5005]\tlr: 1.000e-01, eta: 9:40:05, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3962\n2023-01-04 17:55:43,110 - mmcls - INFO - Epoch [22][1200/5005]\tlr: 1.000e-01, eta: 9:39:56, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4050\n2023-01-04 17:55:51,757 - mmcls - INFO - Epoch [22][1300/5005]\tlr: 1.000e-01, eta: 9:39:47, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3773\n2023-01-04 17:56:00,901 - mmcls - INFO - Epoch [22][1400/5005]\tlr: 1.000e-01, eta: 9:39:39, time: 0.091, data_time: 0.021, memory: 928, loss: 5.3979\n2023-01-04 17:56:09,641 - mmcls - INFO - Epoch [22][1500/5005]\tlr: 1.000e-01, eta: 9:39:30, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4109\n2023-01-04 17:56:18,062 - mmcls - INFO - Epoch [22][1600/5005]\tlr: 1.000e-01, eta: 9:39:19, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4021\n2023-01-04 17:56:26,766 - mmcls - INFO - Epoch [22][1700/5005]\tlr: 1.000e-01, eta: 9:39:10, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4006\n2023-01-04 17:56:35,675 - mmcls - INFO - Epoch [22][1800/5005]\tlr: 1.000e-01, eta: 9:39:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4288\n2023-01-04 17:56:44,598 - mmcls - INFO - Epoch [22][1900/5005]\tlr: 1.000e-01, eta: 9:38:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3963\n2023-01-04 17:56:53,209 - mmcls - INFO - Epoch [22][2000/5005]\tlr: 1.000e-01, eta: 9:38:44, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4060\n2023-01-04 17:57:01,650 - mmcls - INFO - Epoch [22][2100/5005]\tlr: 1.000e-01, eta: 9:38:33, time: 0.084, data_time: 0.004, memory: 928, loss: 5.3951\n2023-01-04 17:57:10,993 - mmcls - INFO - Epoch [22][2200/5005]\tlr: 1.000e-01, eta: 9:38:26, time: 0.093, data_time: 0.003, memory: 928, loss: 5.3856\n2023-01-04 17:57:19,709 - mmcls - INFO - Epoch [22][2300/5005]\tlr: 1.000e-01, eta: 9:38:17, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4025\n2023-01-04 17:57:28,377 - mmcls - INFO - Epoch [22][2400/5005]\tlr: 1.000e-01, eta: 9:38:08, time: 0.087, data_time: 0.020, memory: 928, loss: 5.3818\n2023-01-04 17:57:36,993 - mmcls - INFO - Epoch [22][2500/5005]\tlr: 1.000e-01, eta: 9:37:58, time: 0.086, data_time: 0.019, memory: 928, loss: 5.3739\n2023-01-04 17:57:45,618 - mmcls - INFO - Epoch [22][2600/5005]\tlr: 1.000e-01, eta: 9:37:49, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3967\n2023-01-04 17:57:54,614 - mmcls - INFO - Epoch [22][2700/5005]\tlr: 1.000e-01, eta: 9:37:40, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4046\n2023-01-04 17:58:03,614 - mmcls - INFO - Epoch [22][2800/5005]\tlr: 1.000e-01, eta: 9:37:32, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4117\n2023-01-04 17:58:12,190 - mmcls - INFO - Epoch [22][2900/5005]\tlr: 1.000e-01, eta: 9:37:22, time: 0.086, data_time: 0.029, memory: 928, loss: 5.4045\n2023-01-04 17:58:20,854 - mmcls - INFO - Epoch [22][3000/5005]\tlr: 1.000e-01, eta: 9:37:13, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4048\n2023-01-04 17:58:30,070 - mmcls - INFO - Epoch [22][3100/5005]\tlr: 1.000e-01, eta: 9:37:06, time: 0.092, data_time: 0.020, memory: 928, loss: 5.3888\n2023-01-04 17:58:38,993 - mmcls - INFO - Epoch [22][3200/5005]\tlr: 1.000e-01, eta: 9:36:57, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3896\n2023-01-04 17:58:47,736 - mmcls - INFO - Epoch [22][3300/5005]\tlr: 1.000e-01, eta: 9:36:48, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4106\n2023-01-04 17:58:56,471 - mmcls - INFO - Epoch [22][3400/5005]\tlr: 1.000e-01, eta: 9:36:39, time: 0.087, data_time: 0.028, memory: 928, loss: 5.3933\n2023-01-04 17:59:05,504 - mmcls - INFO - Epoch [22][3500/5005]\tlr: 1.000e-01, eta: 9:36:31, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4048\n2023-01-04 17:59:14,197 - mmcls - INFO - Epoch [22][3600/5005]\tlr: 1.000e-01, eta: 9:36:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3923\n2023-01-04 17:59:23,647 - mmcls - INFO - Epoch [22][3700/5005]\tlr: 1.000e-01, eta: 9:36:15, time: 0.094, data_time: 0.005, memory: 928, loss: 5.3748\n2023-01-04 17:59:32,191 - mmcls - INFO - Epoch [22][3800/5005]\tlr: 1.000e-01, eta: 9:36:05, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4070\n2023-01-04 17:59:41,094 - mmcls - INFO - Epoch [22][3900/5005]\tlr: 1.000e-01, eta: 9:35:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4077\n2023-01-04 17:59:50,228 - mmcls - INFO - Epoch [22][4000/5005]\tlr: 1.000e-01, eta: 9:35:49, time: 0.091, data_time: 0.008, memory: 928, loss: 5.4149\n2023-01-04 17:59:58,819 - mmcls - INFO - Epoch [22][4100/5005]\tlr: 1.000e-01, eta: 9:35:39, time: 0.086, data_time: 0.031, memory: 928, loss: 5.3985\n2023-01-04 18:00:07,607 - mmcls - INFO - Epoch [22][4200/5005]\tlr: 1.000e-01, eta: 9:35:30, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4052\n2023-01-04 18:00:16,463 - mmcls - INFO - Epoch [22][4300/5005]\tlr: 1.000e-01, eta: 9:35:21, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4042\n2023-01-04 18:00:25,396 - mmcls - INFO - Epoch [22][4400/5005]\tlr: 1.000e-01, eta: 9:35:13, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4124\n2023-01-04 18:00:34,111 - mmcls - INFO - Epoch [22][4500/5005]\tlr: 1.000e-01, eta: 9:35:04, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4177\n2023-01-04 18:00:42,872 - mmcls - INFO - Epoch [22][4600/5005]\tlr: 1.000e-01, eta: 9:34:55, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4039\n2023-01-04 18:00:51,735 - mmcls - INFO - Epoch [22][4700/5005]\tlr: 1.000e-01, eta: 9:34:46, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3821\n2023-01-04 18:01:00,556 - mmcls - INFO - Epoch [22][4800/5005]\tlr: 1.000e-01, eta: 9:34:37, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3980\n2023-01-04 18:01:09,382 - mmcls - INFO - Epoch [22][4900/5005]\tlr: 1.000e-01, eta: 9:34:28, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3826\n2023-01-04 18:01:18,213 - mmcls - INFO - Epoch [22][5000/5005]\tlr: 1.000e-01, eta: 9:34:19, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3851\n2023-01-04 18:01:40,948 - mmcls - INFO - Epoch(val) [22][196]\taccuracy_top-1: 5.0060, accuracy_top-5: 13.5180\n2023-01-04 18:01:51,674 - mmcls - INFO - Epoch [23][100/5005]\tlr: 1.000e-01, eta: 9:34:15, time: 0.107, data_time: 0.031, memory: 928, loss: 5.3829\n2023-01-04 18:02:00,277 - mmcls - INFO - Epoch [23][200/5005]\tlr: 1.000e-01, eta: 9:34:06, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4027\n2023-01-04 18:02:08,944 - mmcls - INFO - Epoch [23][300/5005]\tlr: 1.000e-01, eta: 9:33:56, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3938\n2023-01-04 18:02:17,569 - mmcls - INFO - Epoch [23][400/5005]\tlr: 1.000e-01, eta: 9:33:47, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3861\n2023-01-04 18:02:26,615 - mmcls - INFO - Epoch [23][500/5005]\tlr: 1.000e-01, eta: 9:33:39, time: 0.090, data_time: 0.011, memory: 928, loss: 5.3915\n2023-01-04 18:02:35,438 - mmcls - INFO - Epoch [23][600/5005]\tlr: 1.000e-01, eta: 9:33:30, time: 0.088, data_time: 0.030, memory: 928, loss: 5.4085\n2023-01-04 18:02:44,233 - mmcls - INFO - Epoch [23][700/5005]\tlr: 1.000e-01, eta: 9:33:21, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3835\n2023-01-04 18:02:52,816 - mmcls - INFO - Epoch [23][800/5005]\tlr: 1.000e-01, eta: 9:33:11, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3802\n2023-01-04 18:03:01,457 - mmcls - INFO - Epoch [23][900/5005]\tlr: 1.000e-01, eta: 9:33:02, time: 0.086, data_time: 0.007, memory: 928, loss: 5.3821\n2023-01-04 18:03:10,207 - mmcls - INFO - Epoch [23][1000/5005]\tlr: 1.000e-01, eta: 9:32:52, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3773\n2023-01-04 18:03:18,978 - mmcls - INFO - Epoch [23][1100/5005]\tlr: 1.000e-01, eta: 9:32:43, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3844\n2023-01-04 18:03:27,818 - mmcls - INFO - Epoch [23][1200/5005]\tlr: 1.000e-01, eta: 9:32:35, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4021\n2023-01-04 18:03:36,591 - mmcls - INFO - Epoch [23][1300/5005]\tlr: 1.000e-01, eta: 9:32:26, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3911\n2023-01-04 18:03:45,264 - mmcls - INFO - Epoch [23][1400/5005]\tlr: 1.000e-01, eta: 9:32:16, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3993\n2023-01-04 18:03:54,537 - mmcls - INFO - Epoch [23][1500/5005]\tlr: 1.000e-01, eta: 9:32:09, time: 0.093, data_time: 0.001, memory: 928, loss: 5.3988\n2023-01-04 18:04:03,107 - mmcls - INFO - Epoch [23][1600/5005]\tlr: 1.000e-01, eta: 9:31:59, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3985\n2023-01-04 18:04:11,909 - mmcls - INFO - Epoch [23][1700/5005]\tlr: 1.000e-01, eta: 9:31:50, time: 0.088, data_time: 0.021, memory: 928, loss: 5.3910\n2023-01-04 18:04:20,955 - mmcls - INFO - Epoch [23][1800/5005]\tlr: 1.000e-01, eta: 9:31:42, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4131\n2023-01-04 18:04:30,153 - mmcls - INFO - Epoch [23][1900/5005]\tlr: 1.000e-01, eta: 9:31:35, time: 0.092, data_time: 0.033, memory: 928, loss: 5.3974\n2023-01-04 18:04:39,228 - mmcls - INFO - Epoch [23][2000/5005]\tlr: 1.000e-01, eta: 9:31:27, time: 0.091, data_time: 0.009, memory: 928, loss: 5.3972\n2023-01-04 18:04:48,215 - mmcls - INFO - Epoch [23][2100/5005]\tlr: 1.000e-01, eta: 9:31:18, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4060\n2023-01-04 18:04:57,217 - mmcls - INFO - Epoch [23][2200/5005]\tlr: 1.000e-01, eta: 9:31:10, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4008\n2023-01-04 18:05:06,455 - mmcls - INFO - Epoch [23][2300/5005]\tlr: 1.000e-01, eta: 9:31:03, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4051\n2023-01-04 18:05:15,278 - mmcls - INFO - Epoch [23][2400/5005]\tlr: 1.000e-01, eta: 9:30:54, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3964\n2023-01-04 18:05:24,236 - mmcls - INFO - Epoch [23][2500/5005]\tlr: 1.000e-01, eta: 9:30:46, time: 0.090, data_time: 0.016, memory: 928, loss: 5.4027\n2023-01-04 18:05:33,250 - mmcls - INFO - Epoch [23][2600/5005]\tlr: 1.000e-01, eta: 9:30:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3985\n2023-01-04 18:05:41,894 - mmcls - INFO - Epoch [23][2700/5005]\tlr: 1.000e-01, eta: 9:30:28, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4065\n2023-01-04 18:05:50,828 - mmcls - INFO - Epoch [23][2800/5005]\tlr: 1.000e-01, eta: 9:30:19, time: 0.089, data_time: 0.017, memory: 928, loss: 5.3860\n2023-01-04 18:05:59,415 - mmcls - INFO - Epoch [23][2900/5005]\tlr: 1.000e-01, eta: 9:30:10, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4068\n2023-01-04 18:06:08,127 - mmcls - INFO - Epoch [23][3000/5005]\tlr: 1.000e-01, eta: 9:30:01, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4197\n2023-01-04 18:06:16,986 - mmcls - INFO - Epoch [23][3100/5005]\tlr: 1.000e-01, eta: 9:29:52, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4141\n2023-01-04 18:06:25,954 - mmcls - INFO - Epoch [23][3200/5005]\tlr: 1.000e-01, eta: 9:29:43, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4140\n2023-01-04 18:06:34,655 - mmcls - INFO - Epoch [23][3300/5005]\tlr: 1.000e-01, eta: 9:29:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4035\n2023-01-04 18:06:43,311 - mmcls - INFO - Epoch [23][3400/5005]\tlr: 1.000e-01, eta: 9:29:25, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4059\n2023-01-04 18:06:51,768 - mmcls - INFO - Epoch [23][3500/5005]\tlr: 1.000e-01, eta: 9:29:15, time: 0.084, data_time: 0.010, memory: 928, loss: 5.4042\n2023-01-04 18:07:00,501 - mmcls - INFO - Epoch [23][3600/5005]\tlr: 1.000e-01, eta: 9:29:06, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3993\n2023-01-04 18:07:08,995 - mmcls - INFO - Epoch [23][3700/5005]\tlr: 1.000e-01, eta: 9:28:56, time: 0.085, data_time: 0.005, memory: 928, loss: 5.3901\n2023-01-04 18:07:17,656 - mmcls - INFO - Epoch [23][3800/5005]\tlr: 1.000e-01, eta: 9:28:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3985\n2023-01-04 18:07:26,310 - mmcls - INFO - Epoch [23][3900/5005]\tlr: 1.000e-01, eta: 9:28:37, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3923\n2023-01-04 18:07:35,172 - mmcls - INFO - Epoch [23][4000/5005]\tlr: 1.000e-01, eta: 9:28:28, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3730\n2023-01-04 18:07:43,839 - mmcls - INFO - Epoch [23][4100/5005]\tlr: 1.000e-01, eta: 9:28:19, time: 0.087, data_time: 0.023, memory: 928, loss: 5.3854\n2023-01-04 18:07:52,689 - mmcls - INFO - Epoch [23][4200/5005]\tlr: 1.000e-01, eta: 9:28:10, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3674\n2023-01-04 18:08:01,438 - mmcls - INFO - Epoch [23][4300/5005]\tlr: 1.000e-01, eta: 9:28:01, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3765\n2023-01-04 18:08:10,006 - mmcls - INFO - Epoch [23][4400/5005]\tlr: 1.000e-01, eta: 9:27:51, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3885\n2023-01-04 18:08:18,450 - mmcls - INFO - Epoch [23][4500/5005]\tlr: 1.000e-01, eta: 9:27:41, time: 0.084, data_time: 0.001, memory: 928, loss: 5.3972\n2023-01-04 18:08:27,079 - mmcls - INFO - Epoch [23][4600/5005]\tlr: 1.000e-01, eta: 9:27:31, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3883\n2023-01-04 18:08:35,819 - mmcls - INFO - Epoch [23][4700/5005]\tlr: 1.000e-01, eta: 9:27:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3835\n2023-01-04 18:08:44,374 - mmcls - INFO - Epoch [23][4800/5005]\tlr: 1.000e-01, eta: 9:27:13, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4085\n2023-01-04 18:08:52,915 - mmcls - INFO - Epoch [23][4900/5005]\tlr: 1.000e-01, eta: 9:27:03, time: 0.085, data_time: 0.009, memory: 928, loss: 5.3763\n2023-01-04 18:09:01,763 - mmcls - INFO - Epoch [23][5000/5005]\tlr: 1.000e-01, eta: 9:26:54, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3871\n2023-01-04 18:09:25,613 - mmcls - INFO - Epoch(val) [23][196]\taccuracy_top-1: 5.1260, accuracy_top-5: 13.4180\n2023-01-04 18:09:36,819 - mmcls - INFO - Epoch [24][100/5005]\tlr: 1.000e-01, eta: 9:26:51, time: 0.112, data_time: 0.040, memory: 928, loss: 5.3840\n2023-01-04 18:09:45,353 - mmcls - INFO - Epoch [24][200/5005]\tlr: 1.000e-01, eta: 9:26:41, time: 0.085, data_time: 0.042, memory: 928, loss: 5.3884\n2023-01-04 18:09:54,040 - mmcls - INFO - Epoch [24][300/5005]\tlr: 1.000e-01, eta: 9:26:32, time: 0.087, data_time: 0.029, memory: 928, loss: 5.3907\n2023-01-04 18:10:02,755 - mmcls - INFO - Epoch [24][400/5005]\tlr: 1.000e-01, eta: 9:26:23, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3966\n2023-01-04 18:10:11,769 - mmcls - INFO - Epoch [24][500/5005]\tlr: 1.000e-01, eta: 9:26:15, time: 0.090, data_time: 0.005, memory: 928, loss: 5.3844\n2023-01-04 18:10:20,506 - mmcls - INFO - Epoch [24][600/5005]\tlr: 1.000e-01, eta: 9:26:06, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3913\n2023-01-04 18:10:29,279 - mmcls - INFO - Epoch [24][700/5005]\tlr: 1.000e-01, eta: 9:25:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.3846\n2023-01-04 18:10:38,184 - mmcls - INFO - Epoch [24][800/5005]\tlr: 1.000e-01, eta: 9:25:48, time: 0.089, data_time: 0.019, memory: 928, loss: 5.3835\n2023-01-04 18:10:46,832 - mmcls - INFO - Epoch [24][900/5005]\tlr: 1.000e-01, eta: 9:25:39, time: 0.086, data_time: 0.029, memory: 928, loss: 5.3813\n2023-01-04 18:10:55,595 - mmcls - INFO - Epoch [24][1000/5005]\tlr: 1.000e-01, eta: 9:25:30, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4071\n2023-01-04 18:11:04,404 - mmcls - INFO - Epoch [24][1100/5005]\tlr: 1.000e-01, eta: 9:25:21, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4058\n2023-01-04 18:11:12,839 - mmcls - INFO - Epoch [24][1200/5005]\tlr: 1.000e-01, eta: 9:25:10, time: 0.084, data_time: 0.004, memory: 928, loss: 5.3898\n2023-01-04 18:11:21,705 - mmcls - INFO - Epoch [24][1300/5005]\tlr: 1.000e-01, eta: 9:25:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3774\n2023-01-04 18:11:30,514 - mmcls - INFO - Epoch [24][1400/5005]\tlr: 1.000e-01, eta: 9:24:53, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3857\n2023-01-04 18:11:39,189 - mmcls - INFO - Epoch [24][1500/5005]\tlr: 1.000e-01, eta: 9:24:44, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4035\n2023-01-04 18:11:48,070 - mmcls - INFO - Epoch [24][1600/5005]\tlr: 1.000e-01, eta: 9:24:35, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3864\n2023-01-04 18:11:56,870 - mmcls - INFO - Epoch [24][1700/5005]\tlr: 1.000e-01, eta: 9:24:26, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4014\n2023-01-04 18:12:05,678 - mmcls - INFO - Epoch [24][1800/5005]\tlr: 1.000e-01, eta: 9:24:17, time: 0.088, data_time: 0.022, memory: 928, loss: 5.3961\n2023-01-04 18:12:14,314 - mmcls - INFO - Epoch [24][1900/5005]\tlr: 1.000e-01, eta: 9:24:08, time: 0.086, data_time: 0.009, memory: 928, loss: 5.3977\n2023-01-04 18:12:23,207 - mmcls - INFO - Epoch [24][2000/5005]\tlr: 1.000e-01, eta: 9:23:59, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3971\n2023-01-04 18:12:32,327 - mmcls - INFO - Epoch [24][2100/5005]\tlr: 1.000e-01, eta: 9:23:51, time: 0.091, data_time: 0.004, memory: 928, loss: 5.3698\n2023-01-04 18:12:41,070 - mmcls - INFO - Epoch [24][2200/5005]\tlr: 1.000e-01, eta: 9:23:42, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3925\n2023-01-04 18:12:49,713 - mmcls - INFO - Epoch [24][2300/5005]\tlr: 1.000e-01, eta: 9:23:33, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3984\n2023-01-04 18:12:58,533 - mmcls - INFO - Epoch [24][2400/5005]\tlr: 1.000e-01, eta: 9:23:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3947\n2023-01-04 18:13:07,033 - mmcls - INFO - Epoch [24][2500/5005]\tlr: 1.000e-01, eta: 9:23:14, time: 0.085, data_time: 0.008, memory: 928, loss: 5.3935\n2023-01-04 18:13:15,796 - mmcls - INFO - Epoch [24][2600/5005]\tlr: 1.000e-01, eta: 9:23:05, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3984\n2023-01-04 18:13:24,423 - mmcls - INFO - Epoch [24][2700/5005]\tlr: 1.000e-01, eta: 9:22:55, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3907\n2023-01-04 18:13:33,213 - mmcls - INFO - Epoch [24][2800/5005]\tlr: 1.000e-01, eta: 9:22:46, time: 0.088, data_time: 0.027, memory: 928, loss: 5.3955\n2023-01-04 18:13:42,294 - mmcls - INFO - Epoch [24][2900/5005]\tlr: 1.000e-01, eta: 9:22:38, time: 0.091, data_time: 0.005, memory: 928, loss: 5.3672\n2023-01-04 18:13:51,115 - mmcls - INFO - Epoch [24][3000/5005]\tlr: 1.000e-01, eta: 9:22:30, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3905\n2023-01-04 18:14:00,040 - mmcls - INFO - Epoch [24][3100/5005]\tlr: 1.000e-01, eta: 9:22:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3855\n2023-01-04 18:14:08,641 - mmcls - INFO - Epoch [24][3200/5005]\tlr: 1.000e-01, eta: 9:22:12, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3970\n2023-01-04 18:14:17,254 - mmcls - INFO - Epoch [24][3300/5005]\tlr: 1.000e-01, eta: 9:22:02, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3869\n2023-01-04 18:14:25,984 - mmcls - INFO - Epoch [24][3400/5005]\tlr: 1.000e-01, eta: 9:21:53, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3992\n2023-01-04 18:14:34,652 - mmcls - INFO - Epoch [24][3500/5005]\tlr: 1.000e-01, eta: 9:21:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3880\n2023-01-04 18:14:43,196 - mmcls - INFO - Epoch [24][3600/5005]\tlr: 1.000e-01, eta: 9:21:34, time: 0.085, data_time: 0.019, memory: 928, loss: 5.3818\n2023-01-04 18:14:52,183 - mmcls - INFO - Epoch [24][3700/5005]\tlr: 1.000e-01, eta: 9:21:26, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4001\n2023-01-04 18:15:01,392 - mmcls - INFO - Epoch [24][3800/5005]\tlr: 1.000e-01, eta: 9:21:18, time: 0.092, data_time: 0.007, memory: 928, loss: 5.3779\n2023-01-04 18:15:10,293 - mmcls - INFO - Epoch [24][3900/5005]\tlr: 1.000e-01, eta: 9:21:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4144\n2023-01-04 18:15:19,059 - mmcls - INFO - Epoch [24][4000/5005]\tlr: 1.000e-01, eta: 9:21:00, time: 0.088, data_time: 0.000, memory: 928, loss: 5.3858\n2023-01-04 18:15:28,429 - mmcls - INFO - Epoch [24][4100/5005]\tlr: 1.000e-01, eta: 9:20:53, time: 0.094, data_time: 0.032, memory: 928, loss: 5.3903\n2023-01-04 18:15:37,549 - mmcls - INFO - Epoch [24][4200/5005]\tlr: 1.000e-01, eta: 9:20:45, time: 0.091, data_time: 0.025, memory: 928, loss: 5.3683\n2023-01-04 18:15:46,306 - mmcls - INFO - Epoch [24][4300/5005]\tlr: 1.000e-01, eta: 9:20:36, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3871\n2023-01-04 18:15:55,188 - mmcls - INFO - Epoch [24][4400/5005]\tlr: 1.000e-01, eta: 9:20:28, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3826\n2023-01-04 18:16:04,405 - mmcls - INFO - Epoch [24][4500/5005]\tlr: 1.000e-01, eta: 9:20:20, time: 0.092, data_time: 0.001, memory: 928, loss: 5.3702\n2023-01-04 18:16:13,293 - mmcls - INFO - Epoch [24][4600/5005]\tlr: 1.000e-01, eta: 9:20:11, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3964\n2023-01-04 18:16:22,090 - mmcls - INFO - Epoch [24][4700/5005]\tlr: 1.000e-01, eta: 9:20:02, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3935\n2023-01-04 18:16:30,802 - mmcls - INFO - Epoch [24][4800/5005]\tlr: 1.000e-01, eta: 9:19:53, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3951\n2023-01-04 18:16:39,661 - mmcls - INFO - Epoch [24][4900/5005]\tlr: 1.000e-01, eta: 9:19:45, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4091\n2023-01-04 18:16:48,484 - mmcls - INFO - Epoch [24][5000/5005]\tlr: 1.000e-01, eta: 9:19:36, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3854\n2023-01-04 18:17:12,103 - mmcls - INFO - Epoch(val) [24][196]\taccuracy_top-1: 4.0560, accuracy_top-5: 11.3160\n2023-01-04 18:17:22,964 - mmcls - INFO - Epoch [25][100/5005]\tlr: 1.000e-01, eta: 9:19:31, time: 0.108, data_time: 0.043, memory: 928, loss: 5.3704\n2023-01-04 18:17:31,848 - mmcls - INFO - Epoch [25][200/5005]\tlr: 1.000e-01, eta: 9:19:23, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3864\n2023-01-04 18:17:40,790 - mmcls - INFO - Epoch [25][300/5005]\tlr: 1.000e-01, eta: 9:19:14, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3735\n2023-01-04 18:17:49,558 - mmcls - INFO - Epoch [25][400/5005]\tlr: 1.000e-01, eta: 9:19:05, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3943\n2023-01-04 18:17:58,185 - mmcls - INFO - Epoch [25][500/5005]\tlr: 1.000e-01, eta: 9:18:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3699\n2023-01-04 18:18:07,015 - mmcls - INFO - Epoch [25][600/5005]\tlr: 1.000e-01, eta: 9:18:47, time: 0.088, data_time: 0.024, memory: 928, loss: 5.4007\n2023-01-04 18:18:15,558 - mmcls - INFO - Epoch [25][700/5005]\tlr: 1.000e-01, eta: 9:18:37, time: 0.086, data_time: 0.029, memory: 928, loss: 5.3768\n2023-01-04 18:18:24,473 - mmcls - INFO - Epoch [25][800/5005]\tlr: 1.000e-01, eta: 9:18:29, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3905\n2023-01-04 18:18:33,124 - mmcls - INFO - Epoch [25][900/5005]\tlr: 1.000e-01, eta: 9:18:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3918\n2023-01-04 18:18:41,999 - mmcls - INFO - Epoch [25][1000/5005]\tlr: 1.000e-01, eta: 9:18:11, time: 0.089, data_time: 0.032, memory: 928, loss: 5.3763\n2023-01-04 18:18:51,116 - mmcls - INFO - Epoch [25][1100/5005]\tlr: 1.000e-01, eta: 9:18:03, time: 0.091, data_time: 0.010, memory: 928, loss: 5.3902\n2023-01-04 18:18:59,928 - mmcls - INFO - Epoch [25][1200/5005]\tlr: 1.000e-01, eta: 9:17:54, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3740\n2023-01-04 18:19:08,545 - mmcls - INFO - Epoch [25][1300/5005]\tlr: 1.000e-01, eta: 9:17:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4090\n2023-01-04 18:19:17,257 - mmcls - INFO - Epoch [25][1400/5005]\tlr: 1.000e-01, eta: 9:17:35, time: 0.087, data_time: 0.026, memory: 928, loss: 5.3799\n2023-01-04 18:19:25,763 - mmcls - INFO - Epoch [25][1500/5005]\tlr: 1.000e-01, eta: 9:17:25, time: 0.085, data_time: 0.010, memory: 928, loss: 5.3783\n2023-01-04 18:19:34,411 - mmcls - INFO - Epoch [25][1600/5005]\tlr: 1.000e-01, eta: 9:17:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3953\n2023-01-04 18:19:43,204 - mmcls - INFO - Epoch [25][1700/5005]\tlr: 1.000e-01, eta: 9:17:07, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3949\n2023-01-04 18:19:51,860 - mmcls - INFO - Epoch [25][1800/5005]\tlr: 1.000e-01, eta: 9:16:58, time: 0.087, data_time: 0.012, memory: 928, loss: 5.3810\n2023-01-04 18:20:00,778 - mmcls - INFO - Epoch [25][1900/5005]\tlr: 1.000e-01, eta: 9:16:49, time: 0.089, data_time: 0.008, memory: 928, loss: 5.3822\n2023-01-04 18:20:09,243 - mmcls - INFO - Epoch [25][2000/5005]\tlr: 1.000e-01, eta: 9:16:39, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4042\n2023-01-04 18:20:17,813 - mmcls - INFO - Epoch [25][2100/5005]\tlr: 1.000e-01, eta: 9:16:30, time: 0.086, data_time: 0.006, memory: 928, loss: 5.3792\n2023-01-04 18:20:26,534 - mmcls - INFO - Epoch [25][2200/5005]\tlr: 1.000e-01, eta: 9:16:20, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3936\n2023-01-04 18:20:35,195 - mmcls - INFO - Epoch [25][2300/5005]\tlr: 1.000e-01, eta: 9:16:11, time: 0.087, data_time: 0.021, memory: 928, loss: 5.4070\n2023-01-04 18:20:43,834 - mmcls - INFO - Epoch [25][2400/5005]\tlr: 1.000e-01, eta: 9:16:02, time: 0.086, data_time: 0.039, memory: 928, loss: 5.4030\n2023-01-04 18:20:52,529 - mmcls - INFO - Epoch [25][2500/5005]\tlr: 1.000e-01, eta: 9:15:52, time: 0.087, data_time: 0.008, memory: 928, loss: 5.3714\n2023-01-04 18:21:01,206 - mmcls - INFO - Epoch [25][2600/5005]\tlr: 1.000e-01, eta: 9:15:43, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4014\n2023-01-04 18:21:09,962 - mmcls - INFO - Epoch [25][2700/5005]\tlr: 1.000e-01, eta: 9:15:34, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3968\n2023-01-04 18:21:18,682 - mmcls - INFO - Epoch [25][2800/5005]\tlr: 1.000e-01, eta: 9:15:25, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3898\n2023-01-04 18:21:27,465 - mmcls - INFO - Epoch [25][2900/5005]\tlr: 1.000e-01, eta: 9:15:16, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3944\n2023-01-04 18:21:36,103 - mmcls - INFO - Epoch [25][3000/5005]\tlr: 1.000e-01, eta: 9:15:06, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3903\n2023-01-04 18:21:44,657 - mmcls - INFO - Epoch [25][3100/5005]\tlr: 1.000e-01, eta: 9:14:57, time: 0.086, data_time: 0.005, memory: 928, loss: 5.3584\n2023-01-04 18:21:53,537 - mmcls - INFO - Epoch [25][3200/5005]\tlr: 1.000e-01, eta: 9:14:48, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3846\n2023-01-04 18:22:02,385 - mmcls - INFO - Epoch [25][3300/5005]\tlr: 1.000e-01, eta: 9:14:39, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4222\n2023-01-04 18:22:11,230 - mmcls - INFO - Epoch [25][3400/5005]\tlr: 1.000e-01, eta: 9:14:31, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3983\n2023-01-04 18:22:19,947 - mmcls - INFO - Epoch [25][3500/5005]\tlr: 1.000e-01, eta: 9:14:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.3671\n2023-01-04 18:22:28,639 - mmcls - INFO - Epoch [25][3600/5005]\tlr: 1.000e-01, eta: 9:14:12, time: 0.087, data_time: 0.014, memory: 928, loss: 5.3806\n2023-01-04 18:22:37,241 - mmcls - INFO - Epoch [25][3700/5005]\tlr: 1.000e-01, eta: 9:14:03, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4058\n2023-01-04 18:22:45,930 - mmcls - INFO - Epoch [25][3800/5005]\tlr: 1.000e-01, eta: 9:13:53, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3612\n2023-01-04 18:22:54,454 - mmcls - INFO - Epoch [25][3900/5005]\tlr: 1.000e-01, eta: 9:13:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3730\n2023-01-04 18:23:03,414 - mmcls - INFO - Epoch [25][4000/5005]\tlr: 1.000e-01, eta: 9:13:35, time: 0.090, data_time: 0.012, memory: 928, loss: 5.3969\n2023-01-04 18:23:12,144 - mmcls - INFO - Epoch [25][4100/5005]\tlr: 1.000e-01, eta: 9:13:26, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3877\n2023-01-04 18:23:21,012 - mmcls - INFO - Epoch [25][4200/5005]\tlr: 1.000e-01, eta: 9:13:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3754\n2023-01-04 18:23:29,914 - mmcls - INFO - Epoch [25][4300/5005]\tlr: 1.000e-01, eta: 9:13:09, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3717\n2023-01-04 18:23:38,847 - mmcls - INFO - Epoch [25][4400/5005]\tlr: 1.000e-01, eta: 9:13:00, time: 0.089, data_time: 0.018, memory: 928, loss: 5.3945\n2023-01-04 18:23:47,644 - mmcls - INFO - Epoch [25][4500/5005]\tlr: 1.000e-01, eta: 9:12:52, time: 0.088, data_time: 0.024, memory: 928, loss: 5.3747\n2023-01-04 18:23:56,512 - mmcls - INFO - Epoch [25][4600/5005]\tlr: 1.000e-01, eta: 9:12:43, time: 0.089, data_time: 0.042, memory: 928, loss: 5.3712\n2023-01-04 18:24:05,161 - mmcls - INFO - Epoch [25][4700/5005]\tlr: 1.000e-01, eta: 9:12:33, time: 0.086, data_time: 0.024, memory: 928, loss: 5.3906\n2023-01-04 18:24:13,679 - mmcls - INFO - Epoch [25][4800/5005]\tlr: 1.000e-01, eta: 9:12:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3820\n2023-01-04 18:24:22,430 - mmcls - INFO - Epoch [25][4900/5005]\tlr: 1.000e-01, eta: 9:12:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3939\n2023-01-04 18:24:31,393 - mmcls - INFO - Epoch [25][5000/5005]\tlr: 1.000e-01, eta: 9:12:06, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3851\n2023-01-04 18:24:54,191 - mmcls - INFO - Epoch(val) [25][196]\taccuracy_top-1: 4.2000, accuracy_top-5: 11.1960\n2023-01-04 18:25:05,207 - mmcls - INFO - Epoch [26][100/5005]\tlr: 1.000e-01, eta: 9:12:02, time: 0.110, data_time: 0.071, memory: 928, loss: 5.3468\n2023-01-04 18:25:14,289 - mmcls - INFO - Epoch [26][200/5005]\tlr: 1.000e-01, eta: 9:11:54, time: 0.091, data_time: 0.042, memory: 928, loss: 5.3834\n2023-01-04 18:25:22,883 - mmcls - INFO - Epoch [26][300/5005]\tlr: 1.000e-01, eta: 9:11:45, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3680\n2023-01-04 18:25:31,592 - mmcls - INFO - Epoch [26][400/5005]\tlr: 1.000e-01, eta: 9:11:35, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4018\n2023-01-04 18:25:40,442 - mmcls - INFO - Epoch [26][500/5005]\tlr: 1.000e-01, eta: 9:11:27, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4029\n2023-01-04 18:25:49,238 - mmcls - INFO - Epoch [26][600/5005]\tlr: 1.000e-01, eta: 9:11:18, time: 0.088, data_time: 0.024, memory: 928, loss: 5.3931\n2023-01-04 18:25:57,695 - mmcls - INFO - Epoch [26][700/5005]\tlr: 1.000e-01, eta: 9:11:08, time: 0.084, data_time: 0.003, memory: 928, loss: 5.3938\n2023-01-04 18:26:06,934 - mmcls - INFO - Epoch [26][800/5005]\tlr: 1.000e-01, eta: 9:11:00, time: 0.093, data_time: 0.007, memory: 928, loss: 5.3909\n2023-01-04 18:26:15,762 - mmcls - INFO - Epoch [26][900/5005]\tlr: 1.000e-01, eta: 9:10:51, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3885\n2023-01-04 18:26:24,599 - mmcls - INFO - Epoch [26][1000/5005]\tlr: 1.000e-01, eta: 9:10:43, time: 0.088, data_time: 0.013, memory: 928, loss: 5.3812\n2023-01-04 18:26:33,362 - mmcls - INFO - Epoch [26][1100/5005]\tlr: 1.000e-01, eta: 9:10:34, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3779\n2023-01-04 18:26:42,494 - mmcls - INFO - Epoch [26][1200/5005]\tlr: 1.000e-01, eta: 9:10:26, time: 0.091, data_time: 0.029, memory: 928, loss: 5.3847\n2023-01-04 18:26:51,459 - mmcls - INFO - Epoch [26][1300/5005]\tlr: 1.000e-01, eta: 9:10:17, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3889\n2023-01-04 18:27:00,104 - mmcls - INFO - Epoch [26][1400/5005]\tlr: 1.000e-01, eta: 9:10:08, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3544\n2023-01-04 18:27:08,760 - mmcls - INFO - Epoch [26][1500/5005]\tlr: 1.000e-01, eta: 9:09:59, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3843\n2023-01-04 18:27:17,401 - mmcls - INFO - Epoch [26][1600/5005]\tlr: 1.000e-01, eta: 9:09:49, time: 0.086, data_time: 0.014, memory: 928, loss: 5.3992\n2023-01-04 18:27:26,107 - mmcls - INFO - Epoch [26][1700/5005]\tlr: 1.000e-01, eta: 9:09:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3806\n2023-01-04 18:27:34,722 - mmcls - INFO - Epoch [26][1800/5005]\tlr: 1.000e-01, eta: 9:09:30, time: 0.086, data_time: 0.011, memory: 928, loss: 5.3790\n2023-01-04 18:27:43,718 - mmcls - INFO - Epoch [26][1900/5005]\tlr: 1.000e-01, eta: 9:09:22, time: 0.090, data_time: 0.004, memory: 928, loss: 5.3843\n2023-01-04 18:27:52,331 - mmcls - INFO - Epoch [26][2000/5005]\tlr: 1.000e-01, eta: 9:09:13, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3756\n2023-01-04 18:28:01,169 - mmcls - INFO - Epoch [26][2100/5005]\tlr: 1.000e-01, eta: 9:09:04, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3963\n2023-01-04 18:28:09,849 - mmcls - INFO - Epoch [26][2200/5005]\tlr: 1.000e-01, eta: 9:08:55, time: 0.087, data_time: 0.003, memory: 928, loss: 5.3784\n2023-01-04 18:28:18,788 - mmcls - INFO - Epoch [26][2300/5005]\tlr: 1.000e-01, eta: 9:08:46, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3738\n2023-01-04 18:28:27,455 - mmcls - INFO - Epoch [26][2400/5005]\tlr: 1.000e-01, eta: 9:08:37, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3864\n2023-01-04 18:28:36,353 - mmcls - INFO - Epoch [26][2500/5005]\tlr: 1.000e-01, eta: 9:08:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3845\n2023-01-04 18:28:44,916 - mmcls - INFO - Epoch [26][2600/5005]\tlr: 1.000e-01, eta: 9:08:19, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3807\n2023-01-04 18:28:53,604 - mmcls - INFO - Epoch [26][2700/5005]\tlr: 1.000e-01, eta: 9:08:09, time: 0.087, data_time: 0.009, memory: 928, loss: 5.3929\n2023-01-04 18:29:02,442 - mmcls - INFO - Epoch [26][2800/5005]\tlr: 1.000e-01, eta: 9:08:01, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3996\n2023-01-04 18:29:11,397 - mmcls - INFO - Epoch [26][2900/5005]\tlr: 1.000e-01, eta: 9:07:52, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3863\n2023-01-04 18:29:20,064 - mmcls - INFO - Epoch [26][3000/5005]\tlr: 1.000e-01, eta: 9:07:43, time: 0.087, data_time: 0.014, memory: 928, loss: 5.3790\n2023-01-04 18:29:29,042 - mmcls - INFO - Epoch [26][3100/5005]\tlr: 1.000e-01, eta: 9:07:35, time: 0.090, data_time: 0.019, memory: 928, loss: 5.3648\n2023-01-04 18:29:37,877 - mmcls - INFO - Epoch [26][3200/5005]\tlr: 1.000e-01, eta: 9:07:26, time: 0.088, data_time: 0.021, memory: 928, loss: 5.3981\n2023-01-04 18:29:46,926 - mmcls - INFO - Epoch [26][3300/5005]\tlr: 1.000e-01, eta: 9:07:18, time: 0.091, data_time: 0.003, memory: 928, loss: 5.3792\n2023-01-04 18:29:55,984 - mmcls - INFO - Epoch [26][3400/5005]\tlr: 1.000e-01, eta: 9:07:09, time: 0.091, data_time: 0.027, memory: 928, loss: 5.3846\n2023-01-04 18:30:04,702 - mmcls - INFO - Epoch [26][3500/5005]\tlr: 1.000e-01, eta: 9:07:00, time: 0.087, data_time: 0.027, memory: 928, loss: 5.4096\n2023-01-04 18:30:13,158 - mmcls - INFO - Epoch [26][3600/5005]\tlr: 1.000e-01, eta: 9:06:50, time: 0.085, data_time: 0.004, memory: 928, loss: 5.4165\n2023-01-04 18:30:21,808 - mmcls - INFO - Epoch [26][3700/5005]\tlr: 1.000e-01, eta: 9:06:41, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3901\n2023-01-04 18:30:30,393 - mmcls - INFO - Epoch [26][3800/5005]\tlr: 1.000e-01, eta: 9:06:31, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4127\n2023-01-04 18:30:38,863 - mmcls - INFO - Epoch [26][3900/5005]\tlr: 1.000e-01, eta: 9:06:22, time: 0.085, data_time: 0.028, memory: 928, loss: 5.3873\n2023-01-04 18:30:47,481 - mmcls - INFO - Epoch [26][4000/5005]\tlr: 1.000e-01, eta: 9:06:12, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3677\n2023-01-04 18:30:56,101 - mmcls - INFO - Epoch [26][4100/5005]\tlr: 1.000e-01, eta: 9:06:03, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3885\n2023-01-04 18:31:05,147 - mmcls - INFO - Epoch [26][4200/5005]\tlr: 1.000e-01, eta: 9:05:55, time: 0.090, data_time: 0.012, memory: 928, loss: 5.3868\n2023-01-04 18:31:13,841 - mmcls - INFO - Epoch [26][4300/5005]\tlr: 1.000e-01, eta: 9:05:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3804\n2023-01-04 18:31:22,577 - mmcls - INFO - Epoch [26][4400/5005]\tlr: 1.000e-01, eta: 9:05:36, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3834\n2023-01-04 18:31:31,410 - mmcls - INFO - Epoch [26][4500/5005]\tlr: 1.000e-01, eta: 9:05:28, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3638\n2023-01-04 18:31:40,324 - mmcls - INFO - Epoch [26][4600/5005]\tlr: 1.000e-01, eta: 9:05:19, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3844\n2023-01-04 18:31:49,368 - mmcls - INFO - Epoch [26][4700/5005]\tlr: 1.000e-01, eta: 9:05:11, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3777\n2023-01-04 18:31:58,161 - mmcls - INFO - Epoch [26][4800/5005]\tlr: 1.000e-01, eta: 9:05:02, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3926\n2023-01-04 18:32:06,939 - mmcls - INFO - Epoch [26][4900/5005]\tlr: 1.000e-01, eta: 9:04:53, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3962\n2023-01-04 18:32:15,565 - mmcls - INFO - Epoch [26][5000/5005]\tlr: 1.000e-01, eta: 9:04:43, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4013\n2023-01-04 18:32:38,348 - mmcls - INFO - Epoch(val) [26][196]\taccuracy_top-1: 5.4520, accuracy_top-5: 14.4220\n2023-01-04 18:32:49,150 - mmcls - INFO - Epoch [27][100/5005]\tlr: 1.000e-01, eta: 9:04:39, time: 0.108, data_time: 0.052, memory: 928, loss: 5.3623\n2023-01-04 18:32:57,877 - mmcls - INFO - Epoch [27][200/5005]\tlr: 1.000e-01, eta: 9:04:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3792\n2023-01-04 18:33:06,552 - mmcls - INFO - Epoch [27][300/5005]\tlr: 1.000e-01, eta: 9:04:20, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3869\n2023-01-04 18:33:15,484 - mmcls - INFO - Epoch [27][400/5005]\tlr: 1.000e-01, eta: 9:04:12, time: 0.089, data_time: 0.009, memory: 928, loss: 5.3852\n2023-01-04 18:33:24,365 - mmcls - INFO - Epoch [27][500/5005]\tlr: 1.000e-01, eta: 9:04:03, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3943\n2023-01-04 18:33:33,373 - mmcls - INFO - Epoch [27][600/5005]\tlr: 1.000e-01, eta: 9:03:55, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3768\n2023-01-04 18:33:42,202 - mmcls - INFO - Epoch [27][700/5005]\tlr: 1.000e-01, eta: 9:03:46, time: 0.088, data_time: 0.023, memory: 928, loss: 5.3653\n2023-01-04 18:33:50,721 - mmcls - INFO - Epoch [27][800/5005]\tlr: 1.000e-01, eta: 9:03:36, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3819\n2023-01-04 18:33:59,506 - mmcls - INFO - Epoch [27][900/5005]\tlr: 1.000e-01, eta: 9:03:27, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3906\n2023-01-04 18:34:08,463 - mmcls - INFO - Epoch [27][1000/5005]\tlr: 1.000e-01, eta: 9:03:19, time: 0.090, data_time: 0.015, memory: 928, loss: 5.3798\n2023-01-04 18:34:17,313 - mmcls - INFO - Epoch [27][1100/5005]\tlr: 1.000e-01, eta: 9:03:10, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3909\n2023-01-04 18:34:26,160 - mmcls - INFO - Epoch [27][1200/5005]\tlr: 1.000e-01, eta: 9:03:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3894\n2023-01-04 18:34:35,106 - mmcls - INFO - Epoch [27][1300/5005]\tlr: 1.000e-01, eta: 9:02:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3748\n2023-01-04 18:34:43,946 - mmcls - INFO - Epoch [27][1400/5005]\tlr: 1.000e-01, eta: 9:02:44, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3788\n2023-01-04 18:34:52,876 - mmcls - INFO - Epoch [27][1500/5005]\tlr: 1.000e-01, eta: 9:02:35, time: 0.089, data_time: 0.027, memory: 928, loss: 5.3891\n2023-01-04 18:35:01,567 - mmcls - INFO - Epoch [27][1600/5005]\tlr: 1.000e-01, eta: 9:02:26, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3459\n2023-01-04 18:35:10,400 - mmcls - INFO - Epoch [27][1700/5005]\tlr: 1.000e-01, eta: 9:02:17, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3700\n2023-01-04 18:35:19,285 - mmcls - INFO - Epoch [27][1800/5005]\tlr: 1.000e-01, eta: 9:02:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3858\n2023-01-04 18:35:28,231 - mmcls - INFO - Epoch [27][1900/5005]\tlr: 1.000e-01, eta: 9:02:00, time: 0.090, data_time: 0.036, memory: 928, loss: 5.3874\n2023-01-04 18:35:37,093 - mmcls - INFO - Epoch [27][2000/5005]\tlr: 1.000e-01, eta: 9:01:52, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3710\n2023-01-04 18:35:45,917 - mmcls - INFO - Epoch [27][2100/5005]\tlr: 1.000e-01, eta: 9:01:43, time: 0.088, data_time: 0.027, memory: 928, loss: 5.3794\n2023-01-04 18:35:54,512 - mmcls - INFO - Epoch [27][2200/5005]\tlr: 1.000e-01, eta: 9:01:33, time: 0.086, data_time: 0.034, memory: 928, loss: 5.3929\n2023-01-04 18:36:03,347 - mmcls - INFO - Epoch [27][2300/5005]\tlr: 1.000e-01, eta: 9:01:24, time: 0.088, data_time: 0.025, memory: 928, loss: 5.3839\n2023-01-04 18:36:11,826 - mmcls - INFO - Epoch [27][2400/5005]\tlr: 1.000e-01, eta: 9:01:15, time: 0.085, data_time: 0.012, memory: 928, loss: 5.3631\n2023-01-04 18:36:20,525 - mmcls - INFO - Epoch [27][2500/5005]\tlr: 1.000e-01, eta: 9:01:05, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3841\n2023-01-04 18:36:29,059 - mmcls - INFO - Epoch [27][2600/5005]\tlr: 1.000e-01, eta: 9:00:56, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3959\n2023-01-04 18:36:38,038 - mmcls - INFO - Epoch [27][2700/5005]\tlr: 1.000e-01, eta: 9:00:47, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3870\n2023-01-04 18:36:46,751 - mmcls - INFO - Epoch [27][2800/5005]\tlr: 1.000e-01, eta: 9:00:38, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3873\n2023-01-04 18:36:55,589 - mmcls - INFO - Epoch [27][2900/5005]\tlr: 1.000e-01, eta: 9:00:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3926\n2023-01-04 18:37:04,390 - mmcls - INFO - Epoch [27][3000/5005]\tlr: 1.000e-01, eta: 9:00:21, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4006\n2023-01-04 18:37:12,901 - mmcls - INFO - Epoch [27][3100/5005]\tlr: 1.000e-01, eta: 9:00:11, time: 0.085, data_time: 0.016, memory: 928, loss: 5.4055\n2023-01-04 18:37:21,702 - mmcls - INFO - Epoch [27][3200/5005]\tlr: 1.000e-01, eta: 9:00:02, time: 0.088, data_time: 0.014, memory: 928, loss: 5.3666\n2023-01-04 18:37:30,349 - mmcls - INFO - Epoch [27][3300/5005]\tlr: 1.000e-01, eta: 8:59:53, time: 0.086, data_time: 0.006, memory: 928, loss: 5.3924\n2023-01-04 18:37:39,153 - mmcls - INFO - Epoch [27][3400/5005]\tlr: 1.000e-01, eta: 8:59:44, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3895\n2023-01-04 18:37:47,968 - mmcls - INFO - Epoch [27][3500/5005]\tlr: 1.000e-01, eta: 8:59:35, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3736\n2023-01-04 18:37:56,499 - mmcls - INFO - Epoch [27][3600/5005]\tlr: 1.000e-01, eta: 8:59:25, time: 0.085, data_time: 0.007, memory: 928, loss: 5.3913\n2023-01-04 18:38:05,055 - mmcls - INFO - Epoch [27][3700/5005]\tlr: 1.000e-01, eta: 8:59:16, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3944\n2023-01-04 18:38:13,930 - mmcls - INFO - Epoch [27][3800/5005]\tlr: 1.000e-01, eta: 8:59:07, time: 0.089, data_time: 0.017, memory: 928, loss: 5.3846\n2023-01-04 18:38:22,612 - mmcls - INFO - Epoch [27][3900/5005]\tlr: 1.000e-01, eta: 8:58:58, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3655\n2023-01-04 18:38:31,321 - mmcls - INFO - Epoch [27][4000/5005]\tlr: 1.000e-01, eta: 8:58:49, time: 0.087, data_time: 0.017, memory: 928, loss: 5.3776\n2023-01-04 18:38:39,976 - mmcls - INFO - Epoch [27][4100/5005]\tlr: 1.000e-01, eta: 8:58:39, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3855\n2023-01-04 18:38:48,585 - mmcls - INFO - Epoch [27][4200/5005]\tlr: 1.000e-01, eta: 8:58:30, time: 0.086, data_time: 0.027, memory: 928, loss: 5.3915\n2023-01-04 18:38:57,280 - mmcls - INFO - Epoch [27][4300/5005]\tlr: 1.000e-01, eta: 8:58:21, time: 0.087, data_time: 0.009, memory: 928, loss: 5.3656\n2023-01-04 18:39:06,249 - mmcls - INFO - Epoch [27][4400/5005]\tlr: 1.000e-01, eta: 8:58:12, time: 0.090, data_time: 0.004, memory: 928, loss: 5.3900\n2023-01-04 18:39:15,322 - mmcls - INFO - Epoch [27][4500/5005]\tlr: 1.000e-01, eta: 8:58:04, time: 0.091, data_time: 0.005, memory: 928, loss: 5.3900\n2023-01-04 18:39:24,038 - mmcls - INFO - Epoch [27][4600/5005]\tlr: 1.000e-01, eta: 8:57:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3702\n2023-01-04 18:39:32,907 - mmcls - INFO - Epoch [27][4700/5005]\tlr: 1.000e-01, eta: 8:57:46, time: 0.089, data_time: 0.016, memory: 928, loss: 5.3825\n2023-01-04 18:39:41,554 - mmcls - INFO - Epoch [27][4800/5005]\tlr: 1.000e-01, eta: 8:57:37, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3995\n2023-01-04 18:39:50,614 - mmcls - INFO - Epoch [27][4900/5005]\tlr: 1.000e-01, eta: 8:57:29, time: 0.091, data_time: 0.008, memory: 928, loss: 5.3707\n2023-01-04 18:39:59,484 - mmcls - INFO - Epoch [27][5000/5005]\tlr: 1.000e-01, eta: 8:57:20, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3693\n2023-01-04 18:40:22,262 - mmcls - INFO - Epoch(val) [27][196]\taccuracy_top-1: 5.0180, accuracy_top-5: 13.1980\n2023-01-04 18:40:33,567 - mmcls - INFO - Epoch [28][100/5005]\tlr: 1.000e-01, eta: 8:57:16, time: 0.113, data_time: 0.044, memory: 928, loss: 5.3876\n2023-01-04 18:40:42,642 - mmcls - INFO - Epoch [28][200/5005]\tlr: 1.000e-01, eta: 8:57:08, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3861\n2023-01-04 18:40:51,402 - mmcls - INFO - Epoch [28][300/5005]\tlr: 1.000e-01, eta: 8:56:59, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3839\n2023-01-04 18:41:00,108 - mmcls - INFO - Epoch [28][400/5005]\tlr: 1.000e-01, eta: 8:56:50, time: 0.087, data_time: 0.020, memory: 928, loss: 5.3566\n2023-01-04 18:41:08,887 - mmcls - INFO - Epoch [28][500/5005]\tlr: 1.000e-01, eta: 8:56:41, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3774\n2023-01-04 18:41:17,696 - mmcls - INFO - Epoch [28][600/5005]\tlr: 1.000e-01, eta: 8:56:32, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3835\n2023-01-04 18:41:26,315 - mmcls - INFO - Epoch [28][700/5005]\tlr: 1.000e-01, eta: 8:56:23, time: 0.086, data_time: 0.018, memory: 928, loss: 5.3811\n2023-01-04 18:41:34,967 - mmcls - INFO - Epoch [28][800/5005]\tlr: 1.000e-01, eta: 8:56:14, time: 0.086, data_time: 0.036, memory: 928, loss: 5.3869\n2023-01-04 18:41:43,796 - mmcls - INFO - Epoch [28][900/5005]\tlr: 1.000e-01, eta: 8:56:05, time: 0.088, data_time: 0.017, memory: 928, loss: 5.3960\n2023-01-04 18:41:52,538 - mmcls - INFO - Epoch [28][1000/5005]\tlr: 1.000e-01, eta: 8:55:56, time: 0.087, data_time: 0.017, memory: 928, loss: 5.3590\n2023-01-04 18:42:01,645 - mmcls - INFO - Epoch [28][1100/5005]\tlr: 1.000e-01, eta: 8:55:48, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3799\n2023-01-04 18:42:10,314 - mmcls - INFO - Epoch [28][1200/5005]\tlr: 1.000e-01, eta: 8:55:38, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3991\n2023-01-04 18:42:19,500 - mmcls - INFO - Epoch [28][1300/5005]\tlr: 1.000e-01, eta: 8:55:30, time: 0.092, data_time: 0.038, memory: 928, loss: 5.3832\n2023-01-04 18:42:28,293 - mmcls - INFO - Epoch [28][1400/5005]\tlr: 1.000e-01, eta: 8:55:22, time: 0.088, data_time: 0.034, memory: 928, loss: 5.4026\n2023-01-04 18:42:36,952 - mmcls - INFO - Epoch [28][1500/5005]\tlr: 1.000e-01, eta: 8:55:12, time: 0.087, data_time: 0.025, memory: 928, loss: 5.3727\n2023-01-04 18:42:45,870 - mmcls - INFO - Epoch [28][1600/5005]\tlr: 1.000e-01, eta: 8:55:04, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3905\n2023-01-04 18:42:54,805 - mmcls - INFO - Epoch [28][1700/5005]\tlr: 1.000e-01, eta: 8:54:55, time: 0.089, data_time: 0.010, memory: 928, loss: 5.3900\n2023-01-04 18:43:03,518 - mmcls - INFO - Epoch [28][1800/5005]\tlr: 1.000e-01, eta: 8:54:46, time: 0.087, data_time: 0.028, memory: 928, loss: 5.3834\n2023-01-04 18:43:12,540 - mmcls - INFO - Epoch [28][1900/5005]\tlr: 1.000e-01, eta: 8:54:38, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3716\n2023-01-04 18:43:21,353 - mmcls - INFO - Epoch [28][2000/5005]\tlr: 1.000e-01, eta: 8:54:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3705\n2023-01-04 18:43:29,818 - mmcls - INFO - Epoch [28][2100/5005]\tlr: 1.000e-01, eta: 8:54:19, time: 0.085, data_time: 0.018, memory: 928, loss: 5.3626\n2023-01-04 18:43:38,441 - mmcls - INFO - Epoch [28][2200/5005]\tlr: 1.000e-01, eta: 8:54:10, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3844\n2023-01-04 18:43:47,509 - mmcls - INFO - Epoch [28][2300/5005]\tlr: 1.000e-01, eta: 8:54:02, time: 0.091, data_time: 0.014, memory: 928, loss: 5.3867\n2023-01-04 18:43:56,528 - mmcls - INFO - Epoch [28][2400/5005]\tlr: 1.000e-01, eta: 8:53:53, time: 0.090, data_time: 0.015, memory: 928, loss: 5.3629\n2023-01-04 18:44:05,284 - mmcls - INFO - Epoch [28][2500/5005]\tlr: 1.000e-01, eta: 8:53:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3895\n2023-01-04 18:44:14,154 - mmcls - INFO - Epoch [28][2600/5005]\tlr: 1.000e-01, eta: 8:53:36, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3837\n2023-01-04 18:44:22,882 - mmcls - INFO - Epoch [28][2700/5005]\tlr: 1.000e-01, eta: 8:53:26, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3712\n2023-01-04 18:44:31,871 - mmcls - INFO - Epoch [28][2800/5005]\tlr: 1.000e-01, eta: 8:53:18, time: 0.090, data_time: 0.011, memory: 928, loss: 5.3811\n2023-01-04 18:44:40,660 - mmcls - INFO - Epoch [28][2900/5005]\tlr: 1.000e-01, eta: 8:53:09, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3865\n2023-01-04 18:44:49,462 - mmcls - INFO - Epoch [28][3000/5005]\tlr: 1.000e-01, eta: 8:53:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3670\n2023-01-04 18:44:58,131 - mmcls - INFO - Epoch [28][3100/5005]\tlr: 1.000e-01, eta: 8:52:51, time: 0.087, data_time: 0.008, memory: 928, loss: 5.3644\n2023-01-04 18:45:07,098 - mmcls - INFO - Epoch [28][3200/5005]\tlr: 1.000e-01, eta: 8:52:43, time: 0.090, data_time: 0.010, memory: 928, loss: 5.3666\n2023-01-04 18:45:15,651 - mmcls - INFO - Epoch [28][3300/5005]\tlr: 1.000e-01, eta: 8:52:33, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3733\n2023-01-04 18:45:24,288 - mmcls - INFO - Epoch [28][3400/5005]\tlr: 1.000e-01, eta: 8:52:24, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3820\n2023-01-04 18:45:33,010 - mmcls - INFO - Epoch [28][3500/5005]\tlr: 1.000e-01, eta: 8:52:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3760\n2023-01-04 18:45:41,904 - mmcls - INFO - Epoch [28][3600/5005]\tlr: 1.000e-01, eta: 8:52:06, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3810\n2023-01-04 18:45:50,522 - mmcls - INFO - Epoch [28][3700/5005]\tlr: 1.000e-01, eta: 8:51:57, time: 0.086, data_time: 0.009, memory: 928, loss: 5.3713\n2023-01-04 18:45:59,484 - mmcls - INFO - Epoch [28][3800/5005]\tlr: 1.000e-01, eta: 8:51:48, time: 0.090, data_time: 0.016, memory: 928, loss: 5.3935\n2023-01-04 18:46:08,064 - mmcls - INFO - Epoch [28][3900/5005]\tlr: 1.000e-01, eta: 8:51:39, time: 0.086, data_time: 0.012, memory: 928, loss: 5.3830\n2023-01-04 18:46:16,990 - mmcls - INFO - Epoch [28][4000/5005]\tlr: 1.000e-01, eta: 8:51:30, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3978\n2023-01-04 18:46:25,661 - mmcls - INFO - Epoch [28][4100/5005]\tlr: 1.000e-01, eta: 8:51:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3934\n2023-01-04 18:46:34,340 - mmcls - INFO - Epoch [28][4200/5005]\tlr: 1.000e-01, eta: 8:51:12, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3761\n2023-01-04 18:46:43,104 - mmcls - INFO - Epoch [28][4300/5005]\tlr: 1.000e-01, eta: 8:51:03, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3809\n2023-01-04 18:46:51,972 - mmcls - INFO - Epoch [28][4400/5005]\tlr: 1.000e-01, eta: 8:50:54, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4008\n2023-01-04 18:47:00,603 - mmcls - INFO - Epoch [28][4500/5005]\tlr: 1.000e-01, eta: 8:50:45, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3742\n2023-01-04 18:47:09,379 - mmcls - INFO - Epoch [28][4600/5005]\tlr: 1.000e-01, eta: 8:50:36, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3759\n2023-01-04 18:47:18,320 - mmcls - INFO - Epoch [28][4700/5005]\tlr: 1.000e-01, eta: 8:50:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3887\n2023-01-04 18:47:27,060 - mmcls - INFO - Epoch [28][4800/5005]\tlr: 1.000e-01, eta: 8:50:18, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3664\n2023-01-04 18:47:35,488 - mmcls - INFO - Epoch [28][4900/5005]\tlr: 1.000e-01, eta: 8:50:08, time: 0.084, data_time: 0.002, memory: 928, loss: 5.3672\n2023-01-04 18:47:44,414 - mmcls - INFO - Epoch [28][5000/5005]\tlr: 1.000e-01, eta: 8:50:00, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3782\n2023-01-04 18:48:07,111 - mmcls - INFO - Epoch(val) [28][196]\taccuracy_top-1: 3.6180, accuracy_top-5: 10.1440\n2023-01-04 18:48:18,393 - mmcls - INFO - Epoch [29][100/5005]\tlr: 1.000e-01, eta: 8:49:55, time: 0.113, data_time: 0.055, memory: 928, loss: 5.3712\n2023-01-04 18:48:27,362 - mmcls - INFO - Epoch [29][200/5005]\tlr: 1.000e-01, eta: 8:49:47, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3822\n2023-01-04 18:48:36,174 - mmcls - INFO - Epoch [29][300/5005]\tlr: 1.000e-01, eta: 8:49:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3867\n2023-01-04 18:48:45,010 - mmcls - INFO - Epoch [29][400/5005]\tlr: 1.000e-01, eta: 8:49:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3829\n2023-01-04 18:48:53,872 - mmcls - INFO - Epoch [29][500/5005]\tlr: 1.000e-01, eta: 8:49:21, time: 0.089, data_time: 0.007, memory: 928, loss: 5.3860\n2023-01-04 18:49:02,820 - mmcls - INFO - Epoch [29][600/5005]\tlr: 1.000e-01, eta: 8:49:12, time: 0.090, data_time: 0.017, memory: 928, loss: 5.3634\n2023-01-04 18:49:11,559 - mmcls - INFO - Epoch [29][700/5005]\tlr: 1.000e-01, eta: 8:49:03, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4059\n2023-01-04 18:49:20,369 - mmcls - INFO - Epoch [29][800/5005]\tlr: 1.000e-01, eta: 8:48:54, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3751\n2023-01-04 18:49:29,077 - mmcls - INFO - Epoch [29][900/5005]\tlr: 1.000e-01, eta: 8:48:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3905\n2023-01-04 18:49:37,696 - mmcls - INFO - Epoch [29][1000/5005]\tlr: 1.000e-01, eta: 8:48:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3790\n2023-01-04 18:49:46,323 - mmcls - INFO - Epoch [29][1100/5005]\tlr: 1.000e-01, eta: 8:48:26, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3844\n2023-01-04 18:49:55,240 - mmcls - INFO - Epoch [29][1200/5005]\tlr: 1.000e-01, eta: 8:48:18, time: 0.089, data_time: 0.042, memory: 928, loss: 5.3741\n2023-01-04 18:50:04,134 - mmcls - INFO - Epoch [29][1300/5005]\tlr: 1.000e-01, eta: 8:48:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3551\n2023-01-04 18:50:12,811 - mmcls - INFO - Epoch [29][1400/5005]\tlr: 1.000e-01, eta: 8:48:00, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3845\n2023-01-04 18:50:21,861 - mmcls - INFO - Epoch [29][1500/5005]\tlr: 1.000e-01, eta: 8:47:52, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3763\n2023-01-04 18:50:30,636 - mmcls - INFO - Epoch [29][1600/5005]\tlr: 1.000e-01, eta: 8:47:43, time: 0.088, data_time: 0.019, memory: 928, loss: 5.3704\n2023-01-04 18:50:39,448 - mmcls - INFO - Epoch [29][1700/5005]\tlr: 1.000e-01, eta: 8:47:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3663\n2023-01-04 18:50:48,193 - mmcls - INFO - Epoch [29][1800/5005]\tlr: 1.000e-01, eta: 8:47:25, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3689\n2023-01-04 18:50:57,333 - mmcls - INFO - Epoch [29][1900/5005]\tlr: 1.000e-01, eta: 8:47:17, time: 0.091, data_time: 0.004, memory: 928, loss: 5.3776\n2023-01-04 18:51:06,055 - mmcls - INFO - Epoch [29][2000/5005]\tlr: 1.000e-01, eta: 8:47:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3850\n2023-01-04 18:51:15,024 - mmcls - INFO - Epoch [29][2100/5005]\tlr: 1.000e-01, eta: 8:46:59, time: 0.090, data_time: 0.013, memory: 928, loss: 5.3645\n2023-01-04 18:51:23,720 - mmcls - INFO - Epoch [29][2200/5005]\tlr: 1.000e-01, eta: 8:46:50, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4116\n2023-01-04 18:51:32,747 - mmcls - INFO - Epoch [29][2300/5005]\tlr: 1.000e-01, eta: 8:46:42, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3629\n2023-01-04 18:51:41,468 - mmcls - INFO - Epoch [29][2400/5005]\tlr: 1.000e-01, eta: 8:46:33, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3831\n2023-01-04 18:51:50,085 - mmcls - INFO - Epoch [29][2500/5005]\tlr: 1.000e-01, eta: 8:46:23, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3721\n2023-01-04 18:51:58,819 - mmcls - INFO - Epoch [29][2600/5005]\tlr: 1.000e-01, eta: 8:46:14, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3785\n2023-01-04 18:52:07,370 - mmcls - INFO - Epoch [29][2700/5005]\tlr: 1.000e-01, eta: 8:46:05, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3832\n2023-01-04 18:52:16,387 - mmcls - INFO - Epoch [29][2800/5005]\tlr: 1.000e-01, eta: 8:45:56, time: 0.090, data_time: 0.022, memory: 928, loss: 5.3727\n2023-01-04 18:52:25,224 - mmcls - INFO - Epoch [29][2900/5005]\tlr: 1.000e-01, eta: 8:45:48, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3754\n2023-01-04 18:52:34,293 - mmcls - INFO - Epoch [29][3000/5005]\tlr: 1.000e-01, eta: 8:45:39, time: 0.091, data_time: 0.003, memory: 928, loss: 5.3656\n2023-01-04 18:52:43,052 - mmcls - INFO - Epoch [29][3100/5005]\tlr: 1.000e-01, eta: 8:45:30, time: 0.088, data_time: 0.032, memory: 928, loss: 5.3800\n2023-01-04 18:52:51,817 - mmcls - INFO - Epoch [29][3200/5005]\tlr: 1.000e-01, eta: 8:45:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3843\n2023-01-04 18:53:00,160 - mmcls - INFO - Epoch [29][3300/5005]\tlr: 1.000e-01, eta: 8:45:11, time: 0.083, data_time: 0.033, memory: 928, loss: 5.3787\n2023-01-04 18:53:08,925 - mmcls - INFO - Epoch [29][3400/5005]\tlr: 1.000e-01, eta: 8:45:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3722\n2023-01-04 18:53:17,529 - mmcls - INFO - Epoch [29][3500/5005]\tlr: 1.000e-01, eta: 8:44:53, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3754\n2023-01-04 18:53:26,274 - mmcls - INFO - Epoch [29][3600/5005]\tlr: 1.000e-01, eta: 8:44:44, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3644\n2023-01-04 18:53:35,165 - mmcls - INFO - Epoch [29][3700/5005]\tlr: 1.000e-01, eta: 8:44:35, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3805\n2023-01-04 18:53:43,845 - mmcls - INFO - Epoch [29][3800/5005]\tlr: 1.000e-01, eta: 8:44:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3653\n2023-01-04 18:53:52,646 - mmcls - INFO - Epoch [29][3900/5005]\tlr: 1.000e-01, eta: 8:44:17, time: 0.088, data_time: 0.014, memory: 928, loss: 5.3733\n2023-01-04 18:54:01,201 - mmcls - INFO - Epoch [29][4000/5005]\tlr: 1.000e-01, eta: 8:44:08, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3850\n2023-01-04 18:54:09,943 - mmcls - INFO - Epoch [29][4100/5005]\tlr: 1.000e-01, eta: 8:43:59, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3830\n2023-01-04 18:54:18,655 - mmcls - INFO - Epoch [29][4200/5005]\tlr: 1.000e-01, eta: 8:43:50, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3681\n2023-01-04 18:54:27,199 - mmcls - INFO - Epoch [29][4300/5005]\tlr: 1.000e-01, eta: 8:43:40, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3681\n2023-01-04 18:54:36,270 - mmcls - INFO - Epoch [29][4400/5005]\tlr: 1.000e-01, eta: 8:43:32, time: 0.091, data_time: 0.019, memory: 928, loss: 5.3808\n2023-01-04 18:54:44,918 - mmcls - INFO - Epoch [29][4500/5005]\tlr: 1.000e-01, eta: 8:43:23, time: 0.086, data_time: 0.043, memory: 928, loss: 5.3800\n2023-01-04 18:54:53,504 - mmcls - INFO - Epoch [29][4600/5005]\tlr: 1.000e-01, eta: 8:43:13, time: 0.086, data_time: 0.011, memory: 928, loss: 5.3848\n2023-01-04 18:55:01,955 - mmcls - INFO - Epoch [29][4700/5005]\tlr: 1.000e-01, eta: 8:43:03, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3839\n2023-01-04 18:55:10,861 - mmcls - INFO - Epoch [29][4800/5005]\tlr: 1.000e-01, eta: 8:42:55, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3876\n2023-01-04 18:55:19,740 - mmcls - INFO - Epoch [29][4900/5005]\tlr: 1.000e-01, eta: 8:42:46, time: 0.089, data_time: 0.044, memory: 928, loss: 5.3787\n2023-01-04 18:55:28,507 - mmcls - INFO - Epoch [29][5000/5005]\tlr: 1.000e-01, eta: 8:42:37, time: 0.088, data_time: 0.022, memory: 928, loss: 5.3781\n2023-01-04 18:55:51,269 - mmcls - INFO - Epoch(val) [29][196]\taccuracy_top-1: 3.3940, accuracy_top-5: 9.8000\n2023-01-04 18:56:02,418 - mmcls - INFO - Epoch [30][100/5005]\tlr: 1.000e-01, eta: 8:42:32, time: 0.111, data_time: 0.050, memory: 928, loss: 5.3753\n2023-01-04 18:56:11,003 - mmcls - INFO - Epoch [30][200/5005]\tlr: 1.000e-01, eta: 8:42:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3760\n2023-01-04 18:56:19,786 - mmcls - INFO - Epoch [30][300/5005]\tlr: 1.000e-01, eta: 8:42:14, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3743\n2023-01-04 18:56:28,315 - mmcls - INFO - Epoch [30][400/5005]\tlr: 1.000e-01, eta: 8:42:05, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3788\n2023-01-04 18:56:37,004 - mmcls - INFO - Epoch [30][500/5005]\tlr: 1.000e-01, eta: 8:41:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3492\n2023-01-04 18:56:45,484 - mmcls - INFO - Epoch [30][600/5005]\tlr: 1.000e-01, eta: 8:41:46, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4001\n2023-01-04 18:56:54,107 - mmcls - INFO - Epoch [30][700/5005]\tlr: 1.000e-01, eta: 8:41:36, time: 0.086, data_time: 0.023, memory: 928, loss: 5.3629\n2023-01-04 18:57:02,841 - mmcls - INFO - Epoch [30][800/5005]\tlr: 1.000e-01, eta: 8:41:27, time: 0.087, data_time: 0.013, memory: 928, loss: 5.3863\n2023-01-04 18:57:11,621 - mmcls - INFO - Epoch [30][900/5005]\tlr: 1.000e-01, eta: 8:41:18, time: 0.088, data_time: 0.029, memory: 928, loss: 5.3612\n2023-01-04 18:57:20,216 - mmcls - INFO - Epoch [30][1000/5005]\tlr: 1.000e-01, eta: 8:41:09, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3651\n2023-01-04 18:57:28,978 - mmcls - INFO - Epoch [30][1100/5005]\tlr: 1.000e-01, eta: 8:41:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3719\n2023-01-04 18:57:37,420 - mmcls - INFO - Epoch [30][1200/5005]\tlr: 1.000e-01, eta: 8:40:50, time: 0.084, data_time: 0.017, memory: 928, loss: 5.3740\n2023-01-04 18:57:46,083 - mmcls - INFO - Epoch [30][1300/5005]\tlr: 1.000e-01, eta: 8:40:41, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3894\n2023-01-04 18:57:54,722 - mmcls - INFO - Epoch [30][1400/5005]\tlr: 1.000e-01, eta: 8:40:32, time: 0.086, data_time: 0.007, memory: 928, loss: 5.3926\n2023-01-04 18:58:03,622 - mmcls - INFO - Epoch [30][1500/5005]\tlr: 1.000e-01, eta: 8:40:23, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3891\n2023-01-04 18:58:12,500 - mmcls - INFO - Epoch [30][1600/5005]\tlr: 1.000e-01, eta: 8:40:15, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3674\n2023-01-04 18:58:21,399 - mmcls - INFO - Epoch [30][1700/5005]\tlr: 1.000e-01, eta: 8:40:06, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3836\n2023-01-04 18:58:30,113 - mmcls - INFO - Epoch [30][1800/5005]\tlr: 1.000e-01, eta: 8:39:57, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3756\n2023-01-04 18:58:38,815 - mmcls - INFO - Epoch [30][1900/5005]\tlr: 1.000e-01, eta: 8:39:48, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3882\n2023-01-04 18:58:47,586 - mmcls - INFO - Epoch [30][2000/5005]\tlr: 1.000e-01, eta: 8:39:39, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3545\n2023-01-04 18:58:56,072 - mmcls - INFO - Epoch [30][2100/5005]\tlr: 1.000e-01, eta: 8:39:29, time: 0.085, data_time: 0.014, memory: 928, loss: 5.3557\n2023-01-04 18:59:05,171 - mmcls - INFO - Epoch [30][2200/5005]\tlr: 1.000e-01, eta: 8:39:21, time: 0.091, data_time: 0.011, memory: 928, loss: 5.3871\n2023-01-04 18:59:14,254 - mmcls - INFO - Epoch [30][2300/5005]\tlr: 1.000e-01, eta: 8:39:13, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3735\n2023-01-04 18:59:23,101 - mmcls - INFO - Epoch [30][2400/5005]\tlr: 1.000e-01, eta: 8:39:04, time: 0.089, data_time: 0.016, memory: 928, loss: 5.3616\n2023-01-04 18:59:31,718 - mmcls - INFO - Epoch [30][2500/5005]\tlr: 1.000e-01, eta: 8:38:55, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3804\n2023-01-04 18:59:40,612 - mmcls - INFO - Epoch [30][2600/5005]\tlr: 1.000e-01, eta: 8:38:46, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3642\n2023-01-04 18:59:49,229 - mmcls - INFO - Epoch [30][2700/5005]\tlr: 1.000e-01, eta: 8:38:37, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3861\n2023-01-04 18:59:57,785 - mmcls - INFO - Epoch [30][2800/5005]\tlr: 1.000e-01, eta: 8:38:27, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4058\n2023-01-04 19:00:06,576 - mmcls - INFO - Epoch [30][2900/5005]\tlr: 1.000e-01, eta: 8:38:18, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3697\n2023-01-04 19:00:15,779 - mmcls - INFO - Epoch [30][3000/5005]\tlr: 1.000e-01, eta: 8:38:10, time: 0.092, data_time: 0.001, memory: 928, loss: 5.3957\n2023-01-04 19:00:24,264 - mmcls - INFO - Epoch [30][3100/5005]\tlr: 1.000e-01, eta: 8:38:01, time: 0.085, data_time: 0.011, memory: 928, loss: 5.3839\n2023-01-04 19:00:33,733 - mmcls - INFO - Epoch [30][3200/5005]\tlr: 1.000e-01, eta: 8:37:53, time: 0.095, data_time: 0.001, memory: 928, loss: 5.3828\n2023-01-04 19:00:42,440 - mmcls - INFO - Epoch [30][3300/5005]\tlr: 1.000e-01, eta: 8:37:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3857\n2023-01-04 19:00:51,359 - mmcls - INFO - Epoch [30][3400/5005]\tlr: 1.000e-01, eta: 8:37:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3767\n2023-01-04 19:01:00,107 - mmcls - INFO - Epoch [30][3500/5005]\tlr: 1.000e-01, eta: 8:37:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3694\n2023-01-04 19:01:09,088 - mmcls - INFO - Epoch [30][3600/5005]\tlr: 1.000e-01, eta: 8:37:18, time: 0.090, data_time: 0.032, memory: 928, loss: 5.3783\n2023-01-04 19:01:17,757 - mmcls - INFO - Epoch [30][3700/5005]\tlr: 1.000e-01, eta: 8:37:09, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3819\n2023-01-04 19:01:26,831 - mmcls - INFO - Epoch [30][3800/5005]\tlr: 1.000e-01, eta: 8:37:01, time: 0.091, data_time: 0.002, memory: 928, loss: 5.3958\n2023-01-04 19:01:36,048 - mmcls - INFO - Epoch [30][3900/5005]\tlr: 1.000e-01, eta: 8:36:53, time: 0.092, data_time: 0.039, memory: 928, loss: 5.3827\n2023-01-04 19:01:45,112 - mmcls - INFO - Epoch [30][4000/5005]\tlr: 1.000e-01, eta: 8:36:45, time: 0.091, data_time: 0.020, memory: 928, loss: 5.3674\n2023-01-04 19:01:53,685 - mmcls - INFO - Epoch [30][4100/5005]\tlr: 1.000e-01, eta: 8:36:35, time: 0.086, data_time: 0.000, memory: 928, loss: 5.3784\n2023-01-04 19:02:02,377 - mmcls - INFO - Epoch [30][4200/5005]\tlr: 1.000e-01, eta: 8:36:26, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3800\n2023-01-04 19:02:11,039 - mmcls - INFO - Epoch [30][4300/5005]\tlr: 1.000e-01, eta: 8:36:17, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3812\n2023-01-04 19:02:19,718 - mmcls - INFO - Epoch [30][4400/5005]\tlr: 1.000e-01, eta: 8:36:08, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3753\n2023-01-04 19:02:28,675 - mmcls - INFO - Epoch [30][4500/5005]\tlr: 1.000e-01, eta: 8:35:59, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3735\n2023-01-04 19:02:37,423 - mmcls - INFO - Epoch [30][4600/5005]\tlr: 1.000e-01, eta: 8:35:50, time: 0.087, data_time: 0.015, memory: 928, loss: 5.3756\n2023-01-04 19:02:46,355 - mmcls - INFO - Epoch [30][4700/5005]\tlr: 1.000e-01, eta: 8:35:42, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3642\n2023-01-04 19:02:54,795 - mmcls - INFO - Epoch [30][4800/5005]\tlr: 1.000e-01, eta: 8:35:32, time: 0.084, data_time: 0.011, memory: 928, loss: 5.3786\n2023-01-04 19:03:03,731 - mmcls - INFO - Epoch [30][4900/5005]\tlr: 1.000e-01, eta: 8:35:23, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3708\n2023-01-04 19:03:12,494 - mmcls - INFO - Epoch [30][5000/5005]\tlr: 1.000e-01, eta: 8:35:14, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3762\n2023-01-04 19:03:35,286 - mmcls - INFO - Epoch(val) [30][196]\taccuracy_top-1: 4.9000, accuracy_top-5: 13.6880\n2023-01-04 19:03:46,239 - mmcls - INFO - Epoch [31][100/5005]\tlr: 1.000e-02, eta: 8:35:09, time: 0.109, data_time: 0.043, memory: 928, loss: 5.2582\n2023-01-04 19:03:54,975 - mmcls - INFO - Epoch [31][200/5005]\tlr: 1.000e-02, eta: 8:35:00, time: 0.087, data_time: 0.022, memory: 928, loss: 5.2071\n2023-01-04 19:04:04,085 - mmcls - INFO - Epoch [31][300/5005]\tlr: 1.000e-02, eta: 8:34:52, time: 0.091, data_time: 0.003, memory: 928, loss: 5.1841\n2023-01-04 19:04:12,878 - mmcls - INFO - Epoch [31][400/5005]\tlr: 1.000e-02, eta: 8:34:43, time: 0.088, data_time: 0.003, memory: 928, loss: 5.1974\n2023-01-04 19:04:21,818 - mmcls - INFO - Epoch [31][500/5005]\tlr: 1.000e-02, eta: 8:34:34, time: 0.089, data_time: 0.005, memory: 928, loss: 5.1785\n2023-01-04 19:04:30,654 - mmcls - INFO - Epoch [31][600/5005]\tlr: 1.000e-02, eta: 8:34:26, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1665\n2023-01-04 19:04:39,537 - mmcls - INFO - Epoch [31][700/5005]\tlr: 1.000e-02, eta: 8:34:17, time: 0.089, data_time: 0.019, memory: 928, loss: 5.1773\n2023-01-04 19:04:48,454 - mmcls - INFO - Epoch [31][800/5005]\tlr: 1.000e-02, eta: 8:34:08, time: 0.089, data_time: 0.002, memory: 928, loss: 5.1612\n2023-01-04 19:04:57,360 - mmcls - INFO - Epoch [31][900/5005]\tlr: 1.000e-02, eta: 8:34:00, time: 0.089, data_time: 0.003, memory: 928, loss: 5.1607\n2023-01-04 19:05:06,636 - mmcls - INFO - Epoch [31][1000/5005]\tlr: 1.000e-02, eta: 8:33:52, time: 0.093, data_time: 0.012, memory: 928, loss: 5.1604\n2023-01-04 19:05:15,339 - mmcls - INFO - Epoch [31][1100/5005]\tlr: 1.000e-02, eta: 8:33:43, time: 0.087, data_time: 0.024, memory: 928, loss: 5.1634\n2023-01-04 19:05:24,203 - mmcls - INFO - Epoch [31][1200/5005]\tlr: 1.000e-02, eta: 8:33:34, time: 0.089, data_time: 0.013, memory: 928, loss: 5.1740\n2023-01-04 19:05:32,858 - mmcls - INFO - Epoch [31][1300/5005]\tlr: 1.000e-02, eta: 8:33:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1532\n2023-01-04 19:05:41,838 - mmcls - INFO - Epoch [31][1400/5005]\tlr: 1.000e-02, eta: 8:33:16, time: 0.090, data_time: 0.004, memory: 928, loss: 5.1453\n2023-01-04 19:05:50,378 - mmcls - INFO - Epoch [31][1500/5005]\tlr: 1.000e-02, eta: 8:33:07, time: 0.085, data_time: 0.037, memory: 928, loss: 5.1528\n2023-01-04 19:05:59,137 - mmcls - INFO - Epoch [31][1600/5005]\tlr: 1.000e-02, eta: 8:32:58, time: 0.088, data_time: 0.006, memory: 928, loss: 5.1518\n2023-01-04 19:06:08,029 - mmcls - INFO - Epoch [31][1700/5005]\tlr: 1.000e-02, eta: 8:32:49, time: 0.089, data_time: 0.005, memory: 928, loss: 5.1394\n2023-01-04 19:06:16,922 - mmcls - INFO - Epoch [31][1800/5005]\tlr: 1.000e-02, eta: 8:32:40, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1451\n2023-01-04 19:06:25,739 - mmcls - INFO - Epoch [31][1900/5005]\tlr: 1.000e-02, eta: 8:32:32, time: 0.088, data_time: 0.003, memory: 928, loss: 5.1636\n2023-01-04 19:06:34,418 - mmcls - INFO - Epoch [31][2000/5005]\tlr: 1.000e-02, eta: 8:32:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.1387\n2023-01-04 19:06:43,111 - mmcls - INFO - Epoch [31][2100/5005]\tlr: 1.000e-02, eta: 8:32:13, time: 0.087, data_time: 0.018, memory: 928, loss: 5.1626\n2023-01-04 19:06:51,868 - mmcls - INFO - Epoch [31][2200/5005]\tlr: 1.000e-02, eta: 8:32:04, time: 0.087, data_time: 0.003, memory: 928, loss: 5.1355\n2023-01-04 19:07:00,498 - mmcls - INFO - Epoch [31][2300/5005]\tlr: 1.000e-02, eta: 8:31:55, time: 0.086, data_time: 0.011, memory: 928, loss: 5.1361\n2023-01-04 19:07:09,408 - mmcls - INFO - Epoch [31][2400/5005]\tlr: 1.000e-02, eta: 8:31:46, time: 0.089, data_time: 0.020, memory: 928, loss: 5.1508\n2023-01-04 19:07:18,069 - mmcls - INFO - Epoch [31][2500/5005]\tlr: 1.000e-02, eta: 8:31:37, time: 0.087, data_time: 0.006, memory: 928, loss: 5.1319\n2023-01-04 19:07:27,009 - mmcls - INFO - Epoch [31][2600/5005]\tlr: 1.000e-02, eta: 8:31:29, time: 0.089, data_time: 0.002, memory: 928, loss: 5.1236\n2023-01-04 19:07:36,116 - mmcls - INFO - Epoch [31][2700/5005]\tlr: 1.000e-02, eta: 8:31:20, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1384\n2023-01-04 19:07:44,714 - mmcls - INFO - Epoch [31][2800/5005]\tlr: 1.000e-02, eta: 8:31:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.1343\n2023-01-04 19:07:53,373 - mmcls - INFO - Epoch [31][2900/5005]\tlr: 1.000e-02, eta: 8:31:02, time: 0.087, data_time: 0.036, memory: 928, loss: 5.1274\n2023-01-04 19:08:01,951 - mmcls - INFO - Epoch [31][3000/5005]\tlr: 1.000e-02, eta: 8:30:53, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1357\n2023-01-04 19:08:10,823 - mmcls - INFO - Epoch [31][3100/5005]\tlr: 1.000e-02, eta: 8:30:44, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1180\n2023-01-04 19:08:19,588 - mmcls - INFO - Epoch [31][3200/5005]\tlr: 1.000e-02, eta: 8:30:35, time: 0.088, data_time: 0.034, memory: 928, loss: 5.1350\n2023-01-04 19:08:28,253 - mmcls - INFO - Epoch [31][3300/5005]\tlr: 1.000e-02, eta: 8:30:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1346\n2023-01-04 19:08:36,821 - mmcls - INFO - Epoch [31][3400/5005]\tlr: 1.000e-02, eta: 8:30:16, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1200\n2023-01-04 19:08:45,903 - mmcls - INFO - Epoch [31][3500/5005]\tlr: 1.000e-02, eta: 8:30:08, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1295\n2023-01-04 19:08:54,798 - mmcls - INFO - Epoch [31][3600/5005]\tlr: 1.000e-02, eta: 8:29:59, time: 0.089, data_time: 0.021, memory: 928, loss: 5.1441\n2023-01-04 19:09:03,464 - mmcls - INFO - Epoch [31][3700/5005]\tlr: 1.000e-02, eta: 8:29:50, time: 0.087, data_time: 0.014, memory: 928, loss: 5.1324\n2023-01-04 19:09:12,431 - mmcls - INFO - Epoch [31][3800/5005]\tlr: 1.000e-02, eta: 8:29:42, time: 0.090, data_time: 0.012, memory: 928, loss: 5.1372\n2023-01-04 19:09:21,228 - mmcls - INFO - Epoch [31][3900/5005]\tlr: 1.000e-02, eta: 8:29:33, time: 0.088, data_time: 0.031, memory: 928, loss: 5.1017\n2023-01-04 19:09:29,769 - mmcls - INFO - Epoch [31][4000/5005]\tlr: 1.000e-02, eta: 8:29:23, time: 0.085, data_time: 0.012, memory: 928, loss: 5.1249\n2023-01-04 19:09:38,413 - mmcls - INFO - Epoch [31][4100/5005]\tlr: 1.000e-02, eta: 8:29:14, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1439\n2023-01-04 19:09:46,963 - mmcls - INFO - Epoch [31][4200/5005]\tlr: 1.000e-02, eta: 8:29:05, time: 0.086, data_time: 0.006, memory: 928, loss: 5.1332\n2023-01-04 19:09:55,667 - mmcls - INFO - Epoch [31][4300/5005]\tlr: 1.000e-02, eta: 8:28:56, time: 0.087, data_time: 0.020, memory: 928, loss: 5.1506\n2023-01-04 19:10:04,414 - mmcls - INFO - Epoch [31][4400/5005]\tlr: 1.000e-02, eta: 8:28:47, time: 0.087, data_time: 0.027, memory: 928, loss: 5.1356\n2023-01-04 19:10:13,077 - mmcls - INFO - Epoch [31][4500/5005]\tlr: 1.000e-02, eta: 8:28:37, time: 0.087, data_time: 0.007, memory: 928, loss: 5.1190\n2023-01-04 19:10:21,619 - mmcls - INFO - Epoch [31][4600/5005]\tlr: 1.000e-02, eta: 8:28:28, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1200\n2023-01-04 19:10:30,403 - mmcls - INFO - Epoch [31][4700/5005]\tlr: 1.000e-02, eta: 8:28:19, time: 0.088, data_time: 0.030, memory: 928, loss: 5.1254\n2023-01-04 19:10:38,887 - mmcls - INFO - Epoch [31][4800/5005]\tlr: 1.000e-02, eta: 8:28:09, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1057\n2023-01-04 19:10:47,615 - mmcls - INFO - Epoch [31][4900/5005]\tlr: 1.000e-02, eta: 8:28:00, time: 0.087, data_time: 0.014, memory: 928, loss: 5.1071\n2023-01-04 19:10:56,367 - mmcls - INFO - Epoch [31][5000/5005]\tlr: 1.000e-02, eta: 8:27:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1113\n2023-01-04 19:11:19,271 - mmcls - INFO - Epoch(val) [31][196]\taccuracy_top-1: 12.8400, accuracy_top-5: 28.0800\n2023-01-04 19:11:30,364 - mmcls - INFO - Epoch [32][100/5005]\tlr: 1.000e-02, eta: 8:27:46, time: 0.111, data_time: 0.036, memory: 928, loss: 5.0991\n2023-01-04 19:11:38,890 - mmcls - INFO - Epoch [32][200/5005]\tlr: 1.000e-02, eta: 8:27:37, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1179\n2023-01-04 19:11:47,602 - mmcls - INFO - Epoch [32][300/5005]\tlr: 1.000e-02, eta: 8:27:28, time: 0.087, data_time: 0.004, memory: 928, loss: 5.1076\n2023-01-04 19:11:56,217 - mmcls - INFO - Epoch [32][400/5005]\tlr: 1.000e-02, eta: 8:27:18, time: 0.086, data_time: 0.002, memory: 928, loss: 5.1160\n2023-01-04 19:12:05,123 - mmcls - INFO - Epoch [32][500/5005]\tlr: 1.000e-02, eta: 8:27:10, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0991\n2023-01-04 19:12:13,919 - mmcls - INFO - Epoch [32][600/5005]\tlr: 1.000e-02, eta: 8:27:01, time: 0.088, data_time: 0.009, memory: 928, loss: 5.1196\n2023-01-04 19:12:22,376 - mmcls - INFO - Epoch [32][700/5005]\tlr: 1.000e-02, eta: 8:26:51, time: 0.085, data_time: 0.017, memory: 928, loss: 5.1101\n2023-01-04 19:12:31,366 - mmcls - INFO - Epoch [32][800/5005]\tlr: 1.000e-02, eta: 8:26:43, time: 0.090, data_time: 0.004, memory: 928, loss: 5.1287\n2023-01-04 19:12:40,164 - mmcls - INFO - Epoch [32][900/5005]\tlr: 1.000e-02, eta: 8:26:34, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1067\n2023-01-04 19:12:48,723 - mmcls - INFO - Epoch [32][1000/5005]\tlr: 1.000e-02, eta: 8:26:24, time: 0.086, data_time: 0.012, memory: 928, loss: 5.1040\n2023-01-04 19:12:57,619 - mmcls - INFO - Epoch [32][1100/5005]\tlr: 1.000e-02, eta: 8:26:16, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1081\n2023-01-04 19:13:06,461 - mmcls - INFO - Epoch [32][1200/5005]\tlr: 1.000e-02, eta: 8:26:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0977\n2023-01-04 19:13:15,289 - mmcls - INFO - Epoch [32][1300/5005]\tlr: 1.000e-02, eta: 8:25:58, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0836\n2023-01-04 19:13:23,879 - mmcls - INFO - Epoch [32][1400/5005]\tlr: 1.000e-02, eta: 8:25:49, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1137\n2023-01-04 19:13:32,582 - mmcls - INFO - Epoch [32][1500/5005]\tlr: 1.000e-02, eta: 8:25:40, time: 0.087, data_time: 0.000, memory: 928, loss: 5.1134\n2023-01-04 19:13:41,086 - mmcls - INFO - Epoch [32][1600/5005]\tlr: 1.000e-02, eta: 8:25:30, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1298\n2023-01-04 19:13:49,586 - mmcls - INFO - Epoch [32][1700/5005]\tlr: 1.000e-02, eta: 8:25:21, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1113\n2023-01-04 19:13:58,825 - mmcls - INFO - Epoch [32][1800/5005]\tlr: 1.000e-02, eta: 8:25:13, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0963\n2023-01-04 19:14:07,597 - mmcls - INFO - Epoch [32][1900/5005]\tlr: 1.000e-02, eta: 8:25:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1089\n2023-01-04 19:14:16,388 - mmcls - INFO - Epoch [32][2000/5005]\tlr: 1.000e-02, eta: 8:24:55, time: 0.088, data_time: 0.006, memory: 928, loss: 5.1068\n2023-01-04 19:14:25,363 - mmcls - INFO - Epoch [32][2100/5005]\tlr: 1.000e-02, eta: 8:24:46, time: 0.090, data_time: 0.006, memory: 928, loss: 5.1086\n2023-01-04 19:14:34,174 - mmcls - INFO - Epoch [32][2200/5005]\tlr: 1.000e-02, eta: 8:24:37, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0929\n2023-01-04 19:14:42,821 - mmcls - INFO - Epoch [32][2300/5005]\tlr: 1.000e-02, eta: 8:24:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1096\n2023-01-04 19:14:51,426 - mmcls - INFO - Epoch [32][2400/5005]\tlr: 1.000e-02, eta: 8:24:19, time: 0.086, data_time: 0.005, memory: 928, loss: 5.1200\n2023-01-04 19:15:00,077 - mmcls - INFO - Epoch [32][2500/5005]\tlr: 1.000e-02, eta: 8:24:10, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0992\n2023-01-04 19:15:09,077 - mmcls - INFO - Epoch [32][2600/5005]\tlr: 1.000e-02, eta: 8:24:01, time: 0.090, data_time: 0.033, memory: 928, loss: 5.0966\n2023-01-04 19:15:18,006 - mmcls - INFO - Epoch [32][2700/5005]\tlr: 1.000e-02, eta: 8:23:53, time: 0.089, data_time: 0.004, memory: 928, loss: 5.1179\n2023-01-04 19:15:26,745 - mmcls - INFO - Epoch [32][2800/5005]\tlr: 1.000e-02, eta: 8:23:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.1210\n2023-01-04 19:15:36,022 - mmcls - INFO - Epoch [32][2900/5005]\tlr: 1.000e-02, eta: 8:23:36, time: 0.093, data_time: 0.005, memory: 928, loss: 5.1287\n2023-01-04 19:15:44,913 - mmcls - INFO - Epoch [32][3000/5005]\tlr: 1.000e-02, eta: 8:23:27, time: 0.089, data_time: 0.026, memory: 928, loss: 5.1118\n2023-01-04 19:15:53,508 - mmcls - INFO - Epoch [32][3100/5005]\tlr: 1.000e-02, eta: 8:23:18, time: 0.086, data_time: 0.025, memory: 928, loss: 5.1031\n2023-01-04 19:16:02,605 - mmcls - INFO - Epoch [32][3200/5005]\tlr: 1.000e-02, eta: 8:23:10, time: 0.091, data_time: 0.002, memory: 928, loss: 5.1011\n2023-01-04 19:16:11,422 - mmcls - INFO - Epoch [32][3300/5005]\tlr: 1.000e-02, eta: 8:23:01, time: 0.088, data_time: 0.027, memory: 928, loss: 5.1058\n2023-01-04 19:16:20,775 - mmcls - INFO - Epoch [32][3400/5005]\tlr: 1.000e-02, eta: 8:22:53, time: 0.093, data_time: 0.016, memory: 928, loss: 5.1037\n2023-01-04 19:16:29,439 - mmcls - INFO - Epoch [32][3500/5005]\tlr: 1.000e-02, eta: 8:22:44, time: 0.087, data_time: 0.036, memory: 928, loss: 5.0919\n2023-01-04 19:16:38,375 - mmcls - INFO - Epoch [32][3600/5005]\tlr: 1.000e-02, eta: 8:22:35, time: 0.089, data_time: 0.014, memory: 928, loss: 5.1296\n2023-01-04 19:16:47,387 - mmcls - INFO - Epoch [32][3700/5005]\tlr: 1.000e-02, eta: 8:22:27, time: 0.090, data_time: 0.024, memory: 928, loss: 5.1107\n2023-01-04 19:16:56,329 - mmcls - INFO - Epoch [32][3800/5005]\tlr: 1.000e-02, eta: 8:22:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1071\n2023-01-04 19:17:04,999 - mmcls - INFO - Epoch [32][3900/5005]\tlr: 1.000e-02, eta: 8:22:09, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0884\n2023-01-04 19:17:13,901 - mmcls - INFO - Epoch [32][4000/5005]\tlr: 1.000e-02, eta: 8:22:01, time: 0.089, data_time: 0.003, memory: 928, loss: 5.1212\n2023-01-04 19:17:22,400 - mmcls - INFO - Epoch [32][4100/5005]\tlr: 1.000e-02, eta: 8:21:51, time: 0.085, data_time: 0.019, memory: 928, loss: 5.1131\n2023-01-04 19:17:31,089 - mmcls - INFO - Epoch [32][4200/5005]\tlr: 1.000e-02, eta: 8:21:42, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0993\n2023-01-04 19:17:39,659 - mmcls - INFO - Epoch [32][4300/5005]\tlr: 1.000e-02, eta: 8:21:33, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0998\n2023-01-04 19:17:48,264 - mmcls - INFO - Epoch [32][4400/5005]\tlr: 1.000e-02, eta: 8:21:23, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1026\n2023-01-04 19:17:56,915 - mmcls - INFO - Epoch [32][4500/5005]\tlr: 1.000e-02, eta: 8:21:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.1075\n2023-01-04 19:18:05,927 - mmcls - INFO - Epoch [32][4600/5005]\tlr: 1.000e-02, eta: 8:21:06, time: 0.090, data_time: 0.001, memory: 928, loss: 5.1001\n2023-01-04 19:18:14,693 - mmcls - INFO - Epoch [32][4700/5005]\tlr: 1.000e-02, eta: 8:20:57, time: 0.088, data_time: 0.015, memory: 928, loss: 5.1030\n2023-01-04 19:18:23,171 - mmcls - INFO - Epoch [32][4800/5005]\tlr: 1.000e-02, eta: 8:20:47, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0967\n2023-01-04 19:18:31,834 - mmcls - INFO - Epoch [32][4900/5005]\tlr: 1.000e-02, eta: 8:20:38, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0965\n2023-01-04 19:18:40,491 - mmcls - INFO - Epoch [32][5000/5005]\tlr: 1.000e-02, eta: 8:20:29, time: 0.087, data_time: 0.037, memory: 928, loss: 5.1050\n2023-01-04 19:19:03,303 - mmcls - INFO - Epoch(val) [32][196]\taccuracy_top-1: 11.4880, accuracy_top-5: 25.7780\n2023-01-04 19:19:14,181 - mmcls - INFO - Epoch [33][100/5005]\tlr: 1.000e-02, eta: 8:20:23, time: 0.109, data_time: 0.030, memory: 928, loss: 5.1092\n2023-01-04 19:19:23,019 - mmcls - INFO - Epoch [33][200/5005]\tlr: 1.000e-02, eta: 8:20:14, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0894\n2023-01-04 19:19:32,017 - mmcls - INFO - Epoch [33][300/5005]\tlr: 1.000e-02, eta: 8:20:06, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0937\n2023-01-04 19:19:40,867 - mmcls - INFO - Epoch [33][400/5005]\tlr: 1.000e-02, eta: 8:19:57, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0949\n2023-01-04 19:19:49,420 - mmcls - INFO - Epoch [33][500/5005]\tlr: 1.000e-02, eta: 8:19:47, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0952\n2023-01-04 19:19:58,686 - mmcls - INFO - Epoch [33][600/5005]\tlr: 1.000e-02, eta: 8:19:40, time: 0.093, data_time: 0.004, memory: 928, loss: 5.1079\n2023-01-04 19:20:07,489 - mmcls - INFO - Epoch [33][700/5005]\tlr: 1.000e-02, eta: 8:19:31, time: 0.088, data_time: 0.000, memory: 928, loss: 5.0928\n2023-01-04 19:20:16,178 - mmcls - INFO - Epoch [33][800/5005]\tlr: 1.000e-02, eta: 8:19:22, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0647\n2023-01-04 19:20:25,345 - mmcls - INFO - Epoch [33][900/5005]\tlr: 1.000e-02, eta: 8:19:13, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0941\n2023-01-04 19:20:33,851 - mmcls - INFO - Epoch [33][1000/5005]\tlr: 1.000e-02, eta: 8:19:04, time: 0.085, data_time: 0.021, memory: 928, loss: 5.1108\n2023-01-04 19:20:42,548 - mmcls - INFO - Epoch [33][1100/5005]\tlr: 1.000e-02, eta: 8:18:55, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0911\n2023-01-04 19:20:51,773 - mmcls - INFO - Epoch [33][1200/5005]\tlr: 1.000e-02, eta: 8:18:47, time: 0.092, data_time: 0.013, memory: 928, loss: 5.0994\n2023-01-04 19:21:00,625 - mmcls - INFO - Epoch [33][1300/5005]\tlr: 1.000e-02, eta: 8:18:38, time: 0.089, data_time: 0.014, memory: 928, loss: 5.1076\n2023-01-04 19:21:09,410 - mmcls - INFO - Epoch [33][1400/5005]\tlr: 1.000e-02, eta: 8:18:29, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0675\n2023-01-04 19:21:17,955 - mmcls - INFO - Epoch [33][1500/5005]\tlr: 1.000e-02, eta: 8:18:20, time: 0.086, data_time: 0.018, memory: 928, loss: 5.1012\n2023-01-04 19:21:26,940 - mmcls - INFO - Epoch [33][1600/5005]\tlr: 1.000e-02, eta: 8:18:11, time: 0.090, data_time: 0.011, memory: 928, loss: 5.1222\n2023-01-04 19:21:35,874 - mmcls - INFO - Epoch [33][1700/5005]\tlr: 1.000e-02, eta: 8:18:03, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0928\n2023-01-04 19:21:44,855 - mmcls - INFO - Epoch [33][1800/5005]\tlr: 1.000e-02, eta: 8:17:54, time: 0.090, data_time: 0.021, memory: 928, loss: 5.0992\n2023-01-04 19:21:53,605 - mmcls - INFO - Epoch [33][1900/5005]\tlr: 1.000e-02, eta: 8:17:45, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0826\n2023-01-04 19:22:02,117 - mmcls - INFO - Epoch [33][2000/5005]\tlr: 1.000e-02, eta: 8:17:36, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0854\n2023-01-04 19:22:10,948 - mmcls - INFO - Epoch [33][2100/5005]\tlr: 1.000e-02, eta: 8:17:27, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0987\n2023-01-04 19:22:19,983 - mmcls - INFO - Epoch [33][2200/5005]\tlr: 1.000e-02, eta: 8:17:19, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0819\n2023-01-04 19:22:28,974 - mmcls - INFO - Epoch [33][2300/5005]\tlr: 1.000e-02, eta: 8:17:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0972\n2023-01-04 19:22:37,741 - mmcls - INFO - Epoch [33][2400/5005]\tlr: 1.000e-02, eta: 8:17:01, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0937\n2023-01-04 19:22:46,492 - mmcls - INFO - Epoch [33][2500/5005]\tlr: 1.000e-02, eta: 8:16:52, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0865\n2023-01-04 19:22:55,126 - mmcls - INFO - Epoch [33][2600/5005]\tlr: 1.000e-02, eta: 8:16:43, time: 0.086, data_time: 0.042, memory: 928, loss: 5.0941\n2023-01-04 19:23:03,745 - mmcls - INFO - Epoch [33][2700/5005]\tlr: 1.000e-02, eta: 8:16:34, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0979\n2023-01-04 19:23:12,412 - mmcls - INFO - Epoch [33][2800/5005]\tlr: 1.000e-02, eta: 8:16:25, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0922\n2023-01-04 19:23:21,296 - mmcls - INFO - Epoch [33][2900/5005]\tlr: 1.000e-02, eta: 8:16:16, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0786\n2023-01-04 19:23:29,977 - mmcls - INFO - Epoch [33][3000/5005]\tlr: 1.000e-02, eta: 8:16:07, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0543\n2023-01-04 19:23:38,487 - mmcls - INFO - Epoch [33][3100/5005]\tlr: 1.000e-02, eta: 8:15:57, time: 0.085, data_time: 0.014, memory: 928, loss: 5.1033\n2023-01-04 19:23:47,237 - mmcls - INFO - Epoch [33][3200/5005]\tlr: 1.000e-02, eta: 8:15:48, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0949\n2023-01-04 19:23:56,107 - mmcls - INFO - Epoch [33][3300/5005]\tlr: 1.000e-02, eta: 8:15:40, time: 0.089, data_time: 0.015, memory: 928, loss: 5.1019\n2023-01-04 19:24:04,819 - mmcls - INFO - Epoch [33][3400/5005]\tlr: 1.000e-02, eta: 8:15:30, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0794\n2023-01-04 19:24:13,655 - mmcls - INFO - Epoch [33][3500/5005]\tlr: 1.000e-02, eta: 8:15:22, time: 0.088, data_time: 0.002, memory: 928, loss: 5.1083\n2023-01-04 19:24:22,775 - mmcls - INFO - Epoch [33][3600/5005]\tlr: 1.000e-02, eta: 8:15:13, time: 0.091, data_time: 0.009, memory: 928, loss: 5.0831\n2023-01-04 19:24:31,604 - mmcls - INFO - Epoch [33][3700/5005]\tlr: 1.000e-02, eta: 8:15:05, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0862\n2023-01-04 19:24:40,161 - mmcls - INFO - Epoch [33][3800/5005]\tlr: 1.000e-02, eta: 8:14:55, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1065\n2023-01-04 19:24:48,951 - mmcls - INFO - Epoch [33][3900/5005]\tlr: 1.000e-02, eta: 8:14:46, time: 0.088, data_time: 0.015, memory: 928, loss: 5.1013\n2023-01-04 19:24:57,540 - mmcls - INFO - Epoch [33][4000/5005]\tlr: 1.000e-02, eta: 8:14:37, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0995\n2023-01-04 19:25:06,549 - mmcls - INFO - Epoch [33][4100/5005]\tlr: 1.000e-02, eta: 8:14:29, time: 0.090, data_time: 0.011, memory: 928, loss: 5.1103\n2023-01-04 19:25:15,415 - mmcls - INFO - Epoch [33][4200/5005]\tlr: 1.000e-02, eta: 8:14:20, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0903\n2023-01-04 19:25:24,323 - mmcls - INFO - Epoch [33][4300/5005]\tlr: 1.000e-02, eta: 8:14:11, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1081\n2023-01-04 19:25:33,081 - mmcls - INFO - Epoch [33][4400/5005]\tlr: 1.000e-02, eta: 8:14:02, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0961\n2023-01-04 19:25:41,592 - mmcls - INFO - Epoch [33][4500/5005]\tlr: 1.000e-02, eta: 8:13:53, time: 0.085, data_time: 0.020, memory: 928, loss: 5.0964\n2023-01-04 19:25:50,128 - mmcls - INFO - Epoch [33][4600/5005]\tlr: 1.000e-02, eta: 8:13:43, time: 0.085, data_time: 0.009, memory: 928, loss: 5.1194\n2023-01-04 19:25:58,895 - mmcls - INFO - Epoch [33][4700/5005]\tlr: 1.000e-02, eta: 8:13:34, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0888\n2023-01-04 19:26:07,629 - mmcls - INFO - Epoch [33][4800/5005]\tlr: 1.000e-02, eta: 8:13:25, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0929\n2023-01-04 19:26:16,454 - mmcls - INFO - Epoch [33][4900/5005]\tlr: 1.000e-02, eta: 8:13:17, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0943\n2023-01-04 19:26:25,176 - mmcls - INFO - Epoch [33][5000/5005]\tlr: 1.000e-02, eta: 8:13:08, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0902\n2023-01-04 19:26:47,980 - mmcls - INFO - Epoch(val) [33][196]\taccuracy_top-1: 12.5000, accuracy_top-5: 27.3460\n2023-01-04 19:26:58,858 - mmcls - INFO - Epoch [34][100/5005]\tlr: 1.000e-02, eta: 8:13:02, time: 0.109, data_time: 0.056, memory: 928, loss: 5.0965\n2023-01-04 19:27:07,695 - mmcls - INFO - Epoch [34][200/5005]\tlr: 1.000e-02, eta: 8:12:53, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0873\n2023-01-04 19:27:16,881 - mmcls - INFO - Epoch [34][300/5005]\tlr: 1.000e-02, eta: 8:12:45, time: 0.092, data_time: 0.016, memory: 928, loss: 5.0932\n2023-01-04 19:27:25,347 - mmcls - INFO - Epoch [34][400/5005]\tlr: 1.000e-02, eta: 8:12:35, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0757\n2023-01-04 19:27:33,935 - mmcls - INFO - Epoch [34][500/5005]\tlr: 1.000e-02, eta: 8:12:26, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0710\n2023-01-04 19:27:42,669 - mmcls - INFO - Epoch [34][600/5005]\tlr: 1.000e-02, eta: 8:12:17, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0900\n2023-01-04 19:27:51,473 - mmcls - INFO - Epoch [34][700/5005]\tlr: 1.000e-02, eta: 8:12:08, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0997\n2023-01-04 19:28:00,395 - mmcls - INFO - Epoch [34][800/5005]\tlr: 1.000e-02, eta: 8:11:59, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0791\n2023-01-04 19:28:08,975 - mmcls - INFO - Epoch [34][900/5005]\tlr: 1.000e-02, eta: 8:11:50, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0994\n2023-01-04 19:28:17,989 - mmcls - INFO - Epoch [34][1000/5005]\tlr: 1.000e-02, eta: 8:11:42, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0944\n2023-01-04 19:28:26,838 - mmcls - INFO - Epoch [34][1100/5005]\tlr: 1.000e-02, eta: 8:11:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1117\n2023-01-04 19:28:35,556 - mmcls - INFO - Epoch [34][1200/5005]\tlr: 1.000e-02, eta: 8:11:24, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0912\n2023-01-04 19:28:44,460 - mmcls - INFO - Epoch [34][1300/5005]\tlr: 1.000e-02, eta: 8:11:15, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0915\n2023-01-04 19:28:53,299 - mmcls - INFO - Epoch [34][1400/5005]\tlr: 1.000e-02, eta: 8:11:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0901\n2023-01-04 19:29:01,891 - mmcls - INFO - Epoch [34][1500/5005]\tlr: 1.000e-02, eta: 8:10:57, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0852\n2023-01-04 19:29:10,529 - mmcls - INFO - Epoch [34][1600/5005]\tlr: 1.000e-02, eta: 8:10:48, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0889\n2023-01-04 19:29:19,061 - mmcls - INFO - Epoch [34][1700/5005]\tlr: 1.000e-02, eta: 8:10:38, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0745\n2023-01-04 19:29:27,948 - mmcls - INFO - Epoch [34][1800/5005]\tlr: 1.000e-02, eta: 8:10:30, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0924\n2023-01-04 19:29:36,541 - mmcls - INFO - Epoch [34][1900/5005]\tlr: 1.000e-02, eta: 8:10:20, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0734\n2023-01-04 19:29:45,252 - mmcls - INFO - Epoch [34][2000/5005]\tlr: 1.000e-02, eta: 8:10:11, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0779\n2023-01-04 19:29:54,126 - mmcls - INFO - Epoch [34][2100/5005]\tlr: 1.000e-02, eta: 8:10:03, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0825\n2023-01-04 19:30:02,587 - mmcls - INFO - Epoch [34][2200/5005]\tlr: 1.000e-02, eta: 8:09:53, time: 0.085, data_time: 0.007, memory: 928, loss: 5.1003\n2023-01-04 19:30:11,205 - mmcls - INFO - Epoch [34][2300/5005]\tlr: 1.000e-02, eta: 8:09:44, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0885\n2023-01-04 19:30:19,964 - mmcls - INFO - Epoch [34][2400/5005]\tlr: 1.000e-02, eta: 8:09:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0767\n2023-01-04 19:30:28,865 - mmcls - INFO - Epoch [34][2500/5005]\tlr: 1.000e-02, eta: 8:09:26, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0859\n2023-01-04 19:30:37,426 - mmcls - INFO - Epoch [34][2600/5005]\tlr: 1.000e-02, eta: 8:09:17, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0889\n2023-01-04 19:30:46,296 - mmcls - INFO - Epoch [34][2700/5005]\tlr: 1.000e-02, eta: 8:09:08, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1045\n2023-01-04 19:30:55,148 - mmcls - INFO - Epoch [34][2800/5005]\tlr: 1.000e-02, eta: 8:08:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0949\n2023-01-04 19:31:03,758 - mmcls - INFO - Epoch [34][2900/5005]\tlr: 1.000e-02, eta: 8:08:50, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0835\n2023-01-04 19:31:12,833 - mmcls - INFO - Epoch [34][3000/5005]\tlr: 1.000e-02, eta: 8:08:42, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0799\n2023-01-04 19:31:21,392 - mmcls - INFO - Epoch [34][3100/5005]\tlr: 1.000e-02, eta: 8:08:32, time: 0.086, data_time: 0.027, memory: 928, loss: 5.1063\n2023-01-04 19:31:29,961 - mmcls - INFO - Epoch [34][3200/5005]\tlr: 1.000e-02, eta: 8:08:23, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0783\n2023-01-04 19:31:38,548 - mmcls - INFO - Epoch [34][3300/5005]\tlr: 1.000e-02, eta: 8:08:14, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0975\n2023-01-04 19:31:47,080 - mmcls - INFO - Epoch [34][3400/5005]\tlr: 1.000e-02, eta: 8:08:04, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0876\n2023-01-04 19:31:55,840 - mmcls - INFO - Epoch [34][3500/5005]\tlr: 1.000e-02, eta: 8:07:56, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0857\n2023-01-04 19:32:04,810 - mmcls - INFO - Epoch [34][3600/5005]\tlr: 1.000e-02, eta: 8:07:47, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0905\n2023-01-04 19:32:13,667 - mmcls - INFO - Epoch [34][3700/5005]\tlr: 1.000e-02, eta: 8:07:38, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0816\n2023-01-04 19:32:22,388 - mmcls - INFO - Epoch [34][3800/5005]\tlr: 1.000e-02, eta: 8:07:29, time: 0.087, data_time: 0.008, memory: 928, loss: 5.1118\n2023-01-04 19:32:31,295 - mmcls - INFO - Epoch [34][3900/5005]\tlr: 1.000e-02, eta: 8:07:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1047\n2023-01-04 19:32:40,082 - mmcls - INFO - Epoch [34][4000/5005]\tlr: 1.000e-02, eta: 8:07:12, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0788\n2023-01-04 19:32:48,567 - mmcls - INFO - Epoch [34][4100/5005]\tlr: 1.000e-02, eta: 8:07:02, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1008\n2023-01-04 19:32:57,681 - mmcls - INFO - Epoch [34][4200/5005]\tlr: 1.000e-02, eta: 8:06:54, time: 0.091, data_time: 0.016, memory: 928, loss: 5.0874\n2023-01-04 19:33:06,198 - mmcls - INFO - Epoch [34][4300/5005]\tlr: 1.000e-02, eta: 8:06:44, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0843\n2023-01-04 19:33:15,318 - mmcls - INFO - Epoch [34][4400/5005]\tlr: 1.000e-02, eta: 8:06:36, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1033\n2023-01-04 19:33:23,856 - mmcls - INFO - Epoch [34][4500/5005]\tlr: 1.000e-02, eta: 8:06:27, time: 0.085, data_time: 0.026, memory: 928, loss: 5.0869\n2023-01-04 19:33:32,659 - mmcls - INFO - Epoch [34][4600/5005]\tlr: 1.000e-02, eta: 8:06:18, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0929\n2023-01-04 19:33:41,574 - mmcls - INFO - Epoch [34][4700/5005]\tlr: 1.000e-02, eta: 8:06:09, time: 0.089, data_time: 0.027, memory: 928, loss: 5.0878\n2023-01-04 19:33:50,462 - mmcls - INFO - Epoch [34][4800/5005]\tlr: 1.000e-02, eta: 8:06:01, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1109\n2023-01-04 19:33:59,213 - mmcls - INFO - Epoch [34][4900/5005]\tlr: 1.000e-02, eta: 8:05:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0910\n2023-01-04 19:34:08,100 - mmcls - INFO - Epoch [34][5000/5005]\tlr: 1.000e-02, eta: 8:05:43, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0699\n2023-01-04 19:34:30,792 - mmcls - INFO - Epoch(val) [34][196]\taccuracy_top-1: 12.5160, accuracy_top-5: 28.0500\n2023-01-04 19:34:41,782 - mmcls - INFO - Epoch [35][100/5005]\tlr: 1.000e-02, eta: 8:05:37, time: 0.110, data_time: 0.050, memory: 928, loss: 5.0453\n2023-01-04 19:34:50,512 - mmcls - INFO - Epoch [35][200/5005]\tlr: 1.000e-02, eta: 8:05:28, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0713\n2023-01-04 19:34:59,076 - mmcls - INFO - Epoch [35][300/5005]\tlr: 1.000e-02, eta: 8:05:19, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0691\n2023-01-04 19:35:07,930 - mmcls - INFO - Epoch [35][400/5005]\tlr: 1.000e-02, eta: 8:05:10, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0962\n2023-01-04 19:35:16,836 - mmcls - INFO - Epoch [35][500/5005]\tlr: 1.000e-02, eta: 8:05:01, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0798\n2023-01-04 19:35:25,417 - mmcls - INFO - Epoch [35][600/5005]\tlr: 1.000e-02, eta: 8:04:52, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0945\n2023-01-04 19:35:34,480 - mmcls - INFO - Epoch [35][700/5005]\tlr: 1.000e-02, eta: 8:04:44, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0795\n2023-01-04 19:35:43,364 - mmcls - INFO - Epoch [35][800/5005]\tlr: 1.000e-02, eta: 8:04:35, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0906\n2023-01-04 19:35:52,043 - mmcls - INFO - Epoch [35][900/5005]\tlr: 1.000e-02, eta: 8:04:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0928\n2023-01-04 19:36:00,823 - mmcls - INFO - Epoch [35][1000/5005]\tlr: 1.000e-02, eta: 8:04:17, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0498\n2023-01-04 19:36:09,265 - mmcls - INFO - Epoch [35][1100/5005]\tlr: 1.000e-02, eta: 8:04:07, time: 0.084, data_time: 0.003, memory: 928, loss: 5.0853\n2023-01-04 19:36:18,053 - mmcls - INFO - Epoch [35][1200/5005]\tlr: 1.000e-02, eta: 8:03:58, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0772\n2023-01-04 19:36:26,751 - mmcls - INFO - Epoch [35][1300/5005]\tlr: 1.000e-02, eta: 8:03:49, time: 0.087, data_time: 0.011, memory: 928, loss: 5.1008\n2023-01-04 19:36:35,526 - mmcls - INFO - Epoch [35][1400/5005]\tlr: 1.000e-02, eta: 8:03:40, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0896\n2023-01-04 19:36:44,205 - mmcls - INFO - Epoch [35][1500/5005]\tlr: 1.000e-02, eta: 8:03:31, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0915\n2023-01-04 19:36:52,898 - mmcls - INFO - Epoch [35][1600/5005]\tlr: 1.000e-02, eta: 8:03:22, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0775\n2023-01-04 19:37:01,647 - mmcls - INFO - Epoch [35][1700/5005]\tlr: 1.000e-02, eta: 8:03:13, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0773\n2023-01-04 19:37:10,297 - mmcls - INFO - Epoch [35][1800/5005]\tlr: 1.000e-02, eta: 8:03:04, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0896\n2023-01-04 19:37:19,194 - mmcls - INFO - Epoch [35][1900/5005]\tlr: 1.000e-02, eta: 8:02:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0815\n2023-01-04 19:37:28,127 - mmcls - INFO - Epoch [35][2000/5005]\tlr: 1.000e-02, eta: 8:02:47, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0881\n2023-01-04 19:37:36,775 - mmcls - INFO - Epoch [35][2100/5005]\tlr: 1.000e-02, eta: 8:02:38, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1014\n2023-01-04 19:37:45,682 - mmcls - INFO - Epoch [35][2200/5005]\tlr: 1.000e-02, eta: 8:02:29, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0925\n2023-01-04 19:37:54,447 - mmcls - INFO - Epoch [35][2300/5005]\tlr: 1.000e-02, eta: 8:02:20, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0879\n2023-01-04 19:38:03,136 - mmcls - INFO - Epoch [35][2400/5005]\tlr: 1.000e-02, eta: 8:02:11, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0847\n2023-01-04 19:38:11,953 - mmcls - INFO - Epoch [35][2500/5005]\tlr: 1.000e-02, eta: 8:02:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0840\n2023-01-04 19:38:20,438 - mmcls - INFO - Epoch [35][2600/5005]\tlr: 1.000e-02, eta: 8:01:53, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0703\n2023-01-04 19:38:28,975 - mmcls - INFO - Epoch [35][2700/5005]\tlr: 1.000e-02, eta: 8:01:43, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1040\n2023-01-04 19:38:37,594 - mmcls - INFO - Epoch [35][2800/5005]\tlr: 1.000e-02, eta: 8:01:34, time: 0.086, data_time: 0.002, memory: 928, loss: 5.1003\n2023-01-04 19:38:46,318 - mmcls - INFO - Epoch [35][2900/5005]\tlr: 1.000e-02, eta: 8:01:25, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0932\n2023-01-04 19:38:54,862 - mmcls - INFO - Epoch [35][3000/5005]\tlr: 1.000e-02, eta: 8:01:16, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0853\n2023-01-04 19:39:03,662 - mmcls - INFO - Epoch [35][3100/5005]\tlr: 1.000e-02, eta: 8:01:07, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0661\n2023-01-04 19:39:12,306 - mmcls - INFO - Epoch [35][3200/5005]\tlr: 1.000e-02, eta: 8:00:58, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0857\n2023-01-04 19:39:20,955 - mmcls - INFO - Epoch [35][3300/5005]\tlr: 1.000e-02, eta: 8:00:49, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1079\n2023-01-04 19:39:29,653 - mmcls - INFO - Epoch [35][3400/5005]\tlr: 1.000e-02, eta: 8:00:40, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0969\n2023-01-04 19:39:38,279 - mmcls - INFO - Epoch [35][3500/5005]\tlr: 1.000e-02, eta: 8:00:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0894\n2023-01-04 19:39:47,215 - mmcls - INFO - Epoch [35][3600/5005]\tlr: 1.000e-02, eta: 8:00:22, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1026\n2023-01-04 19:39:56,137 - mmcls - INFO - Epoch [35][3700/5005]\tlr: 1.000e-02, eta: 8:00:13, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0736\n2023-01-04 19:40:05,047 - mmcls - INFO - Epoch [35][3800/5005]\tlr: 1.000e-02, eta: 8:00:04, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0821\n2023-01-04 19:40:13,905 - mmcls - INFO - Epoch [35][3900/5005]\tlr: 1.000e-02, eta: 7:59:56, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0937\n2023-01-04 19:40:22,547 - mmcls - INFO - Epoch [35][4000/5005]\tlr: 1.000e-02, eta: 7:59:47, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0821\n2023-01-04 19:40:31,617 - mmcls - INFO - Epoch [35][4100/5005]\tlr: 1.000e-02, eta: 7:59:38, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0784\n2023-01-04 19:40:40,326 - mmcls - INFO - Epoch [35][4200/5005]\tlr: 1.000e-02, eta: 7:59:29, time: 0.087, data_time: 0.027, memory: 928, loss: 5.1167\n2023-01-04 19:40:48,902 - mmcls - INFO - Epoch [35][4300/5005]\tlr: 1.000e-02, eta: 7:59:20, time: 0.086, data_time: 0.008, memory: 928, loss: 5.1089\n2023-01-04 19:40:57,744 - mmcls - INFO - Epoch [35][4400/5005]\tlr: 1.000e-02, eta: 7:59:11, time: 0.089, data_time: 0.004, memory: 928, loss: 5.1060\n2023-01-04 19:41:06,463 - mmcls - INFO - Epoch [35][4500/5005]\tlr: 1.000e-02, eta: 7:59:02, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0787\n2023-01-04 19:41:15,113 - mmcls - INFO - Epoch [35][4600/5005]\tlr: 1.000e-02, eta: 7:58:53, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0824\n2023-01-04 19:41:23,889 - mmcls - INFO - Epoch [35][4700/5005]\tlr: 1.000e-02, eta: 7:58:44, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0816\n2023-01-04 19:41:32,655 - mmcls - INFO - Epoch [35][4800/5005]\tlr: 1.000e-02, eta: 7:58:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1016\n2023-01-04 19:41:41,637 - mmcls - INFO - Epoch [35][4900/5005]\tlr: 1.000e-02, eta: 7:58:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0769\n2023-01-04 19:41:50,836 - mmcls - INFO - Epoch [35][5000/5005]\tlr: 1.000e-02, eta: 7:58:18, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0622\n2023-01-04 19:42:13,522 - mmcls - INFO - Epoch(val) [35][196]\taccuracy_top-1: 12.1480, accuracy_top-5: 27.3840\n2023-01-04 19:42:24,497 - mmcls - INFO - Epoch [36][100/5005]\tlr: 1.000e-02, eta: 7:58:12, time: 0.110, data_time: 0.075, memory: 928, loss: 5.0736\n2023-01-04 19:42:33,231 - mmcls - INFO - Epoch [36][200/5005]\tlr: 1.000e-02, eta: 7:58:03, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0705\n2023-01-04 19:42:41,814 - mmcls - INFO - Epoch [36][300/5005]\tlr: 1.000e-02, eta: 7:57:54, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0774\n2023-01-04 19:42:51,452 - mmcls - INFO - Epoch [36][400/5005]\tlr: 1.000e-02, eta: 7:57:47, time: 0.096, data_time: 0.010, memory: 928, loss: 5.0762\n2023-01-04 19:43:00,072 - mmcls - INFO - Epoch [36][500/5005]\tlr: 1.000e-02, eta: 7:57:38, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0889\n2023-01-04 19:43:08,921 - mmcls - INFO - Epoch [36][600/5005]\tlr: 1.000e-02, eta: 7:57:29, time: 0.089, data_time: 0.032, memory: 928, loss: 5.0620\n2023-01-04 19:43:17,705 - mmcls - INFO - Epoch [36][700/5005]\tlr: 1.000e-02, eta: 7:57:20, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0771\n2023-01-04 19:43:26,487 - mmcls - INFO - Epoch [36][800/5005]\tlr: 1.000e-02, eta: 7:57:11, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0666\n2023-01-04 19:43:35,141 - mmcls - INFO - Epoch [36][900/5005]\tlr: 1.000e-02, eta: 7:57:02, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0675\n2023-01-04 19:43:43,974 - mmcls - INFO - Epoch [36][1000/5005]\tlr: 1.000e-02, eta: 7:56:53, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0984\n2023-01-04 19:43:52,624 - mmcls - INFO - Epoch [36][1100/5005]\tlr: 1.000e-02, eta: 7:56:44, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0815\n2023-01-04 19:44:01,311 - mmcls - INFO - Epoch [36][1200/5005]\tlr: 1.000e-02, eta: 7:56:35, time: 0.087, data_time: 0.015, memory: 928, loss: 5.1041\n2023-01-04 19:44:10,042 - mmcls - INFO - Epoch [36][1300/5005]\tlr: 1.000e-02, eta: 7:56:26, time: 0.087, data_time: 0.023, memory: 928, loss: 5.1002\n2023-01-04 19:44:18,720 - mmcls - INFO - Epoch [36][1400/5005]\tlr: 1.000e-02, eta: 7:56:17, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0779\n2023-01-04 19:44:27,381 - mmcls - INFO - Epoch [36][1500/5005]\tlr: 1.000e-02, eta: 7:56:08, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0871\n2023-01-04 19:44:36,209 - mmcls - INFO - Epoch [36][1600/5005]\tlr: 1.000e-02, eta: 7:55:59, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0830\n2023-01-04 19:44:44,871 - mmcls - INFO - Epoch [36][1700/5005]\tlr: 1.000e-02, eta: 7:55:50, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0908\n2023-01-04 19:44:53,674 - mmcls - INFO - Epoch [36][1800/5005]\tlr: 1.000e-02, eta: 7:55:41, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0831\n2023-01-04 19:45:02,196 - mmcls - INFO - Epoch [36][1900/5005]\tlr: 1.000e-02, eta: 7:55:32, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0863\n2023-01-04 19:45:10,971 - mmcls - INFO - Epoch [36][2000/5005]\tlr: 1.000e-02, eta: 7:55:23, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0649\n2023-01-04 19:45:19,981 - mmcls - INFO - Epoch [36][2100/5005]\tlr: 1.000e-02, eta: 7:55:14, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0742\n2023-01-04 19:45:28,563 - mmcls - INFO - Epoch [36][2200/5005]\tlr: 1.000e-02, eta: 7:55:05, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0737\n2023-01-04 19:45:37,172 - mmcls - INFO - Epoch [36][2300/5005]\tlr: 1.000e-02, eta: 7:54:56, time: 0.086, data_time: 0.026, memory: 928, loss: 5.0883\n2023-01-04 19:45:45,664 - mmcls - INFO - Epoch [36][2400/5005]\tlr: 1.000e-02, eta: 7:54:46, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0773\n2023-01-04 19:45:54,351 - mmcls - INFO - Epoch [36][2500/5005]\tlr: 1.000e-02, eta: 7:54:37, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0614\n2023-01-04 19:46:02,887 - mmcls - INFO - Epoch [36][2600/5005]\tlr: 1.000e-02, eta: 7:54:28, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0787\n2023-01-04 19:46:11,730 - mmcls - INFO - Epoch [36][2700/5005]\tlr: 1.000e-02, eta: 7:54:19, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0856\n2023-01-04 19:46:20,688 - mmcls - INFO - Epoch [36][2800/5005]\tlr: 1.000e-02, eta: 7:54:10, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0809\n2023-01-04 19:46:29,648 - mmcls - INFO - Epoch [36][2900/5005]\tlr: 1.000e-02, eta: 7:54:02, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0923\n2023-01-04 19:46:39,141 - mmcls - INFO - Epoch [36][3000/5005]\tlr: 1.000e-02, eta: 7:53:54, time: 0.095, data_time: 0.048, memory: 928, loss: 5.0789\n2023-01-04 19:46:48,075 - mmcls - INFO - Epoch [36][3100/5005]\tlr: 1.000e-02, eta: 7:53:46, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0629\n2023-01-04 19:46:56,793 - mmcls - INFO - Epoch [36][3200/5005]\tlr: 1.000e-02, eta: 7:53:37, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0736\n2023-01-04 19:47:05,585 - mmcls - INFO - Epoch [36][3300/5005]\tlr: 1.000e-02, eta: 7:53:28, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0486\n2023-01-04 19:47:14,584 - mmcls - INFO - Epoch [36][3400/5005]\tlr: 1.000e-02, eta: 7:53:19, time: 0.090, data_time: 0.022, memory: 928, loss: 5.0770\n2023-01-04 19:47:23,375 - mmcls - INFO - Epoch [36][3500/5005]\tlr: 1.000e-02, eta: 7:53:10, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0839\n2023-01-04 19:47:32,243 - mmcls - INFO - Epoch [36][3600/5005]\tlr: 1.000e-02, eta: 7:53:02, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0668\n2023-01-04 19:47:41,068 - mmcls - INFO - Epoch [36][3700/5005]\tlr: 1.000e-02, eta: 7:52:53, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0616\n2023-01-04 19:47:49,782 - mmcls - INFO - Epoch [36][3800/5005]\tlr: 1.000e-02, eta: 7:52:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0884\n2023-01-04 19:47:58,389 - mmcls - INFO - Epoch [36][3900/5005]\tlr: 1.000e-02, eta: 7:52:35, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0678\n2023-01-04 19:48:07,083 - mmcls - INFO - Epoch [36][4000/5005]\tlr: 1.000e-02, eta: 7:52:26, time: 0.087, data_time: 0.013, memory: 928, loss: 5.1209\n2023-01-04 19:48:15,692 - mmcls - INFO - Epoch [36][4100/5005]\tlr: 1.000e-02, eta: 7:52:16, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0900\n2023-01-04 19:48:24,199 - mmcls - INFO - Epoch [36][4200/5005]\tlr: 1.000e-02, eta: 7:52:07, time: 0.085, data_time: 0.010, memory: 928, loss: 5.1027\n2023-01-04 19:48:32,928 - mmcls - INFO - Epoch [36][4300/5005]\tlr: 1.000e-02, eta: 7:51:58, time: 0.087, data_time: 0.039, memory: 928, loss: 5.0792\n2023-01-04 19:48:41,646 - mmcls - INFO - Epoch [36][4400/5005]\tlr: 1.000e-02, eta: 7:51:49, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0913\n2023-01-04 19:48:50,440 - mmcls - INFO - Epoch [36][4500/5005]\tlr: 1.000e-02, eta: 7:51:40, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0918\n2023-01-04 19:48:59,155 - mmcls - INFO - Epoch [36][4600/5005]\tlr: 1.000e-02, eta: 7:51:31, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0898\n2023-01-04 19:49:07,971 - mmcls - INFO - Epoch [36][4700/5005]\tlr: 1.000e-02, eta: 7:51:22, time: 0.088, data_time: 0.032, memory: 928, loss: 5.0804\n2023-01-04 19:49:16,823 - mmcls - INFO - Epoch [36][4800/5005]\tlr: 1.000e-02, eta: 7:51:13, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0910\n2023-01-04 19:49:26,001 - mmcls - INFO - Epoch [36][4900/5005]\tlr: 1.000e-02, eta: 7:51:05, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0724\n2023-01-04 19:49:34,688 - mmcls - INFO - Epoch [36][5000/5005]\tlr: 1.000e-02, eta: 7:50:56, time: 0.087, data_time: 0.007, memory: 928, loss: 5.1018\n2023-01-04 19:49:57,774 - mmcls - INFO - Epoch(val) [36][196]\taccuracy_top-1: 13.1380, accuracy_top-5: 28.7100\n2023-01-04 19:50:09,102 - mmcls - INFO - Epoch [37][100/5005]\tlr: 1.000e-02, eta: 7:50:51, time: 0.113, data_time: 0.038, memory: 928, loss: 5.0739\n2023-01-04 19:50:17,734 - mmcls - INFO - Epoch [37][200/5005]\tlr: 1.000e-02, eta: 7:50:41, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0647\n2023-01-04 19:50:26,604 - mmcls - INFO - Epoch [37][300/5005]\tlr: 1.000e-02, eta: 7:50:33, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0634\n2023-01-04 19:50:35,262 - mmcls - INFO - Epoch [37][400/5005]\tlr: 1.000e-02, eta: 7:50:24, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0790\n2023-01-04 19:50:43,822 - mmcls - INFO - Epoch [37][500/5005]\tlr: 1.000e-02, eta: 7:50:14, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0797\n2023-01-04 19:50:52,666 - mmcls - INFO - Epoch [37][600/5005]\tlr: 1.000e-02, eta: 7:50:06, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0885\n2023-01-04 19:51:01,436 - mmcls - INFO - Epoch [37][700/5005]\tlr: 1.000e-02, eta: 7:49:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0834\n2023-01-04 19:51:09,876 - mmcls - INFO - Epoch [37][800/5005]\tlr: 1.000e-02, eta: 7:49:47, time: 0.084, data_time: 0.017, memory: 928, loss: 5.0690\n2023-01-04 19:51:18,793 - mmcls - INFO - Epoch [37][900/5005]\tlr: 1.000e-02, eta: 7:49:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0906\n2023-01-04 19:51:27,435 - mmcls - INFO - Epoch [37][1000/5005]\tlr: 1.000e-02, eta: 7:49:29, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0711\n2023-01-04 19:51:36,294 - mmcls - INFO - Epoch [37][1100/5005]\tlr: 1.000e-02, eta: 7:49:21, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0889\n2023-01-04 19:51:45,096 - mmcls - INFO - Epoch [37][1200/5005]\tlr: 1.000e-02, eta: 7:49:12, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0846\n2023-01-04 19:51:53,717 - mmcls - INFO - Epoch [37][1300/5005]\tlr: 1.000e-02, eta: 7:49:02, time: 0.086, data_time: 0.016, memory: 928, loss: 5.0779\n2023-01-04 19:52:02,481 - mmcls - INFO - Epoch [37][1400/5005]\tlr: 1.000e-02, eta: 7:48:54, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0591\n2023-01-04 19:52:11,296 - mmcls - INFO - Epoch [37][1500/5005]\tlr: 1.000e-02, eta: 7:48:45, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0707\n2023-01-04 19:52:19,910 - mmcls - INFO - Epoch [37][1600/5005]\tlr: 1.000e-02, eta: 7:48:36, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0610\n2023-01-04 19:52:28,698 - mmcls - INFO - Epoch [37][1700/5005]\tlr: 1.000e-02, eta: 7:48:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0863\n2023-01-04 19:52:37,110 - mmcls - INFO - Epoch [37][1800/5005]\tlr: 1.000e-02, eta: 7:48:17, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0761\n2023-01-04 19:52:45,878 - mmcls - INFO - Epoch [37][1900/5005]\tlr: 1.000e-02, eta: 7:48:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0768\n2023-01-04 19:52:54,684 - mmcls - INFO - Epoch [37][2000/5005]\tlr: 1.000e-02, eta: 7:47:59, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0929\n2023-01-04 19:53:03,398 - mmcls - INFO - Epoch [37][2100/5005]\tlr: 1.000e-02, eta: 7:47:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0888\n2023-01-04 19:53:12,143 - mmcls - INFO - Epoch [37][2200/5005]\tlr: 1.000e-02, eta: 7:47:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0786\n2023-01-04 19:53:21,372 - mmcls - INFO - Epoch [37][2300/5005]\tlr: 1.000e-02, eta: 7:47:33, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0534\n2023-01-04 19:53:30,294 - mmcls - INFO - Epoch [37][2400/5005]\tlr: 1.000e-02, eta: 7:47:25, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0949\n2023-01-04 19:53:39,188 - mmcls - INFO - Epoch [37][2500/5005]\tlr: 1.000e-02, eta: 7:47:16, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0749\n2023-01-04 19:53:47,814 - mmcls - INFO - Epoch [37][2600/5005]\tlr: 1.000e-02, eta: 7:47:07, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0593\n2023-01-04 19:53:56,537 - mmcls - INFO - Epoch [37][2700/5005]\tlr: 1.000e-02, eta: 7:46:58, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0759\n2023-01-04 19:54:05,470 - mmcls - INFO - Epoch [37][2800/5005]\tlr: 1.000e-02, eta: 7:46:49, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0877\n2023-01-04 19:54:14,242 - mmcls - INFO - Epoch [37][2900/5005]\tlr: 1.000e-02, eta: 7:46:40, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0886\n2023-01-04 19:54:22,999 - mmcls - INFO - Epoch [37][3000/5005]\tlr: 1.000e-02, eta: 7:46:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0612\n2023-01-04 19:54:31,715 - mmcls - INFO - Epoch [37][3100/5005]\tlr: 1.000e-02, eta: 7:46:22, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0807\n2023-01-04 19:54:40,276 - mmcls - INFO - Epoch [37][3200/5005]\tlr: 1.000e-02, eta: 7:46:13, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0873\n2023-01-04 19:54:48,988 - mmcls - INFO - Epoch [37][3300/5005]\tlr: 1.000e-02, eta: 7:46:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0815\n2023-01-04 19:54:57,654 - mmcls - INFO - Epoch [37][3400/5005]\tlr: 1.000e-02, eta: 7:45:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0461\n2023-01-04 19:55:06,444 - mmcls - INFO - Epoch [37][3500/5005]\tlr: 1.000e-02, eta: 7:45:46, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0471\n2023-01-04 19:55:15,179 - mmcls - INFO - Epoch [37][3600/5005]\tlr: 1.000e-02, eta: 7:45:37, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0881\n2023-01-04 19:55:23,763 - mmcls - INFO - Epoch [37][3700/5005]\tlr: 1.000e-02, eta: 7:45:28, time: 0.086, data_time: 0.022, memory: 928, loss: 5.0844\n2023-01-04 19:55:32,535 - mmcls - INFO - Epoch [37][3800/5005]\tlr: 1.000e-02, eta: 7:45:19, time: 0.088, data_time: 0.030, memory: 928, loss: 5.0692\n2023-01-04 19:55:41,696 - mmcls - INFO - Epoch [37][3900/5005]\tlr: 1.000e-02, eta: 7:45:11, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0502\n2023-01-04 19:55:50,323 - mmcls - INFO - Epoch [37][4000/5005]\tlr: 1.000e-02, eta: 7:45:02, time: 0.086, data_time: 0.030, memory: 928, loss: 5.0903\n2023-01-04 19:55:59,319 - mmcls - INFO - Epoch [37][4100/5005]\tlr: 1.000e-02, eta: 7:44:53, time: 0.090, data_time: 0.037, memory: 928, loss: 5.0817\n2023-01-04 19:56:08,223 - mmcls - INFO - Epoch [37][4200/5005]\tlr: 1.000e-02, eta: 7:44:44, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0923\n2023-01-04 19:56:17,124 - mmcls - INFO - Epoch [37][4300/5005]\tlr: 1.000e-02, eta: 7:44:36, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0784\n2023-01-04 19:56:26,030 - mmcls - INFO - Epoch [37][4400/5005]\tlr: 1.000e-02, eta: 7:44:27, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0632\n2023-01-04 19:56:34,730 - mmcls - INFO - Epoch [37][4500/5005]\tlr: 1.000e-02, eta: 7:44:18, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0755\n2023-01-04 19:56:43,691 - mmcls - INFO - Epoch [37][4600/5005]\tlr: 1.000e-02, eta: 7:44:09, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0755\n2023-01-04 19:56:52,206 - mmcls - INFO - Epoch [37][4700/5005]\tlr: 1.000e-02, eta: 7:44:00, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0686\n2023-01-04 19:57:01,158 - mmcls - INFO - Epoch [37][4800/5005]\tlr: 1.000e-02, eta: 7:43:51, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0968\n2023-01-04 19:57:09,646 - mmcls - INFO - Epoch [37][4900/5005]\tlr: 1.000e-02, eta: 7:43:42, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0868\n2023-01-04 19:57:18,681 - mmcls - INFO - Epoch [37][5000/5005]\tlr: 1.000e-02, eta: 7:43:34, time: 0.090, data_time: 0.019, memory: 928, loss: 5.0847\n2023-01-04 19:57:41,509 - mmcls - INFO - Epoch(val) [37][196]\taccuracy_top-1: 11.9320, accuracy_top-5: 26.4800\n2023-01-04 19:57:52,464 - mmcls - INFO - Epoch [38][100/5005]\tlr: 1.000e-02, eta: 7:43:27, time: 0.109, data_time: 0.047, memory: 928, loss: 5.0837\n2023-01-04 19:58:01,382 - mmcls - INFO - Epoch [38][200/5005]\tlr: 1.000e-02, eta: 7:43:19, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0533\n2023-01-04 19:58:10,176 - mmcls - INFO - Epoch [38][300/5005]\tlr: 1.000e-02, eta: 7:43:10, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0433\n2023-01-04 19:58:18,649 - mmcls - INFO - Epoch [38][400/5005]\tlr: 1.000e-02, eta: 7:43:00, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0716\n2023-01-04 19:58:27,208 - mmcls - INFO - Epoch [38][500/5005]\tlr: 1.000e-02, eta: 7:42:51, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0591\n2023-01-04 19:58:36,382 - mmcls - INFO - Epoch [38][600/5005]\tlr: 1.000e-02, eta: 7:42:43, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0693\n2023-01-04 19:58:45,038 - mmcls - INFO - Epoch [38][700/5005]\tlr: 1.000e-02, eta: 7:42:34, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0732\n2023-01-04 19:58:54,159 - mmcls - INFO - Epoch [38][800/5005]\tlr: 1.000e-02, eta: 7:42:25, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0577\n2023-01-04 19:59:03,180 - mmcls - INFO - Epoch [38][900/5005]\tlr: 1.000e-02, eta: 7:42:17, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0663\n2023-01-04 19:59:12,100 - mmcls - INFO - Epoch [38][1000/5005]\tlr: 1.000e-02, eta: 7:42:08, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0743\n2023-01-04 19:59:20,920 - mmcls - INFO - Epoch [38][1100/5005]\tlr: 1.000e-02, eta: 7:41:59, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0776\n2023-01-04 19:59:29,709 - mmcls - INFO - Epoch [38][1200/5005]\tlr: 1.000e-02, eta: 7:41:50, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0632\n2023-01-04 19:59:38,582 - mmcls - INFO - Epoch [38][1300/5005]\tlr: 1.000e-02, eta: 7:41:42, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0799\n2023-01-04 19:59:47,139 - mmcls - INFO - Epoch [38][1400/5005]\tlr: 1.000e-02, eta: 7:41:32, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0876\n2023-01-04 19:59:55,965 - mmcls - INFO - Epoch [38][1500/5005]\tlr: 1.000e-02, eta: 7:41:24, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0824\n2023-01-04 20:00:04,821 - mmcls - INFO - Epoch [38][1600/5005]\tlr: 1.000e-02, eta: 7:41:15, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0748\n2023-01-04 20:00:13,495 - mmcls - INFO - Epoch [38][1700/5005]\tlr: 1.000e-02, eta: 7:41:06, time: 0.087, data_time: 0.006, memory: 928, loss: 5.1020\n2023-01-04 20:00:22,312 - mmcls - INFO - Epoch [38][1800/5005]\tlr: 1.000e-02, eta: 7:40:57, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0813\n2023-01-04 20:00:31,448 - mmcls - INFO - Epoch [38][1900/5005]\tlr: 1.000e-02, eta: 7:40:49, time: 0.091, data_time: 0.003, memory: 928, loss: 5.0741\n2023-01-04 20:00:40,083 - mmcls - INFO - Epoch [38][2000/5005]\tlr: 1.000e-02, eta: 7:40:40, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0698\n2023-01-04 20:00:48,865 - mmcls - INFO - Epoch [38][2100/5005]\tlr: 1.000e-02, eta: 7:40:31, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0978\n2023-01-04 20:00:58,115 - mmcls - INFO - Epoch [38][2200/5005]\tlr: 1.000e-02, eta: 7:40:23, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0757\n2023-01-04 20:01:06,705 - mmcls - INFO - Epoch [38][2300/5005]\tlr: 1.000e-02, eta: 7:40:13, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0706\n2023-01-04 20:01:15,436 - mmcls - INFO - Epoch [38][2400/5005]\tlr: 1.000e-02, eta: 7:40:04, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0861\n2023-01-04 20:01:24,171 - mmcls - INFO - Epoch [38][2500/5005]\tlr: 1.000e-02, eta: 7:39:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0627\n2023-01-04 20:01:33,058 - mmcls - INFO - Epoch [38][2600/5005]\tlr: 1.000e-02, eta: 7:39:47, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0653\n2023-01-04 20:01:41,989 - mmcls - INFO - Epoch [38][2700/5005]\tlr: 1.000e-02, eta: 7:39:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0708\n2023-01-04 20:01:50,403 - mmcls - INFO - Epoch [38][2800/5005]\tlr: 1.000e-02, eta: 7:39:29, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0810\n2023-01-04 20:01:59,397 - mmcls - INFO - Epoch [38][2900/5005]\tlr: 1.000e-02, eta: 7:39:20, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0818\n2023-01-04 20:02:08,519 - mmcls - INFO - Epoch [38][3000/5005]\tlr: 1.000e-02, eta: 7:39:12, time: 0.091, data_time: 0.022, memory: 928, loss: 5.0691\n2023-01-04 20:02:17,417 - mmcls - INFO - Epoch [38][3100/5005]\tlr: 1.000e-02, eta: 7:39:03, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0727\n2023-01-04 20:02:25,899 - mmcls - INFO - Epoch [38][3200/5005]\tlr: 1.000e-02, eta: 7:38:54, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0802\n2023-01-04 20:02:34,484 - mmcls - INFO - Epoch [38][3300/5005]\tlr: 1.000e-02, eta: 7:38:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0965\n2023-01-04 20:02:43,642 - mmcls - INFO - Epoch [38][3400/5005]\tlr: 1.000e-02, eta: 7:38:36, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0645\n2023-01-04 20:02:52,396 - mmcls - INFO - Epoch [38][3500/5005]\tlr: 1.000e-02, eta: 7:38:27, time: 0.088, data_time: 0.043, memory: 928, loss: 5.0835\n2023-01-04 20:03:00,838 - mmcls - INFO - Epoch [38][3600/5005]\tlr: 1.000e-02, eta: 7:38:18, time: 0.084, data_time: 0.019, memory: 928, loss: 5.1039\n2023-01-04 20:03:10,115 - mmcls - INFO - Epoch [38][3700/5005]\tlr: 1.000e-02, eta: 7:38:10, time: 0.093, data_time: 0.003, memory: 928, loss: 5.0805\n2023-01-04 20:03:18,782 - mmcls - INFO - Epoch [38][3800/5005]\tlr: 1.000e-02, eta: 7:38:01, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0800\n2023-01-04 20:03:27,481 - mmcls - INFO - Epoch [38][3900/5005]\tlr: 1.000e-02, eta: 7:37:52, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0706\n2023-01-04 20:03:36,173 - mmcls - INFO - Epoch [38][4000/5005]\tlr: 1.000e-02, eta: 7:37:42, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0786\n2023-01-04 20:03:45,072 - mmcls - INFO - Epoch [38][4100/5005]\tlr: 1.000e-02, eta: 7:37:34, time: 0.089, data_time: 0.045, memory: 928, loss: 5.0937\n2023-01-04 20:03:54,035 - mmcls - INFO - Epoch [38][4200/5005]\tlr: 1.000e-02, eta: 7:37:25, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0639\n2023-01-04 20:04:02,972 - mmcls - INFO - Epoch [38][4300/5005]\tlr: 1.000e-02, eta: 7:37:17, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0945\n2023-01-04 20:04:11,670 - mmcls - INFO - Epoch [38][4400/5005]\tlr: 1.000e-02, eta: 7:37:08, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0697\n2023-01-04 20:04:20,720 - mmcls - INFO - Epoch [38][4500/5005]\tlr: 1.000e-02, eta: 7:36:59, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0867\n2023-01-04 20:04:29,797 - mmcls - INFO - Epoch [38][4600/5005]\tlr: 1.000e-02, eta: 7:36:51, time: 0.091, data_time: 0.006, memory: 928, loss: 5.0668\n2023-01-04 20:04:38,622 - mmcls - INFO - Epoch [38][4700/5005]\tlr: 1.000e-02, eta: 7:36:42, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0826\n2023-01-04 20:04:47,129 - mmcls - INFO - Epoch [38][4800/5005]\tlr: 1.000e-02, eta: 7:36:33, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0747\n2023-01-04 20:04:56,053 - mmcls - INFO - Epoch [38][4900/5005]\tlr: 1.000e-02, eta: 7:36:24, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0993\n2023-01-04 20:05:04,562 - mmcls - INFO - Epoch [38][5000/5005]\tlr: 1.000e-02, eta: 7:36:15, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0788\n2023-01-04 20:05:27,486 - mmcls - INFO - Epoch(val) [38][196]\taccuracy_top-1: 10.7980, accuracy_top-5: 24.9140\n2023-01-04 20:05:38,900 - mmcls - INFO - Epoch [39][100/5005]\tlr: 1.000e-02, eta: 7:36:09, time: 0.114, data_time: 0.041, memory: 928, loss: 5.0572\n2023-01-04 20:05:47,881 - mmcls - INFO - Epoch [39][200/5005]\tlr: 1.000e-02, eta: 7:36:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0685\n2023-01-04 20:05:56,835 - mmcls - INFO - Epoch [39][300/5005]\tlr: 1.000e-02, eta: 7:35:52, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0641\n2023-01-04 20:06:05,526 - mmcls - INFO - Epoch [39][400/5005]\tlr: 1.000e-02, eta: 7:35:43, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0868\n2023-01-04 20:06:14,116 - mmcls - INFO - Epoch [39][500/5005]\tlr: 1.000e-02, eta: 7:35:33, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0443\n2023-01-04 20:06:22,774 - mmcls - INFO - Epoch [39][600/5005]\tlr: 1.000e-02, eta: 7:35:24, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0799\n2023-01-04 20:06:31,341 - mmcls - INFO - Epoch [39][700/5005]\tlr: 1.000e-02, eta: 7:35:15, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0924\n2023-01-04 20:06:40,027 - mmcls - INFO - Epoch [39][800/5005]\tlr: 1.000e-02, eta: 7:35:06, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0464\n2023-01-04 20:06:48,772 - mmcls - INFO - Epoch [39][900/5005]\tlr: 1.000e-02, eta: 7:34:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0660\n2023-01-04 20:06:57,200 - mmcls - INFO - Epoch [39][1000/5005]\tlr: 1.000e-02, eta: 7:34:48, time: 0.084, data_time: 0.009, memory: 928, loss: 5.0772\n2023-01-04 20:07:06,238 - mmcls - INFO - Epoch [39][1100/5005]\tlr: 1.000e-02, eta: 7:34:39, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0597\n2023-01-04 20:07:15,081 - mmcls - INFO - Epoch [39][1200/5005]\tlr: 1.000e-02, eta: 7:34:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0744\n2023-01-04 20:07:23,885 - mmcls - INFO - Epoch [39][1300/5005]\tlr: 1.000e-02, eta: 7:34:21, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0596\n2023-01-04 20:07:32,748 - mmcls - INFO - Epoch [39][1400/5005]\tlr: 1.000e-02, eta: 7:34:13, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0744\n2023-01-04 20:07:41,300 - mmcls - INFO - Epoch [39][1500/5005]\tlr: 1.000e-02, eta: 7:34:03, time: 0.086, data_time: 0.022, memory: 928, loss: 5.0751\n2023-01-04 20:07:50,049 - mmcls - INFO - Epoch [39][1600/5005]\tlr: 1.000e-02, eta: 7:33:54, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0727\n2023-01-04 20:07:58,662 - mmcls - INFO - Epoch [39][1700/5005]\tlr: 1.000e-02, eta: 7:33:45, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0845\n2023-01-04 20:08:07,393 - mmcls - INFO - Epoch [39][1800/5005]\tlr: 1.000e-02, eta: 7:33:36, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0712\n2023-01-04 20:08:15,917 - mmcls - INFO - Epoch [39][1900/5005]\tlr: 1.000e-02, eta: 7:33:27, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0607\n2023-01-04 20:08:24,443 - mmcls - INFO - Epoch [39][2000/5005]\tlr: 1.000e-02, eta: 7:33:18, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0823\n2023-01-04 20:08:33,763 - mmcls - INFO - Epoch [39][2100/5005]\tlr: 1.000e-02, eta: 7:33:10, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0394\n2023-01-04 20:08:42,345 - mmcls - INFO - Epoch [39][2200/5005]\tlr: 1.000e-02, eta: 7:33:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1032\n2023-01-04 20:08:51,214 - mmcls - INFO - Epoch [39][2300/5005]\tlr: 1.000e-02, eta: 7:32:52, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0779\n2023-01-04 20:08:59,838 - mmcls - INFO - Epoch [39][2400/5005]\tlr: 1.000e-02, eta: 7:32:43, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0428\n2023-01-04 20:09:08,605 - mmcls - INFO - Epoch [39][2500/5005]\tlr: 1.000e-02, eta: 7:32:34, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0745\n2023-01-04 20:09:17,317 - mmcls - INFO - Epoch [39][2600/5005]\tlr: 1.000e-02, eta: 7:32:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0682\n2023-01-04 20:09:25,874 - mmcls - INFO - Epoch [39][2700/5005]\tlr: 1.000e-02, eta: 7:32:15, time: 0.085, data_time: 0.017, memory: 928, loss: 5.0605\n2023-01-04 20:09:34,892 - mmcls - INFO - Epoch [39][2800/5005]\tlr: 1.000e-02, eta: 7:32:07, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0655\n2023-01-04 20:09:43,595 - mmcls - INFO - Epoch [39][2900/5005]\tlr: 1.000e-02, eta: 7:31:58, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0941\n2023-01-04 20:09:52,460 - mmcls - INFO - Epoch [39][3000/5005]\tlr: 1.000e-02, eta: 7:31:49, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0639\n2023-01-04 20:10:01,129 - mmcls - INFO - Epoch [39][3100/5005]\tlr: 1.000e-02, eta: 7:31:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0817\n2023-01-04 20:10:09,912 - mmcls - INFO - Epoch [39][3200/5005]\tlr: 1.000e-02, eta: 7:31:31, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0483\n2023-01-04 20:10:18,609 - mmcls - INFO - Epoch [39][3300/5005]\tlr: 1.000e-02, eta: 7:31:22, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0601\n2023-01-04 20:10:27,355 - mmcls - INFO - Epoch [39][3400/5005]\tlr: 1.000e-02, eta: 7:31:13, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0559\n2023-01-04 20:10:36,015 - mmcls - INFO - Epoch [39][3500/5005]\tlr: 1.000e-02, eta: 7:31:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0919\n2023-01-04 20:10:44,713 - mmcls - INFO - Epoch [39][3600/5005]\tlr: 1.000e-02, eta: 7:30:55, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0842\n2023-01-04 20:10:53,547 - mmcls - INFO - Epoch [39][3700/5005]\tlr: 1.000e-02, eta: 7:30:46, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0973\n2023-01-04 20:11:02,330 - mmcls - INFO - Epoch [39][3800/5005]\tlr: 1.000e-02, eta: 7:30:38, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0781\n2023-01-04 20:11:11,001 - mmcls - INFO - Epoch [39][3900/5005]\tlr: 1.000e-02, eta: 7:30:28, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0807\n2023-01-04 20:11:19,861 - mmcls - INFO - Epoch [39][4000/5005]\tlr: 1.000e-02, eta: 7:30:20, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0654\n2023-01-04 20:11:28,520 - mmcls - INFO - Epoch [39][4100/5005]\tlr: 1.000e-02, eta: 7:30:11, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0681\n2023-01-04 20:11:37,209 - mmcls - INFO - Epoch [39][4200/5005]\tlr: 1.000e-02, eta: 7:30:02, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0817\n2023-01-04 20:11:45,978 - mmcls - INFO - Epoch [39][4300/5005]\tlr: 1.000e-02, eta: 7:29:53, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0799\n2023-01-04 20:11:54,547 - mmcls - INFO - Epoch [39][4400/5005]\tlr: 1.000e-02, eta: 7:29:43, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0914\n2023-01-04 20:12:03,916 - mmcls - INFO - Epoch [39][4500/5005]\tlr: 1.000e-02, eta: 7:29:35, time: 0.094, data_time: 0.034, memory: 928, loss: 5.0735\n2023-01-04 20:12:12,546 - mmcls - INFO - Epoch [39][4600/5005]\tlr: 1.000e-02, eta: 7:29:26, time: 0.086, data_time: 0.032, memory: 928, loss: 5.0739\n2023-01-04 20:12:21,242 - mmcls - INFO - Epoch [39][4700/5005]\tlr: 1.000e-02, eta: 7:29:17, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0868\n2023-01-04 20:12:30,023 - mmcls - INFO - Epoch [39][4800/5005]\tlr: 1.000e-02, eta: 7:29:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0604\n2023-01-04 20:12:38,754 - mmcls - INFO - Epoch [39][4900/5005]\tlr: 1.000e-02, eta: 7:28:59, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0916\n2023-01-04 20:12:47,646 - mmcls - INFO - Epoch [39][5000/5005]\tlr: 1.000e-02, eta: 7:28:51, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0765\n2023-01-04 20:13:10,467 - mmcls - INFO - Epoch(val) [39][196]\taccuracy_top-1: 9.6180, accuracy_top-5: 22.2460\n2023-01-04 20:13:21,371 - mmcls - INFO - Epoch [40][100/5005]\tlr: 1.000e-02, eta: 7:28:44, time: 0.109, data_time: 0.044, memory: 928, loss: 5.0832\n2023-01-04 20:13:29,782 - mmcls - INFO - Epoch [40][200/5005]\tlr: 1.000e-02, eta: 7:28:35, time: 0.084, data_time: 0.026, memory: 928, loss: 5.0913\n2023-01-04 20:13:38,253 - mmcls - INFO - Epoch [40][300/5005]\tlr: 1.000e-02, eta: 7:28:25, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0663\n2023-01-04 20:13:47,140 - mmcls - INFO - Epoch [40][400/5005]\tlr: 1.000e-02, eta: 7:28:16, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0617\n2023-01-04 20:13:56,043 - mmcls - INFO - Epoch [40][500/5005]\tlr: 1.000e-02, eta: 7:28:08, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0710\n2023-01-04 20:14:04,687 - mmcls - INFO - Epoch [40][600/5005]\tlr: 1.000e-02, eta: 7:27:59, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0636\n2023-01-04 20:14:13,474 - mmcls - INFO - Epoch [40][700/5005]\tlr: 1.000e-02, eta: 7:27:50, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0731\n2023-01-04 20:14:23,037 - mmcls - INFO - Epoch [40][800/5005]\tlr: 1.000e-02, eta: 7:27:42, time: 0.095, data_time: 0.019, memory: 928, loss: 5.0807\n2023-01-04 20:14:31,908 - mmcls - INFO - Epoch [40][900/5005]\tlr: 1.000e-02, eta: 7:27:33, time: 0.089, data_time: 0.037, memory: 928, loss: 5.0755\n2023-01-04 20:14:40,660 - mmcls - INFO - Epoch [40][1000/5005]\tlr: 1.000e-02, eta: 7:27:24, time: 0.087, data_time: 0.036, memory: 928, loss: 5.0867\n2023-01-04 20:14:49,346 - mmcls - INFO - Epoch [40][1100/5005]\tlr: 1.000e-02, eta: 7:27:15, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0755\n2023-01-04 20:14:58,042 - mmcls - INFO - Epoch [40][1200/5005]\tlr: 1.000e-02, eta: 7:27:06, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0600\n2023-01-04 20:15:06,904 - mmcls - INFO - Epoch [40][1300/5005]\tlr: 1.000e-02, eta: 7:26:58, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0820\n2023-01-04 20:15:15,329 - mmcls - INFO - Epoch [40][1400/5005]\tlr: 1.000e-02, eta: 7:26:48, time: 0.084, data_time: 0.022, memory: 928, loss: 5.0713\n2023-01-04 20:15:24,047 - mmcls - INFO - Epoch [40][1500/5005]\tlr: 1.000e-02, eta: 7:26:39, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0675\n2023-01-04 20:15:32,985 - mmcls - INFO - Epoch [40][1600/5005]\tlr: 1.000e-02, eta: 7:26:31, time: 0.089, data_time: 0.017, memory: 928, loss: 5.0878\n2023-01-04 20:15:41,496 - mmcls - INFO - Epoch [40][1700/5005]\tlr: 1.000e-02, eta: 7:26:21, time: 0.085, data_time: 0.028, memory: 928, loss: 5.0949\n2023-01-04 20:15:50,255 - mmcls - INFO - Epoch [40][1800/5005]\tlr: 1.000e-02, eta: 7:26:12, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0501\n2023-01-04 20:15:58,838 - mmcls - INFO - Epoch [40][1900/5005]\tlr: 1.000e-02, eta: 7:26:03, time: 0.086, data_time: 0.021, memory: 928, loss: 5.0694\n2023-01-04 20:16:07,347 - mmcls - INFO - Epoch [40][2000/5005]\tlr: 1.000e-02, eta: 7:25:54, time: 0.085, data_time: 0.029, memory: 928, loss: 5.0665\n2023-01-04 20:16:16,533 - mmcls - INFO - Epoch [40][2100/5005]\tlr: 1.000e-02, eta: 7:25:46, time: 0.092, data_time: 0.008, memory: 928, loss: 5.0758\n2023-01-04 20:16:25,522 - mmcls - INFO - Epoch [40][2200/5005]\tlr: 1.000e-02, eta: 7:25:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0932\n2023-01-04 20:16:33,934 - mmcls - INFO - Epoch [40][2300/5005]\tlr: 1.000e-02, eta: 7:25:28, time: 0.084, data_time: 0.014, memory: 928, loss: 5.0637\n2023-01-04 20:16:42,392 - mmcls - INFO - Epoch [40][2400/5005]\tlr: 1.000e-02, eta: 7:25:18, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0606\n2023-01-04 20:16:51,283 - mmcls - INFO - Epoch [40][2500/5005]\tlr: 1.000e-02, eta: 7:25:10, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0511\n2023-01-04 20:17:00,539 - mmcls - INFO - Epoch [40][2600/5005]\tlr: 1.000e-02, eta: 7:25:01, time: 0.092, data_time: 0.015, memory: 928, loss: 5.0982\n2023-01-04 20:17:08,960 - mmcls - INFO - Epoch [40][2700/5005]\tlr: 1.000e-02, eta: 7:24:52, time: 0.084, data_time: 0.014, memory: 928, loss: 5.0752\n2023-01-04 20:17:17,919 - mmcls - INFO - Epoch [40][2800/5005]\tlr: 1.000e-02, eta: 7:24:43, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0626\n2023-01-04 20:17:26,453 - mmcls - INFO - Epoch [40][2900/5005]\tlr: 1.000e-02, eta: 7:24:34, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0929\n2023-01-04 20:17:35,049 - mmcls - INFO - Epoch [40][3000/5005]\tlr: 1.000e-02, eta: 7:24:25, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0599\n2023-01-04 20:17:44,076 - mmcls - INFO - Epoch [40][3100/5005]\tlr: 1.000e-02, eta: 7:24:16, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0677\n2023-01-04 20:17:52,882 - mmcls - INFO - Epoch [40][3200/5005]\tlr: 1.000e-02, eta: 7:24:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0986\n2023-01-04 20:18:01,449 - mmcls - INFO - Epoch [40][3300/5005]\tlr: 1.000e-02, eta: 7:23:58, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0488\n2023-01-04 20:18:10,148 - mmcls - INFO - Epoch [40][3400/5005]\tlr: 1.000e-02, eta: 7:23:49, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0796\n2023-01-04 20:18:18,881 - mmcls - INFO - Epoch [40][3500/5005]\tlr: 1.000e-02, eta: 7:23:40, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0564\n2023-01-04 20:18:27,574 - mmcls - INFO - Epoch [40][3600/5005]\tlr: 1.000e-02, eta: 7:23:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0511\n2023-01-04 20:18:36,375 - mmcls - INFO - Epoch [40][3700/5005]\tlr: 1.000e-02, eta: 7:23:23, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0610\n2023-01-04 20:18:45,057 - mmcls - INFO - Epoch [40][3800/5005]\tlr: 1.000e-02, eta: 7:23:13, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0717\n2023-01-04 20:18:53,871 - mmcls - INFO - Epoch [40][3900/5005]\tlr: 1.000e-02, eta: 7:23:05, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0716\n2023-01-04 20:19:02,783 - mmcls - INFO - Epoch [40][4000/5005]\tlr: 1.000e-02, eta: 7:22:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0768\n2023-01-04 20:19:11,638 - mmcls - INFO - Epoch [40][4100/5005]\tlr: 1.000e-02, eta: 7:22:47, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0800\n2023-01-04 20:19:20,278 - mmcls - INFO - Epoch [40][4200/5005]\tlr: 1.000e-02, eta: 7:22:38, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0650\n2023-01-04 20:19:28,776 - mmcls - INFO - Epoch [40][4300/5005]\tlr: 1.000e-02, eta: 7:22:29, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0761\n2023-01-04 20:19:37,461 - mmcls - INFO - Epoch [40][4400/5005]\tlr: 1.000e-02, eta: 7:22:20, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0671\n2023-01-04 20:19:46,062 - mmcls - INFO - Epoch [40][4500/5005]\tlr: 1.000e-02, eta: 7:22:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0691\n2023-01-04 20:19:54,958 - mmcls - INFO - Epoch [40][4600/5005]\tlr: 1.000e-02, eta: 7:22:02, time: 0.089, data_time: 0.029, memory: 928, loss: 5.0673\n2023-01-04 20:20:03,618 - mmcls - INFO - Epoch [40][4700/5005]\tlr: 1.000e-02, eta: 7:21:53, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0730\n2023-01-04 20:20:12,948 - mmcls - INFO - Epoch [40][4800/5005]\tlr: 1.000e-02, eta: 7:21:45, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0769\n2023-01-04 20:20:21,395 - mmcls - INFO - Epoch [40][4900/5005]\tlr: 1.000e-02, eta: 7:21:35, time: 0.084, data_time: 0.007, memory: 928, loss: 5.1038\n2023-01-04 20:20:30,181 - mmcls - INFO - Epoch [40][5000/5005]\tlr: 1.000e-02, eta: 7:21:27, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0749\n2023-01-04 20:20:52,941 - mmcls - INFO - Epoch(val) [40][196]\taccuracy_top-1: 10.6960, accuracy_top-5: 24.3360\n2023-01-04 20:21:03,707 - mmcls - INFO - Epoch [41][100/5005]\tlr: 1.000e-02, eta: 7:21:19, time: 0.107, data_time: 0.046, memory: 928, loss: 5.0581\n2023-01-04 20:21:12,160 - mmcls - INFO - Epoch [41][200/5005]\tlr: 1.000e-02, eta: 7:21:10, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0463\n2023-01-04 20:21:21,093 - mmcls - INFO - Epoch [41][300/5005]\tlr: 1.000e-02, eta: 7:21:01, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0550\n2023-01-04 20:21:29,880 - mmcls - INFO - Epoch [41][400/5005]\tlr: 1.000e-02, eta: 7:20:53, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0780\n2023-01-04 20:21:39,148 - mmcls - INFO - Epoch [41][500/5005]\tlr: 1.000e-02, eta: 7:20:44, time: 0.093, data_time: 0.003, memory: 928, loss: 5.0652\n2023-01-04 20:21:48,144 - mmcls - INFO - Epoch [41][600/5005]\tlr: 1.000e-02, eta: 7:20:36, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0621\n2023-01-04 20:21:56,690 - mmcls - INFO - Epoch [41][700/5005]\tlr: 1.000e-02, eta: 7:20:27, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0770\n2023-01-04 20:22:05,666 - mmcls - INFO - Epoch [41][800/5005]\tlr: 1.000e-02, eta: 7:20:18, time: 0.090, data_time: 0.014, memory: 928, loss: 5.0578\n2023-01-04 20:22:14,574 - mmcls - INFO - Epoch [41][900/5005]\tlr: 1.000e-02, eta: 7:20:09, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0549\n2023-01-04 20:22:23,143 - mmcls - INFO - Epoch [41][1000/5005]\tlr: 1.000e-02, eta: 7:20:00, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0679\n2023-01-04 20:22:31,858 - mmcls - INFO - Epoch [41][1100/5005]\tlr: 1.000e-02, eta: 7:19:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0711\n2023-01-04 20:22:40,833 - mmcls - INFO - Epoch [41][1200/5005]\tlr: 1.000e-02, eta: 7:19:43, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0729\n2023-01-04 20:22:49,928 - mmcls - INFO - Epoch [41][1300/5005]\tlr: 1.000e-02, eta: 7:19:34, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0601\n2023-01-04 20:22:58,810 - mmcls - INFO - Epoch [41][1400/5005]\tlr: 1.000e-02, eta: 7:19:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0664\n2023-01-04 20:23:07,641 - mmcls - INFO - Epoch [41][1500/5005]\tlr: 1.000e-02, eta: 7:19:17, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0570\n2023-01-04 20:23:16,205 - mmcls - INFO - Epoch [41][1600/5005]\tlr: 1.000e-02, eta: 7:19:07, time: 0.086, data_time: 0.028, memory: 928, loss: 5.0685\n2023-01-04 20:23:25,000 - mmcls - INFO - Epoch [41][1700/5005]\tlr: 1.000e-02, eta: 7:18:59, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0730\n2023-01-04 20:23:33,478 - mmcls - INFO - Epoch [41][1800/5005]\tlr: 1.000e-02, eta: 7:18:49, time: 0.085, data_time: 0.016, memory: 928, loss: 5.0538\n2023-01-04 20:23:42,257 - mmcls - INFO - Epoch [41][1900/5005]\tlr: 1.000e-02, eta: 7:18:40, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0515\n2023-01-04 20:23:51,099 - mmcls - INFO - Epoch [41][2000/5005]\tlr: 1.000e-02, eta: 7:18:32, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0501\n2023-01-04 20:23:59,952 - mmcls - INFO - Epoch [41][2100/5005]\tlr: 1.000e-02, eta: 7:18:23, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0713\n2023-01-04 20:24:08,690 - mmcls - INFO - Epoch [41][2200/5005]\tlr: 1.000e-02, eta: 7:18:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0591\n2023-01-04 20:24:17,275 - mmcls - INFO - Epoch [41][2300/5005]\tlr: 1.000e-02, eta: 7:18:05, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0548\n2023-01-04 20:24:26,322 - mmcls - INFO - Epoch [41][2400/5005]\tlr: 1.000e-02, eta: 7:17:56, time: 0.090, data_time: 0.014, memory: 928, loss: 5.0837\n2023-01-04 20:24:35,291 - mmcls - INFO - Epoch [41][2500/5005]\tlr: 1.000e-02, eta: 7:17:48, time: 0.090, data_time: 0.015, memory: 928, loss: 5.0832\n2023-01-04 20:24:43,831 - mmcls - INFO - Epoch [41][2600/5005]\tlr: 1.000e-02, eta: 7:17:38, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0860\n2023-01-04 20:24:52,754 - mmcls - INFO - Epoch [41][2700/5005]\tlr: 1.000e-02, eta: 7:17:30, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0655\n2023-01-04 20:25:01,419 - mmcls - INFO - Epoch [41][2800/5005]\tlr: 1.000e-02, eta: 7:17:21, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0627\n2023-01-04 20:25:10,207 - mmcls - INFO - Epoch [41][2900/5005]\tlr: 1.000e-02, eta: 7:17:12, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0758\n2023-01-04 20:25:18,818 - mmcls - INFO - Epoch [41][3000/5005]\tlr: 1.000e-02, eta: 7:17:03, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0668\n2023-01-04 20:25:27,250 - mmcls - INFO - Epoch [41][3100/5005]\tlr: 1.000e-02, eta: 7:16:53, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0921\n2023-01-04 20:25:35,946 - mmcls - INFO - Epoch [41][3200/5005]\tlr: 1.000e-02, eta: 7:16:44, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0668\n2023-01-04 20:25:44,632 - mmcls - INFO - Epoch [41][3300/5005]\tlr: 1.000e-02, eta: 7:16:35, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0614\n2023-01-04 20:25:53,387 - mmcls - INFO - Epoch [41][3400/5005]\tlr: 1.000e-02, eta: 7:16:26, time: 0.088, data_time: 0.000, memory: 928, loss: 5.0837\n2023-01-04 20:26:02,100 - mmcls - INFO - Epoch [41][3500/5005]\tlr: 1.000e-02, eta: 7:16:17, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0936\n2023-01-04 20:26:11,248 - mmcls - INFO - Epoch [41][3600/5005]\tlr: 1.000e-02, eta: 7:16:09, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0686\n2023-01-04 20:26:20,028 - mmcls - INFO - Epoch [41][3700/5005]\tlr: 1.000e-02, eta: 7:16:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0943\n2023-01-04 20:26:28,657 - mmcls - INFO - Epoch [41][3800/5005]\tlr: 1.000e-02, eta: 7:15:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0612\n2023-01-04 20:26:37,451 - mmcls - INFO - Epoch [41][3900/5005]\tlr: 1.000e-02, eta: 7:15:42, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0660\n2023-01-04 20:26:46,314 - mmcls - INFO - Epoch [41][4000/5005]\tlr: 1.000e-02, eta: 7:15:33, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0639\n2023-01-04 20:26:54,893 - mmcls - INFO - Epoch [41][4100/5005]\tlr: 1.000e-02, eta: 7:15:24, time: 0.086, data_time: 0.023, memory: 928, loss: 5.0848\n2023-01-04 20:27:03,712 - mmcls - INFO - Epoch [41][4200/5005]\tlr: 1.000e-02, eta: 7:15:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0654\n2023-01-04 20:27:12,438 - mmcls - INFO - Epoch [41][4300/5005]\tlr: 1.000e-02, eta: 7:15:06, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0804\n2023-01-04 20:27:21,067 - mmcls - INFO - Epoch [41][4400/5005]\tlr: 1.000e-02, eta: 7:14:57, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0777\n2023-01-04 20:27:29,723 - mmcls - INFO - Epoch [41][4500/5005]\tlr: 1.000e-02, eta: 7:14:48, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0925\n2023-01-04 20:27:38,389 - mmcls - INFO - Epoch [41][4600/5005]\tlr: 1.000e-02, eta: 7:14:39, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0800\n2023-01-04 20:27:47,247 - mmcls - INFO - Epoch [41][4700/5005]\tlr: 1.000e-02, eta: 7:14:30, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0685\n2023-01-04 20:27:55,943 - mmcls - INFO - Epoch [41][4800/5005]\tlr: 1.000e-02, eta: 7:14:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0753\n2023-01-04 20:28:04,652 - mmcls - INFO - Epoch [41][4900/5005]\tlr: 1.000e-02, eta: 7:14:12, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0678\n2023-01-04 20:28:13,605 - mmcls - INFO - Epoch [41][5000/5005]\tlr: 1.000e-02, eta: 7:14:04, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0942\n2023-01-04 20:28:36,304 - mmcls - INFO - Epoch(val) [41][196]\taccuracy_top-1: 11.8540, accuracy_top-5: 26.4460\n2023-01-04 20:28:47,579 - mmcls - INFO - Epoch [42][100/5005]\tlr: 1.000e-02, eta: 7:13:57, time: 0.112, data_time: 0.066, memory: 928, loss: 5.0622\n2023-01-04 20:28:56,202 - mmcls - INFO - Epoch [42][200/5005]\tlr: 1.000e-02, eta: 7:13:48, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0623\n2023-01-04 20:29:04,940 - mmcls - INFO - Epoch [42][300/5005]\tlr: 1.000e-02, eta: 7:13:39, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0377\n2023-01-04 20:29:13,712 - mmcls - INFO - Epoch [42][400/5005]\tlr: 1.000e-02, eta: 7:13:31, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0647\n2023-01-04 20:29:22,358 - mmcls - INFO - Epoch [42][500/5005]\tlr: 1.000e-02, eta: 7:13:21, time: 0.086, data_time: 0.009, memory: 928, loss: 5.1003\n2023-01-04 20:29:31,252 - mmcls - INFO - Epoch [42][600/5005]\tlr: 1.000e-02, eta: 7:13:13, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0679\n2023-01-04 20:29:39,950 - mmcls - INFO - Epoch [42][700/5005]\tlr: 1.000e-02, eta: 7:13:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0634\n2023-01-04 20:29:48,814 - mmcls - INFO - Epoch [42][800/5005]\tlr: 1.000e-02, eta: 7:12:55, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0524\n2023-01-04 20:29:57,529 - mmcls - INFO - Epoch [42][900/5005]\tlr: 1.000e-02, eta: 7:12:46, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0546\n2023-01-04 20:30:06,295 - mmcls - INFO - Epoch [42][1000/5005]\tlr: 1.000e-02, eta: 7:12:37, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0677\n2023-01-04 20:30:14,851 - mmcls - INFO - Epoch [42][1100/5005]\tlr: 1.000e-02, eta: 7:12:28, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0959\n2023-01-04 20:30:23,916 - mmcls - INFO - Epoch [42][1200/5005]\tlr: 1.000e-02, eta: 7:12:19, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0860\n2023-01-04 20:30:32,621 - mmcls - INFO - Epoch [42][1300/5005]\tlr: 1.000e-02, eta: 7:12:10, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0767\n2023-01-04 20:30:41,497 - mmcls - INFO - Epoch [42][1400/5005]\tlr: 1.000e-02, eta: 7:12:02, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0653\n2023-01-04 20:30:50,242 - mmcls - INFO - Epoch [42][1500/5005]\tlr: 1.000e-02, eta: 7:11:53, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0637\n2023-01-04 20:30:58,999 - mmcls - INFO - Epoch [42][1600/5005]\tlr: 1.000e-02, eta: 7:11:44, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0402\n2023-01-04 20:31:07,258 - mmcls - INFO - Epoch [42][1700/5005]\tlr: 1.000e-02, eta: 7:11:34, time: 0.083, data_time: 0.003, memory: 928, loss: 5.0681\n2023-01-04 20:31:16,087 - mmcls - INFO - Epoch [42][1800/5005]\tlr: 1.000e-02, eta: 7:11:25, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0633\n2023-01-04 20:31:25,070 - mmcls - INFO - Epoch [42][1900/5005]\tlr: 1.000e-02, eta: 7:11:17, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0538\n2023-01-04 20:31:33,608 - mmcls - INFO - Epoch [42][2000/5005]\tlr: 1.000e-02, eta: 7:11:08, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0625\n2023-01-04 20:31:42,495 - mmcls - INFO - Epoch [42][2100/5005]\tlr: 1.000e-02, eta: 7:10:59, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0678\n2023-01-04 20:31:51,219 - mmcls - INFO - Epoch [42][2200/5005]\tlr: 1.000e-02, eta: 7:10:50, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0670\n2023-01-04 20:31:59,835 - mmcls - INFO - Epoch [42][2300/5005]\tlr: 1.000e-02, eta: 7:10:41, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0652\n2023-01-04 20:32:08,413 - mmcls - INFO - Epoch [42][2400/5005]\tlr: 1.000e-02, eta: 7:10:32, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0609\n2023-01-04 20:32:17,237 - mmcls - INFO - Epoch [42][2500/5005]\tlr: 1.000e-02, eta: 7:10:23, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0552\n2023-01-04 20:32:25,901 - mmcls - INFO - Epoch [42][2600/5005]\tlr: 1.000e-02, eta: 7:10:14, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0741\n2023-01-04 20:32:34,570 - mmcls - INFO - Epoch [42][2700/5005]\tlr: 1.000e-02, eta: 7:10:05, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0505\n2023-01-04 20:32:43,299 - mmcls - INFO - Epoch [42][2800/5005]\tlr: 1.000e-02, eta: 7:09:56, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0690\n2023-01-04 20:32:52,205 - mmcls - INFO - Epoch [42][2900/5005]\tlr: 1.000e-02, eta: 7:09:47, time: 0.089, data_time: 0.036, memory: 928, loss: 5.0739\n2023-01-04 20:33:01,126 - mmcls - INFO - Epoch [42][3000/5005]\tlr: 1.000e-02, eta: 7:09:38, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0932\n2023-01-04 20:33:10,153 - mmcls - INFO - Epoch [42][3100/5005]\tlr: 1.000e-02, eta: 7:09:30, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0783\n2023-01-04 20:33:18,724 - mmcls - INFO - Epoch [42][3200/5005]\tlr: 1.000e-02, eta: 7:09:21, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0727\n2023-01-04 20:33:27,589 - mmcls - INFO - Epoch [42][3300/5005]\tlr: 1.000e-02, eta: 7:09:12, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0895\n2023-01-04 20:33:36,258 - mmcls - INFO - Epoch [42][3400/5005]\tlr: 1.000e-02, eta: 7:09:03, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0470\n2023-01-04 20:33:44,928 - mmcls - INFO - Epoch [42][3500/5005]\tlr: 1.000e-02, eta: 7:08:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0705\n2023-01-04 20:33:53,608 - mmcls - INFO - Epoch [42][3600/5005]\tlr: 1.000e-02, eta: 7:08:45, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0982\n2023-01-04 20:34:02,343 - mmcls - INFO - Epoch [42][3700/5005]\tlr: 1.000e-02, eta: 7:08:36, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0601\n2023-01-04 20:34:11,470 - mmcls - INFO - Epoch [42][3800/5005]\tlr: 1.000e-02, eta: 7:08:28, time: 0.091, data_time: 0.023, memory: 928, loss: 5.0728\n2023-01-04 20:34:20,379 - mmcls - INFO - Epoch [42][3900/5005]\tlr: 1.000e-02, eta: 7:08:19, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0789\n2023-01-04 20:34:29,887 - mmcls - INFO - Epoch [42][4000/5005]\tlr: 1.000e-02, eta: 7:08:11, time: 0.095, data_time: 0.014, memory: 928, loss: 5.0755\n2023-01-04 20:34:38,694 - mmcls - INFO - Epoch [42][4100/5005]\tlr: 1.000e-02, eta: 7:08:02, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0717\n2023-01-04 20:34:47,385 - mmcls - INFO - Epoch [42][4200/5005]\tlr: 1.000e-02, eta: 7:07:53, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0625\n2023-01-04 20:34:55,957 - mmcls - INFO - Epoch [42][4300/5005]\tlr: 1.000e-02, eta: 7:07:44, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0696\n2023-01-04 20:35:04,792 - mmcls - INFO - Epoch [42][4400/5005]\tlr: 1.000e-02, eta: 7:07:35, time: 0.088, data_time: 0.046, memory: 928, loss: 5.0851\n2023-01-04 20:35:13,693 - mmcls - INFO - Epoch [42][4500/5005]\tlr: 1.000e-02, eta: 7:07:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0616\n2023-01-04 20:35:22,389 - mmcls - INFO - Epoch [42][4600/5005]\tlr: 1.000e-02, eta: 7:07:18, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0524\n2023-01-04 20:35:31,170 - mmcls - INFO - Epoch [42][4700/5005]\tlr: 1.000e-02, eta: 7:07:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0600\n2023-01-04 20:35:40,065 - mmcls - INFO - Epoch [42][4800/5005]\tlr: 1.000e-02, eta: 7:07:00, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0832\n2023-01-04 20:35:49,164 - mmcls - INFO - Epoch [42][4900/5005]\tlr: 1.000e-02, eta: 7:06:52, time: 0.091, data_time: 0.013, memory: 928, loss: 5.0905\n2023-01-04 20:35:57,788 - mmcls - INFO - Epoch [42][5000/5005]\tlr: 1.000e-02, eta: 7:06:42, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0702\n2023-01-04 20:36:20,560 - mmcls - INFO - Epoch(val) [42][196]\taccuracy_top-1: 11.7260, accuracy_top-5: 26.0880\n2023-01-04 20:36:31,464 - mmcls - INFO - Epoch [43][100/5005]\tlr: 1.000e-02, eta: 7:06:35, time: 0.109, data_time: 0.048, memory: 928, loss: 5.0776\n2023-01-04 20:36:40,146 - mmcls - INFO - Epoch [43][200/5005]\tlr: 1.000e-02, eta: 7:06:26, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0646\n2023-01-04 20:36:48,815 - mmcls - INFO - Epoch [43][300/5005]\tlr: 1.000e-02, eta: 7:06:17, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0672\n2023-01-04 20:36:57,311 - mmcls - INFO - Epoch [43][400/5005]\tlr: 1.000e-02, eta: 7:06:08, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0798\n2023-01-04 20:37:05,974 - mmcls - INFO - Epoch [43][500/5005]\tlr: 1.000e-02, eta: 7:05:59, time: 0.087, data_time: 0.032, memory: 928, loss: 5.0827\n2023-01-04 20:37:14,473 - mmcls - INFO - Epoch [43][600/5005]\tlr: 1.000e-02, eta: 7:05:50, time: 0.085, data_time: 0.033, memory: 928, loss: 5.0526\n2023-01-04 20:37:23,190 - mmcls - INFO - Epoch [43][700/5005]\tlr: 1.000e-02, eta: 7:05:41, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0758\n2023-01-04 20:37:32,197 - mmcls - INFO - Epoch [43][800/5005]\tlr: 1.000e-02, eta: 7:05:32, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0598\n2023-01-04 20:37:40,851 - mmcls - INFO - Epoch [43][900/5005]\tlr: 1.000e-02, eta: 7:05:23, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0657\n2023-01-04 20:37:49,346 - mmcls - INFO - Epoch [43][1000/5005]\tlr: 1.000e-02, eta: 7:05:14, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0607\n2023-01-04 20:37:58,226 - mmcls - INFO - Epoch [43][1100/5005]\tlr: 1.000e-02, eta: 7:05:05, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0643\n2023-01-04 20:38:06,722 - mmcls - INFO - Epoch [43][1200/5005]\tlr: 1.000e-02, eta: 7:04:56, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0682\n2023-01-04 20:38:15,399 - mmcls - INFO - Epoch [43][1300/5005]\tlr: 1.000e-02, eta: 7:04:47, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0674\n2023-01-04 20:38:23,961 - mmcls - INFO - Epoch [43][1400/5005]\tlr: 1.000e-02, eta: 7:04:38, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0976\n2023-01-04 20:38:32,701 - mmcls - INFO - Epoch [43][1500/5005]\tlr: 1.000e-02, eta: 7:04:29, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0836\n2023-01-04 20:38:41,459 - mmcls - INFO - Epoch [43][1600/5005]\tlr: 1.000e-02, eta: 7:04:20, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0601\n2023-01-04 20:38:50,526 - mmcls - INFO - Epoch [43][1700/5005]\tlr: 1.000e-02, eta: 7:04:11, time: 0.091, data_time: 0.019, memory: 928, loss: 5.0840\n2023-01-04 20:38:59,161 - mmcls - INFO - Epoch [43][1800/5005]\tlr: 1.000e-02, eta: 7:04:02, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0357\n2023-01-04 20:39:07,857 - mmcls - INFO - Epoch [43][1900/5005]\tlr: 1.000e-02, eta: 7:03:53, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0591\n2023-01-04 20:39:16,812 - mmcls - INFO - Epoch [43][2000/5005]\tlr: 1.000e-02, eta: 7:03:45, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0605\n2023-01-04 20:39:25,624 - mmcls - INFO - Epoch [43][2100/5005]\tlr: 1.000e-02, eta: 7:03:36, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0613\n2023-01-04 20:39:34,217 - mmcls - INFO - Epoch [43][2200/5005]\tlr: 1.000e-02, eta: 7:03:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0700\n2023-01-04 20:39:43,072 - mmcls - INFO - Epoch [43][2300/5005]\tlr: 1.000e-02, eta: 7:03:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0540\n2023-01-04 20:39:51,868 - mmcls - INFO - Epoch [43][2400/5005]\tlr: 1.000e-02, eta: 7:03:09, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0817\n2023-01-04 20:40:00,893 - mmcls - INFO - Epoch [43][2500/5005]\tlr: 1.000e-02, eta: 7:03:01, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0755\n2023-01-04 20:40:09,424 - mmcls - INFO - Epoch [43][2600/5005]\tlr: 1.000e-02, eta: 7:02:51, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0816\n2023-01-04 20:40:18,137 - mmcls - INFO - Epoch [43][2700/5005]\tlr: 1.000e-02, eta: 7:02:42, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0606\n2023-01-04 20:40:26,801 - mmcls - INFO - Epoch [43][2800/5005]\tlr: 1.000e-02, eta: 7:02:33, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0553\n2023-01-04 20:40:35,303 - mmcls - INFO - Epoch [43][2900/5005]\tlr: 1.000e-02, eta: 7:02:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0894\n2023-01-04 20:40:44,079 - mmcls - INFO - Epoch [43][3000/5005]\tlr: 1.000e-02, eta: 7:02:15, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0617\n2023-01-04 20:40:53,252 - mmcls - INFO - Epoch [43][3100/5005]\tlr: 1.000e-02, eta: 7:02:07, time: 0.092, data_time: 0.012, memory: 928, loss: 5.0645\n2023-01-04 20:41:01,973 - mmcls - INFO - Epoch [43][3200/5005]\tlr: 1.000e-02, eta: 7:01:58, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0663\n2023-01-04 20:41:10,877 - mmcls - INFO - Epoch [43][3300/5005]\tlr: 1.000e-02, eta: 7:01:49, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0758\n2023-01-04 20:41:19,724 - mmcls - INFO - Epoch [43][3400/5005]\tlr: 1.000e-02, eta: 7:01:41, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0815\n2023-01-04 20:41:28,493 - mmcls - INFO - Epoch [43][3500/5005]\tlr: 1.000e-02, eta: 7:01:32, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0709\n2023-01-04 20:41:37,221 - mmcls - INFO - Epoch [43][3600/5005]\tlr: 1.000e-02, eta: 7:01:23, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0695\n2023-01-04 20:41:45,977 - mmcls - INFO - Epoch [43][3700/5005]\tlr: 1.000e-02, eta: 7:01:14, time: 0.088, data_time: 0.005, memory: 928, loss: 5.1089\n2023-01-04 20:41:54,822 - mmcls - INFO - Epoch [43][3800/5005]\tlr: 1.000e-02, eta: 7:01:05, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0685\n2023-01-04 20:42:03,401 - mmcls - INFO - Epoch [43][3900/5005]\tlr: 1.000e-02, eta: 7:00:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0760\n2023-01-04 20:42:12,453 - mmcls - INFO - Epoch [43][4000/5005]\tlr: 1.000e-02, eta: 7:00:47, time: 0.091, data_time: 0.045, memory: 928, loss: 5.0559\n2023-01-04 20:42:21,236 - mmcls - INFO - Epoch [43][4100/5005]\tlr: 1.000e-02, eta: 7:00:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0710\n2023-01-04 20:42:30,385 - mmcls - INFO - Epoch [43][4200/5005]\tlr: 1.000e-02, eta: 7:00:30, time: 0.091, data_time: 0.004, memory: 928, loss: 5.0596\n2023-01-04 20:42:39,595 - mmcls - INFO - Epoch [43][4300/5005]\tlr: 1.000e-02, eta: 7:00:22, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0651\n2023-01-04 20:42:48,196 - mmcls - INFO - Epoch [43][4400/5005]\tlr: 1.000e-02, eta: 7:00:13, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0622\n2023-01-04 20:42:57,036 - mmcls - INFO - Epoch [43][4500/5005]\tlr: 1.000e-02, eta: 7:00:04, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0757\n2023-01-04 20:43:05,779 - mmcls - INFO - Epoch [43][4600/5005]\tlr: 1.000e-02, eta: 6:59:55, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0513\n2023-01-04 20:43:14,817 - mmcls - INFO - Epoch [43][4700/5005]\tlr: 1.000e-02, eta: 6:59:46, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0490\n2023-01-04 20:43:23,649 - mmcls - INFO - Epoch [43][4800/5005]\tlr: 1.000e-02, eta: 6:59:38, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0422\n2023-01-04 20:43:32,741 - mmcls - INFO - Epoch [43][4900/5005]\tlr: 1.000e-02, eta: 6:59:29, time: 0.091, data_time: 0.005, memory: 928, loss: 5.0708\n2023-01-04 20:43:41,242 - mmcls - INFO - Epoch [43][5000/5005]\tlr: 1.000e-02, eta: 6:59:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0563\n2023-01-04 20:44:04,049 - mmcls - INFO - Epoch(val) [43][196]\taccuracy_top-1: 11.6040, accuracy_top-5: 26.3460\n2023-01-04 20:44:14,836 - mmcls - INFO - Epoch [44][100/5005]\tlr: 1.000e-02, eta: 6:59:13, time: 0.108, data_time: 0.049, memory: 928, loss: 5.0772\n2023-01-04 20:44:23,598 - mmcls - INFO - Epoch [44][200/5005]\tlr: 1.000e-02, eta: 6:59:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0825\n2023-01-04 20:44:32,551 - mmcls - INFO - Epoch [44][300/5005]\tlr: 1.000e-02, eta: 6:58:55, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0832\n2023-01-04 20:44:41,835 - mmcls - INFO - Epoch [44][400/5005]\tlr: 1.000e-02, eta: 6:58:47, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0910\n2023-01-04 20:44:50,627 - mmcls - INFO - Epoch [44][500/5005]\tlr: 1.000e-02, eta: 6:58:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0409\n2023-01-04 20:44:59,303 - mmcls - INFO - Epoch [44][600/5005]\tlr: 1.000e-02, eta: 6:58:29, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0550\n2023-01-04 20:45:07,942 - mmcls - INFO - Epoch [44][700/5005]\tlr: 1.000e-02, eta: 6:58:20, time: 0.086, data_time: 0.039, memory: 928, loss: 5.0453\n2023-01-04 20:45:16,560 - mmcls - INFO - Epoch [44][800/5005]\tlr: 1.000e-02, eta: 6:58:11, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0976\n2023-01-04 20:45:25,017 - mmcls - INFO - Epoch [44][900/5005]\tlr: 1.000e-02, eta: 6:58:02, time: 0.084, data_time: 0.011, memory: 928, loss: 5.0557\n2023-01-04 20:45:33,898 - mmcls - INFO - Epoch [44][1000/5005]\tlr: 1.000e-02, eta: 6:57:53, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0639\n2023-01-04 20:45:42,702 - mmcls - INFO - Epoch [44][1100/5005]\tlr: 1.000e-02, eta: 6:57:44, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0460\n2023-01-04 20:45:51,471 - mmcls - INFO - Epoch [44][1200/5005]\tlr: 1.000e-02, eta: 6:57:35, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0704\n2023-01-04 20:46:00,090 - mmcls - INFO - Epoch [44][1300/5005]\tlr: 1.000e-02, eta: 6:57:26, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0740\n2023-01-04 20:46:08,693 - mmcls - INFO - Epoch [44][1400/5005]\tlr: 1.000e-02, eta: 6:57:17, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0669\n2023-01-04 20:46:17,273 - mmcls - INFO - Epoch [44][1500/5005]\tlr: 1.000e-02, eta: 6:57:08, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0660\n2023-01-04 20:46:26,085 - mmcls - INFO - Epoch [44][1600/5005]\tlr: 1.000e-02, eta: 6:56:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0485\n2023-01-04 20:46:34,811 - mmcls - INFO - Epoch [44][1700/5005]\tlr: 1.000e-02, eta: 6:56:50, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0563\n2023-01-04 20:46:43,388 - mmcls - INFO - Epoch [44][1800/5005]\tlr: 1.000e-02, eta: 6:56:41, time: 0.086, data_time: 0.032, memory: 928, loss: 5.0676\n2023-01-04 20:46:52,130 - mmcls - INFO - Epoch [44][1900/5005]\tlr: 1.000e-02, eta: 6:56:32, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0914\n2023-01-04 20:47:00,886 - mmcls - INFO - Epoch [44][2000/5005]\tlr: 1.000e-02, eta: 6:56:23, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0438\n2023-01-04 20:47:09,473 - mmcls - INFO - Epoch [44][2100/5005]\tlr: 1.000e-02, eta: 6:56:14, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0603\n2023-01-04 20:47:18,152 - mmcls - INFO - Epoch [44][2200/5005]\tlr: 1.000e-02, eta: 6:56:05, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0636\n2023-01-04 20:47:26,879 - mmcls - INFO - Epoch [44][2300/5005]\tlr: 1.000e-02, eta: 6:55:56, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0646\n2023-01-04 20:47:35,696 - mmcls - INFO - Epoch [44][2400/5005]\tlr: 1.000e-02, eta: 6:55:47, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0420\n2023-01-04 20:47:44,347 - mmcls - INFO - Epoch [44][2500/5005]\tlr: 1.000e-02, eta: 6:55:38, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0511\n2023-01-04 20:47:53,059 - mmcls - INFO - Epoch [44][2600/5005]\tlr: 1.000e-02, eta: 6:55:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0703\n2023-01-04 20:48:01,881 - mmcls - INFO - Epoch [44][2700/5005]\tlr: 1.000e-02, eta: 6:55:20, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0602\n2023-01-04 20:48:10,666 - mmcls - INFO - Epoch [44][2800/5005]\tlr: 1.000e-02, eta: 6:55:12, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0776\n2023-01-04 20:48:19,565 - mmcls - INFO - Epoch [44][2900/5005]\tlr: 1.000e-02, eta: 6:55:03, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0690\n2023-01-04 20:48:28,144 - mmcls - INFO - Epoch [44][3000/5005]\tlr: 1.000e-02, eta: 6:54:54, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0875\n2023-01-04 20:48:36,652 - mmcls - INFO - Epoch [44][3100/5005]\tlr: 1.000e-02, eta: 6:54:44, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0443\n2023-01-04 20:48:45,300 - mmcls - INFO - Epoch [44][3200/5005]\tlr: 1.000e-02, eta: 6:54:35, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0689\n2023-01-04 20:48:53,970 - mmcls - INFO - Epoch [44][3300/5005]\tlr: 1.000e-02, eta: 6:54:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0731\n2023-01-04 20:49:02,433 - mmcls - INFO - Epoch [44][3400/5005]\tlr: 1.000e-02, eta: 6:54:17, time: 0.084, data_time: 0.011, memory: 928, loss: 5.0634\n2023-01-04 20:49:11,496 - mmcls - INFO - Epoch [44][3500/5005]\tlr: 1.000e-02, eta: 6:54:09, time: 0.091, data_time: 0.007, memory: 928, loss: 5.0401\n2023-01-04 20:49:20,066 - mmcls - INFO - Epoch [44][3600/5005]\tlr: 1.000e-02, eta: 6:53:59, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0672\n2023-01-04 20:49:28,728 - mmcls - INFO - Epoch [44][3700/5005]\tlr: 1.000e-02, eta: 6:53:50, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0645\n2023-01-04 20:49:37,489 - mmcls - INFO - Epoch [44][3800/5005]\tlr: 1.000e-02, eta: 6:53:42, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0675\n2023-01-04 20:49:46,297 - mmcls - INFO - Epoch [44][3900/5005]\tlr: 1.000e-02, eta: 6:53:33, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0495\n2023-01-04 20:49:54,993 - mmcls - INFO - Epoch [44][4000/5005]\tlr: 1.000e-02, eta: 6:53:24, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0874\n2023-01-04 20:50:04,009 - mmcls - INFO - Epoch [44][4100/5005]\tlr: 1.000e-02, eta: 6:53:15, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0736\n2023-01-04 20:50:12,663 - mmcls - INFO - Epoch [44][4200/5005]\tlr: 1.000e-02, eta: 6:53:06, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0664\n2023-01-04 20:50:21,289 - mmcls - INFO - Epoch [44][4300/5005]\tlr: 1.000e-02, eta: 6:52:57, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0918\n2023-01-04 20:50:30,196 - mmcls - INFO - Epoch [44][4400/5005]\tlr: 1.000e-02, eta: 6:52:48, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0499\n2023-01-04 20:50:39,108 - mmcls - INFO - Epoch [44][4500/5005]\tlr: 1.000e-02, eta: 6:52:40, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0767\n2023-01-04 20:50:47,869 - mmcls - INFO - Epoch [44][4600/5005]\tlr: 1.000e-02, eta: 6:52:31, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0725\n2023-01-04 20:50:56,518 - mmcls - INFO - Epoch [44][4700/5005]\tlr: 1.000e-02, eta: 6:52:22, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0575\n2023-01-04 20:51:05,370 - mmcls - INFO - Epoch [44][4800/5005]\tlr: 1.000e-02, eta: 6:52:13, time: 0.089, data_time: 0.030, memory: 928, loss: 5.0667\n2023-01-04 20:51:14,112 - mmcls - INFO - Epoch [44][4900/5005]\tlr: 1.000e-02, eta: 6:52:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0553\n2023-01-04 20:51:22,881 - mmcls - INFO - Epoch [44][5000/5005]\tlr: 1.000e-02, eta: 6:51:55, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0691\n2023-01-04 20:51:45,711 - mmcls - INFO - Epoch(val) [44][196]\taccuracy_top-1: 8.6820, accuracy_top-5: 20.0420\n2023-01-04 20:51:56,844 - mmcls - INFO - Epoch [45][100/5005]\tlr: 1.000e-02, eta: 6:51:48, time: 0.111, data_time: 0.052, memory: 928, loss: 5.0665\n2023-01-04 20:52:05,462 - mmcls - INFO - Epoch [45][200/5005]\tlr: 1.000e-02, eta: 6:51:39, time: 0.086, data_time: 0.036, memory: 928, loss: 5.0672\n2023-01-04 20:52:14,187 - mmcls - INFO - Epoch [45][300/5005]\tlr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0270\n2023-01-04 20:52:23,006 - mmcls - INFO - Epoch [45][400/5005]\tlr: 1.000e-02, eta: 6:51:21, time: 0.088, data_time: 0.029, memory: 928, loss: 5.0669\n2023-01-04 20:52:31,710 - mmcls - INFO - Epoch [45][500/5005]\tlr: 1.000e-02, eta: 6:51:12, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0567\n2023-01-04 20:52:40,708 - mmcls - INFO - Epoch [45][600/5005]\tlr: 1.000e-02, eta: 6:51:04, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0459\n2023-01-04 20:52:49,476 - mmcls - INFO - Epoch [45][700/5005]\tlr: 1.000e-02, eta: 6:50:55, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0628\n2023-01-04 20:52:58,055 - mmcls - INFO - Epoch [45][800/5005]\tlr: 1.000e-02, eta: 6:50:46, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0613\n2023-01-04 20:53:06,744 - mmcls - INFO - Epoch [45][900/5005]\tlr: 1.000e-02, eta: 6:50:37, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0691\n2023-01-04 20:53:15,518 - mmcls - INFO - Epoch [45][1000/5005]\tlr: 1.000e-02, eta: 6:50:28, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0711\n2023-01-04 20:53:24,296 - mmcls - INFO - Epoch [45][1100/5005]\tlr: 1.000e-02, eta: 6:50:19, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0812\n2023-01-04 20:53:33,078 - mmcls - INFO - Epoch [45][1200/5005]\tlr: 1.000e-02, eta: 6:50:10, time: 0.088, data_time: 0.029, memory: 928, loss: 5.0744\n2023-01-04 20:53:41,816 - mmcls - INFO - Epoch [45][1300/5005]\tlr: 1.000e-02, eta: 6:50:01, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0693\n2023-01-04 20:53:50,593 - mmcls - INFO - Epoch [45][1400/5005]\tlr: 1.000e-02, eta: 6:49:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0649\n2023-01-04 20:53:59,322 - mmcls - INFO - Epoch [45][1500/5005]\tlr: 1.000e-02, eta: 6:49:44, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0391\n2023-01-04 20:54:07,956 - mmcls - INFO - Epoch [45][1600/5005]\tlr: 1.000e-02, eta: 6:49:35, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0716\n2023-01-04 20:54:16,576 - mmcls - INFO - Epoch [45][1700/5005]\tlr: 1.000e-02, eta: 6:49:25, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0524\n2023-01-04 20:54:25,254 - mmcls - INFO - Epoch [45][1800/5005]\tlr: 1.000e-02, eta: 6:49:16, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0714\n2023-01-04 20:54:33,846 - mmcls - INFO - Epoch [45][1900/5005]\tlr: 1.000e-02, eta: 6:49:07, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0667\n2023-01-04 20:54:42,712 - mmcls - INFO - Epoch [45][2000/5005]\tlr: 1.000e-02, eta: 6:48:59, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0625\n2023-01-04 20:54:51,671 - mmcls - INFO - Epoch [45][2100/5005]\tlr: 1.000e-02, eta: 6:48:50, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0676\n2023-01-04 20:55:00,431 - mmcls - INFO - Epoch [45][2200/5005]\tlr: 1.000e-02, eta: 6:48:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0621\n2023-01-04 20:55:08,983 - mmcls - INFO - Epoch [45][2300/5005]\tlr: 1.000e-02, eta: 6:48:32, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0774\n2023-01-04 20:55:17,726 - mmcls - INFO - Epoch [45][2400/5005]\tlr: 1.000e-02, eta: 6:48:23, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0657\n2023-01-04 20:55:26,956 - mmcls - INFO - Epoch [45][2500/5005]\tlr: 1.000e-02, eta: 6:48:15, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0688\n2023-01-04 20:55:35,523 - mmcls - INFO - Epoch [45][2600/5005]\tlr: 1.000e-02, eta: 6:48:06, time: 0.086, data_time: 0.038, memory: 928, loss: 5.0569\n2023-01-04 20:55:44,440 - mmcls - INFO - Epoch [45][2700/5005]\tlr: 1.000e-02, eta: 6:47:57, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0686\n2023-01-04 20:55:53,120 - mmcls - INFO - Epoch [45][2800/5005]\tlr: 1.000e-02, eta: 6:47:48, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0802\n2023-01-04 20:56:02,157 - mmcls - INFO - Epoch [45][2900/5005]\tlr: 1.000e-02, eta: 6:47:39, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0395\n2023-01-04 20:56:10,732 - mmcls - INFO - Epoch [45][3000/5005]\tlr: 1.000e-02, eta: 6:47:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0658\n2023-01-04 20:56:19,451 - mmcls - INFO - Epoch [45][3100/5005]\tlr: 1.000e-02, eta: 6:47:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0641\n2023-01-04 20:56:28,308 - mmcls - INFO - Epoch [45][3200/5005]\tlr: 1.000e-02, eta: 6:47:12, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0629\n2023-01-04 20:56:36,952 - mmcls - INFO - Epoch [45][3300/5005]\tlr: 1.000e-02, eta: 6:47:03, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0651\n2023-01-04 20:56:45,810 - mmcls - INFO - Epoch [45][3400/5005]\tlr: 1.000e-02, eta: 6:46:55, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0596\n2023-01-04 20:56:54,327 - mmcls - INFO - Epoch [45][3500/5005]\tlr: 1.000e-02, eta: 6:46:45, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0855\n2023-01-04 20:57:03,138 - mmcls - INFO - Epoch [45][3600/5005]\tlr: 1.000e-02, eta: 6:46:37, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0609\n2023-01-04 20:57:12,325 - mmcls - INFO - Epoch [45][3700/5005]\tlr: 1.000e-02, eta: 6:46:28, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0613\n2023-01-04 20:57:21,140 - mmcls - INFO - Epoch [45][3800/5005]\tlr: 1.000e-02, eta: 6:46:19, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0748\n2023-01-04 20:57:29,950 - mmcls - INFO - Epoch [45][3900/5005]\tlr: 1.000e-02, eta: 6:46:11, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0786\n2023-01-04 20:57:38,893 - mmcls - INFO - Epoch [45][4000/5005]\tlr: 1.000e-02, eta: 6:46:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0465\n2023-01-04 20:57:47,730 - mmcls - INFO - Epoch [45][4100/5005]\tlr: 1.000e-02, eta: 6:45:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0639\n2023-01-04 20:57:56,698 - mmcls - INFO - Epoch [45][4200/5005]\tlr: 1.000e-02, eta: 6:45:45, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0486\n2023-01-04 20:58:05,931 - mmcls - INFO - Epoch [45][4300/5005]\tlr: 1.000e-02, eta: 6:45:36, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0808\n2023-01-04 20:58:14,725 - mmcls - INFO - Epoch [45][4400/5005]\tlr: 1.000e-02, eta: 6:45:27, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0549\n2023-01-04 20:58:23,659 - mmcls - INFO - Epoch [45][4500/5005]\tlr: 1.000e-02, eta: 6:45:19, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0560\n2023-01-04 20:58:32,372 - mmcls - INFO - Epoch [45][4600/5005]\tlr: 1.000e-02, eta: 6:45:10, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0666\n2023-01-04 20:58:40,877 - mmcls - INFO - Epoch [45][4700/5005]\tlr: 1.000e-02, eta: 6:45:01, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0685\n2023-01-04 20:58:49,683 - mmcls - INFO - Epoch [45][4800/5005]\tlr: 1.000e-02, eta: 6:44:52, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0597\n2023-01-04 20:58:58,430 - mmcls - INFO - Epoch [45][4900/5005]\tlr: 1.000e-02, eta: 6:44:43, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0577\n2023-01-04 20:59:07,187 - mmcls - INFO - Epoch [45][5000/5005]\tlr: 1.000e-02, eta: 6:44:34, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0605\n2023-01-04 20:59:30,041 - mmcls - INFO - Epoch(val) [45][196]\taccuracy_top-1: 10.7300, accuracy_top-5: 24.4520\n2023-01-04 20:59:40,757 - mmcls - INFO - Epoch [46][100/5005]\tlr: 1.000e-02, eta: 6:44:26, time: 0.107, data_time: 0.051, memory: 928, loss: 5.0394\n2023-01-04 20:59:49,573 - mmcls - INFO - Epoch [46][200/5005]\tlr: 1.000e-02, eta: 6:44:18, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0374\n2023-01-04 20:59:58,069 - mmcls - INFO - Epoch [46][300/5005]\tlr: 1.000e-02, eta: 6:44:08, time: 0.085, data_time: 0.016, memory: 928, loss: 5.0646\n2023-01-04 21:00:07,282 - mmcls - INFO - Epoch [46][400/5005]\tlr: 1.000e-02, eta: 6:44:00, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0305\n2023-01-04 21:00:15,959 - mmcls - INFO - Epoch [46][500/5005]\tlr: 1.000e-02, eta: 6:43:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0543\n2023-01-04 21:00:24,706 - mmcls - INFO - Epoch [46][600/5005]\tlr: 1.000e-02, eta: 6:43:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0567\n2023-01-04 21:00:33,278 - mmcls - INFO - Epoch [46][700/5005]\tlr: 1.000e-02, eta: 6:43:33, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0625\n2023-01-04 21:00:42,227 - mmcls - INFO - Epoch [46][800/5005]\tlr: 1.000e-02, eta: 6:43:24, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0779\n2023-01-04 21:00:51,377 - mmcls - INFO - Epoch [46][900/5005]\tlr: 1.000e-02, eta: 6:43:16, time: 0.091, data_time: 0.002, memory: 928, loss: 5.0522\n2023-01-04 21:01:00,204 - mmcls - INFO - Epoch [46][1000/5005]\tlr: 1.000e-02, eta: 6:43:07, time: 0.088, data_time: 0.031, memory: 928, loss: 5.0681\n2023-01-04 21:01:09,515 - mmcls - INFO - Epoch [46][1100/5005]\tlr: 1.000e-02, eta: 6:42:59, time: 0.093, data_time: 0.007, memory: 928, loss: 5.0773\n2023-01-04 21:01:18,309 - mmcls - INFO - Epoch [46][1200/5005]\tlr: 1.000e-02, eta: 6:42:50, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0576\n2023-01-04 21:01:27,465 - mmcls - INFO - Epoch [46][1300/5005]\tlr: 1.000e-02, eta: 6:42:42, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0543\n2023-01-04 21:01:36,071 - mmcls - INFO - Epoch [46][1400/5005]\tlr: 1.000e-02, eta: 6:42:33, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0670\n2023-01-04 21:01:45,033 - mmcls - INFO - Epoch [46][1500/5005]\tlr: 1.000e-02, eta: 6:42:24, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0700\n2023-01-04 21:01:53,612 - mmcls - INFO - Epoch [46][1600/5005]\tlr: 1.000e-02, eta: 6:42:15, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0835\n2023-01-04 21:02:02,181 - mmcls - INFO - Epoch [46][1700/5005]\tlr: 1.000e-02, eta: 6:42:06, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0349\n2023-01-04 21:02:10,913 - mmcls - INFO - Epoch [46][1800/5005]\tlr: 1.000e-02, eta: 6:41:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0882\n2023-01-04 21:02:19,806 - mmcls - INFO - Epoch [46][1900/5005]\tlr: 1.000e-02, eta: 6:41:48, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0787\n2023-01-04 21:02:28,624 - mmcls - INFO - Epoch [46][2000/5005]\tlr: 1.000e-02, eta: 6:41:39, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0868\n2023-01-04 21:02:37,343 - mmcls - INFO - Epoch [46][2100/5005]\tlr: 1.000e-02, eta: 6:41:30, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0722\n2023-01-04 21:02:45,791 - mmcls - INFO - Epoch [46][2200/5005]\tlr: 1.000e-02, eta: 6:41:21, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0519\n2023-01-04 21:02:54,506 - mmcls - INFO - Epoch [46][2300/5005]\tlr: 1.000e-02, eta: 6:41:12, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0523\n2023-01-04 21:03:03,247 - mmcls - INFO - Epoch [46][2400/5005]\tlr: 1.000e-02, eta: 6:41:03, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0465\n2023-01-04 21:03:11,917 - mmcls - INFO - Epoch [46][2500/5005]\tlr: 1.000e-02, eta: 6:40:54, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0657\n2023-01-04 21:03:20,550 - mmcls - INFO - Epoch [46][2600/5005]\tlr: 1.000e-02, eta: 6:40:45, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0513\n2023-01-04 21:03:29,559 - mmcls - INFO - Epoch [46][2700/5005]\tlr: 1.000e-02, eta: 6:40:37, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0635\n2023-01-04 21:03:38,951 - mmcls - INFO - Epoch [46][2800/5005]\tlr: 1.000e-02, eta: 6:40:28, time: 0.094, data_time: 0.008, memory: 928, loss: 5.0481\n2023-01-04 21:03:47,939 - mmcls - INFO - Epoch [46][2900/5005]\tlr: 1.000e-02, eta: 6:40:20, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0745\n2023-01-04 21:03:56,518 - mmcls - INFO - Epoch [46][3000/5005]\tlr: 1.000e-02, eta: 6:40:11, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0675\n2023-01-04 21:04:05,408 - mmcls - INFO - Epoch [46][3100/5005]\tlr: 1.000e-02, eta: 6:40:02, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0691\n2023-01-04 21:04:13,883 - mmcls - INFO - Epoch [46][3200/5005]\tlr: 1.000e-02, eta: 6:39:53, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0253\n2023-01-04 21:04:22,621 - mmcls - INFO - Epoch [46][3300/5005]\tlr: 1.000e-02, eta: 6:39:44, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0716\n2023-01-04 21:04:32,059 - mmcls - INFO - Epoch [46][3400/5005]\tlr: 1.000e-02, eta: 6:39:36, time: 0.094, data_time: 0.001, memory: 928, loss: 5.0418\n2023-01-04 21:04:41,102 - mmcls - INFO - Epoch [46][3500/5005]\tlr: 1.000e-02, eta: 6:39:27, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0828\n2023-01-04 21:04:49,819 - mmcls - INFO - Epoch [46][3600/5005]\tlr: 1.000e-02, eta: 6:39:18, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0732\n2023-01-04 21:04:58,803 - mmcls - INFO - Epoch [46][3700/5005]\tlr: 1.000e-02, eta: 6:39:10, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0567\n2023-01-04 21:05:07,704 - mmcls - INFO - Epoch [46][3800/5005]\tlr: 1.000e-02, eta: 6:39:01, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0701\n2023-01-04 21:05:16,657 - mmcls - INFO - Epoch [46][3900/5005]\tlr: 1.000e-02, eta: 6:38:52, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0775\n2023-01-04 21:05:25,202 - mmcls - INFO - Epoch [46][4000/5005]\tlr: 1.000e-02, eta: 6:38:43, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0758\n2023-01-04 21:05:34,063 - mmcls - INFO - Epoch [46][4100/5005]\tlr: 1.000e-02, eta: 6:38:34, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0554\n2023-01-04 21:05:42,897 - mmcls - INFO - Epoch [46][4200/5005]\tlr: 1.000e-02, eta: 6:38:25, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0776\n2023-01-04 21:05:51,891 - mmcls - INFO - Epoch [46][4300/5005]\tlr: 1.000e-02, eta: 6:38:17, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0772\n2023-01-04 21:06:00,445 - mmcls - INFO - Epoch [46][4400/5005]\tlr: 1.000e-02, eta: 6:38:08, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0612\n2023-01-04 21:06:09,532 - mmcls - INFO - Epoch [46][4500/5005]\tlr: 1.000e-02, eta: 6:37:59, time: 0.091, data_time: 0.004, memory: 928, loss: 5.0567\n2023-01-04 21:06:18,473 - mmcls - INFO - Epoch [46][4600/5005]\tlr: 1.000e-02, eta: 6:37:51, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0580\n2023-01-04 21:06:27,323 - mmcls - INFO - Epoch [46][4700/5005]\tlr: 1.000e-02, eta: 6:37:42, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0627\n2023-01-04 21:06:35,887 - mmcls - INFO - Epoch [46][4800/5005]\tlr: 1.000e-02, eta: 6:37:33, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0591\n2023-01-04 21:06:44,603 - mmcls - INFO - Epoch [46][4900/5005]\tlr: 1.000e-02, eta: 6:37:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0611\n2023-01-04 21:06:53,238 - mmcls - INFO - Epoch [46][5000/5005]\tlr: 1.000e-02, eta: 6:37:15, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0336\n2023-01-04 21:07:15,983 - mmcls - INFO - Epoch(val) [46][196]\taccuracy_top-1: 10.3480, accuracy_top-5: 23.5820\n2023-01-04 21:07:26,977 - mmcls - INFO - Epoch [47][100/5005]\tlr: 1.000e-02, eta: 6:37:07, time: 0.110, data_time: 0.059, memory: 928, loss: 5.0469\n2023-01-04 21:07:35,736 - mmcls - INFO - Epoch [47][200/5005]\tlr: 1.000e-02, eta: 6:36:59, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0277\n2023-01-04 21:07:44,358 - mmcls - INFO - Epoch [47][300/5005]\tlr: 1.000e-02, eta: 6:36:49, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0629\n2023-01-04 21:07:53,104 - mmcls - INFO - Epoch [47][400/5005]\tlr: 1.000e-02, eta: 6:36:41, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0548\n2023-01-04 21:08:01,948 - mmcls - INFO - Epoch [47][500/5005]\tlr: 1.000e-02, eta: 6:36:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0416\n2023-01-04 21:08:10,667 - mmcls - INFO - Epoch [47][600/5005]\tlr: 1.000e-02, eta: 6:36:23, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0544\n2023-01-04 21:08:19,585 - mmcls - INFO - Epoch [47][700/5005]\tlr: 1.000e-02, eta: 6:36:14, time: 0.089, data_time: 0.015, memory: 928, loss: 5.0688\n2023-01-04 21:08:28,204 - mmcls - INFO - Epoch [47][800/5005]\tlr: 1.000e-02, eta: 6:36:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0584\n2023-01-04 21:08:37,103 - mmcls - INFO - Epoch [47][900/5005]\tlr: 1.000e-02, eta: 6:35:56, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0517\n2023-01-04 21:08:45,875 - mmcls - INFO - Epoch [47][1000/5005]\tlr: 1.000e-02, eta: 6:35:48, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0379\n2023-01-04 21:08:54,238 - mmcls - INFO - Epoch [47][1100/5005]\tlr: 1.000e-02, eta: 6:35:38, time: 0.084, data_time: 0.015, memory: 928, loss: 5.0711\n2023-01-04 21:09:03,341 - mmcls - INFO - Epoch [47][1200/5005]\tlr: 1.000e-02, eta: 6:35:30, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0641\n2023-01-04 21:09:12,311 - mmcls - INFO - Epoch [47][1300/5005]\tlr: 1.000e-02, eta: 6:35:21, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0660\n2023-01-04 21:09:21,138 - mmcls - INFO - Epoch [47][1400/5005]\tlr: 1.000e-02, eta: 6:35:12, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0671\n2023-01-04 21:09:30,052 - mmcls - INFO - Epoch [47][1500/5005]\tlr: 1.000e-02, eta: 6:35:03, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0608\n2023-01-04 21:09:38,572 - mmcls - INFO - Epoch [47][1600/5005]\tlr: 1.000e-02, eta: 6:34:54, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0642\n2023-01-04 21:09:47,220 - mmcls - INFO - Epoch [47][1700/5005]\tlr: 1.000e-02, eta: 6:34:45, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0459\n2023-01-04 21:09:56,193 - mmcls - INFO - Epoch [47][1800/5005]\tlr: 1.000e-02, eta: 6:34:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0462\n2023-01-04 21:10:04,812 - mmcls - INFO - Epoch [47][1900/5005]\tlr: 1.000e-02, eta: 6:34:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0631\n2023-01-04 21:10:13,887 - mmcls - INFO - Epoch [47][2000/5005]\tlr: 1.000e-02, eta: 6:34:19, time: 0.091, data_time: 0.002, memory: 928, loss: 5.0432\n2023-01-04 21:10:22,862 - mmcls - INFO - Epoch [47][2100/5005]\tlr: 1.000e-02, eta: 6:34:10, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0916\n2023-01-04 21:10:31,368 - mmcls - INFO - Epoch [47][2200/5005]\tlr: 1.000e-02, eta: 6:34:01, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0645\n2023-01-04 21:10:40,263 - mmcls - INFO - Epoch [47][2300/5005]\tlr: 1.000e-02, eta: 6:33:53, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0520\n2023-01-04 21:10:48,840 - mmcls - INFO - Epoch [47][2400/5005]\tlr: 1.000e-02, eta: 6:33:43, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0609\n2023-01-04 21:10:57,419 - mmcls - INFO - Epoch [47][2500/5005]\tlr: 1.000e-02, eta: 6:33:34, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0468\n2023-01-04 21:11:06,247 - mmcls - INFO - Epoch [47][2600/5005]\tlr: 1.000e-02, eta: 6:33:26, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0378\n2023-01-04 21:11:14,841 - mmcls - INFO - Epoch [47][2700/5005]\tlr: 1.000e-02, eta: 6:33:16, time: 0.086, data_time: 0.031, memory: 928, loss: 5.0768\n2023-01-04 21:11:23,706 - mmcls - INFO - Epoch [47][2800/5005]\tlr: 1.000e-02, eta: 6:33:08, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0655\n2023-01-04 21:11:32,492 - mmcls - INFO - Epoch [47][2900/5005]\tlr: 1.000e-02, eta: 6:32:59, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0640\n2023-01-04 21:11:40,963 - mmcls - INFO - Epoch [47][3000/5005]\tlr: 1.000e-02, eta: 6:32:50, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0594\n2023-01-04 21:11:49,773 - mmcls - INFO - Epoch [47][3100/5005]\tlr: 1.000e-02, eta: 6:32:41, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0616\n2023-01-04 21:11:58,694 - mmcls - INFO - Epoch [47][3200/5005]\tlr: 1.000e-02, eta: 6:32:32, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0732\n2023-01-04 21:12:07,423 - mmcls - INFO - Epoch [47][3300/5005]\tlr: 1.000e-02, eta: 6:32:23, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0715\n2023-01-04 21:12:16,269 - mmcls - INFO - Epoch [47][3400/5005]\tlr: 1.000e-02, eta: 6:32:14, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0458\n2023-01-04 21:12:24,916 - mmcls - INFO - Epoch [47][3500/5005]\tlr: 1.000e-02, eta: 6:32:05, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0646\n2023-01-04 21:12:33,504 - mmcls - INFO - Epoch [47][3600/5005]\tlr: 1.000e-02, eta: 6:31:56, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0711\n2023-01-04 21:12:42,236 - mmcls - INFO - Epoch [47][3700/5005]\tlr: 1.000e-02, eta: 6:31:47, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0714\n2023-01-04 21:12:51,070 - mmcls - INFO - Epoch [47][3800/5005]\tlr: 1.000e-02, eta: 6:31:39, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0720\n2023-01-04 21:12:59,697 - mmcls - INFO - Epoch [47][3900/5005]\tlr: 1.000e-02, eta: 6:31:29, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0582\n2023-01-04 21:13:08,485 - mmcls - INFO - Epoch [47][4000/5005]\tlr: 1.000e-02, eta: 6:31:21, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0481\n2023-01-04 21:13:17,270 - mmcls - INFO - Epoch [47][4100/5005]\tlr: 1.000e-02, eta: 6:31:12, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0672\n2023-01-04 21:13:26,197 - mmcls - INFO - Epoch [47][4200/5005]\tlr: 1.000e-02, eta: 6:31:03, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0671\n2023-01-04 21:13:34,982 - mmcls - INFO - Epoch [47][4300/5005]\tlr: 1.000e-02, eta: 6:30:54, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0487\n2023-01-04 21:13:43,602 - mmcls - INFO - Epoch [47][4400/5005]\tlr: 1.000e-02, eta: 6:30:45, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0712\n2023-01-04 21:13:52,310 - mmcls - INFO - Epoch [47][4500/5005]\tlr: 1.000e-02, eta: 6:30:36, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0471\n2023-01-04 21:14:00,874 - mmcls - INFO - Epoch [47][4600/5005]\tlr: 1.000e-02, eta: 6:30:27, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0650\n2023-01-04 21:14:09,460 - mmcls - INFO - Epoch [47][4700/5005]\tlr: 1.000e-02, eta: 6:30:18, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0523\n2023-01-04 21:14:18,404 - mmcls - INFO - Epoch [47][4800/5005]\tlr: 1.000e-02, eta: 6:30:09, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0624\n2023-01-04 21:14:27,162 - mmcls - INFO - Epoch [47][4900/5005]\tlr: 1.000e-02, eta: 6:30:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0517\n2023-01-04 21:14:35,893 - mmcls - INFO - Epoch [47][5000/5005]\tlr: 1.000e-02, eta: 6:29:52, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0517\n2023-01-04 21:14:58,538 - mmcls - INFO - Epoch(val) [47][196]\taccuracy_top-1: 10.0200, accuracy_top-5: 23.0860\n2023-01-04 21:15:09,482 - mmcls - INFO - Epoch [48][100/5005]\tlr: 1.000e-02, eta: 6:29:44, time: 0.109, data_time: 0.035, memory: 928, loss: 5.0407\n2023-01-04 21:15:18,449 - mmcls - INFO - Epoch [48][200/5005]\tlr: 1.000e-02, eta: 6:29:36, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0497\n2023-01-04 21:15:27,135 - mmcls - INFO - Epoch [48][300/5005]\tlr: 1.000e-02, eta: 6:29:27, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0289\n2023-01-04 21:15:35,859 - mmcls - INFO - Epoch [48][400/5005]\tlr: 1.000e-02, eta: 6:29:18, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0551\n2023-01-04 21:15:44,829 - mmcls - INFO - Epoch [48][500/5005]\tlr: 1.000e-02, eta: 6:29:09, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0559\n2023-01-04 21:15:53,668 - mmcls - INFO - Epoch [48][600/5005]\tlr: 1.000e-02, eta: 6:29:00, time: 0.088, data_time: 0.027, memory: 928, loss: 5.0563\n2023-01-04 21:16:02,324 - mmcls - INFO - Epoch [48][700/5005]\tlr: 1.000e-02, eta: 6:28:51, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0733\n2023-01-04 21:16:11,210 - mmcls - INFO - Epoch [48][800/5005]\tlr: 1.000e-02, eta: 6:28:42, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0095\n2023-01-04 21:16:19,869 - mmcls - INFO - Epoch [48][900/5005]\tlr: 1.000e-02, eta: 6:28:33, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0185\n2023-01-04 21:16:29,063 - mmcls - INFO - Epoch [48][1000/5005]\tlr: 1.000e-02, eta: 6:28:25, time: 0.092, data_time: 0.017, memory: 928, loss: 5.0451\n2023-01-04 21:16:38,200 - mmcls - INFO - Epoch [48][1100/5005]\tlr: 1.000e-02, eta: 6:28:17, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0713\n2023-01-04 21:16:46,865 - mmcls - INFO - Epoch [48][1200/5005]\tlr: 1.000e-02, eta: 6:28:08, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0441\n2023-01-04 21:16:55,668 - mmcls - INFO - Epoch [48][1300/5005]\tlr: 1.000e-02, eta: 6:27:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0543\n2023-01-04 21:17:04,387 - mmcls - INFO - Epoch [48][1400/5005]\tlr: 1.000e-02, eta: 6:27:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0554\n2023-01-04 21:17:13,258 - mmcls - INFO - Epoch [48][1500/5005]\tlr: 1.000e-02, eta: 6:27:41, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0607\n2023-01-04 21:17:21,853 - mmcls - INFO - Epoch [48][1600/5005]\tlr: 1.000e-02, eta: 6:27:32, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0541\n2023-01-04 21:17:30,637 - mmcls - INFO - Epoch [48][1700/5005]\tlr: 1.000e-02, eta: 6:27:23, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0655\n2023-01-04 21:17:39,242 - mmcls - INFO - Epoch [48][1800/5005]\tlr: 1.000e-02, eta: 6:27:14, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0688\n2023-01-04 21:17:47,783 - mmcls - INFO - Epoch [48][1900/5005]\tlr: 1.000e-02, eta: 6:27:05, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0456\n2023-01-04 21:17:56,368 - mmcls - INFO - Epoch [48][2000/5005]\tlr: 1.000e-02, eta: 6:26:56, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0542\n2023-01-04 21:18:04,991 - mmcls - INFO - Epoch [48][2100/5005]\tlr: 1.000e-02, eta: 6:26:47, time: 0.086, data_time: 0.021, memory: 928, loss: 5.0902\n2023-01-04 21:18:13,868 - mmcls - INFO - Epoch [48][2200/5005]\tlr: 1.000e-02, eta: 6:26:38, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0430\n2023-01-04 21:18:22,581 - mmcls - INFO - Epoch [48][2300/5005]\tlr: 1.000e-02, eta: 6:26:29, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0592\n2023-01-04 21:18:31,239 - mmcls - INFO - Epoch [48][2400/5005]\tlr: 1.000e-02, eta: 6:26:20, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0594\n2023-01-04 21:18:40,083 - mmcls - INFO - Epoch [48][2500/5005]\tlr: 1.000e-02, eta: 6:26:11, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0594\n2023-01-04 21:18:48,797 - mmcls - INFO - Epoch [48][2600/5005]\tlr: 1.000e-02, eta: 6:26:02, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0600\n2023-01-04 21:18:57,550 - mmcls - INFO - Epoch [48][2700/5005]\tlr: 1.000e-02, eta: 6:25:54, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0434\n2023-01-04 21:19:06,168 - mmcls - INFO - Epoch [48][2800/5005]\tlr: 1.000e-02, eta: 6:25:44, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0645\n2023-01-04 21:19:15,103 - mmcls - INFO - Epoch [48][2900/5005]\tlr: 1.000e-02, eta: 6:25:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0660\n2023-01-04 21:19:23,870 - mmcls - INFO - Epoch [48][3000/5005]\tlr: 1.000e-02, eta: 6:25:27, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0774\n2023-01-04 21:19:32,477 - mmcls - INFO - Epoch [48][3100/5005]\tlr: 1.000e-02, eta: 6:25:18, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0717\n2023-01-04 21:19:41,197 - mmcls - INFO - Epoch [48][3200/5005]\tlr: 1.000e-02, eta: 6:25:09, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0459\n2023-01-04 21:19:49,896 - mmcls - INFO - Epoch [48][3300/5005]\tlr: 1.000e-02, eta: 6:25:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0722\n2023-01-04 21:19:58,574 - mmcls - INFO - Epoch [48][3400/5005]\tlr: 1.000e-02, eta: 6:24:51, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0473\n2023-01-04 21:20:07,649 - mmcls - INFO - Epoch [48][3500/5005]\tlr: 1.000e-02, eta: 6:24:42, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0412\n2023-01-04 21:20:16,432 - mmcls - INFO - Epoch [48][3600/5005]\tlr: 1.000e-02, eta: 6:24:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0617\n2023-01-04 21:20:24,935 - mmcls - INFO - Epoch [48][3700/5005]\tlr: 1.000e-02, eta: 6:24:24, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0713\n2023-01-04 21:20:33,618 - mmcls - INFO - Epoch [48][3800/5005]\tlr: 1.000e-02, eta: 6:24:15, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0725\n2023-01-04 21:20:42,253 - mmcls - INFO - Epoch [48][3900/5005]\tlr: 1.000e-02, eta: 6:24:06, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0412\n2023-01-04 21:20:51,127 - mmcls - INFO - Epoch [48][4000/5005]\tlr: 1.000e-02, eta: 6:23:58, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0498\n2023-01-04 21:20:59,883 - mmcls - INFO - Epoch [48][4100/5005]\tlr: 1.000e-02, eta: 6:23:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0655\n2023-01-04 21:21:08,635 - mmcls - INFO - Epoch [48][4200/5005]\tlr: 1.000e-02, eta: 6:23:40, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0387\n2023-01-04 21:21:17,386 - mmcls - INFO - Epoch [48][4300/5005]\tlr: 1.000e-02, eta: 6:23:31, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0589\n2023-01-04 21:21:25,966 - mmcls - INFO - Epoch [48][4400/5005]\tlr: 1.000e-02, eta: 6:23:22, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0620\n2023-01-04 21:21:35,128 - mmcls - INFO - Epoch [48][4500/5005]\tlr: 1.000e-02, eta: 6:23:14, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0437\n2023-01-04 21:21:44,031 - mmcls - INFO - Epoch [48][4600/5005]\tlr: 1.000e-02, eta: 6:23:05, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0335\n2023-01-04 21:21:52,973 - mmcls - INFO - Epoch [48][4700/5005]\tlr: 1.000e-02, eta: 6:22:56, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0562\n2023-01-04 21:22:01,643 - mmcls - INFO - Epoch [48][4800/5005]\tlr: 1.000e-02, eta: 6:22:47, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0595\n2023-01-04 21:22:09,973 - mmcls - INFO - Epoch [48][4900/5005]\tlr: 1.000e-02, eta: 6:22:38, time: 0.083, data_time: 0.006, memory: 928, loss: 5.0888\n2023-01-04 21:22:18,418 - mmcls - INFO - Epoch [48][5000/5005]\tlr: 1.000e-02, eta: 6:22:29, time: 0.084, data_time: 0.015, memory: 928, loss: 5.0605\n2023-01-04 21:22:41,087 - mmcls - INFO - Epoch(val) [48][196]\taccuracy_top-1: 9.2200, accuracy_top-5: 21.4240\n2023-01-04 21:22:52,266 - mmcls - INFO - Epoch [49][100/5005]\tlr: 1.000e-02, eta: 6:22:21, time: 0.112, data_time: 0.053, memory: 928, loss: 5.0634\n2023-01-04 21:23:01,126 - mmcls - INFO - Epoch [49][200/5005]\tlr: 1.000e-02, eta: 6:22:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0418\n2023-01-04 21:23:10,113 - mmcls - INFO - Epoch [49][300/5005]\tlr: 1.000e-02, eta: 6:22:04, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0595\n2023-01-04 21:23:18,944 - mmcls - INFO - Epoch [49][400/5005]\tlr: 1.000e-02, eta: 6:21:55, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0505\n2023-01-04 21:23:27,476 - mmcls - INFO - Epoch [49][500/5005]\tlr: 1.000e-02, eta: 6:21:46, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0409\n2023-01-04 21:23:36,065 - mmcls - INFO - Epoch [49][600/5005]\tlr: 1.000e-02, eta: 6:21:37, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0625\n2023-01-04 21:23:44,853 - mmcls - INFO - Epoch [49][700/5005]\tlr: 1.000e-02, eta: 6:21:28, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0475\n2023-01-04 21:23:53,357 - mmcls - INFO - Epoch [49][800/5005]\tlr: 1.000e-02, eta: 6:21:19, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0469\n2023-01-04 21:24:02,418 - mmcls - INFO - Epoch [49][900/5005]\tlr: 1.000e-02, eta: 6:21:10, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0506\n2023-01-04 21:24:11,160 - mmcls - INFO - Epoch [49][1000/5005]\tlr: 1.000e-02, eta: 6:21:01, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0646\n2023-01-04 21:24:20,163 - mmcls - INFO - Epoch [49][1100/5005]\tlr: 1.000e-02, eta: 6:20:53, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0528\n2023-01-04 21:24:28,707 - mmcls - INFO - Epoch [49][1200/5005]\tlr: 1.000e-02, eta: 6:20:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0488\n2023-01-04 21:24:37,346 - mmcls - INFO - Epoch [49][1300/5005]\tlr: 1.000e-02, eta: 6:20:35, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0736\n2023-01-04 21:24:46,371 - mmcls - INFO - Epoch [49][1400/5005]\tlr: 1.000e-02, eta: 6:20:26, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0490\n2023-01-04 21:24:55,073 - mmcls - INFO - Epoch [49][1500/5005]\tlr: 1.000e-02, eta: 6:20:17, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0417\n2023-01-04 21:25:03,698 - mmcls - INFO - Epoch [49][1600/5005]\tlr: 1.000e-02, eta: 6:20:08, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0343\n2023-01-04 21:25:12,542 - mmcls - INFO - Epoch [49][1700/5005]\tlr: 1.000e-02, eta: 6:19:59, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0646\n2023-01-04 21:25:21,116 - mmcls - INFO - Epoch [49][1800/5005]\tlr: 1.000e-02, eta: 6:19:50, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0557\n2023-01-04 21:25:30,019 - mmcls - INFO - Epoch [49][1900/5005]\tlr: 1.000e-02, eta: 6:19:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0369\n2023-01-04 21:25:38,981 - mmcls - INFO - Epoch [49][2000/5005]\tlr: 1.000e-02, eta: 6:19:33, time: 0.090, data_time: 0.039, memory: 928, loss: 5.0374\n2023-01-04 21:25:47,716 - mmcls - INFO - Epoch [49][2100/5005]\tlr: 1.000e-02, eta: 6:19:24, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0607\n2023-01-04 21:25:56,671 - mmcls - INFO - Epoch [49][2200/5005]\tlr: 1.000e-02, eta: 6:19:15, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0630\n2023-01-04 21:26:05,558 - mmcls - INFO - Epoch [49][2300/5005]\tlr: 1.000e-02, eta: 6:19:07, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0617\n2023-01-04 21:26:14,312 - mmcls - INFO - Epoch [49][2400/5005]\tlr: 1.000e-02, eta: 6:18:58, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0700\n2023-01-04 21:26:23,101 - mmcls - INFO - Epoch [49][2500/5005]\tlr: 1.000e-02, eta: 6:18:49, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0485\n2023-01-04 21:26:31,676 - mmcls - INFO - Epoch [49][2600/5005]\tlr: 1.000e-02, eta: 6:18:40, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0676\n2023-01-04 21:26:40,416 - mmcls - INFO - Epoch [49][2700/5005]\tlr: 1.000e-02, eta: 6:18:31, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0563\n2023-01-04 21:26:49,489 - mmcls - INFO - Epoch [49][2800/5005]\tlr: 1.000e-02, eta: 6:18:22, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0201\n2023-01-04 21:26:58,288 - mmcls - INFO - Epoch [49][2900/5005]\tlr: 1.000e-02, eta: 6:18:13, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0734\n2023-01-04 21:27:07,197 - mmcls - INFO - Epoch [49][3000/5005]\tlr: 1.000e-02, eta: 6:18:05, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0574\n2023-01-04 21:27:16,020 - mmcls - INFO - Epoch [49][3100/5005]\tlr: 1.000e-02, eta: 6:17:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0459\n2023-01-04 21:27:24,794 - mmcls - INFO - Epoch [49][3200/5005]\tlr: 1.000e-02, eta: 6:17:47, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0341\n2023-01-04 21:27:33,426 - mmcls - INFO - Epoch [49][3300/5005]\tlr: 1.000e-02, eta: 6:17:38, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0700\n2023-01-04 21:27:42,299 - mmcls - INFO - Epoch [49][3400/5005]\tlr: 1.000e-02, eta: 6:17:29, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0486\n2023-01-04 21:27:50,900 - mmcls - INFO - Epoch [49][3500/5005]\tlr: 1.000e-02, eta: 6:17:20, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0537\n2023-01-04 21:27:59,757 - mmcls - INFO - Epoch [49][3600/5005]\tlr: 1.000e-02, eta: 6:17:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0641\n2023-01-04 21:28:08,312 - mmcls - INFO - Epoch [49][3700/5005]\tlr: 1.000e-02, eta: 6:17:02, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0542\n2023-01-04 21:28:17,007 - mmcls - INFO - Epoch [49][3800/5005]\tlr: 1.000e-02, eta: 6:16:53, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0710\n2023-01-04 21:28:25,746 - mmcls - INFO - Epoch [49][3900/5005]\tlr: 1.000e-02, eta: 6:16:45, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0458\n2023-01-04 21:28:34,500 - mmcls - INFO - Epoch [49][4000/5005]\tlr: 1.000e-02, eta: 6:16:36, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0665\n2023-01-04 21:28:43,099 - mmcls - INFO - Epoch [49][4100/5005]\tlr: 1.000e-02, eta: 6:16:27, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0674\n2023-01-04 21:28:51,738 - mmcls - INFO - Epoch [49][4200/5005]\tlr: 1.000e-02, eta: 6:16:18, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0631\n2023-01-04 21:29:00,653 - mmcls - INFO - Epoch [49][4300/5005]\tlr: 1.000e-02, eta: 6:16:09, time: 0.089, data_time: 0.024, memory: 928, loss: 5.0624\n2023-01-04 21:29:09,350 - mmcls - INFO - Epoch [49][4400/5005]\tlr: 1.000e-02, eta: 6:16:00, time: 0.087, data_time: 0.037, memory: 928, loss: 5.0556\n2023-01-04 21:29:18,025 - mmcls - INFO - Epoch [49][4500/5005]\tlr: 1.000e-02, eta: 6:15:51, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0414\n2023-01-04 21:29:26,799 - mmcls - INFO - Epoch [49][4600/5005]\tlr: 1.000e-02, eta: 6:15:42, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0864\n2023-01-04 21:29:35,802 - mmcls - INFO - Epoch [49][4700/5005]\tlr: 1.000e-02, eta: 6:15:33, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0650\n2023-01-04 21:29:44,687 - mmcls - INFO - Epoch [49][4800/5005]\tlr: 1.000e-02, eta: 6:15:25, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0347\n2023-01-04 21:29:53,591 - mmcls - INFO - Epoch [49][4900/5005]\tlr: 1.000e-02, eta: 6:15:16, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0323\n2023-01-04 21:30:02,210 - mmcls - INFO - Epoch [49][5000/5005]\tlr: 1.000e-02, eta: 6:15:07, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0635\n2023-01-04 21:30:24,856 - mmcls - INFO - Epoch(val) [49][196]\taccuracy_top-1: 10.5920, accuracy_top-5: 23.9340\n2023-01-04 21:30:35,939 - mmcls - INFO - Epoch [50][100/5005]\tlr: 1.000e-02, eta: 6:15:00, time: 0.111, data_time: 0.037, memory: 928, loss: 5.0506\n2023-01-04 21:30:44,774 - mmcls - INFO - Epoch [50][200/5005]\tlr: 1.000e-02, eta: 6:14:51, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0419\n2023-01-04 21:30:53,679 - mmcls - INFO - Epoch [50][300/5005]\tlr: 1.000e-02, eta: 6:14:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0411\n2023-01-04 21:31:02,645 - mmcls - INFO - Epoch [50][400/5005]\tlr: 1.000e-02, eta: 6:14:33, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0454\n2023-01-04 21:31:11,550 - mmcls - INFO - Epoch [50][500/5005]\tlr: 1.000e-02, eta: 6:14:25, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0566\n2023-01-04 21:31:20,888 - mmcls - INFO - Epoch [50][600/5005]\tlr: 1.000e-02, eta: 6:14:16, time: 0.093, data_time: 0.005, memory: 928, loss: 5.0480\n2023-01-04 21:31:29,467 - mmcls - INFO - Epoch [50][700/5005]\tlr: 1.000e-02, eta: 6:14:07, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0526\n2023-01-04 21:31:38,417 - mmcls - INFO - Epoch [50][800/5005]\tlr: 1.000e-02, eta: 6:13:59, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0239\n2023-01-04 21:31:47,044 - mmcls - INFO - Epoch [50][900/5005]\tlr: 1.000e-02, eta: 6:13:50, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0494\n2023-01-04 21:31:55,738 - mmcls - INFO - Epoch [50][1000/5005]\tlr: 1.000e-02, eta: 6:13:41, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0561\n2023-01-04 21:32:04,540 - mmcls - INFO - Epoch [50][1100/5005]\tlr: 1.000e-02, eta: 6:13:32, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0383\n2023-01-04 21:32:13,393 - mmcls - INFO - Epoch [50][1200/5005]\tlr: 1.000e-02, eta: 6:13:23, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0404\n2023-01-04 21:32:22,176 - mmcls - INFO - Epoch [50][1300/5005]\tlr: 1.000e-02, eta: 6:13:14, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0450\n2023-01-04 21:32:30,898 - mmcls - INFO - Epoch [50][1400/5005]\tlr: 1.000e-02, eta: 6:13:05, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0509\n2023-01-04 21:32:39,426 - mmcls - INFO - Epoch [50][1500/5005]\tlr: 1.000e-02, eta: 6:12:56, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0524\n2023-01-04 21:32:47,990 - mmcls - INFO - Epoch [50][1600/5005]\tlr: 1.000e-02, eta: 6:12:47, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0427\n2023-01-04 21:32:56,865 - mmcls - INFO - Epoch [50][1700/5005]\tlr: 1.000e-02, eta: 6:12:38, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0362\n2023-01-04 21:33:05,681 - mmcls - INFO - Epoch [50][1800/5005]\tlr: 1.000e-02, eta: 6:12:29, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0327\n2023-01-04 21:33:14,349 - mmcls - INFO - Epoch [50][1900/5005]\tlr: 1.000e-02, eta: 6:12:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0570\n2023-01-04 21:33:23,038 - mmcls - INFO - Epoch [50][2000/5005]\tlr: 1.000e-02, eta: 6:12:12, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0502\n2023-01-04 21:33:31,541 - mmcls - INFO - Epoch [50][2100/5005]\tlr: 1.000e-02, eta: 6:12:02, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0692\n2023-01-04 21:33:40,662 - mmcls - INFO - Epoch [50][2200/5005]\tlr: 1.000e-02, eta: 6:11:54, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0438\n2023-01-04 21:33:49,411 - mmcls - INFO - Epoch [50][2300/5005]\tlr: 1.000e-02, eta: 6:11:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0482\n2023-01-04 21:33:58,409 - mmcls - INFO - Epoch [50][2400/5005]\tlr: 1.000e-02, eta: 6:11:36, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0582\n2023-01-04 21:34:07,264 - mmcls - INFO - Epoch [50][2500/5005]\tlr: 1.000e-02, eta: 6:11:28, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0398\n2023-01-04 21:34:15,973 - mmcls - INFO - Epoch [50][2600/5005]\tlr: 1.000e-02, eta: 6:11:19, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0466\n2023-01-04 21:34:24,632 - mmcls - INFO - Epoch [50][2700/5005]\tlr: 1.000e-02, eta: 6:11:10, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0516\n2023-01-04 21:34:33,152 - mmcls - INFO - Epoch [50][2800/5005]\tlr: 1.000e-02, eta: 6:11:01, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0474\n2023-01-04 21:34:41,777 - mmcls - INFO - Epoch [50][2900/5005]\tlr: 1.000e-02, eta: 6:10:52, time: 0.086, data_time: 0.030, memory: 928, loss: 5.0793\n2023-01-04 21:34:50,476 - mmcls - INFO - Epoch [50][3000/5005]\tlr: 1.000e-02, eta: 6:10:43, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0720\n2023-01-04 21:34:59,172 - mmcls - INFO - Epoch [50][3100/5005]\tlr: 1.000e-02, eta: 6:10:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0266\n2023-01-04 21:35:07,872 - mmcls - INFO - Epoch [50][3200/5005]\tlr: 1.000e-02, eta: 6:10:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0627\n2023-01-04 21:35:16,629 - mmcls - INFO - Epoch [50][3300/5005]\tlr: 1.000e-02, eta: 6:10:16, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0473\n2023-01-04 21:35:25,548 - mmcls - INFO - Epoch [50][3400/5005]\tlr: 1.000e-02, eta: 6:10:07, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0554\n2023-01-04 21:35:34,299 - mmcls - INFO - Epoch [50][3500/5005]\tlr: 1.000e-02, eta: 6:09:58, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0568\n2023-01-04 21:35:43,157 - mmcls - INFO - Epoch [50][3600/5005]\tlr: 1.000e-02, eta: 6:09:49, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0457\n2023-01-04 21:35:51,849 - mmcls - INFO - Epoch [50][3700/5005]\tlr: 1.000e-02, eta: 6:09:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0619\n2023-01-04 21:36:00,772 - mmcls - INFO - Epoch [50][3800/5005]\tlr: 1.000e-02, eta: 6:09:32, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0625\n2023-01-04 21:36:09,311 - mmcls - INFO - Epoch [50][3900/5005]\tlr: 1.000e-02, eta: 6:09:23, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0469\n2023-01-04 21:36:18,175 - mmcls - INFO - Epoch [50][4000/5005]\tlr: 1.000e-02, eta: 6:09:14, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0407\n2023-01-04 21:36:27,017 - mmcls - INFO - Epoch [50][4100/5005]\tlr: 1.000e-02, eta: 6:09:05, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0469\n2023-01-04 21:36:35,707 - mmcls - INFO - Epoch [50][4200/5005]\tlr: 1.000e-02, eta: 6:08:56, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0578\n2023-01-04 21:36:44,312 - mmcls - INFO - Epoch [50][4300/5005]\tlr: 1.000e-02, eta: 6:08:47, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0509\n2023-01-04 21:36:53,082 - mmcls - INFO - Epoch [50][4400/5005]\tlr: 1.000e-02, eta: 6:08:38, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0945\n2023-01-04 21:37:01,972 - mmcls - INFO - Epoch [50][4500/5005]\tlr: 1.000e-02, eta: 6:08:30, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0465\n2023-01-04 21:37:10,560 - mmcls - INFO - Epoch [50][4600/5005]\tlr: 1.000e-02, eta: 6:08:21, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0292\n2023-01-04 21:37:19,430 - mmcls - INFO - Epoch [50][4700/5005]\tlr: 1.000e-02, eta: 6:08:12, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0413\n2023-01-04 21:37:28,213 - mmcls - INFO - Epoch [50][4800/5005]\tlr: 1.000e-02, eta: 6:08:03, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0517\n2023-01-04 21:37:36,914 - mmcls - INFO - Epoch [50][4900/5005]\tlr: 1.000e-02, eta: 6:07:54, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0678\n2023-01-04 21:37:45,455 - mmcls - INFO - Epoch [50][5000/5005]\tlr: 1.000e-02, eta: 6:07:45, time: 0.085, data_time: 0.011, memory: 928, loss: 5.0772\n2023-01-04 21:37:45,786 - mmcls - INFO - Saving checkpoint at 50 epochs\n2023-01-04 21:38:08,275 - mmcls - INFO - Epoch(val) [50][196]\taccuracy_top-1: 8.1060, accuracy_top-5: 19.3340\n2023-01-04 21:38:19,342 - mmcls - INFO - Epoch [51][100/5005]\tlr: 1.000e-02, eta: 6:07:37, time: 0.110, data_time: 0.036, memory: 928, loss: 5.0349\n2023-01-04 21:38:28,034 - mmcls - INFO - Epoch [51][200/5005]\tlr: 1.000e-02, eta: 6:07:28, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0299\n2023-01-04 21:38:37,128 - mmcls - INFO - Epoch [51][300/5005]\tlr: 1.000e-02, eta: 6:07:20, time: 0.091, data_time: 0.029, memory: 928, loss: 5.0601\n2023-01-04 21:38:45,993 - mmcls - INFO - Epoch [51][400/5005]\tlr: 1.000e-02, eta: 6:07:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0586\n2023-01-04 21:38:54,667 - mmcls - INFO - Epoch [51][500/5005]\tlr: 1.000e-02, eta: 6:07:02, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0438\n2023-01-04 21:39:03,465 - mmcls - INFO - Epoch [51][600/5005]\tlr: 1.000e-02, eta: 6:06:53, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0593\n2023-01-04 21:39:12,005 - mmcls - INFO - Epoch [51][700/5005]\tlr: 1.000e-02, eta: 6:06:44, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0472\n2023-01-04 21:39:20,780 - mmcls - INFO - Epoch [51][800/5005]\tlr: 1.000e-02, eta: 6:06:35, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0529\n2023-01-04 21:39:29,395 - mmcls - INFO - Epoch [51][900/5005]\tlr: 1.000e-02, eta: 6:06:26, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0313\n2023-01-04 21:39:37,908 - mmcls - INFO - Epoch [51][1000/5005]\tlr: 1.000e-02, eta: 6:06:17, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0529\n2023-01-04 21:39:46,654 - mmcls - INFO - Epoch [51][1100/5005]\tlr: 1.000e-02, eta: 6:06:08, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0560\n2023-01-04 21:39:55,553 - mmcls - INFO - Epoch [51][1200/5005]\tlr: 1.000e-02, eta: 6:06:00, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0344\n2023-01-04 21:40:04,294 - mmcls - INFO - Epoch [51][1300/5005]\tlr: 1.000e-02, eta: 6:05:51, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0500\n2023-01-04 21:40:13,079 - mmcls - INFO - Epoch [51][1400/5005]\tlr: 1.000e-02, eta: 6:05:42, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0335\n2023-01-04 21:40:21,980 - mmcls - INFO - Epoch [51][1500/5005]\tlr: 1.000e-02, eta: 6:05:33, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0562\n2023-01-04 21:40:30,186 - mmcls - INFO - Epoch [51][1600/5005]\tlr: 1.000e-02, eta: 6:05:24, time: 0.082, data_time: 0.016, memory: 928, loss: 5.0581\n2023-01-04 21:40:38,973 - mmcls - INFO - Epoch [51][1700/5005]\tlr: 1.000e-02, eta: 6:05:15, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0424\n2023-01-04 21:40:47,424 - mmcls - INFO - Epoch [51][1800/5005]\tlr: 1.000e-02, eta: 6:05:06, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0304\n2023-01-04 21:40:55,997 - mmcls - INFO - Epoch [51][1900/5005]\tlr: 1.000e-02, eta: 6:04:57, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0476\n2023-01-04 21:41:05,220 - mmcls - INFO - Epoch [51][2000/5005]\tlr: 1.000e-02, eta: 6:04:48, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0431\n2023-01-04 21:41:13,837 - mmcls - INFO - Epoch [51][2100/5005]\tlr: 1.000e-02, eta: 6:04:39, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0462\n2023-01-04 21:41:22,581 - mmcls - INFO - Epoch [51][2200/5005]\tlr: 1.000e-02, eta: 6:04:30, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0577\n2023-01-04 21:41:31,328 - mmcls - INFO - Epoch [51][2300/5005]\tlr: 1.000e-02, eta: 6:04:21, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0193\n2023-01-04 21:41:40,097 - mmcls - INFO - Epoch [51][2400/5005]\tlr: 1.000e-02, eta: 6:04:13, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0572\n2023-01-04 21:41:48,832 - mmcls - INFO - Epoch [51][2500/5005]\tlr: 1.000e-02, eta: 6:04:04, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0377\n2023-01-04 21:41:57,918 - mmcls - INFO - Epoch [51][2600/5005]\tlr: 1.000e-02, eta: 6:03:55, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0368\n2023-01-04 21:42:06,630 - mmcls - INFO - Epoch [51][2700/5005]\tlr: 1.000e-02, eta: 6:03:46, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0502\n2023-01-04 21:42:15,201 - mmcls - INFO - Epoch [51][2800/5005]\tlr: 1.000e-02, eta: 6:03:37, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0640\n2023-01-04 21:42:24,300 - mmcls - INFO - Epoch [51][2900/5005]\tlr: 1.000e-02, eta: 6:03:29, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0747\n2023-01-04 21:42:32,804 - mmcls - INFO - Epoch [51][3000/5005]\tlr: 1.000e-02, eta: 6:03:19, time: 0.085, data_time: 0.035, memory: 928, loss: 5.0510\n2023-01-04 21:42:41,841 - mmcls - INFO - Epoch [51][3100/5005]\tlr: 1.000e-02, eta: 6:03:11, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0493\n2023-01-04 21:42:50,314 - mmcls - INFO - Epoch [51][3200/5005]\tlr: 1.000e-02, eta: 6:03:02, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0731\n2023-01-04 21:42:59,329 - mmcls - INFO - Epoch [51][3300/5005]\tlr: 1.000e-02, eta: 6:02:53, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0360\n2023-01-04 21:43:08,125 - mmcls - INFO - Epoch [51][3400/5005]\tlr: 1.000e-02, eta: 6:02:44, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0515\n2023-01-04 21:43:16,867 - mmcls - INFO - Epoch [51][3500/5005]\tlr: 1.000e-02, eta: 6:02:35, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0532\n2023-01-04 21:43:25,858 - mmcls - INFO - Epoch [51][3600/5005]\tlr: 1.000e-02, eta: 6:02:27, time: 0.090, data_time: 0.049, memory: 928, loss: 5.0267\n2023-01-04 21:43:34,668 - mmcls - INFO - Epoch [51][3700/5005]\tlr: 1.000e-02, eta: 6:02:18, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0502\n2023-01-04 21:43:43,340 - mmcls - INFO - Epoch [51][3800/5005]\tlr: 1.000e-02, eta: 6:02:09, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0566\n2023-01-04 21:43:52,066 - mmcls - INFO - Epoch [51][3900/5005]\tlr: 1.000e-02, eta: 6:02:00, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0628\n2023-01-04 21:44:00,702 - mmcls - INFO - Epoch [51][4000/5005]\tlr: 1.000e-02, eta: 6:01:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0615\n2023-01-04 21:44:09,242 - mmcls - INFO - Epoch [51][4100/5005]\tlr: 1.000e-02, eta: 6:01:42, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0600\n2023-01-04 21:44:17,898 - mmcls - INFO - Epoch [51][4200/5005]\tlr: 1.000e-02, eta: 6:01:33, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0364\n2023-01-04 21:44:26,493 - mmcls - INFO - Epoch [51][4300/5005]\tlr: 1.000e-02, eta: 6:01:24, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0581\n2023-01-04 21:44:35,068 - mmcls - INFO - Epoch [51][4400/5005]\tlr: 1.000e-02, eta: 6:01:15, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0355\n2023-01-04 21:44:43,688 - mmcls - INFO - Epoch [51][4500/5005]\tlr: 1.000e-02, eta: 6:01:06, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0665\n2023-01-04 21:44:52,469 - mmcls - INFO - Epoch [51][4600/5005]\tlr: 1.000e-02, eta: 6:00:57, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0613\n2023-01-04 21:45:01,229 - mmcls - INFO - Epoch [51][4700/5005]\tlr: 1.000e-02, eta: 6:00:48, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0510\n2023-01-04 21:45:09,783 - mmcls - INFO - Epoch [51][4800/5005]\tlr: 1.000e-02, eta: 6:00:39, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0222\n2023-01-04 21:45:18,354 - mmcls - INFO - Epoch [51][4900/5005]\tlr: 1.000e-02, eta: 6:00:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0611\n2023-01-04 21:45:27,292 - mmcls - INFO - Epoch [51][5000/5005]\tlr: 1.000e-02, eta: 6:00:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0498\n2023-01-04 21:45:50,007 - mmcls - INFO - Epoch(val) [51][196]\taccuracy_top-1: 10.1380, accuracy_top-5: 22.9220\n2023-01-04 21:46:01,110 - mmcls - INFO - Epoch [52][100/5005]\tlr: 1.000e-02, eta: 6:00:14, time: 0.111, data_time: 0.065, memory: 928, loss: 5.0261\n2023-01-04 21:46:09,771 - mmcls - INFO - Epoch [52][200/5005]\tlr: 1.000e-02, eta: 6:00:05, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0306\n2023-01-04 21:46:18,742 - mmcls - INFO - Epoch [52][300/5005]\tlr: 1.000e-02, eta: 5:59:56, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0397\n2023-01-04 21:46:27,275 - mmcls - INFO - Epoch [52][400/5005]\tlr: 1.000e-02, eta: 5:59:47, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0465\n2023-01-04 21:46:35,861 - mmcls - INFO - Epoch [52][500/5005]\tlr: 1.000e-02, eta: 5:59:38, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0514\n2023-01-04 21:46:44,493 - mmcls - INFO - Epoch [52][600/5005]\tlr: 1.000e-02, eta: 5:59:29, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0372\n2023-01-04 21:46:53,190 - mmcls - INFO - Epoch [52][700/5005]\tlr: 1.000e-02, eta: 5:59:20, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0539\n2023-01-04 21:47:02,165 - mmcls - INFO - Epoch [52][800/5005]\tlr: 1.000e-02, eta: 5:59:11, time: 0.090, data_time: 0.035, memory: 928, loss: 5.0278\n2023-01-04 21:47:10,902 - mmcls - INFO - Epoch [52][900/5005]\tlr: 1.000e-02, eta: 5:59:03, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0282\n2023-01-04 21:47:19,555 - mmcls - INFO - Epoch [52][1000/5005]\tlr: 1.000e-02, eta: 5:58:54, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0380\n2023-01-04 21:47:28,110 - mmcls - INFO - Epoch [52][1100/5005]\tlr: 1.000e-02, eta: 5:58:45, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0520\n2023-01-04 21:47:36,940 - mmcls - INFO - Epoch [52][1200/5005]\tlr: 1.000e-02, eta: 5:58:36, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0657\n2023-01-04 21:47:45,762 - mmcls - INFO - Epoch [52][1300/5005]\tlr: 1.000e-02, eta: 5:58:27, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0600\n2023-01-04 21:47:54,820 - mmcls - INFO - Epoch [52][1400/5005]\tlr: 1.000e-02, eta: 5:58:18, time: 0.091, data_time: 0.013, memory: 928, loss: 5.0391\n2023-01-04 21:48:03,811 - mmcls - INFO - Epoch [52][1500/5005]\tlr: 1.000e-02, eta: 5:58:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0305\n2023-01-04 21:48:12,981 - mmcls - INFO - Epoch [52][1600/5005]\tlr: 1.000e-02, eta: 5:58:01, time: 0.092, data_time: 0.000, memory: 928, loss: 5.0324\n2023-01-04 21:48:21,632 - mmcls - INFO - Epoch [52][1700/5005]\tlr: 1.000e-02, eta: 5:57:52, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0540\n2023-01-04 21:48:30,582 - mmcls - INFO - Epoch [52][1800/5005]\tlr: 1.000e-02, eta: 5:57:44, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0400\n2023-01-04 21:48:39,204 - mmcls - INFO - Epoch [52][1900/5005]\tlr: 1.000e-02, eta: 5:57:35, time: 0.086, data_time: 0.035, memory: 928, loss: 5.0620\n2023-01-04 21:48:47,551 - mmcls - INFO - Epoch [52][2000/5005]\tlr: 1.000e-02, eta: 5:57:25, time: 0.084, data_time: 0.007, memory: 928, loss: 5.0402\n2023-01-04 21:48:56,670 - mmcls - INFO - Epoch [52][2100/5005]\tlr: 1.000e-02, eta: 5:57:17, time: 0.091, data_time: 0.034, memory: 928, loss: 5.0460\n2023-01-04 21:49:05,662 - mmcls - INFO - Epoch [52][2200/5005]\tlr: 1.000e-02, eta: 5:57:08, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0656\n2023-01-04 21:49:14,312 - mmcls - INFO - Epoch [52][2300/5005]\tlr: 1.000e-02, eta: 5:56:59, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0290\n2023-01-04 21:49:23,024 - mmcls - INFO - Epoch [52][2400/5005]\tlr: 1.000e-02, eta: 5:56:50, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0308\n2023-01-04 21:49:31,598 - mmcls - INFO - Epoch [52][2500/5005]\tlr: 1.000e-02, eta: 5:56:41, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0240\n2023-01-04 21:49:40,713 - mmcls - INFO - Epoch [52][2600/5005]\tlr: 1.000e-02, eta: 5:56:33, time: 0.091, data_time: 0.003, memory: 928, loss: 5.0370\n2023-01-04 21:49:49,244 - mmcls - INFO - Epoch [52][2700/5005]\tlr: 1.000e-02, eta: 5:56:24, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0709\n2023-01-04 21:49:58,013 - mmcls - INFO - Epoch [52][2800/5005]\tlr: 1.000e-02, eta: 5:56:15, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0605\n2023-01-04 21:50:06,772 - mmcls - INFO - Epoch [52][2900/5005]\tlr: 1.000e-02, eta: 5:56:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0499\n2023-01-04 21:50:15,715 - mmcls - INFO - Epoch [52][3000/5005]\tlr: 1.000e-02, eta: 5:55:57, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0480\n2023-01-04 21:50:24,533 - mmcls - INFO - Epoch [52][3100/5005]\tlr: 1.000e-02, eta: 5:55:48, time: 0.088, data_time: 0.028, memory: 928, loss: 5.0488\n2023-01-04 21:50:33,422 - mmcls - INFO - Epoch [52][3200/5005]\tlr: 1.000e-02, eta: 5:55:40, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0567\n2023-01-04 21:50:42,006 - mmcls - INFO - Epoch [52][3300/5005]\tlr: 1.000e-02, eta: 5:55:30, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0536\n2023-01-04 21:50:50,858 - mmcls - INFO - Epoch [52][3400/5005]\tlr: 1.000e-02, eta: 5:55:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0368\n2023-01-04 21:50:59,388 - mmcls - INFO - Epoch [52][3500/5005]\tlr: 1.000e-02, eta: 5:55:13, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0638\n2023-01-04 21:51:08,172 - mmcls - INFO - Epoch [52][3600/5005]\tlr: 1.000e-02, eta: 5:55:04, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0441\n2023-01-04 21:51:16,892 - mmcls - INFO - Epoch [52][3700/5005]\tlr: 1.000e-02, eta: 5:54:55, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0259\n2023-01-04 21:51:25,594 - mmcls - INFO - Epoch [52][3800/5005]\tlr: 1.000e-02, eta: 5:54:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0558\n2023-01-04 21:51:34,224 - mmcls - INFO - Epoch [52][3900/5005]\tlr: 1.000e-02, eta: 5:54:37, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0418\n2023-01-04 21:51:43,060 - mmcls - INFO - Epoch [52][4000/5005]\tlr: 1.000e-02, eta: 5:54:28, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0449\n2023-01-04 21:51:51,994 - mmcls - INFO - Epoch [52][4100/5005]\tlr: 1.000e-02, eta: 5:54:19, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0560\n2023-01-04 21:52:00,654 - mmcls - INFO - Epoch [52][4200/5005]\tlr: 1.000e-02, eta: 5:54:10, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0411\n2023-01-04 21:52:09,406 - mmcls - INFO - Epoch [52][4300/5005]\tlr: 1.000e-02, eta: 5:54:02, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0447\n2023-01-04 21:52:18,476 - mmcls - INFO - Epoch [52][4400/5005]\tlr: 1.000e-02, eta: 5:53:53, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0551\n2023-01-04 21:52:27,011 - mmcls - INFO - Epoch [52][4500/5005]\tlr: 1.000e-02, eta: 5:53:44, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0699\n2023-01-04 21:52:35,670 - mmcls - INFO - Epoch [52][4600/5005]\tlr: 1.000e-02, eta: 5:53:35, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0591\n2023-01-04 21:52:44,319 - mmcls - INFO - Epoch [52][4700/5005]\tlr: 1.000e-02, eta: 5:53:26, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0430\n2023-01-04 21:52:53,107 - mmcls - INFO - Epoch [52][4800/5005]\tlr: 1.000e-02, eta: 5:53:17, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0415\n2023-01-04 21:53:01,716 - mmcls - INFO - Epoch [52][4900/5005]\tlr: 1.000e-02, eta: 5:53:08, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0335\n2023-01-04 21:53:10,702 - mmcls - INFO - Epoch [52][5000/5005]\tlr: 1.000e-02, eta: 5:52:59, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0484\n2023-01-04 21:53:33,522 - mmcls - INFO - Epoch(val) [52][196]\taccuracy_top-1: 11.9200, accuracy_top-5: 26.5460\n2023-01-04 21:53:44,655 - mmcls - INFO - Epoch [53][100/5005]\tlr: 1.000e-02, eta: 5:52:52, time: 0.111, data_time: 0.044, memory: 928, loss: 5.0426\n2023-01-04 21:53:53,554 - mmcls - INFO - Epoch [53][200/5005]\tlr: 1.000e-02, eta: 5:52:43, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0182\n2023-01-04 21:54:02,253 - mmcls - INFO - Epoch [53][300/5005]\tlr: 1.000e-02, eta: 5:52:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0340\n2023-01-04 21:54:10,770 - mmcls - INFO - Epoch [53][400/5005]\tlr: 1.000e-02, eta: 5:52:25, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0263\n2023-01-04 21:54:20,173 - mmcls - INFO - Epoch [53][500/5005]\tlr: 1.000e-02, eta: 5:52:17, time: 0.094, data_time: 0.018, memory: 928, loss: 5.0645\n2023-01-04 21:54:28,772 - mmcls - INFO - Epoch [53][600/5005]\tlr: 1.000e-02, eta: 5:52:08, time: 0.086, data_time: 0.026, memory: 928, loss: 5.0145\n2023-01-04 21:54:37,453 - mmcls - INFO - Epoch [53][700/5005]\tlr: 1.000e-02, eta: 5:51:59, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0361\n2023-01-04 21:54:46,061 - mmcls - INFO - Epoch [53][800/5005]\tlr: 1.000e-02, eta: 5:51:50, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0569\n2023-01-04 21:54:54,708 - mmcls - INFO - Epoch [53][900/5005]\tlr: 1.000e-02, eta: 5:51:41, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0436\n2023-01-04 21:55:03,514 - mmcls - INFO - Epoch [53][1000/5005]\tlr: 1.000e-02, eta: 5:51:32, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0465\n2023-01-04 21:55:12,184 - mmcls - INFO - Epoch [53][1100/5005]\tlr: 1.000e-02, eta: 5:51:23, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0611\n2023-01-04 21:55:20,751 - mmcls - INFO - Epoch [53][1200/5005]\tlr: 1.000e-02, eta: 5:51:14, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0346\n2023-01-04 21:55:29,527 - mmcls - INFO - Epoch [53][1300/5005]\tlr: 1.000e-02, eta: 5:51:05, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0288\n2023-01-04 21:55:38,345 - mmcls - INFO - Epoch [53][1400/5005]\tlr: 1.000e-02, eta: 5:50:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0532\n2023-01-04 21:55:47,695 - mmcls - INFO - Epoch [53][1500/5005]\tlr: 1.000e-02, eta: 5:50:48, time: 0.093, data_time: 0.025, memory: 928, loss: 5.0422\n2023-01-04 21:55:56,487 - mmcls - INFO - Epoch [53][1600/5005]\tlr: 1.000e-02, eta: 5:50:39, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0433\n2023-01-04 21:56:05,452 - mmcls - INFO - Epoch [53][1700/5005]\tlr: 1.000e-02, eta: 5:50:31, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0524\n2023-01-04 21:56:14,310 - mmcls - INFO - Epoch [53][1800/5005]\tlr: 1.000e-02, eta: 5:50:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0582\n2023-01-04 21:56:23,095 - mmcls - INFO - Epoch [53][1900/5005]\tlr: 1.000e-02, eta: 5:50:13, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0445\n2023-01-04 21:56:31,875 - mmcls - INFO - Epoch [53][2000/5005]\tlr: 1.000e-02, eta: 5:50:04, time: 0.088, data_time: 0.024, memory: 928, loss: 5.0421\n2023-01-04 21:56:40,786 - mmcls - INFO - Epoch [53][2100/5005]\tlr: 1.000e-02, eta: 5:49:55, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0428\n2023-01-04 21:56:49,582 - mmcls - INFO - Epoch [53][2200/5005]\tlr: 1.000e-02, eta: 5:49:47, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0444\n2023-01-04 21:56:58,407 - mmcls - INFO - Epoch [53][2300/5005]\tlr: 1.000e-02, eta: 5:49:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0576\n2023-01-04 21:57:07,002 - mmcls - INFO - Epoch [53][2400/5005]\tlr: 1.000e-02, eta: 5:49:29, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0354\n2023-01-04 21:57:15,708 - mmcls - INFO - Epoch [53][2500/5005]\tlr: 1.000e-02, eta: 5:49:20, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0390\n2023-01-04 21:57:24,331 - mmcls - INFO - Epoch [53][2600/5005]\tlr: 1.000e-02, eta: 5:49:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0303\n2023-01-04 21:57:33,207 - mmcls - INFO - Epoch [53][2700/5005]\tlr: 1.000e-02, eta: 5:49:02, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0385\n2023-01-04 21:57:41,814 - mmcls - INFO - Epoch [53][2800/5005]\tlr: 1.000e-02, eta: 5:48:53, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0424\n2023-01-04 21:57:50,515 - mmcls - INFO - Epoch [53][2900/5005]\tlr: 1.000e-02, eta: 5:48:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0237\n2023-01-04 21:57:59,256 - mmcls - INFO - Epoch [53][3000/5005]\tlr: 1.000e-02, eta: 5:48:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0371\n2023-01-04 21:58:08,228 - mmcls - INFO - Epoch [53][3100/5005]\tlr: 1.000e-02, eta: 5:48:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0524\n2023-01-04 21:58:17,261 - mmcls - INFO - Epoch [53][3200/5005]\tlr: 1.000e-02, eta: 5:48:18, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0505\n2023-01-04 21:58:25,732 - mmcls - INFO - Epoch [53][3300/5005]\tlr: 1.000e-02, eta: 5:48:09, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0250\n2023-01-04 21:58:34,256 - mmcls - INFO - Epoch [53][3400/5005]\tlr: 1.000e-02, eta: 5:48:00, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0409\n2023-01-04 21:58:43,116 - mmcls - INFO - Epoch [53][3500/5005]\tlr: 1.000e-02, eta: 5:47:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0281\n2023-01-04 21:58:52,115 - mmcls - INFO - Epoch [53][3600/5005]\tlr: 1.000e-02, eta: 5:47:42, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0480\n2023-01-04 21:59:01,049 - mmcls - INFO - Epoch [53][3700/5005]\tlr: 1.000e-02, eta: 5:47:34, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0545\n2023-01-04 21:59:09,538 - mmcls - INFO - Epoch [53][3800/5005]\tlr: 1.000e-02, eta: 5:47:25, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0584\n2023-01-04 21:59:18,192 - mmcls - INFO - Epoch [53][3900/5005]\tlr: 1.000e-02, eta: 5:47:16, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0424\n2023-01-04 21:59:27,000 - mmcls - INFO - Epoch [53][4000/5005]\tlr: 1.000e-02, eta: 5:47:07, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0406\n2023-01-04 21:59:35,874 - mmcls - INFO - Epoch [53][4100/5005]\tlr: 1.000e-02, eta: 5:46:58, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0378\n2023-01-04 21:59:44,436 - mmcls - INFO - Epoch [53][4200/5005]\tlr: 1.000e-02, eta: 5:46:49, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0389\n2023-01-04 21:59:53,361 - mmcls - INFO - Epoch [53][4300/5005]\tlr: 1.000e-02, eta: 5:46:40, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0418\n2023-01-04 22:00:02,092 - mmcls - INFO - Epoch [53][4400/5005]\tlr: 1.000e-02, eta: 5:46:31, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0311\n2023-01-04 22:00:10,823 - mmcls - INFO - Epoch [53][4500/5005]\tlr: 1.000e-02, eta: 5:46:22, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0558\n2023-01-04 22:00:19,553 - mmcls - INFO - Epoch [53][4600/5005]\tlr: 1.000e-02, eta: 5:46:14, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0347\n2023-01-04 22:00:28,153 - mmcls - INFO - Epoch [53][4700/5005]\tlr: 1.000e-02, eta: 5:46:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0571\n2023-01-04 22:00:36,704 - mmcls - INFO - Epoch [53][4800/5005]\tlr: 1.000e-02, eta: 5:45:55, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0479\n2023-01-04 22:00:45,346 - mmcls - INFO - Epoch [53][4900/5005]\tlr: 1.000e-02, eta: 5:45:46, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0640\n2023-01-04 22:00:54,023 - mmcls - INFO - Epoch [53][5000/5005]\tlr: 1.000e-02, eta: 5:45:38, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0522\n2023-01-04 22:01:16,896 - mmcls - INFO - Epoch(val) [53][196]\taccuracy_top-1: 10.4160, accuracy_top-5: 23.8220\n2023-01-04 22:01:28,070 - mmcls - INFO - Epoch [54][100/5005]\tlr: 1.000e-02, eta: 5:45:30, time: 0.112, data_time: 0.049, memory: 928, loss: 5.0270\n2023-01-04 22:01:37,041 - mmcls - INFO - Epoch [54][200/5005]\tlr: 1.000e-02, eta: 5:45:21, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0383\n2023-01-04 22:01:45,526 - mmcls - INFO - Epoch [54][300/5005]\tlr: 1.000e-02, eta: 5:45:12, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0534\n2023-01-04 22:01:54,463 - mmcls - INFO - Epoch [54][400/5005]\tlr: 1.000e-02, eta: 5:45:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0099\n2023-01-04 22:02:03,553 - mmcls - INFO - Epoch [54][500/5005]\tlr: 1.000e-02, eta: 5:44:55, time: 0.091, data_time: 0.012, memory: 928, loss: 5.0130\n2023-01-04 22:02:12,578 - mmcls - INFO - Epoch [54][600/5005]\tlr: 1.000e-02, eta: 5:44:46, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0427\n2023-01-04 22:02:21,173 - mmcls - INFO - Epoch [54][700/5005]\tlr: 1.000e-02, eta: 5:44:37, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0187\n2023-01-04 22:02:30,196 - mmcls - INFO - Epoch [54][800/5005]\tlr: 1.000e-02, eta: 5:44:29, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0430\n2023-01-04 22:02:38,588 - mmcls - INFO - Epoch [54][900/5005]\tlr: 1.000e-02, eta: 5:44:19, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0536\n2023-01-04 22:02:47,231 - mmcls - INFO - Epoch [54][1000/5005]\tlr: 1.000e-02, eta: 5:44:10, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0354\n2023-01-04 22:02:56,043 - mmcls - INFO - Epoch [54][1100/5005]\tlr: 1.000e-02, eta: 5:44:02, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0387\n2023-01-04 22:03:04,900 - mmcls - INFO - Epoch [54][1200/5005]\tlr: 1.000e-02, eta: 5:43:53, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0419\n2023-01-04 22:03:13,708 - mmcls - INFO - Epoch [54][1300/5005]\tlr: 1.000e-02, eta: 5:43:44, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0179\n2023-01-04 22:03:22,320 - mmcls - INFO - Epoch [54][1400/5005]\tlr: 1.000e-02, eta: 5:43:35, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0199\n2023-01-04 22:03:30,914 - mmcls - INFO - Epoch [54][1500/5005]\tlr: 1.000e-02, eta: 5:43:26, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0286\n2023-01-04 22:03:39,782 - mmcls - INFO - Epoch [54][1600/5005]\tlr: 1.000e-02, eta: 5:43:17, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0517\n2023-01-04 22:03:48,338 - mmcls - INFO - Epoch [54][1700/5005]\tlr: 1.000e-02, eta: 5:43:08, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0766\n2023-01-04 22:03:57,321 - mmcls - INFO - Epoch [54][1800/5005]\tlr: 1.000e-02, eta: 5:43:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0246\n2023-01-04 22:04:05,918 - mmcls - INFO - Epoch [54][1900/5005]\tlr: 1.000e-02, eta: 5:42:51, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0269\n2023-01-04 22:04:14,570 - mmcls - INFO - Epoch [54][2000/5005]\tlr: 1.000e-02, eta: 5:42:42, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0513\n2023-01-04 22:04:23,454 - mmcls - INFO - Epoch [54][2100/5005]\tlr: 1.000e-02, eta: 5:42:33, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0354\n2023-01-04 22:04:32,172 - mmcls - INFO - Epoch [54][2200/5005]\tlr: 1.000e-02, eta: 5:42:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0248\n2023-01-04 22:04:41,278 - mmcls - INFO - Epoch [54][2300/5005]\tlr: 1.000e-02, eta: 5:42:15, time: 0.091, data_time: 0.007, memory: 928, loss: 5.0695\n2023-01-04 22:04:50,008 - mmcls - INFO - Epoch [54][2400/5005]\tlr: 1.000e-02, eta: 5:42:06, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0499\n2023-01-04 22:04:58,756 - mmcls - INFO - Epoch [54][2500/5005]\tlr: 1.000e-02, eta: 5:41:58, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0390\n2023-01-04 22:05:07,488 - mmcls - INFO - Epoch [54][2600/5005]\tlr: 1.000e-02, eta: 5:41:49, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0410\n2023-01-04 22:05:16,122 - mmcls - INFO - Epoch [54][2700/5005]\tlr: 1.000e-02, eta: 5:41:40, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0701\n2023-01-04 22:05:25,126 - mmcls - INFO - Epoch [54][2800/5005]\tlr: 1.000e-02, eta: 5:41:31, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0325\n2023-01-04 22:05:33,759 - mmcls - INFO - Epoch [54][2900/5005]\tlr: 1.000e-02, eta: 5:41:22, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0531\n2023-01-04 22:05:42,677 - mmcls - INFO - Epoch [54][3000/5005]\tlr: 1.000e-02, eta: 5:41:13, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0387\n2023-01-04 22:05:51,406 - mmcls - INFO - Epoch [54][3100/5005]\tlr: 1.000e-02, eta: 5:41:04, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0338\n2023-01-04 22:05:59,978 - mmcls - INFO - Epoch [54][3200/5005]\tlr: 1.000e-02, eta: 5:40:55, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0233\n2023-01-04 22:06:09,075 - mmcls - INFO - Epoch [54][3300/5005]\tlr: 1.000e-02, eta: 5:40:47, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0384\n2023-01-04 22:06:17,938 - mmcls - INFO - Epoch [54][3400/5005]\tlr: 1.000e-02, eta: 5:40:38, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0525\n2023-01-04 22:06:26,712 - mmcls - INFO - Epoch [54][3500/5005]\tlr: 1.000e-02, eta: 5:40:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0373\n2023-01-04 22:06:35,256 - mmcls - INFO - Epoch [54][3600/5005]\tlr: 1.000e-02, eta: 5:40:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0479\n2023-01-04 22:06:44,032 - mmcls - INFO - Epoch [54][3700/5005]\tlr: 1.000e-02, eta: 5:40:11, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0345\n2023-01-04 22:06:52,502 - mmcls - INFO - Epoch [54][3800/5005]\tlr: 1.000e-02, eta: 5:40:02, time: 0.085, data_time: 0.017, memory: 928, loss: 5.0497\n2023-01-04 22:07:01,534 - mmcls - INFO - Epoch [54][3900/5005]\tlr: 1.000e-02, eta: 5:39:54, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0477\n2023-01-04 22:07:10,308 - mmcls - INFO - Epoch [54][4000/5005]\tlr: 1.000e-02, eta: 5:39:45, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0608\n2023-01-04 22:07:18,840 - mmcls - INFO - Epoch [54][4100/5005]\tlr: 1.000e-02, eta: 5:39:36, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0633\n2023-01-04 22:07:27,492 - mmcls - INFO - Epoch [54][4200/5005]\tlr: 1.000e-02, eta: 5:39:27, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0164\n2023-01-04 22:07:36,051 - mmcls - INFO - Epoch [54][4300/5005]\tlr: 1.000e-02, eta: 5:39:18, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0621\n2023-01-04 22:07:44,929 - mmcls - INFO - Epoch [54][4400/5005]\tlr: 1.000e-02, eta: 5:39:09, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0342\n2023-01-04 22:07:53,692 - mmcls - INFO - Epoch [54][4500/5005]\tlr: 1.000e-02, eta: 5:39:00, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0498\n2023-01-04 22:08:02,252 - mmcls - INFO - Epoch [54][4600/5005]\tlr: 1.000e-02, eta: 5:38:51, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0362\n2023-01-04 22:08:11,193 - mmcls - INFO - Epoch [54][4700/5005]\tlr: 1.000e-02, eta: 5:38:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0215\n2023-01-04 22:08:19,886 - mmcls - INFO - Epoch [54][4800/5005]\tlr: 1.000e-02, eta: 5:38:33, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0447\n2023-01-04 22:08:28,645 - mmcls - INFO - Epoch [54][4900/5005]\tlr: 1.000e-02, eta: 5:38:25, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0232\n2023-01-04 22:08:37,431 - mmcls - INFO - Epoch [54][5000/5005]\tlr: 1.000e-02, eta: 5:38:16, time: 0.088, data_time: 0.040, memory: 928, loss: 5.0578\n2023-01-04 22:09:00,261 - mmcls - INFO - Epoch(val) [54][196]\taccuracy_top-1: 10.4900, accuracy_top-5: 23.8420\n2023-01-04 22:09:11,114 - mmcls - INFO - Epoch [55][100/5005]\tlr: 1.000e-02, eta: 5:38:08, time: 0.108, data_time: 0.047, memory: 928, loss: 5.0331\n2023-01-04 22:09:19,951 - mmcls - INFO - Epoch [55][200/5005]\tlr: 1.000e-02, eta: 5:37:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0149\n2023-01-04 22:09:28,979 - mmcls - INFO - Epoch [55][300/5005]\tlr: 1.000e-02, eta: 5:37:50, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0235\n2023-01-04 22:09:37,663 - mmcls - INFO - Epoch [55][400/5005]\tlr: 1.000e-02, eta: 5:37:41, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0489\n2023-01-04 22:09:46,568 - mmcls - INFO - Epoch [55][500/5005]\tlr: 1.000e-02, eta: 5:37:33, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0233\n2023-01-04 22:09:54,876 - mmcls - INFO - Epoch [55][600/5005]\tlr: 1.000e-02, eta: 5:37:23, time: 0.083, data_time: 0.022, memory: 928, loss: 5.0348\n2023-01-04 22:10:03,429 - mmcls - INFO - Epoch [55][700/5005]\tlr: 1.000e-02, eta: 5:37:14, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0198\n2023-01-04 22:10:12,282 - mmcls - INFO - Epoch [55][800/5005]\tlr: 1.000e-02, eta: 5:37:06, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0442\n2023-01-04 22:10:21,113 - mmcls - INFO - Epoch [55][900/5005]\tlr: 1.000e-02, eta: 5:36:57, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0097\n2023-01-04 22:10:29,896 - mmcls - INFO - Epoch [55][1000/5005]\tlr: 1.000e-02, eta: 5:36:48, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0170\n2023-01-04 22:10:38,859 - mmcls - INFO - Epoch [55][1100/5005]\tlr: 1.000e-02, eta: 5:36:39, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0518\n2023-01-04 22:10:47,847 - mmcls - INFO - Epoch [55][1200/5005]\tlr: 1.000e-02, eta: 5:36:31, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0244\n2023-01-04 22:10:57,045 - mmcls - INFO - Epoch [55][1300/5005]\tlr: 1.000e-02, eta: 5:36:22, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0245\n2023-01-04 22:11:05,681 - mmcls - INFO - Epoch [55][1400/5005]\tlr: 1.000e-02, eta: 5:36:13, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0515\n2023-01-04 22:11:14,115 - mmcls - INFO - Epoch [55][1500/5005]\tlr: 1.000e-02, eta: 5:36:04, time: 0.084, data_time: 0.001, memory: 928, loss: 5.0630\n2023-01-04 22:11:23,199 - mmcls - INFO - Epoch [55][1600/5005]\tlr: 1.000e-02, eta: 5:35:55, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0609\n2023-01-04 22:11:31,847 - mmcls - INFO - Epoch [55][1700/5005]\tlr: 1.000e-02, eta: 5:35:46, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0552\n2023-01-04 22:11:40,896 - mmcls - INFO - Epoch [55][1800/5005]\tlr: 1.000e-02, eta: 5:35:38, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0373\n2023-01-04 22:11:49,556 - mmcls - INFO - Epoch [55][1900/5005]\tlr: 1.000e-02, eta: 5:35:29, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0145\n2023-01-04 22:11:58,329 - mmcls - INFO - Epoch [55][2000/5005]\tlr: 1.000e-02, eta: 5:35:20, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0368\n2023-01-04 22:12:07,070 - mmcls - INFO - Epoch [55][2100/5005]\tlr: 1.000e-02, eta: 5:35:11, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0593\n2023-01-04 22:12:15,655 - mmcls - INFO - Epoch [55][2200/5005]\tlr: 1.000e-02, eta: 5:35:02, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0338\n2023-01-04 22:12:24,163 - mmcls - INFO - Epoch [55][2300/5005]\tlr: 1.000e-02, eta: 5:34:53, time: 0.085, data_time: 0.033, memory: 928, loss: 5.0097\n2023-01-04 22:12:32,859 - mmcls - INFO - Epoch [55][2400/5005]\tlr: 1.000e-02, eta: 5:34:44, time: 0.087, data_time: 0.040, memory: 928, loss: 5.0493\n2023-01-04 22:12:41,551 - mmcls - INFO - Epoch [55][2500/5005]\tlr: 1.000e-02, eta: 5:34:35, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0607\n2023-01-04 22:12:50,302 - mmcls - INFO - Epoch [55][2600/5005]\tlr: 1.000e-02, eta: 5:34:26, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0337\n2023-01-04 22:12:58,967 - mmcls - INFO - Epoch [55][2700/5005]\tlr: 1.000e-02, eta: 5:34:17, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0451\n2023-01-04 22:13:07,710 - mmcls - INFO - Epoch [55][2800/5005]\tlr: 1.000e-02, eta: 5:34:09, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0272\n2023-01-04 22:13:16,544 - mmcls - INFO - Epoch [55][2900/5005]\tlr: 1.000e-02, eta: 5:34:00, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0197\n2023-01-04 22:13:25,487 - mmcls - INFO - Epoch [55][3000/5005]\tlr: 1.000e-02, eta: 5:33:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0627\n2023-01-04 22:13:34,521 - mmcls - INFO - Epoch [55][3100/5005]\tlr: 1.000e-02, eta: 5:33:42, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0402\n2023-01-04 22:13:43,088 - mmcls - INFO - Epoch [55][3200/5005]\tlr: 1.000e-02, eta: 5:33:33, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0455\n2023-01-04 22:13:51,926 - mmcls - INFO - Epoch [55][3300/5005]\tlr: 1.000e-02, eta: 5:33:25, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0234\n2023-01-04 22:14:00,953 - mmcls - INFO - Epoch [55][3400/5005]\tlr: 1.000e-02, eta: 5:33:16, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0480\n2023-01-04 22:14:09,639 - mmcls - INFO - Epoch [55][3500/5005]\tlr: 1.000e-02, eta: 5:33:07, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0678\n2023-01-04 22:14:18,476 - mmcls - INFO - Epoch [55][3600/5005]\tlr: 1.000e-02, eta: 5:32:58, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0324\n2023-01-04 22:14:27,277 - mmcls - INFO - Epoch [55][3700/5005]\tlr: 1.000e-02, eta: 5:32:49, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0139\n2023-01-04 22:14:36,134 - mmcls - INFO - Epoch [55][3800/5005]\tlr: 1.000e-02, eta: 5:32:41, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0526\n2023-01-04 22:14:45,221 - mmcls - INFO - Epoch [55][3900/5005]\tlr: 1.000e-02, eta: 5:32:32, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0287\n2023-01-04 22:14:54,078 - mmcls - INFO - Epoch [55][4000/5005]\tlr: 1.000e-02, eta: 5:32:23, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0061\n2023-01-04 22:15:02,599 - mmcls - INFO - Epoch [55][4100/5005]\tlr: 1.000e-02, eta: 5:32:14, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0367\n2023-01-04 22:15:11,338 - mmcls - INFO - Epoch [55][4200/5005]\tlr: 1.000e-02, eta: 5:32:05, time: 0.087, data_time: 0.028, memory: 928, loss: 5.0332\n2023-01-04 22:15:20,600 - mmcls - INFO - Epoch [55][4300/5005]\tlr: 1.000e-02, eta: 5:31:57, time: 0.093, data_time: 0.010, memory: 928, loss: 5.0503\n2023-01-04 22:15:29,284 - mmcls - INFO - Epoch [55][4400/5005]\tlr: 1.000e-02, eta: 5:31:48, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0723\n2023-01-04 22:15:37,925 - mmcls - INFO - Epoch [55][4500/5005]\tlr: 1.000e-02, eta: 5:31:39, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0433\n2023-01-04 22:15:46,765 - mmcls - INFO - Epoch [55][4600/5005]\tlr: 1.000e-02, eta: 5:31:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0463\n2023-01-04 22:15:55,787 - mmcls - INFO - Epoch [55][4700/5005]\tlr: 1.000e-02, eta: 5:31:21, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0248\n2023-01-04 22:16:04,737 - mmcls - INFO - Epoch [55][4800/5005]\tlr: 1.000e-02, eta: 5:31:13, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0463\n2023-01-04 22:16:13,216 - mmcls - INFO - Epoch [55][4900/5005]\tlr: 1.000e-02, eta: 5:31:04, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0404\n2023-01-04 22:16:22,211 - mmcls - INFO - Epoch [55][5000/5005]\tlr: 1.000e-02, eta: 5:30:55, time: 0.090, data_time: 0.035, memory: 928, loss: 5.0492\n2023-01-04 22:16:45,034 - mmcls - INFO - Epoch(val) [55][196]\taccuracy_top-1: 9.7320, accuracy_top-5: 22.4360\n2023-01-04 22:16:55,979 - mmcls - INFO - Epoch [56][100/5005]\tlr: 1.000e-02, eta: 5:30:47, time: 0.109, data_time: 0.039, memory: 928, loss: 5.0028\n2023-01-04 22:17:04,779 - mmcls - INFO - Epoch [56][200/5005]\tlr: 1.000e-02, eta: 5:30:38, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0352\n2023-01-04 22:17:13,500 - mmcls - INFO - Epoch [56][300/5005]\tlr: 1.000e-02, eta: 5:30:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.9977\n2023-01-04 22:17:22,305 - mmcls - INFO - Epoch [56][400/5005]\tlr: 1.000e-02, eta: 5:30:21, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0454\n2023-01-04 22:17:31,042 - mmcls - INFO - Epoch [56][500/5005]\tlr: 1.000e-02, eta: 5:30:12, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0512\n2023-01-04 22:17:39,982 - mmcls - INFO - Epoch [56][600/5005]\tlr: 1.000e-02, eta: 5:30:03, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0445\n2023-01-04 22:17:48,673 - mmcls - INFO - Epoch [56][700/5005]\tlr: 1.000e-02, eta: 5:29:54, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0073\n2023-01-04 22:17:57,358 - mmcls - INFO - Epoch [56][800/5005]\tlr: 1.000e-02, eta: 5:29:45, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0374\n2023-01-04 22:18:05,970 - mmcls - INFO - Epoch [56][900/5005]\tlr: 1.000e-02, eta: 5:29:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0359\n2023-01-04 22:18:14,932 - mmcls - INFO - Epoch [56][1000/5005]\tlr: 1.000e-02, eta: 5:29:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0236\n2023-01-04 22:18:24,090 - mmcls - INFO - Epoch [56][1100/5005]\tlr: 1.000e-02, eta: 5:29:19, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0329\n2023-01-04 22:18:32,567 - mmcls - INFO - Epoch [56][1200/5005]\tlr: 1.000e-02, eta: 5:29:10, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0333\n2023-01-04 22:18:41,414 - mmcls - INFO - Epoch [56][1300/5005]\tlr: 1.000e-02, eta: 5:29:01, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0441\n2023-01-04 22:18:50,006 - mmcls - INFO - Epoch [56][1400/5005]\tlr: 1.000e-02, eta: 5:28:52, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0300\n2023-01-04 22:18:58,931 - mmcls - INFO - Epoch [56][1500/5005]\tlr: 1.000e-02, eta: 5:28:43, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0179\n2023-01-04 22:19:07,766 - mmcls - INFO - Epoch [56][1600/5005]\tlr: 1.000e-02, eta: 5:28:34, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0191\n2023-01-04 22:19:16,611 - mmcls - INFO - Epoch [56][1700/5005]\tlr: 1.000e-02, eta: 5:28:26, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0244\n2023-01-04 22:19:25,196 - mmcls - INFO - Epoch [56][1800/5005]\tlr: 1.000e-02, eta: 5:28:17, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0592\n2023-01-04 22:19:33,969 - mmcls - INFO - Epoch [56][1900/5005]\tlr: 1.000e-02, eta: 5:28:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0259\n2023-01-04 22:19:42,626 - mmcls - INFO - Epoch [56][2000/5005]\tlr: 1.000e-02, eta: 5:27:59, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0367\n2023-01-04 22:19:51,271 - mmcls - INFO - Epoch [56][2100/5005]\tlr: 1.000e-02, eta: 5:27:50, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0610\n2023-01-04 22:20:00,284 - mmcls - INFO - Epoch [56][2200/5005]\tlr: 1.000e-02, eta: 5:27:41, time: 0.090, data_time: 0.017, memory: 928, loss: 5.0355\n2023-01-04 22:20:09,145 - mmcls - INFO - Epoch [56][2300/5005]\tlr: 1.000e-02, eta: 5:27:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0332\n2023-01-04 22:20:17,821 - mmcls - INFO - Epoch [56][2400/5005]\tlr: 1.000e-02, eta: 5:27:24, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0306\n2023-01-04 22:20:26,620 - mmcls - INFO - Epoch [56][2500/5005]\tlr: 1.000e-02, eta: 5:27:15, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0390\n2023-01-04 22:20:35,453 - mmcls - INFO - Epoch [56][2600/5005]\tlr: 1.000e-02, eta: 5:27:06, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0319\n2023-01-04 22:20:44,327 - mmcls - INFO - Epoch [56][2700/5005]\tlr: 1.000e-02, eta: 5:26:57, time: 0.089, data_time: 0.015, memory: 928, loss: 5.0473\n2023-01-04 22:20:53,012 - mmcls - INFO - Epoch [56][2800/5005]\tlr: 1.000e-02, eta: 5:26:48, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0347\n2023-01-04 22:21:01,735 - mmcls - INFO - Epoch [56][2900/5005]\tlr: 1.000e-02, eta: 5:26:39, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0323\n2023-01-04 22:21:10,742 - mmcls - INFO - Epoch [56][3000/5005]\tlr: 1.000e-02, eta: 5:26:31, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0156\n2023-01-04 22:21:19,617 - mmcls - INFO - Epoch [56][3100/5005]\tlr: 1.000e-02, eta: 5:26:22, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0480\n2023-01-04 22:21:28,892 - mmcls - INFO - Epoch [56][3200/5005]\tlr: 1.000e-02, eta: 5:26:13, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0485\n2023-01-04 22:21:37,540 - mmcls - INFO - Epoch [56][3300/5005]\tlr: 1.000e-02, eta: 5:26:05, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0400\n2023-01-04 22:21:46,157 - mmcls - INFO - Epoch [56][3400/5005]\tlr: 1.000e-02, eta: 5:25:56, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0503\n2023-01-04 22:21:54,963 - mmcls - INFO - Epoch [56][3500/5005]\tlr: 1.000e-02, eta: 5:25:47, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0367\n2023-01-04 22:22:03,804 - mmcls - INFO - Epoch [56][3600/5005]\tlr: 1.000e-02, eta: 5:25:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0488\n2023-01-04 22:22:12,675 - mmcls - INFO - Epoch [56][3700/5005]\tlr: 1.000e-02, eta: 5:25:29, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0481\n2023-01-04 22:22:21,479 - mmcls - INFO - Epoch [56][3800/5005]\tlr: 1.000e-02, eta: 5:25:20, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0327\n2023-01-04 22:22:30,054 - mmcls - INFO - Epoch [56][3900/5005]\tlr: 1.000e-02, eta: 5:25:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0492\n2023-01-04 22:22:38,606 - mmcls - INFO - Epoch [56][4000/5005]\tlr: 1.000e-02, eta: 5:25:02, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0294\n2023-01-04 22:22:47,241 - mmcls - INFO - Epoch [56][4100/5005]\tlr: 1.000e-02, eta: 5:24:53, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0436\n2023-01-04 22:22:56,158 - mmcls - INFO - Epoch [56][4200/5005]\tlr: 1.000e-02, eta: 5:24:45, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0341\n2023-01-04 22:23:04,791 - mmcls - INFO - Epoch [56][4300/5005]\tlr: 1.000e-02, eta: 5:24:36, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0342\n2023-01-04 22:23:13,662 - mmcls - INFO - Epoch [56][4400/5005]\tlr: 1.000e-02, eta: 5:24:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0322\n2023-01-04 22:23:22,252 - mmcls - INFO - Epoch [56][4500/5005]\tlr: 1.000e-02, eta: 5:24:18, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0372\n2023-01-04 22:23:31,408 - mmcls - INFO - Epoch [56][4600/5005]\tlr: 1.000e-02, eta: 5:24:09, time: 0.091, data_time: 0.024, memory: 928, loss: 5.0250\n2023-01-04 22:23:40,203 - mmcls - INFO - Epoch [56][4700/5005]\tlr: 1.000e-02, eta: 5:24:01, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0538\n2023-01-04 22:23:48,940 - mmcls - INFO - Epoch [56][4800/5005]\tlr: 1.000e-02, eta: 5:23:52, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0215\n2023-01-04 22:23:57,625 - mmcls - INFO - Epoch [56][4900/5005]\tlr: 1.000e-02, eta: 5:23:43, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0549\n2023-01-04 22:24:06,381 - mmcls - INFO - Epoch [56][5000/5005]\tlr: 1.000e-02, eta: 5:23:34, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0445\n2023-01-04 22:24:29,346 - mmcls - INFO - Epoch(val) [56][196]\taccuracy_top-1: 10.0860, accuracy_top-5: 23.2660\n2023-01-04 22:24:40,189 - mmcls - INFO - Epoch [57][100/5005]\tlr: 1.000e-02, eta: 5:23:26, time: 0.108, data_time: 0.059, memory: 928, loss: 5.0396\n2023-01-04 22:24:49,037 - mmcls - INFO - Epoch [57][200/5005]\tlr: 1.000e-02, eta: 5:23:17, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0213\n2023-01-04 22:24:57,817 - mmcls - INFO - Epoch [57][300/5005]\tlr: 1.000e-02, eta: 5:23:08, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0214\n2023-01-04 22:25:06,506 - mmcls - INFO - Epoch [57][400/5005]\tlr: 1.000e-02, eta: 5:22:59, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0425\n2023-01-04 22:25:15,063 - mmcls - INFO - Epoch [57][500/5005]\tlr: 1.000e-02, eta: 5:22:50, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0301\n2023-01-04 22:25:23,578 - mmcls - INFO - Epoch [57][600/5005]\tlr: 1.000e-02, eta: 5:22:41, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0258\n2023-01-04 22:25:32,152 - mmcls - INFO - Epoch [57][700/5005]\tlr: 1.000e-02, eta: 5:22:32, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0203\n2023-01-04 22:25:40,902 - mmcls - INFO - Epoch [57][800/5005]\tlr: 1.000e-02, eta: 5:22:23, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0304\n2023-01-04 22:25:49,891 - mmcls - INFO - Epoch [57][900/5005]\tlr: 1.000e-02, eta: 5:22:15, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0414\n2023-01-04 22:25:58,530 - mmcls - INFO - Epoch [57][1000/5005]\tlr: 1.000e-02, eta: 5:22:06, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0525\n2023-01-04 22:26:07,327 - mmcls - INFO - Epoch [57][1100/5005]\tlr: 1.000e-02, eta: 5:21:57, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0514\n2023-01-04 22:26:15,902 - mmcls - INFO - Epoch [57][1200/5005]\tlr: 1.000e-02, eta: 5:21:48, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0471\n2023-01-04 22:26:24,512 - mmcls - INFO - Epoch [57][1300/5005]\tlr: 1.000e-02, eta: 5:21:39, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0291\n2023-01-04 22:26:33,252 - mmcls - INFO - Epoch [57][1400/5005]\tlr: 1.000e-02, eta: 5:21:30, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0418\n2023-01-04 22:26:42,386 - mmcls - INFO - Epoch [57][1500/5005]\tlr: 1.000e-02, eta: 5:21:21, time: 0.091, data_time: 0.019, memory: 928, loss: 5.0264\n2023-01-04 22:26:51,105 - mmcls - INFO - Epoch [57][1600/5005]\tlr: 1.000e-02, eta: 5:21:13, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0290\n2023-01-04 22:26:59,919 - mmcls - INFO - Epoch [57][1700/5005]\tlr: 1.000e-02, eta: 5:21:04, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0360\n2023-01-04 22:27:08,634 - mmcls - INFO - Epoch [57][1800/5005]\tlr: 1.000e-02, eta: 5:20:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0403\n2023-01-04 22:27:17,526 - mmcls - INFO - Epoch [57][1900/5005]\tlr: 1.000e-02, eta: 5:20:46, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0068\n2023-01-04 22:27:26,276 - mmcls - INFO - Epoch [57][2000/5005]\tlr: 1.000e-02, eta: 5:20:37, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0311\n2023-01-04 22:27:35,136 - mmcls - INFO - Epoch [57][2100/5005]\tlr: 1.000e-02, eta: 5:20:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0084\n2023-01-04 22:27:44,062 - mmcls - INFO - Epoch [57][2200/5005]\tlr: 1.000e-02, eta: 5:20:20, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0271\n2023-01-04 22:27:52,871 - mmcls - INFO - Epoch [57][2300/5005]\tlr: 1.000e-02, eta: 5:20:11, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0418\n2023-01-04 22:28:01,879 - mmcls - INFO - Epoch [57][2400/5005]\tlr: 1.000e-02, eta: 5:20:02, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0358\n2023-01-04 22:28:10,581 - mmcls - INFO - Epoch [57][2500/5005]\tlr: 1.000e-02, eta: 5:19:53, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0445\n2023-01-04 22:28:19,245 - mmcls - INFO - Epoch [57][2600/5005]\tlr: 1.000e-02, eta: 5:19:44, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0194\n2023-01-04 22:28:28,212 - mmcls - INFO - Epoch [57][2700/5005]\tlr: 1.000e-02, eta: 5:19:36, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0293\n2023-01-04 22:28:36,868 - mmcls - INFO - Epoch [57][2800/5005]\tlr: 1.000e-02, eta: 5:19:27, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0068\n2023-01-04 22:28:45,585 - mmcls - INFO - Epoch [57][2900/5005]\tlr: 1.000e-02, eta: 5:19:18, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0376\n2023-01-04 22:28:54,329 - mmcls - INFO - Epoch [57][3000/5005]\tlr: 1.000e-02, eta: 5:19:09, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0427\n2023-01-04 22:29:03,070 - mmcls - INFO - Epoch [57][3100/5005]\tlr: 1.000e-02, eta: 5:19:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0263\n2023-01-04 22:29:11,663 - mmcls - INFO - Epoch [57][3200/5005]\tlr: 1.000e-02, eta: 5:18:51, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0320\n2023-01-04 22:29:20,172 - mmcls - INFO - Epoch [57][3300/5005]\tlr: 1.000e-02, eta: 5:18:42, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0512\n2023-01-04 22:29:29,005 - mmcls - INFO - Epoch [57][3400/5005]\tlr: 1.000e-02, eta: 5:18:33, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0230\n2023-01-04 22:29:37,608 - mmcls - INFO - Epoch [57][3500/5005]\tlr: 1.000e-02, eta: 5:18:24, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0317\n2023-01-04 22:29:46,336 - mmcls - INFO - Epoch [57][3600/5005]\tlr: 1.000e-02, eta: 5:18:15, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0388\n2023-01-04 22:29:55,120 - mmcls - INFO - Epoch [57][3700/5005]\tlr: 1.000e-02, eta: 5:18:07, time: 0.088, data_time: 0.030, memory: 928, loss: 5.0205\n2023-01-04 22:30:03,759 - mmcls - INFO - Epoch [57][3800/5005]\tlr: 1.000e-02, eta: 5:17:58, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0398\n2023-01-04 22:30:12,456 - mmcls - INFO - Epoch [57][3900/5005]\tlr: 1.000e-02, eta: 5:17:49, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0290\n2023-01-04 22:30:21,180 - mmcls - INFO - Epoch [57][4000/5005]\tlr: 1.000e-02, eta: 5:17:40, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0323\n2023-01-04 22:30:29,999 - mmcls - INFO - Epoch [57][4100/5005]\tlr: 1.000e-02, eta: 5:17:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0462\n2023-01-04 22:30:38,781 - mmcls - INFO - Epoch [57][4200/5005]\tlr: 1.000e-02, eta: 5:17:22, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0442\n2023-01-04 22:30:47,576 - mmcls - INFO - Epoch [57][4300/5005]\tlr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0315\n2023-01-04 22:30:56,459 - mmcls - INFO - Epoch [57][4400/5005]\tlr: 1.000e-02, eta: 5:17:05, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0397\n2023-01-04 22:31:05,297 - mmcls - INFO - Epoch [57][4500/5005]\tlr: 1.000e-02, eta: 5:16:56, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0469\n2023-01-04 22:31:13,749 - mmcls - INFO - Epoch [57][4600/5005]\tlr: 1.000e-02, eta: 5:16:47, time: 0.084, data_time: 0.010, memory: 928, loss: 5.0174\n2023-01-04 22:31:22,695 - mmcls - INFO - Epoch [57][4700/5005]\tlr: 1.000e-02, eta: 5:16:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0357\n2023-01-04 22:31:31,143 - mmcls - INFO - Epoch [57][4800/5005]\tlr: 1.000e-02, eta: 5:16:29, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0157\n2023-01-04 22:31:39,948 - mmcls - INFO - Epoch [57][4900/5005]\tlr: 1.000e-02, eta: 5:16:20, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0180\n2023-01-04 22:31:48,719 - mmcls - INFO - Epoch [57][5000/5005]\tlr: 1.000e-02, eta: 5:16:11, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0599\n2023-01-04 22:32:11,449 - mmcls - INFO - Epoch(val) [57][196]\taccuracy_top-1: 12.0060, accuracy_top-5: 26.7400\n2023-01-04 22:32:22,532 - mmcls - INFO - Epoch [58][100/5005]\tlr: 1.000e-02, eta: 5:16:03, time: 0.111, data_time: 0.033, memory: 928, loss: 5.0181\n2023-01-04 22:32:31,400 - mmcls - INFO - Epoch [58][200/5005]\tlr: 1.000e-02, eta: 5:15:54, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0170\n2023-01-04 22:32:40,434 - mmcls - INFO - Epoch [58][300/5005]\tlr: 1.000e-02, eta: 5:15:46, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0132\n2023-01-04 22:32:49,445 - mmcls - INFO - Epoch [58][400/5005]\tlr: 1.000e-02, eta: 5:15:37, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0203\n2023-01-04 22:32:58,288 - mmcls - INFO - Epoch [58][500/5005]\tlr: 1.000e-02, eta: 5:15:28, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0240\n2023-01-04 22:33:07,259 - mmcls - INFO - Epoch [58][600/5005]\tlr: 1.000e-02, eta: 5:15:20, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0271\n2023-01-04 22:33:16,010 - mmcls - INFO - Epoch [58][700/5005]\tlr: 1.000e-02, eta: 5:15:11, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0142\n2023-01-04 22:33:25,048 - mmcls - INFO - Epoch [58][800/5005]\tlr: 1.000e-02, eta: 5:15:02, time: 0.090, data_time: 0.037, memory: 928, loss: 5.0011\n2023-01-04 22:33:33,919 - mmcls - INFO - Epoch [58][900/5005]\tlr: 1.000e-02, eta: 5:14:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0164\n2023-01-04 22:33:42,552 - mmcls - INFO - Epoch [58][1000/5005]\tlr: 1.000e-02, eta: 5:14:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0210\n2023-01-04 22:33:51,106 - mmcls - INFO - Epoch [58][1100/5005]\tlr: 1.000e-02, eta: 5:14:35, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0428\n2023-01-04 22:34:00,118 - mmcls - INFO - Epoch [58][1200/5005]\tlr: 1.000e-02, eta: 5:14:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0227\n2023-01-04 22:34:09,270 - mmcls - INFO - Epoch [58][1300/5005]\tlr: 1.000e-02, eta: 5:14:18, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0448\n2023-01-04 22:34:17,951 - mmcls - INFO - Epoch [58][1400/5005]\tlr: 1.000e-02, eta: 5:14:09, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0013\n2023-01-04 22:34:26,604 - mmcls - INFO - Epoch [58][1500/5005]\tlr: 1.000e-02, eta: 5:14:00, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0305\n2023-01-04 22:34:35,419 - mmcls - INFO - Epoch [58][1600/5005]\tlr: 1.000e-02, eta: 5:13:52, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0230\n2023-01-04 22:34:43,751 - mmcls - INFO - Epoch [58][1700/5005]\tlr: 1.000e-02, eta: 5:13:42, time: 0.083, data_time: 0.006, memory: 928, loss: 5.0549\n2023-01-04 22:34:52,471 - mmcls - INFO - Epoch [58][1800/5005]\tlr: 1.000e-02, eta: 5:13:33, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0277\n2023-01-04 22:35:01,092 - mmcls - INFO - Epoch [58][1900/5005]\tlr: 1.000e-02, eta: 5:13:24, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0128\n2023-01-04 22:35:09,918 - mmcls - INFO - Epoch [58][2000/5005]\tlr: 1.000e-02, eta: 5:13:16, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0327\n2023-01-04 22:35:18,690 - mmcls - INFO - Epoch [58][2100/5005]\tlr: 1.000e-02, eta: 5:13:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0412\n2023-01-04 22:35:27,677 - mmcls - INFO - Epoch [58][2200/5005]\tlr: 1.000e-02, eta: 5:12:58, time: 0.090, data_time: 0.019, memory: 928, loss: 5.0425\n2023-01-04 22:35:36,370 - mmcls - INFO - Epoch [58][2300/5005]\tlr: 1.000e-02, eta: 5:12:49, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0557\n2023-01-04 22:35:45,170 - mmcls - INFO - Epoch [58][2400/5005]\tlr: 1.000e-02, eta: 5:12:40, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0326\n2023-01-04 22:35:53,987 - mmcls - INFO - Epoch [58][2500/5005]\tlr: 1.000e-02, eta: 5:12:32, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0382\n2023-01-04 22:36:02,680 - mmcls - INFO - Epoch [58][2600/5005]\tlr: 1.000e-02, eta: 5:12:23, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0349\n2023-01-04 22:36:11,428 - mmcls - INFO - Epoch [58][2700/5005]\tlr: 1.000e-02, eta: 5:12:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0435\n2023-01-04 22:36:20,140 - mmcls - INFO - Epoch [58][2800/5005]\tlr: 1.000e-02, eta: 5:12:05, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0579\n2023-01-04 22:36:29,063 - mmcls - INFO - Epoch [58][2900/5005]\tlr: 1.000e-02, eta: 5:11:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0189\n2023-01-04 22:36:37,963 - mmcls - INFO - Epoch [58][3000/5005]\tlr: 1.000e-02, eta: 5:11:47, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0427\n2023-01-04 22:36:46,585 - mmcls - INFO - Epoch [58][3100/5005]\tlr: 1.000e-02, eta: 5:11:38, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0439\n2023-01-04 22:36:55,248 - mmcls - INFO - Epoch [58][3200/5005]\tlr: 1.000e-02, eta: 5:11:30, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0536\n2023-01-04 22:37:04,112 - mmcls - INFO - Epoch [58][3300/5005]\tlr: 1.000e-02, eta: 5:11:21, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0305\n2023-01-04 22:37:12,791 - mmcls - INFO - Epoch [58][3400/5005]\tlr: 1.000e-02, eta: 5:11:12, time: 0.087, data_time: 0.041, memory: 928, loss: 5.0052\n2023-01-04 22:37:21,907 - mmcls - INFO - Epoch [58][3500/5005]\tlr: 1.000e-02, eta: 5:11:03, time: 0.091, data_time: 0.009, memory: 928, loss: 5.0531\n2023-01-04 22:37:30,361 - mmcls - INFO - Epoch [58][3600/5005]\tlr: 1.000e-02, eta: 5:10:54, time: 0.084, data_time: 0.009, memory: 928, loss: 5.0447\n2023-01-04 22:37:38,870 - mmcls - INFO - Epoch [58][3700/5005]\tlr: 1.000e-02, eta: 5:10:45, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0200\n2023-01-04 22:37:48,331 - mmcls - INFO - Epoch [58][3800/5005]\tlr: 1.000e-02, eta: 5:10:37, time: 0.095, data_time: 0.005, memory: 928, loss: 5.0130\n2023-01-04 22:37:56,991 - mmcls - INFO - Epoch [58][3900/5005]\tlr: 1.000e-02, eta: 5:10:28, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0392\n2023-01-04 22:38:05,846 - mmcls - INFO - Epoch [58][4000/5005]\tlr: 1.000e-02, eta: 5:10:19, time: 0.089, data_time: 0.000, memory: 928, loss: 5.0468\n2023-01-04 22:38:14,752 - mmcls - INFO - Epoch [58][4100/5005]\tlr: 1.000e-02, eta: 5:10:10, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0474\n2023-01-04 22:38:23,363 - mmcls - INFO - Epoch [58][4200/5005]\tlr: 1.000e-02, eta: 5:10:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0178\n2023-01-04 22:38:32,073 - mmcls - INFO - Epoch [58][4300/5005]\tlr: 1.000e-02, eta: 5:09:52, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0563\n2023-01-04 22:38:40,849 - mmcls - INFO - Epoch [58][4400/5005]\tlr: 1.000e-02, eta: 5:09:44, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0359\n2023-01-04 22:38:49,714 - mmcls - INFO - Epoch [58][4500/5005]\tlr: 1.000e-02, eta: 5:09:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0156\n2023-01-04 22:38:58,789 - mmcls - INFO - Epoch [58][4600/5005]\tlr: 1.000e-02, eta: 5:09:26, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0316\n2023-01-04 22:39:07,488 - mmcls - INFO - Epoch [58][4700/5005]\tlr: 1.000e-02, eta: 5:09:17, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0522\n2023-01-04 22:39:15,993 - mmcls - INFO - Epoch [58][4800/5005]\tlr: 1.000e-02, eta: 5:09:08, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0448\n2023-01-04 22:39:24,701 - mmcls - INFO - Epoch [58][4900/5005]\tlr: 1.000e-02, eta: 5:08:59, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0266\n2023-01-04 22:39:33,657 - mmcls - INFO - Epoch [58][5000/5005]\tlr: 1.000e-02, eta: 5:08:51, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0255\n2023-01-04 22:39:56,233 - mmcls - INFO - Epoch(val) [58][196]\taccuracy_top-1: 7.0600, accuracy_top-5: 17.0500\n2023-01-04 22:40:07,056 - mmcls - INFO - Epoch [59][100/5005]\tlr: 1.000e-02, eta: 5:08:43, time: 0.108, data_time: 0.041, memory: 928, loss: 5.0297\n2023-01-04 22:40:16,044 - mmcls - INFO - Epoch [59][200/5005]\tlr: 1.000e-02, eta: 5:08:34, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0396\n2023-01-04 22:40:24,667 - mmcls - INFO - Epoch [59][300/5005]\tlr: 1.000e-02, eta: 5:08:25, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0455\n2023-01-04 22:40:33,430 - mmcls - INFO - Epoch [59][400/5005]\tlr: 1.000e-02, eta: 5:08:16, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0306\n2023-01-04 22:40:42,648 - mmcls - INFO - Epoch [59][500/5005]\tlr: 1.000e-02, eta: 5:08:07, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0208\n2023-01-04 22:40:51,453 - mmcls - INFO - Epoch [59][600/5005]\tlr: 1.000e-02, eta: 5:07:59, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0391\n2023-01-04 22:41:00,161 - mmcls - INFO - Epoch [59][700/5005]\tlr: 1.000e-02, eta: 5:07:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0288\n2023-01-04 22:41:08,999 - mmcls - INFO - Epoch [59][800/5005]\tlr: 1.000e-02, eta: 5:07:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0346\n2023-01-04 22:41:17,844 - mmcls - INFO - Epoch [59][900/5005]\tlr: 1.000e-02, eta: 5:07:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0175\n2023-01-04 22:41:26,534 - mmcls - INFO - Epoch [59][1000/5005]\tlr: 1.000e-02, eta: 5:07:23, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0128\n2023-01-04 22:41:35,316 - mmcls - INFO - Epoch [59][1100/5005]\tlr: 1.000e-02, eta: 5:07:14, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0309\n2023-01-04 22:41:43,976 - mmcls - INFO - Epoch [59][1200/5005]\tlr: 1.000e-02, eta: 5:07:06, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0449\n2023-01-04 22:41:52,662 - mmcls - INFO - Epoch [59][1300/5005]\tlr: 1.000e-02, eta: 5:06:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0232\n2023-01-04 22:42:01,496 - mmcls - INFO - Epoch [59][1400/5005]\tlr: 1.000e-02, eta: 5:06:48, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0227\n2023-01-04 22:42:10,407 - mmcls - INFO - Epoch [59][1500/5005]\tlr: 1.000e-02, eta: 5:06:39, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0249\n2023-01-04 22:42:19,141 - mmcls - INFO - Epoch [59][1600/5005]\tlr: 1.000e-02, eta: 5:06:30, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0269\n2023-01-04 22:42:27,852 - mmcls - INFO - Epoch [59][1700/5005]\tlr: 1.000e-02, eta: 5:06:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0308\n2023-01-04 22:42:36,454 - mmcls - INFO - Epoch [59][1800/5005]\tlr: 1.000e-02, eta: 5:06:12, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0151\n2023-01-04 22:42:45,286 - mmcls - INFO - Epoch [59][1900/5005]\tlr: 1.000e-02, eta: 5:06:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0122\n2023-01-04 22:42:54,040 - mmcls - INFO - Epoch [59][2000/5005]\tlr: 1.000e-02, eta: 5:05:55, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0540\n2023-01-04 22:43:02,601 - mmcls - INFO - Epoch [59][2100/5005]\tlr: 1.000e-02, eta: 5:05:46, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0477\n2023-01-04 22:43:11,487 - mmcls - INFO - Epoch [59][2200/5005]\tlr: 1.000e-02, eta: 5:05:37, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0307\n2023-01-04 22:43:20,757 - mmcls - INFO - Epoch [59][2300/5005]\tlr: 1.000e-02, eta: 5:05:28, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0407\n2023-01-04 22:43:29,450 - mmcls - INFO - Epoch [59][2400/5005]\tlr: 1.000e-02, eta: 5:05:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0241\n2023-01-04 22:43:38,135 - mmcls - INFO - Epoch [59][2500/5005]\tlr: 1.000e-02, eta: 5:05:11, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0155\n2023-01-04 22:43:46,782 - mmcls - INFO - Epoch [59][2600/5005]\tlr: 1.000e-02, eta: 5:05:02, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0359\n2023-01-04 22:43:55,812 - mmcls - INFO - Epoch [59][2700/5005]\tlr: 1.000e-02, eta: 5:04:53, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0387\n2023-01-04 22:44:04,541 - mmcls - INFO - Epoch [59][2800/5005]\tlr: 1.000e-02, eta: 5:04:44, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0168\n2023-01-04 22:44:13,329 - mmcls - INFO - Epoch [59][2900/5005]\tlr: 1.000e-02, eta: 5:04:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0316\n2023-01-04 22:44:22,297 - mmcls - INFO - Epoch [59][3000/5005]\tlr: 1.000e-02, eta: 5:04:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0344\n2023-01-04 22:44:31,250 - mmcls - INFO - Epoch [59][3100/5005]\tlr: 1.000e-02, eta: 5:04:18, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0241\n2023-01-04 22:44:39,922 - mmcls - INFO - Epoch [59][3200/5005]\tlr: 1.000e-02, eta: 5:04:09, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0242\n2023-01-04 22:44:48,625 - mmcls - INFO - Epoch [59][3300/5005]\tlr: 1.000e-02, eta: 5:04:00, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0087\n2023-01-04 22:44:57,220 - mmcls - INFO - Epoch [59][3400/5005]\tlr: 1.000e-02, eta: 5:03:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0331\n2023-01-04 22:45:06,376 - mmcls - INFO - Epoch [59][3500/5005]\tlr: 1.000e-02, eta: 5:03:42, time: 0.092, data_time: 0.009, memory: 928, loss: 5.0634\n2023-01-04 22:45:15,103 - mmcls - INFO - Epoch [59][3600/5005]\tlr: 1.000e-02, eta: 5:03:34, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0173\n2023-01-04 22:45:23,681 - mmcls - INFO - Epoch [59][3700/5005]\tlr: 1.000e-02, eta: 5:03:25, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0260\n2023-01-04 22:45:32,580 - mmcls - INFO - Epoch [59][3800/5005]\tlr: 1.000e-02, eta: 5:03:16, time: 0.089, data_time: 0.030, memory: 928, loss: 5.0578\n2023-01-04 22:45:41,158 - mmcls - INFO - Epoch [59][3900/5005]\tlr: 1.000e-02, eta: 5:03:07, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0209\n2023-01-04 22:45:49,901 - mmcls - INFO - Epoch [59][4000/5005]\tlr: 1.000e-02, eta: 5:02:58, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0127\n2023-01-04 22:45:58,830 - mmcls - INFO - Epoch [59][4100/5005]\tlr: 1.000e-02, eta: 5:02:49, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0406\n2023-01-04 22:46:07,380 - mmcls - INFO - Epoch [59][4200/5005]\tlr: 1.000e-02, eta: 5:02:40, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0257\n2023-01-04 22:46:16,223 - mmcls - INFO - Epoch [59][4300/5005]\tlr: 1.000e-02, eta: 5:02:31, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0189\n2023-01-04 22:46:24,872 - mmcls - INFO - Epoch [59][4400/5005]\tlr: 1.000e-02, eta: 5:02:23, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0259\n2023-01-04 22:46:33,597 - mmcls - INFO - Epoch [59][4500/5005]\tlr: 1.000e-02, eta: 5:02:14, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0222\n2023-01-04 22:46:42,222 - mmcls - INFO - Epoch [59][4600/5005]\tlr: 1.000e-02, eta: 5:02:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0466\n2023-01-04 22:46:50,985 - mmcls - INFO - Epoch [59][4700/5005]\tlr: 1.000e-02, eta: 5:01:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0246\n2023-01-04 22:46:59,992 - mmcls - INFO - Epoch [59][4800/5005]\tlr: 1.000e-02, eta: 5:01:47, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0041\n2023-01-04 22:47:08,957 - mmcls - INFO - Epoch [59][4900/5005]\tlr: 1.000e-02, eta: 5:01:38, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0396\n2023-01-04 22:47:17,646 - mmcls - INFO - Epoch [59][5000/5005]\tlr: 1.000e-02, eta: 5:01:30, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0270\n2023-01-04 22:47:40,345 - mmcls - INFO - Epoch(val) [59][196]\taccuracy_top-1: 11.4520, accuracy_top-5: 25.7140\n2023-01-04 22:47:51,280 - mmcls - INFO - Epoch [60][100/5005]\tlr: 1.000e-02, eta: 5:01:21, time: 0.109, data_time: 0.049, memory: 928, loss: 5.0115\n2023-01-04 22:47:59,687 - mmcls - INFO - Epoch [60][200/5005]\tlr: 1.000e-02, eta: 5:01:12, time: 0.084, data_time: 0.003, memory: 928, loss: 5.0339\n2023-01-04 22:48:08,106 - mmcls - INFO - Epoch [60][300/5005]\tlr: 1.000e-02, eta: 5:01:03, time: 0.084, data_time: 0.006, memory: 928, loss: 5.0216\n2023-01-04 22:48:16,788 - mmcls - INFO - Epoch [60][400/5005]\tlr: 1.000e-02, eta: 5:00:54, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0111\n2023-01-04 22:48:25,515 - mmcls - INFO - Epoch [60][500/5005]\tlr: 1.000e-02, eta: 5:00:45, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0312\n2023-01-04 22:48:34,097 - mmcls - INFO - Epoch [60][600/5005]\tlr: 1.000e-02, eta: 5:00:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0208\n2023-01-04 22:48:43,353 - mmcls - INFO - Epoch [60][700/5005]\tlr: 1.000e-02, eta: 5:00:28, time: 0.093, data_time: 0.006, memory: 928, loss: 5.0534\n2023-01-04 22:48:52,065 - mmcls - INFO - Epoch [60][800/5005]\tlr: 1.000e-02, eta: 5:00:19, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0097\n2023-01-04 22:49:00,974 - mmcls - INFO - Epoch [60][900/5005]\tlr: 1.000e-02, eta: 5:00:10, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0172\n2023-01-04 22:49:09,735 - mmcls - INFO - Epoch [60][1000/5005]\tlr: 1.000e-02, eta: 5:00:01, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0144\n2023-01-04 22:49:18,213 - mmcls - INFO - Epoch [60][1100/5005]\tlr: 1.000e-02, eta: 4:59:52, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0425\n2023-01-04 22:49:26,967 - mmcls - INFO - Epoch [60][1200/5005]\tlr: 1.000e-02, eta: 4:59:44, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0159\n2023-01-04 22:49:35,894 - mmcls - INFO - Epoch [60][1300/5005]\tlr: 1.000e-02, eta: 4:59:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0371\n2023-01-04 22:49:44,410 - mmcls - INFO - Epoch [60][1400/5005]\tlr: 1.000e-02, eta: 4:59:26, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0191\n2023-01-04 22:49:53,205 - mmcls - INFO - Epoch [60][1500/5005]\tlr: 1.000e-02, eta: 4:59:17, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0107\n2023-01-04 22:50:02,033 - mmcls - INFO - Epoch [60][1600/5005]\tlr: 1.000e-02, eta: 4:59:08, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0067\n2023-01-04 22:50:10,796 - mmcls - INFO - Epoch [60][1700/5005]\tlr: 1.000e-02, eta: 4:58:59, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0166\n2023-01-04 22:50:19,635 - mmcls - INFO - Epoch [60][1800/5005]\tlr: 1.000e-02, eta: 4:58:51, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0409\n2023-01-04 22:50:28,534 - mmcls - INFO - Epoch [60][1900/5005]\tlr: 1.000e-02, eta: 4:58:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0427\n2023-01-04 22:50:37,097 - mmcls - INFO - Epoch [60][2000/5005]\tlr: 1.000e-02, eta: 4:58:33, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0539\n2023-01-04 22:50:45,578 - mmcls - INFO - Epoch [60][2100/5005]\tlr: 1.000e-02, eta: 4:58:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0326\n2023-01-04 22:50:54,521 - mmcls - INFO - Epoch [60][2200/5005]\tlr: 1.000e-02, eta: 4:58:15, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0433\n2023-01-04 22:51:03,541 - mmcls - INFO - Epoch [60][2300/5005]\tlr: 1.000e-02, eta: 4:58:06, time: 0.090, data_time: 0.021, memory: 928, loss: 5.0275\n2023-01-04 22:51:12,380 - mmcls - INFO - Epoch [60][2400/5005]\tlr: 1.000e-02, eta: 4:57:58, time: 0.088, data_time: 0.034, memory: 928, loss: 5.0238\n2023-01-04 22:51:20,914 - mmcls - INFO - Epoch [60][2500/5005]\tlr: 1.000e-02, eta: 4:57:49, time: 0.085, data_time: 0.031, memory: 928, loss: 5.0311\n2023-01-04 22:51:29,691 - mmcls - INFO - Epoch [60][2600/5005]\tlr: 1.000e-02, eta: 4:57:40, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0194\n2023-01-04 22:51:38,478 - mmcls - INFO - Epoch [60][2700/5005]\tlr: 1.000e-02, eta: 4:57:31, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0292\n2023-01-04 22:51:47,589 - mmcls - INFO - Epoch [60][2800/5005]\tlr: 1.000e-02, eta: 4:57:22, time: 0.091, data_time: 0.013, memory: 928, loss: 4.9874\n2023-01-04 22:51:56,559 - mmcls - INFO - Epoch [60][2900/5005]\tlr: 1.000e-02, eta: 4:57:14, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0309\n2023-01-04 22:52:05,307 - mmcls - INFO - Epoch [60][3000/5005]\tlr: 1.000e-02, eta: 4:57:05, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0166\n2023-01-04 22:52:13,893 - mmcls - INFO - Epoch [60][3100/5005]\tlr: 1.000e-02, eta: 4:56:56, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0460\n2023-01-04 22:52:22,843 - mmcls - INFO - Epoch [60][3200/5005]\tlr: 1.000e-02, eta: 4:56:47, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0205\n2023-01-04 22:52:31,645 - mmcls - INFO - Epoch [60][3300/5005]\tlr: 1.000e-02, eta: 4:56:38, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0356\n2023-01-04 22:52:40,226 - mmcls - INFO - Epoch [60][3400/5005]\tlr: 1.000e-02, eta: 4:56:29, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0323\n2023-01-04 22:52:49,129 - mmcls - INFO - Epoch [60][3500/5005]\tlr: 1.000e-02, eta: 4:56:20, time: 0.089, data_time: 0.024, memory: 928, loss: 5.0215\n2023-01-04 22:52:57,877 - mmcls - INFO - Epoch [60][3600/5005]\tlr: 1.000e-02, eta: 4:56:12, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0400\n2023-01-04 22:53:06,656 - mmcls - INFO - Epoch [60][3700/5005]\tlr: 1.000e-02, eta: 4:56:03, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0111\n2023-01-04 22:53:15,316 - mmcls - INFO - Epoch [60][3800/5005]\tlr: 1.000e-02, eta: 4:55:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0376\n2023-01-04 22:53:24,101 - mmcls - INFO - Epoch [60][3900/5005]\tlr: 1.000e-02, eta: 4:55:45, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0372\n2023-01-04 22:53:32,718 - mmcls - INFO - Epoch [60][4000/5005]\tlr: 1.000e-02, eta: 4:55:36, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0334\n2023-01-04 22:53:41,218 - mmcls - INFO - Epoch [60][4100/5005]\tlr: 1.000e-02, eta: 4:55:27, time: 0.085, data_time: 0.036, memory: 928, loss: 5.0351\n2023-01-04 22:53:50,052 - mmcls - INFO - Epoch [60][4200/5005]\tlr: 1.000e-02, eta: 4:55:18, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0185\n2023-01-04 22:53:58,885 - mmcls - INFO - Epoch [60][4300/5005]\tlr: 1.000e-02, eta: 4:55:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0097\n2023-01-04 22:54:07,829 - mmcls - INFO - Epoch [60][4400/5005]\tlr: 1.000e-02, eta: 4:55:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0191\n2023-01-04 22:54:16,628 - mmcls - INFO - Epoch [60][4500/5005]\tlr: 1.000e-02, eta: 4:54:52, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0269\n2023-01-04 22:54:25,183 - mmcls - INFO - Epoch [60][4600/5005]\tlr: 1.000e-02, eta: 4:54:43, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0271\n2023-01-04 22:54:33,718 - mmcls - INFO - Epoch [60][4700/5005]\tlr: 1.000e-02, eta: 4:54:34, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0408\n2023-01-04 22:54:42,549 - mmcls - INFO - Epoch [60][4800/5005]\tlr: 1.000e-02, eta: 4:54:25, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0258\n2023-01-04 22:54:51,194 - mmcls - INFO - Epoch [60][4900/5005]\tlr: 1.000e-02, eta: 4:54:16, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0400\n2023-01-04 22:55:00,211 - mmcls - INFO - Epoch [60][5000/5005]\tlr: 1.000e-02, eta: 4:54:07, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0143\n2023-01-04 22:55:22,895 - mmcls - INFO - Epoch(val) [60][196]\taccuracy_top-1: 9.5760, accuracy_top-5: 22.2380\n2023-01-04 22:55:33,818 - mmcls - INFO - Epoch [61][100/5005]\tlr: 1.000e-03, eta: 4:53:59, time: 0.109, data_time: 0.054, memory: 928, loss: 4.9478\n2023-01-04 22:55:42,396 - mmcls - INFO - Epoch [61][200/5005]\tlr: 1.000e-03, eta: 4:53:50, time: 0.086, data_time: 0.005, memory: 928, loss: 4.9633\n2023-01-04 22:55:51,260 - mmcls - INFO - Epoch [61][300/5005]\tlr: 1.000e-03, eta: 4:53:41, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9512\n2023-01-04 22:55:59,712 - mmcls - INFO - Epoch [61][400/5005]\tlr: 1.000e-03, eta: 4:53:32, time: 0.084, data_time: 0.004, memory: 928, loss: 4.9229\n2023-01-04 22:56:08,432 - mmcls - INFO - Epoch [61][500/5005]\tlr: 1.000e-03, eta: 4:53:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9093\n2023-01-04 22:56:17,256 - mmcls - INFO - Epoch [61][600/5005]\tlr: 1.000e-03, eta: 4:53:15, time: 0.088, data_time: 0.006, memory: 928, loss: 4.9167\n2023-01-04 22:56:26,081 - mmcls - INFO - Epoch [61][700/5005]\tlr: 1.000e-03, eta: 4:53:06, time: 0.088, data_time: 0.016, memory: 928, loss: 4.9485\n2023-01-04 22:56:34,870 - mmcls - INFO - Epoch [61][800/5005]\tlr: 1.000e-03, eta: 4:52:57, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9358\n2023-01-04 22:56:43,655 - mmcls - INFO - Epoch [61][900/5005]\tlr: 1.000e-03, eta: 4:52:48, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9306\n2023-01-04 22:56:52,317 - mmcls - INFO - Epoch [61][1000/5005]\tlr: 1.000e-03, eta: 4:52:39, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8843\n2023-01-04 22:57:01,463 - mmcls - INFO - Epoch [61][1100/5005]\tlr: 1.000e-03, eta: 4:52:31, time: 0.091, data_time: 0.017, memory: 928, loss: 4.9304\n2023-01-04 22:57:10,234 - mmcls - INFO - Epoch [61][1200/5005]\tlr: 1.000e-03, eta: 4:52:22, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8936\n2023-01-04 22:57:19,006 - mmcls - INFO - Epoch [61][1300/5005]\tlr: 1.000e-03, eta: 4:52:13, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8937\n2023-01-04 22:57:27,613 - mmcls - INFO - Epoch [61][1400/5005]\tlr: 1.000e-03, eta: 4:52:04, time: 0.086, data_time: 0.005, memory: 928, loss: 4.9300\n2023-01-04 22:57:36,319 - mmcls - INFO - Epoch [61][1500/5005]\tlr: 1.000e-03, eta: 4:51:55, time: 0.087, data_time: 0.002, memory: 928, loss: 4.9242\n2023-01-04 22:57:44,987 - mmcls - INFO - Epoch [61][1600/5005]\tlr: 1.000e-03, eta: 4:51:46, time: 0.087, data_time: 0.002, memory: 928, loss: 4.9254\n2023-01-04 22:57:53,466 - mmcls - INFO - Epoch [61][1700/5005]\tlr: 1.000e-03, eta: 4:51:37, time: 0.085, data_time: 0.012, memory: 928, loss: 4.9286\n2023-01-04 22:58:02,361 - mmcls - INFO - Epoch [61][1800/5005]\tlr: 1.000e-03, eta: 4:51:29, time: 0.089, data_time: 0.009, memory: 928, loss: 4.9071\n2023-01-04 22:58:11,155 - mmcls - INFO - Epoch [61][1900/5005]\tlr: 1.000e-03, eta: 4:51:20, time: 0.088, data_time: 0.010, memory: 928, loss: 4.9103\n2023-01-04 22:58:19,839 - mmcls - INFO - Epoch [61][2000/5005]\tlr: 1.000e-03, eta: 4:51:11, time: 0.087, data_time: 0.007, memory: 928, loss: 4.9095\n2023-01-04 22:58:28,318 - mmcls - INFO - Epoch [61][2100/5005]\tlr: 1.000e-03, eta: 4:51:02, time: 0.085, data_time: 0.006, memory: 928, loss: 4.9175\n2023-01-04 22:58:37,053 - mmcls - INFO - Epoch [61][2200/5005]\tlr: 1.000e-03, eta: 4:50:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.9165\n2023-01-04 22:58:45,815 - mmcls - INFO - Epoch [61][2300/5005]\tlr: 1.000e-03, eta: 4:50:44, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9035\n2023-01-04 22:58:54,817 - mmcls - INFO - Epoch [61][2400/5005]\tlr: 1.000e-03, eta: 4:50:35, time: 0.090, data_time: 0.023, memory: 928, loss: 4.8883\n2023-01-04 22:59:03,450 - mmcls - INFO - Epoch [61][2500/5005]\tlr: 1.000e-03, eta: 4:50:26, time: 0.086, data_time: 0.019, memory: 928, loss: 4.9186\n2023-01-04 22:59:12,210 - mmcls - INFO - Epoch [61][2600/5005]\tlr: 1.000e-03, eta: 4:50:18, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9117\n2023-01-04 22:59:20,754 - mmcls - INFO - Epoch [61][2700/5005]\tlr: 1.000e-03, eta: 4:50:09, time: 0.085, data_time: 0.009, memory: 928, loss: 4.9238\n2023-01-04 22:59:29,251 - mmcls - INFO - Epoch [61][2800/5005]\tlr: 1.000e-03, eta: 4:50:00, time: 0.085, data_time: 0.017, memory: 928, loss: 4.9069\n2023-01-04 22:59:38,141 - mmcls - INFO - Epoch [61][2900/5005]\tlr: 1.000e-03, eta: 4:49:51, time: 0.089, data_time: 0.007, memory: 928, loss: 4.9026\n2023-01-04 22:59:47,077 - mmcls - INFO - Epoch [61][3000/5005]\tlr: 1.000e-03, eta: 4:49:42, time: 0.089, data_time: 0.002, memory: 928, loss: 4.9148\n2023-01-04 22:59:56,470 - mmcls - INFO - Epoch [61][3100/5005]\tlr: 1.000e-03, eta: 4:49:34, time: 0.094, data_time: 0.013, memory: 928, loss: 4.9164\n2023-01-04 23:00:05,333 - mmcls - INFO - Epoch [61][3200/5005]\tlr: 1.000e-03, eta: 4:49:25, time: 0.089, data_time: 0.019, memory: 928, loss: 4.9071\n2023-01-04 23:00:14,085 - mmcls - INFO - Epoch [61][3300/5005]\tlr: 1.000e-03, eta: 4:49:16, time: 0.088, data_time: 0.014, memory: 928, loss: 4.9017\n2023-01-04 23:00:22,919 - mmcls - INFO - Epoch [61][3400/5005]\tlr: 1.000e-03, eta: 4:49:07, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8910\n2023-01-04 23:00:31,538 - mmcls - INFO - Epoch [61][3500/5005]\tlr: 1.000e-03, eta: 4:48:58, time: 0.086, data_time: 0.002, memory: 928, loss: 4.9042\n2023-01-04 23:00:40,248 - mmcls - INFO - Epoch [61][3600/5005]\tlr: 1.000e-03, eta: 4:48:49, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9112\n2023-01-04 23:00:48,980 - mmcls - INFO - Epoch [61][3700/5005]\tlr: 1.000e-03, eta: 4:48:40, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9187\n2023-01-04 23:00:57,834 - mmcls - INFO - Epoch [61][3800/5005]\tlr: 1.000e-03, eta: 4:48:32, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8989\n2023-01-04 23:01:06,788 - mmcls - INFO - Epoch [61][3900/5005]\tlr: 1.000e-03, eta: 4:48:23, time: 0.090, data_time: 0.001, memory: 928, loss: 4.9011\n2023-01-04 23:01:15,656 - mmcls - INFO - Epoch [61][4000/5005]\tlr: 1.000e-03, eta: 4:48:14, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9166\n2023-01-04 23:01:24,533 - mmcls - INFO - Epoch [61][4100/5005]\tlr: 1.000e-03, eta: 4:48:05, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9011\n2023-01-04 23:01:33,202 - mmcls - INFO - Epoch [61][4200/5005]\tlr: 1.000e-03, eta: 4:47:56, time: 0.087, data_time: 0.023, memory: 928, loss: 4.9104\n2023-01-04 23:01:41,775 - mmcls - INFO - Epoch [61][4300/5005]\tlr: 1.000e-03, eta: 4:47:47, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8821\n2023-01-04 23:01:50,655 - mmcls - INFO - Epoch [61][4400/5005]\tlr: 1.000e-03, eta: 4:47:39, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8911\n2023-01-04 23:01:59,825 - mmcls - INFO - Epoch [61][4500/5005]\tlr: 1.000e-03, eta: 4:47:30, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8932\n2023-01-04 23:02:08,336 - mmcls - INFO - Epoch [61][4600/5005]\tlr: 1.000e-03, eta: 4:47:21, time: 0.085, data_time: 0.033, memory: 928, loss: 4.9043\n2023-01-04 23:02:17,665 - mmcls - INFO - Epoch [61][4700/5005]\tlr: 1.000e-03, eta: 4:47:13, time: 0.093, data_time: 0.024, memory: 928, loss: 4.8873\n2023-01-04 23:02:26,544 - mmcls - INFO - Epoch [61][4800/5005]\tlr: 1.000e-03, eta: 4:47:04, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9084\n2023-01-04 23:02:35,331 - mmcls - INFO - Epoch [61][4900/5005]\tlr: 1.000e-03, eta: 4:46:55, time: 0.088, data_time: 0.006, memory: 928, loss: 4.9320\n2023-01-04 23:02:44,019 - mmcls - INFO - Epoch [61][5000/5005]\tlr: 1.000e-03, eta: 4:46:46, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8859\n2023-01-04 23:03:06,748 - mmcls - INFO - Epoch(val) [61][196]\taccuracy_top-1: 16.9300, accuracy_top-5: 34.8440\n2023-01-04 23:03:17,855 - mmcls - INFO - Epoch [62][100/5005]\tlr: 1.000e-03, eta: 4:46:38, time: 0.111, data_time: 0.042, memory: 928, loss: 4.8940\n2023-01-04 23:03:26,538 - mmcls - INFO - Epoch [62][200/5005]\tlr: 1.000e-03, eta: 4:46:29, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8911\n2023-01-04 23:03:35,146 - mmcls - INFO - Epoch [62][300/5005]\tlr: 1.000e-03, eta: 4:46:20, time: 0.086, data_time: 0.041, memory: 928, loss: 4.9182\n2023-01-04 23:03:43,781 - mmcls - INFO - Epoch [62][400/5005]\tlr: 1.000e-03, eta: 4:46:11, time: 0.086, data_time: 0.003, memory: 928, loss: 4.9064\n2023-01-04 23:03:52,401 - mmcls - INFO - Epoch [62][500/5005]\tlr: 1.000e-03, eta: 4:46:02, time: 0.086, data_time: 0.030, memory: 928, loss: 4.8994\n2023-01-04 23:04:01,189 - mmcls - INFO - Epoch [62][600/5005]\tlr: 1.000e-03, eta: 4:45:54, time: 0.088, data_time: 0.023, memory: 928, loss: 4.9105\n2023-01-04 23:04:09,921 - mmcls - INFO - Epoch [62][700/5005]\tlr: 1.000e-03, eta: 4:45:45, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9112\n2023-01-04 23:04:18,827 - mmcls - INFO - Epoch [62][800/5005]\tlr: 1.000e-03, eta: 4:45:36, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8993\n2023-01-04 23:04:27,660 - mmcls - INFO - Epoch [62][900/5005]\tlr: 1.000e-03, eta: 4:45:27, time: 0.088, data_time: 0.012, memory: 928, loss: 4.9134\n2023-01-04 23:04:36,218 - mmcls - INFO - Epoch [62][1000/5005]\tlr: 1.000e-03, eta: 4:45:18, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8857\n2023-01-04 23:04:45,127 - mmcls - INFO - Epoch [62][1100/5005]\tlr: 1.000e-03, eta: 4:45:09, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8897\n2023-01-04 23:04:54,193 - mmcls - INFO - Epoch [62][1200/5005]\tlr: 1.000e-03, eta: 4:45:01, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8954\n2023-01-04 23:05:02,945 - mmcls - INFO - Epoch [62][1300/5005]\tlr: 1.000e-03, eta: 4:44:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9047\n2023-01-04 23:05:11,836 - mmcls - INFO - Epoch [62][1400/5005]\tlr: 1.000e-03, eta: 4:44:43, time: 0.089, data_time: 0.002, memory: 928, loss: 4.9060\n2023-01-04 23:05:20,662 - mmcls - INFO - Epoch [62][1500/5005]\tlr: 1.000e-03, eta: 4:44:34, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9029\n2023-01-04 23:05:29,398 - mmcls - INFO - Epoch [62][1600/5005]\tlr: 1.000e-03, eta: 4:44:25, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8921\n2023-01-04 23:05:38,108 - mmcls - INFO - Epoch [62][1700/5005]\tlr: 1.000e-03, eta: 4:44:16, time: 0.087, data_time: 0.015, memory: 928, loss: 4.9103\n2023-01-04 23:05:46,870 - mmcls - INFO - Epoch [62][1800/5005]\tlr: 1.000e-03, eta: 4:44:08, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8937\n2023-01-04 23:05:56,059 - mmcls - INFO - Epoch [62][1900/5005]\tlr: 1.000e-03, eta: 4:43:59, time: 0.092, data_time: 0.006, memory: 928, loss: 4.8919\n2023-01-04 23:06:04,725 - mmcls - INFO - Epoch [62][2000/5005]\tlr: 1.000e-03, eta: 4:43:50, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8823\n2023-01-04 23:06:13,792 - mmcls - INFO - Epoch [62][2100/5005]\tlr: 1.000e-03, eta: 4:43:41, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8990\n2023-01-04 23:06:22,426 - mmcls - INFO - Epoch [62][2200/5005]\tlr: 1.000e-03, eta: 4:43:33, time: 0.086, data_time: 0.003, memory: 928, loss: 4.9090\n2023-01-04 23:06:31,127 - mmcls - INFO - Epoch [62][2300/5005]\tlr: 1.000e-03, eta: 4:43:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8844\n2023-01-04 23:06:39,856 - mmcls - INFO - Epoch [62][2400/5005]\tlr: 1.000e-03, eta: 4:43:15, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8840\n2023-01-04 23:06:48,396 - mmcls - INFO - Epoch [62][2500/5005]\tlr: 1.000e-03, eta: 4:43:06, time: 0.085, data_time: 0.010, memory: 928, loss: 4.9058\n2023-01-04 23:06:57,069 - mmcls - INFO - Epoch [62][2600/5005]\tlr: 1.000e-03, eta: 4:42:57, time: 0.087, data_time: 0.014, memory: 928, loss: 4.9209\n2023-01-04 23:07:05,847 - mmcls - INFO - Epoch [62][2700/5005]\tlr: 1.000e-03, eta: 4:42:48, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8960\n2023-01-04 23:07:14,710 - mmcls - INFO - Epoch [62][2800/5005]\tlr: 1.000e-03, eta: 4:42:39, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9130\n2023-01-04 23:07:23,625 - mmcls - INFO - Epoch [62][2900/5005]\tlr: 1.000e-03, eta: 4:42:31, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8979\n2023-01-04 23:07:32,542 - mmcls - INFO - Epoch [62][3000/5005]\tlr: 1.000e-03, eta: 4:42:22, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8991\n2023-01-04 23:07:41,240 - mmcls - INFO - Epoch [62][3100/5005]\tlr: 1.000e-03, eta: 4:42:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.9058\n2023-01-04 23:07:50,391 - mmcls - INFO - Epoch [62][3200/5005]\tlr: 1.000e-03, eta: 4:42:04, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8916\n2023-01-04 23:07:59,101 - mmcls - INFO - Epoch [62][3300/5005]\tlr: 1.000e-03, eta: 4:41:55, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8846\n2023-01-04 23:08:07,859 - mmcls - INFO - Epoch [62][3400/5005]\tlr: 1.000e-03, eta: 4:41:47, time: 0.088, data_time: 0.010, memory: 928, loss: 4.9022\n2023-01-04 23:08:16,599 - mmcls - INFO - Epoch [62][3500/5005]\tlr: 1.000e-03, eta: 4:41:38, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8640\n2023-01-04 23:08:25,267 - mmcls - INFO - Epoch [62][3600/5005]\tlr: 1.000e-03, eta: 4:41:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8914\n2023-01-04 23:08:34,196 - mmcls - INFO - Epoch [62][3700/5005]\tlr: 1.000e-03, eta: 4:41:20, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8998\n2023-01-04 23:08:42,766 - mmcls - INFO - Epoch [62][3800/5005]\tlr: 1.000e-03, eta: 4:41:11, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8729\n2023-01-04 23:08:51,560 - mmcls - INFO - Epoch [62][3900/5005]\tlr: 1.000e-03, eta: 4:41:02, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8959\n2023-01-04 23:09:00,296 - mmcls - INFO - Epoch [62][4000/5005]\tlr: 1.000e-03, eta: 4:40:53, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8976\n2023-01-04 23:09:09,349 - mmcls - INFO - Epoch [62][4100/5005]\tlr: 1.000e-03, eta: 4:40:45, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8416\n2023-01-04 23:09:17,912 - mmcls - INFO - Epoch [62][4200/5005]\tlr: 1.000e-03, eta: 4:40:36, time: 0.086, data_time: 0.002, memory: 928, loss: 4.9034\n2023-01-04 23:09:27,140 - mmcls - INFO - Epoch [62][4300/5005]\tlr: 1.000e-03, eta: 4:40:27, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8869\n2023-01-04 23:09:35,750 - mmcls - INFO - Epoch [62][4400/5005]\tlr: 1.000e-03, eta: 4:40:18, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8948\n2023-01-04 23:09:44,752 - mmcls - INFO - Epoch [62][4500/5005]\tlr: 1.000e-03, eta: 4:40:10, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8902\n2023-01-04 23:09:53,831 - mmcls - INFO - Epoch [62][4600/5005]\tlr: 1.000e-03, eta: 4:40:01, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8786\n2023-01-04 23:10:02,683 - mmcls - INFO - Epoch [62][4700/5005]\tlr: 1.000e-03, eta: 4:39:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9073\n2023-01-04 23:10:11,640 - mmcls - INFO - Epoch [62][4800/5005]\tlr: 1.000e-03, eta: 4:39:43, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8808\n2023-01-04 23:10:20,416 - mmcls - INFO - Epoch [62][4900/5005]\tlr: 1.000e-03, eta: 4:39:35, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9123\n2023-01-04 23:10:29,151 - mmcls - INFO - Epoch [62][5000/5005]\tlr: 1.000e-03, eta: 4:39:26, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8791\n2023-01-04 23:10:51,913 - mmcls - INFO - Epoch(val) [62][196]\taccuracy_top-1: 16.6240, accuracy_top-5: 34.3200\n2023-01-04 23:11:02,763 - mmcls - INFO - Epoch [63][100/5005]\tlr: 1.000e-03, eta: 4:39:17, time: 0.108, data_time: 0.046, memory: 928, loss: 4.9047\n2023-01-04 23:11:11,397 - mmcls - INFO - Epoch [63][200/5005]\tlr: 1.000e-03, eta: 4:39:08, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8968\n2023-01-04 23:11:20,375 - mmcls - INFO - Epoch [63][300/5005]\tlr: 1.000e-03, eta: 4:39:00, time: 0.090, data_time: 0.030, memory: 928, loss: 4.9014\n2023-01-04 23:11:29,166 - mmcls - INFO - Epoch [63][400/5005]\tlr: 1.000e-03, eta: 4:38:51, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8832\n2023-01-04 23:11:37,921 - mmcls - INFO - Epoch [63][500/5005]\tlr: 1.000e-03, eta: 4:38:42, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8886\n2023-01-04 23:11:46,604 - mmcls - INFO - Epoch [63][600/5005]\tlr: 1.000e-03, eta: 4:38:33, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8711\n2023-01-04 23:11:55,192 - mmcls - INFO - Epoch [63][700/5005]\tlr: 1.000e-03, eta: 4:38:24, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8828\n2023-01-04 23:12:03,840 - mmcls - INFO - Epoch [63][800/5005]\tlr: 1.000e-03, eta: 4:38:15, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8884\n2023-01-04 23:12:12,693 - mmcls - INFO - Epoch [63][900/5005]\tlr: 1.000e-03, eta: 4:38:06, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8770\n2023-01-04 23:12:21,245 - mmcls - INFO - Epoch [63][1000/5005]\tlr: 1.000e-03, eta: 4:37:57, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8505\n2023-01-04 23:12:30,216 - mmcls - INFO - Epoch [63][1100/5005]\tlr: 1.000e-03, eta: 4:37:49, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8761\n2023-01-04 23:12:38,955 - mmcls - INFO - Epoch [63][1200/5005]\tlr: 1.000e-03, eta: 4:37:40, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8922\n2023-01-04 23:12:47,616 - mmcls - INFO - Epoch [63][1300/5005]\tlr: 1.000e-03, eta: 4:37:31, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8874\n2023-01-04 23:12:56,437 - mmcls - INFO - Epoch [63][1400/5005]\tlr: 1.000e-03, eta: 4:37:22, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8836\n2023-01-04 23:13:04,965 - mmcls - INFO - Epoch [63][1500/5005]\tlr: 1.000e-03, eta: 4:37:13, time: 0.085, data_time: 0.015, memory: 928, loss: 4.9097\n2023-01-04 23:13:13,454 - mmcls - INFO - Epoch [63][1600/5005]\tlr: 1.000e-03, eta: 4:37:04, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8910\n2023-01-04 23:13:22,244 - mmcls - INFO - Epoch [63][1700/5005]\tlr: 1.000e-03, eta: 4:36:55, time: 0.088, data_time: 0.026, memory: 928, loss: 4.8722\n2023-01-04 23:13:30,910 - mmcls - INFO - Epoch [63][1800/5005]\tlr: 1.000e-03, eta: 4:36:46, time: 0.087, data_time: 0.040, memory: 928, loss: 4.8679\n2023-01-04 23:13:39,689 - mmcls - INFO - Epoch [63][1900/5005]\tlr: 1.000e-03, eta: 4:36:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8636\n2023-01-04 23:13:48,581 - mmcls - INFO - Epoch [63][2000/5005]\tlr: 1.000e-03, eta: 4:36:29, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9082\n2023-01-04 23:13:57,303 - mmcls - INFO - Epoch [63][2100/5005]\tlr: 1.000e-03, eta: 4:36:20, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8908\n2023-01-04 23:14:05,963 - mmcls - INFO - Epoch [63][2200/5005]\tlr: 1.000e-03, eta: 4:36:11, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8992\n2023-01-04 23:14:15,029 - mmcls - INFO - Epoch [63][2300/5005]\tlr: 1.000e-03, eta: 4:36:02, time: 0.091, data_time: 0.021, memory: 928, loss: 4.9014\n2023-01-04 23:14:23,876 - mmcls - INFO - Epoch [63][2400/5005]\tlr: 1.000e-03, eta: 4:35:54, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9033\n2023-01-04 23:14:32,523 - mmcls - INFO - Epoch [63][2500/5005]\tlr: 1.000e-03, eta: 4:35:45, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8963\n2023-01-04 23:14:41,459 - mmcls - INFO - Epoch [63][2600/5005]\tlr: 1.000e-03, eta: 4:35:36, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8815\n2023-01-04 23:14:50,186 - mmcls - INFO - Epoch [63][2700/5005]\tlr: 1.000e-03, eta: 4:35:27, time: 0.087, data_time: 0.004, memory: 928, loss: 4.9150\n2023-01-04 23:14:58,977 - mmcls - INFO - Epoch [63][2800/5005]\tlr: 1.000e-03, eta: 4:35:18, time: 0.088, data_time: 0.017, memory: 928, loss: 4.9000\n2023-01-04 23:15:08,172 - mmcls - INFO - Epoch [63][2900/5005]\tlr: 1.000e-03, eta: 4:35:10, time: 0.092, data_time: 0.010, memory: 928, loss: 4.8849\n2023-01-04 23:15:17,120 - mmcls - INFO - Epoch [63][3000/5005]\tlr: 1.000e-03, eta: 4:35:01, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8806\n2023-01-04 23:15:25,741 - mmcls - INFO - Epoch [63][3100/5005]\tlr: 1.000e-03, eta: 4:34:52, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8925\n2023-01-04 23:15:34,820 - mmcls - INFO - Epoch [63][3200/5005]\tlr: 1.000e-03, eta: 4:34:43, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8817\n2023-01-04 23:15:43,493 - mmcls - INFO - Epoch [63][3300/5005]\tlr: 1.000e-03, eta: 4:34:34, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8874\n2023-01-04 23:15:52,098 - mmcls - INFO - Epoch [63][3400/5005]\tlr: 1.000e-03, eta: 4:34:25, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8913\n2023-01-04 23:16:01,022 - mmcls - INFO - Epoch [63][3500/5005]\tlr: 1.000e-03, eta: 4:34:17, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8902\n2023-01-04 23:16:09,925 - mmcls - INFO - Epoch [63][3600/5005]\tlr: 1.000e-03, eta: 4:34:08, time: 0.089, data_time: 0.006, memory: 928, loss: 4.9025\n2023-01-04 23:16:18,487 - mmcls - INFO - Epoch [63][3700/5005]\tlr: 1.000e-03, eta: 4:33:59, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8984\n2023-01-04 23:16:27,350 - mmcls - INFO - Epoch [63][3800/5005]\tlr: 1.000e-03, eta: 4:33:50, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8753\n2023-01-04 23:16:36,141 - mmcls - INFO - Epoch [63][3900/5005]\tlr: 1.000e-03, eta: 4:33:41, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8874\n2023-01-04 23:16:44,840 - mmcls - INFO - Epoch [63][4000/5005]\tlr: 1.000e-03, eta: 4:33:32, time: 0.087, data_time: 0.052, memory: 928, loss: 4.9155\n2023-01-04 23:16:53,501 - mmcls - INFO - Epoch [63][4100/5005]\tlr: 1.000e-03, eta: 4:33:24, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8907\n2023-01-04 23:17:02,393 - mmcls - INFO - Epoch [63][4200/5005]\tlr: 1.000e-03, eta: 4:33:15, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8960\n2023-01-04 23:17:11,207 - mmcls - INFO - Epoch [63][4300/5005]\tlr: 1.000e-03, eta: 4:33:06, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8875\n2023-01-04 23:17:20,045 - mmcls - INFO - Epoch [63][4400/5005]\tlr: 1.000e-03, eta: 4:32:57, time: 0.088, data_time: 0.036, memory: 928, loss: 4.8955\n2023-01-04 23:17:28,892 - mmcls - INFO - Epoch [63][4500/5005]\tlr: 1.000e-03, eta: 4:32:48, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8979\n2023-01-04 23:17:37,667 - mmcls - INFO - Epoch [63][4600/5005]\tlr: 1.000e-03, eta: 4:32:40, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8862\n2023-01-04 23:17:46,487 - mmcls - INFO - Epoch [63][4700/5005]\tlr: 1.000e-03, eta: 4:32:31, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8771\n2023-01-04 23:17:55,597 - mmcls - INFO - Epoch [63][4800/5005]\tlr: 1.000e-03, eta: 4:32:22, time: 0.091, data_time: 0.012, memory: 928, loss: 4.8994\n2023-01-04 23:18:04,055 - mmcls - INFO - Epoch [63][4900/5005]\tlr: 1.000e-03, eta: 4:32:13, time: 0.085, data_time: 0.005, memory: 928, loss: 4.9079\n2023-01-04 23:18:12,729 - mmcls - INFO - Epoch [63][5000/5005]\tlr: 1.000e-03, eta: 4:32:04, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8890\n2023-01-04 23:18:35,575 - mmcls - INFO - Epoch(val) [63][196]\taccuracy_top-1: 16.7900, accuracy_top-5: 34.4480\n2023-01-04 23:18:46,870 - mmcls - INFO - Epoch [64][100/5005]\tlr: 1.000e-03, eta: 4:31:56, time: 0.113, data_time: 0.060, memory: 928, loss: 4.8820\n2023-01-04 23:18:55,282 - mmcls - INFO - Epoch [64][200/5005]\tlr: 1.000e-03, eta: 4:31:47, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8840\n2023-01-04 23:19:03,757 - mmcls - INFO - Epoch [64][300/5005]\tlr: 1.000e-03, eta: 4:31:38, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8764\n2023-01-04 23:19:12,436 - mmcls - INFO - Epoch [64][400/5005]\tlr: 1.000e-03, eta: 4:31:29, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8899\n2023-01-04 23:19:21,459 - mmcls - INFO - Epoch [64][500/5005]\tlr: 1.000e-03, eta: 4:31:20, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8375\n2023-01-04 23:19:30,108 - mmcls - INFO - Epoch [64][600/5005]\tlr: 1.000e-03, eta: 4:31:12, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8981\n2023-01-04 23:19:38,797 - mmcls - INFO - Epoch [64][700/5005]\tlr: 1.000e-03, eta: 4:31:03, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8611\n2023-01-04 23:19:47,804 - mmcls - INFO - Epoch [64][800/5005]\tlr: 1.000e-03, eta: 4:30:54, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8695\n2023-01-04 23:19:56,623 - mmcls - INFO - Epoch [64][900/5005]\tlr: 1.000e-03, eta: 4:30:45, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8802\n2023-01-04 23:20:05,263 - mmcls - INFO - Epoch [64][1000/5005]\tlr: 1.000e-03, eta: 4:30:36, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8873\n2023-01-04 23:20:14,093 - mmcls - INFO - Epoch [64][1100/5005]\tlr: 1.000e-03, eta: 4:30:27, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8779\n2023-01-04 23:20:22,918 - mmcls - INFO - Epoch [64][1200/5005]\tlr: 1.000e-03, eta: 4:30:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8971\n2023-01-04 23:20:31,922 - mmcls - INFO - Epoch [64][1300/5005]\tlr: 1.000e-03, eta: 4:30:10, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8835\n2023-01-04 23:20:40,926 - mmcls - INFO - Epoch [64][1400/5005]\tlr: 1.000e-03, eta: 4:30:01, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8779\n2023-01-04 23:20:49,554 - mmcls - INFO - Epoch [64][1500/5005]\tlr: 1.000e-03, eta: 4:29:52, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8925\n2023-01-04 23:20:58,364 - mmcls - INFO - Epoch [64][1600/5005]\tlr: 1.000e-03, eta: 4:29:43, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8894\n2023-01-04 23:21:06,982 - mmcls - INFO - Epoch [64][1700/5005]\tlr: 1.000e-03, eta: 4:29:35, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8843\n2023-01-04 23:21:15,911 - mmcls - INFO - Epoch [64][1800/5005]\tlr: 1.000e-03, eta: 4:29:26, time: 0.089, data_time: 0.037, memory: 928, loss: 4.8998\n2023-01-04 23:21:24,609 - mmcls - INFO - Epoch [64][1900/5005]\tlr: 1.000e-03, eta: 4:29:17, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8826\n2023-01-04 23:21:33,456 - mmcls - INFO - Epoch [64][2000/5005]\tlr: 1.000e-03, eta: 4:29:08, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8938\n2023-01-04 23:21:42,538 - mmcls - INFO - Epoch [64][2100/5005]\tlr: 1.000e-03, eta: 4:28:59, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8773\n2023-01-04 23:21:51,210 - mmcls - INFO - Epoch [64][2200/5005]\tlr: 1.000e-03, eta: 4:28:51, time: 0.087, data_time: 0.034, memory: 928, loss: 4.8949\n2023-01-04 23:21:59,969 - mmcls - INFO - Epoch [64][2300/5005]\tlr: 1.000e-03, eta: 4:28:42, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8914\n2023-01-04 23:22:08,739 - mmcls - INFO - Epoch [64][2400/5005]\tlr: 1.000e-03, eta: 4:28:33, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8888\n2023-01-04 23:22:17,717 - mmcls - INFO - Epoch [64][2500/5005]\tlr: 1.000e-03, eta: 4:28:24, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8706\n2023-01-04 23:22:26,417 - mmcls - INFO - Epoch [64][2600/5005]\tlr: 1.000e-03, eta: 4:28:15, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8902\n2023-01-04 23:22:35,098 - mmcls - INFO - Epoch [64][2700/5005]\tlr: 1.000e-03, eta: 4:28:06, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8686\n2023-01-04 23:22:43,773 - mmcls - INFO - Epoch [64][2800/5005]\tlr: 1.000e-03, eta: 4:27:57, time: 0.087, data_time: 0.018, memory: 928, loss: 4.9081\n2023-01-04 23:22:52,439 - mmcls - INFO - Epoch [64][2900/5005]\tlr: 1.000e-03, eta: 4:27:49, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8847\n2023-01-04 23:23:01,239 - mmcls - INFO - Epoch [64][3000/5005]\tlr: 1.000e-03, eta: 4:27:40, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8771\n2023-01-04 23:23:09,943 - mmcls - INFO - Epoch [64][3100/5005]\tlr: 1.000e-03, eta: 4:27:31, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8938\n2023-01-04 23:23:18,716 - mmcls - INFO - Epoch [64][3200/5005]\tlr: 1.000e-03, eta: 4:27:22, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8824\n2023-01-04 23:23:27,058 - mmcls - INFO - Epoch [64][3300/5005]\tlr: 1.000e-03, eta: 4:27:13, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8806\n2023-01-04 23:23:35,555 - mmcls - INFO - Epoch [64][3400/5005]\tlr: 1.000e-03, eta: 4:27:04, time: 0.085, data_time: 0.039, memory: 928, loss: 4.8810\n2023-01-04 23:23:44,352 - mmcls - INFO - Epoch [64][3500/5005]\tlr: 1.000e-03, eta: 4:26:55, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8815\n2023-01-04 23:23:52,977 - mmcls - INFO - Epoch [64][3600/5005]\tlr: 1.000e-03, eta: 4:26:46, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8703\n2023-01-04 23:24:01,845 - mmcls - INFO - Epoch [64][3700/5005]\tlr: 1.000e-03, eta: 4:26:37, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8731\n2023-01-04 23:24:10,647 - mmcls - INFO - Epoch [64][3800/5005]\tlr: 1.000e-03, eta: 4:26:29, time: 0.088, data_time: 0.000, memory: 928, loss: 4.8932\n2023-01-04 23:24:19,427 - mmcls - INFO - Epoch [64][3900/5005]\tlr: 1.000e-03, eta: 4:26:20, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9059\n2023-01-04 23:24:28,066 - mmcls - INFO - Epoch [64][4000/5005]\tlr: 1.000e-03, eta: 4:26:11, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8792\n2023-01-04 23:24:36,910 - mmcls - INFO - Epoch [64][4100/5005]\tlr: 1.000e-03, eta: 4:26:02, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9011\n2023-01-04 23:24:45,446 - mmcls - INFO - Epoch [64][4200/5005]\tlr: 1.000e-03, eta: 4:25:53, time: 0.085, data_time: 0.001, memory: 928, loss: 4.9015\n2023-01-04 23:24:54,463 - mmcls - INFO - Epoch [64][4300/5005]\tlr: 1.000e-03, eta: 4:25:44, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8976\n2023-01-04 23:25:03,021 - mmcls - INFO - Epoch [64][4400/5005]\tlr: 1.000e-03, eta: 4:25:35, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8582\n2023-01-04 23:25:11,650 - mmcls - INFO - Epoch [64][4500/5005]\tlr: 1.000e-03, eta: 4:25:26, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8975\n2023-01-04 23:25:20,498 - mmcls - INFO - Epoch [64][4600/5005]\tlr: 1.000e-03, eta: 4:25:18, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9002\n2023-01-04 23:25:29,406 - mmcls - INFO - Epoch [64][4700/5005]\tlr: 1.000e-03, eta: 4:25:09, time: 0.089, data_time: 0.000, memory: 928, loss: 4.8712\n2023-01-04 23:25:38,489 - mmcls - INFO - Epoch [64][4800/5005]\tlr: 1.000e-03, eta: 4:25:00, time: 0.091, data_time: 0.013, memory: 928, loss: 4.8980\n2023-01-04 23:25:47,042 - mmcls - INFO - Epoch [64][4900/5005]\tlr: 1.000e-03, eta: 4:24:51, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8995\n2023-01-04 23:25:55,870 - mmcls - INFO - Epoch [64][5000/5005]\tlr: 1.000e-03, eta: 4:24:42, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8918\n2023-01-04 23:26:18,886 - mmcls - INFO - Epoch(val) [64][196]\taccuracy_top-1: 16.8880, accuracy_top-5: 34.5660\n2023-01-04 23:26:29,645 - mmcls - INFO - Epoch [65][100/5005]\tlr: 1.000e-03, eta: 4:24:34, time: 0.107, data_time: 0.038, memory: 928, loss: 4.8802\n2023-01-04 23:26:38,314 - mmcls - INFO - Epoch [65][200/5005]\tlr: 1.000e-03, eta: 4:24:25, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8793\n2023-01-04 23:26:47,326 - mmcls - INFO - Epoch [65][300/5005]\tlr: 1.000e-03, eta: 4:24:16, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8770\n2023-01-04 23:26:56,077 - mmcls - INFO - Epoch [65][400/5005]\tlr: 1.000e-03, eta: 4:24:08, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8568\n2023-01-04 23:27:05,076 - mmcls - INFO - Epoch [65][500/5005]\tlr: 1.000e-03, eta: 4:23:59, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8777\n2023-01-04 23:27:13,794 - mmcls - INFO - Epoch [65][600/5005]\tlr: 1.000e-03, eta: 4:23:50, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8719\n2023-01-04 23:27:22,491 - mmcls - INFO - Epoch [65][700/5005]\tlr: 1.000e-03, eta: 4:23:41, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8883\n2023-01-04 23:27:31,262 - mmcls - INFO - Epoch [65][800/5005]\tlr: 1.000e-03, eta: 4:23:32, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8907\n2023-01-04 23:27:39,952 - mmcls - INFO - Epoch [65][900/5005]\tlr: 1.000e-03, eta: 4:23:23, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8764\n2023-01-04 23:27:48,541 - mmcls - INFO - Epoch [65][1000/5005]\tlr: 1.000e-03, eta: 4:23:14, time: 0.086, data_time: 0.001, memory: 928, loss: 4.9004\n2023-01-04 23:27:57,398 - mmcls - INFO - Epoch [65][1100/5005]\tlr: 1.000e-03, eta: 4:23:06, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8753\n2023-01-04 23:28:05,906 - mmcls - INFO - Epoch [65][1200/5005]\tlr: 1.000e-03, eta: 4:22:57, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8878\n2023-01-04 23:28:14,551 - mmcls - INFO - Epoch [65][1300/5005]\tlr: 1.000e-03, eta: 4:22:48, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8942\n2023-01-04 23:28:23,196 - mmcls - INFO - Epoch [65][1400/5005]\tlr: 1.000e-03, eta: 4:22:39, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8966\n2023-01-04 23:28:31,713 - mmcls - INFO - Epoch [65][1500/5005]\tlr: 1.000e-03, eta: 4:22:30, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8843\n2023-01-04 23:28:40,654 - mmcls - INFO - Epoch [65][1600/5005]\tlr: 1.000e-03, eta: 4:22:21, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8917\n2023-01-04 23:28:49,353 - mmcls - INFO - Epoch [65][1700/5005]\tlr: 1.000e-03, eta: 4:22:12, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8737\n2023-01-04 23:28:57,960 - mmcls - INFO - Epoch [65][1800/5005]\tlr: 1.000e-03, eta: 4:22:03, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8759\n2023-01-04 23:29:06,884 - mmcls - INFO - Epoch [65][1900/5005]\tlr: 1.000e-03, eta: 4:21:55, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9071\n2023-01-04 23:29:15,705 - mmcls - INFO - Epoch [65][2000/5005]\tlr: 1.000e-03, eta: 4:21:46, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8794\n2023-01-04 23:29:24,856 - mmcls - INFO - Epoch [65][2100/5005]\tlr: 1.000e-03, eta: 4:21:37, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8597\n2023-01-04 23:29:33,348 - mmcls - INFO - Epoch [65][2200/5005]\tlr: 1.000e-03, eta: 4:21:28, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8604\n2023-01-04 23:29:41,963 - mmcls - INFO - Epoch [65][2300/5005]\tlr: 1.000e-03, eta: 4:21:19, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8828\n2023-01-04 23:29:50,852 - mmcls - INFO - Epoch [65][2400/5005]\tlr: 1.000e-03, eta: 4:21:10, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8934\n2023-01-04 23:29:59,569 - mmcls - INFO - Epoch [65][2500/5005]\tlr: 1.000e-03, eta: 4:21:02, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8687\n2023-01-04 23:30:08,514 - mmcls - INFO - Epoch [65][2600/5005]\tlr: 1.000e-03, eta: 4:20:53, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8959\n2023-01-04 23:30:16,994 - mmcls - INFO - Epoch [65][2700/5005]\tlr: 1.000e-03, eta: 4:20:44, time: 0.085, data_time: 0.018, memory: 928, loss: 4.8872\n2023-01-04 23:30:25,773 - mmcls - INFO - Epoch [65][2800/5005]\tlr: 1.000e-03, eta: 4:20:35, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8804\n2023-01-04 23:30:34,417 - mmcls - INFO - Epoch [65][2900/5005]\tlr: 1.000e-03, eta: 4:20:26, time: 0.086, data_time: 0.025, memory: 928, loss: 4.9052\n2023-01-04 23:30:42,944 - mmcls - INFO - Epoch [65][3000/5005]\tlr: 1.000e-03, eta: 4:20:17, time: 0.085, data_time: 0.014, memory: 928, loss: 4.8638\n2023-01-04 23:30:51,995 - mmcls - INFO - Epoch [65][3100/5005]\tlr: 1.000e-03, eta: 4:20:08, time: 0.091, data_time: 0.035, memory: 928, loss: 4.9013\n2023-01-04 23:31:00,748 - mmcls - INFO - Epoch [65][3200/5005]\tlr: 1.000e-03, eta: 4:20:00, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9178\n2023-01-04 23:31:09,687 - mmcls - INFO - Epoch [65][3300/5005]\tlr: 1.000e-03, eta: 4:19:51, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8575\n2023-01-04 23:31:18,257 - mmcls - INFO - Epoch [65][3400/5005]\tlr: 1.000e-03, eta: 4:19:42, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8720\n2023-01-04 23:31:26,936 - mmcls - INFO - Epoch [65][3500/5005]\tlr: 1.000e-03, eta: 4:19:33, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8856\n2023-01-04 23:31:35,682 - mmcls - INFO - Epoch [65][3600/5005]\tlr: 1.000e-03, eta: 4:19:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8886\n2023-01-04 23:31:44,478 - mmcls - INFO - Epoch [65][3700/5005]\tlr: 1.000e-03, eta: 4:19:15, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8651\n2023-01-04 23:31:53,266 - mmcls - INFO - Epoch [65][3800/5005]\tlr: 1.000e-03, eta: 4:19:06, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8934\n2023-01-04 23:32:02,047 - mmcls - INFO - Epoch [65][3900/5005]\tlr: 1.000e-03, eta: 4:18:58, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8680\n2023-01-04 23:32:10,707 - mmcls - INFO - Epoch [65][4000/5005]\tlr: 1.000e-03, eta: 4:18:49, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8643\n2023-01-04 23:32:19,718 - mmcls - INFO - Epoch [65][4100/5005]\tlr: 1.000e-03, eta: 4:18:40, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8962\n2023-01-04 23:32:28,406 - mmcls - INFO - Epoch [65][4200/5005]\tlr: 1.000e-03, eta: 4:18:31, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8790\n2023-01-04 23:32:37,150 - mmcls - INFO - Epoch [65][4300/5005]\tlr: 1.000e-03, eta: 4:18:22, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8984\n2023-01-04 23:32:45,694 - mmcls - INFO - Epoch [65][4400/5005]\tlr: 1.000e-03, eta: 4:18:13, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8740\n2023-01-04 23:32:54,333 - mmcls - INFO - Epoch [65][4500/5005]\tlr: 1.000e-03, eta: 4:18:04, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8624\n2023-01-04 23:33:03,153 - mmcls - INFO - Epoch [65][4600/5005]\tlr: 1.000e-03, eta: 4:17:56, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8888\n2023-01-04 23:33:12,209 - mmcls - INFO - Epoch [65][4700/5005]\tlr: 1.000e-03, eta: 4:17:47, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8910\n2023-01-04 23:33:20,859 - mmcls - INFO - Epoch [65][4800/5005]\tlr: 1.000e-03, eta: 4:17:38, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8707\n2023-01-04 23:33:29,749 - mmcls - INFO - Epoch [65][4900/5005]\tlr: 1.000e-03, eta: 4:17:29, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8810\n2023-01-04 23:33:39,091 - mmcls - INFO - Epoch [65][5000/5005]\tlr: 1.000e-03, eta: 4:17:21, time: 0.093, data_time: 0.001, memory: 928, loss: 4.9083\n2023-01-04 23:34:01,824 - mmcls - INFO - Epoch(val) [65][196]\taccuracy_top-1: 17.4340, accuracy_top-5: 35.4260\n2023-01-04 23:34:12,738 - mmcls - INFO - Epoch [66][100/5005]\tlr: 1.000e-03, eta: 4:17:12, time: 0.109, data_time: 0.044, memory: 928, loss: 4.8719\n2023-01-04 23:34:21,293 - mmcls - INFO - Epoch [66][200/5005]\tlr: 1.000e-03, eta: 4:17:03, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8626\n2023-01-04 23:34:30,296 - mmcls - INFO - Epoch [66][300/5005]\tlr: 1.000e-03, eta: 4:16:55, time: 0.090, data_time: 0.010, memory: 928, loss: 4.8619\n2023-01-04 23:34:38,681 - mmcls - INFO - Epoch [66][400/5005]\tlr: 1.000e-03, eta: 4:16:46, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8595\n2023-01-04 23:34:47,249 - mmcls - INFO - Epoch [66][500/5005]\tlr: 1.000e-03, eta: 4:16:37, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8764\n2023-01-04 23:34:56,521 - mmcls - INFO - Epoch [66][600/5005]\tlr: 1.000e-03, eta: 4:16:28, time: 0.093, data_time: 0.010, memory: 928, loss: 4.8638\n2023-01-04 23:35:05,277 - mmcls - INFO - Epoch [66][700/5005]\tlr: 1.000e-03, eta: 4:16:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8969\n2023-01-04 23:35:13,992 - mmcls - INFO - Epoch [66][800/5005]\tlr: 1.000e-03, eta: 4:16:10, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8657\n2023-01-04 23:35:22,820 - mmcls - INFO - Epoch [66][900/5005]\tlr: 1.000e-03, eta: 4:16:02, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8925\n2023-01-04 23:35:31,181 - mmcls - INFO - Epoch [66][1000/5005]\tlr: 1.000e-03, eta: 4:15:53, time: 0.083, data_time: 0.008, memory: 928, loss: 4.8843\n2023-01-04 23:35:39,947 - mmcls - INFO - Epoch [66][1100/5005]\tlr: 1.000e-03, eta: 4:15:44, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8858\n2023-01-04 23:35:48,760 - mmcls - INFO - Epoch [66][1200/5005]\tlr: 1.000e-03, eta: 4:15:35, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8557\n2023-01-04 23:35:57,382 - mmcls - INFO - Epoch [66][1300/5005]\tlr: 1.000e-03, eta: 4:15:26, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8619\n2023-01-04 23:36:06,102 - mmcls - INFO - Epoch [66][1400/5005]\tlr: 1.000e-03, eta: 4:15:17, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8702\n2023-01-04 23:36:15,183 - mmcls - INFO - Epoch [66][1500/5005]\tlr: 1.000e-03, eta: 4:15:08, time: 0.091, data_time: 0.015, memory: 928, loss: 4.8633\n2023-01-04 23:36:24,083 - mmcls - INFO - Epoch [66][1600/5005]\tlr: 1.000e-03, eta: 4:15:00, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8855\n2023-01-04 23:36:32,757 - mmcls - INFO - Epoch [66][1700/5005]\tlr: 1.000e-03, eta: 4:14:51, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8785\n2023-01-04 23:36:41,621 - mmcls - INFO - Epoch [66][1800/5005]\tlr: 1.000e-03, eta: 4:14:42, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8684\n2023-01-04 23:36:50,329 - mmcls - INFO - Epoch [66][1900/5005]\tlr: 1.000e-03, eta: 4:14:33, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8824\n2023-01-04 23:36:59,149 - mmcls - INFO - Epoch [66][2000/5005]\tlr: 1.000e-03, eta: 4:14:24, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8916\n2023-01-04 23:37:07,857 - mmcls - INFO - Epoch [66][2100/5005]\tlr: 1.000e-03, eta: 4:14:15, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8749\n2023-01-04 23:37:16,616 - mmcls - INFO - Epoch [66][2200/5005]\tlr: 1.000e-03, eta: 4:14:07, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8810\n2023-01-04 23:37:25,198 - mmcls - INFO - Epoch [66][2300/5005]\tlr: 1.000e-03, eta: 4:13:58, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8822\n2023-01-04 23:37:33,857 - mmcls - INFO - Epoch [66][2400/5005]\tlr: 1.000e-03, eta: 4:13:49, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8722\n2023-01-04 23:37:42,514 - mmcls - INFO - Epoch [66][2500/5005]\tlr: 1.000e-03, eta: 4:13:40, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8779\n2023-01-04 23:37:51,169 - mmcls - INFO - Epoch [66][2600/5005]\tlr: 1.000e-03, eta: 4:13:31, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8940\n2023-01-04 23:38:00,054 - mmcls - INFO - Epoch [66][2700/5005]\tlr: 1.000e-03, eta: 4:13:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8833\n2023-01-04 23:38:08,893 - mmcls - INFO - Epoch [66][2800/5005]\tlr: 1.000e-03, eta: 4:13:13, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8818\n2023-01-04 23:38:17,578 - mmcls - INFO - Epoch [66][2900/5005]\tlr: 1.000e-03, eta: 4:13:04, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8931\n2023-01-04 23:38:26,273 - mmcls - INFO - Epoch [66][3000/5005]\tlr: 1.000e-03, eta: 4:12:56, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8605\n2023-01-04 23:38:35,145 - mmcls - INFO - Epoch [66][3100/5005]\tlr: 1.000e-03, eta: 4:12:47, time: 0.089, data_time: 0.004, memory: 928, loss: 4.9019\n2023-01-04 23:38:43,777 - mmcls - INFO - Epoch [66][3200/5005]\tlr: 1.000e-03, eta: 4:12:38, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8786\n2023-01-04 23:38:52,430 - mmcls - INFO - Epoch [66][3300/5005]\tlr: 1.000e-03, eta: 4:12:29, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8830\n2023-01-04 23:39:01,089 - mmcls - INFO - Epoch [66][3400/5005]\tlr: 1.000e-03, eta: 4:12:20, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8768\n2023-01-04 23:39:10,060 - mmcls - INFO - Epoch [66][3500/5005]\tlr: 1.000e-03, eta: 4:12:11, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8914\n2023-01-04 23:39:18,922 - mmcls - INFO - Epoch [66][3600/5005]\tlr: 1.000e-03, eta: 4:12:03, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8860\n2023-01-04 23:39:27,410 - mmcls - INFO - Epoch [66][3700/5005]\tlr: 1.000e-03, eta: 4:11:54, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8650\n2023-01-04 23:39:36,420 - mmcls - INFO - Epoch [66][3800/5005]\tlr: 1.000e-03, eta: 4:11:45, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8998\n2023-01-04 23:39:45,278 - mmcls - INFO - Epoch [66][3900/5005]\tlr: 1.000e-03, eta: 4:11:36, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8663\n2023-01-04 23:39:54,178 - mmcls - INFO - Epoch [66][4000/5005]\tlr: 1.000e-03, eta: 4:11:27, time: 0.089, data_time: 0.004, memory: 928, loss: 4.9098\n2023-01-04 23:40:02,751 - mmcls - INFO - Epoch [66][4100/5005]\tlr: 1.000e-03, eta: 4:11:18, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8860\n2023-01-04 23:40:11,862 - mmcls - INFO - Epoch [66][4200/5005]\tlr: 1.000e-03, eta: 4:11:10, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8639\n2023-01-04 23:40:20,646 - mmcls - INFO - Epoch [66][4300/5005]\tlr: 1.000e-03, eta: 4:11:01, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8634\n2023-01-04 23:40:29,490 - mmcls - INFO - Epoch [66][4400/5005]\tlr: 1.000e-03, eta: 4:10:52, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8696\n2023-01-04 23:40:38,678 - mmcls - INFO - Epoch [66][4500/5005]\tlr: 1.000e-03, eta: 4:10:43, time: 0.092, data_time: 0.014, memory: 928, loss: 4.8733\n2023-01-04 23:40:47,783 - mmcls - INFO - Epoch [66][4600/5005]\tlr: 1.000e-03, eta: 4:10:35, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8837\n2023-01-04 23:40:56,307 - mmcls - INFO - Epoch [66][4700/5005]\tlr: 1.000e-03, eta: 4:10:26, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8904\n2023-01-04 23:41:05,031 - mmcls - INFO - Epoch [66][4800/5005]\tlr: 1.000e-03, eta: 4:10:17, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8559\n2023-01-04 23:41:13,939 - mmcls - INFO - Epoch [66][4900/5005]\tlr: 1.000e-03, eta: 4:10:08, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8859\n2023-01-04 23:41:22,699 - mmcls - INFO - Epoch [66][5000/5005]\tlr: 1.000e-03, eta: 4:09:59, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8721\n2023-01-04 23:41:45,506 - mmcls - INFO - Epoch(val) [66][196]\taccuracy_top-1: 17.7020, accuracy_top-5: 35.9180\n2023-01-04 23:41:56,346 - mmcls - INFO - Epoch [67][100/5005]\tlr: 1.000e-03, eta: 4:09:51, time: 0.108, data_time: 0.059, memory: 928, loss: 4.8816\n2023-01-04 23:42:05,168 - mmcls - INFO - Epoch [67][200/5005]\tlr: 1.000e-03, eta: 4:09:42, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8948\n2023-01-04 23:42:13,874 - mmcls - INFO - Epoch [67][300/5005]\tlr: 1.000e-03, eta: 4:09:33, time: 0.087, data_time: 0.030, memory: 928, loss: 4.8563\n2023-01-04 23:42:22,765 - mmcls - INFO - Epoch [67][400/5005]\tlr: 1.000e-03, eta: 4:09:24, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8880\n2023-01-04 23:42:31,323 - mmcls - INFO - Epoch [67][500/5005]\tlr: 1.000e-03, eta: 4:09:16, time: 0.085, data_time: 0.030, memory: 928, loss: 4.8686\n2023-01-04 23:42:40,109 - mmcls - INFO - Epoch [67][600/5005]\tlr: 1.000e-03, eta: 4:09:07, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8639\n2023-01-04 23:42:48,867 - mmcls - INFO - Epoch [67][700/5005]\tlr: 1.000e-03, eta: 4:08:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8596\n2023-01-04 23:42:57,669 - mmcls - INFO - Epoch [67][800/5005]\tlr: 1.000e-03, eta: 4:08:49, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8882\n2023-01-04 23:43:06,355 - mmcls - INFO - Epoch [67][900/5005]\tlr: 1.000e-03, eta: 4:08:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8748\n2023-01-04 23:43:14,985 - mmcls - INFO - Epoch [67][1000/5005]\tlr: 1.000e-03, eta: 4:08:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8598\n2023-01-04 23:43:24,135 - mmcls - INFO - Epoch [67][1100/5005]\tlr: 1.000e-03, eta: 4:08:23, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8664\n2023-01-04 23:43:33,471 - mmcls - INFO - Epoch [67][1200/5005]\tlr: 1.000e-03, eta: 4:08:14, time: 0.093, data_time: 0.005, memory: 928, loss: 4.8786\n2023-01-04 23:43:41,946 - mmcls - INFO - Epoch [67][1300/5005]\tlr: 1.000e-03, eta: 4:08:05, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8753\n2023-01-04 23:43:50,973 - mmcls - INFO - Epoch [67][1400/5005]\tlr: 1.000e-03, eta: 4:07:56, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8917\n2023-01-04 23:43:59,489 - mmcls - INFO - Epoch [67][1500/5005]\tlr: 1.000e-03, eta: 4:07:47, time: 0.085, data_time: 0.019, memory: 928, loss: 4.8910\n2023-01-04 23:44:08,337 - mmcls - INFO - Epoch [67][1600/5005]\tlr: 1.000e-03, eta: 4:07:39, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8889\n2023-01-04 23:44:16,907 - mmcls - INFO - Epoch [67][1700/5005]\tlr: 1.000e-03, eta: 4:07:30, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8941\n2023-01-04 23:44:25,353 - mmcls - INFO - Epoch [67][1800/5005]\tlr: 1.000e-03, eta: 4:07:21, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8872\n2023-01-04 23:44:34,319 - mmcls - INFO - Epoch [67][1900/5005]\tlr: 1.000e-03, eta: 4:07:12, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8920\n2023-01-04 23:44:42,949 - mmcls - INFO - Epoch [67][2000/5005]\tlr: 1.000e-03, eta: 4:07:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8658\n2023-01-04 23:44:51,755 - mmcls - INFO - Epoch [67][2100/5005]\tlr: 1.000e-03, eta: 4:06:54, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8720\n2023-01-04 23:45:00,416 - mmcls - INFO - Epoch [67][2200/5005]\tlr: 1.000e-03, eta: 4:06:45, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8641\n2023-01-04 23:45:09,122 - mmcls - INFO - Epoch [67][2300/5005]\tlr: 1.000e-03, eta: 4:06:36, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8848\n2023-01-04 23:45:17,658 - mmcls - INFO - Epoch [67][2400/5005]\tlr: 1.000e-03, eta: 4:06:27, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8987\n2023-01-04 23:45:26,817 - mmcls - INFO - Epoch [67][2500/5005]\tlr: 1.000e-03, eta: 4:06:19, time: 0.092, data_time: 0.031, memory: 928, loss: 4.8885\n2023-01-04 23:45:35,767 - mmcls - INFO - Epoch [67][2600/5005]\tlr: 1.000e-03, eta: 4:06:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8962\n2023-01-04 23:45:44,384 - mmcls - INFO - Epoch [67][2700/5005]\tlr: 1.000e-03, eta: 4:06:01, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8777\n2023-01-04 23:45:53,327 - mmcls - INFO - Epoch [67][2800/5005]\tlr: 1.000e-03, eta: 4:05:52, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8684\n2023-01-04 23:46:02,189 - mmcls - INFO - Epoch [67][2900/5005]\tlr: 1.000e-03, eta: 4:05:44, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9032\n2023-01-04 23:46:11,045 - mmcls - INFO - Epoch [67][3000/5005]\tlr: 1.000e-03, eta: 4:05:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8638\n2023-01-04 23:46:19,861 - mmcls - INFO - Epoch [67][3100/5005]\tlr: 1.000e-03, eta: 4:05:26, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8620\n2023-01-04 23:46:28,480 - mmcls - INFO - Epoch [67][3200/5005]\tlr: 1.000e-03, eta: 4:05:17, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8483\n2023-01-04 23:46:37,307 - mmcls - INFO - Epoch [67][3300/5005]\tlr: 1.000e-03, eta: 4:05:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8887\n2023-01-04 23:46:46,156 - mmcls - INFO - Epoch [67][3400/5005]\tlr: 1.000e-03, eta: 4:04:59, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8667\n2023-01-04 23:46:55,042 - mmcls - INFO - Epoch [67][3500/5005]\tlr: 1.000e-03, eta: 4:04:51, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8884\n2023-01-04 23:47:03,861 - mmcls - INFO - Epoch [67][3600/5005]\tlr: 1.000e-03, eta: 4:04:42, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8715\n2023-01-04 23:47:12,706 - mmcls - INFO - Epoch [67][3700/5005]\tlr: 1.000e-03, eta: 4:04:33, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8789\n2023-01-04 23:47:21,368 - mmcls - INFO - Epoch [67][3800/5005]\tlr: 1.000e-03, eta: 4:04:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8784\n2023-01-04 23:47:30,105 - mmcls - INFO - Epoch [67][3900/5005]\tlr: 1.000e-03, eta: 4:04:15, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8617\n2023-01-04 23:47:39,387 - mmcls - INFO - Epoch [67][4000/5005]\tlr: 1.000e-03, eta: 4:04:07, time: 0.093, data_time: 0.008, memory: 928, loss: 4.8880\n2023-01-04 23:47:48,280 - mmcls - INFO - Epoch [67][4100/5005]\tlr: 1.000e-03, eta: 4:03:58, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8820\n2023-01-04 23:47:57,419 - mmcls - INFO - Epoch [67][4200/5005]\tlr: 1.000e-03, eta: 4:03:49, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8721\n2023-01-04 23:48:05,945 - mmcls - INFO - Epoch [67][4300/5005]\tlr: 1.000e-03, eta: 4:03:40, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8834\n2023-01-04 23:48:14,474 - mmcls - INFO - Epoch [67][4400/5005]\tlr: 1.000e-03, eta: 4:03:31, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8897\n2023-01-04 23:48:23,418 - mmcls - INFO - Epoch [67][4500/5005]\tlr: 1.000e-03, eta: 4:03:23, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8939\n2023-01-04 23:48:32,531 - mmcls - INFO - Epoch [67][4600/5005]\tlr: 1.000e-03, eta: 4:03:14, time: 0.091, data_time: 0.022, memory: 928, loss: 4.8811\n2023-01-04 23:48:41,242 - mmcls - INFO - Epoch [67][4700/5005]\tlr: 1.000e-03, eta: 4:03:05, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8667\n2023-01-04 23:48:50,206 - mmcls - INFO - Epoch [67][4800/5005]\tlr: 1.000e-03, eta: 4:02:56, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8631\n2023-01-04 23:48:58,857 - mmcls - INFO - Epoch [67][4900/5005]\tlr: 1.000e-03, eta: 4:02:47, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8960\n2023-01-04 23:49:07,379 - mmcls - INFO - Epoch [67][5000/5005]\tlr: 1.000e-03, eta: 4:02:39, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8581\n2023-01-04 23:49:30,058 - mmcls - INFO - Epoch(val) [67][196]\taccuracy_top-1: 17.0160, accuracy_top-5: 34.7920\n2023-01-04 23:49:41,210 - mmcls - INFO - Epoch [68][100/5005]\tlr: 1.000e-03, eta: 4:02:30, time: 0.111, data_time: 0.032, memory: 928, loss: 4.8622\n2023-01-04 23:49:49,936 - mmcls - INFO - Epoch [68][200/5005]\tlr: 1.000e-03, eta: 4:02:21, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8635\n2023-01-04 23:49:58,705 - mmcls - INFO - Epoch [68][300/5005]\tlr: 1.000e-03, eta: 4:02:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8769\n2023-01-04 23:50:07,290 - mmcls - INFO - Epoch [68][400/5005]\tlr: 1.000e-03, eta: 4:02:04, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8922\n2023-01-04 23:50:16,427 - mmcls - INFO - Epoch [68][500/5005]\tlr: 1.000e-03, eta: 4:01:55, time: 0.091, data_time: 0.028, memory: 928, loss: 4.8619\n2023-01-04 23:50:25,106 - mmcls - INFO - Epoch [68][600/5005]\tlr: 1.000e-03, eta: 4:01:46, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8714\n2023-01-04 23:50:33,940 - mmcls - INFO - Epoch [68][700/5005]\tlr: 1.000e-03, eta: 4:01:37, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8811\n2023-01-04 23:50:42,712 - mmcls - INFO - Epoch [68][800/5005]\tlr: 1.000e-03, eta: 4:01:28, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8763\n2023-01-04 23:50:51,443 - mmcls - INFO - Epoch [68][900/5005]\tlr: 1.000e-03, eta: 4:01:20, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8823\n2023-01-04 23:51:00,214 - mmcls - INFO - Epoch [68][1000/5005]\tlr: 1.000e-03, eta: 4:01:11, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8799\n2023-01-04 23:51:09,010 - mmcls - INFO - Epoch [68][1100/5005]\tlr: 1.000e-03, eta: 4:01:02, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8813\n2023-01-04 23:51:17,728 - mmcls - INFO - Epoch [68][1200/5005]\tlr: 1.000e-03, eta: 4:00:53, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8923\n2023-01-04 23:51:26,446 - mmcls - INFO - Epoch [68][1300/5005]\tlr: 1.000e-03, eta: 4:00:44, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8537\n2023-01-04 23:51:35,344 - mmcls - INFO - Epoch [68][1400/5005]\tlr: 1.000e-03, eta: 4:00:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8500\n2023-01-04 23:51:44,174 - mmcls - INFO - Epoch [68][1500/5005]\tlr: 1.000e-03, eta: 4:00:27, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8816\n2023-01-04 23:51:52,951 - mmcls - INFO - Epoch [68][1600/5005]\tlr: 1.000e-03, eta: 4:00:18, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8681\n2023-01-04 23:52:02,093 - mmcls - INFO - Epoch [68][1700/5005]\tlr: 1.000e-03, eta: 4:00:09, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8773\n2023-01-04 23:52:10,774 - mmcls - INFO - Epoch [68][1800/5005]\tlr: 1.000e-03, eta: 4:00:00, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8730\n2023-01-04 23:52:19,276 - mmcls - INFO - Epoch [68][1900/5005]\tlr: 1.000e-03, eta: 3:59:51, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8962\n2023-01-04 23:52:28,587 - mmcls - INFO - Epoch [68][2000/5005]\tlr: 1.000e-03, eta: 3:59:43, time: 0.093, data_time: 0.015, memory: 928, loss: 4.8722\n2023-01-04 23:52:37,819 - mmcls - INFO - Epoch [68][2100/5005]\tlr: 1.000e-03, eta: 3:59:34, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8861\n2023-01-04 23:52:46,468 - mmcls - INFO - Epoch [68][2200/5005]\tlr: 1.000e-03, eta: 3:59:25, time: 0.086, data_time: 0.029, memory: 928, loss: 4.8775\n2023-01-04 23:52:55,131 - mmcls - INFO - Epoch [68][2300/5005]\tlr: 1.000e-03, eta: 3:59:16, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8888\n2023-01-04 23:53:04,126 - mmcls - INFO - Epoch [68][2400/5005]\tlr: 1.000e-03, eta: 3:59:08, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8787\n2023-01-04 23:53:12,679 - mmcls - INFO - Epoch [68][2500/5005]\tlr: 1.000e-03, eta: 3:58:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8574\n2023-01-04 23:53:21,310 - mmcls - INFO - Epoch [68][2600/5005]\tlr: 1.000e-03, eta: 3:58:50, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8665\n2023-01-04 23:53:30,154 - mmcls - INFO - Epoch [68][2700/5005]\tlr: 1.000e-03, eta: 3:58:41, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8714\n2023-01-04 23:53:38,599 - mmcls - INFO - Epoch [68][2800/5005]\tlr: 1.000e-03, eta: 3:58:32, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8776\n2023-01-04 23:53:47,360 - mmcls - INFO - Epoch [68][2900/5005]\tlr: 1.000e-03, eta: 3:58:23, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9054\n2023-01-04 23:53:56,108 - mmcls - INFO - Epoch [68][3000/5005]\tlr: 1.000e-03, eta: 3:58:14, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8674\n2023-01-04 23:54:05,001 - mmcls - INFO - Epoch [68][3100/5005]\tlr: 1.000e-03, eta: 3:58:05, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8663\n2023-01-04 23:54:13,696 - mmcls - INFO - Epoch [68][3200/5005]\tlr: 1.000e-03, eta: 3:57:57, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8473\n2023-01-04 23:54:22,486 - mmcls - INFO - Epoch [68][3300/5005]\tlr: 1.000e-03, eta: 3:57:48, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8792\n2023-01-04 23:54:31,398 - mmcls - INFO - Epoch [68][3400/5005]\tlr: 1.000e-03, eta: 3:57:39, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8593\n2023-01-04 23:54:40,000 - mmcls - INFO - Epoch [68][3500/5005]\tlr: 1.000e-03, eta: 3:57:30, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8625\n2023-01-04 23:54:48,645 - mmcls - INFO - Epoch [68][3600/5005]\tlr: 1.000e-03, eta: 3:57:21, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8958\n2023-01-04 23:54:57,245 - mmcls - INFO - Epoch [68][3700/5005]\tlr: 1.000e-03, eta: 3:57:12, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8881\n2023-01-04 23:55:05,844 - mmcls - INFO - Epoch [68][3800/5005]\tlr: 1.000e-03, eta: 3:57:03, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8873\n2023-01-04 23:55:14,881 - mmcls - INFO - Epoch [68][3900/5005]\tlr: 1.000e-03, eta: 3:56:55, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8843\n2023-01-04 23:55:23,500 - mmcls - INFO - Epoch [68][4000/5005]\tlr: 1.000e-03, eta: 3:56:46, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8890\n2023-01-04 23:55:32,287 - mmcls - INFO - Epoch [68][4100/5005]\tlr: 1.000e-03, eta: 3:56:37, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8805\n2023-01-04 23:55:40,880 - mmcls - INFO - Epoch [68][4200/5005]\tlr: 1.000e-03, eta: 3:56:28, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8844\n2023-01-04 23:55:49,411 - mmcls - INFO - Epoch [68][4300/5005]\tlr: 1.000e-03, eta: 3:56:19, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8895\n2023-01-04 23:55:58,194 - mmcls - INFO - Epoch [68][4400/5005]\tlr: 1.000e-03, eta: 3:56:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8899\n2023-01-04 23:56:07,164 - mmcls - INFO - Epoch [68][4500/5005]\tlr: 1.000e-03, eta: 3:56:01, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8638\n2023-01-04 23:56:15,691 - mmcls - INFO - Epoch [68][4600/5005]\tlr: 1.000e-03, eta: 3:55:52, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8600\n2023-01-04 23:56:24,460 - mmcls - INFO - Epoch [68][4700/5005]\tlr: 1.000e-03, eta: 3:55:44, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8878\n2023-01-04 23:56:33,249 - mmcls - INFO - Epoch [68][4800/5005]\tlr: 1.000e-03, eta: 3:55:35, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8801\n2023-01-04 23:56:42,020 - mmcls - INFO - Epoch [68][4900/5005]\tlr: 1.000e-03, eta: 3:55:26, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8783\n2023-01-04 23:56:51,069 - mmcls - INFO - Epoch [68][5000/5005]\tlr: 1.000e-03, eta: 3:55:17, time: 0.091, data_time: 0.008, memory: 928, loss: 4.8874\n2023-01-04 23:57:13,756 - mmcls - INFO - Epoch(val) [68][196]\taccuracy_top-1: 16.9000, accuracy_top-5: 34.5600\n2023-01-04 23:57:25,022 - mmcls - INFO - Epoch [69][100/5005]\tlr: 1.000e-03, eta: 3:55:09, time: 0.112, data_time: 0.041, memory: 928, loss: 4.8800\n2023-01-04 23:57:33,656 - mmcls - INFO - Epoch [69][200/5005]\tlr: 1.000e-03, eta: 3:55:00, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8672\n2023-01-04 23:57:42,348 - mmcls - INFO - Epoch [69][300/5005]\tlr: 1.000e-03, eta: 3:54:51, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8708\n2023-01-04 23:57:51,243 - mmcls - INFO - Epoch [69][400/5005]\tlr: 1.000e-03, eta: 3:54:42, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8780\n2023-01-04 23:58:00,122 - mmcls - INFO - Epoch [69][500/5005]\tlr: 1.000e-03, eta: 3:54:34, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8509\n2023-01-04 23:58:08,486 - mmcls - INFO - Epoch [69][600/5005]\tlr: 1.000e-03, eta: 3:54:25, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8737\n2023-01-04 23:58:17,205 - mmcls - INFO - Epoch [69][700/5005]\tlr: 1.000e-03, eta: 3:54:16, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8714\n2023-01-04 23:58:25,876 - mmcls - INFO - Epoch [69][800/5005]\tlr: 1.000e-03, eta: 3:54:07, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8936\n2023-01-04 23:58:34,593 - mmcls - INFO - Epoch [69][900/5005]\tlr: 1.000e-03, eta: 3:53:58, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8604\n2023-01-04 23:58:43,959 - mmcls - INFO - Epoch [69][1000/5005]\tlr: 1.000e-03, eta: 3:53:49, time: 0.094, data_time: 0.016, memory: 928, loss: 4.8431\n2023-01-04 23:58:52,753 - mmcls - INFO - Epoch [69][1100/5005]\tlr: 1.000e-03, eta: 3:53:41, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8885\n2023-01-04 23:59:01,692 - mmcls - INFO - Epoch [69][1200/5005]\tlr: 1.000e-03, eta: 3:53:32, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8380\n2023-01-04 23:59:10,304 - mmcls - INFO - Epoch [69][1300/5005]\tlr: 1.000e-03, eta: 3:53:23, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8660\n2023-01-04 23:59:18,763 - mmcls - INFO - Epoch [69][1400/5005]\tlr: 1.000e-03, eta: 3:53:14, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8692\n2023-01-04 23:59:27,413 - mmcls - INFO - Epoch [69][1500/5005]\tlr: 1.000e-03, eta: 3:53:05, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8889\n2023-01-04 23:59:36,641 - mmcls - INFO - Epoch [69][1600/5005]\tlr: 1.000e-03, eta: 3:52:56, time: 0.092, data_time: 0.017, memory: 928, loss: 4.8861\n2023-01-04 23:59:45,433 - mmcls - INFO - Epoch [69][1700/5005]\tlr: 1.000e-03, eta: 3:52:48, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8720\n2023-01-04 23:59:54,087 - mmcls - INFO - Epoch [69][1800/5005]\tlr: 1.000e-03, eta: 3:52:39, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8657\n2023-01-05 00:00:02,483 - mmcls - INFO - Epoch [69][1900/5005]\tlr: 1.000e-03, eta: 3:52:30, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8584\n2023-01-05 00:00:11,163 - mmcls - INFO - Epoch [69][2000/5005]\tlr: 1.000e-03, eta: 3:52:21, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8690\n2023-01-05 00:00:19,929 - mmcls - INFO - Epoch [69][2100/5005]\tlr: 1.000e-03, eta: 3:52:12, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8747\n2023-01-05 00:00:28,625 - mmcls - INFO - Epoch [69][2200/5005]\tlr: 1.000e-03, eta: 3:52:03, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8500\n2023-01-05 00:00:37,245 - mmcls - INFO - Epoch [69][2300/5005]\tlr: 1.000e-03, eta: 3:51:54, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8776\n2023-01-05 00:00:46,190 - mmcls - INFO - Epoch [69][2400/5005]\tlr: 1.000e-03, eta: 3:51:45, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8644\n2023-01-05 00:00:55,112 - mmcls - INFO - Epoch [69][2500/5005]\tlr: 1.000e-03, eta: 3:51:37, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8599\n2023-01-05 00:01:03,582 - mmcls - INFO - Epoch [69][2600/5005]\tlr: 1.000e-03, eta: 3:51:28, time: 0.085, data_time: 0.002, memory: 928, loss: 4.9178\n2023-01-05 00:01:12,304 - mmcls - INFO - Epoch [69][2700/5005]\tlr: 1.000e-03, eta: 3:51:19, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8607\n2023-01-05 00:01:21,374 - mmcls - INFO - Epoch [69][2800/5005]\tlr: 1.000e-03, eta: 3:51:10, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8790\n2023-01-05 00:01:30,004 - mmcls - INFO - Epoch [69][2900/5005]\tlr: 1.000e-03, eta: 3:51:01, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8949\n2023-01-05 00:01:38,617 - mmcls - INFO - Epoch [69][3000/5005]\tlr: 1.000e-03, eta: 3:50:52, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8941\n2023-01-05 00:01:47,489 - mmcls - INFO - Epoch [69][3100/5005]\tlr: 1.000e-03, eta: 3:50:44, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8734\n2023-01-05 00:01:56,164 - mmcls - INFO - Epoch [69][3200/5005]\tlr: 1.000e-03, eta: 3:50:35, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8704\n2023-01-05 00:02:05,053 - mmcls - INFO - Epoch [69][3300/5005]\tlr: 1.000e-03, eta: 3:50:26, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8813\n2023-01-05 00:02:14,371 - mmcls - INFO - Epoch [69][3400/5005]\tlr: 1.000e-03, eta: 3:50:17, time: 0.093, data_time: 0.001, memory: 928, loss: 4.8810\n2023-01-05 00:02:23,182 - mmcls - INFO - Epoch [69][3500/5005]\tlr: 1.000e-03, eta: 3:50:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8820\n2023-01-05 00:02:31,800 - mmcls - INFO - Epoch [69][3600/5005]\tlr: 1.000e-03, eta: 3:50:00, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8552\n2023-01-05 00:02:40,344 - mmcls - INFO - Epoch [69][3700/5005]\tlr: 1.000e-03, eta: 3:49:51, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8743\n2023-01-05 00:02:49,067 - mmcls - INFO - Epoch [69][3800/5005]\tlr: 1.000e-03, eta: 3:49:42, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8621\n2023-01-05 00:02:57,828 - mmcls - INFO - Epoch [69][3900/5005]\tlr: 1.000e-03, eta: 3:49:33, time: 0.087, data_time: 0.048, memory: 928, loss: 4.8747\n2023-01-05 00:03:06,437 - mmcls - INFO - Epoch [69][4000/5005]\tlr: 1.000e-03, eta: 3:49:24, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8791\n2023-01-05 00:03:15,316 - mmcls - INFO - Epoch [69][4100/5005]\tlr: 1.000e-03, eta: 3:49:15, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8846\n2023-01-05 00:03:23,871 - mmcls - INFO - Epoch [69][4200/5005]\tlr: 1.000e-03, eta: 3:49:06, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8583\n2023-01-05 00:03:32,641 - mmcls - INFO - Epoch [69][4300/5005]\tlr: 1.000e-03, eta: 3:48:57, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8596\n2023-01-05 00:03:41,518 - mmcls - INFO - Epoch [69][4400/5005]\tlr: 1.000e-03, eta: 3:48:49, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8673\n2023-01-05 00:03:50,233 - mmcls - INFO - Epoch [69][4500/5005]\tlr: 1.000e-03, eta: 3:48:40, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8681\n2023-01-05 00:03:58,860 - mmcls - INFO - Epoch [69][4600/5005]\tlr: 1.000e-03, eta: 3:48:31, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8951\n2023-01-05 00:04:07,620 - mmcls - INFO - Epoch [69][4700/5005]\tlr: 1.000e-03, eta: 3:48:22, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8700\n2023-01-05 00:04:16,518 - mmcls - INFO - Epoch [69][4800/5005]\tlr: 1.000e-03, eta: 3:48:13, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8707\n2023-01-05 00:04:24,964 - mmcls - INFO - Epoch [69][4900/5005]\tlr: 1.000e-03, eta: 3:48:04, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8905\n2023-01-05 00:04:33,658 - mmcls - INFO - Epoch [69][5000/5005]\tlr: 1.000e-03, eta: 3:47:55, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8730\n2023-01-05 00:04:56,340 - mmcls - INFO - Epoch(val) [69][196]\taccuracy_top-1: 16.9820, accuracy_top-5: 34.5400\n2023-01-05 00:05:07,324 - mmcls - INFO - Epoch [70][100/5005]\tlr: 1.000e-03, eta: 3:47:47, time: 0.110, data_time: 0.035, memory: 928, loss: 4.8873\n2023-01-05 00:05:15,972 - mmcls - INFO - Epoch [70][200/5005]\tlr: 1.000e-03, eta: 3:47:38, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8527\n2023-01-05 00:05:24,888 - mmcls - INFO - Epoch [70][300/5005]\tlr: 1.000e-03, eta: 3:47:29, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8633\n2023-01-05 00:05:33,847 - mmcls - INFO - Epoch [70][400/5005]\tlr: 1.000e-03, eta: 3:47:21, time: 0.090, data_time: 0.018, memory: 928, loss: 4.8507\n2023-01-05 00:05:42,554 - mmcls - INFO - Epoch [70][500/5005]\tlr: 1.000e-03, eta: 3:47:12, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8924\n2023-01-05 00:05:51,301 - mmcls - INFO - Epoch [70][600/5005]\tlr: 1.000e-03, eta: 3:47:03, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8713\n2023-01-05 00:05:59,797 - mmcls - INFO - Epoch [70][700/5005]\tlr: 1.000e-03, eta: 3:46:54, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8692\n2023-01-05 00:06:08,550 - mmcls - INFO - Epoch [70][800/5005]\tlr: 1.000e-03, eta: 3:46:45, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8720\n2023-01-05 00:06:17,063 - mmcls - INFO - Epoch [70][900/5005]\tlr: 1.000e-03, eta: 3:46:36, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8778\n2023-01-05 00:06:25,666 - mmcls - INFO - Epoch [70][1000/5005]\tlr: 1.000e-03, eta: 3:46:27, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8837\n2023-01-05 00:06:34,636 - mmcls - INFO - Epoch [70][1100/5005]\tlr: 1.000e-03, eta: 3:46:18, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8838\n2023-01-05 00:06:43,523 - mmcls - INFO - Epoch [70][1200/5005]\tlr: 1.000e-03, eta: 3:46:10, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8657\n2023-01-05 00:06:52,197 - mmcls - INFO - Epoch [70][1300/5005]\tlr: 1.000e-03, eta: 3:46:01, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8582\n2023-01-05 00:07:00,916 - mmcls - INFO - Epoch [70][1400/5005]\tlr: 1.000e-03, eta: 3:45:52, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8710\n2023-01-05 00:07:09,919 - mmcls - INFO - Epoch [70][1500/5005]\tlr: 1.000e-03, eta: 3:45:43, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8698\n2023-01-05 00:07:18,605 - mmcls - INFO - Epoch [70][1600/5005]\tlr: 1.000e-03, eta: 3:45:34, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8633\n2023-01-05 00:07:27,291 - mmcls - INFO - Epoch [70][1700/5005]\tlr: 1.000e-03, eta: 3:45:25, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8965\n2023-01-05 00:07:35,991 - mmcls - INFO - Epoch [70][1800/5005]\tlr: 1.000e-03, eta: 3:45:17, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8596\n2023-01-05 00:07:44,851 - mmcls - INFO - Epoch [70][1900/5005]\tlr: 1.000e-03, eta: 3:45:08, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8913\n2023-01-05 00:07:53,910 - mmcls - INFO - Epoch [70][2000/5005]\tlr: 1.000e-03, eta: 3:44:59, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8542\n2023-01-05 00:08:02,666 - mmcls - INFO - Epoch [70][2100/5005]\tlr: 1.000e-03, eta: 3:44:50, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8681\n2023-01-05 00:08:11,492 - mmcls - INFO - Epoch [70][2200/5005]\tlr: 1.000e-03, eta: 3:44:41, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8940\n2023-01-05 00:08:20,208 - mmcls - INFO - Epoch [70][2300/5005]\tlr: 1.000e-03, eta: 3:44:33, time: 0.087, data_time: 0.000, memory: 928, loss: 4.8851\n2023-01-05 00:08:29,118 - mmcls - INFO - Epoch [70][2400/5005]\tlr: 1.000e-03, eta: 3:44:24, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8535\n2023-01-05 00:08:37,870 - mmcls - INFO - Epoch [70][2500/5005]\tlr: 1.000e-03, eta: 3:44:15, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8747\n2023-01-05 00:08:46,349 - mmcls - INFO - Epoch [70][2600/5005]\tlr: 1.000e-03, eta: 3:44:06, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8675\n2023-01-05 00:08:55,106 - mmcls - INFO - Epoch [70][2700/5005]\tlr: 1.000e-03, eta: 3:43:57, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8685\n2023-01-05 00:09:03,838 - mmcls - INFO - Epoch [70][2800/5005]\tlr: 1.000e-03, eta: 3:43:48, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8689\n2023-01-05 00:09:12,626 - mmcls - INFO - Epoch [70][2900/5005]\tlr: 1.000e-03, eta: 3:43:40, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8740\n2023-01-05 00:09:21,495 - mmcls - INFO - Epoch [70][3000/5005]\tlr: 1.000e-03, eta: 3:43:31, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8864\n2023-01-05 00:09:30,403 - mmcls - INFO - Epoch [70][3100/5005]\tlr: 1.000e-03, eta: 3:43:22, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8725\n2023-01-05 00:09:39,212 - mmcls - INFO - Epoch [70][3200/5005]\tlr: 1.000e-03, eta: 3:43:13, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8650\n2023-01-05 00:09:47,811 - mmcls - INFO - Epoch [70][3300/5005]\tlr: 1.000e-03, eta: 3:43:04, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8784\n2023-01-05 00:09:56,638 - mmcls - INFO - Epoch [70][3400/5005]\tlr: 1.000e-03, eta: 3:42:55, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9096\n2023-01-05 00:10:05,851 - mmcls - INFO - Epoch [70][3500/5005]\tlr: 1.000e-03, eta: 3:42:47, time: 0.092, data_time: 0.018, memory: 928, loss: 4.8744\n2023-01-05 00:10:14,694 - mmcls - INFO - Epoch [70][3600/5005]\tlr: 1.000e-03, eta: 3:42:38, time: 0.088, data_time: 0.033, memory: 928, loss: 4.8656\n2023-01-05 00:10:23,899 - mmcls - INFO - Epoch [70][3700/5005]\tlr: 1.000e-03, eta: 3:42:29, time: 0.092, data_time: 0.011, memory: 928, loss: 4.8701\n2023-01-05 00:10:32,683 - mmcls - INFO - Epoch [70][3800/5005]\tlr: 1.000e-03, eta: 3:42:21, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8555\n2023-01-05 00:10:41,332 - mmcls - INFO - Epoch [70][3900/5005]\tlr: 1.000e-03, eta: 3:42:12, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8985\n2023-01-05 00:10:50,274 - mmcls - INFO - Epoch [70][4000/5005]\tlr: 1.000e-03, eta: 3:42:03, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8728\n2023-01-05 00:10:59,082 - mmcls - INFO - Epoch [70][4100/5005]\tlr: 1.000e-03, eta: 3:41:54, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8810\n2023-01-05 00:11:07,953 - mmcls - INFO - Epoch [70][4200/5005]\tlr: 1.000e-03, eta: 3:41:45, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8775\n2023-01-05 00:11:16,807 - mmcls - INFO - Epoch [70][4300/5005]\tlr: 1.000e-03, eta: 3:41:36, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8780\n2023-01-05 00:11:25,463 - mmcls - INFO - Epoch [70][4400/5005]\tlr: 1.000e-03, eta: 3:41:28, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8782\n2023-01-05 00:11:34,051 - mmcls - INFO - Epoch [70][4500/5005]\tlr: 1.000e-03, eta: 3:41:19, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8369\n2023-01-05 00:11:42,897 - mmcls - INFO - Epoch [70][4600/5005]\tlr: 1.000e-03, eta: 3:41:10, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8753\n2023-01-05 00:11:51,581 - mmcls - INFO - Epoch [70][4700/5005]\tlr: 1.000e-03, eta: 3:41:01, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8615\n2023-01-05 00:12:00,386 - mmcls - INFO - Epoch [70][4800/5005]\tlr: 1.000e-03, eta: 3:40:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8493\n2023-01-05 00:12:09,267 - mmcls - INFO - Epoch [70][4900/5005]\tlr: 1.000e-03, eta: 3:40:43, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8460\n2023-01-05 00:12:17,846 - mmcls - INFO - Epoch [70][5000/5005]\tlr: 1.000e-03, eta: 3:40:34, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8738\n2023-01-05 00:12:40,679 - mmcls - INFO - Epoch(val) [70][196]\taccuracy_top-1: 17.3400, accuracy_top-5: 35.3940\n2023-01-05 00:12:51,694 - mmcls - INFO - Epoch [71][100/5005]\tlr: 1.000e-03, eta: 3:40:26, time: 0.110, data_time: 0.056, memory: 928, loss: 4.8616\n2023-01-05 00:13:00,373 - mmcls - INFO - Epoch [71][200/5005]\tlr: 1.000e-03, eta: 3:40:17, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8524\n2023-01-05 00:13:09,257 - mmcls - INFO - Epoch [71][300/5005]\tlr: 1.000e-03, eta: 3:40:08, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9004\n2023-01-05 00:13:17,864 - mmcls - INFO - Epoch [71][400/5005]\tlr: 1.000e-03, eta: 3:39:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8683\n2023-01-05 00:13:26,645 - mmcls - INFO - Epoch [71][500/5005]\tlr: 1.000e-03, eta: 3:39:51, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8562\n2023-01-05 00:13:35,302 - mmcls - INFO - Epoch [71][600/5005]\tlr: 1.000e-03, eta: 3:39:42, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8668\n2023-01-05 00:13:44,070 - mmcls - INFO - Epoch [71][700/5005]\tlr: 1.000e-03, eta: 3:39:33, time: 0.088, data_time: 0.000, memory: 928, loss: 4.8796\n2023-01-05 00:13:52,952 - mmcls - INFO - Epoch [71][800/5005]\tlr: 1.000e-03, eta: 3:39:24, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8640\n2023-01-05 00:14:01,799 - mmcls - INFO - Epoch [71][900/5005]\tlr: 1.000e-03, eta: 3:39:15, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8672\n2023-01-05 00:14:10,391 - mmcls - INFO - Epoch [71][1000/5005]\tlr: 1.000e-03, eta: 3:39:06, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8831\n2023-01-05 00:14:19,151 - mmcls - INFO - Epoch [71][1100/5005]\tlr: 1.000e-03, eta: 3:38:58, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8725\n2023-01-05 00:14:28,175 - mmcls - INFO - Epoch [71][1200/5005]\tlr: 1.000e-03, eta: 3:38:49, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8448\n2023-01-05 00:14:37,008 - mmcls - INFO - Epoch [71][1300/5005]\tlr: 1.000e-03, eta: 3:38:40, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8750\n2023-01-05 00:14:45,955 - mmcls - INFO - Epoch [71][1400/5005]\tlr: 1.000e-03, eta: 3:38:31, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8685\n2023-01-05 00:14:55,157 - mmcls - INFO - Epoch [71][1500/5005]\tlr: 1.000e-03, eta: 3:38:23, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8683\n2023-01-05 00:15:03,813 - mmcls - INFO - Epoch [71][1600/5005]\tlr: 1.000e-03, eta: 3:38:14, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8777\n2023-01-05 00:15:12,476 - mmcls - INFO - Epoch [71][1700/5005]\tlr: 1.000e-03, eta: 3:38:05, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8487\n2023-01-05 00:15:21,545 - mmcls - INFO - Epoch [71][1800/5005]\tlr: 1.000e-03, eta: 3:37:56, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8679\n2023-01-05 00:15:30,241 - mmcls - INFO - Epoch [71][1900/5005]\tlr: 1.000e-03, eta: 3:37:47, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8487\n2023-01-05 00:15:39,010 - mmcls - INFO - Epoch [71][2000/5005]\tlr: 1.000e-03, eta: 3:37:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8539\n2023-01-05 00:15:47,698 - mmcls - INFO - Epoch [71][2100/5005]\tlr: 1.000e-03, eta: 3:37:30, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8679\n2023-01-05 00:15:56,695 - mmcls - INFO - Epoch [71][2200/5005]\tlr: 1.000e-03, eta: 3:37:21, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8665\n2023-01-05 00:16:05,537 - mmcls - INFO - Epoch [71][2300/5005]\tlr: 1.000e-03, eta: 3:37:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8411\n2023-01-05 00:16:14,011 - mmcls - INFO - Epoch [71][2400/5005]\tlr: 1.000e-03, eta: 3:37:03, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8707\n2023-01-05 00:16:22,753 - mmcls - INFO - Epoch [71][2500/5005]\tlr: 1.000e-03, eta: 3:36:54, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8691\n2023-01-05 00:16:31,792 - mmcls - INFO - Epoch [71][2600/5005]\tlr: 1.000e-03, eta: 3:36:45, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8571\n2023-01-05 00:16:40,500 - mmcls - INFO - Epoch [71][2700/5005]\tlr: 1.000e-03, eta: 3:36:37, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8647\n2023-01-05 00:16:49,553 - mmcls - INFO - Epoch [71][2800/5005]\tlr: 1.000e-03, eta: 3:36:28, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8495\n2023-01-05 00:16:58,200 - mmcls - INFO - Epoch [71][2900/5005]\tlr: 1.000e-03, eta: 3:36:19, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8764\n2023-01-05 00:17:07,008 - mmcls - INFO - Epoch [71][3000/5005]\tlr: 1.000e-03, eta: 3:36:10, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8741\n2023-01-05 00:17:15,776 - mmcls - INFO - Epoch [71][3100/5005]\tlr: 1.000e-03, eta: 3:36:01, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8768\n2023-01-05 00:17:24,575 - mmcls - INFO - Epoch [71][3200/5005]\tlr: 1.000e-03, eta: 3:35:53, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8824\n2023-01-05 00:17:33,804 - mmcls - INFO - Epoch [71][3300/5005]\tlr: 1.000e-03, eta: 3:35:44, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8552\n2023-01-05 00:17:42,909 - mmcls - INFO - Epoch [71][3400/5005]\tlr: 1.000e-03, eta: 3:35:35, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8745\n2023-01-05 00:17:51,721 - mmcls - INFO - Epoch [71][3500/5005]\tlr: 1.000e-03, eta: 3:35:26, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8744\n2023-01-05 00:18:00,268 - mmcls - INFO - Epoch [71][3600/5005]\tlr: 1.000e-03, eta: 3:35:17, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8521\n2023-01-05 00:18:08,920 - mmcls - INFO - Epoch [71][3700/5005]\tlr: 1.000e-03, eta: 3:35:09, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8563\n2023-01-05 00:18:17,823 - mmcls - INFO - Epoch [71][3800/5005]\tlr: 1.000e-03, eta: 3:35:00, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8897\n2023-01-05 00:18:26,251 - mmcls - INFO - Epoch [71][3900/5005]\tlr: 1.000e-03, eta: 3:34:51, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8614\n2023-01-05 00:18:34,914 - mmcls - INFO - Epoch [71][4000/5005]\tlr: 1.000e-03, eta: 3:34:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8802\n2023-01-05 00:18:43,983 - mmcls - INFO - Epoch [71][4100/5005]\tlr: 1.000e-03, eta: 3:34:33, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8715\n2023-01-05 00:18:52,679 - mmcls - INFO - Epoch [71][4200/5005]\tlr: 1.000e-03, eta: 3:34:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8739\n2023-01-05 00:19:01,360 - mmcls - INFO - Epoch [71][4300/5005]\tlr: 1.000e-03, eta: 3:34:15, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8918\n2023-01-05 00:19:10,101 - mmcls - INFO - Epoch [71][4400/5005]\tlr: 1.000e-03, eta: 3:34:07, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8827\n2023-01-05 00:19:18,574 - mmcls - INFO - Epoch [71][4500/5005]\tlr: 1.000e-03, eta: 3:33:58, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8876\n2023-01-05 00:19:27,760 - mmcls - INFO - Epoch [71][4600/5005]\tlr: 1.000e-03, eta: 3:33:49, time: 0.092, data_time: 0.008, memory: 928, loss: 4.8749\n2023-01-05 00:19:36,381 - mmcls - INFO - Epoch [71][4700/5005]\tlr: 1.000e-03, eta: 3:33:40, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8509\n2023-01-05 00:19:45,093 - mmcls - INFO - Epoch [71][4800/5005]\tlr: 1.000e-03, eta: 3:33:31, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8691\n2023-01-05 00:19:53,904 - mmcls - INFO - Epoch [71][4900/5005]\tlr: 1.000e-03, eta: 3:33:22, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8734\n2023-01-05 00:20:02,691 - mmcls - INFO - Epoch [71][5000/5005]\tlr: 1.000e-03, eta: 3:33:14, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8400\n2023-01-05 00:20:25,525 - mmcls - INFO - Epoch(val) [71][196]\taccuracy_top-1: 16.8620, accuracy_top-5: 34.5980\n2023-01-05 00:20:36,519 - mmcls - INFO - Epoch [72][100/5005]\tlr: 1.000e-03, eta: 3:33:05, time: 0.110, data_time: 0.048, memory: 928, loss: 4.8613\n2023-01-05 00:20:45,421 - mmcls - INFO - Epoch [72][200/5005]\tlr: 1.000e-03, eta: 3:32:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8537\n2023-01-05 00:20:54,185 - mmcls - INFO - Epoch [72][300/5005]\tlr: 1.000e-03, eta: 3:32:47, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8569\n2023-01-05 00:21:02,856 - mmcls - INFO - Epoch [72][400/5005]\tlr: 1.000e-03, eta: 3:32:39, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8578\n2023-01-05 00:21:11,259 - mmcls - INFO - Epoch [72][500/5005]\tlr: 1.000e-03, eta: 3:32:30, time: 0.084, data_time: 0.008, memory: 928, loss: 4.8873\n2023-01-05 00:21:20,141 - mmcls - INFO - Epoch [72][600/5005]\tlr: 1.000e-03, eta: 3:32:21, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8587\n2023-01-05 00:21:28,895 - mmcls - INFO - Epoch [72][700/5005]\tlr: 1.000e-03, eta: 3:32:12, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8640\n2023-01-05 00:21:37,596 - mmcls - INFO - Epoch [72][800/5005]\tlr: 1.000e-03, eta: 3:32:03, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8776\n2023-01-05 00:21:46,372 - mmcls - INFO - Epoch [72][900/5005]\tlr: 1.000e-03, eta: 3:31:54, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8651\n2023-01-05 00:21:55,139 - mmcls - INFO - Epoch [72][1000/5005]\tlr: 1.000e-03, eta: 3:31:45, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8300\n2023-01-05 00:22:03,839 - mmcls - INFO - Epoch [72][1100/5005]\tlr: 1.000e-03, eta: 3:31:37, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8716\n2023-01-05 00:22:12,625 - mmcls - INFO - Epoch [72][1200/5005]\tlr: 1.000e-03, eta: 3:31:28, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8676\n2023-01-05 00:22:21,332 - mmcls - INFO - Epoch [72][1300/5005]\tlr: 1.000e-03, eta: 3:31:19, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8464\n2023-01-05 00:22:30,144 - mmcls - INFO - Epoch [72][1400/5005]\tlr: 1.000e-03, eta: 3:31:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8741\n2023-01-05 00:22:38,833 - mmcls - INFO - Epoch [72][1500/5005]\tlr: 1.000e-03, eta: 3:31:01, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8649\n2023-01-05 00:22:47,583 - mmcls - INFO - Epoch [72][1600/5005]\tlr: 1.000e-03, eta: 3:30:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8866\n2023-01-05 00:22:56,293 - mmcls - INFO - Epoch [72][1700/5005]\tlr: 1.000e-03, eta: 3:30:44, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8595\n2023-01-05 00:23:04,805 - mmcls - INFO - Epoch [72][1800/5005]\tlr: 1.000e-03, eta: 3:30:35, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8892\n2023-01-05 00:23:13,655 - mmcls - INFO - Epoch [72][1900/5005]\tlr: 1.000e-03, eta: 3:30:26, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8733\n2023-01-05 00:23:22,272 - mmcls - INFO - Epoch [72][2000/5005]\tlr: 1.000e-03, eta: 3:30:17, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8778\n2023-01-05 00:23:31,029 - mmcls - INFO - Epoch [72][2100/5005]\tlr: 1.000e-03, eta: 3:30:08, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8713\n2023-01-05 00:23:39,661 - mmcls - INFO - Epoch [72][2200/5005]\tlr: 1.000e-03, eta: 3:29:59, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8775\n2023-01-05 00:23:48,588 - mmcls - INFO - Epoch [72][2300/5005]\tlr: 1.000e-03, eta: 3:29:50, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8738\n2023-01-05 00:23:57,330 - mmcls - INFO - Epoch [72][2400/5005]\tlr: 1.000e-03, eta: 3:29:42, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8711\n2023-01-05 00:24:06,308 - mmcls - INFO - Epoch [72][2500/5005]\tlr: 1.000e-03, eta: 3:29:33, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8783\n2023-01-05 00:24:14,901 - mmcls - INFO - Epoch [72][2600/5005]\tlr: 1.000e-03, eta: 3:29:24, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8817\n2023-01-05 00:24:23,425 - mmcls - INFO - Epoch [72][2700/5005]\tlr: 1.000e-03, eta: 3:29:15, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8786\n2023-01-05 00:24:32,321 - mmcls - INFO - Epoch [72][2800/5005]\tlr: 1.000e-03, eta: 3:29:06, time: 0.089, data_time: 0.039, memory: 928, loss: 4.8602\n2023-01-05 00:24:41,052 - mmcls - INFO - Epoch [72][2900/5005]\tlr: 1.000e-03, eta: 3:28:57, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8585\n2023-01-05 00:24:50,101 - mmcls - INFO - Epoch [72][3000/5005]\tlr: 1.000e-03, eta: 3:28:49, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8557\n2023-01-05 00:24:58,808 - mmcls - INFO - Epoch [72][3100/5005]\tlr: 1.000e-03, eta: 3:28:40, time: 0.087, data_time: 0.025, memory: 928, loss: 4.8677\n2023-01-05 00:25:07,422 - mmcls - INFO - Epoch [72][3200/5005]\tlr: 1.000e-03, eta: 3:28:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8699\n2023-01-05 00:25:16,485 - mmcls - INFO - Epoch [72][3300/5005]\tlr: 1.000e-03, eta: 3:28:22, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8788\n2023-01-05 00:25:25,185 - mmcls - INFO - Epoch [72][3400/5005]\tlr: 1.000e-03, eta: 3:28:13, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8840\n2023-01-05 00:25:34,054 - mmcls - INFO - Epoch [72][3500/5005]\tlr: 1.000e-03, eta: 3:28:05, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8655\n2023-01-05 00:25:42,674 - mmcls - INFO - Epoch [72][3600/5005]\tlr: 1.000e-03, eta: 3:27:56, time: 0.086, data_time: 0.035, memory: 928, loss: 4.8585\n2023-01-05 00:25:51,519 - mmcls - INFO - Epoch [72][3700/5005]\tlr: 1.000e-03, eta: 3:27:47, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8454\n2023-01-05 00:26:00,101 - mmcls - INFO - Epoch [72][3800/5005]\tlr: 1.000e-03, eta: 3:27:38, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8555\n2023-01-05 00:26:08,834 - mmcls - INFO - Epoch [72][3900/5005]\tlr: 1.000e-03, eta: 3:27:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8716\n2023-01-05 00:26:17,600 - mmcls - INFO - Epoch [72][4000/5005]\tlr: 1.000e-03, eta: 3:27:20, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8546\n2023-01-05 00:26:26,790 - mmcls - INFO - Epoch [72][4100/5005]\tlr: 1.000e-03, eta: 3:27:12, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8682\n2023-01-05 00:26:35,535 - mmcls - INFO - Epoch [72][4200/5005]\tlr: 1.000e-03, eta: 3:27:03, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8605\n2023-01-05 00:26:44,153 - mmcls - INFO - Epoch [72][4300/5005]\tlr: 1.000e-03, eta: 3:26:54, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8543\n2023-01-05 00:26:52,790 - mmcls - INFO - Epoch [72][4400/5005]\tlr: 1.000e-03, eta: 3:26:45, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8461\n2023-01-05 00:27:01,461 - mmcls - INFO - Epoch [72][4500/5005]\tlr: 1.000e-03, eta: 3:26:36, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8723\n2023-01-05 00:27:10,277 - mmcls - INFO - Epoch [72][4600/5005]\tlr: 1.000e-03, eta: 3:26:27, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8553\n2023-01-05 00:27:18,981 - mmcls - INFO - Epoch [72][4700/5005]\tlr: 1.000e-03, eta: 3:26:18, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8650\n2023-01-05 00:27:27,352 - mmcls - INFO - Epoch [72][4800/5005]\tlr: 1.000e-03, eta: 3:26:09, time: 0.084, data_time: 0.012, memory: 928, loss: 4.8635\n2023-01-05 00:27:36,004 - mmcls - INFO - Epoch [72][4900/5005]\tlr: 1.000e-03, eta: 3:26:01, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8662\n2023-01-05 00:27:44,521 - mmcls - INFO - Epoch [72][5000/5005]\tlr: 1.000e-03, eta: 3:25:52, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8865\n2023-01-05 00:28:07,186 - mmcls - INFO - Epoch(val) [72][196]\taccuracy_top-1: 16.0420, accuracy_top-5: 33.1360\n2023-01-05 00:28:18,233 - mmcls - INFO - Epoch [73][100/5005]\tlr: 1.000e-03, eta: 3:25:43, time: 0.110, data_time: 0.044, memory: 928, loss: 4.8622\n2023-01-05 00:28:26,837 - mmcls - INFO - Epoch [73][200/5005]\tlr: 1.000e-03, eta: 3:25:34, time: 0.086, data_time: 0.032, memory: 928, loss: 4.8472\n2023-01-05 00:28:35,647 - mmcls - INFO - Epoch [73][300/5005]\tlr: 1.000e-03, eta: 3:25:25, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8675\n2023-01-05 00:28:44,336 - mmcls - INFO - Epoch [73][400/5005]\tlr: 1.000e-03, eta: 3:25:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8541\n2023-01-05 00:28:52,841 - mmcls - INFO - Epoch [73][500/5005]\tlr: 1.000e-03, eta: 3:25:08, time: 0.085, data_time: 0.014, memory: 928, loss: 4.8728\n2023-01-05 00:29:01,827 - mmcls - INFO - Epoch [73][600/5005]\tlr: 1.000e-03, eta: 3:24:59, time: 0.090, data_time: 0.011, memory: 928, loss: 4.8832\n2023-01-05 00:29:10,613 - mmcls - INFO - Epoch [73][700/5005]\tlr: 1.000e-03, eta: 3:24:50, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8476\n2023-01-05 00:29:19,585 - mmcls - INFO - Epoch [73][800/5005]\tlr: 1.000e-03, eta: 3:24:41, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8668\n2023-01-05 00:29:28,126 - mmcls - INFO - Epoch [73][900/5005]\tlr: 1.000e-03, eta: 3:24:32, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8873\n2023-01-05 00:29:36,785 - mmcls - INFO - Epoch [73][1000/5005]\tlr: 1.000e-03, eta: 3:24:23, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8562\n2023-01-05 00:29:45,480 - mmcls - INFO - Epoch [73][1100/5005]\tlr: 1.000e-03, eta: 3:24:15, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8601\n2023-01-05 00:29:53,894 - mmcls - INFO - Epoch [73][1200/5005]\tlr: 1.000e-03, eta: 3:24:06, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8552\n2023-01-05 00:30:02,849 - mmcls - INFO - Epoch [73][1300/5005]\tlr: 1.000e-03, eta: 3:23:57, time: 0.090, data_time: 0.038, memory: 928, loss: 4.8576\n2023-01-05 00:30:11,542 - mmcls - INFO - Epoch [73][1400/5005]\tlr: 1.000e-03, eta: 3:23:48, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8810\n2023-01-05 00:30:20,106 - mmcls - INFO - Epoch [73][1500/5005]\tlr: 1.000e-03, eta: 3:23:39, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8661\n2023-01-05 00:30:28,904 - mmcls - INFO - Epoch [73][1600/5005]\tlr: 1.000e-03, eta: 3:23:30, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8708\n2023-01-05 00:30:37,939 - mmcls - INFO - Epoch [73][1700/5005]\tlr: 1.000e-03, eta: 3:23:22, time: 0.090, data_time: 0.010, memory: 928, loss: 4.8451\n2023-01-05 00:30:46,663 - mmcls - INFO - Epoch [73][1800/5005]\tlr: 1.000e-03, eta: 3:23:13, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8664\n2023-01-05 00:30:55,736 - mmcls - INFO - Epoch [73][1900/5005]\tlr: 1.000e-03, eta: 3:23:04, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8761\n2023-01-05 00:31:04,332 - mmcls - INFO - Epoch [73][2000/5005]\tlr: 1.000e-03, eta: 3:22:55, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8537\n2023-01-05 00:31:12,928 - mmcls - INFO - Epoch [73][2100/5005]\tlr: 1.000e-03, eta: 3:22:46, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8481\n2023-01-05 00:31:21,521 - mmcls - INFO - Epoch [73][2200/5005]\tlr: 1.000e-03, eta: 3:22:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8515\n2023-01-05 00:31:30,482 - mmcls - INFO - Epoch [73][2300/5005]\tlr: 1.000e-03, eta: 3:22:28, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8954\n2023-01-05 00:31:39,714 - mmcls - INFO - Epoch [73][2400/5005]\tlr: 1.000e-03, eta: 3:22:20, time: 0.092, data_time: 0.004, memory: 928, loss: 4.8625\n2023-01-05 00:31:48,369 - mmcls - INFO - Epoch [73][2500/5005]\tlr: 1.000e-03, eta: 3:22:11, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8904\n2023-01-05 00:31:56,845 - mmcls - INFO - Epoch [73][2600/5005]\tlr: 1.000e-03, eta: 3:22:02, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8850\n2023-01-05 00:32:05,588 - mmcls - INFO - Epoch [73][2700/5005]\tlr: 1.000e-03, eta: 3:21:53, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8550\n2023-01-05 00:32:14,435 - mmcls - INFO - Epoch [73][2800/5005]\tlr: 1.000e-03, eta: 3:21:44, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8532\n2023-01-05 00:32:23,389 - mmcls - INFO - Epoch [73][2900/5005]\tlr: 1.000e-03, eta: 3:21:36, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8680\n2023-01-05 00:32:32,334 - mmcls - INFO - Epoch [73][3000/5005]\tlr: 1.000e-03, eta: 3:21:27, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8478\n2023-01-05 00:32:40,925 - mmcls - INFO - Epoch [73][3100/5005]\tlr: 1.000e-03, eta: 3:21:18, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8511\n2023-01-05 00:32:49,723 - mmcls - INFO - Epoch [73][3200/5005]\tlr: 1.000e-03, eta: 3:21:09, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8595\n2023-01-05 00:32:58,252 - mmcls - INFO - Epoch [73][3300/5005]\tlr: 1.000e-03, eta: 3:21:00, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8380\n2023-01-05 00:33:07,146 - mmcls - INFO - Epoch [73][3400/5005]\tlr: 1.000e-03, eta: 3:20:51, time: 0.089, data_time: 0.036, memory: 928, loss: 4.8731\n2023-01-05 00:33:15,774 - mmcls - INFO - Epoch [73][3500/5005]\tlr: 1.000e-03, eta: 3:20:43, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8588\n2023-01-05 00:33:24,744 - mmcls - INFO - Epoch [73][3600/5005]\tlr: 1.000e-03, eta: 3:20:34, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8772\n2023-01-05 00:33:33,546 - mmcls - INFO - Epoch [73][3700/5005]\tlr: 1.000e-03, eta: 3:20:25, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8435\n2023-01-05 00:33:42,486 - mmcls - INFO - Epoch [73][3800/5005]\tlr: 1.000e-03, eta: 3:20:16, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8682\n2023-01-05 00:33:51,253 - mmcls - INFO - Epoch [73][3900/5005]\tlr: 1.000e-03, eta: 3:20:07, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8604\n2023-01-05 00:34:00,375 - mmcls - INFO - Epoch [73][4000/5005]\tlr: 1.000e-03, eta: 3:19:59, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8677\n2023-01-05 00:34:09,113 - mmcls - INFO - Epoch [73][4100/5005]\tlr: 1.000e-03, eta: 3:19:50, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8499\n2023-01-05 00:34:17,688 - mmcls - INFO - Epoch [73][4200/5005]\tlr: 1.000e-03, eta: 3:19:41, time: 0.086, data_time: 0.028, memory: 928, loss: 4.8681\n2023-01-05 00:34:26,398 - mmcls - INFO - Epoch [73][4300/5005]\tlr: 1.000e-03, eta: 3:19:32, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8651\n2023-01-05 00:34:35,002 - mmcls - INFO - Epoch [73][4400/5005]\tlr: 1.000e-03, eta: 3:19:23, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8642\n2023-01-05 00:34:43,804 - mmcls - INFO - Epoch [73][4500/5005]\tlr: 1.000e-03, eta: 3:19:14, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8524\n2023-01-05 00:34:52,416 - mmcls - INFO - Epoch [73][4600/5005]\tlr: 1.000e-03, eta: 3:19:05, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8462\n2023-01-05 00:35:01,236 - mmcls - INFO - Epoch [73][4700/5005]\tlr: 1.000e-03, eta: 3:18:57, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8693\n2023-01-05 00:35:10,025 - mmcls - INFO - Epoch [73][4800/5005]\tlr: 1.000e-03, eta: 3:18:48, time: 0.088, data_time: 0.003, memory: 928, loss: 4.9035\n2023-01-05 00:35:18,453 - mmcls - INFO - Epoch [73][4900/5005]\tlr: 1.000e-03, eta: 3:18:39, time: 0.084, data_time: 0.018, memory: 928, loss: 4.8719\n2023-01-05 00:35:27,467 - mmcls - INFO - Epoch [73][5000/5005]\tlr: 1.000e-03, eta: 3:18:30, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8903\n2023-01-05 00:35:50,071 - mmcls - INFO - Epoch(val) [73][196]\taccuracy_top-1: 16.3260, accuracy_top-5: 33.7100\n2023-01-05 00:36:01,155 - mmcls - INFO - Epoch [74][100/5005]\tlr: 1.000e-03, eta: 3:18:22, time: 0.111, data_time: 0.066, memory: 928, loss: 4.8668\n2023-01-05 00:36:10,132 - mmcls - INFO - Epoch [74][200/5005]\tlr: 1.000e-03, eta: 3:18:13, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8603\n2023-01-05 00:36:18,715 - mmcls - INFO - Epoch [74][300/5005]\tlr: 1.000e-03, eta: 3:18:04, time: 0.086, data_time: 0.034, memory: 928, loss: 4.8595\n2023-01-05 00:36:27,450 - mmcls - INFO - Epoch [74][400/5005]\tlr: 1.000e-03, eta: 3:17:55, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8609\n2023-01-05 00:36:36,395 - mmcls - INFO - Epoch [74][500/5005]\tlr: 1.000e-03, eta: 3:17:46, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8581\n2023-01-05 00:36:45,251 - mmcls - INFO - Epoch [74][600/5005]\tlr: 1.000e-03, eta: 3:17:37, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8786\n2023-01-05 00:36:54,027 - mmcls - INFO - Epoch [74][700/5005]\tlr: 1.000e-03, eta: 3:17:29, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8395\n2023-01-05 00:37:03,249 - mmcls - INFO - Epoch [74][800/5005]\tlr: 1.000e-03, eta: 3:17:20, time: 0.092, data_time: 0.004, memory: 928, loss: 4.8609\n2023-01-05 00:37:12,035 - mmcls - INFO - Epoch [74][900/5005]\tlr: 1.000e-03, eta: 3:17:11, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8656\n2023-01-05 00:37:21,021 - mmcls - INFO - Epoch [74][1000/5005]\tlr: 1.000e-03, eta: 3:17:02, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8665\n2023-01-05 00:37:29,489 - mmcls - INFO - Epoch [74][1100/5005]\tlr: 1.000e-03, eta: 3:16:53, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8716\n2023-01-05 00:37:38,129 - mmcls - INFO - Epoch [74][1200/5005]\tlr: 1.000e-03, eta: 3:16:45, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8764\n2023-01-05 00:37:46,887 - mmcls - INFO - Epoch [74][1300/5005]\tlr: 1.000e-03, eta: 3:16:36, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8489\n2023-01-05 00:37:55,566 - mmcls - INFO - Epoch [74][1400/5005]\tlr: 1.000e-03, eta: 3:16:27, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8737\n2023-01-05 00:38:04,443 - mmcls - INFO - Epoch [74][1500/5005]\tlr: 1.000e-03, eta: 3:16:18, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8751\n2023-01-05 00:38:13,006 - mmcls - INFO - Epoch [74][1600/5005]\tlr: 1.000e-03, eta: 3:16:09, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8728\n2023-01-05 00:38:21,761 - mmcls - INFO - Epoch [74][1700/5005]\tlr: 1.000e-03, eta: 3:16:00, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8722\n2023-01-05 00:38:30,429 - mmcls - INFO - Epoch [74][1800/5005]\tlr: 1.000e-03, eta: 3:15:52, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8598\n2023-01-05 00:38:39,223 - mmcls - INFO - Epoch [74][1900/5005]\tlr: 1.000e-03, eta: 3:15:43, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8960\n2023-01-05 00:38:47,821 - mmcls - INFO - Epoch [74][2000/5005]\tlr: 1.000e-03, eta: 3:15:34, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8601\n2023-01-05 00:38:56,499 - mmcls - INFO - Epoch [74][2100/5005]\tlr: 1.000e-03, eta: 3:15:25, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8469\n2023-01-05 00:39:05,421 - mmcls - INFO - Epoch [74][2200/5005]\tlr: 1.000e-03, eta: 3:15:16, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8542\n2023-01-05 00:39:14,130 - mmcls - INFO - Epoch [74][2300/5005]\tlr: 1.000e-03, eta: 3:15:07, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8574\n2023-01-05 00:39:23,225 - mmcls - INFO - Epoch [74][2400/5005]\tlr: 1.000e-03, eta: 3:14:59, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8574\n2023-01-05 00:39:31,925 - mmcls - INFO - Epoch [74][2500/5005]\tlr: 1.000e-03, eta: 3:14:50, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8370\n2023-01-05 00:39:40,731 - mmcls - INFO - Epoch [74][2600/5005]\tlr: 1.000e-03, eta: 3:14:41, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8645\n2023-01-05 00:39:49,460 - mmcls - INFO - Epoch [74][2700/5005]\tlr: 1.000e-03, eta: 3:14:32, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8629\n2023-01-05 00:39:58,108 - mmcls - INFO - Epoch [74][2800/5005]\tlr: 1.000e-03, eta: 3:14:23, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8659\n2023-01-05 00:40:06,805 - mmcls - INFO - Epoch [74][2900/5005]\tlr: 1.000e-03, eta: 3:14:14, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8387\n2023-01-05 00:40:15,674 - mmcls - INFO - Epoch [74][3000/5005]\tlr: 1.000e-03, eta: 3:14:06, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8633\n2023-01-05 00:40:24,317 - mmcls - INFO - Epoch [74][3100/5005]\tlr: 1.000e-03, eta: 3:13:57, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8790\n2023-01-05 00:40:33,190 - mmcls - INFO - Epoch [74][3200/5005]\tlr: 1.000e-03, eta: 3:13:48, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8566\n2023-01-05 00:40:42,495 - mmcls - INFO - Epoch [74][3300/5005]\tlr: 1.000e-03, eta: 3:13:39, time: 0.093, data_time: 0.010, memory: 928, loss: 4.8403\n2023-01-05 00:40:51,134 - mmcls - INFO - Epoch [74][3400/5005]\tlr: 1.000e-03, eta: 3:13:30, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8479\n2023-01-05 00:41:00,108 - mmcls - INFO - Epoch [74][3500/5005]\tlr: 1.000e-03, eta: 3:13:22, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8878\n2023-01-05 00:41:09,033 - mmcls - INFO - Epoch [74][3600/5005]\tlr: 1.000e-03, eta: 3:13:13, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8683\n2023-01-05 00:41:17,792 - mmcls - INFO - Epoch [74][3700/5005]\tlr: 1.000e-03, eta: 3:13:04, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8910\n2023-01-05 00:41:27,070 - mmcls - INFO - Epoch [74][3800/5005]\tlr: 1.000e-03, eta: 3:12:55, time: 0.093, data_time: 0.026, memory: 928, loss: 4.8694\n2023-01-05 00:41:35,781 - mmcls - INFO - Epoch [74][3900/5005]\tlr: 1.000e-03, eta: 3:12:46, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8537\n2023-01-05 00:41:44,534 - mmcls - INFO - Epoch [74][4000/5005]\tlr: 1.000e-03, eta: 3:12:38, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8638\n2023-01-05 00:41:53,287 - mmcls - INFO - Epoch [74][4100/5005]\tlr: 1.000e-03, eta: 3:12:29, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8543\n2023-01-05 00:42:02,096 - mmcls - INFO - Epoch [74][4200/5005]\tlr: 1.000e-03, eta: 3:12:20, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8955\n2023-01-05 00:42:10,735 - mmcls - INFO - Epoch [74][4300/5005]\tlr: 1.000e-03, eta: 3:12:11, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8578\n2023-01-05 00:42:19,128 - mmcls - INFO - Epoch [74][4400/5005]\tlr: 1.000e-03, eta: 3:12:02, time: 0.084, data_time: 0.008, memory: 928, loss: 4.8647\n2023-01-05 00:42:28,118 - mmcls - INFO - Epoch [74][4500/5005]\tlr: 1.000e-03, eta: 3:11:53, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8614\n2023-01-05 00:42:37,060 - mmcls - INFO - Epoch [74][4600/5005]\tlr: 1.000e-03, eta: 3:11:45, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8679\n2023-01-05 00:42:45,847 - mmcls - INFO - Epoch [74][4700/5005]\tlr: 1.000e-03, eta: 3:11:36, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8627\n2023-01-05 00:42:54,716 - mmcls - INFO - Epoch [74][4800/5005]\tlr: 1.000e-03, eta: 3:11:27, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8784\n2023-01-05 00:43:03,432 - mmcls - INFO - Epoch [74][4900/5005]\tlr: 1.000e-03, eta: 3:11:18, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8597\n2023-01-05 00:43:12,102 - mmcls - INFO - Epoch [74][5000/5005]\tlr: 1.000e-03, eta: 3:11:09, time: 0.087, data_time: 0.031, memory: 928, loss: 4.8712\n2023-01-05 00:43:34,868 - mmcls - INFO - Epoch(val) [74][196]\taccuracy_top-1: 17.3060, accuracy_top-5: 35.3580\n2023-01-05 00:43:45,803 - mmcls - INFO - Epoch [75][100/5005]\tlr: 1.000e-03, eta: 3:11:01, time: 0.109, data_time: 0.060, memory: 928, loss: 4.8869\n2023-01-05 00:43:54,176 - mmcls - INFO - Epoch [75][200/5005]\tlr: 1.000e-03, eta: 3:10:52, time: 0.084, data_time: 0.026, memory: 928, loss: 4.8715\n2023-01-05 00:44:02,984 - mmcls - INFO - Epoch [75][300/5005]\tlr: 1.000e-03, eta: 3:10:43, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8694\n2023-01-05 00:44:11,687 - mmcls - INFO - Epoch [75][400/5005]\tlr: 1.000e-03, eta: 3:10:34, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8737\n2023-01-05 00:44:20,689 - mmcls - INFO - Epoch [75][500/5005]\tlr: 1.000e-03, eta: 3:10:25, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8470\n2023-01-05 00:44:29,498 - mmcls - INFO - Epoch [75][600/5005]\tlr: 1.000e-03, eta: 3:10:16, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8654\n2023-01-05 00:44:38,601 - mmcls - INFO - Epoch [75][700/5005]\tlr: 1.000e-03, eta: 3:10:08, time: 0.091, data_time: 0.031, memory: 928, loss: 4.8678\n2023-01-05 00:44:47,019 - mmcls - INFO - Epoch [75][800/5005]\tlr: 1.000e-03, eta: 3:09:59, time: 0.084, data_time: 0.002, memory: 928, loss: 4.8940\n2023-01-05 00:44:55,639 - mmcls - INFO - Epoch [75][900/5005]\tlr: 1.000e-03, eta: 3:09:50, time: 0.086, data_time: 0.042, memory: 928, loss: 4.8516\n2023-01-05 00:45:04,649 - mmcls - INFO - Epoch [75][1000/5005]\tlr: 1.000e-03, eta: 3:09:41, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8646\n2023-01-05 00:45:13,526 - mmcls - INFO - Epoch [75][1100/5005]\tlr: 1.000e-03, eta: 3:09:32, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8635\n2023-01-05 00:45:21,980 - mmcls - INFO - Epoch [75][1200/5005]\tlr: 1.000e-03, eta: 3:09:23, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8419\n2023-01-05 00:45:30,769 - mmcls - INFO - Epoch [75][1300/5005]\tlr: 1.000e-03, eta: 3:09:15, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8670\n2023-01-05 00:45:39,473 - mmcls - INFO - Epoch [75][1400/5005]\tlr: 1.000e-03, eta: 3:09:06, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8055\n2023-01-05 00:45:48,368 - mmcls - INFO - Epoch [75][1500/5005]\tlr: 1.000e-03, eta: 3:08:57, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8797\n2023-01-05 00:45:57,121 - mmcls - INFO - Epoch [75][1600/5005]\tlr: 1.000e-03, eta: 3:08:48, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8273\n2023-01-05 00:46:05,816 - mmcls - INFO - Epoch [75][1700/5005]\tlr: 1.000e-03, eta: 3:08:39, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8801\n2023-01-05 00:46:14,663 - mmcls - INFO - Epoch [75][1800/5005]\tlr: 1.000e-03, eta: 3:08:30, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8682\n2023-01-05 00:46:23,664 - mmcls - INFO - Epoch [75][1900/5005]\tlr: 1.000e-03, eta: 3:08:22, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8710\n2023-01-05 00:46:32,277 - mmcls - INFO - Epoch [75][2000/5005]\tlr: 1.000e-03, eta: 3:08:13, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8631\n2023-01-05 00:46:41,000 - mmcls - INFO - Epoch [75][2100/5005]\tlr: 1.000e-03, eta: 3:08:04, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8506\n2023-01-05 00:46:49,579 - mmcls - INFO - Epoch [75][2200/5005]\tlr: 1.000e-03, eta: 3:07:55, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8464\n2023-01-05 00:46:58,499 - mmcls - INFO - Epoch [75][2300/5005]\tlr: 1.000e-03, eta: 3:07:46, time: 0.089, data_time: 0.027, memory: 928, loss: 4.8689\n2023-01-05 00:47:06,984 - mmcls - INFO - Epoch [75][2400/5005]\tlr: 1.000e-03, eta: 3:07:37, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8561\n2023-01-05 00:47:15,929 - mmcls - INFO - Epoch [75][2500/5005]\tlr: 1.000e-03, eta: 3:07:29, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8506\n2023-01-05 00:47:24,737 - mmcls - INFO - Epoch [75][2600/5005]\tlr: 1.000e-03, eta: 3:07:20, time: 0.088, data_time: 0.046, memory: 928, loss: 4.8391\n2023-01-05 00:47:33,733 - mmcls - INFO - Epoch [75][2700/5005]\tlr: 1.000e-03, eta: 3:07:11, time: 0.090, data_time: 0.011, memory: 928, loss: 4.8637\n2023-01-05 00:47:42,763 - mmcls - INFO - Epoch [75][2800/5005]\tlr: 1.000e-03, eta: 3:07:02, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8876\n2023-01-05 00:47:51,290 - mmcls - INFO - Epoch [75][2900/5005]\tlr: 1.000e-03, eta: 3:06:53, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8712\n2023-01-05 00:48:00,114 - mmcls - INFO - Epoch [75][3000/5005]\tlr: 1.000e-03, eta: 3:06:45, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8680\n2023-01-05 00:48:09,086 - mmcls - INFO - Epoch [75][3100/5005]\tlr: 1.000e-03, eta: 3:06:36, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8688\n2023-01-05 00:48:18,237 - mmcls - INFO - Epoch [75][3200/5005]\tlr: 1.000e-03, eta: 3:06:27, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8663\n2023-01-05 00:48:27,123 - mmcls - INFO - Epoch [75][3300/5005]\tlr: 1.000e-03, eta: 3:06:18, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8426\n2023-01-05 00:48:35,879 - mmcls - INFO - Epoch [75][3400/5005]\tlr: 1.000e-03, eta: 3:06:09, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8769\n2023-01-05 00:48:44,684 - mmcls - INFO - Epoch [75][3500/5005]\tlr: 1.000e-03, eta: 3:06:01, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8614\n2023-01-05 00:48:53,309 - mmcls - INFO - Epoch [75][3600/5005]\tlr: 1.000e-03, eta: 3:05:52, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8538\n2023-01-05 00:49:02,111 - mmcls - INFO - Epoch [75][3700/5005]\tlr: 1.000e-03, eta: 3:05:43, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8478\n2023-01-05 00:49:10,943 - mmcls - INFO - Epoch [75][3800/5005]\tlr: 1.000e-03, eta: 3:05:34, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8587\n2023-01-05 00:49:19,812 - mmcls - INFO - Epoch [75][3900/5005]\tlr: 1.000e-03, eta: 3:05:25, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8758\n2023-01-05 00:49:28,610 - mmcls - INFO - Epoch [75][4000/5005]\tlr: 1.000e-03, eta: 3:05:17, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8756\n2023-01-05 00:49:37,431 - mmcls - INFO - Epoch [75][4100/5005]\tlr: 1.000e-03, eta: 3:05:08, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8394\n2023-01-05 00:49:46,190 - mmcls - INFO - Epoch [75][4200/5005]\tlr: 1.000e-03, eta: 3:04:59, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8807\n2023-01-05 00:49:54,982 - mmcls - INFO - Epoch [75][4300/5005]\tlr: 1.000e-03, eta: 3:04:50, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8618\n2023-01-05 00:50:03,770 - mmcls - INFO - Epoch [75][4400/5005]\tlr: 1.000e-03, eta: 3:04:41, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8819\n2023-01-05 00:50:13,251 - mmcls - INFO - Epoch [75][4500/5005]\tlr: 1.000e-03, eta: 3:04:33, time: 0.095, data_time: 0.003, memory: 928, loss: 4.8551\n2023-01-05 00:50:21,752 - mmcls - INFO - Epoch [75][4600/5005]\tlr: 1.000e-03, eta: 3:04:24, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8581\n2023-01-05 00:50:30,540 - mmcls - INFO - Epoch [75][4700/5005]\tlr: 1.000e-03, eta: 3:04:15, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8531\n2023-01-05 00:50:39,565 - mmcls - INFO - Epoch [75][4800/5005]\tlr: 1.000e-03, eta: 3:04:06, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8689\n2023-01-05 00:50:48,262 - mmcls - INFO - Epoch [75][4900/5005]\tlr: 1.000e-03, eta: 3:03:57, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8646\n2023-01-05 00:50:57,120 - mmcls - INFO - Epoch [75][5000/5005]\tlr: 1.000e-03, eta: 3:03:49, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8584\n2023-01-05 00:51:19,793 - mmcls - INFO - Epoch(val) [75][196]\taccuracy_top-1: 17.0480, accuracy_top-5: 34.8280\n2023-01-05 00:51:31,006 - mmcls - INFO - Epoch [76][100/5005]\tlr: 1.000e-03, eta: 3:03:40, time: 0.112, data_time: 0.049, memory: 928, loss: 4.8401\n2023-01-05 00:51:40,164 - mmcls - INFO - Epoch [76][200/5005]\tlr: 1.000e-03, eta: 3:03:31, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8603\n2023-01-05 00:51:48,865 - mmcls - INFO - Epoch [76][300/5005]\tlr: 1.000e-03, eta: 3:03:22, time: 0.087, data_time: 0.031, memory: 928, loss: 4.8468\n2023-01-05 00:51:57,511 - mmcls - INFO - Epoch [76][400/5005]\tlr: 1.000e-03, eta: 3:03:14, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8592\n2023-01-05 00:52:06,472 - mmcls - INFO - Epoch [76][500/5005]\tlr: 1.000e-03, eta: 3:03:05, time: 0.090, data_time: 0.024, memory: 928, loss: 4.8757\n2023-01-05 00:52:15,319 - mmcls - INFO - Epoch [76][600/5005]\tlr: 1.000e-03, eta: 3:02:56, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8630\n2023-01-05 00:52:24,041 - mmcls - INFO - Epoch [76][700/5005]\tlr: 1.000e-03, eta: 3:02:47, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8606\n2023-01-05 00:52:32,712 - mmcls - INFO - Epoch [76][800/5005]\tlr: 1.000e-03, eta: 3:02:38, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8624\n2023-01-05 00:52:41,255 - mmcls - INFO - Epoch [76][900/5005]\tlr: 1.000e-03, eta: 3:02:29, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8694\n2023-01-05 00:52:50,156 - mmcls - INFO - Epoch [76][1000/5005]\tlr: 1.000e-03, eta: 3:02:21, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8564\n2023-01-05 00:52:58,820 - mmcls - INFO - Epoch [76][1100/5005]\tlr: 1.000e-03, eta: 3:02:12, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8834\n2023-01-05 00:53:08,000 - mmcls - INFO - Epoch [76][1200/5005]\tlr: 1.000e-03, eta: 3:02:03, time: 0.092, data_time: 0.019, memory: 928, loss: 4.8723\n2023-01-05 00:53:16,806 - mmcls - INFO - Epoch [76][1300/5005]\tlr: 1.000e-03, eta: 3:01:54, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8654\n2023-01-05 00:53:25,546 - mmcls - INFO - Epoch [76][1400/5005]\tlr: 1.000e-03, eta: 3:01:45, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8668\n2023-01-05 00:53:34,315 - mmcls - INFO - Epoch [76][1500/5005]\tlr: 1.000e-03, eta: 3:01:36, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8700\n2023-01-05 00:53:43,201 - mmcls - INFO - Epoch [76][1600/5005]\tlr: 1.000e-03, eta: 3:01:28, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8451\n2023-01-05 00:53:51,775 - mmcls - INFO - Epoch [76][1700/5005]\tlr: 1.000e-03, eta: 3:01:19, time: 0.086, data_time: 0.000, memory: 928, loss: 4.8622\n2023-01-05 00:54:00,823 - mmcls - INFO - Epoch [76][1800/5005]\tlr: 1.000e-03, eta: 3:01:10, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8574\n2023-01-05 00:54:09,607 - mmcls - INFO - Epoch [76][1900/5005]\tlr: 1.000e-03, eta: 3:01:01, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8626\n2023-01-05 00:54:18,148 - mmcls - INFO - Epoch [76][2000/5005]\tlr: 1.000e-03, eta: 3:00:52, time: 0.085, data_time: 0.019, memory: 928, loss: 4.9024\n2023-01-05 00:54:27,012 - mmcls - INFO - Epoch [76][2100/5005]\tlr: 1.000e-03, eta: 3:00:44, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8727\n2023-01-05 00:54:36,112 - mmcls - INFO - Epoch [76][2200/5005]\tlr: 1.000e-03, eta: 3:00:35, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8684\n2023-01-05 00:54:45,182 - mmcls - INFO - Epoch [76][2300/5005]\tlr: 1.000e-03, eta: 3:00:26, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8504\n2023-01-05 00:54:53,870 - mmcls - INFO - Epoch [76][2400/5005]\tlr: 1.000e-03, eta: 3:00:17, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8636\n2023-01-05 00:55:02,759 - mmcls - INFO - Epoch [76][2500/5005]\tlr: 1.000e-03, eta: 3:00:08, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8579\n2023-01-05 00:55:11,402 - mmcls - INFO - Epoch [76][2600/5005]\tlr: 1.000e-03, eta: 3:00:00, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8691\n2023-01-05 00:55:20,472 - mmcls - INFO - Epoch [76][2700/5005]\tlr: 1.000e-03, eta: 2:59:51, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8763\n2023-01-05 00:55:29,582 - mmcls - INFO - Epoch [76][2800/5005]\tlr: 1.000e-03, eta: 2:59:42, time: 0.091, data_time: 0.022, memory: 928, loss: 4.8627\n2023-01-05 00:55:38,362 - mmcls - INFO - Epoch [76][2900/5005]\tlr: 1.000e-03, eta: 2:59:33, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8548\n2023-01-05 00:55:47,061 - mmcls - INFO - Epoch [76][3000/5005]\tlr: 1.000e-03, eta: 2:59:24, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8449\n2023-01-05 00:55:55,950 - mmcls - INFO - Epoch [76][3100/5005]\tlr: 1.000e-03, eta: 2:59:16, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8670\n2023-01-05 00:56:04,737 - mmcls - INFO - Epoch [76][3200/5005]\tlr: 1.000e-03, eta: 2:59:07, time: 0.088, data_time: 0.038, memory: 928, loss: 4.8575\n2023-01-05 00:56:13,382 - mmcls - INFO - Epoch [76][3300/5005]\tlr: 1.000e-03, eta: 2:58:58, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8628\n2023-01-05 00:56:22,048 - mmcls - INFO - Epoch [76][3400/5005]\tlr: 1.000e-03, eta: 2:58:49, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8594\n2023-01-05 00:56:30,893 - mmcls - INFO - Epoch [76][3500/5005]\tlr: 1.000e-03, eta: 2:58:40, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8622\n2023-01-05 00:56:39,756 - mmcls - INFO - Epoch [76][3600/5005]\tlr: 1.000e-03, eta: 2:58:31, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8759\n2023-01-05 00:56:48,366 - mmcls - INFO - Epoch [76][3700/5005]\tlr: 1.000e-03, eta: 2:58:23, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8671\n2023-01-05 00:56:57,052 - mmcls - INFO - Epoch [76][3800/5005]\tlr: 1.000e-03, eta: 2:58:14, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8466\n2023-01-05 00:57:05,840 - mmcls - INFO - Epoch [76][3900/5005]\tlr: 1.000e-03, eta: 2:58:05, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8598\n2023-01-05 00:57:14,602 - mmcls - INFO - Epoch [76][4000/5005]\tlr: 1.000e-03, eta: 2:57:56, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8630\n2023-01-05 00:57:23,333 - mmcls - INFO - Epoch [76][4100/5005]\tlr: 1.000e-03, eta: 2:57:47, time: 0.087, data_time: 0.041, memory: 928, loss: 4.8550\n2023-01-05 00:57:32,105 - mmcls - INFO - Epoch [76][4200/5005]\tlr: 1.000e-03, eta: 2:57:38, time: 0.088, data_time: 0.040, memory: 928, loss: 4.8545\n2023-01-05 00:57:41,062 - mmcls - INFO - Epoch [76][4300/5005]\tlr: 1.000e-03, eta: 2:57:30, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8422\n2023-01-05 00:57:49,833 - mmcls - INFO - Epoch [76][4400/5005]\tlr: 1.000e-03, eta: 2:57:21, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8640\n2023-01-05 00:57:58,878 - mmcls - INFO - Epoch [76][4500/5005]\tlr: 1.000e-03, eta: 2:57:12, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8739\n2023-01-05 00:58:07,677 - mmcls - INFO - Epoch [76][4600/5005]\tlr: 1.000e-03, eta: 2:57:03, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8568\n2023-01-05 00:58:16,390 - mmcls - INFO - Epoch [76][4700/5005]\tlr: 1.000e-03, eta: 2:56:54, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8552\n2023-01-05 00:58:24,905 - mmcls - INFO - Epoch [76][4800/5005]\tlr: 1.000e-03, eta: 2:56:46, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8372\n2023-01-05 00:58:33,668 - mmcls - INFO - Epoch [76][4900/5005]\tlr: 1.000e-03, eta: 2:56:37, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8626\n2023-01-05 00:58:42,728 - mmcls - INFO - Epoch [76][5000/5005]\tlr: 1.000e-03, eta: 2:56:28, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8779\n2023-01-05 00:59:05,266 - mmcls - INFO - Epoch(val) [76][196]\taccuracy_top-1: 17.2600, accuracy_top-5: 35.3860\n2023-01-05 00:59:16,234 - mmcls - INFO - Epoch [77][100/5005]\tlr: 1.000e-03, eta: 2:56:19, time: 0.109, data_time: 0.045, memory: 928, loss: 4.8389\n2023-01-05 00:59:25,099 - mmcls - INFO - Epoch [77][200/5005]\tlr: 1.000e-03, eta: 2:56:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8584\n2023-01-05 00:59:33,602 - mmcls - INFO - Epoch [77][300/5005]\tlr: 1.000e-03, eta: 2:56:02, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8558\n2023-01-05 00:59:42,324 - mmcls - INFO - Epoch [77][400/5005]\tlr: 1.000e-03, eta: 2:55:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8669\n2023-01-05 00:59:51,021 - mmcls - INFO - Epoch [77][500/5005]\tlr: 1.000e-03, eta: 2:55:44, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8428\n2023-01-05 01:00:00,002 - mmcls - INFO - Epoch [77][600/5005]\tlr: 1.000e-03, eta: 2:55:35, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8525\n2023-01-05 01:00:09,111 - mmcls - INFO - Epoch [77][700/5005]\tlr: 1.000e-03, eta: 2:55:26, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8559\n2023-01-05 01:00:18,371 - mmcls - INFO - Epoch [77][800/5005]\tlr: 1.000e-03, eta: 2:55:18, time: 0.093, data_time: 0.017, memory: 928, loss: 4.8624\n2023-01-05 01:00:26,996 - mmcls - INFO - Epoch [77][900/5005]\tlr: 1.000e-03, eta: 2:55:09, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8912\n2023-01-05 01:00:36,034 - mmcls - INFO - Epoch [77][1000/5005]\tlr: 1.000e-03, eta: 2:55:00, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8897\n2023-01-05 01:00:44,972 - mmcls - INFO - Epoch [77][1100/5005]\tlr: 1.000e-03, eta: 2:54:51, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8813\n2023-01-05 01:00:53,566 - mmcls - INFO - Epoch [77][1200/5005]\tlr: 1.000e-03, eta: 2:54:42, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8551\n2023-01-05 01:01:02,226 - mmcls - INFO - Epoch [77][1300/5005]\tlr: 1.000e-03, eta: 2:54:33, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8341\n2023-01-05 01:01:10,918 - mmcls - INFO - Epoch [77][1400/5005]\tlr: 1.000e-03, eta: 2:54:25, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8846\n2023-01-05 01:01:19,896 - mmcls - INFO - Epoch [77][1500/5005]\tlr: 1.000e-03, eta: 2:54:16, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8643\n2023-01-05 01:01:28,584 - mmcls - INFO - Epoch [77][1600/5005]\tlr: 1.000e-03, eta: 2:54:07, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8657\n2023-01-05 01:01:37,400 - mmcls - INFO - Epoch [77][1700/5005]\tlr: 1.000e-03, eta: 2:53:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8532\n2023-01-05 01:01:46,214 - mmcls - INFO - Epoch [77][1800/5005]\tlr: 1.000e-03, eta: 2:53:49, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8582\n2023-01-05 01:01:55,038 - mmcls - INFO - Epoch [77][1900/5005]\tlr: 1.000e-03, eta: 2:53:41, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8489\n2023-01-05 01:02:03,603 - mmcls - INFO - Epoch [77][2000/5005]\tlr: 1.000e-03, eta: 2:53:32, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8591\n2023-01-05 01:02:12,464 - mmcls - INFO - Epoch [77][2100/5005]\tlr: 1.000e-03, eta: 2:53:23, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8561\n2023-01-05 01:02:21,296 - mmcls - INFO - Epoch [77][2200/5005]\tlr: 1.000e-03, eta: 2:53:14, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8689\n2023-01-05 01:02:30,160 - mmcls - INFO - Epoch [77][2300/5005]\tlr: 1.000e-03, eta: 2:53:05, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8436\n2023-01-05 01:02:38,875 - mmcls - INFO - Epoch [77][2400/5005]\tlr: 1.000e-03, eta: 2:52:56, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8752\n2023-01-05 01:02:47,598 - mmcls - INFO - Epoch [77][2500/5005]\tlr: 1.000e-03, eta: 2:52:48, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8406\n2023-01-05 01:02:56,189 - mmcls - INFO - Epoch [77][2600/5005]\tlr: 1.000e-03, eta: 2:52:39, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8608\n2023-01-05 01:03:05,032 - mmcls - INFO - Epoch [77][2700/5005]\tlr: 1.000e-03, eta: 2:52:30, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8772\n2023-01-05 01:03:13,776 - mmcls - INFO - Epoch [77][2800/5005]\tlr: 1.000e-03, eta: 2:52:21, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8623\n2023-01-05 01:03:22,599 - mmcls - INFO - Epoch [77][2900/5005]\tlr: 1.000e-03, eta: 2:52:12, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8576\n2023-01-05 01:03:31,415 - mmcls - INFO - Epoch [77][3000/5005]\tlr: 1.000e-03, eta: 2:52:03, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8425\n2023-01-05 01:03:40,301 - mmcls - INFO - Epoch [77][3100/5005]\tlr: 1.000e-03, eta: 2:51:55, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8339\n2023-01-05 01:03:49,008 - mmcls - INFO - Epoch [77][3200/5005]\tlr: 1.000e-03, eta: 2:51:46, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8443\n2023-01-05 01:03:57,886 - mmcls - INFO - Epoch [77][3300/5005]\tlr: 1.000e-03, eta: 2:51:37, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8360\n2023-01-05 01:04:06,927 - mmcls - INFO - Epoch [77][3400/5005]\tlr: 1.000e-03, eta: 2:51:28, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8546\n2023-01-05 01:04:15,718 - mmcls - INFO - Epoch [77][3500/5005]\tlr: 1.000e-03, eta: 2:51:19, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8761\n2023-01-05 01:04:24,492 - mmcls - INFO - Epoch [77][3600/5005]\tlr: 1.000e-03, eta: 2:51:11, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8493\n2023-01-05 01:04:33,094 - mmcls - INFO - Epoch [77][3700/5005]\tlr: 1.000e-03, eta: 2:51:02, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8656\n2023-01-05 01:04:41,595 - mmcls - INFO - Epoch [77][3800/5005]\tlr: 1.000e-03, eta: 2:50:53, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8492\n2023-01-05 01:04:50,482 - mmcls - INFO - Epoch [77][3900/5005]\tlr: 1.000e-03, eta: 2:50:44, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8776\n2023-01-05 01:04:59,126 - mmcls - INFO - Epoch [77][4000/5005]\tlr: 1.000e-03, eta: 2:50:35, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8510\n2023-01-05 01:05:08,119 - mmcls - INFO - Epoch [77][4100/5005]\tlr: 1.000e-03, eta: 2:50:26, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8685\n2023-01-05 01:05:17,088 - mmcls - INFO - Epoch [77][4200/5005]\tlr: 1.000e-03, eta: 2:50:18, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8318\n2023-01-05 01:05:25,742 - mmcls - INFO - Epoch [77][4300/5005]\tlr: 1.000e-03, eta: 2:50:09, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8442\n2023-01-05 01:05:34,643 - mmcls - INFO - Epoch [77][4400/5005]\tlr: 1.000e-03, eta: 2:50:00, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8761\n2023-01-05 01:05:43,259 - mmcls - INFO - Epoch [77][4500/5005]\tlr: 1.000e-03, eta: 2:49:51, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8608\n2023-01-05 01:05:51,859 - mmcls - INFO - Epoch [77][4600/5005]\tlr: 1.000e-03, eta: 2:49:42, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8584\n2023-01-05 01:06:00,370 - mmcls - INFO - Epoch [77][4700/5005]\tlr: 1.000e-03, eta: 2:49:33, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8456\n2023-01-05 01:06:09,034 - mmcls - INFO - Epoch [77][4800/5005]\tlr: 1.000e-03, eta: 2:49:24, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8744\n2023-01-05 01:06:17,889 - mmcls - INFO - Epoch [77][4900/5005]\tlr: 1.000e-03, eta: 2:49:16, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8703\n2023-01-05 01:06:26,615 - mmcls - INFO - Epoch [77][5000/5005]\tlr: 1.000e-03, eta: 2:49:07, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8373\n2023-01-05 01:06:49,397 - mmcls - INFO - Epoch(val) [77][196]\taccuracy_top-1: 17.0560, accuracy_top-5: 35.1240\n2023-01-05 01:07:00,471 - mmcls - INFO - Epoch [78][100/5005]\tlr: 1.000e-03, eta: 2:48:58, time: 0.111, data_time: 0.049, memory: 928, loss: 4.8439\n2023-01-05 01:07:09,349 - mmcls - INFO - Epoch [78][200/5005]\tlr: 1.000e-03, eta: 2:48:49, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8772\n2023-01-05 01:07:18,173 - mmcls - INFO - Epoch [78][300/5005]\tlr: 1.000e-03, eta: 2:48:40, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8574\n2023-01-05 01:07:26,976 - mmcls - INFO - Epoch [78][400/5005]\tlr: 1.000e-03, eta: 2:48:32, time: 0.088, data_time: 0.032, memory: 928, loss: 4.8356\n2023-01-05 01:07:35,537 - mmcls - INFO - Epoch [78][500/5005]\tlr: 1.000e-03, eta: 2:48:23, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8823\n2023-01-05 01:07:44,160 - mmcls - INFO - Epoch [78][600/5005]\tlr: 1.000e-03, eta: 2:48:14, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8403\n2023-01-05 01:07:52,884 - mmcls - INFO - Epoch [78][700/5005]\tlr: 1.000e-03, eta: 2:48:05, time: 0.087, data_time: 0.039, memory: 928, loss: 4.8555\n2023-01-05 01:08:01,728 - mmcls - INFO - Epoch [78][800/5005]\tlr: 1.000e-03, eta: 2:47:56, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8828\n2023-01-05 01:08:10,445 - mmcls - INFO - Epoch [78][900/5005]\tlr: 1.000e-03, eta: 2:47:47, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8560\n2023-01-05 01:08:19,187 - mmcls - INFO - Epoch [78][1000/5005]\tlr: 1.000e-03, eta: 2:47:39, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8778\n2023-01-05 01:08:27,685 - mmcls - INFO - Epoch [78][1100/5005]\tlr: 1.000e-03, eta: 2:47:30, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8677\n2023-01-05 01:08:36,533 - mmcls - INFO - Epoch [78][1200/5005]\tlr: 1.000e-03, eta: 2:47:21, time: 0.089, data_time: 0.031, memory: 928, loss: 4.8514\n2023-01-05 01:08:45,239 - mmcls - INFO - Epoch [78][1300/5005]\tlr: 1.000e-03, eta: 2:47:12, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8509\n2023-01-05 01:08:53,903 - mmcls - INFO - Epoch [78][1400/5005]\tlr: 1.000e-03, eta: 2:47:03, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8675\n2023-01-05 01:09:02,898 - mmcls - INFO - Epoch [78][1500/5005]\tlr: 1.000e-03, eta: 2:46:54, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8532\n2023-01-05 01:09:11,891 - mmcls - INFO - Epoch [78][1600/5005]\tlr: 1.000e-03, eta: 2:46:46, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8400\n2023-01-05 01:09:20,963 - mmcls - INFO - Epoch [78][1700/5005]\tlr: 1.000e-03, eta: 2:46:37, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8742\n2023-01-05 01:09:29,958 - mmcls - INFO - Epoch [78][1800/5005]\tlr: 1.000e-03, eta: 2:46:28, time: 0.090, data_time: 0.019, memory: 928, loss: 4.8560\n2023-01-05 01:09:38,538 - mmcls - INFO - Epoch [78][1900/5005]\tlr: 1.000e-03, eta: 2:46:19, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8619\n2023-01-05 01:09:47,291 - mmcls - INFO - Epoch [78][2000/5005]\tlr: 1.000e-03, eta: 2:46:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8648\n2023-01-05 01:09:56,043 - mmcls - INFO - Epoch [78][2100/5005]\tlr: 1.000e-03, eta: 2:46:02, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8638\n2023-01-05 01:10:04,922 - mmcls - INFO - Epoch [78][2200/5005]\tlr: 1.000e-03, eta: 2:45:53, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8733\n2023-01-05 01:10:13,552 - mmcls - INFO - Epoch [78][2300/5005]\tlr: 1.000e-03, eta: 2:45:44, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8758\n2023-01-05 01:10:22,147 - mmcls - INFO - Epoch [78][2400/5005]\tlr: 1.000e-03, eta: 2:45:35, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8551\n2023-01-05 01:10:31,062 - mmcls - INFO - Epoch [78][2500/5005]\tlr: 1.000e-03, eta: 2:45:26, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8545\n2023-01-05 01:10:40,316 - mmcls - INFO - Epoch [78][2600/5005]\tlr: 1.000e-03, eta: 2:45:18, time: 0.092, data_time: 0.000, memory: 928, loss: 4.8449\n2023-01-05 01:10:49,136 - mmcls - INFO - Epoch [78][2700/5005]\tlr: 1.000e-03, eta: 2:45:09, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8625\n2023-01-05 01:10:57,921 - mmcls - INFO - Epoch [78][2800/5005]\tlr: 1.000e-03, eta: 2:45:00, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8479\n2023-01-05 01:11:06,613 - mmcls - INFO - Epoch [78][2900/5005]\tlr: 1.000e-03, eta: 2:44:51, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8645\n2023-01-05 01:11:15,335 - mmcls - INFO - Epoch [78][3000/5005]\tlr: 1.000e-03, eta: 2:44:42, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8824\n2023-01-05 01:11:23,867 - mmcls - INFO - Epoch [78][3100/5005]\tlr: 1.000e-03, eta: 2:44:33, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8611\n2023-01-05 01:11:32,764 - mmcls - INFO - Epoch [78][3200/5005]\tlr: 1.000e-03, eta: 2:44:25, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8685\n2023-01-05 01:11:41,290 - mmcls - INFO - Epoch [78][3300/5005]\tlr: 1.000e-03, eta: 2:44:16, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8751\n2023-01-05 01:11:50,130 - mmcls - INFO - Epoch [78][3400/5005]\tlr: 1.000e-03, eta: 2:44:07, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8535\n2023-01-05 01:11:58,884 - mmcls - INFO - Epoch [78][3500/5005]\tlr: 1.000e-03, eta: 2:43:58, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8566\n2023-01-05 01:12:07,563 - mmcls - INFO - Epoch [78][3600/5005]\tlr: 1.000e-03, eta: 2:43:49, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8524\n2023-01-05 01:12:16,311 - mmcls - INFO - Epoch [78][3700/5005]\tlr: 1.000e-03, eta: 2:43:40, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8591\n2023-01-05 01:12:25,208 - mmcls - INFO - Epoch [78][3800/5005]\tlr: 1.000e-03, eta: 2:43:32, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8598\n2023-01-05 01:12:34,130 - mmcls - INFO - Epoch [78][3900/5005]\tlr: 1.000e-03, eta: 2:43:23, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8642\n2023-01-05 01:12:42,894 - mmcls - INFO - Epoch [78][4000/5005]\tlr: 1.000e-03, eta: 2:43:14, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8635\n2023-01-05 01:12:51,764 - mmcls - INFO - Epoch [78][4100/5005]\tlr: 1.000e-03, eta: 2:43:05, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8449\n2023-01-05 01:13:00,427 - mmcls - INFO - Epoch [78][4200/5005]\tlr: 1.000e-03, eta: 2:42:56, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8622\n2023-01-05 01:13:09,178 - mmcls - INFO - Epoch [78][4300/5005]\tlr: 1.000e-03, eta: 2:42:47, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8502\n2023-01-05 01:13:18,034 - mmcls - INFO - Epoch [78][4400/5005]\tlr: 1.000e-03, eta: 2:42:39, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8425\n2023-01-05 01:13:26,665 - mmcls - INFO - Epoch [78][4500/5005]\tlr: 1.000e-03, eta: 2:42:30, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8653\n2023-01-05 01:13:35,635 - mmcls - INFO - Epoch [78][4600/5005]\tlr: 1.000e-03, eta: 2:42:21, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8419\n2023-01-05 01:13:44,435 - mmcls - INFO - Epoch [78][4700/5005]\tlr: 1.000e-03, eta: 2:42:12, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8518\n2023-01-05 01:13:53,176 - mmcls - INFO - Epoch [78][4800/5005]\tlr: 1.000e-03, eta: 2:42:03, time: 0.087, data_time: 0.036, memory: 928, loss: 4.8658\n2023-01-05 01:14:01,737 - mmcls - INFO - Epoch [78][4900/5005]\tlr: 1.000e-03, eta: 2:41:54, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8579\n2023-01-05 01:14:10,369 - mmcls - INFO - Epoch [78][5000/5005]\tlr: 1.000e-03, eta: 2:41:46, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8430\n2023-01-05 01:14:33,109 - mmcls - INFO - Epoch(val) [78][196]\taccuracy_top-1: 16.1780, accuracy_top-5: 33.4500\n2023-01-05 01:14:44,140 - mmcls - INFO - Epoch [79][100/5005]\tlr: 1.000e-03, eta: 2:41:37, time: 0.110, data_time: 0.036, memory: 928, loss: 4.8404\n2023-01-05 01:14:52,810 - mmcls - INFO - Epoch [79][200/5005]\tlr: 1.000e-03, eta: 2:41:28, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8484\n2023-01-05 01:15:01,575 - mmcls - INFO - Epoch [79][300/5005]\tlr: 1.000e-03, eta: 2:41:19, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8760\n2023-01-05 01:15:10,500 - mmcls - INFO - Epoch [79][400/5005]\tlr: 1.000e-03, eta: 2:41:10, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8541\n2023-01-05 01:15:19,033 - mmcls - INFO - Epoch [79][500/5005]\tlr: 1.000e-03, eta: 2:41:01, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8657\n2023-01-05 01:15:27,876 - mmcls - INFO - Epoch [79][600/5005]\tlr: 1.000e-03, eta: 2:40:53, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8440\n2023-01-05 01:15:36,712 - mmcls - INFO - Epoch [79][700/5005]\tlr: 1.000e-03, eta: 2:40:44, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8593\n2023-01-05 01:15:45,482 - mmcls - INFO - Epoch [79][800/5005]\tlr: 1.000e-03, eta: 2:40:35, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8615\n2023-01-05 01:15:54,299 - mmcls - INFO - Epoch [79][900/5005]\tlr: 1.000e-03, eta: 2:40:26, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8547\n2023-01-05 01:16:02,819 - mmcls - INFO - Epoch [79][1000/5005]\tlr: 1.000e-03, eta: 2:40:17, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8619\n2023-01-05 01:16:11,793 - mmcls - INFO - Epoch [79][1100/5005]\tlr: 1.000e-03, eta: 2:40:09, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8618\n2023-01-05 01:16:20,713 - mmcls - INFO - Epoch [79][1200/5005]\tlr: 1.000e-03, eta: 2:40:00, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8623\n2023-01-05 01:16:29,509 - mmcls - INFO - Epoch [79][1300/5005]\tlr: 1.000e-03, eta: 2:39:51, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8558\n2023-01-05 01:16:38,282 - mmcls - INFO - Epoch [79][1400/5005]\tlr: 1.000e-03, eta: 2:39:42, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8550\n2023-01-05 01:16:47,001 - mmcls - INFO - Epoch [79][1500/5005]\tlr: 1.000e-03, eta: 2:39:33, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8585\n2023-01-05 01:16:55,506 - mmcls - INFO - Epoch [79][1600/5005]\tlr: 1.000e-03, eta: 2:39:24, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8562\n2023-01-05 01:17:04,317 - mmcls - INFO - Epoch [79][1700/5005]\tlr: 1.000e-03, eta: 2:39:16, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8466\n2023-01-05 01:17:13,211 - mmcls - INFO - Epoch [79][1800/5005]\tlr: 1.000e-03, eta: 2:39:07, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8541\n2023-01-05 01:17:21,858 - mmcls - INFO - Epoch [79][1900/5005]\tlr: 1.000e-03, eta: 2:38:58, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8572\n2023-01-05 01:17:30,679 - mmcls - INFO - Epoch [79][2000/5005]\tlr: 1.000e-03, eta: 2:38:49, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8547\n2023-01-05 01:17:39,500 - mmcls - INFO - Epoch [79][2100/5005]\tlr: 1.000e-03, eta: 2:38:40, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8549\n2023-01-05 01:17:48,130 - mmcls - INFO - Epoch [79][2200/5005]\tlr: 1.000e-03, eta: 2:38:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8582\n2023-01-05 01:17:57,354 - mmcls - INFO - Epoch [79][2300/5005]\tlr: 1.000e-03, eta: 2:38:23, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8589\n2023-01-05 01:18:06,104 - mmcls - INFO - Epoch [79][2400/5005]\tlr: 1.000e-03, eta: 2:38:14, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8672\n2023-01-05 01:18:14,763 - mmcls - INFO - Epoch [79][2500/5005]\tlr: 1.000e-03, eta: 2:38:05, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8689\n2023-01-05 01:18:23,388 - mmcls - INFO - Epoch [79][2600/5005]\tlr: 1.000e-03, eta: 2:37:56, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8521\n2023-01-05 01:18:32,243 - mmcls - INFO - Epoch [79][2700/5005]\tlr: 1.000e-03, eta: 2:37:47, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8614\n2023-01-05 01:18:41,053 - mmcls - INFO - Epoch [79][2800/5005]\tlr: 1.000e-03, eta: 2:37:38, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8486\n2023-01-05 01:18:49,523 - mmcls - INFO - Epoch [79][2900/5005]\tlr: 1.000e-03, eta: 2:37:30, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8646\n2023-01-05 01:18:58,324 - mmcls - INFO - Epoch [79][3000/5005]\tlr: 1.000e-03, eta: 2:37:21, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8668\n2023-01-05 01:19:07,223 - mmcls - INFO - Epoch [79][3100/5005]\tlr: 1.000e-03, eta: 2:37:12, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8517\n2023-01-05 01:19:16,065 - mmcls - INFO - Epoch [79][3200/5005]\tlr: 1.000e-03, eta: 2:37:03, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8765\n2023-01-05 01:19:24,889 - mmcls - INFO - Epoch [79][3300/5005]\tlr: 1.000e-03, eta: 2:36:54, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8621\n2023-01-05 01:19:33,464 - mmcls - INFO - Epoch [79][3400/5005]\tlr: 1.000e-03, eta: 2:36:45, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8652\n2023-01-05 01:19:42,143 - mmcls - INFO - Epoch [79][3500/5005]\tlr: 1.000e-03, eta: 2:36:37, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8692\n2023-01-05 01:19:50,998 - mmcls - INFO - Epoch [79][3600/5005]\tlr: 1.000e-03, eta: 2:36:28, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8737\n2023-01-05 01:20:00,055 - mmcls - INFO - Epoch [79][3700/5005]\tlr: 1.000e-03, eta: 2:36:19, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8474\n2023-01-05 01:20:08,977 - mmcls - INFO - Epoch [79][3800/5005]\tlr: 1.000e-03, eta: 2:36:10, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8701\n2023-01-05 01:20:17,959 - mmcls - INFO - Epoch [79][3900/5005]\tlr: 1.000e-03, eta: 2:36:02, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8524\n2023-01-05 01:20:26,671 - mmcls - INFO - Epoch [79][4000/5005]\tlr: 1.000e-03, eta: 2:35:53, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8483\n2023-01-05 01:20:35,401 - mmcls - INFO - Epoch [79][4100/5005]\tlr: 1.000e-03, eta: 2:35:44, time: 0.087, data_time: 0.025, memory: 928, loss: 4.8561\n2023-01-05 01:20:44,276 - mmcls - INFO - Epoch [79][4200/5005]\tlr: 1.000e-03, eta: 2:35:35, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8445\n2023-01-05 01:20:52,972 - mmcls - INFO - Epoch [79][4300/5005]\tlr: 1.000e-03, eta: 2:35:26, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8759\n2023-01-05 01:21:01,779 - mmcls - INFO - Epoch [79][4400/5005]\tlr: 1.000e-03, eta: 2:35:17, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8626\n2023-01-05 01:21:10,326 - mmcls - INFO - Epoch [79][4500/5005]\tlr: 1.000e-03, eta: 2:35:08, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8356\n2023-01-05 01:21:18,926 - mmcls - INFO - Epoch [79][4600/5005]\tlr: 1.000e-03, eta: 2:35:00, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8550\n2023-01-05 01:21:27,638 - mmcls - INFO - Epoch [79][4700/5005]\tlr: 1.000e-03, eta: 2:34:51, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8505\n2023-01-05 01:21:36,493 - mmcls - INFO - Epoch [79][4800/5005]\tlr: 1.000e-03, eta: 2:34:42, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8852\n2023-01-05 01:21:45,356 - mmcls - INFO - Epoch [79][4900/5005]\tlr: 1.000e-03, eta: 2:34:33, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8421\n2023-01-05 01:21:54,025 - mmcls - INFO - Epoch [79][5000/5005]\tlr: 1.000e-03, eta: 2:34:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8585\n2023-01-05 01:22:16,935 - mmcls - INFO - Epoch(val) [79][196]\taccuracy_top-1: 17.1500, accuracy_top-5: 35.0720\n2023-01-05 01:22:28,055 - mmcls - INFO - Epoch [80][100/5005]\tlr: 1.000e-03, eta: 2:34:16, time: 0.111, data_time: 0.033, memory: 928, loss: 4.8536\n2023-01-05 01:22:36,878 - mmcls - INFO - Epoch [80][200/5005]\tlr: 1.000e-03, eta: 2:34:07, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8568\n2023-01-05 01:22:45,669 - mmcls - INFO - Epoch [80][300/5005]\tlr: 1.000e-03, eta: 2:33:58, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8524\n2023-01-05 01:22:54,427 - mmcls - INFO - Epoch [80][400/5005]\tlr: 1.000e-03, eta: 2:33:49, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8615\n2023-01-05 01:23:03,550 - mmcls - INFO - Epoch [80][500/5005]\tlr: 1.000e-03, eta: 2:33:40, time: 0.091, data_time: 0.023, memory: 928, loss: 4.8367\n2023-01-05 01:23:12,177 - mmcls - INFO - Epoch [80][600/5005]\tlr: 1.000e-03, eta: 2:33:31, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8312\n2023-01-05 01:23:21,138 - mmcls - INFO - Epoch [80][700/5005]\tlr: 1.000e-03, eta: 2:33:23, time: 0.090, data_time: 0.026, memory: 928, loss: 4.8388\n2023-01-05 01:23:29,834 - mmcls - INFO - Epoch [80][800/5005]\tlr: 1.000e-03, eta: 2:33:14, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8412\n2023-01-05 01:23:38,536 - mmcls - INFO - Epoch [80][900/5005]\tlr: 1.000e-03, eta: 2:33:05, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8760\n2023-01-05 01:23:47,347 - mmcls - INFO - Epoch [80][1000/5005]\tlr: 1.000e-03, eta: 2:32:56, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8657\n2023-01-05 01:23:56,024 - mmcls - INFO - Epoch [80][1100/5005]\tlr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8731\n2023-01-05 01:24:04,669 - mmcls - INFO - Epoch [80][1200/5005]\tlr: 1.000e-03, eta: 2:32:38, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8590\n2023-01-05 01:24:13,244 - mmcls - INFO - Epoch [80][1300/5005]\tlr: 1.000e-03, eta: 2:32:30, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8586\n2023-01-05 01:24:22,202 - mmcls - INFO - Epoch [80][1400/5005]\tlr: 1.000e-03, eta: 2:32:21, time: 0.089, data_time: 0.027, memory: 928, loss: 4.8513\n2023-01-05 01:24:31,179 - mmcls - INFO - Epoch [80][1500/5005]\tlr: 1.000e-03, eta: 2:32:12, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8440\n2023-01-05 01:24:39,875 - mmcls - INFO - Epoch [80][1600/5005]\tlr: 1.000e-03, eta: 2:32:03, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8456\n2023-01-05 01:24:48,579 - mmcls - INFO - Epoch [80][1700/5005]\tlr: 1.000e-03, eta: 2:31:54, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8488\n2023-01-05 01:24:57,675 - mmcls - INFO - Epoch [80][1800/5005]\tlr: 1.000e-03, eta: 2:31:46, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8474\n2023-01-05 01:25:06,265 - mmcls - INFO - Epoch [80][1900/5005]\tlr: 1.000e-03, eta: 2:31:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8617\n2023-01-05 01:25:15,005 - mmcls - INFO - Epoch [80][2000/5005]\tlr: 1.000e-03, eta: 2:31:28, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8506\n2023-01-05 01:25:23,557 - mmcls - INFO - Epoch [80][2100/5005]\tlr: 1.000e-03, eta: 2:31:19, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8667\n2023-01-05 01:25:32,447 - mmcls - INFO - Epoch [80][2200/5005]\tlr: 1.000e-03, eta: 2:31:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8536\n2023-01-05 01:25:41,449 - mmcls - INFO - Epoch [80][2300/5005]\tlr: 1.000e-03, eta: 2:31:01, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8593\n2023-01-05 01:25:50,165 - mmcls - INFO - Epoch [80][2400/5005]\tlr: 1.000e-03, eta: 2:30:53, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8626\n2023-01-05 01:25:58,989 - mmcls - INFO - Epoch [80][2500/5005]\tlr: 1.000e-03, eta: 2:30:44, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8520\n2023-01-05 01:26:07,581 - mmcls - INFO - Epoch [80][2600/5005]\tlr: 1.000e-03, eta: 2:30:35, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8702\n2023-01-05 01:26:16,573 - mmcls - INFO - Epoch [80][2700/5005]\tlr: 1.000e-03, eta: 2:30:26, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8350\n2023-01-05 01:26:24,978 - mmcls - INFO - Epoch [80][2800/5005]\tlr: 1.000e-03, eta: 2:30:17, time: 0.084, data_time: 0.021, memory: 928, loss: 4.8634\n2023-01-05 01:26:34,149 - mmcls - INFO - Epoch [80][2900/5005]\tlr: 1.000e-03, eta: 2:30:09, time: 0.092, data_time: 0.005, memory: 928, loss: 4.8503\n2023-01-05 01:26:42,791 - mmcls - INFO - Epoch [80][3000/5005]\tlr: 1.000e-03, eta: 2:30:00, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8634\n2023-01-05 01:26:52,070 - mmcls - INFO - Epoch [80][3100/5005]\tlr: 1.000e-03, eta: 2:29:51, time: 0.093, data_time: 0.005, memory: 928, loss: 4.8677\n2023-01-05 01:27:00,788 - mmcls - INFO - Epoch [80][3200/5005]\tlr: 1.000e-03, eta: 2:29:42, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8438\n2023-01-05 01:27:09,599 - mmcls - INFO - Epoch [80][3300/5005]\tlr: 1.000e-03, eta: 2:29:33, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8509\n2023-01-05 01:27:18,428 - mmcls - INFO - Epoch [80][3400/5005]\tlr: 1.000e-03, eta: 2:29:25, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8776\n2023-01-05 01:27:26,992 - mmcls - INFO - Epoch [80][3500/5005]\tlr: 1.000e-03, eta: 2:29:16, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8878\n2023-01-05 01:27:35,835 - mmcls - INFO - Epoch [80][3600/5005]\tlr: 1.000e-03, eta: 2:29:07, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8599\n2023-01-05 01:27:44,400 - mmcls - INFO - Epoch [80][3700/5005]\tlr: 1.000e-03, eta: 2:28:58, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8541\n2023-01-05 01:27:53,074 - mmcls - INFO - Epoch [80][3800/5005]\tlr: 1.000e-03, eta: 2:28:49, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8593\n2023-01-05 01:28:01,458 - mmcls - INFO - Epoch [80][3900/5005]\tlr: 1.000e-03, eta: 2:28:40, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8701\n2023-01-05 01:28:10,299 - mmcls - INFO - Epoch [80][4000/5005]\tlr: 1.000e-03, eta: 2:28:31, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8443\n2023-01-05 01:28:19,070 - mmcls - INFO - Epoch [80][4100/5005]\tlr: 1.000e-03, eta: 2:28:23, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8559\n2023-01-05 01:28:27,881 - mmcls - INFO - Epoch [80][4200/5005]\tlr: 1.000e-03, eta: 2:28:14, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8453\n2023-01-05 01:28:36,928 - mmcls - INFO - Epoch [80][4300/5005]\tlr: 1.000e-03, eta: 2:28:05, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8524\n2023-01-05 01:28:45,820 - mmcls - INFO - Epoch [80][4400/5005]\tlr: 1.000e-03, eta: 2:27:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8564\n2023-01-05 01:28:54,486 - mmcls - INFO - Epoch [80][4500/5005]\tlr: 1.000e-03, eta: 2:27:47, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8528\n2023-01-05 01:29:02,956 - mmcls - INFO - Epoch [80][4600/5005]\tlr: 1.000e-03, eta: 2:27:38, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8334\n2023-01-05 01:29:11,490 - mmcls - INFO - Epoch [80][4700/5005]\tlr: 1.000e-03, eta: 2:27:30, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8453\n2023-01-05 01:29:20,234 - mmcls - INFO - Epoch [80][4800/5005]\tlr: 1.000e-03, eta: 2:27:21, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8783\n2023-01-05 01:29:28,988 - mmcls - INFO - Epoch [80][4900/5005]\tlr: 1.000e-03, eta: 2:27:12, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8544\n2023-01-05 01:29:37,649 - mmcls - INFO - Epoch [80][5000/5005]\tlr: 1.000e-03, eta: 2:27:03, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8479\n2023-01-05 01:30:00,215 - mmcls - INFO - Epoch(val) [80][196]\taccuracy_top-1: 16.7800, accuracy_top-5: 34.2860\n2023-01-05 01:30:10,948 - mmcls - INFO - Epoch [81][100/5005]\tlr: 1.000e-03, eta: 2:26:54, time: 0.107, data_time: 0.060, memory: 928, loss: 4.8361\n2023-01-05 01:30:19,805 - mmcls - INFO - Epoch [81][200/5005]\tlr: 1.000e-03, eta: 2:26:45, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8606\n2023-01-05 01:30:28,473 - mmcls - INFO - Epoch [81][300/5005]\tlr: 1.000e-03, eta: 2:26:36, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8562\n2023-01-05 01:30:37,286 - mmcls - INFO - Epoch [81][400/5005]\tlr: 1.000e-03, eta: 2:26:28, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8588\n2023-01-05 01:30:46,216 - mmcls - INFO - Epoch [81][500/5005]\tlr: 1.000e-03, eta: 2:26:19, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8557\n2023-01-05 01:30:54,885 - mmcls - INFO - Epoch [81][600/5005]\tlr: 1.000e-03, eta: 2:26:10, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8466\n2023-01-05 01:31:04,244 - mmcls - INFO - Epoch [81][700/5005]\tlr: 1.000e-03, eta: 2:26:01, time: 0.094, data_time: 0.002, memory: 928, loss: 4.8486\n2023-01-05 01:31:13,078 - mmcls - INFO - Epoch [81][800/5005]\tlr: 1.000e-03, eta: 2:25:53, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8598\n2023-01-05 01:31:21,933 - mmcls - INFO - Epoch [81][900/5005]\tlr: 1.000e-03, eta: 2:25:44, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8446\n2023-01-05 01:31:30,502 - mmcls - INFO - Epoch [81][1000/5005]\tlr: 1.000e-03, eta: 2:25:35, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8546\n2023-01-05 01:31:39,350 - mmcls - INFO - Epoch [81][1100/5005]\tlr: 1.000e-03, eta: 2:25:26, time: 0.089, data_time: 0.023, memory: 928, loss: 4.8482\n2023-01-05 01:31:48,043 - mmcls - INFO - Epoch [81][1200/5005]\tlr: 1.000e-03, eta: 2:25:17, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8398\n2023-01-05 01:31:56,828 - mmcls - INFO - Epoch [81][1300/5005]\tlr: 1.000e-03, eta: 2:25:08, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8337\n2023-01-05 01:32:05,211 - mmcls - INFO - Epoch [81][1400/5005]\tlr: 1.000e-03, eta: 2:24:59, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8578\n2023-01-05 01:32:14,057 - mmcls - INFO - Epoch [81][1500/5005]\tlr: 1.000e-03, eta: 2:24:51, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8526\n2023-01-05 01:32:22,959 - mmcls - INFO - Epoch [81][1600/5005]\tlr: 1.000e-03, eta: 2:24:42, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8450\n2023-01-05 01:32:31,964 - mmcls - INFO - Epoch [81][1700/5005]\tlr: 1.000e-03, eta: 2:24:33, time: 0.090, data_time: 0.039, memory: 928, loss: 4.8611\n2023-01-05 01:32:41,120 - mmcls - INFO - Epoch [81][1800/5005]\tlr: 1.000e-03, eta: 2:24:24, time: 0.091, data_time: 0.012, memory: 928, loss: 4.8681\n2023-01-05 01:32:49,832 - mmcls - INFO - Epoch [81][1900/5005]\tlr: 1.000e-03, eta: 2:24:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8484\n2023-01-05 01:32:58,710 - mmcls - INFO - Epoch [81][2000/5005]\tlr: 1.000e-03, eta: 2:24:07, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8551\n2023-01-05 01:33:07,483 - mmcls - INFO - Epoch [81][2100/5005]\tlr: 1.000e-03, eta: 2:23:58, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8441\n2023-01-05 01:33:16,114 - mmcls - INFO - Epoch [81][2200/5005]\tlr: 1.000e-03, eta: 2:23:49, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8587\n2023-01-05 01:33:24,845 - mmcls - INFO - Epoch [81][2300/5005]\tlr: 1.000e-03, eta: 2:23:40, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8513\n2023-01-05 01:33:33,469 - mmcls - INFO - Epoch [81][2400/5005]\tlr: 1.000e-03, eta: 2:23:31, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8674\n2023-01-05 01:33:42,068 - mmcls - INFO - Epoch [81][2500/5005]\tlr: 1.000e-03, eta: 2:23:22, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8660\n2023-01-05 01:33:50,911 - mmcls - INFO - Epoch [81][2600/5005]\tlr: 1.000e-03, eta: 2:23:14, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8645\n2023-01-05 01:33:59,308 - mmcls - INFO - Epoch [81][2700/5005]\tlr: 1.000e-03, eta: 2:23:05, time: 0.084, data_time: 0.015, memory: 928, loss: 4.8532\n2023-01-05 01:34:08,347 - mmcls - INFO - Epoch [81][2800/5005]\tlr: 1.000e-03, eta: 2:22:56, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8462\n2023-01-05 01:34:16,819 - mmcls - INFO - Epoch [81][2900/5005]\tlr: 1.000e-03, eta: 2:22:47, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8655\n2023-01-05 01:34:25,474 - mmcls - INFO - Epoch [81][3000/5005]\tlr: 1.000e-03, eta: 2:22:38, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8503\n2023-01-05 01:34:34,050 - mmcls - INFO - Epoch [81][3100/5005]\tlr: 1.000e-03, eta: 2:22:29, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8650\n2023-01-05 01:34:42,746 - mmcls - INFO - Epoch [81][3200/5005]\tlr: 1.000e-03, eta: 2:22:21, time: 0.087, data_time: 0.000, memory: 928, loss: 4.8297\n2023-01-05 01:34:51,253 - mmcls - INFO - Epoch [81][3300/5005]\tlr: 1.000e-03, eta: 2:22:12, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8463\n2023-01-05 01:34:59,930 - mmcls - INFO - Epoch [81][3400/5005]\tlr: 1.000e-03, eta: 2:22:03, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8523\n2023-01-05 01:35:08,738 - mmcls - INFO - Epoch [81][3500/5005]\tlr: 1.000e-03, eta: 2:21:54, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8497\n2023-01-05 01:35:17,138 - mmcls - INFO - Epoch [81][3600/5005]\tlr: 1.000e-03, eta: 2:21:45, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8627\n2023-01-05 01:35:25,980 - mmcls - INFO - Epoch [81][3700/5005]\tlr: 1.000e-03, eta: 2:21:36, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8585\n2023-01-05 01:35:34,661 - mmcls - INFO - Epoch [81][3800/5005]\tlr: 1.000e-03, eta: 2:21:27, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8400\n2023-01-05 01:35:43,178 - mmcls - INFO - Epoch [81][3900/5005]\tlr: 1.000e-03, eta: 2:21:19, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8853\n2023-01-05 01:35:51,707 - mmcls - INFO - Epoch [81][4000/5005]\tlr: 1.000e-03, eta: 2:21:10, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8593\n2023-01-05 01:36:00,626 - mmcls - INFO - Epoch [81][4100/5005]\tlr: 1.000e-03, eta: 2:21:01, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8264\n2023-01-05 01:36:09,120 - mmcls - INFO - Epoch [81][4200/5005]\tlr: 1.000e-03, eta: 2:20:52, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8559\n2023-01-05 01:36:17,882 - mmcls - INFO - Epoch [81][4300/5005]\tlr: 1.000e-03, eta: 2:20:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8559\n2023-01-05 01:36:27,426 - mmcls - INFO - Epoch [81][4400/5005]\tlr: 1.000e-03, eta: 2:20:34, time: 0.095, data_time: 0.024, memory: 928, loss: 4.8414\n2023-01-05 01:36:36,035 - mmcls - INFO - Epoch [81][4500/5005]\tlr: 1.000e-03, eta: 2:20:26, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8816\n2023-01-05 01:36:44,631 - mmcls - INFO - Epoch [81][4600/5005]\tlr: 1.000e-03, eta: 2:20:17, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8446\n2023-01-05 01:36:53,493 - mmcls - INFO - Epoch [81][4700/5005]\tlr: 1.000e-03, eta: 2:20:08, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8760\n2023-01-05 01:37:01,986 - mmcls - INFO - Epoch [81][4800/5005]\tlr: 1.000e-03, eta: 2:19:59, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8693\n2023-01-05 01:37:10,642 - mmcls - INFO - Epoch [81][4900/5005]\tlr: 1.000e-03, eta: 2:19:50, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8500\n2023-01-05 01:37:19,354 - mmcls - INFO - Epoch [81][5000/5005]\tlr: 1.000e-03, eta: 2:19:41, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8820\n2023-01-05 01:37:42,058 - mmcls - INFO - Epoch(val) [81][196]\taccuracy_top-1: 16.6260, accuracy_top-5: 34.3740\n2023-01-05 01:37:52,659 - mmcls - INFO - Epoch [82][100/5005]\tlr: 1.000e-03, eta: 2:19:32, time: 0.106, data_time: 0.059, memory: 928, loss: 4.8756\n2023-01-05 01:38:01,275 - mmcls - INFO - Epoch [82][200/5005]\tlr: 1.000e-03, eta: 2:19:24, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8492\n2023-01-05 01:38:10,143 - mmcls - INFO - Epoch [82][300/5005]\tlr: 1.000e-03, eta: 2:19:15, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8713\n2023-01-05 01:38:18,837 - mmcls - INFO - Epoch [82][400/5005]\tlr: 1.000e-03, eta: 2:19:06, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8564\n2023-01-05 01:38:27,481 - mmcls - INFO - Epoch [82][500/5005]\tlr: 1.000e-03, eta: 2:18:57, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8299\n2023-01-05 01:38:36,151 - mmcls - INFO - Epoch [82][600/5005]\tlr: 1.000e-03, eta: 2:18:48, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8492\n2023-01-05 01:38:45,008 - mmcls - INFO - Epoch [82][700/5005]\tlr: 1.000e-03, eta: 2:18:39, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8467\n2023-01-05 01:38:54,068 - mmcls - INFO - Epoch [82][800/5005]\tlr: 1.000e-03, eta: 2:18:31, time: 0.091, data_time: 0.023, memory: 928, loss: 4.8575\n2023-01-05 01:39:02,918 - mmcls - INFO - Epoch [82][900/5005]\tlr: 1.000e-03, eta: 2:18:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8630\n2023-01-05 01:39:11,643 - mmcls - INFO - Epoch [82][1000/5005]\tlr: 1.000e-03, eta: 2:18:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8519\n2023-01-05 01:39:20,782 - mmcls - INFO - Epoch [82][1100/5005]\tlr: 1.000e-03, eta: 2:18:04, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8503\n2023-01-05 01:39:29,675 - mmcls - INFO - Epoch [82][1200/5005]\tlr: 1.000e-03, eta: 2:17:55, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8569\n2023-01-05 01:39:38,377 - mmcls - INFO - Epoch [82][1300/5005]\tlr: 1.000e-03, eta: 2:17:47, time: 0.087, data_time: 0.038, memory: 928, loss: 4.8723\n2023-01-05 01:39:47,093 - mmcls - INFO - Epoch [82][1400/5005]\tlr: 1.000e-03, eta: 2:17:38, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8627\n2023-01-05 01:39:55,845 - mmcls - INFO - Epoch [82][1500/5005]\tlr: 1.000e-03, eta: 2:17:29, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8439\n2023-01-05 01:40:04,716 - mmcls - INFO - Epoch [82][1600/5005]\tlr: 1.000e-03, eta: 2:17:20, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8470\n2023-01-05 01:40:13,557 - mmcls - INFO - Epoch [82][1700/5005]\tlr: 1.000e-03, eta: 2:17:11, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8721\n2023-01-05 01:40:22,574 - mmcls - INFO - Epoch [82][1800/5005]\tlr: 1.000e-03, eta: 2:17:03, time: 0.090, data_time: 0.030, memory: 928, loss: 4.8447\n2023-01-05 01:40:31,254 - mmcls - INFO - Epoch [82][1900/5005]\tlr: 1.000e-03, eta: 2:16:54, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8608\n2023-01-05 01:40:40,167 - mmcls - INFO - Epoch [82][2000/5005]\tlr: 1.000e-03, eta: 2:16:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8518\n2023-01-05 01:40:48,952 - mmcls - INFO - Epoch [82][2100/5005]\tlr: 1.000e-03, eta: 2:16:36, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8589\n2023-01-05 01:40:57,673 - mmcls - INFO - Epoch [82][2200/5005]\tlr: 1.000e-03, eta: 2:16:27, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8617\n2023-01-05 01:41:06,631 - mmcls - INFO - Epoch [82][2300/5005]\tlr: 1.000e-03, eta: 2:16:19, time: 0.090, data_time: 0.021, memory: 928, loss: 4.8331\n2023-01-05 01:41:15,656 - mmcls - INFO - Epoch [82][2400/5005]\tlr: 1.000e-03, eta: 2:16:10, time: 0.090, data_time: 0.014, memory: 928, loss: 4.8559\n2023-01-05 01:41:25,062 - mmcls - INFO - Epoch [82][2500/5005]\tlr: 1.000e-03, eta: 2:16:01, time: 0.094, data_time: 0.029, memory: 928, loss: 4.8735\n2023-01-05 01:41:33,837 - mmcls - INFO - Epoch [82][2600/5005]\tlr: 1.000e-03, eta: 2:15:52, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8373\n2023-01-05 01:41:42,594 - mmcls - INFO - Epoch [82][2700/5005]\tlr: 1.000e-03, eta: 2:15:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8600\n2023-01-05 01:41:51,965 - mmcls - INFO - Epoch [82][2800/5005]\tlr: 1.000e-03, eta: 2:15:35, time: 0.094, data_time: 0.005, memory: 928, loss: 4.8515\n2023-01-05 01:42:00,730 - mmcls - INFO - Epoch [82][2900/5005]\tlr: 1.000e-03, eta: 2:15:26, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8256\n2023-01-05 01:42:09,571 - mmcls - INFO - Epoch [82][3000/5005]\tlr: 1.000e-03, eta: 2:15:17, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8626\n2023-01-05 01:42:18,431 - mmcls - INFO - Epoch [82][3100/5005]\tlr: 1.000e-03, eta: 2:15:08, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8513\n2023-01-05 01:42:27,183 - mmcls - INFO - Epoch [82][3200/5005]\tlr: 1.000e-03, eta: 2:14:59, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8607\n2023-01-05 01:42:36,102 - mmcls - INFO - Epoch [82][3300/5005]\tlr: 1.000e-03, eta: 2:14:51, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8648\n2023-01-05 01:42:45,309 - mmcls - INFO - Epoch [82][3400/5005]\tlr: 1.000e-03, eta: 2:14:42, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8657\n2023-01-05 01:42:54,015 - mmcls - INFO - Epoch [82][3500/5005]\tlr: 1.000e-03, eta: 2:14:33, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8461\n2023-01-05 01:43:02,910 - mmcls - INFO - Epoch [82][3600/5005]\tlr: 1.000e-03, eta: 2:14:24, time: 0.089, data_time: 0.031, memory: 928, loss: 4.8238\n2023-01-05 01:43:11,594 - mmcls - INFO - Epoch [82][3700/5005]\tlr: 1.000e-03, eta: 2:14:15, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8561\n2023-01-05 01:43:20,599 - mmcls - INFO - Epoch [82][3800/5005]\tlr: 1.000e-03, eta: 2:14:07, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8635\n2023-01-05 01:43:29,554 - mmcls - INFO - Epoch [82][3900/5005]\tlr: 1.000e-03, eta: 2:13:58, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8517\n2023-01-05 01:43:38,330 - mmcls - INFO - Epoch [82][4000/5005]\tlr: 1.000e-03, eta: 2:13:49, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8570\n2023-01-05 01:43:46,915 - mmcls - INFO - Epoch [82][4100/5005]\tlr: 1.000e-03, eta: 2:13:40, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8726\n2023-01-05 01:43:55,479 - mmcls - INFO - Epoch [82][4200/5005]\tlr: 1.000e-03, eta: 2:13:31, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8669\n2023-01-05 01:44:04,067 - mmcls - INFO - Epoch [82][4300/5005]\tlr: 1.000e-03, eta: 2:13:23, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8340\n2023-01-05 01:44:12,683 - mmcls - INFO - Epoch [82][4400/5005]\tlr: 1.000e-03, eta: 2:13:14, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8520\n2023-01-05 01:44:21,479 - mmcls - INFO - Epoch [82][4500/5005]\tlr: 1.000e-03, eta: 2:13:05, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8730\n2023-01-05 01:44:30,305 - mmcls - INFO - Epoch [82][4600/5005]\tlr: 1.000e-03, eta: 2:12:56, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8573\n2023-01-05 01:44:38,834 - mmcls - INFO - Epoch [82][4700/5005]\tlr: 1.000e-03, eta: 2:12:47, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8644\n2023-01-05 01:44:47,840 - mmcls - INFO - Epoch [82][4800/5005]\tlr: 1.000e-03, eta: 2:12:38, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8462\n2023-01-05 01:44:56,579 - mmcls - INFO - Epoch [82][4900/5005]\tlr: 1.000e-03, eta: 2:12:30, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8502\n2023-01-05 01:45:05,252 - mmcls - INFO - Epoch [82][5000/5005]\tlr: 1.000e-03, eta: 2:12:21, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8404\n2023-01-05 01:45:28,042 - mmcls - INFO - Epoch(val) [82][196]\taccuracy_top-1: 17.0900, accuracy_top-5: 34.6640\n2023-01-05 01:45:39,105 - mmcls - INFO - Epoch [83][100/5005]\tlr: 1.000e-03, eta: 2:12:12, time: 0.110, data_time: 0.030, memory: 928, loss: 4.8576\n2023-01-05 01:45:47,841 - mmcls - INFO - Epoch [83][200/5005]\tlr: 1.000e-03, eta: 2:12:03, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8518\n2023-01-05 01:45:56,378 - mmcls - INFO - Epoch [83][300/5005]\tlr: 1.000e-03, eta: 2:11:54, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8433\n2023-01-05 01:46:05,521 - mmcls - INFO - Epoch [83][400/5005]\tlr: 1.000e-03, eta: 2:11:45, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8639\n2023-01-05 01:46:14,255 - mmcls - INFO - Epoch [83][500/5005]\tlr: 1.000e-03, eta: 2:11:37, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8559\n2023-01-05 01:46:23,039 - mmcls - INFO - Epoch [83][600/5005]\tlr: 1.000e-03, eta: 2:11:28, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8550\n2023-01-05 01:46:31,416 - mmcls - INFO - Epoch [83][700/5005]\tlr: 1.000e-03, eta: 2:11:19, time: 0.084, data_time: 0.006, memory: 928, loss: 4.8473\n2023-01-05 01:46:40,180 - mmcls - INFO - Epoch [83][800/5005]\tlr: 1.000e-03, eta: 2:11:10, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8508\n2023-01-05 01:46:48,827 - mmcls - INFO - Epoch [83][900/5005]\tlr: 1.000e-03, eta: 2:11:01, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8509\n2023-01-05 01:46:57,665 - mmcls - INFO - Epoch [83][1000/5005]\tlr: 1.000e-03, eta: 2:10:52, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8363\n2023-01-05 01:47:06,399 - mmcls - INFO - Epoch [83][1100/5005]\tlr: 1.000e-03, eta: 2:10:43, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8617\n2023-01-05 01:47:15,274 - mmcls - INFO - Epoch [83][1200/5005]\tlr: 1.000e-03, eta: 2:10:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8492\n2023-01-05 01:47:23,992 - mmcls - INFO - Epoch [83][1300/5005]\tlr: 1.000e-03, eta: 2:10:26, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8754\n2023-01-05 01:47:32,586 - mmcls - INFO - Epoch [83][1400/5005]\tlr: 1.000e-03, eta: 2:10:17, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8604\n2023-01-05 01:47:41,532 - mmcls - INFO - Epoch [83][1500/5005]\tlr: 1.000e-03, eta: 2:10:08, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8756\n2023-01-05 01:47:50,687 - mmcls - INFO - Epoch [83][1600/5005]\tlr: 1.000e-03, eta: 2:09:59, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8536\n2023-01-05 01:47:59,448 - mmcls - INFO - Epoch [83][1700/5005]\tlr: 1.000e-03, eta: 2:09:51, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8519\n2023-01-05 01:48:08,086 - mmcls - INFO - Epoch [83][1800/5005]\tlr: 1.000e-03, eta: 2:09:42, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8343\n2023-01-05 01:48:17,001 - mmcls - INFO - Epoch [83][1900/5005]\tlr: 1.000e-03, eta: 2:09:33, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8706\n2023-01-05 01:48:25,598 - mmcls - INFO - Epoch [83][2000/5005]\tlr: 1.000e-03, eta: 2:09:24, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8421\n2023-01-05 01:48:34,214 - mmcls - INFO - Epoch [83][2100/5005]\tlr: 1.000e-03, eta: 2:09:15, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8620\n2023-01-05 01:48:43,039 - mmcls - INFO - Epoch [83][2200/5005]\tlr: 1.000e-03, eta: 2:09:06, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8349\n2023-01-05 01:48:51,710 - mmcls - INFO - Epoch [83][2300/5005]\tlr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8471\n2023-01-05 01:49:00,408 - mmcls - INFO - Epoch [83][2400/5005]\tlr: 1.000e-03, eta: 2:08:49, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8593\n2023-01-05 01:49:09,250 - mmcls - INFO - Epoch [83][2500/5005]\tlr: 1.000e-03, eta: 2:08:40, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8465\n2023-01-05 01:49:18,014 - mmcls - INFO - Epoch [83][2600/5005]\tlr: 1.000e-03, eta: 2:08:31, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8618\n2023-01-05 01:49:26,607 - mmcls - INFO - Epoch [83][2700/5005]\tlr: 1.000e-03, eta: 2:08:22, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8526\n2023-01-05 01:49:35,603 - mmcls - INFO - Epoch [83][2800/5005]\tlr: 1.000e-03, eta: 2:08:14, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8453\n2023-01-05 01:49:44,566 - mmcls - INFO - Epoch [83][2900/5005]\tlr: 1.000e-03, eta: 2:08:05, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8427\n2023-01-05 01:49:53,236 - mmcls - INFO - Epoch [83][3000/5005]\tlr: 1.000e-03, eta: 2:07:56, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8574\n2023-01-05 01:50:02,105 - mmcls - INFO - Epoch [83][3100/5005]\tlr: 1.000e-03, eta: 2:07:47, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8392\n2023-01-05 01:50:10,626 - mmcls - INFO - Epoch [83][3200/5005]\tlr: 1.000e-03, eta: 2:07:38, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8485\n2023-01-05 01:50:19,659 - mmcls - INFO - Epoch [83][3300/5005]\tlr: 1.000e-03, eta: 2:07:29, time: 0.090, data_time: 0.020, memory: 928, loss: 4.8363\n2023-01-05 01:50:28,333 - mmcls - INFO - Epoch [83][3400/5005]\tlr: 1.000e-03, eta: 2:07:21, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8672\n2023-01-05 01:50:37,102 - mmcls - INFO - Epoch [83][3500/5005]\tlr: 1.000e-03, eta: 2:07:12, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8524\n2023-01-05 01:50:46,274 - mmcls - INFO - Epoch [83][3600/5005]\tlr: 1.000e-03, eta: 2:07:03, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8450\n2023-01-05 01:50:54,935 - mmcls - INFO - Epoch [83][3700/5005]\tlr: 1.000e-03, eta: 2:06:54, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8435\n2023-01-05 01:51:03,566 - mmcls - INFO - Epoch [83][3800/5005]\tlr: 1.000e-03, eta: 2:06:45, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8591\n2023-01-05 01:51:12,332 - mmcls - INFO - Epoch [83][3900/5005]\tlr: 1.000e-03, eta: 2:06:37, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8554\n2023-01-05 01:51:20,968 - mmcls - INFO - Epoch [83][4000/5005]\tlr: 1.000e-03, eta: 2:06:28, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8601\n2023-01-05 01:51:30,187 - mmcls - INFO - Epoch [83][4100/5005]\tlr: 1.000e-03, eta: 2:06:19, time: 0.092, data_time: 0.021, memory: 928, loss: 4.8652\n2023-01-05 01:51:38,864 - mmcls - INFO - Epoch [83][4200/5005]\tlr: 1.000e-03, eta: 2:06:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8544\n2023-01-05 01:51:47,489 - mmcls - INFO - Epoch [83][4300/5005]\tlr: 1.000e-03, eta: 2:06:01, time: 0.086, data_time: 0.046, memory: 928, loss: 4.8589\n2023-01-05 01:51:56,343 - mmcls - INFO - Epoch [83][4400/5005]\tlr: 1.000e-03, eta: 2:05:52, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8714\n2023-01-05 01:52:05,141 - mmcls - INFO - Epoch [83][4500/5005]\tlr: 1.000e-03, eta: 2:05:44, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8463\n2023-01-05 01:52:14,208 - mmcls - INFO - Epoch [83][4600/5005]\tlr: 1.000e-03, eta: 2:05:35, time: 0.091, data_time: 0.030, memory: 928, loss: 4.8600\n2023-01-05 01:52:22,705 - mmcls - INFO - Epoch [83][4700/5005]\tlr: 1.000e-03, eta: 2:05:26, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8701\n2023-01-05 01:52:31,437 - mmcls - INFO - Epoch [83][4800/5005]\tlr: 1.000e-03, eta: 2:05:17, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8813\n2023-01-05 01:52:40,396 - mmcls - INFO - Epoch [83][4900/5005]\tlr: 1.000e-03, eta: 2:05:08, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8450\n2023-01-05 01:52:48,996 - mmcls - INFO - Epoch [83][5000/5005]\tlr: 1.000e-03, eta: 2:04:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8343\n2023-01-05 01:53:11,891 - mmcls - INFO - Epoch(val) [83][196]\taccuracy_top-1: 16.6600, accuracy_top-5: 34.1460\n2023-01-05 01:53:23,225 - mmcls - INFO - Epoch [84][100/5005]\tlr: 1.000e-03, eta: 2:04:51, time: 0.113, data_time: 0.034, memory: 928, loss: 4.8309\n2023-01-05 01:53:32,045 - mmcls - INFO - Epoch [84][200/5005]\tlr: 1.000e-03, eta: 2:04:42, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8655\n2023-01-05 01:53:40,670 - mmcls - INFO - Epoch [84][300/5005]\tlr: 1.000e-03, eta: 2:04:33, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8596\n2023-01-05 01:53:49,370 - mmcls - INFO - Epoch [84][400/5005]\tlr: 1.000e-03, eta: 2:04:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8489\n2023-01-05 01:53:58,131 - mmcls - INFO - Epoch [84][500/5005]\tlr: 1.000e-03, eta: 2:04:15, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8479\n2023-01-05 01:54:07,132 - mmcls - INFO - Epoch [84][600/5005]\tlr: 1.000e-03, eta: 2:04:07, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8240\n2023-01-05 01:54:16,089 - mmcls - INFO - Epoch [84][700/5005]\tlr: 1.000e-03, eta: 2:03:58, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8626\n2023-01-05 01:54:25,034 - mmcls - INFO - Epoch [84][800/5005]\tlr: 1.000e-03, eta: 2:03:49, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8689\n2023-01-05 01:54:33,754 - mmcls - INFO - Epoch [84][900/5005]\tlr: 1.000e-03, eta: 2:03:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8464\n2023-01-05 01:54:42,802 - mmcls - INFO - Epoch [84][1000/5005]\tlr: 1.000e-03, eta: 2:03:31, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8531\n2023-01-05 01:54:51,532 - mmcls - INFO - Epoch [84][1100/5005]\tlr: 1.000e-03, eta: 2:03:23, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8582\n2023-01-05 01:55:00,179 - mmcls - INFO - Epoch [84][1200/5005]\tlr: 1.000e-03, eta: 2:03:14, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8595\n2023-01-05 01:55:08,860 - mmcls - INFO - Epoch [84][1300/5005]\tlr: 1.000e-03, eta: 2:03:05, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8319\n2023-01-05 01:55:17,499 - mmcls - INFO - Epoch [84][1400/5005]\tlr: 1.000e-03, eta: 2:02:56, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8500\n2023-01-05 01:55:26,023 - mmcls - INFO - Epoch [84][1500/5005]\tlr: 1.000e-03, eta: 2:02:47, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8363\n2023-01-05 01:55:34,851 - mmcls - INFO - Epoch [84][1600/5005]\tlr: 1.000e-03, eta: 2:02:38, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8444\n2023-01-05 01:55:43,471 - mmcls - INFO - Epoch [84][1700/5005]\tlr: 1.000e-03, eta: 2:02:29, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8374\n2023-01-05 01:55:52,322 - mmcls - INFO - Epoch [84][1800/5005]\tlr: 1.000e-03, eta: 2:02:21, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8347\n2023-01-05 01:56:01,326 - mmcls - INFO - Epoch [84][1900/5005]\tlr: 1.000e-03, eta: 2:02:12, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8284\n2023-01-05 01:56:10,126 - mmcls - INFO - Epoch [84][2000/5005]\tlr: 1.000e-03, eta: 2:02:03, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8765\n2023-01-05 01:56:18,590 - mmcls - INFO - Epoch [84][2100/5005]\tlr: 1.000e-03, eta: 2:01:54, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8394\n2023-01-05 01:56:27,102 - mmcls - INFO - Epoch [84][2200/5005]\tlr: 1.000e-03, eta: 2:01:45, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8411\n2023-01-05 01:56:36,236 - mmcls - INFO - Epoch [84][2300/5005]\tlr: 1.000e-03, eta: 2:01:37, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8721\n2023-01-05 01:56:44,813 - mmcls - INFO - Epoch [84][2400/5005]\tlr: 1.000e-03, eta: 2:01:28, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8272\n2023-01-05 01:56:53,424 - mmcls - INFO - Epoch [84][2500/5005]\tlr: 1.000e-03, eta: 2:01:19, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8660\n2023-01-05 01:57:02,200 - mmcls - INFO - Epoch [84][2600/5005]\tlr: 1.000e-03, eta: 2:01:10, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8458\n2023-01-05 01:57:10,851 - mmcls - INFO - Epoch [84][2700/5005]\tlr: 1.000e-03, eta: 2:01:01, time: 0.086, data_time: 0.026, memory: 928, loss: 4.8659\n2023-01-05 01:57:19,395 - mmcls - INFO - Epoch [84][2800/5005]\tlr: 1.000e-03, eta: 2:00:52, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8658\n2023-01-05 01:57:28,261 - mmcls - INFO - Epoch [84][2900/5005]\tlr: 1.000e-03, eta: 2:00:43, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8484\n2023-01-05 01:57:37,338 - mmcls - INFO - Epoch [84][3000/5005]\tlr: 1.000e-03, eta: 2:00:35, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8463\n2023-01-05 01:57:45,980 - mmcls - INFO - Epoch [84][3100/5005]\tlr: 1.000e-03, eta: 2:00:26, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8274\n2023-01-05 01:57:54,827 - mmcls - INFO - Epoch [84][3200/5005]\tlr: 1.000e-03, eta: 2:00:17, time: 0.089, data_time: 0.032, memory: 928, loss: 4.8229\n2023-01-05 01:58:03,887 - mmcls - INFO - Epoch [84][3300/5005]\tlr: 1.000e-03, eta: 2:00:08, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8560\n2023-01-05 01:58:12,483 - mmcls - INFO - Epoch [84][3400/5005]\tlr: 1.000e-03, eta: 1:59:59, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8479\n2023-01-05 01:58:21,690 - mmcls - INFO - Epoch [84][3500/5005]\tlr: 1.000e-03, eta: 1:59:51, time: 0.092, data_time: 0.014, memory: 928, loss: 4.8610\n2023-01-05 01:58:30,231 - mmcls - INFO - Epoch [84][3600/5005]\tlr: 1.000e-03, eta: 1:59:42, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8645\n2023-01-05 01:58:39,119 - mmcls - INFO - Epoch [84][3700/5005]\tlr: 1.000e-03, eta: 1:59:33, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8655\n2023-01-05 01:58:47,438 - mmcls - INFO - Epoch [84][3800/5005]\tlr: 1.000e-03, eta: 1:59:24, time: 0.083, data_time: 0.018, memory: 928, loss: 4.8718\n2023-01-05 01:58:56,387 - mmcls - INFO - Epoch [84][3900/5005]\tlr: 1.000e-03, eta: 1:59:15, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8524\n2023-01-05 01:59:05,214 - mmcls - INFO - Epoch [84][4000/5005]\tlr: 1.000e-03, eta: 1:59:07, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8422\n2023-01-05 01:59:13,966 - mmcls - INFO - Epoch [84][4100/5005]\tlr: 1.000e-03, eta: 1:58:58, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8537\n2023-01-05 01:59:22,634 - mmcls - INFO - Epoch [84][4200/5005]\tlr: 1.000e-03, eta: 1:58:49, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8776\n2023-01-05 01:59:31,439 - mmcls - INFO - Epoch [84][4300/5005]\tlr: 1.000e-03, eta: 1:58:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8673\n2023-01-05 01:59:40,301 - mmcls - INFO - Epoch [84][4400/5005]\tlr: 1.000e-03, eta: 1:58:31, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8693\n2023-01-05 01:59:48,668 - mmcls - INFO - Epoch [84][4500/5005]\tlr: 1.000e-03, eta: 1:58:22, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8619\n2023-01-05 01:59:57,233 - mmcls - INFO - Epoch [84][4600/5005]\tlr: 1.000e-03, eta: 1:58:13, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8401\n2023-01-05 02:00:05,868 - mmcls - INFO - Epoch [84][4700/5005]\tlr: 1.000e-03, eta: 1:58:05, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8621\n2023-01-05 02:00:14,728 - mmcls - INFO - Epoch [84][4800/5005]\tlr: 1.000e-03, eta: 1:57:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8602\n2023-01-05 02:00:23,491 - mmcls - INFO - Epoch [84][4900/5005]\tlr: 1.000e-03, eta: 1:57:47, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8368\n2023-01-05 02:00:32,374 - mmcls - INFO - Epoch [84][5000/5005]\tlr: 1.000e-03, eta: 1:57:38, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8704\n2023-01-05 02:00:55,074 - mmcls - INFO - Epoch(val) [84][196]\taccuracy_top-1: 16.9240, accuracy_top-5: 34.8840\n2023-01-05 02:01:06,082 - mmcls - INFO - Epoch [85][100/5005]\tlr: 1.000e-03, eta: 1:57:29, time: 0.110, data_time: 0.033, memory: 928, loss: 4.8306\n2023-01-05 02:01:14,651 - mmcls - INFO - Epoch [85][200/5005]\tlr: 1.000e-03, eta: 1:57:20, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8531\n2023-01-05 02:01:23,495 - mmcls - INFO - Epoch [85][300/5005]\tlr: 1.000e-03, eta: 1:57:12, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8576\n2023-01-05 02:01:32,240 - mmcls - INFO - Epoch [85][400/5005]\tlr: 1.000e-03, eta: 1:57:03, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8379\n2023-01-05 02:01:40,808 - mmcls - INFO - Epoch [85][500/5005]\tlr: 1.000e-03, eta: 1:56:54, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8471\n2023-01-05 02:01:49,550 - mmcls - INFO - Epoch [85][600/5005]\tlr: 1.000e-03, eta: 1:56:45, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8562\n2023-01-05 02:01:58,402 - mmcls - INFO - Epoch [85][700/5005]\tlr: 1.000e-03, eta: 1:56:36, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8383\n2023-01-05 02:02:07,433 - mmcls - INFO - Epoch [85][800/5005]\tlr: 1.000e-03, eta: 1:56:28, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8558\n2023-01-05 02:02:16,487 - mmcls - INFO - Epoch [85][900/5005]\tlr: 1.000e-03, eta: 1:56:19, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8571\n2023-01-05 02:02:25,245 - mmcls - INFO - Epoch [85][1000/5005]\tlr: 1.000e-03, eta: 1:56:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8309\n2023-01-05 02:02:33,914 - mmcls - INFO - Epoch [85][1100/5005]\tlr: 1.000e-03, eta: 1:56:01, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8562\n2023-01-05 02:02:42,915 - mmcls - INFO - Epoch [85][1200/5005]\tlr: 1.000e-03, eta: 1:55:52, time: 0.090, data_time: 0.014, memory: 928, loss: 4.8372\n2023-01-05 02:02:51,525 - mmcls - INFO - Epoch [85][1300/5005]\tlr: 1.000e-03, eta: 1:55:43, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8502\n2023-01-05 02:03:00,532 - mmcls - INFO - Epoch [85][1400/5005]\tlr: 1.000e-03, eta: 1:55:35, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8514\n2023-01-05 02:03:09,100 - mmcls - INFO - Epoch [85][1500/5005]\tlr: 1.000e-03, eta: 1:55:26, time: 0.086, data_time: 0.034, memory: 928, loss: 4.8446\n2023-01-05 02:03:17,707 - mmcls - INFO - Epoch [85][1600/5005]\tlr: 1.000e-03, eta: 1:55:17, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8535\n2023-01-05 02:03:26,547 - mmcls - INFO - Epoch [85][1700/5005]\tlr: 1.000e-03, eta: 1:55:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8585\n2023-01-05 02:03:35,269 - mmcls - INFO - Epoch [85][1800/5005]\tlr: 1.000e-03, eta: 1:54:59, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8750\n2023-01-05 02:03:43,953 - mmcls - INFO - Epoch [85][1900/5005]\tlr: 1.000e-03, eta: 1:54:50, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8378\n2023-01-05 02:03:52,960 - mmcls - INFO - Epoch [85][2000/5005]\tlr: 1.000e-03, eta: 1:54:42, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8432\n2023-01-05 02:04:01,543 - mmcls - INFO - Epoch [85][2100/5005]\tlr: 1.000e-03, eta: 1:54:33, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8452\n2023-01-05 02:04:10,286 - mmcls - INFO - Epoch [85][2200/5005]\tlr: 1.000e-03, eta: 1:54:24, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8517\n2023-01-05 02:04:19,327 - mmcls - INFO - Epoch [85][2300/5005]\tlr: 1.000e-03, eta: 1:54:15, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8627\n2023-01-05 02:04:28,167 - mmcls - INFO - Epoch [85][2400/5005]\tlr: 1.000e-03, eta: 1:54:06, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8438\n2023-01-05 02:04:36,961 - mmcls - INFO - Epoch [85][2500/5005]\tlr: 1.000e-03, eta: 1:53:58, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8545\n2023-01-05 02:04:45,535 - mmcls - INFO - Epoch [85][2600/5005]\tlr: 1.000e-03, eta: 1:53:49, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8602\n2023-01-05 02:04:54,198 - mmcls - INFO - Epoch [85][2700/5005]\tlr: 1.000e-03, eta: 1:53:40, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8458\n2023-01-05 02:05:02,972 - mmcls - INFO - Epoch [85][2800/5005]\tlr: 1.000e-03, eta: 1:53:31, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8397\n2023-01-05 02:05:11,824 - mmcls - INFO - Epoch [85][2900/5005]\tlr: 1.000e-03, eta: 1:53:22, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8657\n2023-01-05 02:05:20,530 - mmcls - INFO - Epoch [85][3000/5005]\tlr: 1.000e-03, eta: 1:53:13, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8462\n2023-01-05 02:05:29,263 - mmcls - INFO - Epoch [85][3100/5005]\tlr: 1.000e-03, eta: 1:53:05, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8388\n2023-01-05 02:05:37,953 - mmcls - INFO - Epoch [85][3200/5005]\tlr: 1.000e-03, eta: 1:52:56, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8593\n2023-01-05 02:05:46,657 - mmcls - INFO - Epoch [85][3300/5005]\tlr: 1.000e-03, eta: 1:52:47, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8744\n2023-01-05 02:05:55,269 - mmcls - INFO - Epoch [85][3400/5005]\tlr: 1.000e-03, eta: 1:52:38, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8577\n2023-01-05 02:06:04,002 - mmcls - INFO - Epoch [85][3500/5005]\tlr: 1.000e-03, eta: 1:52:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8511\n2023-01-05 02:06:13,011 - mmcls - INFO - Epoch [85][3600/5005]\tlr: 1.000e-03, eta: 1:52:20, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8768\n2023-01-05 02:06:21,841 - mmcls - INFO - Epoch [85][3700/5005]\tlr: 1.000e-03, eta: 1:52:12, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8473\n2023-01-05 02:06:30,706 - mmcls - INFO - Epoch [85][3800/5005]\tlr: 1.000e-03, eta: 1:52:03, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8491\n2023-01-05 02:06:39,175 - mmcls - INFO - Epoch [85][3900/5005]\tlr: 1.000e-03, eta: 1:51:54, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8455\n2023-01-05 02:06:47,900 - mmcls - INFO - Epoch [85][4000/5005]\tlr: 1.000e-03, eta: 1:51:45, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8687\n2023-01-05 02:06:56,498 - mmcls - INFO - Epoch [85][4100/5005]\tlr: 1.000e-03, eta: 1:51:36, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8599\n2023-01-05 02:07:05,375 - mmcls - INFO - Epoch [85][4200/5005]\tlr: 1.000e-03, eta: 1:51:28, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8295\n2023-01-05 02:07:14,239 - mmcls - INFO - Epoch [85][4300/5005]\tlr: 1.000e-03, eta: 1:51:19, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8291\n2023-01-05 02:07:22,861 - mmcls - INFO - Epoch [85][4400/5005]\tlr: 1.000e-03, eta: 1:51:10, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8463\n2023-01-05 02:07:31,548 - mmcls - INFO - Epoch [85][4500/5005]\tlr: 1.000e-03, eta: 1:51:01, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8371\n2023-01-05 02:07:40,203 - mmcls - INFO - Epoch [85][4600/5005]\tlr: 1.000e-03, eta: 1:50:52, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8712\n2023-01-05 02:07:49,040 - mmcls - INFO - Epoch [85][4700/5005]\tlr: 1.000e-03, eta: 1:50:43, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8420\n2023-01-05 02:07:57,985 - mmcls - INFO - Epoch [85][4800/5005]\tlr: 1.000e-03, eta: 1:50:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8376\n2023-01-05 02:08:06,586 - mmcls - INFO - Epoch [85][4900/5005]\tlr: 1.000e-03, eta: 1:50:26, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8517\n2023-01-05 02:08:15,686 - mmcls - INFO - Epoch [85][5000/5005]\tlr: 1.000e-03, eta: 1:50:17, time: 0.091, data_time: 0.016, memory: 928, loss: 4.8659\n2023-01-05 02:08:38,390 - mmcls - INFO - Epoch(val) [85][196]\taccuracy_top-1: 16.3500, accuracy_top-5: 33.4720\n2023-01-05 02:08:49,295 - mmcls - INFO - Epoch [86][100/5005]\tlr: 1.000e-03, eta: 1:50:08, time: 0.109, data_time: 0.046, memory: 928, loss: 4.8427\n2023-01-05 02:08:57,933 - mmcls - INFO - Epoch [86][200/5005]\tlr: 1.000e-03, eta: 1:49:59, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8610\n2023-01-05 02:09:06,809 - mmcls - INFO - Epoch [86][300/5005]\tlr: 1.000e-03, eta: 1:49:50, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8347\n2023-01-05 02:09:16,144 - mmcls - INFO - Epoch [86][400/5005]\tlr: 1.000e-03, eta: 1:49:42, time: 0.093, data_time: 0.002, memory: 928, loss: 4.8431\n2023-01-05 02:09:24,983 - mmcls - INFO - Epoch [86][500/5005]\tlr: 1.000e-03, eta: 1:49:33, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8223\n2023-01-05 02:09:33,863 - mmcls - INFO - Epoch [86][600/5005]\tlr: 1.000e-03, eta: 1:49:24, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8497\n2023-01-05 02:09:42,169 - mmcls - INFO - Epoch [86][700/5005]\tlr: 1.000e-03, eta: 1:49:15, time: 0.083, data_time: 0.012, memory: 928, loss: 4.8412\n2023-01-05 02:09:50,900 - mmcls - INFO - Epoch [86][800/5005]\tlr: 1.000e-03, eta: 1:49:06, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8390\n2023-01-05 02:09:59,929 - mmcls - INFO - Epoch [86][900/5005]\tlr: 1.000e-03, eta: 1:48:57, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8618\n2023-01-05 02:10:08,685 - mmcls - INFO - Epoch [86][1000/5005]\tlr: 1.000e-03, eta: 1:48:49, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8592\n2023-01-05 02:10:17,506 - mmcls - INFO - Epoch [86][1100/5005]\tlr: 1.000e-03, eta: 1:48:40, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8448\n2023-01-05 02:10:26,071 - mmcls - INFO - Epoch [86][1200/5005]\tlr: 1.000e-03, eta: 1:48:31, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8635\n2023-01-05 02:10:35,180 - mmcls - INFO - Epoch [86][1300/5005]\tlr: 1.000e-03, eta: 1:48:22, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8285\n2023-01-05 02:10:43,986 - mmcls - INFO - Epoch [86][1400/5005]\tlr: 1.000e-03, eta: 1:48:13, time: 0.088, data_time: 0.039, memory: 928, loss: 4.8579\n2023-01-05 02:10:52,789 - mmcls - INFO - Epoch [86][1500/5005]\tlr: 1.000e-03, eta: 1:48:05, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8585\n2023-01-05 02:11:01,331 - mmcls - INFO - Epoch [86][1600/5005]\tlr: 1.000e-03, eta: 1:47:56, time: 0.085, data_time: 0.018, memory: 928, loss: 4.8560\n2023-01-05 02:11:10,452 - mmcls - INFO - Epoch [86][1700/5005]\tlr: 1.000e-03, eta: 1:47:47, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8557\n2023-01-05 02:11:19,215 - mmcls - INFO - Epoch [86][1800/5005]\tlr: 1.000e-03, eta: 1:47:38, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8595\n2023-01-05 02:11:27,873 - mmcls - INFO - Epoch [86][1900/5005]\tlr: 1.000e-03, eta: 1:47:29, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8340\n2023-01-05 02:11:36,772 - mmcls - INFO - Epoch [86][2000/5005]\tlr: 1.000e-03, eta: 1:47:21, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8510\n2023-01-05 02:11:45,513 - mmcls - INFO - Epoch [86][2100/5005]\tlr: 1.000e-03, eta: 1:47:12, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8462\n2023-01-05 02:11:54,081 - mmcls - INFO - Epoch [86][2200/5005]\tlr: 1.000e-03, eta: 1:47:03, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8541\n2023-01-05 02:12:02,956 - mmcls - INFO - Epoch [86][2300/5005]\tlr: 1.000e-03, eta: 1:46:54, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8818\n2023-01-05 02:12:11,711 - mmcls - INFO - Epoch [86][2400/5005]\tlr: 1.000e-03, eta: 1:46:45, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8672\n2023-01-05 02:12:20,306 - mmcls - INFO - Epoch [86][2500/5005]\tlr: 1.000e-03, eta: 1:46:36, time: 0.086, data_time: 0.026, memory: 928, loss: 4.8486\n2023-01-05 02:12:28,928 - mmcls - INFO - Epoch [86][2600/5005]\tlr: 1.000e-03, eta: 1:46:28, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8654\n2023-01-05 02:12:37,535 - mmcls - INFO - Epoch [86][2700/5005]\tlr: 1.000e-03, eta: 1:46:19, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8551\n2023-01-05 02:12:46,264 - mmcls - INFO - Epoch [86][2800/5005]\tlr: 1.000e-03, eta: 1:46:10, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8320\n2023-01-05 02:12:55,103 - mmcls - INFO - Epoch [86][2900/5005]\tlr: 1.000e-03, eta: 1:46:01, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8586\n2023-01-05 02:13:03,937 - mmcls - INFO - Epoch [86][3000/5005]\tlr: 1.000e-03, eta: 1:45:52, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8419\n2023-01-05 02:13:12,753 - mmcls - INFO - Epoch [86][3100/5005]\tlr: 1.000e-03, eta: 1:45:43, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8466\n2023-01-05 02:13:21,331 - mmcls - INFO - Epoch [86][3200/5005]\tlr: 1.000e-03, eta: 1:45:35, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8532\n2023-01-05 02:13:30,193 - mmcls - INFO - Epoch [86][3300/5005]\tlr: 1.000e-03, eta: 1:45:26, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8275\n2023-01-05 02:13:38,992 - mmcls - INFO - Epoch [86][3400/5005]\tlr: 1.000e-03, eta: 1:45:17, time: 0.088, data_time: 0.042, memory: 928, loss: 4.8447\n2023-01-05 02:13:47,687 - mmcls - INFO - Epoch [86][3500/5005]\tlr: 1.000e-03, eta: 1:45:08, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8503\n2023-01-05 02:13:56,459 - mmcls - INFO - Epoch [86][3600/5005]\tlr: 1.000e-03, eta: 1:44:59, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8554\n2023-01-05 02:14:04,991 - mmcls - INFO - Epoch [86][3700/5005]\tlr: 1.000e-03, eta: 1:44:50, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8330\n2023-01-05 02:14:13,542 - mmcls - INFO - Epoch [86][3800/5005]\tlr: 1.000e-03, eta: 1:44:42, time: 0.086, data_time: 0.029, memory: 928, loss: 4.8545\n2023-01-05 02:14:22,270 - mmcls - INFO - Epoch [86][3900/5005]\tlr: 1.000e-03, eta: 1:44:33, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8726\n2023-01-05 02:14:31,188 - mmcls - INFO - Epoch [86][4000/5005]\tlr: 1.000e-03, eta: 1:44:24, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8371\n2023-01-05 02:14:40,238 - mmcls - INFO - Epoch [86][4100/5005]\tlr: 1.000e-03, eta: 1:44:15, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8509\n2023-01-05 02:14:49,033 - mmcls - INFO - Epoch [86][4200/5005]\tlr: 1.000e-03, eta: 1:44:06, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8492\n2023-01-05 02:14:57,845 - mmcls - INFO - Epoch [86][4300/5005]\tlr: 1.000e-03, eta: 1:43:58, time: 0.088, data_time: 0.021, memory: 928, loss: 4.8347\n2023-01-05 02:15:06,962 - mmcls - INFO - Epoch [86][4400/5005]\tlr: 1.000e-03, eta: 1:43:49, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8423\n2023-01-05 02:15:15,594 - mmcls - INFO - Epoch [86][4500/5005]\tlr: 1.000e-03, eta: 1:43:40, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8684\n2023-01-05 02:15:24,509 - mmcls - INFO - Epoch [86][4600/5005]\tlr: 1.000e-03, eta: 1:43:31, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8756\n2023-01-05 02:15:33,581 - mmcls - INFO - Epoch [86][4700/5005]\tlr: 1.000e-03, eta: 1:43:22, time: 0.091, data_time: 0.029, memory: 928, loss: 4.8588\n2023-01-05 02:15:42,259 - mmcls - INFO - Epoch [86][4800/5005]\tlr: 1.000e-03, eta: 1:43:13, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8442\n2023-01-05 02:15:50,804 - mmcls - INFO - Epoch [86][4900/5005]\tlr: 1.000e-03, eta: 1:43:05, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8566\n2023-01-05 02:15:59,888 - mmcls - INFO - Epoch [86][5000/5005]\tlr: 1.000e-03, eta: 1:42:56, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8488\n2023-01-05 02:16:22,643 - mmcls - INFO - Epoch(val) [86][196]\taccuracy_top-1: 17.6400, accuracy_top-5: 35.9040\n2023-01-05 02:16:33,615 - mmcls - INFO - Epoch [87][100/5005]\tlr: 1.000e-03, eta: 1:42:47, time: 0.109, data_time: 0.045, memory: 928, loss: 4.8608\n2023-01-05 02:16:42,438 - mmcls - INFO - Epoch [87][200/5005]\tlr: 1.000e-03, eta: 1:42:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8451\n2023-01-05 02:16:51,291 - mmcls - INFO - Epoch [87][300/5005]\tlr: 1.000e-03, eta: 1:42:29, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8263\n2023-01-05 02:16:59,780 - mmcls - INFO - Epoch [87][400/5005]\tlr: 1.000e-03, eta: 1:42:20, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8432\n2023-01-05 02:17:09,144 - mmcls - INFO - Epoch [87][500/5005]\tlr: 1.000e-03, eta: 1:42:12, time: 0.094, data_time: 0.004, memory: 928, loss: 4.8377\n2023-01-05 02:17:17,924 - mmcls - INFO - Epoch [87][600/5005]\tlr: 1.000e-03, eta: 1:42:03, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8355\n2023-01-05 02:17:26,719 - mmcls - INFO - Epoch [87][700/5005]\tlr: 1.000e-03, eta: 1:41:54, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8458\n2023-01-05 02:17:35,443 - mmcls - INFO - Epoch [87][800/5005]\tlr: 1.000e-03, eta: 1:41:45, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8570\n2023-01-05 02:17:44,080 - mmcls - INFO - Epoch [87][900/5005]\tlr: 1.000e-03, eta: 1:41:36, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8562\n2023-01-05 02:17:52,691 - mmcls - INFO - Epoch [87][1000/5005]\tlr: 1.000e-03, eta: 1:41:28, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8522\n2023-01-05 02:18:01,087 - mmcls - INFO - Epoch [87][1100/5005]\tlr: 1.000e-03, eta: 1:41:19, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8633\n2023-01-05 02:18:10,079 - mmcls - INFO - Epoch [87][1200/5005]\tlr: 1.000e-03, eta: 1:41:10, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8479\n2023-01-05 02:18:19,045 - mmcls - INFO - Epoch [87][1300/5005]\tlr: 1.000e-03, eta: 1:41:01, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8434\n2023-01-05 02:18:27,836 - mmcls - INFO - Epoch [87][1400/5005]\tlr: 1.000e-03, eta: 1:40:52, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8609\n2023-01-05 02:18:36,620 - mmcls - INFO - Epoch [87][1500/5005]\tlr: 1.000e-03, eta: 1:40:43, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8305\n2023-01-05 02:18:45,307 - mmcls - INFO - Epoch [87][1600/5005]\tlr: 1.000e-03, eta: 1:40:35, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8521\n2023-01-05 02:18:53,985 - mmcls - INFO - Epoch [87][1700/5005]\tlr: 1.000e-03, eta: 1:40:26, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8505\n2023-01-05 02:19:02,907 - mmcls - INFO - Epoch [87][1800/5005]\tlr: 1.000e-03, eta: 1:40:17, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8404\n2023-01-05 02:19:11,951 - mmcls - INFO - Epoch [87][1900/5005]\tlr: 1.000e-03, eta: 1:40:08, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8668\n2023-01-05 02:19:20,338 - mmcls - INFO - Epoch [87][2000/5005]\tlr: 1.000e-03, eta: 1:39:59, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8553\n2023-01-05 02:19:29,004 - mmcls - INFO - Epoch [87][2100/5005]\tlr: 1.000e-03, eta: 1:39:50, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8498\n2023-01-05 02:19:37,914 - mmcls - INFO - Epoch [87][2200/5005]\tlr: 1.000e-03, eta: 1:39:42, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8718\n2023-01-05 02:19:46,625 - mmcls - INFO - Epoch [87][2300/5005]\tlr: 1.000e-03, eta: 1:39:33, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8506\n2023-01-05 02:19:55,051 - mmcls - INFO - Epoch [87][2400/5005]\tlr: 1.000e-03, eta: 1:39:24, time: 0.084, data_time: 0.025, memory: 928, loss: 4.8398\n2023-01-05 02:20:04,034 - mmcls - INFO - Epoch [87][2500/5005]\tlr: 1.000e-03, eta: 1:39:15, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8634\n2023-01-05 02:20:12,782 - mmcls - INFO - Epoch [87][2600/5005]\tlr: 1.000e-03, eta: 1:39:06, time: 0.087, data_time: 0.022, memory: 928, loss: 4.8737\n2023-01-05 02:20:21,411 - mmcls - INFO - Epoch [87][2700/5005]\tlr: 1.000e-03, eta: 1:38:57, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8472\n2023-01-05 02:20:30,129 - mmcls - INFO - Epoch [87][2800/5005]\tlr: 1.000e-03, eta: 1:38:49, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8562\n2023-01-05 02:20:39,066 - mmcls - INFO - Epoch [87][2900/5005]\tlr: 1.000e-03, eta: 1:38:40, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8504\n2023-01-05 02:20:47,875 - mmcls - INFO - Epoch [87][3000/5005]\tlr: 1.000e-03, eta: 1:38:31, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8359\n2023-01-05 02:20:56,575 - mmcls - INFO - Epoch [87][3100/5005]\tlr: 1.000e-03, eta: 1:38:22, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8402\n2023-01-05 02:21:05,359 - mmcls - INFO - Epoch [87][3200/5005]\tlr: 1.000e-03, eta: 1:38:13, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8285\n2023-01-05 02:21:14,183 - mmcls - INFO - Epoch [87][3300/5005]\tlr: 1.000e-03, eta: 1:38:05, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8518\n2023-01-05 02:21:23,126 - mmcls - INFO - Epoch [87][3400/5005]\tlr: 1.000e-03, eta: 1:37:56, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8312\n2023-01-05 02:21:31,929 - mmcls - INFO - Epoch [87][3500/5005]\tlr: 1.000e-03, eta: 1:37:47, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8419\n2023-01-05 02:21:40,558 - mmcls - INFO - Epoch [87][3600/5005]\tlr: 1.000e-03, eta: 1:37:38, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8463\n2023-01-05 02:21:49,290 - mmcls - INFO - Epoch [87][3700/5005]\tlr: 1.000e-03, eta: 1:37:29, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8625\n2023-01-05 02:21:58,112 - mmcls - INFO - Epoch [87][3800/5005]\tlr: 1.000e-03, eta: 1:37:21, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8552\n2023-01-05 02:22:06,965 - mmcls - INFO - Epoch [87][3900/5005]\tlr: 1.000e-03, eta: 1:37:12, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8432\n2023-01-05 02:22:15,668 - mmcls - INFO - Epoch [87][4000/5005]\tlr: 1.000e-03, eta: 1:37:03, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8456\n2023-01-05 02:22:24,562 - mmcls - INFO - Epoch [87][4100/5005]\tlr: 1.000e-03, eta: 1:36:54, time: 0.089, data_time: 0.042, memory: 928, loss: 4.8406\n2023-01-05 02:22:33,292 - mmcls - INFO - Epoch [87][4200/5005]\tlr: 1.000e-03, eta: 1:36:45, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8503\n2023-01-05 02:22:42,088 - mmcls - INFO - Epoch [87][4300/5005]\tlr: 1.000e-03, eta: 1:36:36, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8391\n2023-01-05 02:22:50,864 - mmcls - INFO - Epoch [87][4400/5005]\tlr: 1.000e-03, eta: 1:36:28, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8417\n2023-01-05 02:22:59,734 - mmcls - INFO - Epoch [87][4500/5005]\tlr: 1.000e-03, eta: 1:36:19, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8492\n2023-01-05 02:23:08,892 - mmcls - INFO - Epoch [87][4600/5005]\tlr: 1.000e-03, eta: 1:36:10, time: 0.092, data_time: 0.009, memory: 928, loss: 4.8582\n2023-01-05 02:23:17,494 - mmcls - INFO - Epoch [87][4700/5005]\tlr: 1.000e-03, eta: 1:36:01, time: 0.086, data_time: 0.036, memory: 928, loss: 4.8259\n2023-01-05 02:23:26,680 - mmcls - INFO - Epoch [87][4800/5005]\tlr: 1.000e-03, eta: 1:35:52, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8661\n2023-01-05 02:23:35,320 - mmcls - INFO - Epoch [87][4900/5005]\tlr: 1.000e-03, eta: 1:35:44, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8436\n2023-01-05 02:23:44,226 - mmcls - INFO - Epoch [87][5000/5005]\tlr: 1.000e-03, eta: 1:35:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8754\n2023-01-05 02:24:06,949 - mmcls - INFO - Epoch(val) [87][196]\taccuracy_top-1: 15.9120, accuracy_top-5: 32.8820\n2023-01-05 02:24:17,876 - mmcls - INFO - Epoch [88][100/5005]\tlr: 1.000e-03, eta: 1:35:26, time: 0.109, data_time: 0.036, memory: 928, loss: 4.8391\n2023-01-05 02:24:26,956 - mmcls - INFO - Epoch [88][200/5005]\tlr: 1.000e-03, eta: 1:35:17, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8376\n2023-01-05 02:24:35,655 - mmcls - INFO - Epoch [88][300/5005]\tlr: 1.000e-03, eta: 1:35:08, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8463\n2023-01-05 02:24:44,383 - mmcls - INFO - Epoch [88][400/5005]\tlr: 1.000e-03, eta: 1:34:59, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8326\n2023-01-05 02:24:53,345 - mmcls - INFO - Epoch [88][500/5005]\tlr: 1.000e-03, eta: 1:34:51, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8497\n2023-01-05 02:25:01,787 - mmcls - INFO - Epoch [88][600/5005]\tlr: 1.000e-03, eta: 1:34:42, time: 0.084, data_time: 0.021, memory: 928, loss: 4.8341\n2023-01-05 02:25:10,407 - mmcls - INFO - Epoch [88][700/5005]\tlr: 1.000e-03, eta: 1:34:33, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8269\n2023-01-05 02:25:19,287 - mmcls - INFO - Epoch [88][800/5005]\tlr: 1.000e-03, eta: 1:34:24, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8325\n2023-01-05 02:25:28,115 - mmcls - INFO - Epoch [88][900/5005]\tlr: 1.000e-03, eta: 1:34:15, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8559\n2023-01-05 02:25:36,663 - mmcls - INFO - Epoch [88][1000/5005]\tlr: 1.000e-03, eta: 1:34:06, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8571\n2023-01-05 02:25:45,163 - mmcls - INFO - Epoch [88][1100/5005]\tlr: 1.000e-03, eta: 1:33:58, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8530\n2023-01-05 02:25:54,070 - mmcls - INFO - Epoch [88][1200/5005]\tlr: 1.000e-03, eta: 1:33:49, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8429\n2023-01-05 02:26:02,583 - mmcls - INFO - Epoch [88][1300/5005]\tlr: 1.000e-03, eta: 1:33:40, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8483\n2023-01-05 02:26:11,390 - mmcls - INFO - Epoch [88][1400/5005]\tlr: 1.000e-03, eta: 1:33:31, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8475\n2023-01-05 02:26:20,189 - mmcls - INFO - Epoch [88][1500/5005]\tlr: 1.000e-03, eta: 1:33:22, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8529\n2023-01-05 02:26:28,842 - mmcls - INFO - Epoch [88][1600/5005]\tlr: 1.000e-03, eta: 1:33:13, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8537\n2023-01-05 02:26:37,342 - mmcls - INFO - Epoch [88][1700/5005]\tlr: 1.000e-03, eta: 1:33:05, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8597\n2023-01-05 02:26:45,938 - mmcls - INFO - Epoch [88][1800/5005]\tlr: 1.000e-03, eta: 1:32:56, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8495\n2023-01-05 02:26:54,850 - mmcls - INFO - Epoch [88][1900/5005]\tlr: 1.000e-03, eta: 1:32:47, time: 0.089, data_time: 0.049, memory: 928, loss: 4.8449\n2023-01-05 02:27:03,626 - mmcls - INFO - Epoch [88][2000/5005]\tlr: 1.000e-03, eta: 1:32:38, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8463\n2023-01-05 02:27:12,583 - mmcls - INFO - Epoch [88][2100/5005]\tlr: 1.000e-03, eta: 1:32:29, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8330\n2023-01-05 02:27:21,092 - mmcls - INFO - Epoch [88][2200/5005]\tlr: 1.000e-03, eta: 1:32:20, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8307\n2023-01-05 02:27:29,805 - mmcls - INFO - Epoch [88][2300/5005]\tlr: 1.000e-03, eta: 1:32:12, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8559\n2023-01-05 02:27:38,702 - mmcls - INFO - Epoch [88][2400/5005]\tlr: 1.000e-03, eta: 1:32:03, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8616\n2023-01-05 02:27:47,500 - mmcls - INFO - Epoch [88][2500/5005]\tlr: 1.000e-03, eta: 1:31:54, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8602\n2023-01-05 02:27:56,310 - mmcls - INFO - Epoch [88][2600/5005]\tlr: 1.000e-03, eta: 1:31:45, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8695\n2023-01-05 02:28:04,799 - mmcls - INFO - Epoch [88][2700/5005]\tlr: 1.000e-03, eta: 1:31:36, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8287\n2023-01-05 02:28:13,432 - mmcls - INFO - Epoch [88][2800/5005]\tlr: 1.000e-03, eta: 1:31:27, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8328\n2023-01-05 02:28:21,863 - mmcls - INFO - Epoch [88][2900/5005]\tlr: 1.000e-03, eta: 1:31:19, time: 0.084, data_time: 0.031, memory: 928, loss: 4.8575\n2023-01-05 02:28:30,472 - mmcls - INFO - Epoch [88][3000/5005]\tlr: 1.000e-03, eta: 1:31:10, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8477\n2023-01-05 02:28:39,207 - mmcls - INFO - Epoch [88][3100/5005]\tlr: 1.000e-03, eta: 1:31:01, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8438\n2023-01-05 02:28:47,795 - mmcls - INFO - Epoch [88][3200/5005]\tlr: 1.000e-03, eta: 1:30:52, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8362\n2023-01-05 02:28:56,607 - mmcls - INFO - Epoch [88][3300/5005]\tlr: 1.000e-03, eta: 1:30:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8194\n2023-01-05 02:29:05,282 - mmcls - INFO - Epoch [88][3400/5005]\tlr: 1.000e-03, eta: 1:30:34, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8494\n2023-01-05 02:29:14,360 - mmcls - INFO - Epoch [88][3500/5005]\tlr: 1.000e-03, eta: 1:30:26, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8377\n2023-01-05 02:29:23,012 - mmcls - INFO - Epoch [88][3600/5005]\tlr: 1.000e-03, eta: 1:30:17, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8666\n2023-01-05 02:29:31,645 - mmcls - INFO - Epoch [88][3700/5005]\tlr: 1.000e-03, eta: 1:30:08, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8453\n2023-01-05 02:29:40,736 - mmcls - INFO - Epoch [88][3800/5005]\tlr: 1.000e-03, eta: 1:29:59, time: 0.091, data_time: 0.014, memory: 928, loss: 4.8536\n2023-01-05 02:29:49,676 - mmcls - INFO - Epoch [88][3900/5005]\tlr: 1.000e-03, eta: 1:29:50, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8641\n2023-01-05 02:29:58,833 - mmcls - INFO - Epoch [88][4000/5005]\tlr: 1.000e-03, eta: 1:29:42, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8154\n2023-01-05 02:30:07,432 - mmcls - INFO - Epoch [88][4100/5005]\tlr: 1.000e-03, eta: 1:29:33, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8474\n2023-01-05 02:30:16,309 - mmcls - INFO - Epoch [88][4200/5005]\tlr: 1.000e-03, eta: 1:29:24, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8669\n2023-01-05 02:30:25,036 - mmcls - INFO - Epoch [88][4300/5005]\tlr: 1.000e-03, eta: 1:29:15, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8627\n2023-01-05 02:30:34,018 - mmcls - INFO - Epoch [88][4400/5005]\tlr: 1.000e-03, eta: 1:29:06, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8511\n2023-01-05 02:30:42,792 - mmcls - INFO - Epoch [88][4500/5005]\tlr: 1.000e-03, eta: 1:28:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8397\n2023-01-05 02:30:51,977 - mmcls - INFO - Epoch [88][4600/5005]\tlr: 1.000e-03, eta: 1:28:49, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8778\n2023-01-05 02:31:00,974 - mmcls - INFO - Epoch [88][4700/5005]\tlr: 1.000e-03, eta: 1:28:40, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8450\n2023-01-05 02:31:10,477 - mmcls - INFO - Epoch [88][4800/5005]\tlr: 1.000e-03, eta: 1:28:31, time: 0.095, data_time: 0.045, memory: 928, loss: 4.8335\n2023-01-05 02:31:19,158 - mmcls - INFO - Epoch [88][4900/5005]\tlr: 1.000e-03, eta: 1:28:22, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8387\n2023-01-05 02:31:28,276 - mmcls - INFO - Epoch [88][5000/5005]\tlr: 1.000e-03, eta: 1:28:14, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8634\n2023-01-05 02:31:51,147 - mmcls - INFO - Epoch(val) [88][196]\taccuracy_top-1: 17.1040, accuracy_top-5: 35.0520\n2023-01-05 02:32:02,257 - mmcls - INFO - Epoch [89][100/5005]\tlr: 1.000e-03, eta: 1:28:05, time: 0.111, data_time: 0.034, memory: 928, loss: 4.8365\n2023-01-05 02:32:11,149 - mmcls - INFO - Epoch [89][200/5005]\tlr: 1.000e-03, eta: 1:27:56, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8566\n2023-01-05 02:32:19,826 - mmcls - INFO - Epoch [89][300/5005]\tlr: 1.000e-03, eta: 1:27:47, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8039\n2023-01-05 02:32:28,497 - mmcls - INFO - Epoch [89][400/5005]\tlr: 1.000e-03, eta: 1:27:38, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8263\n2023-01-05 02:32:37,555 - mmcls - INFO - Epoch [89][500/5005]\tlr: 1.000e-03, eta: 1:27:29, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8393\n2023-01-05 02:32:45,943 - mmcls - INFO - Epoch [89][600/5005]\tlr: 1.000e-03, eta: 1:27:21, time: 0.084, data_time: 0.002, memory: 928, loss: 4.8505\n2023-01-05 02:32:54,867 - mmcls - INFO - Epoch [89][700/5005]\tlr: 1.000e-03, eta: 1:27:12, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8300\n2023-01-05 02:33:03,663 - mmcls - INFO - Epoch [89][800/5005]\tlr: 1.000e-03, eta: 1:27:03, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8684\n2023-01-05 02:33:12,144 - mmcls - INFO - Epoch [89][900/5005]\tlr: 1.000e-03, eta: 1:26:54, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8442\n2023-01-05 02:33:20,702 - mmcls - INFO - Epoch [89][1000/5005]\tlr: 1.000e-03, eta: 1:26:45, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8350\n2023-01-05 02:33:29,675 - mmcls - INFO - Epoch [89][1100/5005]\tlr: 1.000e-03, eta: 1:26:36, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8267\n2023-01-05 02:33:38,433 - mmcls - INFO - Epoch [89][1200/5005]\tlr: 1.000e-03, eta: 1:26:28, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8348\n2023-01-05 02:33:47,322 - mmcls - INFO - Epoch [89][1300/5005]\tlr: 1.000e-03, eta: 1:26:19, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8056\n2023-01-05 02:33:55,892 - mmcls - INFO - Epoch [89][1400/5005]\tlr: 1.000e-03, eta: 1:26:10, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8377\n2023-01-05 02:34:04,651 - mmcls - INFO - Epoch [89][1500/5005]\tlr: 1.000e-03, eta: 1:26:01, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8557\n2023-01-05 02:34:13,345 - mmcls - INFO - Epoch [89][1600/5005]\tlr: 1.000e-03, eta: 1:25:52, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8528\n2023-01-05 02:34:22,339 - mmcls - INFO - Epoch [89][1700/5005]\tlr: 1.000e-03, eta: 1:25:43, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8382\n2023-01-05 02:34:31,562 - mmcls - INFO - Epoch [89][1800/5005]\tlr: 1.000e-03, eta: 1:25:35, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8376\n2023-01-05 02:34:40,079 - mmcls - INFO - Epoch [89][1900/5005]\tlr: 1.000e-03, eta: 1:25:26, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8416\n2023-01-05 02:34:48,618 - mmcls - INFO - Epoch [89][2000/5005]\tlr: 1.000e-03, eta: 1:25:17, time: 0.085, data_time: 0.023, memory: 928, loss: 4.8645\n2023-01-05 02:34:57,570 - mmcls - INFO - Epoch [89][2100/5005]\tlr: 1.000e-03, eta: 1:25:08, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8560\n2023-01-05 02:35:06,131 - mmcls - INFO - Epoch [89][2200/5005]\tlr: 1.000e-03, eta: 1:24:59, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8378\n2023-01-05 02:35:14,827 - mmcls - INFO - Epoch [89][2300/5005]\tlr: 1.000e-03, eta: 1:24:51, time: 0.087, data_time: 0.030, memory: 928, loss: 4.8586\n2023-01-05 02:35:23,571 - mmcls - INFO - Epoch [89][2400/5005]\tlr: 1.000e-03, eta: 1:24:42, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8531\n2023-01-05 02:35:32,309 - mmcls - INFO - Epoch [89][2500/5005]\tlr: 1.000e-03, eta: 1:24:33, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8440\n2023-01-05 02:35:41,158 - mmcls - INFO - Epoch [89][2600/5005]\tlr: 1.000e-03, eta: 1:24:24, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8390\n2023-01-05 02:35:50,141 - mmcls - INFO - Epoch [89][2700/5005]\tlr: 1.000e-03, eta: 1:24:15, time: 0.090, data_time: 0.023, memory: 928, loss: 4.8443\n2023-01-05 02:35:58,737 - mmcls - INFO - Epoch [89][2800/5005]\tlr: 1.000e-03, eta: 1:24:06, time: 0.086, data_time: 0.032, memory: 928, loss: 4.8515\n2023-01-05 02:36:07,504 - mmcls - INFO - Epoch [89][2900/5005]\tlr: 1.000e-03, eta: 1:23:58, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8325\n2023-01-05 02:36:16,590 - mmcls - INFO - Epoch [89][3000/5005]\tlr: 1.000e-03, eta: 1:23:49, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8525\n2023-01-05 02:36:25,128 - mmcls - INFO - Epoch [89][3100/5005]\tlr: 1.000e-03, eta: 1:23:40, time: 0.086, data_time: 0.028, memory: 928, loss: 4.8629\n2023-01-05 02:36:33,637 - mmcls - INFO - Epoch [89][3200/5005]\tlr: 1.000e-03, eta: 1:23:31, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8501\n2023-01-05 02:36:42,271 - mmcls - INFO - Epoch [89][3300/5005]\tlr: 1.000e-03, eta: 1:23:22, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8373\n2023-01-05 02:36:51,156 - mmcls - INFO - Epoch [89][3400/5005]\tlr: 1.000e-03, eta: 1:23:14, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8604\n2023-01-05 02:36:59,978 - mmcls - INFO - Epoch [89][3500/5005]\tlr: 1.000e-03, eta: 1:23:05, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8558\n2023-01-05 02:37:08,646 - mmcls - INFO - Epoch [89][3600/5005]\tlr: 1.000e-03, eta: 1:22:56, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8420\n2023-01-05 02:37:17,354 - mmcls - INFO - Epoch [89][3700/5005]\tlr: 1.000e-03, eta: 1:22:47, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8343\n2023-01-05 02:37:26,015 - mmcls - INFO - Epoch [89][3800/5005]\tlr: 1.000e-03, eta: 1:22:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8641\n2023-01-05 02:37:34,825 - mmcls - INFO - Epoch [89][3900/5005]\tlr: 1.000e-03, eta: 1:22:29, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8482\n2023-01-05 02:37:43,426 - mmcls - INFO - Epoch [89][4000/5005]\tlr: 1.000e-03, eta: 1:22:21, time: 0.086, data_time: 0.027, memory: 928, loss: 4.8735\n2023-01-05 02:37:52,161 - mmcls - INFO - Epoch [89][4100/5005]\tlr: 1.000e-03, eta: 1:22:12, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8382\n2023-01-05 02:38:00,782 - mmcls - INFO - Epoch [89][4200/5005]\tlr: 1.000e-03, eta: 1:22:03, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8433\n2023-01-05 02:38:09,522 - mmcls - INFO - Epoch [89][4300/5005]\tlr: 1.000e-03, eta: 1:21:54, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8633\n2023-01-05 02:38:18,347 - mmcls - INFO - Epoch [89][4400/5005]\tlr: 1.000e-03, eta: 1:21:45, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8445\n2023-01-05 02:38:27,252 - mmcls - INFO - Epoch [89][4500/5005]\tlr: 1.000e-03, eta: 1:21:36, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8596\n2023-01-05 02:38:35,995 - mmcls - INFO - Epoch [89][4600/5005]\tlr: 1.000e-03, eta: 1:21:28, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8359\n2023-01-05 02:38:44,835 - mmcls - INFO - Epoch [89][4700/5005]\tlr: 1.000e-03, eta: 1:21:19, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8624\n2023-01-05 02:38:53,486 - mmcls - INFO - Epoch [89][4800/5005]\tlr: 1.000e-03, eta: 1:21:10, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8367\n2023-01-05 02:39:02,203 - mmcls - INFO - Epoch [89][4900/5005]\tlr: 1.000e-03, eta: 1:21:01, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8603\n2023-01-05 02:39:10,956 - mmcls - INFO - Epoch [89][5000/5005]\tlr: 1.000e-03, eta: 1:20:52, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8591\n2023-01-05 02:39:33,920 - mmcls - INFO - Epoch(val) [89][196]\taccuracy_top-1: 17.4020, accuracy_top-5: 35.2540\n2023-01-05 02:39:44,599 - mmcls - INFO - Epoch [90][100/5005]\tlr: 1.000e-03, eta: 1:20:43, time: 0.107, data_time: 0.038, memory: 928, loss: 4.8325\n2023-01-05 02:39:53,528 - mmcls - INFO - Epoch [90][200/5005]\tlr: 1.000e-03, eta: 1:20:34, time: 0.089, data_time: 0.040, memory: 928, loss: 4.8339\n2023-01-05 02:40:02,257 - mmcls - INFO - Epoch [90][300/5005]\tlr: 1.000e-03, eta: 1:20:26, time: 0.087, data_time: 0.037, memory: 928, loss: 4.8012\n2023-01-05 02:40:11,176 - mmcls - INFO - Epoch [90][400/5005]\tlr: 1.000e-03, eta: 1:20:17, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8500\n2023-01-05 02:40:19,655 - mmcls - INFO - Epoch [90][500/5005]\tlr: 1.000e-03, eta: 1:20:08, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8364\n2023-01-05 02:40:28,432 - mmcls - INFO - Epoch [90][600/5005]\tlr: 1.000e-03, eta: 1:19:59, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8467\n2023-01-05 02:40:37,281 - mmcls - INFO - Epoch [90][700/5005]\tlr: 1.000e-03, eta: 1:19:50, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8232\n2023-01-05 02:40:46,123 - mmcls - INFO - Epoch [90][800/5005]\tlr: 1.000e-03, eta: 1:19:42, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8489\n2023-01-05 02:40:54,738 - mmcls - INFO - Epoch [90][900/5005]\tlr: 1.000e-03, eta: 1:19:33, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8566\n2023-01-05 02:41:03,381 - mmcls - INFO - Epoch [90][1000/5005]\tlr: 1.000e-03, eta: 1:19:24, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8341\n2023-01-05 02:41:12,178 - mmcls - INFO - Epoch [90][1100/5005]\tlr: 1.000e-03, eta: 1:19:15, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8731\n2023-01-05 02:41:21,068 - mmcls - INFO - Epoch [90][1200/5005]\tlr: 1.000e-03, eta: 1:19:06, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8522\n2023-01-05 02:41:29,727 - mmcls - INFO - Epoch [90][1300/5005]\tlr: 1.000e-03, eta: 1:18:57, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8484\n2023-01-05 02:41:38,711 - mmcls - INFO - Epoch [90][1400/5005]\tlr: 1.000e-03, eta: 1:18:49, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8354\n2023-01-05 02:41:47,237 - mmcls - INFO - Epoch [90][1500/5005]\tlr: 1.000e-03, eta: 1:18:40, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8602\n2023-01-05 02:41:55,888 - mmcls - INFO - Epoch [90][1600/5005]\tlr: 1.000e-03, eta: 1:18:31, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8360\n2023-01-05 02:42:04,660 - mmcls - INFO - Epoch [90][1700/5005]\tlr: 1.000e-03, eta: 1:18:22, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8257\n2023-01-05 02:42:13,400 - mmcls - INFO - Epoch [90][1800/5005]\tlr: 1.000e-03, eta: 1:18:13, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8352\n2023-01-05 02:42:21,886 - mmcls - INFO - Epoch [90][1900/5005]\tlr: 1.000e-03, eta: 1:18:04, time: 0.085, data_time: 0.036, memory: 928, loss: 4.8354\n2023-01-05 02:42:30,827 - mmcls - INFO - Epoch [90][2000/5005]\tlr: 1.000e-03, eta: 1:17:56, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8363\n2023-01-05 02:42:39,404 - mmcls - INFO - Epoch [90][2100/5005]\tlr: 1.000e-03, eta: 1:17:47, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8616\n2023-01-05 02:42:48,365 - mmcls - INFO - Epoch [90][2200/5005]\tlr: 1.000e-03, eta: 1:17:38, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8454\n2023-01-05 02:42:56,925 - mmcls - INFO - Epoch [90][2300/5005]\tlr: 1.000e-03, eta: 1:17:29, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8592\n2023-01-05 02:43:05,745 - mmcls - INFO - Epoch [90][2400/5005]\tlr: 1.000e-03, eta: 1:17:20, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8410\n2023-01-05 02:43:14,352 - mmcls - INFO - Epoch [90][2500/5005]\tlr: 1.000e-03, eta: 1:17:12, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8631\n2023-01-05 02:43:23,173 - mmcls - INFO - Epoch [90][2600/5005]\tlr: 1.000e-03, eta: 1:17:03, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8395\n2023-01-05 02:43:32,042 - mmcls - INFO - Epoch [90][2700/5005]\tlr: 1.000e-03, eta: 1:16:54, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8630\n2023-01-05 02:43:40,937 - mmcls - INFO - Epoch [90][2800/5005]\tlr: 1.000e-03, eta: 1:16:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8312\n2023-01-05 02:43:50,055 - mmcls - INFO - Epoch [90][2900/5005]\tlr: 1.000e-03, eta: 1:16:36, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8602\n2023-01-05 02:43:58,356 - mmcls - INFO - Epoch [90][3000/5005]\tlr: 1.000e-03, eta: 1:16:27, time: 0.083, data_time: 0.001, memory: 928, loss: 4.8435\n2023-01-05 02:44:06,888 - mmcls - INFO - Epoch [90][3100/5005]\tlr: 1.000e-03, eta: 1:16:19, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8575\n2023-01-05 02:44:15,515 - mmcls - INFO - Epoch [90][3200/5005]\tlr: 1.000e-03, eta: 1:16:10, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8388\n2023-01-05 02:44:24,310 - mmcls - INFO - Epoch [90][3300/5005]\tlr: 1.000e-03, eta: 1:16:01, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8766\n2023-01-05 02:44:33,046 - mmcls - INFO - Epoch [90][3400/5005]\tlr: 1.000e-03, eta: 1:15:52, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8320\n2023-01-05 02:44:42,219 - mmcls - INFO - Epoch [90][3500/5005]\tlr: 1.000e-03, eta: 1:15:43, time: 0.092, data_time: 0.037, memory: 928, loss: 4.8620\n2023-01-05 02:44:51,077 - mmcls - INFO - Epoch [90][3600/5005]\tlr: 1.000e-03, eta: 1:15:35, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8645\n2023-01-05 02:45:00,127 - mmcls - INFO - Epoch [90][3700/5005]\tlr: 1.000e-03, eta: 1:15:26, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8689\n2023-01-05 02:45:08,814 - mmcls - INFO - Epoch [90][3800/5005]\tlr: 1.000e-03, eta: 1:15:17, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8319\n2023-01-05 02:45:17,699 - mmcls - INFO - Epoch [90][3900/5005]\tlr: 1.000e-03, eta: 1:15:08, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8282\n2023-01-05 02:45:26,664 - mmcls - INFO - Epoch [90][4000/5005]\tlr: 1.000e-03, eta: 1:14:59, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8468\n2023-01-05 02:45:35,580 - mmcls - INFO - Epoch [90][4100/5005]\tlr: 1.000e-03, eta: 1:14:51, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8697\n2023-01-05 02:45:44,312 - mmcls - INFO - Epoch [90][4200/5005]\tlr: 1.000e-03, eta: 1:14:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8194\n2023-01-05 02:45:52,812 - mmcls - INFO - Epoch [90][4300/5005]\tlr: 1.000e-03, eta: 1:14:33, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8378\n2023-01-05 02:46:01,658 - mmcls - INFO - Epoch [90][4400/5005]\tlr: 1.000e-03, eta: 1:14:24, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8373\n2023-01-05 02:46:10,239 - mmcls - INFO - Epoch [90][4500/5005]\tlr: 1.000e-03, eta: 1:14:15, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8491\n2023-01-05 02:46:19,415 - mmcls - INFO - Epoch [90][4600/5005]\tlr: 1.000e-03, eta: 1:14:06, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8326\n2023-01-05 02:46:28,087 - mmcls - INFO - Epoch [90][4700/5005]\tlr: 1.000e-03, eta: 1:13:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8409\n2023-01-05 02:46:36,791 - mmcls - INFO - Epoch [90][4800/5005]\tlr: 1.000e-03, eta: 1:13:49, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8408\n2023-01-05 02:46:45,560 - mmcls - INFO - Epoch [90][4900/5005]\tlr: 1.000e-03, eta: 1:13:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8471\n2023-01-05 02:46:54,458 - mmcls - INFO - Epoch [90][5000/5005]\tlr: 1.000e-03, eta: 1:13:31, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8458\n2023-01-05 02:47:17,377 - mmcls - INFO - Epoch(val) [90][196]\taccuracy_top-1: 16.9780, accuracy_top-5: 34.3680\n2023-01-05 02:47:28,615 - mmcls - INFO - Epoch [91][100/5005]\tlr: 1.000e-04, eta: 1:13:22, time: 0.112, data_time: 0.040, memory: 928, loss: 4.8306\n2023-01-05 02:47:37,267 - mmcls - INFO - Epoch [91][200/5005]\tlr: 1.000e-04, eta: 1:13:13, time: 0.087, data_time: 0.037, memory: 928, loss: 4.8325\n2023-01-05 02:47:46,207 - mmcls - INFO - Epoch [91][300/5005]\tlr: 1.000e-04, eta: 1:13:04, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8458\n2023-01-05 02:47:54,867 - mmcls - INFO - Epoch [91][400/5005]\tlr: 1.000e-04, eta: 1:12:56, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8029\n2023-01-05 02:48:03,695 - mmcls - INFO - Epoch [91][500/5005]\tlr: 1.000e-04, eta: 1:12:47, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8246\n2023-01-05 02:48:12,680 - mmcls - INFO - Epoch [91][600/5005]\tlr: 1.000e-04, eta: 1:12:38, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8345\n2023-01-05 02:48:21,650 - mmcls - INFO - Epoch [91][700/5005]\tlr: 1.000e-04, eta: 1:12:29, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8067\n2023-01-05 02:48:30,465 - mmcls - INFO - Epoch [91][800/5005]\tlr: 1.000e-04, eta: 1:12:20, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8132\n2023-01-05 02:48:39,290 - mmcls - INFO - Epoch [91][900/5005]\tlr: 1.000e-04, eta: 1:12:12, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8067\n2023-01-05 02:48:47,918 - mmcls - INFO - Epoch [91][1000/5005]\tlr: 1.000e-04, eta: 1:12:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8180\n2023-01-05 02:48:56,687 - mmcls - INFO - Epoch [91][1100/5005]\tlr: 1.000e-04, eta: 1:11:54, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8156\n2023-01-05 02:49:05,617 - mmcls - INFO - Epoch [91][1200/5005]\tlr: 1.000e-04, eta: 1:11:45, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8042\n2023-01-05 02:49:14,290 - mmcls - INFO - Epoch [91][1300/5005]\tlr: 1.000e-04, eta: 1:11:36, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8372\n2023-01-05 02:49:22,791 - mmcls - INFO - Epoch [91][1400/5005]\tlr: 1.000e-04, eta: 1:11:27, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8038\n2023-01-05 02:49:31,849 - mmcls - INFO - Epoch [91][1500/5005]\tlr: 1.000e-04, eta: 1:11:19, time: 0.091, data_time: 0.025, memory: 928, loss: 4.8461\n2023-01-05 02:49:40,564 - mmcls - INFO - Epoch [91][1600/5005]\tlr: 1.000e-04, eta: 1:11:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8343\n2023-01-05 02:49:49,333 - mmcls - INFO - Epoch [91][1700/5005]\tlr: 1.000e-04, eta: 1:11:01, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8366\n2023-01-05 02:49:58,119 - mmcls - INFO - Epoch [91][1800/5005]\tlr: 1.000e-04, eta: 1:10:52, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8079\n2023-01-05 02:50:06,988 - mmcls - INFO - Epoch [91][1900/5005]\tlr: 1.000e-04, eta: 1:10:43, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8062\n2023-01-05 02:50:15,973 - mmcls - INFO - Epoch [91][2000/5005]\tlr: 1.000e-04, eta: 1:10:35, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8197\n2023-01-05 02:50:24,793 - mmcls - INFO - Epoch [91][2100/5005]\tlr: 1.000e-04, eta: 1:10:26, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8111\n2023-01-05 02:50:33,610 - mmcls - INFO - Epoch [91][2200/5005]\tlr: 1.000e-04, eta: 1:10:17, time: 0.088, data_time: 0.021, memory: 928, loss: 4.8034\n2023-01-05 02:50:42,394 - mmcls - INFO - Epoch [91][2300/5005]\tlr: 1.000e-04, eta: 1:10:08, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7990\n2023-01-05 02:50:51,050 - mmcls - INFO - Epoch [91][2400/5005]\tlr: 1.000e-04, eta: 1:09:59, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8190\n2023-01-05 02:50:59,738 - mmcls - INFO - Epoch [91][2500/5005]\tlr: 1.000e-04, eta: 1:09:51, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8012\n2023-01-05 02:51:08,344 - mmcls - INFO - Epoch [91][2600/5005]\tlr: 1.000e-04, eta: 1:09:42, time: 0.086, data_time: 0.035, memory: 928, loss: 4.7962\n2023-01-05 02:51:16,892 - mmcls - INFO - Epoch [91][2700/5005]\tlr: 1.000e-04, eta: 1:09:33, time: 0.085, data_time: 0.026, memory: 928, loss: 4.7916\n2023-01-05 02:51:25,907 - mmcls - INFO - Epoch [91][2800/5005]\tlr: 1.000e-04, eta: 1:09:24, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8051\n2023-01-05 02:51:34,493 - mmcls - INFO - Epoch [91][2900/5005]\tlr: 1.000e-04, eta: 1:09:15, time: 0.086, data_time: 0.004, memory: 928, loss: 4.7996\n2023-01-05 02:51:43,563 - mmcls - INFO - Epoch [91][3000/5005]\tlr: 1.000e-04, eta: 1:09:06, time: 0.091, data_time: 0.001, memory: 928, loss: 4.7972\n2023-01-05 02:51:52,485 - mmcls - INFO - Epoch [91][3100/5005]\tlr: 1.000e-04, eta: 1:08:58, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8008\n2023-01-05 02:52:01,348 - mmcls - INFO - Epoch [91][3200/5005]\tlr: 1.000e-04, eta: 1:08:49, time: 0.089, data_time: 0.013, memory: 928, loss: 4.7929\n2023-01-05 02:52:10,008 - mmcls - INFO - Epoch [91][3300/5005]\tlr: 1.000e-04, eta: 1:08:40, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8345\n2023-01-05 02:52:18,479 - mmcls - INFO - Epoch [91][3400/5005]\tlr: 1.000e-04, eta: 1:08:31, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8183\n2023-01-05 02:52:27,555 - mmcls - INFO - Epoch [91][3500/5005]\tlr: 1.000e-04, eta: 1:08:22, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8089\n2023-01-05 02:52:36,233 - mmcls - INFO - Epoch [91][3600/5005]\tlr: 1.000e-04, eta: 1:08:14, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8167\n2023-01-05 02:52:45,086 - mmcls - INFO - Epoch [91][3700/5005]\tlr: 1.000e-04, eta: 1:08:05, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8070\n2023-01-05 02:52:54,045 - mmcls - INFO - Epoch [91][3800/5005]\tlr: 1.000e-04, eta: 1:07:56, time: 0.090, data_time: 0.024, memory: 928, loss: 4.8132\n2023-01-05 02:53:02,556 - mmcls - INFO - Epoch [91][3900/5005]\tlr: 1.000e-04, eta: 1:07:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8166\n2023-01-05 02:53:11,725 - mmcls - INFO - Epoch [91][4000/5005]\tlr: 1.000e-04, eta: 1:07:38, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8275\n2023-01-05 02:53:20,037 - mmcls - INFO - Epoch [91][4100/5005]\tlr: 1.000e-04, eta: 1:07:29, time: 0.083, data_time: 0.003, memory: 928, loss: 4.8297\n2023-01-05 02:53:28,908 - mmcls - INFO - Epoch [91][4200/5005]\tlr: 1.000e-04, eta: 1:07:21, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8247\n2023-01-05 02:53:37,464 - mmcls - INFO - Epoch [91][4300/5005]\tlr: 1.000e-04, eta: 1:07:12, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8146\n2023-01-05 02:53:46,200 - mmcls - INFO - Epoch [91][4400/5005]\tlr: 1.000e-04, eta: 1:07:03, time: 0.087, data_time: 0.051, memory: 928, loss: 4.8200\n2023-01-05 02:53:55,021 - mmcls - INFO - Epoch [91][4500/5005]\tlr: 1.000e-04, eta: 1:06:54, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8209\n2023-01-05 02:54:03,655 - mmcls - INFO - Epoch [91][4600/5005]\tlr: 1.000e-04, eta: 1:06:45, time: 0.086, data_time: 0.007, memory: 928, loss: 4.7911\n2023-01-05 02:54:12,319 - mmcls - INFO - Epoch [91][4700/5005]\tlr: 1.000e-04, eta: 1:06:36, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8074\n2023-01-05 02:54:21,215 - mmcls - INFO - Epoch [91][4800/5005]\tlr: 1.000e-04, eta: 1:06:28, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7989\n2023-01-05 02:54:29,938 - mmcls - INFO - Epoch [91][4900/5005]\tlr: 1.000e-04, eta: 1:06:19, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8099\n2023-01-05 02:54:38,613 - mmcls - INFO - Epoch [91][5000/5005]\tlr: 1.000e-04, eta: 1:06:10, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8069\n2023-01-05 02:55:01,557 - mmcls - INFO - Epoch(val) [91][196]\taccuracy_top-1: 18.5500, accuracy_top-5: 37.0220\n2023-01-05 02:55:12,525 - mmcls - INFO - Epoch [92][100/5005]\tlr: 1.000e-04, eta: 1:06:01, time: 0.109, data_time: 0.035, memory: 928, loss: 4.8098\n2023-01-05 02:55:21,407 - mmcls - INFO - Epoch [92][200/5005]\tlr: 1.000e-04, eta: 1:05:52, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8128\n2023-01-05 02:55:30,147 - mmcls - INFO - Epoch [92][300/5005]\tlr: 1.000e-04, eta: 1:05:43, time: 0.088, data_time: 0.001, memory: 928, loss: 4.7982\n2023-01-05 02:55:38,739 - mmcls - INFO - Epoch [92][400/5005]\tlr: 1.000e-04, eta: 1:05:34, time: 0.086, data_time: 0.017, memory: 928, loss: 4.7963\n2023-01-05 02:55:47,389 - mmcls - INFO - Epoch [92][500/5005]\tlr: 1.000e-04, eta: 1:05:26, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8138\n2023-01-05 02:55:56,250 - mmcls - INFO - Epoch [92][600/5005]\tlr: 1.000e-04, eta: 1:05:17, time: 0.089, data_time: 0.036, memory: 928, loss: 4.8248\n2023-01-05 02:56:05,023 - mmcls - INFO - Epoch [92][700/5005]\tlr: 1.000e-04, eta: 1:05:08, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8066\n2023-01-05 02:56:13,569 - mmcls - INFO - Epoch [92][800/5005]\tlr: 1.000e-04, eta: 1:04:59, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8054\n2023-01-05 02:56:22,318 - mmcls - INFO - Epoch [92][900/5005]\tlr: 1.000e-04, eta: 1:04:50, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8247\n2023-01-05 02:56:31,058 - mmcls - INFO - Epoch [92][1000/5005]\tlr: 1.000e-04, eta: 1:04:42, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8009\n2023-01-05 02:56:39,711 - mmcls - INFO - Epoch [92][1100/5005]\tlr: 1.000e-04, eta: 1:04:33, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7968\n2023-01-05 02:56:48,741 - mmcls - INFO - Epoch [92][1200/5005]\tlr: 1.000e-04, eta: 1:04:24, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8163\n2023-01-05 02:56:57,196 - mmcls - INFO - Epoch [92][1300/5005]\tlr: 1.000e-04, eta: 1:04:15, time: 0.084, data_time: 0.011, memory: 928, loss: 4.7932\n2023-01-05 02:57:05,991 - mmcls - INFO - Epoch [92][1400/5005]\tlr: 1.000e-04, eta: 1:04:06, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8529\n2023-01-05 02:57:14,921 - mmcls - INFO - Epoch [92][1500/5005]\tlr: 1.000e-04, eta: 1:03:57, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8308\n2023-01-05 02:57:23,532 - mmcls - INFO - Epoch [92][1600/5005]\tlr: 1.000e-04, eta: 1:03:49, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7987\n2023-01-05 02:57:32,394 - mmcls - INFO - Epoch [92][1700/5005]\tlr: 1.000e-04, eta: 1:03:40, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8108\n2023-01-05 02:57:41,083 - mmcls - INFO - Epoch [92][1800/5005]\tlr: 1.000e-04, eta: 1:03:31, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8087\n2023-01-05 02:57:50,184 - mmcls - INFO - Epoch [92][1900/5005]\tlr: 1.000e-04, eta: 1:03:22, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8077\n2023-01-05 02:57:58,908 - mmcls - INFO - Epoch [92][2000/5005]\tlr: 1.000e-04, eta: 1:03:13, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8165\n2023-01-05 02:58:07,659 - mmcls - INFO - Epoch [92][2100/5005]\tlr: 1.000e-04, eta: 1:03:05, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8039\n2023-01-05 02:58:16,405 - mmcls - INFO - Epoch [92][2200/5005]\tlr: 1.000e-04, eta: 1:02:56, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8136\n2023-01-05 02:58:25,290 - mmcls - INFO - Epoch [92][2300/5005]\tlr: 1.000e-04, eta: 1:02:47, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8190\n2023-01-05 02:58:34,132 - mmcls - INFO - Epoch [92][2400/5005]\tlr: 1.000e-04, eta: 1:02:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8189\n2023-01-05 02:58:42,877 - mmcls - INFO - Epoch [92][2500/5005]\tlr: 1.000e-04, eta: 1:02:29, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7943\n2023-01-05 02:58:51,865 - mmcls - INFO - Epoch [92][2600/5005]\tlr: 1.000e-04, eta: 1:02:20, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8129\n2023-01-05 02:59:00,269 - mmcls - INFO - Epoch [92][2700/5005]\tlr: 1.000e-04, eta: 1:02:12, time: 0.084, data_time: 0.001, memory: 928, loss: 4.7938\n2023-01-05 02:59:09,228 - mmcls - INFO - Epoch [92][2800/5005]\tlr: 1.000e-04, eta: 1:02:03, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8149\n2023-01-05 02:59:17,878 - mmcls - INFO - Epoch [92][2900/5005]\tlr: 1.000e-04, eta: 1:01:54, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8234\n2023-01-05 02:59:26,735 - mmcls - INFO - Epoch [92][3000/5005]\tlr: 1.000e-04, eta: 1:01:45, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8061\n2023-01-05 02:59:35,353 - mmcls - INFO - Epoch [92][3100/5005]\tlr: 1.000e-04, eta: 1:01:36, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8277\n2023-01-05 02:59:44,338 - mmcls - INFO - Epoch [92][3200/5005]\tlr: 1.000e-04, eta: 1:01:28, time: 0.090, data_time: 0.001, memory: 928, loss: 4.7997\n2023-01-05 02:59:53,193 - mmcls - INFO - Epoch [92][3300/5005]\tlr: 1.000e-04, eta: 1:01:19, time: 0.088, data_time: 0.000, memory: 928, loss: 4.7896\n2023-01-05 03:00:01,898 - mmcls - INFO - Epoch [92][3400/5005]\tlr: 1.000e-04, eta: 1:01:10, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8192\n2023-01-05 03:00:10,839 - mmcls - INFO - Epoch [92][3500/5005]\tlr: 1.000e-04, eta: 1:01:01, time: 0.089, data_time: 0.029, memory: 928, loss: 4.7862\n2023-01-05 03:00:19,449 - mmcls - INFO - Epoch [92][3600/5005]\tlr: 1.000e-04, eta: 1:00:52, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8120\n2023-01-05 03:00:28,255 - mmcls - INFO - Epoch [92][3700/5005]\tlr: 1.000e-04, eta: 1:00:43, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8099\n2023-01-05 03:00:37,022 - mmcls - INFO - Epoch [92][3800/5005]\tlr: 1.000e-04, eta: 1:00:35, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8103\n2023-01-05 03:00:45,616 - mmcls - INFO - Epoch [92][3900/5005]\tlr: 1.000e-04, eta: 1:00:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7919\n2023-01-05 03:00:54,201 - mmcls - INFO - Epoch [92][4000/5005]\tlr: 1.000e-04, eta: 1:00:17, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8486\n2023-01-05 03:01:02,984 - mmcls - INFO - Epoch [92][4100/5005]\tlr: 1.000e-04, eta: 1:00:08, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8258\n2023-01-05 03:01:11,956 - mmcls - INFO - Epoch [92][4200/5005]\tlr: 1.000e-04, eta: 0:59:59, time: 0.090, data_time: 0.020, memory: 928, loss: 4.8090\n2023-01-05 03:01:20,774 - mmcls - INFO - Epoch [92][4300/5005]\tlr: 1.000e-04, eta: 0:59:51, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7937\n2023-01-05 03:01:29,645 - mmcls - INFO - Epoch [92][4400/5005]\tlr: 1.000e-04, eta: 0:59:42, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8018\n2023-01-05 03:01:38,040 - mmcls - INFO - Epoch [92][4500/5005]\tlr: 1.000e-04, eta: 0:59:33, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8008\n2023-01-05 03:01:47,090 - mmcls - INFO - Epoch [92][4600/5005]\tlr: 1.000e-04, eta: 0:59:24, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8092\n2023-01-05 03:01:55,871 - mmcls - INFO - Epoch [92][4700/5005]\tlr: 1.000e-04, eta: 0:59:15, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8041\n2023-01-05 03:02:04,870 - mmcls - INFO - Epoch [92][4800/5005]\tlr: 1.000e-04, eta: 0:59:07, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8238\n2023-01-05 03:02:13,741 - mmcls - INFO - Epoch [92][4900/5005]\tlr: 1.000e-04, eta: 0:58:58, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8358\n2023-01-05 03:02:22,510 - mmcls - INFO - Epoch [92][5000/5005]\tlr: 1.000e-04, eta: 0:58:49, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8168\n2023-01-05 03:02:45,083 - mmcls - INFO - Epoch(val) [92][196]\taccuracy_top-1: 17.9720, accuracy_top-5: 36.1720\n2023-01-05 03:02:56,088 - mmcls - INFO - Epoch [93][100/5005]\tlr: 1.000e-04, eta: 0:58:40, time: 0.110, data_time: 0.047, memory: 928, loss: 4.8204\n2023-01-05 03:03:04,775 - mmcls - INFO - Epoch [93][200/5005]\tlr: 1.000e-04, eta: 0:58:31, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8055\n2023-01-05 03:03:13,625 - mmcls - INFO - Epoch [93][300/5005]\tlr: 1.000e-04, eta: 0:58:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8442\n2023-01-05 03:03:22,397 - mmcls - INFO - Epoch [93][400/5005]\tlr: 1.000e-04, eta: 0:58:13, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8066\n2023-01-05 03:03:31,297 - mmcls - INFO - Epoch [93][500/5005]\tlr: 1.000e-04, eta: 0:58:05, time: 0.089, data_time: 0.012, memory: 928, loss: 4.7997\n2023-01-05 03:03:40,117 - mmcls - INFO - Epoch [93][600/5005]\tlr: 1.000e-04, eta: 0:57:56, time: 0.088, data_time: 0.032, memory: 928, loss: 4.7881\n2023-01-05 03:03:49,334 - mmcls - INFO - Epoch [93][700/5005]\tlr: 1.000e-04, eta: 0:57:47, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8098\n2023-01-05 03:03:57,934 - mmcls - INFO - Epoch [93][800/5005]\tlr: 1.000e-04, eta: 0:57:38, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7972\n2023-01-05 03:04:06,986 - mmcls - INFO - Epoch [93][900/5005]\tlr: 1.000e-04, eta: 0:57:29, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8088\n2023-01-05 03:04:15,352 - mmcls - INFO - Epoch [93][1000/5005]\tlr: 1.000e-04, eta: 0:57:20, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8066\n2023-01-05 03:04:24,483 - mmcls - INFO - Epoch [93][1100/5005]\tlr: 1.000e-04, eta: 0:57:12, time: 0.091, data_time: 0.007, memory: 928, loss: 4.7859\n2023-01-05 03:04:33,238 - mmcls - INFO - Epoch [93][1200/5005]\tlr: 1.000e-04, eta: 0:57:03, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8039\n2023-01-05 03:04:41,912 - mmcls - INFO - Epoch [93][1300/5005]\tlr: 1.000e-04, eta: 0:56:54, time: 0.087, data_time: 0.013, memory: 928, loss: 4.7932\n2023-01-05 03:04:50,435 - mmcls - INFO - Epoch [93][1400/5005]\tlr: 1.000e-04, eta: 0:56:45, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8173\n2023-01-05 03:04:59,467 - mmcls - INFO - Epoch [93][1500/5005]\tlr: 1.000e-04, eta: 0:56:36, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8043\n2023-01-05 03:05:08,518 - mmcls - INFO - Epoch [93][1600/5005]\tlr: 1.000e-04, eta: 0:56:28, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8059\n2023-01-05 03:05:17,339 - mmcls - INFO - Epoch [93][1700/5005]\tlr: 1.000e-04, eta: 0:56:19, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8162\n2023-01-05 03:05:25,955 - mmcls - INFO - Epoch [93][1800/5005]\tlr: 1.000e-04, eta: 0:56:10, time: 0.086, data_time: 0.028, memory: 928, loss: 4.7858\n2023-01-05 03:05:34,727 - mmcls - INFO - Epoch [93][1900/5005]\tlr: 1.000e-04, eta: 0:56:01, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8097\n2023-01-05 03:05:43,367 - mmcls - INFO - Epoch [93][2000/5005]\tlr: 1.000e-04, eta: 0:55:52, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8031\n2023-01-05 03:05:51,839 - mmcls - INFO - Epoch [93][2100/5005]\tlr: 1.000e-04, eta: 0:55:43, time: 0.085, data_time: 0.017, memory: 928, loss: 4.7843\n2023-01-05 03:06:00,298 - mmcls - INFO - Epoch [93][2200/5005]\tlr: 1.000e-04, eta: 0:55:35, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8292\n2023-01-05 03:06:09,202 - mmcls - INFO - Epoch [93][2300/5005]\tlr: 1.000e-04, eta: 0:55:26, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8119\n2023-01-05 03:06:17,769 - mmcls - INFO - Epoch [93][2400/5005]\tlr: 1.000e-04, eta: 0:55:17, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7988\n2023-01-05 03:06:27,023 - mmcls - INFO - Epoch [93][2500/5005]\tlr: 1.000e-04, eta: 0:55:08, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8243\n2023-01-05 03:06:35,972 - mmcls - INFO - Epoch [93][2600/5005]\tlr: 1.000e-04, eta: 0:54:59, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8212\n2023-01-05 03:06:44,779 - mmcls - INFO - Epoch [93][2700/5005]\tlr: 1.000e-04, eta: 0:54:51, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8087\n2023-01-05 03:06:53,354 - mmcls - INFO - Epoch [93][2800/5005]\tlr: 1.000e-04, eta: 0:54:42, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7920\n2023-01-05 03:07:02,010 - mmcls - INFO - Epoch [93][2900/5005]\tlr: 1.000e-04, eta: 0:54:33, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8113\n2023-01-05 03:07:10,791 - mmcls - INFO - Epoch [93][3000/5005]\tlr: 1.000e-04, eta: 0:54:24, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8257\n2023-01-05 03:07:19,461 - mmcls - INFO - Epoch [93][3100/5005]\tlr: 1.000e-04, eta: 0:54:15, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8100\n2023-01-05 03:07:28,068 - mmcls - INFO - Epoch [93][3200/5005]\tlr: 1.000e-04, eta: 0:54:06, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8289\n2023-01-05 03:07:36,930 - mmcls - INFO - Epoch [93][3300/5005]\tlr: 1.000e-04, eta: 0:53:58, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8319\n2023-01-05 03:07:45,671 - mmcls - INFO - Epoch [93][3400/5005]\tlr: 1.000e-04, eta: 0:53:49, time: 0.087, data_time: 0.006, memory: 928, loss: 4.7993\n2023-01-05 03:07:54,123 - mmcls - INFO - Epoch [93][3500/5005]\tlr: 1.000e-04, eta: 0:53:40, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8148\n2023-01-05 03:08:03,368 - mmcls - INFO - Epoch [93][3600/5005]\tlr: 1.000e-04, eta: 0:53:31, time: 0.092, data_time: 0.032, memory: 928, loss: 4.8034\n2023-01-05 03:08:12,409 - mmcls - INFO - Epoch [93][3700/5005]\tlr: 1.000e-04, eta: 0:53:22, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8218\n2023-01-05 03:08:21,095 - mmcls - INFO - Epoch [93][3800/5005]\tlr: 1.000e-04, eta: 0:53:14, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8373\n2023-01-05 03:08:30,026 - mmcls - INFO - Epoch [93][3900/5005]\tlr: 1.000e-04, eta: 0:53:05, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7915\n2023-01-05 03:08:38,535 - mmcls - INFO - Epoch [93][4000/5005]\tlr: 1.000e-04, eta: 0:52:56, time: 0.085, data_time: 0.016, memory: 928, loss: 4.7961\n2023-01-05 03:08:47,109 - mmcls - INFO - Epoch [93][4100/5005]\tlr: 1.000e-04, eta: 0:52:47, time: 0.086, data_time: 0.010, memory: 928, loss: 4.7894\n2023-01-05 03:08:56,605 - mmcls - INFO - Epoch [93][4200/5005]\tlr: 1.000e-04, eta: 0:52:38, time: 0.095, data_time: 0.020, memory: 928, loss: 4.8144\n2023-01-05 03:09:04,972 - mmcls - INFO - Epoch [93][4300/5005]\tlr: 1.000e-04, eta: 0:52:29, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8016\n2023-01-05 03:09:13,682 - mmcls - INFO - Epoch [93][4400/5005]\tlr: 1.000e-04, eta: 0:52:21, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8104\n2023-01-05 03:09:22,430 - mmcls - INFO - Epoch [93][4500/5005]\tlr: 1.000e-04, eta: 0:52:12, time: 0.088, data_time: 0.007, memory: 928, loss: 4.7822\n2023-01-05 03:09:31,133 - mmcls - INFO - Epoch [93][4600/5005]\tlr: 1.000e-04, eta: 0:52:03, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7990\n2023-01-05 03:09:40,063 - mmcls - INFO - Epoch [93][4700/5005]\tlr: 1.000e-04, eta: 0:51:54, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8212\n2023-01-05 03:09:48,997 - mmcls - INFO - Epoch [93][4800/5005]\tlr: 1.000e-04, eta: 0:51:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7983\n2023-01-05 03:09:57,471 - mmcls - INFO - Epoch [93][4900/5005]\tlr: 1.000e-04, eta: 0:51:37, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8213\n2023-01-05 03:10:06,269 - mmcls - INFO - Epoch [93][5000/5005]\tlr: 1.000e-04, eta: 0:51:28, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8219\n2023-01-05 03:10:29,129 - mmcls - INFO - Epoch(val) [93][196]\taccuracy_top-1: 18.5320, accuracy_top-5: 36.9080\n2023-01-05 03:10:39,918 - mmcls - INFO - Epoch [94][100/5005]\tlr: 1.000e-04, eta: 0:51:19, time: 0.108, data_time: 0.033, memory: 928, loss: 4.7942\n2023-01-05 03:10:48,687 - mmcls - INFO - Epoch [94][200/5005]\tlr: 1.000e-04, eta: 0:51:10, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8304\n2023-01-05 03:10:57,375 - mmcls - INFO - Epoch [94][300/5005]\tlr: 1.000e-04, eta: 0:51:01, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8012\n2023-01-05 03:11:05,937 - mmcls - INFO - Epoch [94][400/5005]\tlr: 1.000e-04, eta: 0:50:52, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8189\n2023-01-05 03:11:14,792 - mmcls - INFO - Epoch [94][500/5005]\tlr: 1.000e-04, eta: 0:50:43, time: 0.089, data_time: 0.023, memory: 928, loss: 4.7939\n2023-01-05 03:11:23,772 - mmcls - INFO - Epoch [94][600/5005]\tlr: 1.000e-04, eta: 0:50:35, time: 0.090, data_time: 0.037, memory: 928, loss: 4.8085\n2023-01-05 03:11:32,450 - mmcls - INFO - Epoch [94][700/5005]\tlr: 1.000e-04, eta: 0:50:26, time: 0.087, data_time: 0.017, memory: 928, loss: 4.7847\n2023-01-05 03:11:41,129 - mmcls - INFO - Epoch [94][800/5005]\tlr: 1.000e-04, eta: 0:50:17, time: 0.087, data_time: 0.012, memory: 928, loss: 4.7933\n2023-01-05 03:11:49,762 - mmcls - INFO - Epoch [94][900/5005]\tlr: 1.000e-04, eta: 0:50:08, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8108\n2023-01-05 03:11:58,580 - mmcls - INFO - Epoch [94][1000/5005]\tlr: 1.000e-04, eta: 0:49:59, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8027\n2023-01-05 03:12:07,234 - mmcls - INFO - Epoch [94][1100/5005]\tlr: 1.000e-04, eta: 0:49:50, time: 0.087, data_time: 0.041, memory: 928, loss: 4.7987\n2023-01-05 03:12:16,186 - mmcls - INFO - Epoch [94][1200/5005]\tlr: 1.000e-04, eta: 0:49:42, time: 0.089, data_time: 0.015, memory: 928, loss: 4.7938\n2023-01-05 03:12:25,070 - mmcls - INFO - Epoch [94][1300/5005]\tlr: 1.000e-04, eta: 0:49:33, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8410\n2023-01-05 03:12:33,545 - mmcls - INFO - Epoch [94][1400/5005]\tlr: 1.000e-04, eta: 0:49:24, time: 0.085, data_time: 0.001, memory: 928, loss: 4.7984\n2023-01-05 03:12:42,655 - mmcls - INFO - Epoch [94][1500/5005]\tlr: 1.000e-04, eta: 0:49:15, time: 0.091, data_time: 0.024, memory: 928, loss: 4.8265\n2023-01-05 03:12:51,544 - mmcls - INFO - Epoch [94][1600/5005]\tlr: 1.000e-04, eta: 0:49:06, time: 0.089, data_time: 0.006, memory: 928, loss: 4.7939\n2023-01-05 03:13:00,390 - mmcls - INFO - Epoch [94][1700/5005]\tlr: 1.000e-04, eta: 0:48:58, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8089\n2023-01-05 03:13:09,061 - mmcls - INFO - Epoch [94][1800/5005]\tlr: 1.000e-04, eta: 0:48:49, time: 0.087, data_time: 0.010, memory: 928, loss: 4.7918\n2023-01-05 03:13:18,075 - mmcls - INFO - Epoch [94][1900/5005]\tlr: 1.000e-04, eta: 0:48:40, time: 0.090, data_time: 0.012, memory: 928, loss: 4.7989\n2023-01-05 03:13:26,913 - mmcls - INFO - Epoch [94][2000/5005]\tlr: 1.000e-04, eta: 0:48:31, time: 0.088, data_time: 0.001, memory: 928, loss: 4.7927\n2023-01-05 03:13:35,824 - mmcls - INFO - Epoch [94][2100/5005]\tlr: 1.000e-04, eta: 0:48:22, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8310\n2023-01-05 03:13:44,695 - mmcls - INFO - Epoch [94][2200/5005]\tlr: 1.000e-04, eta: 0:48:14, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8045\n2023-01-05 03:13:53,702 - mmcls - INFO - Epoch [94][2300/5005]\tlr: 1.000e-04, eta: 0:48:05, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8083\n2023-01-05 03:14:02,195 - mmcls - INFO - Epoch [94][2400/5005]\tlr: 1.000e-04, eta: 0:47:56, time: 0.085, data_time: 0.002, memory: 928, loss: 4.7988\n2023-01-05 03:14:11,439 - mmcls - INFO - Epoch [94][2500/5005]\tlr: 1.000e-04, eta: 0:47:47, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8233\n2023-01-05 03:14:20,081 - mmcls - INFO - Epoch [94][2600/5005]\tlr: 1.000e-04, eta: 0:47:38, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8073\n2023-01-05 03:14:29,140 - mmcls - INFO - Epoch [94][2700/5005]\tlr: 1.000e-04, eta: 0:47:29, time: 0.091, data_time: 0.019, memory: 928, loss: 4.8062\n2023-01-05 03:14:37,856 - mmcls - INFO - Epoch [94][2800/5005]\tlr: 1.000e-04, eta: 0:47:21, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7926\n2023-01-05 03:14:46,581 - mmcls - INFO - Epoch [94][2900/5005]\tlr: 1.000e-04, eta: 0:47:12, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8382\n2023-01-05 03:14:55,212 - mmcls - INFO - Epoch [94][3000/5005]\tlr: 1.000e-04, eta: 0:47:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.7977\n2023-01-05 03:15:03,862 - mmcls - INFO - Epoch [94][3100/5005]\tlr: 1.000e-04, eta: 0:46:54, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8157\n2023-01-05 03:15:13,094 - mmcls - INFO - Epoch [94][3200/5005]\tlr: 1.000e-04, eta: 0:46:45, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8025\n2023-01-05 03:15:21,711 - mmcls - INFO - Epoch [94][3300/5005]\tlr: 1.000e-04, eta: 0:46:37, time: 0.086, data_time: 0.001, memory: 928, loss: 4.7945\n2023-01-05 03:15:30,608 - mmcls - INFO - Epoch [94][3400/5005]\tlr: 1.000e-04, eta: 0:46:28, time: 0.089, data_time: 0.025, memory: 928, loss: 4.8084\n2023-01-05 03:15:39,284 - mmcls - INFO - Epoch [94][3500/5005]\tlr: 1.000e-04, eta: 0:46:19, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8113\n2023-01-05 03:15:47,832 - mmcls - INFO - Epoch [94][3600/5005]\tlr: 1.000e-04, eta: 0:46:10, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8000\n2023-01-05 03:15:56,329 - mmcls - INFO - Epoch [94][3700/5005]\tlr: 1.000e-04, eta: 0:46:01, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8043\n2023-01-05 03:16:05,235 - mmcls - INFO - Epoch [94][3800/5005]\tlr: 1.000e-04, eta: 0:45:52, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8157\n2023-01-05 03:16:14,095 - mmcls - INFO - Epoch [94][3900/5005]\tlr: 1.000e-04, eta: 0:45:44, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8005\n2023-01-05 03:16:22,683 - mmcls - INFO - Epoch [94][4000/5005]\tlr: 1.000e-04, eta: 0:45:35, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8124\n2023-01-05 03:16:31,274 - mmcls - INFO - Epoch [94][4100/5005]\tlr: 1.000e-04, eta: 0:45:26, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8335\n2023-01-05 03:16:39,946 - mmcls - INFO - Epoch [94][4200/5005]\tlr: 1.000e-04, eta: 0:45:17, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8224\n2023-01-05 03:16:49,055 - mmcls - INFO - Epoch [94][4300/5005]\tlr: 1.000e-04, eta: 0:45:08, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8104\n2023-01-05 03:16:57,939 - mmcls - INFO - Epoch [94][4400/5005]\tlr: 1.000e-04, eta: 0:45:00, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8015\n2023-01-05 03:17:06,772 - mmcls - INFO - Epoch [94][4500/5005]\tlr: 1.000e-04, eta: 0:44:51, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8274\n2023-01-05 03:17:15,546 - mmcls - INFO - Epoch [94][4600/5005]\tlr: 1.000e-04, eta: 0:44:42, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8102\n2023-01-05 03:17:24,079 - mmcls - INFO - Epoch [94][4700/5005]\tlr: 1.000e-04, eta: 0:44:33, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8074\n2023-01-05 03:17:32,772 - mmcls - INFO - Epoch [94][4800/5005]\tlr: 1.000e-04, eta: 0:44:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7868\n2023-01-05 03:17:41,223 - mmcls - INFO - Epoch [94][4900/5005]\tlr: 1.000e-04, eta: 0:44:15, time: 0.085, data_time: 0.012, memory: 928, loss: 4.7958\n2023-01-05 03:17:50,013 - mmcls - INFO - Epoch [94][5000/5005]\tlr: 1.000e-04, eta: 0:44:07, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8064\n2023-01-05 03:18:12,756 - mmcls - INFO - Epoch(val) [94][196]\taccuracy_top-1: 17.7680, accuracy_top-5: 35.9220\n2023-01-05 03:18:23,926 - mmcls - INFO - Epoch [95][100/5005]\tlr: 1.000e-04, eta: 0:43:58, time: 0.112, data_time: 0.046, memory: 928, loss: 4.7896\n2023-01-05 03:18:32,572 - mmcls - INFO - Epoch [95][200/5005]\tlr: 1.000e-04, eta: 0:43:49, time: 0.086, data_time: 0.004, memory: 928, loss: 4.7944\n2023-01-05 03:18:41,396 - mmcls - INFO - Epoch [95][300/5005]\tlr: 1.000e-04, eta: 0:43:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8014\n2023-01-05 03:18:50,229 - mmcls - INFO - Epoch [95][400/5005]\tlr: 1.000e-04, eta: 0:43:31, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8127\n2023-01-05 03:18:58,992 - mmcls - INFO - Epoch [95][500/5005]\tlr: 1.000e-04, eta: 0:43:22, time: 0.088, data_time: 0.019, memory: 928, loss: 4.7864\n2023-01-05 03:19:07,737 - mmcls - INFO - Epoch [95][600/5005]\tlr: 1.000e-04, eta: 0:43:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8083\n2023-01-05 03:19:16,337 - mmcls - INFO - Epoch [95][700/5005]\tlr: 1.000e-04, eta: 0:43:05, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8056\n2023-01-05 03:19:25,039 - mmcls - INFO - Epoch [95][800/5005]\tlr: 1.000e-04, eta: 0:42:56, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8101\n2023-01-05 03:19:33,559 - mmcls - INFO - Epoch [95][900/5005]\tlr: 1.000e-04, eta: 0:42:47, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8059\n2023-01-05 03:19:42,290 - mmcls - INFO - Epoch [95][1000/5005]\tlr: 1.000e-04, eta: 0:42:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8195\n2023-01-05 03:19:51,266 - mmcls - INFO - Epoch [95][1100/5005]\tlr: 1.000e-04, eta: 0:42:29, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8051\n2023-01-05 03:20:00,111 - mmcls - INFO - Epoch [95][1200/5005]\tlr: 1.000e-04, eta: 0:42:21, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8139\n2023-01-05 03:20:08,942 - mmcls - INFO - Epoch [95][1300/5005]\tlr: 1.000e-04, eta: 0:42:12, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8192\n2023-01-05 03:20:17,850 - mmcls - INFO - Epoch [95][1400/5005]\tlr: 1.000e-04, eta: 0:42:03, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8016\n2023-01-05 03:20:26,790 - mmcls - INFO - Epoch [95][1500/5005]\tlr: 1.000e-04, eta: 0:41:54, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8163\n2023-01-05 03:20:35,597 - mmcls - INFO - Epoch [95][1600/5005]\tlr: 1.000e-04, eta: 0:41:45, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8106\n2023-01-05 03:20:44,362 - mmcls - INFO - Epoch [95][1700/5005]\tlr: 1.000e-04, eta: 0:41:36, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8108\n2023-01-05 03:20:53,223 - mmcls - INFO - Epoch [95][1800/5005]\tlr: 1.000e-04, eta: 0:41:28, time: 0.089, data_time: 0.003, memory: 928, loss: 4.7929\n2023-01-05 03:21:01,895 - mmcls - INFO - Epoch [95][1900/5005]\tlr: 1.000e-04, eta: 0:41:19, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8070\n2023-01-05 03:21:10,796 - mmcls - INFO - Epoch [95][2000/5005]\tlr: 1.000e-04, eta: 0:41:10, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8140\n2023-01-05 03:21:19,365 - mmcls - INFO - Epoch [95][2100/5005]\tlr: 1.000e-04, eta: 0:41:01, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8066\n2023-01-05 03:21:27,946 - mmcls - INFO - Epoch [95][2200/5005]\tlr: 1.000e-04, eta: 0:40:52, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8057\n2023-01-05 03:21:36,865 - mmcls - INFO - Epoch [95][2300/5005]\tlr: 1.000e-04, eta: 0:40:44, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8004\n2023-01-05 03:21:45,727 - mmcls - INFO - Epoch [95][2400/5005]\tlr: 1.000e-04, eta: 0:40:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8067\n2023-01-05 03:21:54,468 - mmcls - INFO - Epoch [95][2500/5005]\tlr: 1.000e-04, eta: 0:40:26, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8204\n2023-01-05 03:22:03,177 - mmcls - INFO - Epoch [95][2600/5005]\tlr: 1.000e-04, eta: 0:40:17, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8326\n2023-01-05 03:22:11,691 - mmcls - INFO - Epoch [95][2700/5005]\tlr: 1.000e-04, eta: 0:40:08, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8079\n2023-01-05 03:22:20,515 - mmcls - INFO - Epoch [95][2800/5005]\tlr: 1.000e-04, eta: 0:39:59, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8075\n2023-01-05 03:22:29,544 - mmcls - INFO - Epoch [95][2900/5005]\tlr: 1.000e-04, eta: 0:39:51, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8063\n2023-01-05 03:22:38,155 - mmcls - INFO - Epoch [95][3000/5005]\tlr: 1.000e-04, eta: 0:39:42, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8226\n2023-01-05 03:22:47,248 - mmcls - INFO - Epoch [95][3100/5005]\tlr: 1.000e-04, eta: 0:39:33, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8069\n2023-01-05 03:22:56,192 - mmcls - INFO - Epoch [95][3200/5005]\tlr: 1.000e-04, eta: 0:39:24, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8132\n2023-01-05 03:23:04,979 - mmcls - INFO - Epoch [95][3300/5005]\tlr: 1.000e-04, eta: 0:39:15, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8046\n2023-01-05 03:23:13,606 - mmcls - INFO - Epoch [95][3400/5005]\tlr: 1.000e-04, eta: 0:39:07, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8140\n2023-01-05 03:23:22,470 - mmcls - INFO - Epoch [95][3500/5005]\tlr: 1.000e-04, eta: 0:38:58, time: 0.088, data_time: 0.031, memory: 928, loss: 4.7987\n2023-01-05 03:23:31,419 - mmcls - INFO - Epoch [95][3600/5005]\tlr: 1.000e-04, eta: 0:38:49, time: 0.090, data_time: 0.006, memory: 928, loss: 4.7870\n2023-01-05 03:23:40,138 - mmcls - INFO - Epoch [95][3700/5005]\tlr: 1.000e-04, eta: 0:38:40, time: 0.087, data_time: 0.011, memory: 928, loss: 4.7965\n2023-01-05 03:23:49,039 - mmcls - INFO - Epoch [95][3800/5005]\tlr: 1.000e-04, eta: 0:38:31, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8255\n2023-01-05 03:23:57,702 - mmcls - INFO - Epoch [95][3900/5005]\tlr: 1.000e-04, eta: 0:38:23, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8198\n2023-01-05 03:24:06,580 - mmcls - INFO - Epoch [95][4000/5005]\tlr: 1.000e-04, eta: 0:38:14, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8008\n2023-01-05 03:24:15,110 - mmcls - INFO - Epoch [95][4100/5005]\tlr: 1.000e-04, eta: 0:38:05, time: 0.085, data_time: 0.040, memory: 928, loss: 4.8036\n2023-01-05 03:24:23,612 - mmcls - INFO - Epoch [95][4200/5005]\tlr: 1.000e-04, eta: 0:37:56, time: 0.085, data_time: 0.010, memory: 928, loss: 4.7962\n2023-01-05 03:24:32,324 - mmcls - INFO - Epoch [95][4300/5005]\tlr: 1.000e-04, eta: 0:37:47, time: 0.087, data_time: 0.013, memory: 928, loss: 4.7882\n2023-01-05 03:24:41,031 - mmcls - INFO - Epoch [95][4400/5005]\tlr: 1.000e-04, eta: 0:37:38, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8053\n2023-01-05 03:24:50,201 - mmcls - INFO - Epoch [95][4500/5005]\tlr: 1.000e-04, eta: 0:37:30, time: 0.092, data_time: 0.027, memory: 928, loss: 4.8043\n2023-01-05 03:24:58,791 - mmcls - INFO - Epoch [95][4600/5005]\tlr: 1.000e-04, eta: 0:37:21, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8071\n2023-01-05 03:25:07,647 - mmcls - INFO - Epoch [95][4700/5005]\tlr: 1.000e-04, eta: 0:37:12, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8230\n2023-01-05 03:25:16,573 - mmcls - INFO - Epoch [95][4800/5005]\tlr: 1.000e-04, eta: 0:37:03, time: 0.089, data_time: 0.024, memory: 928, loss: 4.7982\n2023-01-05 03:25:25,188 - mmcls - INFO - Epoch [95][4900/5005]\tlr: 1.000e-04, eta: 0:36:54, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8006\n2023-01-05 03:25:33,905 - mmcls - INFO - Epoch [95][5000/5005]\tlr: 1.000e-04, eta: 0:36:46, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8114\n2023-01-05 03:25:56,826 - mmcls - INFO - Epoch(val) [95][196]\taccuracy_top-1: 17.8940, accuracy_top-5: 36.2120\n2023-01-05 03:26:07,873 - mmcls - INFO - Epoch [96][100/5005]\tlr: 1.000e-04, eta: 0:36:36, time: 0.110, data_time: 0.041, memory: 928, loss: 4.8120\n2023-01-05 03:26:16,424 - mmcls - INFO - Epoch [96][200/5005]\tlr: 1.000e-04, eta: 0:36:28, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8125\n2023-01-05 03:26:25,242 - mmcls - INFO - Epoch [96][300/5005]\tlr: 1.000e-04, eta: 0:36:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8130\n2023-01-05 03:26:33,671 - mmcls - INFO - Epoch [96][400/5005]\tlr: 1.000e-04, eta: 0:36:10, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8179\n2023-01-05 03:26:42,519 - mmcls - INFO - Epoch [96][500/5005]\tlr: 1.000e-04, eta: 0:36:01, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8182\n2023-01-05 03:26:51,123 - mmcls - INFO - Epoch [96][600/5005]\tlr: 1.000e-04, eta: 0:35:52, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8135\n2023-01-05 03:27:00,051 - mmcls - INFO - Epoch [96][700/5005]\tlr: 1.000e-04, eta: 0:35:43, time: 0.089, data_time: 0.044, memory: 928, loss: 4.7894\n2023-01-05 03:27:08,735 - mmcls - INFO - Epoch [96][800/5005]\tlr: 1.000e-04, eta: 0:35:35, time: 0.087, data_time: 0.026, memory: 928, loss: 4.7824\n2023-01-05 03:27:17,658 - mmcls - INFO - Epoch [96][900/5005]\tlr: 1.000e-04, eta: 0:35:26, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7921\n2023-01-05 03:27:26,325 - mmcls - INFO - Epoch [96][1000/5005]\tlr: 1.000e-04, eta: 0:35:17, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7873\n2023-01-05 03:27:34,901 - mmcls - INFO - Epoch [96][1100/5005]\tlr: 1.000e-04, eta: 0:35:08, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8131\n2023-01-05 03:27:43,517 - mmcls - INFO - Epoch [96][1200/5005]\tlr: 1.000e-04, eta: 0:34:59, time: 0.086, data_time: 0.014, memory: 928, loss: 4.7975\n2023-01-05 03:27:52,005 - mmcls - INFO - Epoch [96][1300/5005]\tlr: 1.000e-04, eta: 0:34:51, time: 0.085, data_time: 0.007, memory: 928, loss: 4.7983\n2023-01-05 03:28:00,530 - mmcls - INFO - Epoch [96][1400/5005]\tlr: 1.000e-04, eta: 0:34:42, time: 0.085, data_time: 0.041, memory: 928, loss: 4.8043\n2023-01-05 03:28:09,525 - mmcls - INFO - Epoch [96][1500/5005]\tlr: 1.000e-04, eta: 0:34:33, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8022\n2023-01-05 03:28:18,270 - mmcls - INFO - Epoch [96][1600/5005]\tlr: 1.000e-04, eta: 0:34:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8274\n2023-01-05 03:28:27,311 - mmcls - INFO - Epoch [96][1700/5005]\tlr: 1.000e-04, eta: 0:34:15, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8471\n2023-01-05 03:28:35,870 - mmcls - INFO - Epoch [96][1800/5005]\tlr: 1.000e-04, eta: 0:34:06, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8113\n2023-01-05 03:28:44,608 - mmcls - INFO - Epoch [96][1900/5005]\tlr: 1.000e-04, eta: 0:33:58, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8279\n2023-01-05 03:28:53,377 - mmcls - INFO - Epoch [96][2000/5005]\tlr: 1.000e-04, eta: 0:33:49, time: 0.088, data_time: 0.008, memory: 928, loss: 4.7871\n2023-01-05 03:29:02,332 - mmcls - INFO - Epoch [96][2100/5005]\tlr: 1.000e-04, eta: 0:33:40, time: 0.090, data_time: 0.002, memory: 928, loss: 4.7868\n2023-01-05 03:29:11,073 - mmcls - INFO - Epoch [96][2200/5005]\tlr: 1.000e-04, eta: 0:33:31, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8107\n2023-01-05 03:29:19,928 - mmcls - INFO - Epoch [96][2300/5005]\tlr: 1.000e-04, eta: 0:33:22, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8083\n2023-01-05 03:29:28,838 - mmcls - INFO - Epoch [96][2400/5005]\tlr: 1.000e-04, eta: 0:33:14, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8133\n2023-01-05 03:29:37,662 - mmcls - INFO - Epoch [96][2500/5005]\tlr: 1.000e-04, eta: 0:33:05, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7908\n2023-01-05 03:29:46,225 - mmcls - INFO - Epoch [96][2600/5005]\tlr: 1.000e-04, eta: 0:32:56, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8170\n2023-01-05 03:29:54,741 - mmcls - INFO - Epoch [96][2700/5005]\tlr: 1.000e-04, eta: 0:32:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8089\n2023-01-05 03:30:03,924 - mmcls - INFO - Epoch [96][2800/5005]\tlr: 1.000e-04, eta: 0:32:38, time: 0.092, data_time: 0.001, memory: 928, loss: 4.7918\n2023-01-05 03:30:12,823 - mmcls - INFO - Epoch [96][2900/5005]\tlr: 1.000e-04, eta: 0:32:29, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7909\n2023-01-05 03:30:21,654 - mmcls - INFO - Epoch [96][3000/5005]\tlr: 1.000e-04, eta: 0:32:21, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8227\n2023-01-05 03:30:30,425 - mmcls - INFO - Epoch [96][3100/5005]\tlr: 1.000e-04, eta: 0:32:12, time: 0.088, data_time: 0.004, memory: 928, loss: 4.7947\n2023-01-05 03:30:38,969 - mmcls - INFO - Epoch [96][3200/5005]\tlr: 1.000e-04, eta: 0:32:03, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8023\n2023-01-05 03:30:47,678 - mmcls - INFO - Epoch [96][3300/5005]\tlr: 1.000e-04, eta: 0:31:54, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8106\n2023-01-05 03:30:56,495 - mmcls - INFO - Epoch [96][3400/5005]\tlr: 1.000e-04, eta: 0:31:45, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7973\n2023-01-05 03:31:05,456 - mmcls - INFO - Epoch [96][3500/5005]\tlr: 1.000e-04, eta: 0:31:37, time: 0.090, data_time: 0.004, memory: 928, loss: 4.7867\n2023-01-05 03:31:14,245 - mmcls - INFO - Epoch [96][3600/5005]\tlr: 1.000e-04, eta: 0:31:28, time: 0.088, data_time: 0.038, memory: 928, loss: 4.8036\n2023-01-05 03:31:22,942 - mmcls - INFO - Epoch [96][3700/5005]\tlr: 1.000e-04, eta: 0:31:19, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8013\n2023-01-05 03:31:31,644 - mmcls - INFO - Epoch [96][3800/5005]\tlr: 1.000e-04, eta: 0:31:10, time: 0.087, data_time: 0.006, memory: 928, loss: 4.7957\n2023-01-05 03:31:40,277 - mmcls - INFO - Epoch [96][3900/5005]\tlr: 1.000e-04, eta: 0:31:01, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8008\n2023-01-05 03:31:49,019 - mmcls - INFO - Epoch [96][4000/5005]\tlr: 1.000e-04, eta: 0:30:53, time: 0.087, data_time: 0.019, memory: 928, loss: 4.7980\n2023-01-05 03:31:57,771 - mmcls - INFO - Epoch [96][4100/5005]\tlr: 1.000e-04, eta: 0:30:44, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8253\n2023-01-05 03:32:06,660 - mmcls - INFO - Epoch [96][4200/5005]\tlr: 1.000e-04, eta: 0:30:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7995\n2023-01-05 03:32:15,760 - mmcls - INFO - Epoch [96][4300/5005]\tlr: 1.000e-04, eta: 0:30:26, time: 0.091, data_time: 0.016, memory: 928, loss: 4.8229\n2023-01-05 03:32:24,624 - mmcls - INFO - Epoch [96][4400/5005]\tlr: 1.000e-04, eta: 0:30:17, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8014\n2023-01-05 03:32:33,378 - mmcls - INFO - Epoch [96][4500/5005]\tlr: 1.000e-04, eta: 0:30:08, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8111\n2023-01-05 03:32:42,097 - mmcls - INFO - Epoch [96][4600/5005]\tlr: 1.000e-04, eta: 0:30:00, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8146\n2023-01-05 03:32:50,817 - mmcls - INFO - Epoch [96][4700/5005]\tlr: 1.000e-04, eta: 0:29:51, time: 0.087, data_time: 0.009, memory: 928, loss: 4.7987\n2023-01-05 03:32:59,744 - mmcls - INFO - Epoch [96][4800/5005]\tlr: 1.000e-04, eta: 0:29:42, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8251\n2023-01-05 03:33:08,329 - mmcls - INFO - Epoch [96][4900/5005]\tlr: 1.000e-04, eta: 0:29:33, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8285\n2023-01-05 03:33:16,807 - mmcls - INFO - Epoch [96][5000/5005]\tlr: 1.000e-04, eta: 0:29:24, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8078\n2023-01-05 03:33:39,637 - mmcls - INFO - Epoch(val) [96][196]\taccuracy_top-1: 18.4520, accuracy_top-5: 36.9060\n2023-01-05 03:33:50,749 - mmcls - INFO - Epoch [97][100/5005]\tlr: 1.000e-04, eta: 0:29:15, time: 0.111, data_time: 0.043, memory: 928, loss: 4.8057\n2023-01-05 03:33:59,420 - mmcls - INFO - Epoch [97][200/5005]\tlr: 1.000e-04, eta: 0:29:06, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7847\n2023-01-05 03:34:08,095 - mmcls - INFO - Epoch [97][300/5005]\tlr: 1.000e-04, eta: 0:28:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8017\n2023-01-05 03:34:16,800 - mmcls - INFO - Epoch [97][400/5005]\tlr: 1.000e-04, eta: 0:28:49, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8163\n2023-01-05 03:34:25,577 - mmcls - INFO - Epoch [97][500/5005]\tlr: 1.000e-04, eta: 0:28:40, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8063\n2023-01-05 03:34:34,244 - mmcls - INFO - Epoch [97][600/5005]\tlr: 1.000e-04, eta: 0:28:31, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7935\n2023-01-05 03:34:42,900 - mmcls - INFO - Epoch [97][700/5005]\tlr: 1.000e-04, eta: 0:28:22, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7884\n2023-01-05 03:34:51,575 - mmcls - INFO - Epoch [97][800/5005]\tlr: 1.000e-04, eta: 0:28:13, time: 0.087, data_time: 0.009, memory: 928, loss: 4.7847\n2023-01-05 03:35:00,359 - mmcls - INFO - Epoch [97][900/5005]\tlr: 1.000e-04, eta: 0:28:05, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8012\n2023-01-05 03:35:09,145 - mmcls - INFO - Epoch [97][1000/5005]\tlr: 1.000e-04, eta: 0:27:56, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7932\n2023-01-05 03:35:17,881 - mmcls - INFO - Epoch [97][1100/5005]\tlr: 1.000e-04, eta: 0:27:47, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8130\n2023-01-05 03:35:26,367 - mmcls - INFO - Epoch [97][1200/5005]\tlr: 1.000e-04, eta: 0:27:38, time: 0.085, data_time: 0.005, memory: 928, loss: 4.7939\n2023-01-05 03:35:35,092 - mmcls - INFO - Epoch [97][1300/5005]\tlr: 1.000e-04, eta: 0:27:29, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8184\n2023-01-05 03:35:43,946 - mmcls - INFO - Epoch [97][1400/5005]\tlr: 1.000e-04, eta: 0:27:21, time: 0.089, data_time: 0.029, memory: 928, loss: 4.7932\n2023-01-05 03:35:52,534 - mmcls - INFO - Epoch [97][1500/5005]\tlr: 1.000e-04, eta: 0:27:12, time: 0.086, data_time: 0.008, memory: 928, loss: 4.7885\n2023-01-05 03:36:01,313 - mmcls - INFO - Epoch [97][1600/5005]\tlr: 1.000e-04, eta: 0:27:03, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8089\n2023-01-05 03:36:10,199 - mmcls - INFO - Epoch [97][1700/5005]\tlr: 1.000e-04, eta: 0:26:54, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7953\n2023-01-05 03:36:19,043 - mmcls - INFO - Epoch [97][1800/5005]\tlr: 1.000e-04, eta: 0:26:45, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7979\n2023-01-05 03:36:27,796 - mmcls - INFO - Epoch [97][1900/5005]\tlr: 1.000e-04, eta: 0:26:36, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8199\n2023-01-05 03:36:36,356 - mmcls - INFO - Epoch [97][2000/5005]\tlr: 1.000e-04, eta: 0:26:28, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8167\n2023-01-05 03:36:45,418 - mmcls - INFO - Epoch [97][2100/5005]\tlr: 1.000e-04, eta: 0:26:19, time: 0.091, data_time: 0.004, memory: 928, loss: 4.7934\n2023-01-05 03:36:54,155 - mmcls - INFO - Epoch [97][2200/5005]\tlr: 1.000e-04, eta: 0:26:10, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7985\n2023-01-05 03:37:02,622 - mmcls - INFO - Epoch [97][2300/5005]\tlr: 1.000e-04, eta: 0:26:01, time: 0.085, data_time: 0.009, memory: 928, loss: 4.7916\n2023-01-05 03:37:11,473 - mmcls - INFO - Epoch [97][2400/5005]\tlr: 1.000e-04, eta: 0:25:52, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8093\n2023-01-05 03:37:20,007 - mmcls - INFO - Epoch [97][2500/5005]\tlr: 1.000e-04, eta: 0:25:44, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8249\n2023-01-05 03:37:28,538 - mmcls - INFO - Epoch [97][2600/5005]\tlr: 1.000e-04, eta: 0:25:35, time: 0.085, data_time: 0.004, memory: 928, loss: 4.7859\n2023-01-05 03:37:37,177 - mmcls - INFO - Epoch [97][2700/5005]\tlr: 1.000e-04, eta: 0:25:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8140\n2023-01-05 03:37:45,994 - mmcls - INFO - Epoch [97][2800/5005]\tlr: 1.000e-04, eta: 0:25:17, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8023\n2023-01-05 03:37:54,853 - mmcls - INFO - Epoch [97][2900/5005]\tlr: 1.000e-04, eta: 0:25:08, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8099\n2023-01-05 03:38:03,667 - mmcls - INFO - Epoch [97][3000/5005]\tlr: 1.000e-04, eta: 0:24:59, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8126\n2023-01-05 03:38:12,356 - mmcls - INFO - Epoch [97][3100/5005]\tlr: 1.000e-04, eta: 0:24:51, time: 0.087, data_time: 0.004, memory: 928, loss: 4.7859\n2023-01-05 03:38:21,151 - mmcls - INFO - Epoch [97][3200/5005]\tlr: 1.000e-04, eta: 0:24:42, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8095\n2023-01-05 03:38:29,934 - mmcls - INFO - Epoch [97][3300/5005]\tlr: 1.000e-04, eta: 0:24:33, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8119\n2023-01-05 03:38:38,933 - mmcls - INFO - Epoch [97][3400/5005]\tlr: 1.000e-04, eta: 0:24:24, time: 0.090, data_time: 0.002, memory: 928, loss: 4.7874\n2023-01-05 03:38:47,849 - mmcls - INFO - Epoch [97][3500/5005]\tlr: 1.000e-04, eta: 0:24:15, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8024\n2023-01-05 03:38:56,975 - mmcls - INFO - Epoch [97][3600/5005]\tlr: 1.000e-04, eta: 0:24:07, time: 0.091, data_time: 0.002, memory: 928, loss: 4.7848\n2023-01-05 03:39:06,080 - mmcls - INFO - Epoch [97][3700/5005]\tlr: 1.000e-04, eta: 0:23:58, time: 0.091, data_time: 0.014, memory: 928, loss: 4.8113\n2023-01-05 03:39:14,514 - mmcls - INFO - Epoch [97][3800/5005]\tlr: 1.000e-04, eta: 0:23:49, time: 0.084, data_time: 0.017, memory: 928, loss: 4.8099\n2023-01-05 03:39:23,234 - mmcls - INFO - Epoch [97][3900/5005]\tlr: 1.000e-04, eta: 0:23:40, time: 0.087, data_time: 0.005, memory: 928, loss: 4.7995\n2023-01-05 03:39:32,195 - mmcls - INFO - Epoch [97][4000/5005]\tlr: 1.000e-04, eta: 0:23:31, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8082\n2023-01-05 03:39:40,852 - mmcls - INFO - Epoch [97][4100/5005]\tlr: 1.000e-04, eta: 0:23:23, time: 0.087, data_time: 0.039, memory: 928, loss: 4.7936\n2023-01-05 03:39:49,683 - mmcls - INFO - Epoch [97][4200/5005]\tlr: 1.000e-04, eta: 0:23:14, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8125\n2023-01-05 03:39:58,544 - mmcls - INFO - Epoch [97][4300/5005]\tlr: 1.000e-04, eta: 0:23:05, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8047\n2023-01-05 03:40:07,449 - mmcls - INFO - Epoch [97][4400/5005]\tlr: 1.000e-04, eta: 0:22:56, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8048\n2023-01-05 03:40:16,004 - mmcls - INFO - Epoch [97][4500/5005]\tlr: 1.000e-04, eta: 0:22:47, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7977\n2023-01-05 03:40:24,709 - mmcls - INFO - Epoch [97][4600/5005]\tlr: 1.000e-04, eta: 0:22:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8375\n2023-01-05 03:40:33,240 - mmcls - INFO - Epoch [97][4700/5005]\tlr: 1.000e-04, eta: 0:22:30, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8009\n2023-01-05 03:40:41,847 - mmcls - INFO - Epoch [97][4800/5005]\tlr: 1.000e-04, eta: 0:22:21, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8044\n2023-01-05 03:40:50,437 - mmcls - INFO - Epoch [97][4900/5005]\tlr: 1.000e-04, eta: 0:22:12, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8204\n2023-01-05 03:40:59,522 - mmcls - INFO - Epoch [97][5000/5005]\tlr: 1.000e-04, eta: 0:22:03, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8015\n2023-01-05 03:41:22,377 - mmcls - INFO - Epoch(val) [97][196]\taccuracy_top-1: 18.5960, accuracy_top-5: 37.2300\n2023-01-05 03:41:33,245 - mmcls - INFO - Epoch [98][100/5005]\tlr: 1.000e-04, eta: 0:21:54, time: 0.108, data_time: 0.046, memory: 928, loss: 4.7892\n2023-01-05 03:41:42,277 - mmcls - INFO - Epoch [98][200/5005]\tlr: 1.000e-04, eta: 0:21:45, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8031\n2023-01-05 03:41:50,868 - mmcls - INFO - Epoch [98][300/5005]\tlr: 1.000e-04, eta: 0:21:36, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7911\n2023-01-05 03:41:59,517 - mmcls - INFO - Epoch [98][400/5005]\tlr: 1.000e-04, eta: 0:21:28, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7772\n2023-01-05 03:42:08,254 - mmcls - INFO - Epoch [98][500/5005]\tlr: 1.000e-04, eta: 0:21:19, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8198\n2023-01-05 03:42:17,355 - mmcls - INFO - Epoch [98][600/5005]\tlr: 1.000e-04, eta: 0:21:10, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8002\n2023-01-05 03:42:26,266 - mmcls - INFO - Epoch [98][700/5005]\tlr: 1.000e-04, eta: 0:21:01, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8111\n2023-01-05 03:42:35,101 - mmcls - INFO - Epoch [98][800/5005]\tlr: 1.000e-04, eta: 0:20:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8054\n2023-01-05 03:42:43,906 - mmcls - INFO - Epoch [98][900/5005]\tlr: 1.000e-04, eta: 0:20:44, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7904\n2023-01-05 03:42:52,693 - mmcls - INFO - Epoch [98][1000/5005]\tlr: 1.000e-04, eta: 0:20:35, time: 0.088, data_time: 0.031, memory: 928, loss: 4.8030\n2023-01-05 03:43:01,357 - mmcls - INFO - Epoch [98][1100/5005]\tlr: 1.000e-04, eta: 0:20:26, time: 0.087, data_time: 0.044, memory: 928, loss: 4.8011\n2023-01-05 03:43:10,177 - mmcls - INFO - Epoch [98][1200/5005]\tlr: 1.000e-04, eta: 0:20:17, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8050\n2023-01-05 03:43:19,050 - mmcls - INFO - Epoch [98][1300/5005]\tlr: 1.000e-04, eta: 0:20:08, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8062\n2023-01-05 03:43:27,776 - mmcls - INFO - Epoch [98][1400/5005]\tlr: 1.000e-04, eta: 0:19:59, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7876\n2023-01-05 03:43:36,483 - mmcls - INFO - Epoch [98][1500/5005]\tlr: 1.000e-04, eta: 0:19:51, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8080\n2023-01-05 03:43:45,218 - mmcls - INFO - Epoch [98][1600/5005]\tlr: 1.000e-04, eta: 0:19:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8042\n2023-01-05 03:43:54,102 - mmcls - INFO - Epoch [98][1700/5005]\tlr: 1.000e-04, eta: 0:19:33, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8048\n2023-01-05 03:44:02,899 - mmcls - INFO - Epoch [98][1800/5005]\tlr: 1.000e-04, eta: 0:19:24, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7982\n2023-01-05 03:44:11,480 - mmcls - INFO - Epoch [98][1900/5005]\tlr: 1.000e-04, eta: 0:19:15, time: 0.086, data_time: 0.018, memory: 928, loss: 4.7762\n2023-01-05 03:44:20,259 - mmcls - INFO - Epoch [98][2000/5005]\tlr: 1.000e-04, eta: 0:19:07, time: 0.088, data_time: 0.007, memory: 928, loss: 4.7872\n2023-01-05 03:44:28,918 - mmcls - INFO - Epoch [98][2100/5005]\tlr: 1.000e-04, eta: 0:18:58, time: 0.087, data_time: 0.016, memory: 928, loss: 4.7957\n2023-01-05 03:44:37,415 - mmcls - INFO - Epoch [98][2200/5005]\tlr: 1.000e-04, eta: 0:18:49, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8210\n2023-01-05 03:44:46,099 - mmcls - INFO - Epoch [98][2300/5005]\tlr: 1.000e-04, eta: 0:18:40, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8106\n2023-01-05 03:44:54,935 - mmcls - INFO - Epoch [98][2400/5005]\tlr: 1.000e-04, eta: 0:18:31, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8189\n2023-01-05 03:45:03,845 - mmcls - INFO - Epoch [98][2500/5005]\tlr: 1.000e-04, eta: 0:18:22, time: 0.089, data_time: 0.041, memory: 928, loss: 4.7799\n2023-01-05 03:45:12,442 - mmcls - INFO - Epoch [98][2600/5005]\tlr: 1.000e-04, eta: 0:18:14, time: 0.086, data_time: 0.001, memory: 928, loss: 4.7971\n2023-01-05 03:45:21,557 - mmcls - INFO - Epoch [98][2700/5005]\tlr: 1.000e-04, eta: 0:18:05, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8195\n2023-01-05 03:45:30,445 - mmcls - INFO - Epoch [98][2800/5005]\tlr: 1.000e-04, eta: 0:17:56, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8054\n2023-01-05 03:45:39,271 - mmcls - INFO - Epoch [98][2900/5005]\tlr: 1.000e-04, eta: 0:17:47, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8131\n2023-01-05 03:45:48,201 - mmcls - INFO - Epoch [98][3000/5005]\tlr: 1.000e-04, eta: 0:17:38, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8175\n2023-01-05 03:45:57,049 - mmcls - INFO - Epoch [98][3100/5005]\tlr: 1.000e-04, eta: 0:17:30, time: 0.089, data_time: 0.011, memory: 928, loss: 4.7953\n2023-01-05 03:46:05,922 - mmcls - INFO - Epoch [98][3200/5005]\tlr: 1.000e-04, eta: 0:17:21, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7929\n2023-01-05 03:46:15,122 - mmcls - INFO - Epoch [98][3300/5005]\tlr: 1.000e-04, eta: 0:17:12, time: 0.092, data_time: 0.011, memory: 928, loss: 4.8008\n2023-01-05 03:46:23,822 - mmcls - INFO - Epoch [98][3400/5005]\tlr: 1.000e-04, eta: 0:17:03, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7855\n2023-01-05 03:46:32,674 - mmcls - INFO - Epoch [98][3500/5005]\tlr: 1.000e-04, eta: 0:16:54, time: 0.089, data_time: 0.021, memory: 928, loss: 4.7998\n2023-01-05 03:46:41,386 - mmcls - INFO - Epoch [98][3600/5005]\tlr: 1.000e-04, eta: 0:16:46, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8201\n2023-01-05 03:46:49,948 - mmcls - INFO - Epoch [98][3700/5005]\tlr: 1.000e-04, eta: 0:16:37, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8087\n2023-01-05 03:46:58,538 - mmcls - INFO - Epoch [98][3800/5005]\tlr: 1.000e-04, eta: 0:16:28, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8110\n2023-01-05 03:47:07,115 - mmcls - INFO - Epoch [98][3900/5005]\tlr: 1.000e-04, eta: 0:16:19, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8128\n2023-01-05 03:47:15,744 - mmcls - INFO - Epoch [98][4000/5005]\tlr: 1.000e-04, eta: 0:16:10, time: 0.086, data_time: 0.006, memory: 928, loss: 4.7944\n2023-01-05 03:47:24,294 - mmcls - INFO - Epoch [98][4100/5005]\tlr: 1.000e-04, eta: 0:16:01, time: 0.086, data_time: 0.017, memory: 928, loss: 4.7931\n2023-01-05 03:47:32,994 - mmcls - INFO - Epoch [98][4200/5005]\tlr: 1.000e-04, eta: 0:15:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8234\n2023-01-05 03:47:41,656 - mmcls - INFO - Epoch [98][4300/5005]\tlr: 1.000e-04, eta: 0:15:44, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8025\n2023-01-05 03:47:50,193 - mmcls - INFO - Epoch [98][4400/5005]\tlr: 1.000e-04, eta: 0:15:35, time: 0.085, data_time: 0.008, memory: 928, loss: 4.7958\n2023-01-05 03:47:59,112 - mmcls - INFO - Epoch [98][4500/5005]\tlr: 1.000e-04, eta: 0:15:26, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8123\n2023-01-05 03:48:07,847 - mmcls - INFO - Epoch [98][4600/5005]\tlr: 1.000e-04, eta: 0:15:17, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7840\n2023-01-05 03:48:16,633 - mmcls - INFO - Epoch [98][4700/5005]\tlr: 1.000e-04, eta: 0:15:09, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8031\n2023-01-05 03:48:25,534 - mmcls - INFO - Epoch [98][4800/5005]\tlr: 1.000e-04, eta: 0:15:00, time: 0.089, data_time: 0.003, memory: 928, loss: 4.7970\n2023-01-05 03:48:34,150 - mmcls - INFO - Epoch [98][4900/5005]\tlr: 1.000e-04, eta: 0:14:51, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8093\n2023-01-05 03:48:43,043 - mmcls - INFO - Epoch [98][5000/5005]\tlr: 1.000e-04, eta: 0:14:42, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8219\n2023-01-05 03:49:05,592 - mmcls - INFO - Epoch(val) [98][196]\taccuracy_top-1: 18.7140, accuracy_top-5: 37.3520\n2023-01-05 03:49:16,644 - mmcls - INFO - Epoch [99][100/5005]\tlr: 1.000e-04, eta: 0:14:33, time: 0.110, data_time: 0.051, memory: 928, loss: 4.7947\n2023-01-05 03:49:25,611 - mmcls - INFO - Epoch [99][200/5005]\tlr: 1.000e-04, eta: 0:14:24, time: 0.090, data_time: 0.033, memory: 928, loss: 4.8044\n2023-01-05 03:49:34,332 - mmcls - INFO - Epoch [99][300/5005]\tlr: 1.000e-04, eta: 0:14:15, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8025\n2023-01-05 03:49:43,013 - mmcls - INFO - Epoch [99][400/5005]\tlr: 1.000e-04, eta: 0:14:06, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8046\n2023-01-05 03:49:51,709 - mmcls - INFO - Epoch [99][500/5005]\tlr: 1.000e-04, eta: 0:13:58, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8021\n2023-01-05 03:50:00,520 - mmcls - INFO - Epoch [99][600/5005]\tlr: 1.000e-04, eta: 0:13:49, time: 0.088, data_time: 0.003, memory: 928, loss: 4.7923\n2023-01-05 03:50:09,188 - mmcls - INFO - Epoch [99][700/5005]\tlr: 1.000e-04, eta: 0:13:40, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8230\n2023-01-05 03:50:17,875 - mmcls - INFO - Epoch [99][800/5005]\tlr: 1.000e-04, eta: 0:13:31, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8027\n2023-01-05 03:50:26,409 - mmcls - INFO - Epoch [99][900/5005]\tlr: 1.000e-04, eta: 0:13:22, time: 0.085, data_time: 0.030, memory: 928, loss: 4.8009\n2023-01-05 03:50:35,286 - mmcls - INFO - Epoch [99][1000/5005]\tlr: 1.000e-04, eta: 0:13:14, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8187\n2023-01-05 03:50:44,074 - mmcls - INFO - Epoch [99][1100/5005]\tlr: 1.000e-04, eta: 0:13:05, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8035\n2023-01-05 03:50:53,072 - mmcls - INFO - Epoch [99][1200/5005]\tlr: 1.000e-04, eta: 0:12:56, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8112\n2023-01-05 03:51:01,854 - mmcls - INFO - Epoch [99][1300/5005]\tlr: 1.000e-04, eta: 0:12:47, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7996\n2023-01-05 03:51:10,928 - mmcls - INFO - Epoch [99][1400/5005]\tlr: 1.000e-04, eta: 0:12:38, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8163\n2023-01-05 03:51:19,748 - mmcls - INFO - Epoch [99][1500/5005]\tlr: 1.000e-04, eta: 0:12:30, time: 0.088, data_time: 0.050, memory: 928, loss: 4.7957\n2023-01-05 03:51:28,677 - mmcls - INFO - Epoch [99][1600/5005]\tlr: 1.000e-04, eta: 0:12:21, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8120\n2023-01-05 03:51:37,524 - mmcls - INFO - Epoch [99][1700/5005]\tlr: 1.000e-04, eta: 0:12:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8040\n2023-01-05 03:51:46,231 - mmcls - INFO - Epoch [99][1800/5005]\tlr: 1.000e-04, eta: 0:12:03, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7894\n2023-01-05 03:51:54,869 - mmcls - INFO - Epoch [99][1900/5005]\tlr: 1.000e-04, eta: 0:11:54, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8028\n2023-01-05 03:52:03,665 - mmcls - INFO - Epoch [99][2000/5005]\tlr: 1.000e-04, eta: 0:11:45, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8107\n2023-01-05 03:52:12,853 - mmcls - INFO - Epoch [99][2100/5005]\tlr: 1.000e-04, eta: 0:11:37, time: 0.092, data_time: 0.001, memory: 928, loss: 4.7782\n2023-01-05 03:52:21,722 - mmcls - INFO - Epoch [99][2200/5005]\tlr: 1.000e-04, eta: 0:11:28, time: 0.089, data_time: 0.008, memory: 928, loss: 4.7992\n2023-01-05 03:52:30,604 - mmcls - INFO - Epoch [99][2300/5005]\tlr: 1.000e-04, eta: 0:11:19, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8301\n2023-01-05 03:52:39,478 - mmcls - INFO - Epoch [99][2400/5005]\tlr: 1.000e-04, eta: 0:11:10, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8064\n2023-01-05 03:52:48,517 - mmcls - INFO - Epoch [99][2500/5005]\tlr: 1.000e-04, eta: 0:11:01, time: 0.090, data_time: 0.019, memory: 928, loss: 4.8092\n2023-01-05 03:52:57,467 - mmcls - INFO - Epoch [99][2600/5005]\tlr: 1.000e-04, eta: 0:10:53, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8187\n2023-01-05 03:53:06,323 - mmcls - INFO - Epoch [99][2700/5005]\tlr: 1.000e-04, eta: 0:10:44, time: 0.088, data_time: 0.037, memory: 928, loss: 4.7863\n2023-01-05 03:53:14,880 - mmcls - INFO - Epoch [99][2800/5005]\tlr: 1.000e-04, eta: 0:10:35, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8086\n2023-01-05 03:53:23,843 - mmcls - INFO - Epoch [99][2900/5005]\tlr: 1.000e-04, eta: 0:10:26, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8193\n2023-01-05 03:53:32,497 - mmcls - INFO - Epoch [99][3000/5005]\tlr: 1.000e-04, eta: 0:10:17, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7975\n2023-01-05 03:53:41,286 - mmcls - INFO - Epoch [99][3100/5005]\tlr: 1.000e-04, eta: 0:10:08, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8067\n2023-01-05 03:53:49,836 - mmcls - INFO - Epoch [99][3200/5005]\tlr: 1.000e-04, eta: 0:10:00, time: 0.086, data_time: 0.027, memory: 928, loss: 4.7834\n2023-01-05 03:53:58,723 - mmcls - INFO - Epoch [99][3300/5005]\tlr: 1.000e-04, eta: 0:09:51, time: 0.089, data_time: 0.030, memory: 928, loss: 4.7978\n2023-01-05 03:54:07,545 - mmcls - INFO - Epoch [99][3400/5005]\tlr: 1.000e-04, eta: 0:09:42, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8117\n2023-01-05 03:54:16,173 - mmcls - INFO - Epoch [99][3500/5005]\tlr: 1.000e-04, eta: 0:09:33, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8134\n2023-01-05 03:54:24,904 - mmcls - INFO - Epoch [99][3600/5005]\tlr: 1.000e-04, eta: 0:09:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8025\n2023-01-05 03:54:33,472 - mmcls - INFO - Epoch [99][3700/5005]\tlr: 1.000e-04, eta: 0:09:16, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8141\n2023-01-05 03:54:42,455 - mmcls - INFO - Epoch [99][3800/5005]\tlr: 1.000e-04, eta: 0:09:07, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8044\n2023-01-05 03:54:51,097 - mmcls - INFO - Epoch [99][3900/5005]\tlr: 1.000e-04, eta: 0:08:58, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8147\n2023-01-05 03:54:59,956 - mmcls - INFO - Epoch [99][4000/5005]\tlr: 1.000e-04, eta: 0:08:49, time: 0.089, data_time: 0.026, memory: 928, loss: 4.7990\n2023-01-05 03:55:08,715 - mmcls - INFO - Epoch [99][4100/5005]\tlr: 1.000e-04, eta: 0:08:40, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8067\n2023-01-05 03:55:17,406 - mmcls - INFO - Epoch [99][4200/5005]\tlr: 1.000e-04, eta: 0:08:32, time: 0.087, data_time: 0.011, memory: 928, loss: 4.7970\n2023-01-05 03:55:26,161 - mmcls - INFO - Epoch [99][4300/5005]\tlr: 1.000e-04, eta: 0:08:23, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8082\n2023-01-05 03:55:34,743 - mmcls - INFO - Epoch [99][4400/5005]\tlr: 1.000e-04, eta: 0:08:14, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8091\n2023-01-05 03:55:43,471 - mmcls - INFO - Epoch [99][4500/5005]\tlr: 1.000e-04, eta: 0:08:05, time: 0.087, data_time: 0.045, memory: 928, loss: 4.8049\n2023-01-05 03:55:52,032 - mmcls - INFO - Epoch [99][4600/5005]\tlr: 1.000e-04, eta: 0:07:56, time: 0.086, data_time: 0.032, memory: 928, loss: 4.7934\n2023-01-05 03:56:00,776 - mmcls - INFO - Epoch [99][4700/5005]\tlr: 1.000e-04, eta: 0:07:47, time: 0.087, data_time: 0.040, memory: 928, loss: 4.8344\n2023-01-05 03:56:09,487 - mmcls - INFO - Epoch [99][4800/5005]\tlr: 1.000e-04, eta: 0:07:39, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8118\n2023-01-05 03:56:17,971 - mmcls - INFO - Epoch [99][4900/5005]\tlr: 1.000e-04, eta: 0:07:30, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8184\n2023-01-05 03:56:26,967 - mmcls - INFO - Epoch [99][5000/5005]\tlr: 1.000e-04, eta: 0:07:21, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8199\n2023-01-05 03:56:49,946 - mmcls - INFO - Epoch(val) [99][196]\taccuracy_top-1: 18.5760, accuracy_top-5: 37.2920\n2023-01-05 03:57:00,684 - mmcls - INFO - Epoch [100][100/5005]\tlr: 1.000e-04, eta: 0:07:12, time: 0.107, data_time: 0.051, memory: 928, loss: 4.7705\n2023-01-05 03:57:09,459 - mmcls - INFO - Epoch [100][200/5005]\tlr: 1.000e-04, eta: 0:07:03, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8188\n2023-01-05 03:57:18,422 - mmcls - INFO - Epoch [100][300/5005]\tlr: 1.000e-04, eta: 0:06:54, time: 0.090, data_time: 0.038, memory: 928, loss: 4.7933\n2023-01-05 03:57:27,452 - mmcls - INFO - Epoch [100][400/5005]\tlr: 1.000e-04, eta: 0:06:45, time: 0.090, data_time: 0.006, memory: 928, loss: 4.7989\n2023-01-05 03:57:36,096 - mmcls - INFO - Epoch [100][500/5005]\tlr: 1.000e-04, eta: 0:06:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8135\n2023-01-05 03:57:45,176 - mmcls - INFO - Epoch [100][600/5005]\tlr: 1.000e-04, eta: 0:06:28, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8263\n2023-01-05 03:57:53,810 - mmcls - INFO - Epoch [100][700/5005]\tlr: 1.000e-04, eta: 0:06:19, time: 0.086, data_time: 0.007, memory: 928, loss: 4.7979\n2023-01-05 03:58:02,861 - mmcls - INFO - Epoch [100][800/5005]\tlr: 1.000e-04, eta: 0:06:10, time: 0.091, data_time: 0.009, memory: 928, loss: 4.7869\n2023-01-05 03:58:11,382 - mmcls - INFO - Epoch [100][900/5005]\tlr: 1.000e-04, eta: 0:06:01, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8078\n2023-01-05 03:58:20,174 - mmcls - INFO - Epoch [100][1000/5005]\tlr: 1.000e-04, eta: 0:05:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8131\n2023-01-05 03:58:29,005 - mmcls - INFO - Epoch [100][1100/5005]\tlr: 1.000e-04, eta: 0:05:44, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7880\n2023-01-05 03:58:37,743 - mmcls - INFO - Epoch [100][1200/5005]\tlr: 1.000e-04, eta: 0:05:35, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8248\n2023-01-05 03:58:46,700 - mmcls - INFO - Epoch [100][1300/5005]\tlr: 1.000e-04, eta: 0:05:26, time: 0.090, data_time: 0.005, memory: 928, loss: 4.7793\n2023-01-05 03:58:55,524 - mmcls - INFO - Epoch [100][1400/5005]\tlr: 1.000e-04, eta: 0:05:17, time: 0.088, data_time: 0.017, memory: 928, loss: 4.7909\n2023-01-05 03:59:04,159 - mmcls - INFO - Epoch [100][1500/5005]\tlr: 1.000e-04, eta: 0:05:08, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7871\n2023-01-05 03:59:12,866 - mmcls - INFO - Epoch [100][1600/5005]\tlr: 1.000e-04, eta: 0:05:00, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8125\n2023-01-05 03:59:21,751 - mmcls - INFO - Epoch [100][1700/5005]\tlr: 1.000e-04, eta: 0:04:51, time: 0.089, data_time: 0.018, memory: 928, loss: 4.7996\n2023-01-05 03:59:30,704 - mmcls - INFO - Epoch [100][1800/5005]\tlr: 1.000e-04, eta: 0:04:42, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8011\n2023-01-05 03:59:39,691 - mmcls - INFO - Epoch [100][1900/5005]\tlr: 1.000e-04, eta: 0:04:33, time: 0.090, data_time: 0.019, memory: 928, loss: 4.7970\n2023-01-05 03:59:48,366 - mmcls - INFO - Epoch [100][2000/5005]\tlr: 1.000e-04, eta: 0:04:24, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8005\n2023-01-05 03:59:57,024 - mmcls - INFO - Epoch [100][2100/5005]\tlr: 1.000e-04, eta: 0:04:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8135\n2023-01-05 04:00:05,469 - mmcls - INFO - Epoch [100][2200/5005]\tlr: 1.000e-04, eta: 0:04:07, time: 0.084, data_time: 0.006, memory: 928, loss: 4.8023\n2023-01-05 04:00:14,680 - mmcls - INFO - Epoch [100][2300/5005]\tlr: 1.000e-04, eta: 0:03:58, time: 0.092, data_time: 0.006, memory: 928, loss: 4.7886\n2023-01-05 04:00:23,155 - mmcls - INFO - Epoch [100][2400/5005]\tlr: 1.000e-04, eta: 0:03:49, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8179\n2023-01-05 04:00:31,891 - mmcls - INFO - Epoch [100][2500/5005]\tlr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7970\n2023-01-05 04:00:40,654 - mmcls - INFO - Epoch [100][2600/5005]\tlr: 1.000e-04, eta: 0:03:31, time: 0.088, data_time: 0.015, memory: 928, loss: 4.7952\n2023-01-05 04:00:49,413 - mmcls - INFO - Epoch [100][2700/5005]\tlr: 1.000e-04, eta: 0:03:23, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8035\n2023-01-05 04:00:58,226 - mmcls - INFO - Epoch [100][2800/5005]\tlr: 1.000e-04, eta: 0:03:14, time: 0.088, data_time: 0.046, memory: 928, loss: 4.7887\n2023-01-05 04:01:06,990 - mmcls - INFO - Epoch [100][2900/5005]\tlr: 1.000e-04, eta: 0:03:05, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8201\n2023-01-05 04:01:15,865 - mmcls - INFO - Epoch [100][3000/5005]\tlr: 1.000e-04, eta: 0:02:56, time: 0.089, data_time: 0.007, memory: 928, loss: 4.7942\n2023-01-05 04:01:24,381 - mmcls - INFO - Epoch [100][3100/5005]\tlr: 1.000e-04, eta: 0:02:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8035\n2023-01-05 04:01:33,103 - mmcls - INFO - Epoch [100][3200/5005]\tlr: 1.000e-04, eta: 0:02:39, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8038\n2023-01-05 04:01:41,526 - mmcls - INFO - Epoch [100][3300/5005]\tlr: 1.000e-04, eta: 0:02:30, time: 0.084, data_time: 0.009, memory: 928, loss: 4.8017\n2023-01-05 04:01:50,540 - mmcls - INFO - Epoch [100][3400/5005]\tlr: 1.000e-04, eta: 0:02:21, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8016\n2023-01-05 04:01:59,397 - mmcls - INFO - Epoch [100][3500/5005]\tlr: 1.000e-04, eta: 0:02:12, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8070\n2023-01-05 04:02:08,247 - mmcls - INFO - Epoch [100][3600/5005]\tlr: 1.000e-04, eta: 0:02:03, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8186\n2023-01-05 04:02:16,812 - mmcls - INFO - Epoch [100][3700/5005]\tlr: 1.000e-04, eta: 0:01:55, time: 0.086, data_time: 0.020, memory: 928, loss: 4.7825\n2023-01-05 04:02:25,621 - mmcls - INFO - Epoch [100][3800/5005]\tlr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7748\n2023-01-05 04:02:34,349 - mmcls - INFO - Epoch [100][3900/5005]\tlr: 1.000e-04, eta: 0:01:37, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8049\n2023-01-05 04:02:42,995 - mmcls - INFO - Epoch [100][4000/5005]\tlr: 1.000e-04, eta: 0:01:28, time: 0.086, data_time: 0.029, memory: 928, loss: 4.7859\n2023-01-05 04:02:52,005 - mmcls - INFO - Epoch [100][4100/5005]\tlr: 1.000e-04, eta: 0:01:19, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8139\n2023-01-05 04:03:00,776 - mmcls - INFO - Epoch [100][4200/5005]\tlr: 1.000e-04, eta: 0:01:10, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8102\n2023-01-05 04:03:09,332 - mmcls - INFO - Epoch [100][4300/5005]\tlr: 1.000e-04, eta: 0:01:02, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8002\n2023-01-05 04:03:18,503 - mmcls - INFO - Epoch [100][4400/5005]\tlr: 1.000e-04, eta: 0:00:53, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8026\n2023-01-05 04:03:27,258 - mmcls - INFO - Epoch [100][4500/5005]\tlr: 1.000e-04, eta: 0:00:44, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8206\n2023-01-05 04:03:36,024 - mmcls - INFO - Epoch [100][4600/5005]\tlr: 1.000e-04, eta: 0:00:35, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8141\n2023-01-05 04:03:44,622 - mmcls - INFO - Epoch [100][4700/5005]\tlr: 1.000e-04, eta: 0:00:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7949\n2023-01-05 04:03:53,354 - mmcls - INFO - Epoch [100][4800/5005]\tlr: 1.000e-04, eta: 0:00:18, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8127\n2023-01-05 04:04:02,081 - mmcls - INFO - Epoch [100][4900/5005]\tlr: 1.000e-04, eta: 0:00:09, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8232\n2023-01-05 04:04:10,928 - mmcls - INFO - Epoch [100][5000/5005]\tlr: 1.000e-04, eta: 0:00:00, time: 0.089, data_time: 0.011, memory: 928, loss: 4.7976\n2023-01-05 04:04:11,341 - mmcls - INFO - Saving checkpoint at 100 epochs\n2023-01-05 04:04:33,598 - mmcls - INFO - Epoch(val) [100][196]\taccuracy_top-1: 18.3480, accuracy_top-5: 36.7180\n"
  },
  {
    "path": "face_recognition/README.md",
    "content": "Official Implementation of the experiments on discriminative tasks with polynomial networks as described in the CVPR'20 paper [Π-nets: Deep Polynomial Neural Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf) and its [extension](https://arxiv.org/abs/2006.13026).\n\n### Model Training\n\n1. Download the training dataset [`MS1MV3-RetinaFace`](https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/).\nThe training dataset includes the following 6 files:\n\n```Shell\n    ms1m-retinaface/\n       train.idx\n       train.rec\n       property\n       lfw.bin\n       cfp_fp.bin\n       agedb_30.bin\n```\n\nThe first three files are the training dataset while the last three files are verification sets.\n\n2. Edit config file:\n```Shell\ncp sample_config.py config.py\nvim config.py # edit dataset path etc..\n```\n\n3. Our experiments were conducted on the 8*2080ti GPU. The training script is like this:\n\n```Shell\nCUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_parall.py --network r50 --loss arcface --dataset retina > log_ms1mretina_ploynet50_arcface.txt  2>&1 \n```\n\n4. Compared with the [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition) code, we only add three lines between line 409 and 412 of the symbol/fresnet.py:\n\n```Shell\nact2 = Act(data=bn3*shortcut, act_type='tanh', name=name + '_tanh1')\nconv3 = Conv(data=act2, num_filter=num_filter, kernel=(1, 1), stride=(1,1), pad=(0, 0),\n             no_bias=True, workspace=workspace, name=name + '_conv3')\nbn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4')\n```\n5. Pre-trained models can be downloaded from [here](https://drive.google.com/file/d/1Y9250PK5aK5oxyrvWVqcQsVdfqjtFL53/view?usp=sharing).\n\n6. Results on [IJB-C](https://github.com/deepinsight/insightface/tree/master/Evaluation/IJB):\n\n| Method    | TAR@FAR=1e-5 | TAR@FAR=1e-4 |\n| -------   | ------       | --------- | \n|ResNet50   | 94.28        | 96.17     |  \n|Prodpoly-ResNet50   | 95.16 | 96.58     | \n\n\n### Dependencies\nRequirements:\n- python 3.x\n- mxnet\n- opencv\n\n\n"
  },
  {
    "path": "face_recognition/__init__.py",
    "content": ""
  },
  {
    "path": "face_recognition/config.py",
    "content": "import numpy as np\nimport os\nfrom easydict import EasyDict as edict\n\nconfig = edict()\n\nconfig.bn_mom = 0.9\nconfig.workspace = 256\nconfig.emb_size = 512\nconfig.ckpt_embedding = True\nconfig.net_se = 0\nconfig.net_act = 'prelu'\nconfig.net_unit = 3\nconfig.net_input = 1\nconfig.net_blocks = [1,4,6,2]\nconfig.net_output = 'E'\nconfig.net_multiplier = 1.0\nconfig.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\nconfig.ce_loss = True\nconfig.fc7_lr_mult = 1.0\nconfig.fc7_wd_mult = 1.0\nconfig.fc7_no_bias = False\nconfig.max_steps = 0\nconfig.data_rand_mirror = True\nconfig.data_cutoff = False\nconfig.data_color = 0\nconfig.data_images_filter = 0\nconfig.count_flops = True\nconfig.memonger = False #not work now\n\n\n# network settings\nnetwork = edict()\n\nnetwork.r100 = edict()\nnetwork.r100.net_name = 'fresnet'\nnetwork.r100.num_layers = 100\n\nnetwork.r100fc = edict()\nnetwork.r100fc.net_name = 'fresnet'\nnetwork.r100fc.num_layers = 100\nnetwork.r100fc.net_output = 'FC'\n\nnetwork.r50 = edict()\nnetwork.r50.net_name = 'fresnet'\nnetwork.r50.num_layers = 50\n\nnetwork.r50v1 = edict()\nnetwork.r50v1.net_name = 'fresnet'\nnetwork.r50v1.num_layers = 50\nnetwork.r50v1.net_unit = 1\n\nnetwork.d169 = edict()\nnetwork.d169.net_name = 'fdensenet'\nnetwork.d169.num_layers = 169\nnetwork.d169.per_batch_size = 64\nnetwork.d169.densenet_dropout = 0.0\n\nnetwork.d201 = edict()\nnetwork.d201.net_name = 'fdensenet'\nnetwork.d201.num_layers = 201\nnetwork.d201.per_batch_size = 64\nnetwork.d201.densenet_dropout = 0.0\n\nnetwork.y1 = edict()\nnetwork.y1.net_name = 'fmobilefacenet'\nnetwork.y1.emb_size = 128\nnetwork.y1.net_output = 'GDC'\n\nnetwork.y2 = edict()\nnetwork.y2.net_name = 'fmobilefacenet'\nnetwork.y2.emb_size = 256\nnetwork.y2.net_output = 'GDC'\nnetwork.y2.net_blocks = [2,8,16,4]\n\nnetwork.m1 = edict()\nnetwork.m1.net_name = 'fmobilenet'\nnetwork.m1.emb_size = 256\nnetwork.m1.net_output = 'GDC'\nnetwork.m1.net_multiplier = 1.0\n\nnetwork.m05 = edict()\nnetwork.m05.net_name = 'fmobilenet'\nnetwork.m05.emb_size = 256\nnetwork.m05.net_output = 'GDC'\nnetwork.m05.net_multiplier = 0.5\n\nnetwork.mnas = edict()\nnetwork.mnas.net_name = 'fmnasnet'\nnetwork.mnas.emb_size = 256\nnetwork.mnas.net_output = 'GDC'\nnetwork.mnas.net_multiplier = 1.0\n\nnetwork.mnas05 = edict()\nnetwork.mnas05.net_name = 'fmnasnet'\nnetwork.mnas05.emb_size = 256\nnetwork.mnas05.net_output = 'GDC'\nnetwork.mnas05.net_multiplier = 0.5\n\nnetwork.mnas025 = edict()\nnetwork.mnas025.net_name = 'fmnasnet'\nnetwork.mnas025.emb_size = 256\nnetwork.mnas025.net_output = 'GDC'\nnetwork.mnas025.net_multiplier = 0.25\n\nnetwork.vargfacenet = edict()\nnetwork.vargfacenet.net_name = 'vargfacenet'\nnetwork.vargfacenet.net_multiplier = 1.25\nnetwork.vargfacenet.emb_size = 512\nnetwork.vargfacenet.net_output='J'\n\n# dataset settings\ndataset = edict()\n\ndataset.cmsw = edict()\ndataset.cmsw.dataset = 'cmsw'\ndataset.cmsw.dataset_path = '/home/dengjiankang/datasets/cmsw'\ndataset.cmsw.num_classes = 492795\ndataset.cmsw.image_shape = (112,112,3)\ndataset.cmsw.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\n\ndataset.retina = edict()\ndataset.retina.dataset = 'retina'\ndataset.retina.dataset_path = '/home/dengjiankang/datasets/ms1m-retinaface'\ndataset.retina.num_classes = 93431\ndataset.retina.image_shape = (112,112,3)\ndataset.retina.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\n\nloss = edict()\nloss.softmax = edict()\nloss.softmax.loss_name = 'softmax'\n\nloss.nsoftmax = edict()\nloss.nsoftmax.loss_name = 'margin_softmax'\nloss.nsoftmax.loss_s = 64.0\nloss.nsoftmax.loss_m1 = 1.0\nloss.nsoftmax.loss_m2 = 0.0\nloss.nsoftmax.loss_m3 = 0.0\n\nloss.arcface = edict()\nloss.arcface.loss_name = 'margin_softmax'\nloss.arcface.loss_s = 64.0\nloss.arcface.loss_m1 = 1.0\nloss.arcface.loss_m2 = 0.5\nloss.arcface.loss_m3 = 0.0\n\nloss.cosface = edict()\nloss.cosface.loss_name = 'margin_softmax'\nloss.cosface.loss_s = 64.0\nloss.cosface.loss_m1 = 1.0\nloss.cosface.loss_m2 = 0.0\nloss.cosface.loss_m3 = 0.35\n\nloss.combined = edict()\nloss.combined.loss_name = 'margin_softmax'\nloss.combined.loss_s = 64.0\nloss.combined.loss_m1 = 1.0\nloss.combined.loss_m2 = 0.3\nloss.combined.loss_m3 = 0.2\n\nloss.triplet = edict()\nloss.triplet.loss_name = 'triplet'\nloss.triplet.images_per_identity = 5\nloss.triplet.triplet_alpha = 0.3\nloss.triplet.triplet_bag_size = 7200\nloss.triplet.triplet_max_ap = 0.0\nloss.triplet.per_batch_size = 60\nloss.triplet.lr = 0.05\n\nloss.atriplet = edict()\nloss.atriplet.loss_name = 'atriplet'\nloss.atriplet.images_per_identity = 5\nloss.atriplet.triplet_alpha = 0.35\nloss.atriplet.triplet_bag_size = 7200\nloss.atriplet.triplet_max_ap = 0.0\nloss.atriplet.per_batch_size = 60\nloss.atriplet.lr = 0.05\n\n# default settings\ndefault = edict()\n\n# default network\ndefault.network = 'r50'\ndefault.pretrained = ''\ndefault.pretrained_epoch = 1\n# default dataset\ndefault.dataset = 'retina'\ndefault.loss = 'arcface'\ndefault.frequent = 20\ndefault.verbose = 2000\ndefault.kvstore = 'device'\n\ndefault.end_epoch = 10000\ndefault.lr = 0.1\ndefault.wd = 0.0005\ndefault.mom = 0.9\ndefault.per_batch_size = 64\ndefault.ckpt = 2\n# default.lr_steps = '300000,450000,600000'\ndefault.lr_steps = '150000,240000,330000'\n# default.lr_steps = '100000,160000,220000'\ndefault.models_root = './models'\n\n\ndef generate_config(_network, _dataset, _loss):\n    for k, v in loss[_loss].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    for k, v in network[_network].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    for k, v in dataset[_dataset].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    config.loss = _loss\n    config.network = _network\n    config.dataset = _dataset\n    config.num_workers = 1\n    if 'DMLC_NUM_WORKER' in os.environ:\n      config.num_workers = int(os.environ['DMLC_NUM_WORKER'])\n\n"
  },
  {
    "path": "face_recognition/data/build_eval_pack.py",
    "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n#import mxnet as mx\n#from mxnet import ndarray as nd\nimport argparse\nimport cv2\nimport pickle\nimport numpy as np\nimport sys\nimport os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'common'))\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'RetinaFace'))\nimport face_align\nfrom retinaface import RetinaFace\n\ndef to_rgb(img):\n    w, h = img.shape\n    ret = np.empty((w, h, 3), dtype=np.uint8)\n    ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img\n    return ret\n\n\ndef IOU(Reframe,GTframe):\n  x1 = Reframe[0];\n  y1 = Reframe[1];\n  width1 = Reframe[2]-Reframe[0];\n  height1 = Reframe[3]-Reframe[1];\n\n  x2 = GTframe[0]\n  y2 = GTframe[1]\n  width2 = GTframe[2]-GTframe[0]\n  height2 = GTframe[3]-GTframe[1]\n\n  endx = max(x1+width1,x2+width2)\n  startx = min(x1,x2)\n  width = width1+width2-(endx-startx)\n\n  endy = max(y1+height1,y2+height2)\n  starty = min(y1,y2)\n  height = height1+height2-(endy-starty)\n\n  if width <=0 or height <= 0:\n    ratio = 0\n  else:\n    Area = width*height\n    Area1 = width1*height1\n    Area2 = width2*height2\n    ratio = Area*1./(Area1+Area2-Area)\n  return ratio\n\nparser = argparse.ArgumentParser(description='Package eval images')\n# general\nparser.add_argument('--data-dir', default='', help='')\nparser.add_argument('--image-size', type=int, default=112, help='')\nparser.add_argument('--gpu', type=int, default=0, help='')\nparser.add_argument('--det-prefix', type=str, default='./model/R50', help='')\nparser.add_argument('--output', default='./', help='path to save.')\nparser.add_argument('--align-mode', default='arcface', help='align mode.')\nargs = parser.parse_args()\n\ngpu_id = args.gpu\n\ndetector = RetinaFace(args.det_prefix, 0, gpu_id, network='net3')\ntarget_size = 400\nmax_size = 800\n\ndef get_norm_crop(image_path):\n  im = cv2.imread(image_path)\n  im_shape = im.shape\n  im_size_min = np.min(im_shape[0:2])\n  im_size_max = np.max(im_shape[0:2])\n  im_scale = float(target_size) / float(im_size_min)\n  # prevent bigger axis from being more than max_size:\n  if np.round(im_scale * im_size_max) > max_size:\n      im_scale = float(max_size) / float(im_size_max)\n  bbox, landmark = detector.detect(im, threshold=0.5, scales=[im_scale])\n  #print(im.shape, bbox.shape, landmark.shape)\n  if bbox.shape[0]==0:\n    bbox, landmark = detector.detect(im, threshold=0.05, scales=[im_scale*0.75, im_scale, im_scale*2.0])\n    print('refine', im.shape, bbox.shape, landmark.shape)\n  nrof_faces = bbox.shape[0]\n  if nrof_faces>0:\n    det = bbox[:,0:4]\n    img_size = np.asarray(im.shape)[0:2]\n    bindex = 0\n    if nrof_faces>1:\n      bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])\n      img_center = img_size / 2\n      offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])\n      offset_dist_squared = np.sum(np.power(offsets,2.0),0)\n      bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering\n    #_bbox = bounding_boxes[bindex, 0:4]\n    _landmark = landmark[bindex]\n    warped = face_align.norm_crop(im, landmark = _landmark, image_size=args.image_size, mode=args.align_mode)\n    return warped\n  else:\n    return None\n\n\nbins = []\nissame_list = []\npp = 0\nfor line in open(os.path.join(args.data_dir, 'pairs_label.txt'), 'r'):\n  pp+=1\n  if pp%100==0:\n    print('processing', pp)\n  line = line.strip().split()\n  assert len(line)==3\n  path1 = os.path.join(args.data_dir, line[0])\n  path2 = os.path.join(args.data_dir, line[1])\n  im1 = get_norm_crop(path1)\n  im2 = get_norm_crop(path2)\n  issame = True\n  if line[2]=='0':\n    issame = False\n  issame_list.append(issame)\n  for im in [im1, im2]:\n    _, s = cv2.imencode('.jpg', im)\n    bins.append(s)\n\nwith open(args.output, 'wb') as f:\n  pickle.dump((bins, issame_list), f, protocol=pickle.HIGHEST_PROTOCOL)\n\n"
  },
  {
    "path": "face_recognition/data/lfw/pairs_label.txt",
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_Griese/Brian_Griese_0002.jpg\tLaura_Elena_Harring/Laura_Elena_Harring_0001.jpg\t0\nBrian_Griese/Brian_Griese_0002.jpg\tNicolas_Lapentti/Nicolas_Lapentti_0002.jpg\t0\nBryan_Adams/Bryan_Adams_0001.jpg\tMichael_Kors/Michael_Kors_0001.jpg\t0\nBryan_Adams/Bryan_Adams_0001.jpg\tMohamed_Seineldin/Mohamed_Seineldin_0001.jpg\t0\nCalbert_Cheaney/Calbert_Cheaney_0001.jpg\tIan_Smith/Ian_Smith_0001.jpg\t0\nCalbert_Cheaney/Calbert_Cheaney_0001.jpg\tRobert_Downey_Jr/Robert_Downey_Jr_0001.jpg\t0\nCarl_Reiner/Carl_Reiner_0001.jpg\tHamid_Efendi/Hamid_Efendi_0001.jpg\t0\nCarl_Reiner/Carl_Reiner_0002.jpg\tJohn_Engler/John_Engler_0001.jpg\t0\nCarl_Reiner/Carl_Reiner_0002.jpg\tPrince_Rainier_III/Prince_Rainier_III_0001.jpg\t0\nCarl_Reiner/Carl_Reiner_0002.jpg\tTom_Glavine/Tom_Glavine_0002.jpg\t0\nCarlo_Azeglio_Ciampi/Carlo_Azeglio_Ciampi_0001.jpg\tFrancis_Ford_Coppola/Francis_Ford_Coppola_0001.jpg\t0\nCarlos_Arroyo/Carlos_Arroyo_0001.jpg\tShane_Phillips/Shane_Phillips_0001.jpg\t0\nCarlos_Paternina/Carlos_Paternina_0001.jpg\tEmily_Stevens/Emily_Stevens_0001.jpg\t0\nCarlos_Paternina/Carlos_Paternina_0001.jpg\tPaul_Sarbanes/Paul_Sarbanes_0001.jpg\t0\nCasey_Mears/Casey_Mears_0001.jpg\tMike_Davis/Mike_Davis_0001.jpg\t0\nCasey_Mears/Casey_Mears_0001.jpg\tYukiko_Okudo/Yukiko_Okudo_0001.jpg\t0\nCathy_Freeman/Cathy_Freeman_0002.jpg\tWilliam_Martin/William_Martin_0002.jpg\t0\nCecilia_Cheung/Cecilia_Cheung_0001.jpg\tDaryl_Parks/Daryl_Parks_0001.jpg\t0\nCecilia_Cheung/Cecilia_Cheung_0001.jpg\tPascal_Affi_Nguessan/Pascal_Affi_Nguessan_0001.jpg\t0\nChen_Tsai-chin/Chen_Tsai-chin_0001.jpg\tDereck_Whittenburg/Dereck_Whittenburg_0001.jpg\t0\nChen_Tsai-chin/Chen_Tsai-chin_0001.jpg\tMamdouh_Habib/Mamdouh_Habib_0001.jpg\t0\nCho_Myung-kyun/Cho_Myung-kyun_0001.jpg\tDavid_Bell/David_Bell_0001.jpg\t0\nCho_Myung-kyun/Cho_Myung-kyun_0001.jpg\tFernando_Sanz/Fernando_Sanz_0001.jpg\t0\nCho_Myung-kyun/Cho_Myung-kyun_0001.jpg\tGeorgia_Giddings/Georgia_Giddings_0001.jpg\t0\nCho_Myung-kyun/Cho_Myung-kyun_0001.jpg\tRichard_Fine/Richard_Fine_0001.jpg\t0\nChoi_Yun-yong/Choi_Yun-yong_0001.jpg\tChuck_Eidson/Chuck_Eidson_0001.jpg\t0\nChris_Dodd/Chris_Dodd_0001.jpg\tTaylor_Twellman/Taylor_Twellman_0001.jpg\t0\nChris_Swecker/Chris_Swecker_0001.jpg\tTom_Vilsack/Tom_Vilsack_0001.jpg\t0\nChristian_Lacroix/Christian_Lacroix_0001.jpg\tLaura_Elena_Harring/Laura_Elena_Harring_0001.jpg\t0\nChristian_Lacroix/Christian_Lacroix_0001.jpg\tOrnella_Muti/Ornella_Muti_0001.jpg\t0\nChuck_Eidson/Chuck_Eidson_0001.jpg\tSigourney_Weaver/Sigourney_Weaver_0001.jpg\t0\nClare_Short/Clare_Short_0004.jpg\tDon_Carcieri/Don_Carcieri_0001.jpg\t0\nCoco_dEste/Coco_dEste_0001.jpg\tDarvis_Patton/Darvis_Patton_0001.jpg\t0\nCoco_dEste/Coco_dEste_0001.jpg\tMelina_Kanakaredes/Melina_Kanakaredes_0001.jpg\t0\nCoco_dEste/Coco_dEste_0001.jpg\tTom_Rouen/Tom_Rouen_0001.jpg\t0\nColeen_Rowley/Coleen_Rowley_0001.jpg\tNong_Duc_Manh/Nong_Duc_Manh_0001.jpg\t0\nCorinne_Coman/Corinne_Coman_0002.jpg\tFrank_Beamer/Frank_Beamer_0001.jpg\t0\nDale_Earnhardt_Jr/Dale_Earnhardt_Jr_0001.jpg\tNick_Reilly/Nick_Reilly_0001.jpg\t0\nDario_Franchitti/Dario_Franchitti_0001.jpg\tHenry_Castellanos/Henry_Castellanos_0001.jpg\t0\nDarren_Campel/Darren_Campel_0001.jpg\tHilary_McKay/Hilary_McKay_0001.jpg\t0\nDarvis_Patton/Darvis_Patton_0001.jpg\tGerard_Tronche/Gerard_Tronche_0001.jpg\t0\nDarvis_Patton/Darvis_Patton_0001.jpg\tWilliam_Macy/William_Macy_0004.jpg\t0\nDaryl_Parks/Daryl_Parks_0001.jpg\tGuus_Hiddink/Guus_Hiddink_0001.jpg\t0\nDaryl_Sabara/Daryl_Sabara_0001.jpg\tNick_Reilly/Nick_Reilly_0001.jpg\t0\nDaryl_Sabara/Daryl_Sabara_0001.jpg\tValentina_Tereshkova/Valentina_Tereshkova_0001.jpg\t0\nDave_Johnson/Dave_Johnson_0001.jpg\tHoward_Stern/Howard_Stern_0001.jpg\t0\nDave_Tucker/Dave_Tucker_0001.jpg\tGary_Gitnick/Gary_Gitnick_0001.jpg\t0\nDavid_Collenette/David_Collenette_0001.jpg\tSalman_Khan/Salman_Khan_0001.jpg\t0\nDavid_Westerfield/David_Westerfield_0001.jpg\tStan_Kroenke/Stan_Kroenke_0001.jpg\t0\nDean_Jacek/Dean_Jacek_0001.jpg\tLarry_Wilmore/Larry_Wilmore_0001.jpg\t0\nDemi_Moore/Demi_Moore_0002.jpg\tFred_Thompson/Fred_Thompson_0001.jpg\t0\nDemi_Moore/Demi_Moore_0002.jpg\tLinus_Roache/Linus_Roache_0001.jpg\t0\nDereck_Whittenburg/Dereck_Whittenburg_0001.jpg\tLindsey_Graham/Lindsey_Graham_0002.jpg\t0\nDianne_Reeves/Dianne_Reeves_0001.jpg\tLarry_Wilmore/Larry_Wilmore_0001.jpg\t0\nDianne_Reeves/Dianne_Reeves_0001.jpg\tRomeo_Gigli/Romeo_Gigli_0001.jpg\t0\nDon_Carcieri/Don_Carcieri_0001.jpg\tJanica_Kostelic/Janica_Kostelic_0001.jpg\t0\nDora_Bakoyianni/Dora_Bakoyianni_0001.jpg\tRichard_Sambrook/Richard_Sambrook_0002.jpg\t0\nDora_Bakoyianni/Dora_Bakoyianni_0001.jpg\tSaeb_Erekat/Saeb_Erekat_0002.jpg\t0\nDoris_Roberts/Doris_Roberts_0001.jpg\tNong_Duc_Manh/Nong_Duc_Manh_0001.jpg\t0\nDoug_Wilson/Doug_Wilson_0001.jpg\tSzu_Yu_Chen/Szu_Yu_Chen_0001.jpg\t0\nDouglas_Gansler/Douglas_Gansler_0001.jpg\tMartin_Brooke/Martin_Brooke_0001.jpg\t0\nDouglas_Gansler/Douglas_Gansler_0001.jpg\tRonald_Kadish/Ronald_Kadish_0001.jpg\t0\nDwayne_Wade/Dwayne_Wade_0001.jp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  },
  {
    "path": "face_recognition/data/rec2image.py",
    "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport os\nimport sys\nimport mxnet as mx\nfrom mxnet import ndarray as nd\nimport random\nimport argparse\nimport cv2\nimport time\nimport sklearn\nimport numpy as np\n\n\ndef main(args):\n  include_datasets = args.include.split(',')\n  rec_list = []\n  for ds in include_datasets:\n    path_imgrec = os.path.join(ds, 'train.rec')\n    path_imgidx = os.path.join(ds, 'train.idx')\n    imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type\n    rec_list.append(imgrec)\n  if not os.path.exists(args.output):\n    os.makedirs(args.output)\n  for ds_id in range(len(rec_list)):\n    id_list = []\n    imgrec = rec_list[ds_id]\n    s = imgrec.read_idx(0)\n    header, _ = mx.recordio.unpack(s)\n    assert header.flag>0\n    print('header0 label', header.label)\n    header0 = (int(header.label[0]), int(header.label[1]))\n    seq_identity = range(int(header.label[0]), int(header.label[1]))\n    pp=0\n    for identity in seq_identity:\n      id_dir = os.path.join(args.output, \"%d_%d\"%(ds_id, identity))\n      os.makedirs(id_dir)\n      pp+=1\n      if pp%10==0:\n        print('processing id', pp)\n      s = imgrec.read_idx(identity)\n      header, _ = mx.recordio.unpack(s)\n      imgid = 0\n      for _idx in range(int(header.label[0]), int(header.label[1])):\n        s = imgrec.read_idx(_idx)\n        _header, _img = mx.recordio.unpack(s)\n        _img = mx.image.imdecode(_img).asnumpy()[:,:,::-1] # to bgr\n        image_path = os.path.join(id_dir, \"%d.jpg\"%imgid)\n        cv2.imwrite(image_path, _img)\n        imgid+=1\n\n\n\n\nif __name__ == '__main__':\n  parser = argparse.ArgumentParser(description='do dataset merge')\n  # general\n  parser.add_argument('--include', default='', type=str, help='')\n  parser.add_argument('--output', default='', type=str, help='')\n  args = parser.parse_args()\n  main(args)\n\n"
  },
  {
    "path": "face_recognition/eval/lfw.py",
    "content": "\"\"\"Helper for evaluation on the Labeled Faces in the Wild dataset \n\"\"\"\n\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nfrom scipy import misc\nfrom sklearn.model_selection import KFold\nfrom scipy import interpolate\nimport sklearn\nfrom sklearn.decomposition import PCA\nimport mxnet as mx\nfrom mxnet import ndarray as nd\n\n\n\ndef calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0):\n    assert(embeddings1.shape[0] == embeddings2.shape[0])\n    assert(embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n    \n    tprs = np.zeros((nrof_folds,nrof_thresholds))\n    fprs = np.zeros((nrof_folds,nrof_thresholds))\n    accuracy = np.zeros((nrof_folds))\n    indices = np.arange(nrof_pairs)\n    #print('pca', pca)\n    \n    if pca==0:\n      diff = np.subtract(embeddings1, embeddings2)\n      dist = np.sum(np.square(diff),1)\n    \n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n        #print('train_set', train_set)\n        #print('test_set', test_set)\n        if pca>0:\n          print('doing pca on', fold_idx)\n          embed1_train = embeddings1[train_set]\n          embed2_train = embeddings2[train_set]\n          _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 )\n          #print(_embed_train.shape)\n          pca_model = PCA(n_components=pca)\n          pca_model.fit(_embed_train)\n          embed1 = pca_model.transform(embeddings1)\n          embed2 = pca_model.transform(embeddings2)\n          embed1 = sklearn.preprocessing.normalize(embed1)\n          embed2 = sklearn.preprocessing.normalize(embed2)\n          #print(embed1.shape, embed2.shape)\n          diff = np.subtract(embed1, embed2)\n          dist = np.sum(np.square(diff),1)\n        \n        # Find the best threshold for the fold\n        acc_train = np.zeros((nrof_thresholds))\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])\n        best_threshold_index = np.argmax(acc_train)\n        for threshold_idx, threshold in enumerate(thresholds):\n            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])\n        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])\n          \n    tpr = np.mean(tprs,0)\n    fpr = np.mean(fprs,0)\n    return tpr, fpr, accuracy\n\ndef calculate_accuracy(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    tp = np.sum(np.logical_and(predict_issame, actual_issame))\n    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))\n    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n  \n    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)\n    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)\n    acc = float(tp+tn)/dist.size\n    return tpr, fpr, acc\n\n\n  \ndef calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):\n    assert(embeddings1.shape[0] == embeddings2.shape[0])\n    assert(embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = KFold(n_splits=nrof_folds, shuffle=False)\n    \n    val = np.zeros(nrof_folds)\n    far = np.zeros(nrof_folds)\n    \n    diff = np.subtract(embeddings1, embeddings2)\n    dist = np.sum(np.square(diff),1)\n    indices = np.arange(nrof_pairs)\n    \n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n      \n        # Find the threshold that gives FAR = far_target\n        far_train = np.zeros(nrof_thresholds)\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])\n        if np.max(far_train)>=far_target:\n            f = interpolate.interp1d(far_train, thresholds, kind='slinear')\n            threshold = f(far_target)\n        else:\n            threshold = 0.0\n    \n        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])\n  \n    val_mean = np.mean(val)\n    far_mean = np.mean(far)\n    val_std = np.std(val)\n    return val_mean, val_std, far_mean\n\n\ndef calculate_val_far(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    n_same = np.sum(actual_issame)\n    n_diff = np.sum(np.logical_not(actual_issame))\n    val = float(true_accept) / float(n_same)\n    far = float(false_accept) / float(n_diff)\n    return val, far\n\ndef evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):\n    # Calculate evaluation metrics\n    thresholds = np.arange(0, 4, 0.01)\n    embeddings1 = embeddings[0::2]\n    embeddings2 = embeddings[1::2]\n    tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,\n        np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)\n    thresholds = np.arange(0, 4, 0.001)\n    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,\n        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)\n    return tpr, fpr, accuracy, val, val_std, far\n\ndef get_paths(lfw_dir, pairs, file_ext):\n    nrof_skipped_pairs = 0\n    path_list = []\n    issame_list = []\n    for pair in pairs:\n        if len(pair) == 3:\n            path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)\n            path1 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])+'.'+file_ext)\n            issame = True\n        elif len(pair) == 4:\n            path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext)\n            path1 = os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])+'.'+file_ext)\n            issame = False\n        if os.path.exists(path0) and os.path.exists(path1):    # Only add the pair if both paths exist\n            path_list += (path0,path1)\n            issame_list.append(issame)\n        else:\n            print('not exists', path0, path1)\n            nrof_skipped_pairs += 1\n    if nrof_skipped_pairs>0:\n        print('Skipped %d image pairs' % nrof_skipped_pairs)\n    \n    return path_list, issame_list\n\ndef read_pairs(pairs_filename):\n    pairs = []\n    with open(pairs_filename, 'r') as f:\n        for line in f.readlines()[1:]:\n            pair = line.strip().split()\n            pairs.append(pair)\n    return np.array(pairs)\n\n\ndef load_dataset(lfw_dir, image_size):\n  lfw_pairs = read_pairs(os.path.join(lfw_dir, 'pairs.txt'))\n  lfw_paths, issame_list = get_paths(lfw_dir, lfw_pairs, 'jpg')\n  lfw_data_list = []\n  for flip in [0,1]:\n    lfw_data = nd.empty((len(lfw_paths), 3, image_size[0], image_size[1]))\n    lfw_data_list.append(lfw_data)\n  i = 0\n  for path in lfw_paths:\n    with open(path, 'rb') as fin:\n      _bin = fin.read()\n      img = mx.image.imdecode(_bin)\n      img = nd.transpose(img, axes=(2, 0, 1))\n      for flip in [0,1]:\n        if flip==1:\n          img = mx.ndarray.flip(data=img, axis=2)\n        lfw_data_list[flip][i][:] = img\n      i+=1\n      if i%1000==0:\n        print('loading lfw', i)\n  print(lfw_data_list[0].shape)\n  print(lfw_data_list[1].shape)\n  return (lfw_data_list, issame_list)\n\ndef test(lfw_set, mx_model, batch_size):\n  print('testing lfw..')\n  lfw_data_list = lfw_set[0]\n  issame_list = lfw_set[1]\n  model = mx_model\n  embeddings_list = []\n  for i in range( len(lfw_data_list) ):\n    lfw_data = lfw_data_list[i]\n    embeddings = None\n    ba = 0\n    while ba<lfw_data.shape[0]:\n      bb = min(ba+batch_size, lfw_data.shape[0])\n      _data = nd.slice_axis(lfw_data, axis=0, begin=ba, end=bb)\n      _label = nd.ones( (bb-ba,) )\n      #print(_data.shape, _label.shape)\n      db = mx.io.DataBatch(data=(_data,), label=(_label,))\n      model.forward(db, is_train=False)\n      net_out = model.get_outputs()\n      #_arg, _aux = model.get_params()\n      #__arg = {}\n      #for k,v in _arg.iteritems():\n      #  __arg[k] = v.as_in_context(_ctx)\n      #_arg = __arg\n      #_arg[\"data\"] = _data.as_in_context(_ctx)\n      #_arg[\"softmax_label\"] = _label.as_in_context(_ctx)\n      #for k,v in _arg.iteritems():\n      #  print(k,v.context)\n      #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req=\"null\", aux_states=_aux)\n      #exe.forward(is_train=False)\n      #net_out = exe.outputs\n      _embeddings = net_out[0].asnumpy()\n      #print(_embeddings.shape)\n      if embeddings is None:\n        embeddings = np.zeros( (lfw_data.shape[0], _embeddings.shape[1]) )\n      embeddings[ba:bb,:] = _embeddings\n      ba = bb\n    embeddings_list.append(embeddings)\n\n  _xnorm = 0.0\n  _xnorm_cnt = 0\n  for embed in embeddings_list:\n    for i in range(embed.shape[0]):\n      _em = embed[i]\n      _norm=np.linalg.norm(_em)\n      #print(_em.shape, _norm)\n      _xnorm+=_norm\n      _xnorm_cnt+=1\n  _xnorm /= _xnorm_cnt\n\n  embeddings = embeddings_list[0].copy()\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)\n  acc1, std1 = np.mean(accuracy), np.std(accuracy)\n  #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))\n  #embeddings = np.concatenate(embeddings_list, axis=1)\n  embeddings = embeddings_list[0] + embeddings_list[1]\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  print(embeddings.shape)\n  _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)\n  acc2, std2 = np.mean(accuracy), np.std(accuracy)\n  return acc1, std1, acc2, std2, _xnorm, embeddings_list\n\n"
  },
  {
    "path": "face_recognition/eval/verification.py",
    "content": "\"\"\"Helper for evaluation on the Labeled Faces in the Wild dataset \n\"\"\"\n\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n# \n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n# \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport argparse\nimport sys\nimport numpy as np\nfrom scipy import misc\nfrom sklearn.model_selection import KFold\nfrom scipy import interpolate\nimport sklearn\nimport cv2\nimport math\nimport datetime\nimport pickle\nfrom sklearn.decomposition import PCA\nimport mxnet as mx\nfrom mxnet import ndarray as nd\n\n\nclass LFold:\n  def __init__(self, n_splits = 2, shuffle = False):\n    self.n_splits = n_splits\n    if self.n_splits>1:\n      self.k_fold = KFold(n_splits = n_splits, shuffle = shuffle)\n\n  def split(self, indices):\n    if self.n_splits>1:\n      return self.k_fold.split(indices)\n    else:\n      return [(indices, indices)]\n\n\ndef calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0):\n    assert(embeddings1.shape[0] == embeddings2.shape[0])\n    assert(embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = LFold(n_splits=nrof_folds, shuffle=False)\n    \n    tprs = np.zeros((nrof_folds,nrof_thresholds))\n    fprs = np.zeros((nrof_folds,nrof_thresholds))\n    accuracy = np.zeros((nrof_folds))\n    indices = np.arange(nrof_pairs)\n    #print('pca', pca)\n    \n    if pca==0:\n      diff = np.subtract(embeddings1, embeddings2)\n      dist = np.sum(np.square(diff),1)\n    \n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n        #print('train_set', train_set)\n        #print('test_set', test_set)\n        if pca>0:\n          print('doing pca on', fold_idx)\n          embed1_train = embeddings1[train_set]\n          embed2_train = embeddings2[train_set]\n          _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 )\n          #print(_embed_train.shape)\n          pca_model = PCA(n_components=pca)\n          pca_model.fit(_embed_train)\n          embed1 = pca_model.transform(embeddings1)\n          embed2 = pca_model.transform(embeddings2)\n          embed1 = sklearn.preprocessing.normalize(embed1)\n          embed2 = sklearn.preprocessing.normalize(embed2)\n          #print(embed1.shape, embed2.shape)\n          diff = np.subtract(embed1, embed2)\n          dist = np.sum(np.square(diff),1)\n        \n        # Find the best threshold for the fold\n        acc_train = np.zeros((nrof_thresholds))\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set])\n        best_threshold_index = np.argmax(acc_train)\n        #print('threshold', thresholds[best_threshold_index])\n        for threshold_idx, threshold in enumerate(thresholds):\n            tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])\n        _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])\n          \n    tpr = np.mean(tprs,0)\n    fpr = np.mean(fprs,0)\n    return tpr, fpr, accuracy\n\ndef calculate_accuracy(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    tp = np.sum(np.logical_and(predict_issame, actual_issame))\n    fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame)))\n    fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame))\n  \n    tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn)\n    fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn)\n    acc = float(tp+tn)/dist.size\n    return tpr, fpr, acc\n\n\n  \ndef calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):\n    assert(embeddings1.shape[0] == embeddings2.shape[0])\n    assert(embeddings1.shape[1] == embeddings2.shape[1])\n    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n    nrof_thresholds = len(thresholds)\n    k_fold = LFold(n_splits=nrof_folds, shuffle=False)\n    \n    val = np.zeros(nrof_folds)\n    far = np.zeros(nrof_folds)\n    \n    diff = np.subtract(embeddings1, embeddings2)\n    dist = np.sum(np.square(diff),1)\n    indices = np.arange(nrof_pairs)\n    \n    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n      \n        # Find the threshold that gives FAR = far_target\n        far_train = np.zeros(nrof_thresholds)\n        for threshold_idx, threshold in enumerate(thresholds):\n            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])\n        if np.max(far_train)>=far_target:\n            f = interpolate.interp1d(far_train, thresholds, kind='slinear')\n            threshold = f(far_target)\n        else:\n            threshold = 0.0\n    \n        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])\n  \n    val_mean = np.mean(val)\n    far_mean = np.mean(far)\n    val_std = np.std(val)\n    return val_mean, val_std, far_mean\n\n\ndef calculate_val_far(threshold, dist, actual_issame):\n    predict_issame = np.less(dist, threshold)\n    true_accept = np.sum(np.logical_and(predict_issame, actual_issame))\n    false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame)))\n    n_same = np.sum(actual_issame)\n    n_diff = np.sum(np.logical_not(actual_issame))\n    #print(true_accept, false_accept)\n    #print(n_same, n_diff)\n    val = float(true_accept) / float(n_same)\n    far = float(false_accept) / float(n_diff)\n    return val, far\n\ndef evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0):\n    # Calculate evaluation metrics\n    thresholds = np.arange(0, 4, 0.01)\n    embeddings1 = embeddings[0::2]\n    embeddings2 = embeddings[1::2]\n    tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2,\n        np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca)\n    thresholds = np.arange(0, 4, 0.001)\n    val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2,\n        np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds)\n    return tpr, fpr, accuracy, val, val_std, far\n\ndef load_bin(path, image_size):\n  try:\n    with open(path, 'rb') as f:\n      bins, issame_list = pickle.load(f) #py2\n  except UnicodeDecodeError as e:\n    with open(path, 'rb') as f:\n      bins, issame_list = pickle.load(f, encoding='bytes') #py3\n  data_list = []\n  for flip in [0,1]:\n    data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1]))\n    data_list.append(data)\n  for i in range(len(issame_list)*2):\n    _bin = bins[i]\n    img = mx.image.imdecode(_bin)\n    if img.shape[1]!=image_size[0]:\n      img = mx.image.resize_short(img, image_size[0])\n    img = nd.transpose(img, axes=(2, 0, 1))\n    for flip in [0,1]:\n      if flip==1:\n        img = mx.ndarray.flip(data=img, axis=2)\n      data_list[flip][i][:] = img\n    if i%1000==0:\n      print('loading bin', i)\n  print(data_list[0].shape)\n  return (data_list, issame_list)\n\ndef test(data_set, mx_model, batch_size, nfolds=10, data_extra = None, label_shape = None):\n  print('testing verification..')\n  data_list = data_set[0]\n  issame_list = data_set[1]\n  model = mx_model\n  embeddings_list = []\n  if data_extra is not None:\n    _data_extra = nd.array(data_extra)\n  time_consumed = 0.0\n  if label_shape is None:\n    _label = nd.ones( (batch_size,) )\n  else:\n    _label = nd.ones( label_shape )\n  for i in range( len(data_list) ):\n    data = data_list[i]\n    embeddings = None\n    ba = 0\n    while ba<data.shape[0]:\n      bb = min(ba+batch_size, data.shape[0])\n      count = bb-ba\n      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)\n      #print(_data.shape, _label.shape)\n      time0 = datetime.datetime.now()\n      if data_extra is None:\n        db = mx.io.DataBatch(data=(_data,), label=(_label,))\n      else:\n        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))\n      model.forward(db, is_train=False)\n      net_out = model.get_outputs()\n      #_arg, _aux = model.get_params()\n      #__arg = {}\n      #for k,v in _arg.iteritems():\n      #  __arg[k] = v.as_in_context(_ctx)\n      #_arg = __arg\n      #_arg[\"data\"] = _data.as_in_context(_ctx)\n      #_arg[\"softmax_label\"] = _label.as_in_context(_ctx)\n      #for k,v in _arg.iteritems():\n      #  print(k,v.context)\n      #exe = sym.bind(_ctx, _arg ,args_grad=None, grad_req=\"null\", aux_states=_aux)\n      #exe.forward(is_train=False)\n      #net_out = exe.outputs\n      _embeddings = net_out[0].asnumpy()\n      time_now = datetime.datetime.now()\n      diff = time_now - time0\n      time_consumed+=diff.total_seconds()\n      #print(_embeddings.shape)\n      if embeddings is None:\n        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )\n      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]\n      ba = bb\n    embeddings_list.append(embeddings)\n\n  _xnorm = 0.0\n  _xnorm_cnt = 0\n  for embed in embeddings_list:\n    for i in range(embed.shape[0]):\n      _em = embed[i]\n      _norm=np.linalg.norm(_em)\n      #print(_em.shape, _norm)\n      _xnorm+=_norm\n      _xnorm_cnt+=1\n  _xnorm /= _xnorm_cnt\n\n  embeddings = embeddings_list[0].copy()\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  acc1 = 0.0\n  std1 = 0.0\n  #_, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=10)\n  #acc1, std1 = np.mean(accuracy), np.std(accuracy)\n\n  #print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))\n  #embeddings = np.concatenate(embeddings_list, axis=1)\n  embeddings = embeddings_list[0] + embeddings_list[1]\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  print(embeddings.shape)\n  print('infer time', time_consumed)\n  _, _, accuracy, val, val_std, far = evaluate(embeddings, issame_list, nrof_folds=nfolds)\n  acc2, std2 = np.mean(accuracy), np.std(accuracy)\n  return acc1, std1, acc2, std2, _xnorm, embeddings_list\n\ndef test_badcase(data_set, mx_model, batch_size, name='', data_extra = None, label_shape = None):\n  print('testing verification badcase..')\n  data_list = data_set[0]\n  issame_list = data_set[1]\n  model = mx_model\n  embeddings_list = []\n  if data_extra is not None:\n    _data_extra = nd.array(data_extra)\n  time_consumed = 0.0\n  if label_shape is None:\n    _label = nd.ones( (batch_size,) )\n  else:\n    _label = nd.ones( label_shape )\n  for i in range( len(data_list) ):\n    data = data_list[i]\n    embeddings = None\n    ba = 0\n    while ba<data.shape[0]:\n      bb = min(ba+batch_size, data.shape[0])\n      count = bb-ba\n      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)\n      #print(_data.shape, _label.shape)\n      time0 = datetime.datetime.now()\n      if data_extra is None:\n        db = mx.io.DataBatch(data=(_data,), label=(_label,))\n      else:\n        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))\n      model.forward(db, is_train=False)\n      net_out = model.get_outputs()\n      _embeddings = net_out[0].asnumpy()\n      time_now = datetime.datetime.now()\n      diff = time_now - time0\n      time_consumed+=diff.total_seconds()\n      if embeddings is None:\n        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )\n      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]\n      ba = bb\n    embeddings_list.append(embeddings)\n  embeddings = embeddings_list[0] + embeddings_list[1]\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  thresholds = np.arange(0, 4, 0.01)\n  actual_issame = np.asarray(issame_list)\n  nrof_folds = 10\n  embeddings1 = embeddings[0::2]\n  embeddings2 = embeddings[1::2]\n  assert(embeddings1.shape[0] == embeddings2.shape[0])\n  assert(embeddings1.shape[1] == embeddings2.shape[1])\n  nrof_pairs = min(len(actual_issame), embeddings1.shape[0])\n  nrof_thresholds = len(thresholds)\n  k_fold = LFold(n_splits=nrof_folds, shuffle=False)\n  \n  tprs = np.zeros((nrof_folds,nrof_thresholds))\n  fprs = np.zeros((nrof_folds,nrof_thresholds))\n  accuracy = np.zeros((nrof_folds))\n  indices = np.arange(nrof_pairs)\n  \n  diff = np.subtract(embeddings1, embeddings2)\n  dist = np.sum(np.square(diff),1)\n  data = data_list[0]\n\n  pouts = []\n  nouts = []\n  \n  for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):\n       \n      # Find the best threshold for the fold\n      acc_train = np.zeros((nrof_thresholds))\n      #print(train_set)\n      #print(train_set.__class__)\n      for threshold_idx, threshold in enumerate(thresholds):\n          p2 = dist[train_set]\n          p3 = actual_issame[train_set]\n          _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, p2, p3)\n      best_threshold_index = np.argmax(acc_train)\n      for threshold_idx, threshold in enumerate(thresholds):\n          tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set])\n      _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set])\n      best_threshold = thresholds[best_threshold_index]\n      for iid in test_set:\n        ida = iid*2\n        idb = ida+1\n        asame = actual_issame[iid]\n        _dist = dist[iid]\n        violate = _dist - best_threshold\n        if not asame:\n          violate *= -1.0\n        if violate>0.0:\n          imga = data[ida].asnumpy().transpose( (1,2,0) )[...,::-1] #to bgr\n          imgb = data[idb].asnumpy().transpose( (1,2,0) )[...,::-1]\n          #print(imga.shape, imgb.shape, violate, asame, _dist)\n          if asame:\n            pouts.append( (imga, imgb, _dist, best_threshold, ida) )\n          else:\n            nouts.append( (imga, imgb, _dist, best_threshold, ida) )\n\n        \n  tpr = np.mean(tprs,0)\n  fpr = np.mean(fprs,0)\n  acc = np.mean(accuracy)\n  pouts = sorted(pouts, key = lambda x: x[2], reverse=True)\n  nouts = sorted(nouts, key = lambda x: x[2], reverse=False)\n  print(len(pouts), len(nouts))\n  print('acc', acc)\n  gap = 10\n  image_shape = (112,224,3)\n  out_dir = \"./badcases\"\n  if not os.path.exists(out_dir):\n    os.makedirs(out_dir)\n  if len(nouts)>0:\n    threshold = nouts[0][3]\n  else:\n    threshold = pouts[-1][3]\n  \n  for item in [(pouts, 'positive(false_negative).png'), (nouts, 'negative(false_positive).png')]:\n    cols = 4\n    rows = 8000\n    outs = item[0]\n    if len(outs)==0:\n      continue\n    #if len(outs)==9:\n    #  cols = 3\n    #  rows = 3\n\n    _rows = int(math.ceil(len(outs)/cols))\n    rows = min(rows, _rows)\n    hack = {}\n\n    if name.startswith('cfp') and item[1].startswith('pos'):\n      hack = {0:'manual/238_13.jpg.jpg', 6:'manual/088_14.jpg.jpg', 10:'manual/470_14.jpg.jpg', 25:'manual/238_13.jpg.jpg', 28:'manual/143_11.jpg.jpg'}\n\n    filename = item[1]\n    if len(name)>0:\n      filename = name+\"_\"+filename\n    filename = os.path.join(out_dir, filename)\n    img = np.zeros( (image_shape[0]*rows+20, image_shape[1]*cols+(cols-1)*gap, 3), dtype=np.uint8 )\n    img[:,:,:] = 255\n    text_color = (0,0,153)\n    text_color = (255,178,102)\n    text_color = (153,255,51)\n    for outi, out in enumerate(outs):\n      row = outi//cols\n      col = outi%cols\n      if row==rows:\n        break\n      imga = out[0].copy()\n      imgb = out[1].copy()\n      if outi in hack:\n        idx = out[4]\n        print('noise idx',idx)\n        aa = hack[outi]\n        imgb = cv2.imread(aa)\n        #if aa==1:\n        #  imgb = cv2.transpose(imgb)\n        #  imgb = cv2.flip(imgb, 1)\n        #elif aa==3:\n        #  imgb = cv2.transpose(imgb)\n        #  imgb = cv2.flip(imgb, 0)\n        #else:\n        #  for ii in range(2):\n        #    imgb = cv2.transpose(imgb)\n        #    imgb = cv2.flip(imgb, 1)\n      dist = out[2]\n      _img = np.concatenate( (imga, imgb), axis=1 )\n      k = \"%.3f\"%dist\n      #print(k)\n      font = cv2.FONT_HERSHEY_SIMPLEX\n      cv2.putText(_img,k,(80,image_shape[0]//2+7), font, 0.6, text_color, 2)\n      #_filename = filename+\"_%d.png\"%outi\n      #cv2.imwrite(_filename, _img)\n      img[row*image_shape[0]:(row+1)*image_shape[0], (col*image_shape[1]+gap*col):((col+1)*image_shape[1]+gap*col),:] = _img\n    #threshold = outs[0][3]\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    k = \"threshold: %.3f\"%threshold\n    cv2.putText(img,k,(img.shape[1]//2-70,img.shape[0]-5), font, 0.6, text_color, 2)\n    cv2.imwrite(filename, img)\n\ndef dumpR(data_set, mx_model, batch_size, name='', data_extra = None, label_shape = None):\n  print('dump verification embedding..')\n  data_list = data_set[0]\n  issame_list = data_set[1]\n  model = mx_model\n  embeddings_list = []\n  if data_extra is not None:\n    _data_extra = nd.array(data_extra)\n  time_consumed = 0.0\n  if label_shape is None:\n    _label = nd.ones( (batch_size,) )\n  else:\n    _label = nd.ones( label_shape )\n  for i in range( len(data_list) ):\n    data = data_list[i]\n    embeddings = None\n    ba = 0\n    while ba<data.shape[0]:\n      bb = min(ba+batch_size, data.shape[0])\n      count = bb-ba\n      _data = nd.slice_axis(data, axis=0, begin=bb-batch_size, end=bb)\n      #print(_data.shape, _label.shape)\n      time0 = datetime.datetime.now()\n      if data_extra is None:\n        db = mx.io.DataBatch(data=(_data,), label=(_label,))\n      else:\n        db = mx.io.DataBatch(data=(_data,_data_extra), label=(_label,))\n      model.forward(db, is_train=False)\n      net_out = model.get_outputs()\n      _embeddings = net_out[0].asnumpy()\n      time_now = datetime.datetime.now()\n      diff = time_now - time0\n      time_consumed+=diff.total_seconds()\n      if embeddings is None:\n        embeddings = np.zeros( (data.shape[0], _embeddings.shape[1]) )\n      embeddings[ba:bb,:] = _embeddings[(batch_size-count):,:]\n      ba = bb\n    embeddings_list.append(embeddings)\n  embeddings = embeddings_list[0] + embeddings_list[1]\n  embeddings = sklearn.preprocessing.normalize(embeddings)\n  actual_issame = np.asarray(issame_list)\n  outname = os.path.join('temp.bin')\n  with open(outname, 'wb') as f:\n    pickle.dump((embeddings, issame_list), f, protocol=pickle.HIGHEST_PROTOCOL)\n\nif __name__ == '__main__':\n\n  parser = argparse.ArgumentParser(description='do verification')\n  # general\n  parser.add_argument('--data-dir', default='', help='')\n  parser.add_argument('--model', default='../model/softmax,50', help='path to load model.')\n  parser.add_argument('--target', default='lfw,cfp_ff,cfp_fp,agedb_30', help='test targets.')\n  parser.add_argument('--gpu', default=0, type=int, help='gpu id')\n  parser.add_argument('--batch-size', default=32, type=int, help='')\n  parser.add_argument('--max', default='', type=str, help='')\n  parser.add_argument('--mode', default=0, type=int, help='')\n  parser.add_argument('--nfolds', default=10, type=int, help='')\n  args = parser.parse_args()\n  #sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))\n  #import face_image\n  #prop = face_image.load_property(args.data_dir)\n  #image_size = prop.image_size\n  image_size = [112,112]\n  print('image_size', image_size)\n  ctx = mx.gpu(args.gpu)\n  nets = []\n  vec = args.model.split(',')\n  prefix = args.model.split(',')[0]\n  epochs = []\n  if len(vec)==1:\n    pdir = os.path.dirname(prefix)\n    for fname in os.listdir(pdir):\n      if not fname.endswith('.params'):\n        continue\n      _file = os.path.join(pdir, fname)\n      if _file.startswith(prefix):\n        epoch = int(fname.split('.')[0].split('-')[1])\n        epochs.append(epoch)\n    epochs = sorted(epochs, reverse=True)\n    if len(args.max)>0:\n      _max = [int(x) for x in args.max.split(',')]\n      assert len(_max)==2\n      if len(epochs)>_max[1]:\n        epochs = epochs[_max[0]:_max[1]]\n\n  else:\n    epochs = [int(x) for x in vec[1].split('|')]\n  print('model number', len(epochs))\n  time0 = datetime.datetime.now()\n  for epoch in epochs:\n    print('loading',prefix, epoch)\n    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)\n    #arg_params, aux_params = ch_dev(arg_params, aux_params, ctx)\n    all_layers = sym.get_internals()\n    sym = all_layers['fc1_output']\n    model = mx.mod.Module(symbol=sym, context=ctx, label_names = None)\n    #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])\n    model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))])\n    model.set_params(arg_params, aux_params)\n    nets.append(model)\n  time_now = datetime.datetime.now()\n  diff = time_now - time0\n  print('model loading time', diff.total_seconds())\n\n  ver_list = []\n  ver_name_list = []\n  for name in args.target.split(','):\n    path = os.path.join(args.data_dir,name+\".bin\")\n    if os.path.exists(path):\n      print('loading.. ', name)\n      data_set = load_bin(path, image_size)\n      ver_list.append(data_set)\n      ver_name_list.append(name)\n\n  if args.mode==0:\n    for i in range(len(ver_list)):\n      results = []\n      for model in nets:\n        acc1, std1, acc2, std2, xnorm, embeddings_list = test(ver_list[i], model, args.batch_size, args.nfolds)\n        print('[%s]XNorm: %f' % (ver_name_list[i], xnorm))\n        print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1))\n        print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2))\n        results.append(acc2)\n      print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results)))\n  elif args.mode==1:\n    model = nets[0]\n    test_badcase(ver_list[0], model, args.batch_size, args.target)\n  else:\n    model = nets[0]\n    dumpR(ver_list[0], model, args.batch_size, args.target)\n\n\n"
  },
  {
    "path": "face_recognition/flops_counter.py",
    "content": "\n'''\n@author: insightface\n'''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport sys\nimport os\nimport json\nimport argparse\nimport numpy as np\nimport mxnet as mx\n\n\ndef is_no_bias(attr):\n  ret = False\n  if 'no_bias' in attr and (attr['no_bias']==True or attr['no_bias']=='True'):\n    ret = True\n  return ret\n\ndef count_fc_flops(input_filter, output_filter, attr):\n  #print(input_filter, output_filter ,attr)\n  ret = 2*input_filter*output_filter\n  if is_no_bias(attr):\n    ret -= output_filter\n  return int(ret)\n\n\ndef count_conv_flops(input_shape, output_shape, attr):\n  kernel = attr['kernel'][1:-1].split(',')\n  kernel = [int(x) for x in kernel]\n\n  #print('kernel', kernel)\n  if is_no_bias(attr):\n    ret = (2*input_shape[1]*kernel[0]*kernel[1]-1)*output_shape[2]*output_shape[3]*output_shape[1]\n  else:\n    ret = 2*input_shape[1]*kernel[0]*kernel[1]*output_shape[2]*output_shape[3]*output_shape[1]\n  num_group = 1\n  if 'num_group' in attr:\n    num_group = int(attr['num_group'])\n  ret /= num_group\n  return int(ret)\n\n\ndef count_flops(sym, **data_shapes):\n  all_layers = sym.get_internals()\n  #print(all_layers)\n  arg_shapes, out_shapes, aux_shapes = all_layers.infer_shape(**data_shapes)\n  out_shape_dict = dict(zip(all_layers.list_outputs(), out_shapes))\n\n  nodes = json.loads(sym.tojson())['nodes']\n  nodeid_shape = {}\n  for nodeid, node in enumerate(nodes):\n    name = node['name']\n    layer_name = name+\"_output\"\n    if layer_name in out_shape_dict:\n      nodeid_shape[nodeid] = out_shape_dict[layer_name]\n  #print(nodeid_shape)\n  FLOPs = 0\n  for nodeid, node in enumerate(nodes):\n    flops = 0\n    if node['op']=='Convolution':\n      output_shape = nodeid_shape[nodeid]\n      name = node['name']\n      attr = node['attrs']\n      input_nodeid = node['inputs'][0][0]\n      input_shape = nodeid_shape[input_nodeid]\n      flops = count_conv_flops(input_shape, output_shape, attr)\n    elif node['op']=='FullyConnected':\n      attr = node['attrs']\n      output_shape = nodeid_shape[nodeid]\n      input_nodeid = node['inputs'][0][0]\n      input_shape = nodeid_shape[input_nodeid]\n      output_filter = output_shape[1]\n      input_filter = input_shape[1]*input_shape[2]*input_shape[3]\n      #assert len(input_shape)==4 and input_shape[2]==1 and input_shape[3]==1\n      flops = count_fc_flops(input_filter, output_filter, attr)\n    #print(node, flops)\n    FLOPs += flops\n\n  return FLOPs\n\ndef flops_str(FLOPs):\n  preset = [ (1e12, 'T'), (1e9, 'G'), (1e6, 'M'), (1e3, 'K') ]\n\n  for p in preset:\n    if FLOPs//p[0]>0:\n      N = FLOPs/p[0]\n      ret = \"%.1f%s\"%(N, p[1])\n      return ret\n  ret = \"%.1f\"%(FLOPs)\n  return ret\n\nif __name__ == '__main__':\n  parser = argparse.ArgumentParser(description='flops counter')\n  # general\n  #parser.add_argument('--model', default='../models2/y2-arcface-retinat1/model,1', help='path to load model.')\n  #parser.add_argument('--model', default='../models2/r100fc-arcface-retinaa/model,1', help='path to load model.')\n  parser.add_argument('--model', default='../models2/r50fc-arcface-emore/model,1', help='path to load model.')\n  args = parser.parse_args()\n  _vec = args.model.split(',')\n  assert len(_vec)==2\n  prefix = _vec[0]\n  epoch = int(_vec[1])\n  print('loading',prefix, epoch)\n  sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)\n  all_layers = sym.get_internals()\n  sym = all_layers['fc1_output']\n  FLOPs = count_flops(sym, data=(1,3,112,112))\n  print('FLOPs:', FLOPs)\n\n"
  },
  {
    "path": "face_recognition/image_iter.py",
    "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport random\nimport logging\nimport sys\nimport numbers\nimport math\nimport sklearn\nimport datetime\nimport numpy as np\nimport cv2\n\nimport mxnet as mx\nfrom mxnet import ndarray as nd\nfrom mxnet import io\nfrom mxnet import recordio\n\nlogger = logging.getLogger()\n\n\nclass FaceImageIter(io.DataIter):\n\n    def __init__(self, batch_size, data_shape,\n                 path_imgrec = None,\n                 shuffle=False, aug_list=None, mean = None,\n                 rand_mirror = False, cutoff = 0, color_jittering = 0,\n                 images_filter = 0,\n                 data_name='data', label_name='softmax_label', **kwargs):\n        super(FaceImageIter, self).__init__()\n        assert path_imgrec\n        if path_imgrec:\n            logging.info('loading recordio %s...',\n                         path_imgrec)\n            path_imgidx = path_imgrec[0:-4]+\".idx\"\n            self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type\n            s = self.imgrec.read_idx(0)\n            header, _ = recordio.unpack(s)\n            if header.flag>0:\n              print('header0 label', header.label)\n              self.header0 = (int(header.label[0]), int(header.label[1]))\n              #assert(header.flag==1)\n              #self.imgidx = range(1, int(header.label[0]))\n              self.imgidx = []\n              self.id2range = {}\n              self.seq_identity = range(int(header.label[0]), int(header.label[1]))\n              for identity in self.seq_identity:\n                s = self.imgrec.read_idx(identity)\n                header, _ = recordio.unpack(s)\n                a,b = int(header.label[0]), int(header.label[1])\n                count = b-a\n                if count<images_filter:\n                  continue\n                self.id2range[identity] = (a,b)\n                self.imgidx += range(a, b)\n              print('id2range', len(self.id2range))\n            else:\n              self.imgidx = list(self.imgrec.keys)\n            if shuffle:\n              self.seq = self.imgidx\n              self.oseq = self.imgidx\n              print(len(self.seq))\n            else:\n              self.seq = None\n\n        self.mean = mean\n        self.nd_mean = None\n        if self.mean:\n          self.mean = np.array(self.mean, dtype=np.float32).reshape(1,1,3)\n          self.nd_mean = mx.nd.array(self.mean).reshape((1,1,3))\n\n        self.check_data_shape(data_shape)\n        self.provide_data = [(data_name, (batch_size,) + data_shape)]\n        self.batch_size = batch_size\n        self.data_shape = data_shape\n        self.shuffle = shuffle\n        self.image_size = '%d,%d'%(data_shape[1],data_shape[2])\n        self.rand_mirror = rand_mirror\n        print('rand_mirror', rand_mirror)\n        self.cutoff = cutoff\n        self.color_jittering = color_jittering\n        self.CJA = mx.image.ColorJitterAug(0.125, 0.125, 0.125)\n        self.provide_label = [(label_name, (batch_size,))]\n        #print(self.provide_label[0][1])\n        self.cur = 0\n        self.nbatch = 0\n        self.is_init = False\n\n\n    def reset(self):\n        \"\"\"Resets the iterator to the beginning of the data.\"\"\"\n        print('call reset()')\n        self.cur = 0\n        if self.shuffle:\n          random.shuffle(self.seq)\n        if self.seq is None and self.imgrec is not None:\n            self.imgrec.reset()\n\n    def num_samples(self):\n      return len(self.seq)\n\n    def next_sample(self):\n        \"\"\"Helper function for reading in next sample.\"\"\"\n        #set total batch size, for example, 1800, and maximum size for each people, for example 45\n        if self.seq is not None:\n          while True:\n            if self.cur >= len(self.seq):\n                raise StopIteration\n            idx = self.seq[self.cur]\n            self.cur += 1\n            if self.imgrec is not None:\n              s = self.imgrec.read_idx(idx)\n              header, img = recordio.unpack(s)\n              label = header.label\n              if not isinstance(label, numbers.Number):\n                label = label[0]\n              return label, img, None, None\n            else:\n              label, fname, bbox, landmark = self.imglist[idx]\n              return label, self.read_image(fname), bbox, landmark\n        else:\n            s = self.imgrec.read()\n            if s is None:\n                raise StopIteration\n            header, img = recordio.unpack(s)\n            return header.label, img, None, None\n\n    def brightness_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      src *= alpha\n      return src\n\n    def contrast_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      coef = nd.array([[[0.299, 0.587, 0.114]]])\n      gray = src * coef\n      gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray)\n      src *= alpha\n      src += gray\n      return src\n\n    def saturation_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      coef = nd.array([[[0.299, 0.587, 0.114]]])\n      gray = src * coef\n      gray = nd.sum(gray, axis=2, keepdims=True)\n      gray *= (1.0 - alpha)\n      src *= alpha\n      src += gray\n      return src\n\n    def color_aug(self, img, x):\n      #augs = [self.brightness_aug, self.contrast_aug, self.saturation_aug]\n      #random.shuffle(augs)\n      #for aug in augs:\n      #  #print(img.shape)\n      #  img = aug(img, x)\n      #  #print(img.shape)\n      #return img\n      return self.CJA(img)\n\n    def mirror_aug(self, img):\n      _rd = random.randint(0,1)\n      if _rd==1:\n        for c in range(img.shape[2]):\n          img[:,:,c] = np.fliplr(img[:,:,c])\n      return img\n\n    def compress_aug(self, img):\n      from PIL import Image\n      from io import BytesIO\n      buf = BytesIO()\n      img = Image.fromarray(img.asnumpy(), 'RGB')\n      q = random.randint(2, 20)\n      img.save(buf, format='JPEG', quality=q)\n      buf = buf.getvalue()\n      img = Image.open(BytesIO(buf))\n      return nd.array(np.asarray(img, 'float32'))\n\n\n    def next(self):\n        if not self.is_init:\n          self.reset()\n          self.is_init = True\n        \"\"\"Returns the next batch of data.\"\"\"\n        #print('in next', self.cur, self.labelcur)\n        self.nbatch+=1\n        batch_size = self.batch_size\n        c, h, w = self.data_shape\n        batch_data = nd.empty((batch_size, c, h, w))\n        if self.provide_label is not None:\n          batch_label = nd.empty(self.provide_label[0][1])\n        i = 0\n        try:\n            while i < batch_size:\n                label, s, bbox, landmark = self.next_sample()\n                _data = self.imdecode(s)\n                if _data.shape[0]!=self.data_shape[1]:\n                  _data = mx.image.resize_short(_data, self.data_shape[1])\n                if self.rand_mirror:\n                  _rd = random.randint(0,1)\n                  if _rd==1:\n                    _data = mx.ndarray.flip(data=_data, axis=1)\n                if self.color_jittering>0:\n                  if self.color_jittering>1:\n                    _rd = random.randint(0,1)\n                    if _rd==1:\n                      _data = self.compress_aug(_data)\n                  #print('do color aug')\n                  _data = _data.astype('float32', copy=False)\n                  #print(_data.__class__)\n                  _data = self.color_aug(_data, 0.125)\n                if self.nd_mean is not None:\n                  _data = _data.astype('float32', copy=False)\n                  _data -= self.nd_mean\n                  _data *= 0.0078125\n                if self.cutoff>0:\n                  _rd = random.randint(0,1)\n                  if _rd==1:\n                    #print('do cutoff aug', self.cutoff)\n                    centerh = random.randint(0, _data.shape[0]-1)\n                    centerw = random.randint(0, _data.shape[1]-1)\n                    half = self.cutoff//2\n                    starth = max(0, centerh-half)\n                    endh = min(_data.shape[0], centerh+half)\n                    startw = max(0, centerw-half)\n                    endw = min(_data.shape[1], centerw+half)\n                    #print(starth, endh, startw, endw, _data.shape)\n                    _data[starth:endh, startw:endw, :] = 128\n                data = [_data]\n                try:\n                    self.check_valid_image(data)\n                except RuntimeError as e:\n                    logging.debug('Invalid image, skipping:  %s', str(e))\n                    continue\n                #print('aa',data[0].shape)\n                #data = self.augmentation_transform(data)\n                #print('bb',data[0].shape)\n                for datum in data:\n                    assert i < batch_size, 'Batch size must be multiples of augmenter output length'\n                    #print(datum.shape)\n                    batch_data[i][:] = self.postprocess_data(datum)\n                    batch_label[i][:] = label\n                    i += 1\n        except StopIteration:\n            if i<batch_size:\n                raise StopIteration\n\n        return io.DataBatch([batch_data], [batch_label], batch_size - i)\n\n    def check_data_shape(self, data_shape):\n        \"\"\"Checks if the input data shape is valid\"\"\"\n        if not len(data_shape) == 3:\n            raise ValueError('data_shape should have length 3, with dimensions CxHxW')\n        if not data_shape[0] == 3:\n            raise ValueError('This iterator expects inputs to have 3 channels.')\n\n    def check_valid_image(self, data):\n        \"\"\"Checks if the input data is valid\"\"\"\n        if len(data[0].shape) == 0:\n            raise RuntimeError('Data shape is wrong')\n\n    def imdecode(self, s):\n        \"\"\"Decodes a string or byte string to an NDArray.\n        See mx.img.imdecode for more details.\"\"\"\n        img = mx.image.imdecode(s) #mx.ndarray\n        return img\n\n    def read_image(self, fname):\n        \"\"\"Reads an input image `fname` and returns the decoded raw bytes.\n\n        Example usage:\n        ----------\n        >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.\n        \"\"\"\n        with open(os.path.join(self.path_root, fname), 'rb') as fin:\n            img = fin.read()\n        return img\n\n    def augmentation_transform(self, data):\n        \"\"\"Transforms input data with specified augmentation.\"\"\"\n        for aug in self.auglist:\n            data = [ret for src in data for ret in aug(src)]\n        return data\n\n    def postprocess_data(self, datum):\n        \"\"\"Final postprocessing step before image is loaded into the batch.\"\"\"\n        return nd.transpose(datum, axes=(2, 0, 1))\n\nclass FaceImageIterList(io.DataIter):\n  def __init__(self, iter_list):\n    assert len(iter_list)>0\n    self.provide_data = iter_list[0].provide_data\n    self.provide_label = iter_list[0].provide_label\n    self.iter_list = iter_list\n    self.cur_iter = None\n\n  def reset(self):\n    self.cur_iter.reset()\n\n  def next(self):\n    self.cur_iter = random.choice(self.iter_list)\n    while True:\n      try:\n        ret = self.cur_iter.next()\n      except StopIteration:\n        self.cur_iter.reset()\n        continue\n      return ret\n\n\n"
  },
  {
    "path": "face_recognition/metric.py",
    "content": "import numpy as np\nimport mxnet as mx\n\nclass AccMetric(mx.metric.EvalMetric):\n  def __init__(self):\n    self.axis = 1\n    super(AccMetric, self).__init__(\n        'acc', axis=self.axis,\n        output_names=None, label_names=None)\n    self.losses = []\n    self.count = 0\n\n  def update(self, labels, preds):\n    self.count+=1\n    label = labels[0]\n    pred_label = preds[1]\n    #print('ACC', label.shape, pred_label.shape)\n    if pred_label.shape != label.shape:\n        pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)\n    pred_label = pred_label.asnumpy().astype('int32').flatten()\n    label = label.asnumpy()\n    if label.ndim==2:\n      label = label[:,0]\n    label = label.astype('int32').flatten()\n    assert label.shape==pred_label.shape\n    self.sum_metric += (pred_label.flat == label.flat).sum()\n    self.num_inst += len(pred_label.flat)\n\nclass LossValueMetric(mx.metric.EvalMetric):\n  def __init__(self):\n    self.axis = 1\n    super(LossValueMetric, self).__init__(\n        'lossvalue', axis=self.axis,\n        output_names=None, label_names=None)\n    self.losses = []\n\n  def update(self, labels, preds):\n    #label = labels[0].asnumpy()\n    pred = preds[-1].asnumpy()\n    #print('in loss', pred.shape)\n    #print(pred)\n    loss = pred[0]\n    self.sum_metric += loss\n    self.num_inst += 1.0\n    #gt_label = preds[-2].asnumpy()\n    #print(gt_label)\n"
  },
  {
    "path": "face_recognition/models/MS1Mretina-polynet50-arcface/log_retina_r50newton_arcface.txt",
    "content": "gpu num: 8\nprefix ./models/r50-arcface-retina/model\nimage_size [112, 112]\nnum_classes 93431\nCalled with argument: Namespace(batch_size=512, ckpt=3, ctx_num=8, ctx_num_classes=11679, dataset='retina', extra_model_name='', frequent=20, image_channel=3, kvstore='device', local_class_start=0, local_num_classes=93432, loss='arcface', lr=0.1, lr_steps='100000,160000,220000', models_root='./models', mom=0.9, network='r50', per_batch_size=64, pretrained='', pretrained_epoch=1, rescale_threshold=0, verbose=2000, wd=0.0005, worker_id=0) {'bn_mom': 0.9, 'workspace': 256, 'emb_size': 512, 'ckpt_embedding': True, 'net_se': 0, 'net_act': 'prelu', 'net_unit': 3, 'net_input': 1, 'net_blocks': [1, 4, 6, 2], 'net_output': 'E', 'net_multiplier': 1.0, 'val_targets': ['lfw', 'cfp_fp', 'agedb_30'], 'ce_loss': True, 'fc7_lr_mult': 1.0, 'fc7_wd_mult': 1.0, 'fc7_no_bias': False, 'max_steps': 0, 'data_rand_mirror': True, 'data_cutoff': False, 'data_color': 0, 'data_images_filter': 0, 'count_flops': True, 'memonger': False, 'loss_name': 'margin_softmax', 'loss_s': 64.0, 'loss_m1': 1.0, 'loss_m2': 0.5, 'loss_m3': 0.0, 'net_name': 'fresnet', 'num_layers': 50, 'dataset': 'retina', 'dataset_path': '/home/dengjiankang/datasets/ms1m-retinaface', 'num_classes': 93431, 'image_shape': [112, 112, 3], 'loss': 'arcface', 'network': 'r50', 'num_workers': 1, 'batch_size': 512, 'per_batch_size': 64}\n0 1 E 3 prelu False\nNetwork FLOPs: 13.2G\nINFO:root:loading recordio /home/dengjiankang/datasets/ms1m-retinaface/train.rec...\nheader0 label [5179511. 5272942.]\nid2range 93431\n5179510\nrand_mirror True\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\n(12000, 3, 112, 112)\nver lfw\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\nloading bin 12000\nloading bin 13000\n(14000, 3, 112, 112)\nver cfp_fp\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\n(12000, 3, 112, 112)\nver agedb_30\nlr_steps [100000, 160000, 220000]\ncall reset()in_bind\n False [('data', (512, 3, 112, 112))] [('softmax_label', (512,))]\n_data_shape (512, 3, 112, 112) [('softmax_label', (512,))]\n[05:01:09] src/kvstore/././comm.h:744: only 0 out of 56 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/kvstore/././comm.h:753: ........\n[05:01:09] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (set the environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)\nINFO:root:Iter[0] Batch [20]\tSpeed: 581.65 samples/sec\nINFO:root:Iter[0] Batch [40]\tSpeed: 553.74 samples/sec\nINFO:root:Iter[0] Batch [60]\tSpeed: 607.71 samples/sec\nINFO:root:Iter[0] Batch [80]\tSpeed: 768.20 samples/sec\nINFO:root:Iter[0] Batch [100]\tSpeed: 775.93 samples/sec\nINFO:root:Iter[0] Batch [120]\tSpeed: 812.92 samples/sec\nINFO:root:Iter[0] Batch [140]\tSpeed: 820.32 samples/sec\nINFO:root:Iter[0] Batch [160]\tSpeed: 837.52 samples/sec\nINFO:root:Iter[0] Batch [180]\tSpeed: 857.11 samples/sec\nINFO:root:Iter[0] Batch [200]\tSpeed: 821.90 samples/sec\nINFO:root:Iter[0] Batch [220]\tSpeed: 871.63 samples/sec\nINFO:root:Iter[0] Batch [240]\tSpeed: 886.23 samples/sec\nINFO:root:Iter[0] Batch [260]\tSpeed: 856.74 samples/sec\nINFO:root:Iter[0] Batch [280]\tSpeed: 864.63 samples/sec\nINFO:root:Iter[0] Batch [300]\tSpeed: 879.61 samples/sec\nINFO:root:Iter[0] Batch [320]\tSpeed: 885.36 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verification..\n(12000, 512)\ninfer time 4.898198000000001\n[lfw][12000]XNorm: 22.295786\n[lfw][12000]Accuracy-Flip: 0.99500+-0.00342\ntesting verification..\n(14000, 512)\ninfer time 5.716751000000001\n[cfp_fp][12000]XNorm: 20.151784\n[cfp_fp][12000]Accuracy-Flip: 0.94714+-0.01171\ntesting verification..\n(12000, 512)\ninfer time 4.177675\n[agedb_30][12000]XNorm: 21.588955\n[agedb_30][12000]Accuracy-Flip: 0.94633+-0.00985\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[12000]Accuracy-Highest: 0.94633\nINFO:root:Iter[1] Batch [1900]\tSpeed: 244.65 samples/sec\nINFO:root:Iter[1] Batch [1920]\tSpeed: 811.70 samples/sec\nINFO:root:Iter[1] Batch [1940]\tSpeed: 804.90 samples/sec\nINFO:root:Iter[1] Batch [1960]\tSpeed: 778.85 samples/sec\nINFO:root:Iter[1] Batch [1980]\tSpeed: 725.87 samples/sec\nINFO:root:Iter[1] Batch [2000]\tSpeed: 678.95 samples/sec\nINFO:root:Iter[1] Batch [2020]\tSpeed: 645.01 samples/sec\nINFO:root:Iter[1] Batch 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21.444361\n[agedb_30][14000]Accuracy-Flip: 0.95533+-0.00983\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[14000]Accuracy-Highest: 0.95533\nINFO:root:Iter[1] Batch [3900]\tSpeed: 234.59 samples/sec\nINFO:root:Iter[1] Batch [3920]\tSpeed: 704.59 samples/sec\nINFO:root:Iter[1] Batch [3940]\tSpeed: 699.22 samples/sec\nINFO:root:Iter[1] Batch [3960]\tSpeed: 729.53 samples/sec\nINFO:root:Iter[1] Batch [3980]\tSpeed: 689.91 samples/sec\nINFO:root:Iter[1] Batch [4000]\tSpeed: 678.71 samples/sec\nINFO:root:Iter[1] Batch [4020]\tSpeed: 702.99 samples/sec\nINFO:root:Iter[1] Batch [4040]\tSpeed: 631.42 samples/sec\nINFO:root:Iter[1] Batch [4060]\tSpeed: 638.87 samples/sec\nINFO:root:Iter[1] Batch [4080]\tSpeed: 609.54 samples/sec\nINFO:root:Iter[1] Batch [4100]\tSpeed: 604.52 samples/sec\nINFO:root:Iter[1] Batch [4120]\tSpeed: 621.41 samples/sec\nINFO:root:Iter[1] Batch [4140]\tSpeed: 629.25 samples/sec\nINFO:root:Iter[1] Batch [4160]\tSpeed: 613.51 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verification..\n(12000, 512)\ninfer time 4.629894\n[lfw][56000]XNorm: 22.606347\n[lfw][56000]Accuracy-Flip: 0.99600+-0.00403\ntesting verification..\n(14000, 512)\ninfer time 5.317968999999999\n[cfp_fp][56000]XNorm: 20.260585\n[cfp_fp][56000]Accuracy-Flip: 0.95743+-0.01047\ntesting verification..\n(12000, 512)\ninfer time 4.739341999999999\n[agedb_30][56000]XNorm: 22.605472\n[agedb_30][56000]Accuracy-Flip: 0.96967+-0.00770\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[56000]Accuracy-Highest: 0.96967\nINFO:root:Iter[5] Batch [5420]\tSpeed: 242.92 samples/sec\nINFO:root:Iter[5] Batch [5440]\tSpeed: 795.35 samples/sec\nINFO:root:Iter[5] Batch [5460]\tSpeed: 751.39 samples/sec\nINFO:root:Iter[5] Batch [5480]\tSpeed: 709.02 samples/sec\nINFO:root:Iter[5] Batch [5500]\tSpeed: 657.94 samples/sec\nINFO:root:Iter[5] Batch [5520]\tSpeed: 670.26 samples/sec\nINFO:root:Iter[5] Batch [5540]\tSpeed: 644.64 samples/sec\nINFO:root:Iter[5] Batch 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4.350040999999998\n[cfp_fp][74000]XNorm: 19.616758\n[cfp_fp][74000]Accuracy-Flip: 0.95957+-0.00860\ntesting verification..\n(12000, 512)\ninfer time 3.763639\n[agedb_30][74000]XNorm: 21.882216\n[agedb_30][74000]Accuracy-Flip: 0.96783+-0.00650\n[74000]Accuracy-Highest: 0.96967\nINFO:root:Iter[7] Batch [3200]\tSpeed: 299.49 samples/sec\nINFO:root:Iter[7] Batch [3220]\tSpeed: 870.25 samples/sec\nINFO:root:Iter[7] Batch [3240]\tSpeed: 839.35 samples/sec\nINFO:root:Iter[7] Batch [3260]\tSpeed: 833.55 samples/sec\nINFO:root:Iter[7] Batch [3280]\tSpeed: 831.17 samples/sec\nINFO:root:Iter[7] Batch [3300]\tSpeed: 852.89 samples/sec\nINFO:root:Iter[7] Batch [3320]\tSpeed: 812.90 samples/sec\nINFO:root:Iter[7] Batch [3340]\tSpeed: 841.42 samples/sec\nINFO:root:Iter[7] Batch [3360]\tSpeed: 857.06 samples/sec\nINFO:root:Iter[7] Batch [3380]\tSpeed: 874.36 samples/sec\nINFO:root:Iter[7] Batch [3400]\tSpeed: 809.13 samples/sec\nINFO:root:Iter[7] Batch [3420]\tSpeed: 864.35 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3.7476700000000003\n[lfw][104000]XNorm: 22.333480\n[lfw][104000]Accuracy-Flip: 0.99733+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 4.354682\n[cfp_fp][104000]XNorm: 21.109632\n[cfp_fp][104000]Accuracy-Flip: 0.98243+-0.00557\ntesting verification..\n(12000, 512)\ninfer time 3.7134919999999996\n[agedb_30][104000]XNorm: 22.814853\n[agedb_30][104000]Accuracy-Flip: 0.97583+-0.00700\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[104000]Accuracy-Highest: 0.97583\nINFO:root:Iter[10] Batch [2840]\tSpeed: 299.86 samples/sec\nINFO:root:Iter[10] Batch [2860]\tSpeed: 849.56 samples/sec\nINFO:root:Iter[10] Batch [2880]\tSpeed: 867.13 samples/sec\nINFO:root:Iter[10] Batch [2900]\tSpeed: 847.29 samples/sec\nINFO:root:Iter[10] Batch [2920]\tSpeed: 831.29 samples/sec\nINFO:root:Iter[10] Batch [2940]\tSpeed: 822.39 samples/sec\nINFO:root:Iter[10] Batch [2960]\tSpeed: 804.93 samples/sec\nINFO:root:Iter[10] Batch [2980]\tSpeed: 819.86 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samples/sec\nINFO:root:Iter[10] Batch [8820]\tSpeed: 854.30 samples/sec\n{fc7_acc} 110000 0.48242188\nlr-batch-epoch: 0.010000000000000002 8839 10\ntesting verification..\n(12000, 512)\ninfer time 3.737724\n[lfw][110000]XNorm: 22.443803\n[lfw][110000]Accuracy-Flip: 0.99750+-0.00250\ntesting verification..\n(14000, 512)\ninfer time 4.402831\n[cfp_fp][110000]XNorm: 21.488400\n[cfp_fp][110000]Accuracy-Flip: 0.98214+-0.00488\ntesting verification..\n(12000, 512)\ninfer time 3.718142\n[agedb_30][110000]XNorm: 23.123208\n[agedb_30][110000]Accuracy-Flip: 0.97967+-0.00730\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[110000]Accuracy-Highest: 0.97967\nINFO:root:Iter[10] Batch [8840]\tSpeed: 297.13 samples/sec\nINFO:root:Iter[10] Batch [8860]\tSpeed: 850.45 samples/sec\nINFO:root:Iter[10] Batch [8880]\tSpeed: 854.53 samples/sec\nINFO:root:Iter[10] Batch [8900]\tSpeed: 842.29 samples/sec\nINFO:root:Iter[10] Batch [8920]\tSpeed: 834.12 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3.858423999999999\n[lfw][118000]XNorm: 22.523597\n[lfw][118000]Accuracy-Flip: 0.99800+-0.00233\ntesting verification..\n(14000, 512)\ninfer time 4.317844000000003\n[cfp_fp][118000]XNorm: 21.405447\n[cfp_fp][118000]Accuracy-Flip: 0.98471+-0.00483\ntesting verification..\n(12000, 512)\ninfer time 3.6936679999999997\n[agedb_30][118000]XNorm: 23.261558\n[agedb_30][118000]Accuracy-Flip: 0.97950+-0.00837\n[118000]Accuracy-Highest: 0.98083\nINFO:root:Iter[11] Batch [6740]\tSpeed: 299.12 samples/sec\nINFO:root:Iter[11] Batch [6760]\tSpeed: 833.10 samples/sec\nINFO:root:Iter[11] Batch [6780]\tSpeed: 814.37 samples/sec\nINFO:root:Iter[11] Batch [6800]\tSpeed: 835.82 samples/sec\nINFO:root:Iter[11] Batch [6820]\tSpeed: 859.61 samples/sec\nINFO:root:Iter[11] Batch [6840]\tSpeed: 823.84 samples/sec\nINFO:root:Iter[11] Batch [6860]\tSpeed: 819.18 samples/sec\nINFO:root:Iter[11] Batch [6880]\tSpeed: 824.04 samples/sec\nINFO:root:Iter[11] Batch [6900]\tSpeed: 856.97 samples/sec\nINFO:root:Iter[11] 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time 4.310547\n[cfp_fp][130000]XNorm: 21.100618\n[cfp_fp][130000]Accuracy-Flip: 0.98400+-0.00588\ntesting verification..\n(12000, 512)\ninfer time 3.7245030000000003\n[agedb_30][130000]XNorm: 22.150837\n[agedb_30][130000]Accuracy-Flip: 0.98017+-0.00815\n[130000]Accuracy-Highest: 0.98083\nINFO:root:Iter[12] Batch [8620]\tSpeed: 293.36 samples/sec\nINFO:root:Iter[12] Batch [8640]\tSpeed: 838.69 samples/sec\nINFO:root:Iter[12] Batch [8660]\tSpeed: 847.73 samples/sec\nINFO:root:Iter[12] Batch [8680]\tSpeed: 842.76 samples/sec\nINFO:root:Iter[12] Batch [8700]\tSpeed: 822.13 samples/sec\nINFO:root:Iter[12] Batch [8720]\tSpeed: 825.32 samples/sec\nINFO:root:Iter[12] Batch [8740]\tSpeed: 810.77 samples/sec\nINFO:root:Iter[12] Batch [8760]\tSpeed: 816.47 samples/sec\nINFO:root:Iter[12] Batch [8780]\tSpeed: 855.92 samples/sec\nINFO:root:Iter[12] Batch [8800]\tSpeed: 849.55 samples/sec\nINFO:root:Iter[12] Batch [8820]\tSpeed: 828.76 samples/sec\nINFO:root:Iter[12] Batch [8840]\tSpeed: 844.90 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2491 13\ntesting verification..\n(12000, 512)\ninfer time 3.6866640000000004\n[lfw][134000]XNorm: 22.149758\n[lfw][134000]Accuracy-Flip: 0.99817+-0.00229\ntesting verification..\n(14000, 512)\ninfer time 4.29976\n[cfp_fp][134000]XNorm: 21.457099\n[cfp_fp][134000]Accuracy-Flip: 0.98557+-0.00463\ntesting verification..\n(12000, 512)\ninfer time 3.723896\n[agedb_30][134000]XNorm: 22.593258\n[agedb_30][134000]Accuracy-Flip: 0.97983+-0.00647\n[134000]Accuracy-Highest: 0.98083\nINFO:root:Iter[13] Batch [2500]\tSpeed: 297.41 samples/sec\nINFO:root:Iter[13] Batch [2520]\tSpeed: 826.72 samples/sec\nINFO:root:Iter[13] Batch [2540]\tSpeed: 845.39 samples/sec\nINFO:root:Iter[13] Batch [2560]\tSpeed: 852.25 samples/sec\nINFO:root:Iter[13] Batch [2580]\tSpeed: 839.52 samples/sec\nINFO:root:Iter[13] Batch [2600]\tSpeed: 865.16 samples/sec\nINFO:root:Iter[13] Batch [2620]\tSpeed: 847.05 samples/sec\nINFO:root:Iter[13] Batch [2640]\tSpeed: 843.10 samples/sec\nINFO:root:Iter[13] Batch [2660]\tSpeed: 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3.7134789999999995\n[lfw][140000]XNorm: 22.747301\n[lfw][140000]Accuracy-Flip: 0.99750+-0.00250\ntesting verification..\n(14000, 512)\ninfer time 4.383935000000001\n[cfp_fp][140000]XNorm: 21.669952\n[cfp_fp][140000]Accuracy-Flip: 0.98500+-0.00453\ntesting verification..\n(12000, 512)\ninfer time 3.721106\n[agedb_30][140000]XNorm: 23.064378\n[agedb_30][140000]Accuracy-Flip: 0.97967+-0.00806\n[140000]Accuracy-Highest: 0.98083\nINFO:root:Iter[13] Batch [8500]\tSpeed: 293.89 samples/sec\nINFO:root:Iter[13] Batch [8520]\tSpeed: 869.31 samples/sec\nINFO:root:Iter[13] Batch [8540]\tSpeed: 838.77 samples/sec\nINFO:root:Iter[13] Batch [8560]\tSpeed: 854.38 samples/sec\nINFO:root:Iter[13] Batch [8580]\tSpeed: 860.96 samples/sec\nINFO:root:Iter[13] Batch [8600]\tSpeed: 892.81 samples/sec\nINFO:root:Iter[13] Batch [8620]\tSpeed: 834.36 samples/sec\nINFO:root:Iter[13] Batch [8640]\tSpeed: 873.78 samples/sec\nINFO:root:Iter[13] Batch [8660]\tSpeed: 850.73 samples/sec\nINFO:root:Iter[13] Batch 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reset()\nINFO:root:Iter[15] Batch [20]\tSpeed: 862.08 samples/sec\nINFO:root:Iter[15] Batch [40]\tSpeed: 894.59 samples/sec\nINFO:root:Iter[15] Batch [60]\tSpeed: 889.37 samples/sec\nINFO:root:Iter[15] Batch [80]\tSpeed: 868.14 samples/sec\nINFO:root:Iter[15] Batch [100]\tSpeed: 859.72 samples/sec\nINFO:root:Iter[15] Batch [120]\tSpeed: 867.68 samples/sec\nINFO:root:Iter[15] Batch [140]\tSpeed: 871.48 samples/sec\nINFO:root:Iter[15] Batch [160]\tSpeed: 876.74 samples/sec\nINFO:root:Iter[15] Batch [180]\tSpeed: 859.76 samples/sec\nINFO:root:Iter[15] Batch [200]\tSpeed: 870.12 samples/sec\nINFO:root:Iter[15] Batch [220]\tSpeed: 845.47 samples/sec\nINFO:root:Iter[15] Batch [240]\tSpeed: 853.92 samples/sec\n{fc7_acc} 152000 0.4765625\nlr-batch-epoch: 0.010000000000000002 259 15\ntesting verification..\n(12000, 512)\ninfer time 3.690974\n[lfw][152000]XNorm: 22.426722\n[lfw][152000]Accuracy-Flip: 0.99817+-0.00241\ntesting verification..\n(14000, 512)\ninfer time 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0.46484375\nlr-batch-epoch: 0.010000000000000002 4259 15\ntesting verification..\n(12000, 512)\ninfer time 3.7139059999999993\n[lfw][156000]XNorm: 21.793911\n[lfw][156000]Accuracy-Flip: 0.99750+-0.00310\ntesting verification..\n(14000, 512)\ninfer time 4.353741\n[cfp_fp][156000]XNorm: 20.881752\n[cfp_fp][156000]Accuracy-Flip: 0.98214+-0.00500\ntesting verification..\n(12000, 512)\ninfer time 3.7686550000000008\n[agedb_30][156000]XNorm: 22.799360\n[agedb_30][156000]Accuracy-Flip: 0.97900+-0.00779\n[156000]Accuracy-Highest: 0.98083\nINFO:root:Iter[15] Batch [4260]\tSpeed: 306.47 samples/sec\nINFO:root:Iter[15] Batch [4280]\tSpeed: 829.79 samples/sec\nINFO:root:Iter[15] Batch [4300]\tSpeed: 830.73 samples/sec\nINFO:root:Iter[15] Batch [4320]\tSpeed: 812.62 samples/sec\nINFO:root:Iter[15] Batch [4340]\tSpeed: 839.89 samples/sec\nINFO:root:Iter[15] Batch [4360]\tSpeed: 839.29 samples/sec\nINFO:root:Iter[15] Batch [4380]\tSpeed: 820.88 samples/sec\nINFO:root:Iter[15] Batch [4400]\tSpeed: 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verification..\n(12000, 512)\ninfer time 3.747420000000001\n[agedb_30][158000]XNorm: 23.818229\n[agedb_30][158000]Accuracy-Flip: 0.98100+-0.00646\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[158000]Accuracy-Highest: 0.98100\nINFO:root:Iter[15] Batch [6260]\tSpeed: 297.85 samples/sec\nINFO:root:Iter[15] Batch [6280]\tSpeed: 810.00 samples/sec\nINFO:root:Iter[15] Batch [6300]\tSpeed: 818.38 samples/sec\nINFO:root:Iter[15] Batch [6320]\tSpeed: 858.55 samples/sec\nINFO:root:Iter[15] Batch [6340]\tSpeed: 834.14 samples/sec\nINFO:root:Iter[15] Batch [6360]\tSpeed: 869.71 samples/sec\nINFO:root:Iter[15] Batch [6380]\tSpeed: 822.73 samples/sec\nINFO:root:Iter[15] Batch [6400]\tSpeed: 873.70 samples/sec\nINFO:root:Iter[15] Batch [6420]\tSpeed: 811.39 samples/sec\nINFO:root:Iter[15] Batch [6440]\tSpeed: 833.71 samples/sec\nINFO:root:Iter[15] Batch [6460]\tSpeed: 853.26 samples/sec\nINFO:root:Iter[15] Batch [6480]\tSpeed: 815.40 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samples/sec\nINFO:root:Iter[15] Batch [8140]\tSpeed: 887.95 samples/sec\nINFO:root:Iter[15] Batch [8160]\tSpeed: 885.90 samples/sec\nINFO:root:Iter[15] Batch [8180]\tSpeed: 853.14 samples/sec\nINFO:root:Iter[15] Batch [8200]\tSpeed: 902.79 samples/sec\nINFO:root:Iter[15] Batch [8220]\tSpeed: 866.35 samples/sec\nINFO:root:Iter[15] Batch [8240]\tSpeed: 878.06 samples/sec\n{fc7_acc} 160000 0.43554688\nlr change to 0.0010000000000000002\nlr-batch-epoch: 0.0010000000000000002 8259 15\ntesting verification..\n(12000, 512)\ninfer time 3.747035999999999\n[lfw][160000]XNorm: 22.706179\n[lfw][160000]Accuracy-Flip: 0.99817+-0.00241\ntesting verification..\n(14000, 512)\ninfer time 4.444989\n[cfp_fp][160000]XNorm: 21.663541\n[cfp_fp][160000]Accuracy-Flip: 0.98557+-0.00353\ntesting verification..\n(12000, 512)\ninfer time 3.7130609999999993\n[agedb_30][160000]XNorm: 23.358417\n[agedb_30][160000]Accuracy-Flip: 0.98200+-0.00869\nsaving 1\nINFO:root:Saved checkpoint to 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samples/sec\nINFO:root:Iter[16] Batch [80]\tSpeed: 848.88 samples/sec\nINFO:root:Iter[16] Batch [100]\tSpeed: 835.68 samples/sec\nINFO:root:Iter[16] Batch [120]\tSpeed: 862.46 samples/sec\nINFO:root:Iter[16] Batch [140]\tSpeed: 872.13 samples/sec\n{fc7_acc} 162000 0.65234375\nlr-batch-epoch: 0.0010000000000000002 143 16\ntesting verification..\n(12000, 512)\ninfer time 3.6968080000000003\n[lfw][162000]XNorm: 22.147852\n[lfw][162000]Accuracy-Flip: 0.99850+-0.00189\ntesting verification..\n(14000, 512)\ninfer time 4.299741000000001\n[cfp_fp][162000]XNorm: 21.627607\n[cfp_fp][162000]Accuracy-Flip: 0.98700+-0.00341\ntesting verification..\n(12000, 512)\ninfer time 3.82574\n[agedb_30][162000]XNorm: 23.081771\n[agedb_30][162000]Accuracy-Flip: 0.98283+-0.00789\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[162000]Accuracy-Highest: 0.98283\nINFO:root:Iter[16] Batch [160]\tSpeed: 292.24 samples/sec\nINFO:root:Iter[16] Batch [180]\tSpeed: 872.39 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2143 16\ntesting verification..\n(12000, 512)\ninfer time 3.7348539999999986\n[lfw][164000]XNorm: 22.316927\n[lfw][164000]Accuracy-Flip: 0.99850+-0.00203\ntesting verification..\n(14000, 512)\ninfer time 4.376252\n[cfp_fp][164000]XNorm: 21.936219\n[cfp_fp][164000]Accuracy-Flip: 0.98686+-0.00408\ntesting verification..\n(12000, 512)\ninfer time 3.8226910000000003\n[agedb_30][164000]XNorm: 23.280371\n[agedb_30][164000]Accuracy-Flip: 0.98183+-0.00776\n[164000]Accuracy-Highest: 0.98283\nINFO:root:Iter[16] Batch [2160]\tSpeed: 298.57 samples/sec\nINFO:root:Iter[16] Batch [2180]\tSpeed: 832.60 samples/sec\nINFO:root:Iter[16] Batch [2200]\tSpeed: 857.45 samples/sec\nINFO:root:Iter[16] Batch [2220]\tSpeed: 860.12 samples/sec\nINFO:root:Iter[16] Batch [2240]\tSpeed: 802.67 samples/sec\nINFO:root:Iter[16] Batch [2260]\tSpeed: 824.62 samples/sec\nINFO:root:Iter[16] Batch [2280]\tSpeed: 839.06 samples/sec\nINFO:root:Iter[16] Batch [2300]\tSpeed: 846.35 samples/sec\nINFO:root:Iter[16] Batch 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3.706925000000001\n[agedb_30][166000]XNorm: 23.254591\n[agedb_30][166000]Accuracy-Flip: 0.98200+-0.00767\n[166000]Accuracy-Highest: 0.98283\nINFO:root:Iter[16] Batch [4160]\tSpeed: 300.63 samples/sec\nINFO:root:Iter[16] Batch [4180]\tSpeed: 839.75 samples/sec\nINFO:root:Iter[16] Batch [4200]\tSpeed: 836.98 samples/sec\nINFO:root:Iter[16] Batch [4220]\tSpeed: 832.30 samples/sec\nINFO:root:Iter[16] Batch [4240]\tSpeed: 855.66 samples/sec\nINFO:root:Iter[16] Batch [4260]\tSpeed: 853.44 samples/sec\nINFO:root:Iter[16] Batch [4280]\tSpeed: 862.77 samples/sec\nINFO:root:Iter[16] Batch [4300]\tSpeed: 821.51 samples/sec\nINFO:root:Iter[16] Batch [4320]\tSpeed: 862.07 samples/sec\nINFO:root:Iter[16] Batch [4340]\tSpeed: 852.35 samples/sec\nINFO:root:Iter[16] Batch [4360]\tSpeed: 867.63 samples/sec\nINFO:root:Iter[16] Batch [4380]\tSpeed: 845.14 samples/sec\nINFO:root:Iter[16] Batch [4400]\tSpeed: 849.18 samples/sec\nINFO:root:Iter[16] Batch [4420]\tSpeed: 858.06 samples/sec\nINFO:root:Iter[16] 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3.698393\n[lfw][170000]XNorm: 22.223124\n[lfw][170000]Accuracy-Flip: 0.99850+-0.00203\ntesting verification..\n(14000, 512)\ninfer time 4.359666000000001\n[cfp_fp][170000]XNorm: 21.965220\n[cfp_fp][170000]Accuracy-Flip: 0.98871+-0.00353\ntesting verification..\n(12000, 512)\ninfer time 3.74349\n[agedb_30][170000]XNorm: 23.422694\n[agedb_30][170000]Accuracy-Flip: 0.98183+-0.00818\n[170000]Accuracy-Highest: 0.98283\nINFO:root:Iter[16] Batch [8160]\tSpeed: 297.50 samples/sec\nINFO:root:Iter[16] Batch [8180]\tSpeed: 847.24 samples/sec\nINFO:root:Iter[16] Batch [8200]\tSpeed: 815.55 samples/sec\nINFO:root:Iter[16] Batch [8220]\tSpeed: 820.36 samples/sec\nINFO:root:Iter[16] Batch [8240]\tSpeed: 822.25 samples/sec\nINFO:root:Iter[16] Batch [8260]\tSpeed: 829.08 samples/sec\nINFO:root:Iter[16] Batch [8280]\tSpeed: 858.61 samples/sec\nINFO:root:Iter[16] Batch [8300]\tSpeed: 854.75 samples/sec\nINFO:root:Iter[16] Batch [8320]\tSpeed: 856.35 samples/sec\nINFO:root:Iter[16] Batch [8340]\tSpeed: 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3.6654420000000014\n[lfw][176000]XNorm: 21.916601\n[lfw][176000]Accuracy-Flip: 0.99850+-0.00189\ntesting verification..\n(14000, 512)\ninfer time 4.3355820000000005\n[cfp_fp][176000]XNorm: 22.058505\n[cfp_fp][176000]Accuracy-Flip: 0.98829+-0.00403\ntesting verification..\n(12000, 512)\ninfer time 3.728072000000001\n[agedb_30][176000]XNorm: 23.131193\n[agedb_30][176000]Accuracy-Flip: 0.98350+-0.00743\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[176000]Accuracy-Highest: 0.98350\nINFO:root:Iter[17] Batch [4040]\tSpeed: 293.78 samples/sec\nINFO:root:Iter[17] Batch [4060]\tSpeed: 828.61 samples/sec\nINFO:root:Iter[17] Batch [4080]\tSpeed: 837.63 samples/sec\nINFO:root:Iter[17] Batch [4100]\tSpeed: 838.96 samples/sec\nINFO:root:Iter[17] Batch [4120]\tSpeed: 827.80 samples/sec\nINFO:root:Iter[17] Batch [4140]\tSpeed: 804.14 samples/sec\nINFO:root:Iter[17] Batch [4160]\tSpeed: 843.41 samples/sec\nINFO:root:Iter[17] Batch [4180]\tSpeed: 821.78 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verification..\n(12000, 512)\ninfer time 3.7979759999999994\n[agedb_30][178000]XNorm: 23.282595\n[agedb_30][178000]Accuracy-Flip: 0.98250+-0.00743\n[178000]Accuracy-Highest: 0.98350\nINFO:root:Iter[17] Batch [6040]\tSpeed: 298.39 samples/sec\nINFO:root:Iter[17] Batch [6060]\tSpeed: 840.86 samples/sec\nINFO:root:Iter[17] Batch [6080]\tSpeed: 836.79 samples/sec\nINFO:root:Iter[17] Batch [6100]\tSpeed: 841.68 samples/sec\nINFO:root:Iter[17] Batch [6120]\tSpeed: 850.37 samples/sec\nINFO:root:Iter[17] Batch [6140]\tSpeed: 837.14 samples/sec\nINFO:root:Iter[17] Batch [6160]\tSpeed: 845.69 samples/sec\nINFO:root:Iter[17] Batch [6180]\tSpeed: 850.26 samples/sec\nINFO:root:Iter[17] Batch [6200]\tSpeed: 870.37 samples/sec\nINFO:root:Iter[17] Batch [6220]\tSpeed: 861.46 samples/sec\nINFO:root:Iter[17] Batch [6240]\tSpeed: 859.43 samples/sec\nINFO:root:Iter[17] Batch [6260]\tSpeed: 859.77 samples/sec\nINFO:root:Iter[17] Batch [6280]\tSpeed: 867.64 samples/sec\nINFO:root:Iter[17] Batch 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0.7109375\nlr-batch-epoch: 0.0010000000000000002 5911 18\ntesting verification..\n(12000, 512)\ninfer time 3.7200599999999997\n[lfw][188000]XNorm: 22.099083\n[lfw][188000]Accuracy-Flip: 0.99850+-0.00189\ntesting verification..\n(14000, 512)\ninfer time 4.353227999999998\n[cfp_fp][188000]XNorm: 22.428772\n[cfp_fp][188000]Accuracy-Flip: 0.98700+-0.00380\ntesting verification..\n(12000, 512)\ninfer time 3.735055\n[agedb_30][188000]XNorm: 23.237059\n[agedb_30][188000]Accuracy-Flip: 0.98050+-0.00695\n[188000]Accuracy-Highest: 0.98350\nINFO:root:Iter[18] Batch [5920]\tSpeed: 302.60 samples/sec\nINFO:root:Iter[18] Batch [5940]\tSpeed: 830.02 samples/sec\nINFO:root:Iter[18] Batch [5960]\tSpeed: 838.13 samples/sec\nINFO:root:Iter[18] Batch [5980]\tSpeed: 851.17 samples/sec\nINFO:root:Iter[18] Batch [6000]\tSpeed: 857.39 samples/sec\nINFO:root:Iter[18] Batch [6020]\tSpeed: 858.11 samples/sec\nINFO:root:Iter[18] Batch [6040]\tSpeed: 824.23 samples/sec\nINFO:root:Iter[18] Batch [6060]\tSpeed: 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verification..\n(12000, 512)\ninfer time 3.7665740000000008\n[agedb_30][190000]XNorm: 23.114838\n[agedb_30][190000]Accuracy-Flip: 0.98317+-0.00740\n[190000]Accuracy-Highest: 0.98350\nINFO:root:Iter[18] Batch [7920]\tSpeed: 299.78 samples/sec\nINFO:root:Iter[18] Batch [7940]\tSpeed: 853.05 samples/sec\nINFO:root:Iter[18] Batch [7960]\tSpeed: 832.77 samples/sec\nINFO:root:Iter[18] Batch [7980]\tSpeed: 832.41 samples/sec\nINFO:root:Iter[18] Batch [8000]\tSpeed: 872.54 samples/sec\nINFO:root:Iter[18] Batch [8020]\tSpeed: 861.89 samples/sec\nINFO:root:Iter[18] Batch [8040]\tSpeed: 842.42 samples/sec\nINFO:root:Iter[18] Batch [8060]\tSpeed: 830.96 samples/sec\nINFO:root:Iter[18] Batch [8080]\tSpeed: 819.68 samples/sec\nINFO:root:Iter[18] Batch [8100]\tSpeed: 846.21 samples/sec\nINFO:root:Iter[18] Batch [8120]\tSpeed: 845.88 samples/sec\nINFO:root:Iter[18] Batch [8140]\tSpeed: 845.50 samples/sec\nINFO:root:Iter[18] Batch [8160]\tSpeed: 842.43 samples/sec\nINFO:root:Iter[18] Batch 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3.6762269999999986\n[lfw][200000]XNorm: 22.069662\n[lfw][200000]Accuracy-Flip: 0.99850+-0.00189\ntesting verification..\n(14000, 512)\ninfer time 4.3890649999999996\n[cfp_fp][200000]XNorm: 22.408883\n[cfp_fp][200000]Accuracy-Flip: 0.98929+-0.00363\ntesting verification..\n(12000, 512)\ninfer time 3.71312\n[agedb_30][200000]XNorm: 23.278020\n[agedb_30][200000]Accuracy-Flip: 0.98200+-0.00690\n[200000]Accuracy-Highest: 0.98350\nINFO:root:Iter[19] Batch [7800]\tSpeed: 300.35 samples/sec\nINFO:root:Iter[19] Batch [7820]\tSpeed: 843.24 samples/sec\nINFO:root:Iter[19] Batch [7840]\tSpeed: 832.25 samples/sec\nINFO:root:Iter[19] Batch [7860]\tSpeed: 852.43 samples/sec\nINFO:root:Iter[19] Batch [7880]\tSpeed: 861.92 samples/sec\nINFO:root:Iter[19] Batch [7900]\tSpeed: 855.33 samples/sec\nINFO:root:Iter[19] Batch [7920]\tSpeed: 848.08 samples/sec\nINFO:root:Iter[19] Batch [7940]\tSpeed: 835.82 samples/sec\nINFO:root:Iter[19] Batch [7960]\tSpeed: 847.20 samples/sec\nINFO:root:Iter[19] Batch 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3.7666229999999996\n[agedb_30][208000]XNorm: 23.267387\n[agedb_30][208000]Accuracy-Flip: 0.98217+-0.00753\n[208000]Accuracy-Highest: 0.98450\nINFO:root:Iter[20] Batch [5680]\tSpeed: 298.89 samples/sec\nINFO:root:Iter[20] Batch [5700]\tSpeed: 837.73 samples/sec\nINFO:root:Iter[20] Batch [5720]\tSpeed: 813.02 samples/sec\nINFO:root:Iter[20] Batch [5740]\tSpeed: 844.33 samples/sec\nINFO:root:Iter[20] Batch [5760]\tSpeed: 810.52 samples/sec\nINFO:root:Iter[20] Batch [5780]\tSpeed: 838.38 samples/sec\nINFO:root:Iter[20] Batch [5800]\tSpeed: 836.68 samples/sec\nINFO:root:Iter[20] Batch [5820]\tSpeed: 827.81 samples/sec\nINFO:root:Iter[20] Batch [5840]\tSpeed: 865.49 samples/sec\nINFO:root:Iter[20] Batch [5860]\tSpeed: 865.14 samples/sec\nINFO:root:Iter[20] Batch [5880]\tSpeed: 845.95 samples/sec\nINFO:root:Iter[20] Batch [5900]\tSpeed: 848.76 samples/sec\nINFO:root:Iter[20] Batch [5920]\tSpeed: 826.70 samples/sec\nINFO:root:Iter[20] Batch [5940]\tSpeed: 851.55 samples/sec\nINFO:root:Iter[20] 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time 4.347269000000001\n[cfp_fp][224000]XNorm: 22.580874\n[cfp_fp][224000]Accuracy-Flip: 0.98914+-0.00273\ntesting verification..\n(12000, 512)\ninfer time 3.749904\n[agedb_30][224000]XNorm: 23.170913\n[agedb_30][224000]Accuracy-Flip: 0.98217+-0.00723\n[224000]Accuracy-Highest: 0.98450\nINFO:root:Iter[22] Batch [1460]\tSpeed: 299.17 samples/sec\nINFO:root:Iter[22] Batch [1480]\tSpeed: 834.01 samples/sec\nINFO:root:Iter[22] Batch [1500]\tSpeed: 815.45 samples/sec\nINFO:root:Iter[22] Batch [1520]\tSpeed: 844.93 samples/sec\nINFO:root:Iter[22] Batch [1540]\tSpeed: 829.08 samples/sec\nINFO:root:Iter[22] Batch [1560]\tSpeed: 831.11 samples/sec\nINFO:root:Iter[22] Batch [1580]\tSpeed: 818.87 samples/sec\nINFO:root:Iter[22] Batch [1600]\tSpeed: 823.97 samples/sec\nINFO:root:Iter[22] Batch [1620]\tSpeed: 839.25 samples/sec\nINFO:root:Iter[22] Batch [1640]\tSpeed: 862.33 samples/sec\nINFO:root:Iter[22] Batch [1660]\tSpeed: 846.83 samples/sec\nINFO:root:Iter[22] Batch [1680]\tSpeed: 830.82 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time 4.333293999999999\n[cfp_fp][230000]XNorm: 22.620853\n[cfp_fp][230000]Accuracy-Flip: 0.98886+-0.00343\ntesting verification..\n(12000, 512)\ninfer time 3.705837000000001\n[agedb_30][230000]XNorm: 23.173331\n[agedb_30][230000]Accuracy-Flip: 0.98417+-0.00625\n[230000]Accuracy-Highest: 0.98450\nINFO:root:Iter[22] Batch [7460]\tSpeed: 296.31 samples/sec\nINFO:root:Iter[22] Batch [7480]\tSpeed: 840.70 samples/sec\nINFO:root:Iter[22] Batch [7500]\tSpeed: 857.12 samples/sec\nINFO:root:Iter[22] Batch [7520]\tSpeed: 824.66 samples/sec\nINFO:root:Iter[22] Batch [7540]\tSpeed: 840.25 samples/sec\nINFO:root:Iter[22] Batch [7560]\tSpeed: 808.85 samples/sec\nINFO:root:Iter[22] Batch [7580]\tSpeed: 814.26 samples/sec\nINFO:root:Iter[22] Batch [7600]\tSpeed: 792.21 samples/sec\nINFO:root:Iter[22] Batch [7620]\tSpeed: 855.31 samples/sec\nINFO:root:Iter[22] Batch [7640]\tSpeed: 857.99 samples/sec\nINFO:root:Iter[22] Batch [7660]\tSpeed: 858.53 samples/sec\nINFO:root:Iter[22] Batch [7680]\tSpeed: 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0.7714844\nlr-batch-epoch: 0.00010000000000000003 1331 23\ntesting verification..\n(12000, 512)\ninfer time 3.7026880000000006\n[lfw][234000]XNorm: 21.844835\n[lfw][234000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.399933000000001\n[cfp_fp][234000]XNorm: 22.596082\n[cfp_fp][234000]Accuracy-Flip: 0.98857+-0.00271\ntesting verification..\n(12000, 512)\ninfer time 3.6841390000000005\n[agedb_30][234000]XNorm: 23.070350\n[agedb_30][234000]Accuracy-Flip: 0.98233+-0.00720\n[234000]Accuracy-Highest: 0.98450\nINFO:root:Iter[23] Batch [1340]\tSpeed: 301.91 samples/sec\nINFO:root:Iter[23] Batch [1360]\tSpeed: 833.35 samples/sec\nINFO:root:Iter[23] Batch [1380]\tSpeed: 840.73 samples/sec\nINFO:root:Iter[23] Batch [1400]\tSpeed: 852.95 samples/sec\nINFO:root:Iter[23] Batch [1420]\tSpeed: 865.11 samples/sec\nINFO:root:Iter[23] Batch [1440]\tSpeed: 850.11 samples/sec\nINFO:root:Iter[23] Batch [1460]\tSpeed: 840.71 samples/sec\nINFO:root:Iter[23] Batch 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22.693386\n[cfp_fp][236000]Accuracy-Flip: 0.98900+-0.00313\ntesting verification..\n(12000, 512)\ninfer time 3.7499080000000005\n[agedb_30][236000]XNorm: 23.162510\n[agedb_30][236000]Accuracy-Flip: 0.98317+-0.00713\n[236000]Accuracy-Highest: 0.98450\nINFO:root:Iter[23] Batch [3340]\tSpeed: 297.79 samples/sec\nINFO:root:Iter[23] Batch [3360]\tSpeed: 850.04 samples/sec\nINFO:root:Iter[23] Batch [3380]\tSpeed: 839.11 samples/sec\nINFO:root:Iter[23] Batch [3400]\tSpeed: 825.39 samples/sec\nINFO:root:Iter[23] Batch [3420]\tSpeed: 822.54 samples/sec\nINFO:root:Iter[23] Batch [3440]\tSpeed: 858.84 samples/sec\nINFO:root:Iter[23] Batch [3460]\tSpeed: 853.66 samples/sec\nINFO:root:Iter[23] Batch [3480]\tSpeed: 821.82 samples/sec\nINFO:root:Iter[23] Batch [3500]\tSpeed: 833.08 samples/sec\nINFO:root:Iter[23] Batch [3520]\tSpeed: 841.99 samples/sec\nINFO:root:Iter[23] Batch [3540]\tSpeed: 832.78 samples/sec\nINFO:root:Iter[23] Batch [3560]\tSpeed: 880.02 samples/sec\nINFO:root:Iter[23] Batch 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time 4.389742\n[cfp_fp][242000]XNorm: 22.568605\n[cfp_fp][242000]Accuracy-Flip: 0.98886+-0.00305\ntesting verification..\n(12000, 512)\ninfer time 3.7907069999999994\n[agedb_30][242000]XNorm: 23.054570\n[agedb_30][242000]Accuracy-Flip: 0.98300+-0.00694\n[242000]Accuracy-Highest: 0.98450\nINFO:root:Iter[23] Batch [9340]\tSpeed: 294.39 samples/sec\nINFO:root:Iter[23] Batch [9360]\tSpeed: 848.01 samples/sec\nINFO:root:Iter[23] Batch [9380]\tSpeed: 814.14 samples/sec\nINFO:root:Iter[23] Batch [9400]\tSpeed: 818.51 samples/sec\nINFO:root:Iter[23] Batch [9420]\tSpeed: 835.22 samples/sec\nINFO:root:Iter[23] Batch [9440]\tSpeed: 854.07 samples/sec\nINFO:root:Iter[23] Batch [9460]\tSpeed: 842.23 samples/sec\nINFO:root:Iter[23] Batch [9480]\tSpeed: 847.01 samples/sec\nINFO:root:Iter[23] Batch [9500]\tSpeed: 860.69 samples/sec\nINFO:root:Iter[23] Batch [9520]\tSpeed: 847.83 samples/sec\nINFO:root:Iter[23] Batch [9540]\tSpeed: 854.49 samples/sec\nINFO:root:Iter[23] Batch [9560]\tSpeed: 847.42 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0.80859375\nlr-batch-epoch: 0.00010000000000000003 3215 24\ntesting verification..\n(12000, 512)\ninfer time 3.770176\n[lfw][246000]XNorm: 21.929517\n[lfw][246000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.438579000000001\n[cfp_fp][246000]XNorm: 22.636126\n[cfp_fp][246000]Accuracy-Flip: 0.98957+-0.00293\ntesting verification..\n(12000, 512)\ninfer time 3.788399\n[agedb_30][246000]XNorm: 23.138202\n[agedb_30][246000]Accuracy-Flip: 0.98250+-0.00739\n[246000]Accuracy-Highest: 0.98450\nINFO:root:Iter[24] Batch [3220]\tSpeed: 298.77 samples/sec\nINFO:root:Iter[24] Batch [3240]\tSpeed: 860.37 samples/sec\nINFO:root:Iter[24] Batch [3260]\tSpeed: 842.16 samples/sec\nINFO:root:Iter[24] Batch [3280]\tSpeed: 846.08 samples/sec\nINFO:root:Iter[24] Batch [3300]\tSpeed: 801.91 samples/sec\nINFO:root:Iter[24] Batch [3320]\tSpeed: 827.72 samples/sec\nINFO:root:Iter[24] Batch [3340]\tSpeed: 835.26 samples/sec\nINFO:root:Iter[24] Batch [3360]\tSpeed: 833.26 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512)\ninfer time 3.7687120000000003\n[agedb_30][248000]XNorm: 23.093899\n[agedb_30][248000]Accuracy-Flip: 0.98383+-0.00646\n[248000]Accuracy-Highest: 0.98450\nINFO:root:Iter[24] Batch [5220]\tSpeed: 302.63 samples/sec\nINFO:root:Iter[24] Batch [5240]\tSpeed: 843.74 samples/sec\nINFO:root:Iter[24] Batch [5260]\tSpeed: 832.26 samples/sec\nINFO:root:Iter[24] Batch [5280]\tSpeed: 861.72 samples/sec\nINFO:root:Iter[24] Batch [5300]\tSpeed: 825.12 samples/sec\nINFO:root:Iter[24] Batch [5320]\tSpeed: 787.62 samples/sec\nINFO:root:Iter[24] Batch [5340]\tSpeed: 808.17 samples/sec\nINFO:root:Iter[24] Batch [5360]\tSpeed: 816.05 samples/sec\nINFO:root:Iter[24] Batch [5380]\tSpeed: 828.55 samples/sec\nINFO:root:Iter[24] Batch [5400]\tSpeed: 816.23 samples/sec\nINFO:root:Iter[24] Batch [5420]\tSpeed: 850.75 samples/sec\nINFO:root:Iter[24] Batch [5440]\tSpeed: 825.08 samples/sec\nINFO:root:Iter[24] Batch [5460]\tSpeed: 826.88 samples/sec\nINFO:root:Iter[24] Batch [5480]\tSpeed: 855.06 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3.727699\n[agedb_30][260000]XNorm: 23.116018\n[agedb_30][260000]Accuracy-Flip: 0.98233+-0.00720\n[260000]Accuracy-Highest: 0.98450\nINFO:root:Iter[25] Batch [7100]\tSpeed: 308.66 samples/sec\nINFO:root:Iter[25] Batch [7120]\tSpeed: 833.95 samples/sec\nINFO:root:Iter[25] Batch [7140]\tSpeed: 824.00 samples/sec\nINFO:root:Iter[25] Batch [7160]\tSpeed: 835.48 samples/sec\nINFO:root:Iter[25] Batch [7180]\tSpeed: 826.46 samples/sec\nINFO:root:Iter[25] Batch [7200]\tSpeed: 816.49 samples/sec\nINFO:root:Iter[25] Batch [7220]\tSpeed: 802.84 samples/sec\nINFO:root:Iter[25] Batch [7240]\tSpeed: 850.99 samples/sec\nINFO:root:Iter[25] Batch [7260]\tSpeed: 869.58 samples/sec\nINFO:root:Iter[25] Batch [7280]\tSpeed: 860.60 samples/sec\nINFO:root:Iter[25] Batch [7300]\tSpeed: 842.24 samples/sec\nINFO:root:Iter[25] Batch [7320]\tSpeed: 843.94 samples/sec\nINFO:root:Iter[25] Batch [7340]\tSpeed: 825.28 samples/sec\nINFO:root:Iter[25] Batch [7360]\tSpeed: 840.30 samples/sec\nINFO:root:Iter[25] Batch 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cost=6122.383\ncall reset()\nINFO:root:Iter[26] Batch [20]\tSpeed: 886.75 samples/sec\nINFO:root:Iter[26] Batch [40]\tSpeed: 890.05 samples/sec\nINFO:root:Iter[26] Batch [60]\tSpeed: 862.63 samples/sec\nINFO:root:Iter[26] Batch [80]\tSpeed: 865.88 samples/sec\nINFO:root:Iter[26] Batch [100]\tSpeed: 846.38 samples/sec\nINFO:root:Iter[26] Batch [120]\tSpeed: 820.14 samples/sec\nINFO:root:Iter[26] Batch [140]\tSpeed: 820.17 samples/sec\nINFO:root:Iter[26] Batch [160]\tSpeed: 840.28 samples/sec\nINFO:root:Iter[26] Batch [180]\tSpeed: 871.57 samples/sec\nINFO:root:Iter[26] Batch [200]\tSpeed: 850.02 samples/sec\nINFO:root:Iter[26] Batch [220]\tSpeed: 855.08 samples/sec\nINFO:root:Iter[26] Batch [240]\tSpeed: 875.13 samples/sec\nINFO:root:Iter[26] Batch [260]\tSpeed: 875.96 samples/sec\nINFO:root:Iter[26] Batch [280]\tSpeed: 883.28 samples/sec\nINFO:root:Iter[26] Batch [300]\tSpeed: 864.51 samples/sec\nINFO:root:Iter[26] Batch [320]\tSpeed: 888.78 samples/sec\nINFO:root:Iter[26] Batch 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26\ntesting verification..\n(12000, 512)\ninfer time 3.6828010000000004\n[lfw][264000]XNorm: 21.909757\n[lfw][264000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.348984000000001\n[cfp_fp][264000]XNorm: 22.671618\n[cfp_fp][264000]Accuracy-Flip: 0.98957+-0.00286\ntesting verification..\n(12000, 512)\ninfer time 3.7288200000000002\n[agedb_30][264000]XNorm: 23.125675\n[agedb_30][264000]Accuracy-Flip: 0.98433+-0.00606\n[264000]Accuracy-Highest: 0.98450\nINFO:root:Iter[26] Batch [1000]\tSpeed: 296.34 samples/sec\nINFO:root:Iter[26] Batch [1020]\tSpeed: 834.22 samples/sec\nINFO:root:Iter[26] Batch [1040]\tSpeed: 825.41 samples/sec\nINFO:root:Iter[26] Batch [1060]\tSpeed: 832.69 samples/sec\nINFO:root:Iter[26] Batch [1080]\tSpeed: 862.23 samples/sec\nINFO:root:Iter[26] Batch [1100]\tSpeed: 862.25 samples/sec\nINFO:root:Iter[26] Batch [1120]\tSpeed: 859.79 samples/sec\nINFO:root:Iter[26] Batch [1140]\tSpeed: 859.40 samples/sec\nINFO:root:Iter[26] Batch 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verification..\n(12000, 512)\ninfer time 3.7589659999999996\n[agedb_30][266000]XNorm: 23.096936\n[agedb_30][266000]Accuracy-Flip: 0.98283+-0.00727\n[266000]Accuracy-Highest: 0.98450\nINFO:root:Iter[26] Batch [3000]\tSpeed: 295.89 samples/sec\nINFO:root:Iter[26] Batch [3020]\tSpeed: 850.58 samples/sec\nINFO:root:Iter[26] Batch [3040]\tSpeed: 839.13 samples/sec\nINFO:root:Iter[26] Batch [3060]\tSpeed: 852.19 samples/sec\nINFO:root:Iter[26] Batch [3080]\tSpeed: 818.86 samples/sec\nINFO:root:Iter[26] Batch [3100]\tSpeed: 832.82 samples/sec\nINFO:root:Iter[26] Batch [3120]\tSpeed: 833.99 samples/sec\nINFO:root:Iter[26] Batch [3140]\tSpeed: 845.40 samples/sec\nINFO:root:Iter[26] Batch [3160]\tSpeed: 838.52 samples/sec\nINFO:root:Iter[26] Batch [3180]\tSpeed: 840.62 samples/sec\nINFO:root:Iter[26] Batch [3200]\tSpeed: 828.96 samples/sec\nINFO:root:Iter[26] Batch [3220]\tSpeed: 854.40 samples/sec\nINFO:root:Iter[26] Batch [3240]\tSpeed: 863.68 samples/sec\nINFO:root:Iter[26] Batch 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0.7910156\nlr-batch-epoch: 0.00010000000000000003 6983 26\ntesting verification..\n(12000, 512)\ninfer time 3.7880840000000005\n[lfw][270000]XNorm: 21.875213\n[lfw][270000]Accuracy-Flip: 0.99817+-0.00203\ntesting verification..\n(14000, 512)\ninfer time 4.334204\n[cfp_fp][270000]XNorm: 22.686974\n[cfp_fp][270000]Accuracy-Flip: 0.98871+-0.00329\ntesting verification..\n(12000, 512)\ninfer time 3.768275\n[agedb_30][270000]XNorm: 23.119831\n[agedb_30][270000]Accuracy-Flip: 0.98417+-0.00611\n[270000]Accuracy-Highest: 0.98450\nINFO:root:Iter[26] Batch [7000]\tSpeed: 295.13 samples/sec\nINFO:root:Iter[26] Batch [7020]\tSpeed: 831.24 samples/sec\nINFO:root:Iter[26] Batch [7040]\tSpeed: 848.00 samples/sec\nINFO:root:Iter[26] Batch [7060]\tSpeed: 817.50 samples/sec\nINFO:root:Iter[26] Batch [7080]\tSpeed: 816.68 samples/sec\nINFO:root:Iter[26] Batch [7100]\tSpeed: 809.23 samples/sec\nINFO:root:Iter[26] Batch [7120]\tSpeed: 820.92 samples/sec\nINFO:root:Iter[26] Batch [7140]\tSpeed: 835.98 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samples/sec\nINFO:root:Iter[27] Batch [800]\tSpeed: 877.71 samples/sec\nINFO:root:Iter[27] Batch [820]\tSpeed: 868.22 samples/sec\nINFO:root:Iter[27] Batch [840]\tSpeed: 901.16 samples/sec\nINFO:root:Iter[27] Batch [860]\tSpeed: 871.30 samples/sec\n{fc7_acc} 274000 0.8183594\nlr-batch-epoch: 0.00010000000000000003 867 27\ntesting verification..\n(12000, 512)\ninfer time 3.7452680000000007\n[lfw][274000]XNorm: 21.876404\n[lfw][274000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.341177\n[cfp_fp][274000]XNorm: 22.699434\n[cfp_fp][274000]Accuracy-Flip: 0.98943+-0.00287\ntesting verification..\n(12000, 512)\ninfer time 3.773223\n[agedb_30][274000]XNorm: 23.086900\n[agedb_30][274000]Accuracy-Flip: 0.98217+-0.00771\n[274000]Accuracy-Highest: 0.98450\nINFO:root:Iter[27] Batch [880]\tSpeed: 296.67 samples/sec\nINFO:root:Iter[27] Batch [900]\tSpeed: 863.67 samples/sec\nINFO:root:Iter[27] Batch [920]\tSpeed: 843.65 samples/sec\nINFO:root:Iter[27] Batch 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verification..\n(12000, 512)\ninfer time 3.7080830000000002\n[lfw][276000]XNorm: 21.905753\n[lfw][276000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.397842000000001\n[cfp_fp][276000]XNorm: 22.672234\n[cfp_fp][276000]Accuracy-Flip: 0.98886+-0.00325\ntesting verification..\n(12000, 512)\ninfer time 3.7725990000000005\n[agedb_30][276000]XNorm: 23.100533\n[agedb_30][276000]Accuracy-Flip: 0.98233+-0.00750\n[276000]Accuracy-Highest: 0.98450\nINFO:root:Iter[27] Batch [2880]\tSpeed: 297.01 samples/sec\nINFO:root:Iter[27] Batch [2900]\tSpeed: 832.39 samples/sec\nINFO:root:Iter[27] Batch [2920]\tSpeed: 830.55 samples/sec\nINFO:root:Iter[27] Batch [2940]\tSpeed: 831.65 samples/sec\nINFO:root:Iter[27] Batch [2960]\tSpeed: 838.61 samples/sec\nINFO:root:Iter[27] Batch [2980]\tSpeed: 823.05 samples/sec\nINFO:root:Iter[27] Batch [3000]\tSpeed: 829.91 samples/sec\nINFO:root:Iter[27] Batch [3020]\tSpeed: 845.96 samples/sec\nINFO:root:Iter[27] Batch [3040]\tSpeed: 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verification..\n(12000, 512)\ninfer time 3.7327110000000006\n[lfw][282000]XNorm: 21.879665\n[lfw][282000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.576282000000001\n[cfp_fp][282000]XNorm: 22.726157\n[cfp_fp][282000]Accuracy-Flip: 0.98971+-0.00270\ntesting verification..\n(12000, 512)\ninfer time 3.729272\n[agedb_30][282000]XNorm: 23.085668\n[agedb_30][282000]Accuracy-Flip: 0.98500+-0.00587\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[282000]Accuracy-Highest: 0.98500\nINFO:root:Iter[27] Batch [8880]\tSpeed: 287.97 samples/sec\nINFO:root:Iter[27] Batch [8900]\tSpeed: 822.76 samples/sec\nINFO:root:Iter[27] Batch [8920]\tSpeed: 855.90 samples/sec\nINFO:root:Iter[27] Batch [8940]\tSpeed: 851.11 samples/sec\nINFO:root:Iter[27] Batch [8960]\tSpeed: 830.83 samples/sec\nINFO:root:Iter[27] Batch [8980]\tSpeed: 858.67 samples/sec\nINFO:root:Iter[27] Batch [9000]\tSpeed: 844.99 samples/sec\nINFO:root:Iter[27] 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0.98843+-0.00289\ntesting verification..\n(12000, 512)\ninfer time 3.7245760000000003\n[agedb_30][284000]XNorm: 23.100871\n[agedb_30][284000]Accuracy-Flip: 0.98383+-0.00746\n[284000]Accuracy-Highest: 0.98500\nINFO:root:Iter[28] Batch [760]\tSpeed: 299.53 samples/sec\nINFO:root:Iter[28] Batch [780]\tSpeed: 821.72 samples/sec\nINFO:root:Iter[28] Batch [800]\tSpeed: 834.34 samples/sec\nINFO:root:Iter[28] Batch [820]\tSpeed: 810.24 samples/sec\nINFO:root:Iter[28] Batch [840]\tSpeed: 833.03 samples/sec\nINFO:root:Iter[28] Batch [860]\tSpeed: 859.35 samples/sec\nINFO:root:Iter[28] Batch [880]\tSpeed: 845.94 samples/sec\nINFO:root:Iter[28] Batch [900]\tSpeed: 837.70 samples/sec\nINFO:root:Iter[28] Batch [920]\tSpeed: 829.30 samples/sec\nINFO:root:Iter[28] Batch [940]\tSpeed: 853.91 samples/sec\nINFO:root:Iter[28] Batch [960]\tSpeed: 837.03 samples/sec\nINFO:root:Iter[28] Batch [980]\tSpeed: 842.59 samples/sec\nINFO:root:Iter[28] Batch [1000]\tSpeed: 828.27 samples/sec\nINFO:root:Iter[28] 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0.8261719\nlr-batch-epoch: 0.00010000000000000003 4751 28\ntesting verification..\n(12000, 512)\ninfer time 3.699165\n[lfw][288000]XNorm: 21.932219\n[lfw][288000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.337257\n[cfp_fp][288000]XNorm: 22.748199\n[cfp_fp][288000]Accuracy-Flip: 0.98914+-0.00357\ntesting verification..\n(12000, 512)\ninfer time 3.9130559999999983\n[agedb_30][288000]XNorm: 23.160412\n[agedb_30][288000]Accuracy-Flip: 0.98317+-0.00724\n[288000]Accuracy-Highest: 0.98500\nINFO:root:Iter[28] Batch [4760]\tSpeed: 296.11 samples/sec\nINFO:root:Iter[28] Batch [4780]\tSpeed: 839.54 samples/sec\nINFO:root:Iter[28] Batch [4800]\tSpeed: 847.43 samples/sec\nINFO:root:Iter[28] Batch [4820]\tSpeed: 814.58 samples/sec\nINFO:root:Iter[28] Batch [4840]\tSpeed: 852.55 samples/sec\nINFO:root:Iter[28] Batch [4860]\tSpeed: 853.28 samples/sec\nINFO:root:Iter[28] Batch [4880]\tSpeed: 825.32 samples/sec\nINFO:root:Iter[28] Batch [4900]\tSpeed: 820.03 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samples/sec\nINFO:root:Iter[28] Batch [6560]\tSpeed: 861.24 samples/sec\nINFO:root:Iter[28] Batch [6580]\tSpeed: 857.31 samples/sec\nINFO:root:Iter[28] Batch [6600]\tSpeed: 869.75 samples/sec\nINFO:root:Iter[28] Batch [6620]\tSpeed: 869.76 samples/sec\nINFO:root:Iter[28] Batch [6640]\tSpeed: 871.42 samples/sec\nINFO:root:Iter[28] Batch [6660]\tSpeed: 846.86 samples/sec\nINFO:root:Iter[28] Batch [6680]\tSpeed: 859.93 samples/sec\nINFO:root:Iter[28] Batch [6700]\tSpeed: 861.87 samples/sec\nINFO:root:Iter[28] Batch [6720]\tSpeed: 865.90 samples/sec\nINFO:root:Iter[28] Batch [6740]\tSpeed: 878.17 samples/sec\n{fc7_acc} 290000 0.7910156\nlr-batch-epoch: 0.00010000000000000003 6751 28\ntesting verification..\n(12000, 512)\ninfer time 3.735802\n[lfw][290000]XNorm: 21.800522\n[lfw][290000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.347777000000002\n[cfp_fp][290000]XNorm: 22.599933\n[cfp_fp][290000]Accuracy-Flip: 0.98871+-0.00234\ntesting verification..\n(12000, 512)\ninfer time 3.7770970000000004\n[agedb_30][290000]XNorm: 23.014746\n[agedb_30][290000]Accuracy-Flip: 0.98500+-0.00619\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[290000]Accuracy-Highest: 0.98500\nINFO:root:Iter[28] Batch [6760]\tSpeed: 293.33 samples/sec\nINFO:root:Iter[28] Batch [6780]\tSpeed: 857.73 samples/sec\nINFO:root:Iter[28] Batch [6800]\tSpeed: 839.10 samples/sec\nINFO:root:Iter[28] Batch [6820]\tSpeed: 842.95 samples/sec\nINFO:root:Iter[28] Batch [6840]\tSpeed: 847.79 samples/sec\nINFO:root:Iter[28] Batch [6860]\tSpeed: 821.85 samples/sec\nINFO:root:Iter[28] Batch [6880]\tSpeed: 834.02 samples/sec\nINFO:root:Iter[28] Batch [6900]\tSpeed: 844.64 samples/sec\nINFO:root:Iter[28] Batch [6920]\tSpeed: 853.06 samples/sec\nINFO:root:Iter[28] Batch [6940]\tSpeed: 833.21 samples/sec\nINFO:root:Iter[28] Batch [6960]\tSpeed: 841.62 samples/sec\nINFO:root:Iter[28] Batch [6980]\tSpeed: 831.08 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22.689612\n[cfp_fp][296000]Accuracy-Flip: 0.98971+-0.00262\ntesting verification..\n(12000, 512)\ninfer time 3.729677999999999\n[agedb_30][296000]XNorm: 23.111305\n[agedb_30][296000]Accuracy-Flip: 0.98350+-0.00689\n[296000]Accuracy-Highest: 0.98500\nINFO:root:Iter[29] Batch [2640]\tSpeed: 301.29 samples/sec\nINFO:root:Iter[29] Batch [2660]\tSpeed: 838.45 samples/sec\nINFO:root:Iter[29] Batch [2680]\tSpeed: 813.15 samples/sec\nINFO:root:Iter[29] Batch [2700]\tSpeed: 835.59 samples/sec\nINFO:root:Iter[29] Batch [2720]\tSpeed: 845.71 samples/sec\nINFO:root:Iter[29] Batch [2740]\tSpeed: 817.01 samples/sec\nINFO:root:Iter[29] Batch [2760]\tSpeed: 819.73 samples/sec\nINFO:root:Iter[29] Batch [2780]\tSpeed: 840.17 samples/sec\nINFO:root:Iter[29] Batch [2800]\tSpeed: 835.34 samples/sec\nINFO:root:Iter[29] Batch [2820]\tSpeed: 861.13 samples/sec\nINFO:root:Iter[29] Batch [2840]\tSpeed: 809.56 samples/sec\nINFO:root:Iter[29] Batch [2860]\tSpeed: 848.45 samples/sec\nINFO:root:Iter[29] Batch 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0.7949219\nlr-batch-epoch: 0.00010000000000000003 2519 30\ntesting verification..\n(12000, 512)\ninfer time 3.774218\n[lfw][306000]XNorm: 21.916169\n[lfw][306000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.416122999999998\n[cfp_fp][306000]XNorm: 22.788253\n[cfp_fp][306000]Accuracy-Flip: 0.98971+-0.00262\ntesting verification..\n(12000, 512)\ninfer time 3.734230000000001\n[agedb_30][306000]XNorm: 23.135613\n[agedb_30][306000]Accuracy-Flip: 0.98283+-0.00749\n[306000]Accuracy-Highest: 0.98500\nINFO:root:Iter[30] Batch [2520]\tSpeed: 302.08 samples/sec\nINFO:root:Iter[30] Batch [2540]\tSpeed: 841.41 samples/sec\nINFO:root:Iter[30] Batch [2560]\tSpeed: 823.83 samples/sec\nINFO:root:Iter[30] Batch [2580]\tSpeed: 822.46 samples/sec\nINFO:root:Iter[30] Batch [2600]\tSpeed: 852.63 samples/sec\nINFO:root:Iter[30] Batch [2620]\tSpeed: 842.97 samples/sec\nINFO:root:Iter[30] Batch [2640]\tSpeed: 830.93 samples/sec\nINFO:root:Iter[30] Batch [2660]\tSpeed: 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verification..\n(12000, 512)\ninfer time 3.8158220000000003\n[agedb_30][308000]XNorm: 23.112907\n[agedb_30][308000]Accuracy-Flip: 0.98333+-0.00699\n[308000]Accuracy-Highest: 0.98500\nINFO:root:Iter[30] Batch [4520]\tSpeed: 301.67 samples/sec\nINFO:root:Iter[30] Batch [4540]\tSpeed: 843.74 samples/sec\nINFO:root:Iter[30] Batch [4560]\tSpeed: 815.45 samples/sec\nINFO:root:Iter[30] Batch [4580]\tSpeed: 849.54 samples/sec\nINFO:root:Iter[30] Batch [4600]\tSpeed: 775.76 samples/sec\nINFO:root:Iter[30] Batch [4620]\tSpeed: 821.93 samples/sec\nINFO:root:Iter[30] Batch [4640]\tSpeed: 822.81 samples/sec\nINFO:root:Iter[30] Batch [4660]\tSpeed: 859.69 samples/sec\nINFO:root:Iter[30] Batch [4680]\tSpeed: 800.44 samples/sec\nINFO:root:Iter[30] Batch [4700]\tSpeed: 829.89 samples/sec\nINFO:root:Iter[30] Batch [4720]\tSpeed: 829.25 samples/sec\nINFO:root:Iter[30] Batch [4740]\tSpeed: 865.18 samples/sec\nINFO:root:Iter[30] Batch [4760]\tSpeed: 852.01 samples/sec\nINFO:root:Iter[30] Batch 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verification..\n(12000, 512)\ninfer time 3.77228\n[agedb_30][320000]XNorm: 23.095152\n[agedb_30][320000]Accuracy-Flip: 0.98283+-0.00742\n[320000]Accuracy-Highest: 0.98500\nINFO:root:Iter[31] Batch [6420]\tSpeed: 296.25 samples/sec\nINFO:root:Iter[31] Batch [6440]\tSpeed: 844.60 samples/sec\nINFO:root:Iter[31] Batch [6460]\tSpeed: 839.39 samples/sec\nINFO:root:Iter[31] Batch [6480]\tSpeed: 864.15 samples/sec\nINFO:root:Iter[31] Batch [6500]\tSpeed: 860.42 samples/sec\nINFO:root:Iter[31] Batch [6520]\tSpeed: 860.01 samples/sec\nINFO:root:Iter[31] Batch [6540]\tSpeed: 869.49 samples/sec\nINFO:root:Iter[31] Batch [6560]\tSpeed: 860.78 samples/sec\nINFO:root:Iter[31] Batch [6580]\tSpeed: 842.73 samples/sec\nINFO:root:Iter[31] Batch [6600]\tSpeed: 837.46 samples/sec\nINFO:root:Iter[31] Batch [6620]\tSpeed: 833.67 samples/sec\nINFO:root:Iter[31] Batch [6640]\tSpeed: 847.35 samples/sec\nINFO:root:Iter[31] Batch [6660]\tSpeed: 861.83 samples/sec\nINFO:root:Iter[31] Batch [6680]\tSpeed: 849.23 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samples/sec\nINFO:root:Epoch[31] Time cost=6123.379\ncall reset()\nINFO:root:Iter[32] Batch [20]\tSpeed: 875.64 samples/sec\nINFO:root:Iter[32] Batch [40]\tSpeed: 858.02 samples/sec\nINFO:root:Iter[32] Batch [60]\tSpeed: 871.30 samples/sec\nINFO:root:Iter[32] Batch [80]\tSpeed: 870.02 samples/sec\nINFO:root:Iter[32] Batch [100]\tSpeed: 891.64 samples/sec\nINFO:root:Iter[32] Batch [120]\tSpeed: 821.16 samples/sec\nINFO:root:Iter[32] Batch [140]\tSpeed: 866.10 samples/sec\nINFO:root:Iter[32] Batch [160]\tSpeed: 872.93 samples/sec\nINFO:root:Iter[32] Batch [180]\tSpeed: 846.15 samples/sec\nINFO:root:Iter[32] Batch [200]\tSpeed: 871.77 samples/sec\nINFO:root:Iter[32] Batch [220]\tSpeed: 853.00 samples/sec\nINFO:root:Iter[32] Batch [240]\tSpeed: 864.31 samples/sec\nINFO:root:Iter[32] Batch [260]\tSpeed: 886.39 samples/sec\nINFO:root:Iter[32] Batch [280]\tSpeed: 853.95 samples/sec\n{fc7_acc} 324000 0.7871094\nlr-batch-epoch: 0.00010000000000000003 287 32\ntesting verification..\n(12000, 512)\ninfer time 3.7533279999999998\n[lfw][324000]XNorm: 21.889460\n[lfw][324000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.360629000000001\n[cfp_fp][324000]XNorm: 22.721149\n[cfp_fp][324000]Accuracy-Flip: 0.98971+-0.00254\ntesting verification..\n(12000, 512)\ninfer time 3.7445629999999994\n[agedb_30][324000]XNorm: 23.027542\n[agedb_30][324000]Accuracy-Flip: 0.98483+-0.00584\n[324000]Accuracy-Highest: 0.98500\nINFO:root:Iter[32] Batch [300]\tSpeed: 302.55 samples/sec\nINFO:root:Iter[32] Batch [320]\tSpeed: 847.30 samples/sec\nINFO:root:Iter[32] Batch [340]\tSpeed: 861.35 samples/sec\nINFO:root:Iter[32] Batch [360]\tSpeed: 849.77 samples/sec\nINFO:root:Iter[32] Batch [380]\tSpeed: 835.54 samples/sec\nINFO:root:Iter[32] Batch [400]\tSpeed: 855.14 samples/sec\nINFO:root:Iter[32] Batch [420]\tSpeed: 850.02 samples/sec\nINFO:root:Iter[32] Batch [440]\tSpeed: 860.63 samples/sec\nINFO:root:Iter[32] Batch [460]\tSpeed: 825.34 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samples/sec\nINFO:root:Iter[32] Batch [2120]\tSpeed: 870.88 samples/sec\nINFO:root:Iter[32] Batch [2140]\tSpeed: 871.80 samples/sec\nINFO:root:Iter[32] Batch [2160]\tSpeed: 866.87 samples/sec\nINFO:root:Iter[32] Batch [2180]\tSpeed: 855.89 samples/sec\nINFO:root:Iter[32] Batch [2200]\tSpeed: 851.22 samples/sec\nINFO:root:Iter[32] Batch [2220]\tSpeed: 870.56 samples/sec\nINFO:root:Iter[32] Batch [2240]\tSpeed: 837.19 samples/sec\nINFO:root:Iter[32] Batch [2260]\tSpeed: 862.14 samples/sec\nINFO:root:Iter[32] Batch [2280]\tSpeed: 847.37 samples/sec\n{fc7_acc} 326000 0.8125\nlr-batch-epoch: 0.00010000000000000003 2287 32\ntesting verification..\n(12000, 512)\ninfer time 3.69318\n[lfw][326000]XNorm: 21.893645\n[lfw][326000]Accuracy-Flip: 0.99817+-0.00203\ntesting verification..\n(14000, 512)\ninfer time 4.330982\n[cfp_fp][326000]XNorm: 22.745846\n[cfp_fp][326000]Accuracy-Flip: 0.98886+-0.00246\ntesting verification..\n(12000, 512)\ninfer time 3.7934330000000016\n[agedb_30][326000]XNorm: 23.061304\n[agedb_30][326000]Accuracy-Flip: 0.98467+-0.00609\n[326000]Accuracy-Highest: 0.98500\nINFO:root:Iter[32] Batch [2300]\tSpeed: 302.85 samples/sec\nINFO:root:Iter[32] Batch [2320]\tSpeed: 850.11 samples/sec\nINFO:root:Iter[32] Batch [2340]\tSpeed: 841.64 samples/sec\nINFO:root:Iter[32] Batch [2360]\tSpeed: 832.44 samples/sec\nINFO:root:Iter[32] Batch [2380]\tSpeed: 845.26 samples/sec\nINFO:root:Iter[32] Batch [2400]\tSpeed: 817.58 samples/sec\nINFO:root:Iter[32] Batch [2420]\tSpeed: 818.21 samples/sec\nINFO:root:Iter[32] Batch [2440]\tSpeed: 827.31 samples/sec\nINFO:root:Iter[32] Batch [2460]\tSpeed: 854.99 samples/sec\nINFO:root:Iter[32] Batch [2480]\tSpeed: 852.61 samples/sec\nINFO:root:Iter[32] Batch [2500]\tSpeed: 837.28 samples/sec\nINFO:root:Iter[32] Batch [2520]\tSpeed: 852.86 samples/sec\nINFO:root:Iter[32] Batch [2540]\tSpeed: 862.16 samples/sec\nINFO:root:Iter[32] Batch [2560]\tSpeed: 864.22 samples/sec\nINFO:root:Iter[32] Batch [2580]\tSpeed: 846.96 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3.7011299999999996\n[lfw][330000]XNorm: 21.876165\n[lfw][330000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.39658\n[cfp_fp][330000]XNorm: 22.720489\n[cfp_fp][330000]Accuracy-Flip: 0.98871+-0.00243\ntesting verification..\n(12000, 512)\ninfer time 3.8188219999999995\n[agedb_30][330000]XNorm: 23.116132\n[agedb_30][330000]Accuracy-Flip: 0.98383+-0.00730\n[330000]Accuracy-Highest: 0.98500\nINFO:root:Iter[32] Batch [6300]\tSpeed: 300.86 samples/sec\nINFO:root:Iter[32] Batch [6320]\tSpeed: 858.64 samples/sec\nINFO:root:Iter[32] Batch [6340]\tSpeed: 855.01 samples/sec\nINFO:root:Iter[32] Batch [6360]\tSpeed: 844.34 samples/sec\nINFO:root:Iter[32] Batch [6380]\tSpeed: 834.69 samples/sec\nINFO:root:Iter[32] Batch [6400]\tSpeed: 867.08 samples/sec\nINFO:root:Iter[32] Batch [6420]\tSpeed: 838.74 samples/sec\nINFO:root:Iter[32] Batch [6440]\tSpeed: 848.97 samples/sec\nINFO:root:Iter[32] Batch [6460]\tSpeed: 833.02 samples/sec\nINFO:root:Iter[32] Batch 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3.7277899999999997\n[lfw][336000]XNorm: 21.972699\n[lfw][336000]Accuracy-Flip: 0.99833+-0.00211\ntesting verification..\n(14000, 512)\ninfer time 4.376143999999998\n[cfp_fp][336000]XNorm: 22.795769\n[cfp_fp][336000]Accuracy-Flip: 0.99029+-0.00262\ntesting verification..\n(12000, 512)\ninfer time 3.717962\n[agedb_30][336000]XNorm: 23.141228\n[agedb_30][336000]Accuracy-Flip: 0.98450+-0.00650\n[336000]Accuracy-Highest: 0.98500\nINFO:root:Iter[33] Batch [2180]\tSpeed: 298.45 samples/sec\nINFO:root:Iter[33] Batch [2200]\tSpeed: 800.90 samples/sec\nINFO:root:Iter[33] Batch [2220]\tSpeed: 845.96 samples/sec\nINFO:root:Iter[33] Batch [2240]\tSpeed: 832.28 samples/sec\nINFO:root:Iter[33] Batch [2260]\tSpeed: 827.44 samples/sec\nINFO:root:Iter[33] Batch [2280]\tSpeed: 842.05 samples/sec\nINFO:root:Iter[33] Batch [2300]\tSpeed: 868.15 samples/sec\nINFO:root:Iter[33] Batch [2320]\tSpeed: 865.21 samples/sec\nINFO:root:Iter[33] Batch [2340]\tSpeed: 892.57 samples/sec\nINFO:root:Iter[33] Batch 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verification..\n(12000, 512)\ninfer time 3.6822339999999993\n[lfw][342000]XNorm: 21.761155\n[lfw][342000]Accuracy-Flip: 0.99800+-0.00194\ntesting verification..\n(14000, 512)\ninfer time 4.399541\n[cfp_fp][342000]XNorm: 22.673288\n[cfp_fp][342000]Accuracy-Flip: 0.98986+-0.00274\ntesting verification..\n(12000, 512)\ninfer time 3.8138129999999997\n[agedb_30][342000]XNorm: 22.957931\n[agedb_30][342000]Accuracy-Flip: 0.98383+-0.00730\n[342000]Accuracy-Highest: 0.98500\nINFO:root:Iter[33] Batch [8180]\tSpeed: 296.88 samples/sec\nINFO:root:Iter[33] Batch [8200]\tSpeed: 847.64 samples/sec\nINFO:root:Iter[33] Batch [8220]\tSpeed: 820.67 samples/sec\nINFO:root:Iter[33] Batch [8240]\tSpeed: 811.66 samples/sec\nINFO:root:Iter[33] Batch [8260]\tSpeed: 842.09 samples/sec\nINFO:root:Iter[33] Batch [8280]\tSpeed: 870.67 samples/sec\nINFO:root:Iter[33] Batch [8300]\tSpeed: 833.99 samples/sec\nINFO:root:Iter[33] Batch [8320]\tSpeed: 835.02 samples/sec\nINFO:root:Iter[33] Batch [8340]\tSpeed: 796.11 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[8680]\tSpeed: 860.04 samples/sec\nINFO:root:Iter[33] Batch [8700]\tSpeed: 839.82 samples/sec\nTraceback (most recent call last):\n  File \"train_parall.py\", line 353, in <module>\n    main()\n  File \"train_parall.py\", line 350, in main\n    train_net(args)\n  File \"train_parall.py\", line 345, in train_net\n    epoch_end_callback = epoch_cb )\n  File \"/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py\", line 475, in fit\n    self.update()\n  File \"/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py\", line 323, in update\n    self._curr_module.update()\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/module/module.py\", line 666, in update\n    self._kvstore, self._exec_group.param_names)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/model.py\", line 160, in _update_params_on_kvstore\n    kvstore.pull(name, arg_list, priority=-index)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/kvstore.py\", line 301, in pull\n    ckeys, cvals, use_str_keys = _ctype_key_value(key, out)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/kvstore.py\", line 65, in _ctype_key_value\n    return (c_keys, c_handle_array(vals), use_str_keys)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/base.py\", line 409, in c_handle_array\n    arr[:] = [o.handle for o in objs]\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/base.py\", line 409, in <listcomp>\n    arr[:] = [o.handle for o in objs]\nKeyboardInterrupt\n"
  },
  {
    "path": "face_recognition/models/MS1Mretina-polynet50-arcface/model-symbol.json",
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  {
    "path": "face_recognition/models/MS1Mretina-resnet50-arcface/log_retina_r50_arcface.txt",
    "content": "gpu num: 8\nprefix ./models/r50-arcface-retina/model\nimage_size [112, 112]\nnum_classes 93431\nCalled with argument: Namespace(batch_size=512, ckpt=3, ctx_num=8, ctx_num_classes=11679, dataset='retina', extra_model_name='', frequent=20, image_channel=3, kvstore='device', local_class_start=0, local_num_classes=93432, loss='arcface', lr=0.1, lr_steps='100000,160000,220000', models_root='./models', mom=0.9, network='r50', per_batch_size=64, pretrained='', pretrained_epoch=1, rescale_threshold=0, verbose=2000, wd=0.0005, worker_id=0) {'bn_mom': 0.9, 'workspace': 256, 'emb_size': 512, 'ckpt_embedding': True, 'net_se': 0, 'net_act': 'prelu', 'net_unit': 3, 'net_input': 1, 'net_blocks': [1, 4, 6, 2], 'net_output': 'E', 'net_multiplier': 1.0, 'val_targets': ['lfw', 'cfp_fp', 'agedb_30'], 'ce_loss': True, 'fc7_lr_mult': 1.0, 'fc7_wd_mult': 1.0, 'fc7_no_bias': False, 'max_steps': 0, 'data_rand_mirror': True, 'data_cutoff': False, 'data_color': 0, 'data_images_filter': 0, 'count_flops': True, 'memonger': False, 'loss_name': 'margin_softmax', 'loss_s': 64.0, 'loss_m1': 1.0, 'loss_m2': 0.5, 'loss_m3': 0.0, 'net_name': 'fresnet', 'num_layers': 50, 'dataset': 'retina', 'dataset_path': '../datasets/ms1m-retinaface', 'num_classes': 93431, 'image_shape': [112, 112, 3], 'loss': 'arcface', 'network': 'r50', 'num_workers': 1, 'batch_size': 512, 'per_batch_size': 64}\n0 1 E 3 prelu False\nNetwork FLOPs: 12.6G\nINFO:root:loading recordio ../datasets/ms1m-retinaface/train.rec...\nheader0 label [5179511. 5272942.]\nid2range 93431\n5179510\nrand_mirror True\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\n(12000, 3, 112, 112)\nver lfw\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\nloading bin 12000\nloading bin 13000\n(14000, 3, 112, 112)\nver cfp_fp\nloading bin 0\nloading bin 1000\nloading bin 2000\nloading bin 3000\nloading bin 4000\nloading bin 5000\nloading bin 6000\nloading bin 7000\nloading bin 8000\nloading bin 9000\nloading bin 10000\nloading bin 11000\n(12000, 3, 112, 112)\nver agedb_30\nlr_steps [100000, 160000, 220000]\ncall reset()in_bind\n False [('data', (512, 3, 112, 112))] [('softmax_label', (512,))]\n_data_shape (512, 3, 112, 112) [('softmax_label', (512,))]\n[18:18:28] src/kvstore/././comm.h:744: only 0 out of 56 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:28] src/kvstore/././comm.h:753: ........\n[18:18:29] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (set the environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)\nINFO:root:Iter[0] Batch [20]\tSpeed: 523.28 samples/sec\nINFO:root:Iter[0] Batch [40]\tSpeed: 580.59 samples/sec\nINFO:root:Iter[0] Batch [60]\tSpeed: 591.03 samples/sec\nINFO:root:Iter[0] Batch [80]\tSpeed: 604.40 samples/sec\nINFO:root:Iter[0] Batch [100]\tSpeed: 568.77 samples/sec\nINFO:root:Iter[0] Batch [120]\tSpeed: 556.98 samples/sec\nINFO:root:Iter[0] Batch [140]\tSpeed: 558.32 samples/sec\nINFO:root:Iter[0] Batch [160]\tSpeed: 578.28 samples/sec\nINFO:root:Iter[0] Batch [180]\tSpeed: 581.61 samples/sec\nINFO:root:Iter[0] Batch [200]\tSpeed: 579.75 samples/sec\nINFO:root:Iter[0] Batch [220]\tSpeed: 535.07 samples/sec\nINFO:root:Iter[0] Batch [240]\tSpeed: 553.90 samples/sec\nINFO:root:Iter[0] Batch [260]\tSpeed: 576.64 samples/sec\nINFO:root:Iter[0] Batch [280]\tSpeed: 560.29 samples/sec\nINFO:root:Iter[0] Batch [300]\tSpeed: 571.43 samples/sec\nINFO:root:Iter[0] Batch [320]\tSpeed: 593.56 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1999 0\ntesting verification..\n(12000, 512)\ninfer time 4.409407000000002\n[lfw][2000]XNorm: 21.780299\n[lfw][2000]Accuracy-Flip: 0.93867+-0.00945\ntesting verification..\n(14000, 512)\ninfer time 4.746598999999999\n[cfp_fp][2000]XNorm: 18.694772\n[cfp_fp][2000]Accuracy-Flip: 0.74800+-0.01314\ntesting verification..\n(12000, 512)\ninfer time 4.061450000000001\n[agedb_30][2000]XNorm: 20.538235\n[agedb_30][2000]Accuracy-Flip: 0.77350+-0.02001\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[2000]Accuracy-Highest: 0.77350\nINFO:root:Iter[0] Batch [2000]\tSpeed: 265.37 samples/sec\nINFO:root:Iter[0] Batch [2020]\tSpeed: 855.82 samples/sec\nINFO:root:Iter[0] Batch [2040]\tSpeed: 786.01 samples/sec\nINFO:root:Iter[0] Batch [2060]\tSpeed: 842.24 samples/sec\nINFO:root:Iter[0] Batch [2080]\tSpeed: 789.51 samples/sec\nINFO:root:Iter[0] Batch [2100]\tSpeed: 815.07 samples/sec\nINFO:root:Iter[0] Batch [2120]\tSpeed: 862.04 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verification..\n(12000, 512)\ninfer time 4.1605810000000005\n[lfw][12000]XNorm: 22.699552\n[lfw][12000]Accuracy-Flip: 0.99167+-0.00387\ntesting verification..\n(14000, 512)\ninfer time 5.022702\n[cfp_fp][12000]XNorm: 20.320430\n[cfp_fp][12000]Accuracy-Flip: 0.93171+-0.01430\ntesting verification..\n(12000, 512)\ninfer time 4.043082\n[agedb_30][12000]XNorm: 22.693639\n[agedb_30][12000]Accuracy-Flip: 0.93800+-0.01542\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[12000]Accuracy-Highest: 0.93800\nINFO:root:Iter[1] Batch [1900]\tSpeed: 263.49 samples/sec\nINFO:root:Iter[1] Batch [1920]\tSpeed: 845.92 samples/sec\nINFO:root:Iter[1] Batch [1940]\tSpeed: 818.03 samples/sec\nINFO:root:Iter[1] Batch [1960]\tSpeed: 820.49 samples/sec\nINFO:root:Iter[1] Batch [1980]\tSpeed: 798.45 samples/sec\nINFO:root:Iter[1] Batch [2000]\tSpeed: 867.20 samples/sec\nINFO:root:Iter[1] Batch [2020]\tSpeed: 776.90 samples/sec\nINFO:root:Iter[1] Batch [2040]\tSpeed: 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5.5791340000000025\n[cfp_fp][66000]XNorm: 19.670671\n[cfp_fp][66000]Accuracy-Flip: 0.94771+-0.01127\ntesting verification..\n(12000, 512)\ninfer time 4.826205000000001\n[agedb_30][66000]XNorm: 21.799397\n[agedb_30][66000]Accuracy-Flip: 0.95883+-0.00873\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[66000]Accuracy-Highest: 0.95883\nINFO:root:Iter[6] Batch [5320]\tSpeed: 190.55 samples/sec\nINFO:root:Iter[6] Batch [5340]\tSpeed: 568.29 samples/sec\nINFO:root:Iter[6] Batch [5360]\tSpeed: 558.49 samples/sec\nINFO:root:Iter[6] Batch [5380]\tSpeed: 555.72 samples/sec\nINFO:root:Iter[6] Batch [5400]\tSpeed: 562.08 samples/sec\nINFO:root:Iter[6] Batch [5420]\tSpeed: 561.61 samples/sec\nINFO:root:Iter[6] Batch [5440]\tSpeed: 555.94 samples/sec\nINFO:root:Iter[6] Batch [5460]\tSpeed: 551.72 samples/sec\nINFO:root:Iter[6] Batch [5480]\tSpeed: 545.08 samples/sec\nINFO:root:Iter[6] Batch [5500]\tSpeed: 560.38 samples/sec\nINFO:root:Iter[6] Batch 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verification..\n(12000, 512)\ninfer time 4.63958\n[lfw][86000]XNorm: 22.475337\n[lfw][86000]Accuracy-Flip: 0.99567+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 5.524654999999999\n[cfp_fp][86000]XNorm: 19.521985\n[cfp_fp][86000]Accuracy-Flip: 0.94943+-0.01163\ntesting verification..\n(12000, 512)\ninfer time 4.538101\n[agedb_30][86000]XNorm: 22.040609\n[agedb_30][86000]Accuracy-Flip: 0.95867+-0.01030\n[86000]Accuracy-Highest: 0.95883\nINFO:root:Iter[8] Batch [5080]\tSpeed: 244.87 samples/sec\nINFO:root:Iter[8] Batch [5100]\tSpeed: 700.02 samples/sec\nINFO:root:Iter[8] Batch [5120]\tSpeed: 692.61 samples/sec\nINFO:root:Iter[8] Batch [5140]\tSpeed: 676.74 samples/sec\nINFO:root:Iter[8] Batch [5160]\tSpeed: 685.91 samples/sec\nINFO:root:Iter[8] Batch [5180]\tSpeed: 666.88 samples/sec\nINFO:root:Iter[8] Batch [5200]\tSpeed: 670.17 samples/sec\nINFO:root:Iter[8] Batch [5220]\tSpeed: 654.97 samples/sec\nINFO:root:Iter[8] Batch [5240]\tSpeed: 659.38 samples/sec\nINFO:root:Iter[8] 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4.584187999999999\n[agedb_30][106000]XNorm: 22.632446\n[agedb_30][106000]Accuracy-Flip: 0.97367+-0.00666\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[106000]Accuracy-Highest: 0.97367\nINFO:root:Iter[10] Batch [4840]\tSpeed: 229.86 samples/sec\nINFO:root:Iter[10] Batch [4860]\tSpeed: 608.26 samples/sec\nINFO:root:Iter[10] Batch [4880]\tSpeed: 607.58 samples/sec\nINFO:root:Iter[10] Batch [4900]\tSpeed: 608.56 samples/sec\nINFO:root:Iter[10] Batch [4920]\tSpeed: 613.73 samples/sec\nINFO:root:Iter[10] Batch [4940]\tSpeed: 624.88 samples/sec\nINFO:root:Iter[10] Batch [4960]\tSpeed: 604.76 samples/sec\nINFO:root:Iter[10] Batch [4980]\tSpeed: 585.59 samples/sec\nINFO:root:Iter[10] Batch [5000]\tSpeed: 613.24 samples/sec\nINFO:root:Iter[10] Batch [5020]\tSpeed: 583.24 samples/sec\nINFO:root:Iter[10] Batch [5040]\tSpeed: 593.99 samples/sec\nINFO:root:Iter[10] Batch [5060]\tSpeed: 593.04 samples/sec\nINFO:root:Iter[10] Batch [5080]\tSpeed: 587.73 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8839 10\ntesting verification..\n(12000, 512)\ninfer time 4.741572000000001\n[lfw][110000]XNorm: 22.297012\n[lfw][110000]Accuracy-Flip: 0.99683+-0.00263\ntesting verification..\n(14000, 512)\ninfer time 5.615647\n[cfp_fp][110000]XNorm: 20.665292\n[cfp_fp][110000]Accuracy-Flip: 0.98057+-0.00561\ntesting verification..\n(12000, 512)\ninfer time 4.741881000000001\n[agedb_30][110000]XNorm: 22.270407\n[agedb_30][110000]Accuracy-Flip: 0.97650+-0.00736\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[110000]Accuracy-Highest: 0.97650\nINFO:root:Iter[10] Batch [8840]\tSpeed: 229.47 samples/sec\nINFO:root:Iter[10] Batch [8860]\tSpeed: 574.92 samples/sec\nINFO:root:Iter[10] Batch [8880]\tSpeed: 569.74 samples/sec\nINFO:root:Iter[10] Batch [8900]\tSpeed: 580.66 samples/sec\nINFO:root:Iter[10] Batch [8920]\tSpeed: 577.36 samples/sec\nINFO:root:Iter[10] Batch [8940]\tSpeed: 588.25 samples/sec\nINFO:root:Iter[10] Batch [8960]\tSpeed: 583.13 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21.033314\n[cfp_fp][112000]Accuracy-Flip: 0.98229+-0.00617\ntesting verification..\n(12000, 512)\ninfer time 4.698333000000001\n[agedb_30][112000]XNorm: 22.653178\n[agedb_30][112000]Accuracy-Flip: 0.97367+-0.00799\n[112000]Accuracy-Highest: 0.97650\nINFO:root:Iter[11] Batch [740]\tSpeed: 238.94 samples/sec\nINFO:root:Iter[11] Batch [760]\tSpeed: 606.35 samples/sec\nINFO:root:Iter[11] Batch [780]\tSpeed: 594.88 samples/sec\nINFO:root:Iter[11] Batch [800]\tSpeed: 598.95 samples/sec\nINFO:root:Iter[11] Batch [820]\tSpeed: 603.97 samples/sec\nINFO:root:Iter[11] Batch [840]\tSpeed: 591.64 samples/sec\nINFO:root:Iter[11] Batch [860]\tSpeed: 597.79 samples/sec\nINFO:root:Iter[11] Batch [880]\tSpeed: 587.20 samples/sec\nINFO:root:Iter[11] Batch [900]\tSpeed: 594.20 samples/sec\nINFO:root:Iter[11] Batch [920]\tSpeed: 582.27 samples/sec\nINFO:root:Iter[11] Batch [940]\tSpeed: 584.67 samples/sec\nINFO:root:Iter[11] Batch [960]\tSpeed: 587.48 samples/sec\nINFO:root:Iter[11] Batch [980]\tSpeed: 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samples/sec\nINFO:root:Iter[11] Batch [6540]\tSpeed: 588.73 samples/sec\nINFO:root:Iter[11] Batch [6560]\tSpeed: 585.72 samples/sec\nINFO:root:Iter[11] Batch [6580]\tSpeed: 582.05 samples/sec\nINFO:root:Iter[11] Batch [6600]\tSpeed: 596.01 samples/sec\nINFO:root:Iter[11] Batch [6620]\tSpeed: 563.80 samples/sec\nINFO:root:Iter[11] Batch [6640]\tSpeed: 585.65 samples/sec\nINFO:root:Iter[11] Batch [6660]\tSpeed: 587.75 samples/sec\nINFO:root:Iter[11] Batch [6680]\tSpeed: 583.64 samples/sec\nINFO:root:Iter[11] Batch [6700]\tSpeed: 596.08 samples/sec\nINFO:root:Iter[11] Batch [6720]\tSpeed: 573.78 samples/sec\n{fc7_acc} 118000 0.40234375\nlr-batch-epoch: 0.010000000000000002 6723 11\ntesting verification..\n(12000, 512)\ninfer time 4.751512000000001\n[lfw][118000]XNorm: 22.677340\n[lfw][118000]Accuracy-Flip: 0.99750+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 5.538938999999998\n[cfp_fp][118000]XNorm: 20.836081\n[cfp_fp][118000]Accuracy-Flip: 0.98029+-0.00663\ntesting verification..\n(12000, 512)\ninfer time 4.806184\n[agedb_30][118000]XNorm: 22.510237\n[agedb_30][118000]Accuracy-Flip: 0.97417+-0.00704\n[118000]Accuracy-Highest: 0.97650\nINFO:root:Iter[11] Batch [6740]\tSpeed: 228.41 samples/sec\nINFO:root:Iter[11] Batch [6760]\tSpeed: 577.42 samples/sec\nINFO:root:Iter[11] Batch [6780]\tSpeed: 566.35 samples/sec\nINFO:root:Iter[11] Batch [6800]\tSpeed: 563.56 samples/sec\nINFO:root:Iter[11] Batch [6820]\tSpeed: 538.48 samples/sec\nINFO:root:Iter[11] Batch [6840]\tSpeed: 569.54 samples/sec\nINFO:root:Iter[11] Batch [6860]\tSpeed: 563.91 samples/sec\nINFO:root:Iter[11] Batch [6880]\tSpeed: 571.61 samples/sec\nINFO:root:Iter[11] Batch [6900]\tSpeed: 568.29 samples/sec\nINFO:root:Iter[11] Batch [6920]\tSpeed: 569.89 samples/sec\nINFO:root:Iter[11] Batch [6940]\tSpeed: 559.01 samples/sec\nINFO:root:Iter[11] Batch [6960]\tSpeed: 560.94 samples/sec\nINFO:root:Iter[11] Batch [6980]\tSpeed: 551.60 samples/sec\nINFO:root:Iter[11] Batch [7000]\tSpeed: 589.77 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samples/sec\nINFO:root:Iter[11] Batch [10100]\tSpeed: 602.78 samples/sec\nINFO:root:Epoch[11] Time cost=8975.600\ncall reset()\nINFO:root:Iter[12] Batch [20]\tSpeed: 599.19 samples/sec\nINFO:root:Iter[12] Batch [40]\tSpeed: 602.04 samples/sec\nINFO:root:Iter[12] Batch [60]\tSpeed: 605.02 samples/sec\nINFO:root:Iter[12] Batch [80]\tSpeed: 593.15 samples/sec\nINFO:root:Iter[12] Batch [100]\tSpeed: 594.78 samples/sec\nINFO:root:Iter[12] Batch [120]\tSpeed: 598.74 samples/sec\nINFO:root:Iter[12] Batch [140]\tSpeed: 608.03 samples/sec\nINFO:root:Iter[12] Batch [160]\tSpeed: 589.29 samples/sec\nINFO:root:Iter[12] Batch [180]\tSpeed: 591.06 samples/sec\nINFO:root:Iter[12] Batch [200]\tSpeed: 594.61 samples/sec\nINFO:root:Iter[12] Batch [220]\tSpeed: 597.00 samples/sec\nINFO:root:Iter[12] Batch [240]\tSpeed: 585.88 samples/sec\nINFO:root:Iter[12] Batch [260]\tSpeed: 599.24 samples/sec\nINFO:root:Iter[12] Batch [280]\tSpeed: 616.78 samples/sec\nINFO:root:Iter[12] Batch [300]\tSpeed: 611.80 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verification..\n(12000, 512)\ninfer time 4.466574\n[lfw][122000]XNorm: 22.985673\n[lfw][122000]Accuracy-Flip: 0.99733+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 5.522776\n[cfp_fp][122000]XNorm: 21.301214\n[cfp_fp][122000]Accuracy-Flip: 0.97986+-0.00551\ntesting verification..\n(12000, 512)\ninfer time 4.732594\n[agedb_30][122000]XNorm: 22.473095\n[agedb_30][122000]Accuracy-Flip: 0.97550+-0.00898\n[122000]Accuracy-Highest: 0.97650\nINFO:root:Iter[12] Batch [620]\tSpeed: 237.37 samples/sec\nINFO:root:Iter[12] Batch [640]\tSpeed: 570.74 samples/sec\nINFO:root:Iter[12] Batch [660]\tSpeed: 576.02 samples/sec\nINFO:root:Iter[12] Batch [680]\tSpeed: 566.22 samples/sec\nINFO:root:Iter[12] Batch [700]\tSpeed: 572.50 samples/sec\nINFO:root:Iter[12] Batch [720]\tSpeed: 559.78 samples/sec\nINFO:root:Iter[12] Batch [740]\tSpeed: 566.02 samples/sec\nINFO:root:Iter[12] Batch [760]\tSpeed: 558.75 samples/sec\nINFO:root:Iter[12] Batch [780]\tSpeed: 579.32 samples/sec\nINFO:root:Iter[12] 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22.677550\n[agedb_30][124000]Accuracy-Flip: 0.97467+-0.00670\n[124000]Accuracy-Highest: 0.97650\nINFO:root:Iter[12] Batch [2620]\tSpeed: 232.39 samples/sec\nINFO:root:Iter[12] Batch [2640]\tSpeed: 560.10 samples/sec\nINFO:root:Iter[12] Batch [2660]\tSpeed: 569.12 samples/sec\nINFO:root:Iter[12] Batch [2680]\tSpeed: 574.07 samples/sec\nINFO:root:Iter[12] Batch [2700]\tSpeed: 565.43 samples/sec\nINFO:root:Iter[12] Batch [2720]\tSpeed: 549.31 samples/sec\nINFO:root:Iter[12] Batch [2740]\tSpeed: 589.04 samples/sec\nINFO:root:Iter[12] Batch [2760]\tSpeed: 567.31 samples/sec\nINFO:root:Iter[12] Batch [2780]\tSpeed: 549.61 samples/sec\nINFO:root:Iter[12] Batch [2800]\tSpeed: 564.00 samples/sec\nINFO:root:Iter[12] Batch [2820]\tSpeed: 555.34 samples/sec\nINFO:root:Iter[12] Batch [2840]\tSpeed: 566.37 samples/sec\nINFO:root:Iter[12] Batch [2860]\tSpeed: 588.59 samples/sec\nINFO:root:Iter[12] Batch [2880]\tSpeed: 574.10 samples/sec\nINFO:root:Iter[12] Batch [2900]\tSpeed: 582.45 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samples/sec\nINFO:root:Iter[12] Batch [6340]\tSpeed: 615.00 samples/sec\nINFO:root:Iter[12] Batch [6360]\tSpeed: 592.14 samples/sec\nINFO:root:Iter[12] Batch [6380]\tSpeed: 585.34 samples/sec\nINFO:root:Iter[12] Batch [6400]\tSpeed: 589.44 samples/sec\nINFO:root:Iter[12] Batch [6420]\tSpeed: 593.20 samples/sec\nINFO:root:Iter[12] Batch [6440]\tSpeed: 606.49 samples/sec\nINFO:root:Iter[12] Batch [6460]\tSpeed: 86.88 samples/sec\nINFO:root:Iter[12] Batch [6480]\tSpeed: 67.97 samples/sec\nINFO:root:Iter[12] Batch [6500]\tSpeed: 72.60 samples/sec\nINFO:root:Iter[12] Batch [6520]\tSpeed: 70.74 samples/sec\nINFO:root:Iter[12] Batch [6540]\tSpeed: 71.79 samples/sec\nINFO:root:Iter[12] Batch [6560]\tSpeed: 75.25 samples/sec\nINFO:root:Iter[12] Batch [6580]\tSpeed: 78.05 samples/sec\nINFO:root:Iter[12] Batch [6600]\tSpeed: 91.46 samples/sec\n{fc7_acc} 128000 0.3828125\nlr-batch-epoch: 0.010000000000000002 6607 12\ntesting verification..\n(12000, 512)\ninfer time 5.707871000000002\n[lfw][128000]XNorm: 22.870186\n[lfw][128000]Accuracy-Flip: 0.99633+-0.00233\ntesting verification..\n(14000, 512)\ninfer time 5.441767999999999\n[cfp_fp][128000]XNorm: 21.229069\n[cfp_fp][128000]Accuracy-Flip: 0.98286+-0.00546\ntesting verification..\n(12000, 512)\ninfer time 4.7241\n[agedb_30][128000]XNorm: 22.650301\n[agedb_30][128000]Accuracy-Flip: 0.97583+-0.00793\n[128000]Accuracy-Highest: 0.97650\nINFO:root:Iter[12] Batch [6620]\tSpeed: 145.79 samples/sec\nINFO:root:Iter[12] Batch [6640]\tSpeed: 571.87 samples/sec\nINFO:root:Iter[12] Batch [6660]\tSpeed: 566.59 samples/sec\nINFO:root:Iter[12] Batch [6680]\tSpeed: 570.51 samples/sec\nINFO:root:Iter[12] Batch [6700]\tSpeed: 566.87 samples/sec\nINFO:root:Iter[12] Batch [6720]\tSpeed: 577.21 samples/sec\nINFO:root:Iter[12] Batch [6740]\tSpeed: 566.88 samples/sec\nINFO:root:Iter[12] Batch [6760]\tSpeed: 581.46 samples/sec\nINFO:root:Iter[12] Batch [6780]\tSpeed: 590.16 samples/sec\nINFO:root:Iter[12] Batch 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samples/sec\nINFO:root:Iter[13] Batch [2240]\tSpeed: 610.73 samples/sec\nINFO:root:Iter[13] Batch [2260]\tSpeed: 594.51 samples/sec\nINFO:root:Iter[13] Batch [2280]\tSpeed: 612.98 samples/sec\nINFO:root:Iter[13] Batch [2300]\tSpeed: 597.59 samples/sec\nINFO:root:Iter[13] Batch [2320]\tSpeed: 597.65 samples/sec\nINFO:root:Iter[13] Batch [2340]\tSpeed: 595.38 samples/sec\nINFO:root:Iter[13] Batch [2360]\tSpeed: 595.55 samples/sec\nINFO:root:Iter[13] Batch [2380]\tSpeed: 600.97 samples/sec\nINFO:root:Iter[13] Batch [2400]\tSpeed: 609.54 samples/sec\nINFO:root:Iter[13] Batch [2420]\tSpeed: 599.75 samples/sec\nINFO:root:Iter[13] Batch [2440]\tSpeed: 616.53 samples/sec\nINFO:root:Iter[13] Batch [2460]\tSpeed: 602.85 samples/sec\nINFO:root:Iter[13] Batch [2480]\tSpeed: 589.78 samples/sec\n{fc7_acc} 134000 0.35351562\nlr-batch-epoch: 0.010000000000000002 2491 13\ntesting verification..\n(12000, 512)\ninfer time 4.402627000000001\n[lfw][134000]XNorm: 21.994982\n[lfw][134000]Accuracy-Flip: 0.99733+-0.00351\ntesting verification..\n(14000, 512)\ninfer time 5.678553\n[cfp_fp][134000]XNorm: 20.484188\n[cfp_fp][134000]Accuracy-Flip: 0.98086+-0.00520\ntesting verification..\n(12000, 512)\ninfer time 4.694464000000001\n[agedb_30][134000]XNorm: 21.956600\n[agedb_30][134000]Accuracy-Flip: 0.97650+-0.00747\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[134000]Accuracy-Highest: 0.97650\nINFO:root:Iter[13] Batch [2500]\tSpeed: 232.36 samples/sec\nINFO:root:Iter[13] Batch [2520]\tSpeed: 579.60 samples/sec\nINFO:root:Iter[13] Batch [2540]\tSpeed: 577.99 samples/sec\nINFO:root:Iter[13] Batch [2560]\tSpeed: 563.34 samples/sec\nINFO:root:Iter[13] Batch [2580]\tSpeed: 557.64 samples/sec\nINFO:root:Iter[13] Batch [2600]\tSpeed: 560.16 samples/sec\nINFO:root:Iter[13] Batch [2620]\tSpeed: 568.21 samples/sec\nINFO:root:Iter[13] Batch [2640]\tSpeed: 582.11 samples/sec\nINFO:root:Iter[13] Batch [2660]\tSpeed: 582.89 samples/sec\nINFO:root:Iter[13] 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samples/sec\nINFO:root:Iter[14] Batch [2340]\tSpeed: 614.56 samples/sec\nINFO:root:Iter[14] Batch [2360]\tSpeed: 626.02 samples/sec\n{fc7_acc} 144000 0.38085938\nlr-batch-epoch: 0.010000000000000002 2375 14\ntesting verification..\n(12000, 512)\ninfer time 4.659367\n[lfw][144000]XNorm: 22.737742\n[lfw][144000]Accuracy-Flip: 0.99667+-0.00365\ntesting verification..\n(14000, 512)\ninfer time 5.400553000000001\n[cfp_fp][144000]XNorm: 21.160134\n[cfp_fp][144000]Accuracy-Flip: 0.98014+-0.00627\ntesting verification..\n(12000, 512)\ninfer time 4.036646999999999\n[agedb_30][144000]XNorm: 22.582316\n[agedb_30][144000]Accuracy-Flip: 0.97750+-0.00638\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[144000]Accuracy-Highest: 0.97750\nINFO:root:Iter[14] Batch [2380]\tSpeed: 242.91 samples/sec\nINFO:root:Iter[14] Batch [2400]\tSpeed: 772.32 samples/sec\nINFO:root:Iter[14] Batch [2420]\tSpeed: 735.79 samples/sec\nINFO:root:Iter[14] Batch [2440]\tSpeed: 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samples/sec\nINFO:root:Iter[14] Batch [10100]\tSpeed: 630.75 samples/sec\nINFO:root:Epoch[14] Time cost=8295.043\ncall reset()\nINFO:root:Iter[15] Batch [20]\tSpeed: 667.22 samples/sec\nINFO:root:Iter[15] Batch [40]\tSpeed: 627.42 samples/sec\nINFO:root:Iter[15] Batch [60]\tSpeed: 614.44 samples/sec\nINFO:root:Iter[15] Batch [80]\tSpeed: 627.35 samples/sec\nINFO:root:Iter[15] Batch [100]\tSpeed: 618.88 samples/sec\nINFO:root:Iter[15] Batch [120]\tSpeed: 655.73 samples/sec\nINFO:root:Iter[15] Batch [140]\tSpeed: 640.13 samples/sec\nINFO:root:Iter[15] Batch [160]\tSpeed: 612.55 samples/sec\nINFO:root:Iter[15] Batch [180]\tSpeed: 628.54 samples/sec\nINFO:root:Iter[15] Batch [200]\tSpeed: 624.67 samples/sec\nINFO:root:Iter[15] Batch [220]\tSpeed: 624.77 samples/sec\nINFO:root:Iter[15] Batch [240]\tSpeed: 616.06 samples/sec\n{fc7_acc} 152000 0.42578125\nlr-batch-epoch: 0.010000000000000002 259 15\ntesting verification..\n(12000, 512)\ninfer time 4.533885999999999\n[lfw][152000]XNorm: 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samples/sec\nINFO:root:Iter[16] Batch [120]\tSpeed: 592.77 samples/sec\nINFO:root:Iter[16] Batch [140]\tSpeed: 605.99 samples/sec\n{fc7_acc} 162000 0.5566406\nlr-batch-epoch: 0.0010000000000000002 143 16\ntesting verification..\n(12000, 512)\ninfer time 4.580578000000002\n[lfw][162000]XNorm: 22.549730\n[lfw][162000]Accuracy-Flip: 0.99667+-0.00365\ntesting verification..\n(14000, 512)\ninfer time 5.459996999999999\n[cfp_fp][162000]XNorm: 21.397352\n[cfp_fp][162000]Accuracy-Flip: 0.98614+-0.00320\ntesting verification..\n(12000, 512)\ninfer time 4.389001999999998\n[agedb_30][162000]XNorm: 22.674503\n[agedb_30][162000]Accuracy-Flip: 0.97833+-0.00715\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[162000]Accuracy-Highest: 0.97833\nINFO:root:Iter[16] Batch [160]\tSpeed: 246.51 samples/sec\nINFO:root:Iter[16] Batch [180]\tSpeed: 751.21 samples/sec\nINFO:root:Iter[16] Batch [200]\tSpeed: 717.94 samples/sec\nINFO:root:Iter[16] Batch [220]\tSpeed: 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samples/sec\nINFO:root:Iter[16] Batch [1880]\tSpeed: 609.38 samples/sec\nINFO:root:Iter[16] Batch [1900]\tSpeed: 595.63 samples/sec\nINFO:root:Iter[16] Batch [1920]\tSpeed: 607.24 samples/sec\nINFO:root:Iter[16] Batch [1940]\tSpeed: 610.05 samples/sec\nINFO:root:Iter[16] Batch [1960]\tSpeed: 635.34 samples/sec\nINFO:root:Iter[16] Batch [1980]\tSpeed: 609.83 samples/sec\nINFO:root:Iter[16] Batch [2000]\tSpeed: 597.80 samples/sec\nINFO:root:Iter[16] Batch [2020]\tSpeed: 610.56 samples/sec\nINFO:root:Iter[16] Batch [2040]\tSpeed: 623.07 samples/sec\nINFO:root:Iter[16] Batch [2060]\tSpeed: 615.91 samples/sec\nINFO:root:Iter[16] Batch [2080]\tSpeed: 611.28 samples/sec\nINFO:root:Iter[16] Batch [2100]\tSpeed: 606.68 samples/sec\nINFO:root:Iter[16] Batch [2120]\tSpeed: 619.37 samples/sec\nINFO:root:Iter[16] Batch [2140]\tSpeed: 626.26 samples/sec\n{fc7_acc} 164000 0.5644531\nlr-batch-epoch: 0.0010000000000000002 2143 16\ntesting verification..\n(12000, 512)\ninfer time 4.534564\n[lfw][164000]XNorm: 22.454099\n[lfw][164000]Accuracy-Flip: 0.99800+-0.00221\ntesting verification..\n(14000, 512)\ninfer time 5.473105000000001\n[cfp_fp][164000]XNorm: 21.505601\n[cfp_fp][164000]Accuracy-Flip: 0.98571+-0.00373\ntesting verification..\n(12000, 512)\ninfer time 4.781446\n[agedb_30][164000]XNorm: 22.757843\n[agedb_30][164000]Accuracy-Flip: 0.97883+-0.00711\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[164000]Accuracy-Highest: 0.97883\nINFO:root:Iter[16] Batch [2160]\tSpeed: 244.47 samples/sec\nINFO:root:Iter[16] Batch [2180]\tSpeed: 718.18 samples/sec\nINFO:root:Iter[16] Batch [2200]\tSpeed: 621.95 samples/sec\nINFO:root:Iter[16] Batch [2220]\tSpeed: 665.88 samples/sec\nINFO:root:Iter[16] Batch [2240]\tSpeed: 678.97 samples/sec\nINFO:root:Iter[16] Batch [2260]\tSpeed: 674.64 samples/sec\nINFO:root:Iter[16] Batch [2280]\tSpeed: 665.88 samples/sec\nINFO:root:Iter[16] Batch [2300]\tSpeed: 666.06 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samples/sec\nINFO:root:Iter[16] Batch [3960]\tSpeed: 612.47 samples/sec\nINFO:root:Iter[16] Batch [3980]\tSpeed: 609.52 samples/sec\nINFO:root:Iter[16] Batch [4000]\tSpeed: 605.37 samples/sec\nINFO:root:Iter[16] Batch [4020]\tSpeed: 636.57 samples/sec\nINFO:root:Iter[16] Batch [4040]\tSpeed: 611.86 samples/sec\nINFO:root:Iter[16] Batch [4060]\tSpeed: 647.61 samples/sec\nINFO:root:Iter[16] Batch [4080]\tSpeed: 605.27 samples/sec\nINFO:root:Iter[16] Batch [4100]\tSpeed: 616.15 samples/sec\nINFO:root:Iter[16] Batch [4120]\tSpeed: 649.78 samples/sec\nINFO:root:Iter[16] Batch [4140]\tSpeed: 623.27 samples/sec\n{fc7_acc} 166000 0.62109375\nlr-batch-epoch: 0.0010000000000000002 4143 16\ntesting verification..\n(12000, 512)\ninfer time 4.581860999999999\n[lfw][166000]XNorm: 22.590786\n[lfw][166000]Accuracy-Flip: 0.99767+-0.00238\ntesting verification..\n(14000, 512)\ninfer time 5.272571000000002\n[cfp_fp][166000]XNorm: 21.677773\n[cfp_fp][166000]Accuracy-Flip: 0.98714+-0.00271\ntesting verification..\n(12000, 512)\ninfer time 4.427877\n[agedb_30][166000]XNorm: 22.724973\n[agedb_30][166000]Accuracy-Flip: 0.97817+-0.00717\n[166000]Accuracy-Highest: 0.97883\nINFO:root:Iter[16] Batch [4160]\tSpeed: 253.29 samples/sec\nINFO:root:Iter[16] Batch [4180]\tSpeed: 692.72 samples/sec\nINFO:root:Iter[16] Batch [4200]\tSpeed: 664.88 samples/sec\nINFO:root:Iter[16] Batch [4220]\tSpeed: 637.52 samples/sec\nINFO:root:Iter[16] Batch [4240]\tSpeed: 642.40 samples/sec\nINFO:root:Iter[16] Batch [4260]\tSpeed: 614.27 samples/sec\nINFO:root:Iter[16] Batch [4280]\tSpeed: 623.61 samples/sec\nINFO:root:Iter[16] Batch [4300]\tSpeed: 662.40 samples/sec\nINFO:root:Iter[16] Batch [4320]\tSpeed: 610.89 samples/sec\nINFO:root:Iter[16] Batch [4340]\tSpeed: 625.47 samples/sec\nINFO:root:Iter[16] Batch [4360]\tSpeed: 639.18 samples/sec\nINFO:root:Iter[16] Batch [4380]\tSpeed: 629.22 samples/sec\nINFO:root:Iter[16] Batch [4400]\tSpeed: 612.20 samples/sec\nINFO:root:Iter[16] Batch [4420]\tSpeed: 619.70 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samples/sec\nINFO:root:Iter[16] Batch [6080]\tSpeed: 636.28 samples/sec\nINFO:root:Iter[16] Batch [6100]\tSpeed: 619.28 samples/sec\nINFO:root:Iter[16] Batch [6120]\tSpeed: 626.96 samples/sec\nINFO:root:Iter[16] Batch [6140]\tSpeed: 623.12 samples/sec\n{fc7_acc} 168000 0.60546875\nlr-batch-epoch: 0.0010000000000000002 6143 16\ntesting verification..\n(12000, 512)\ninfer time 4.629511\n[lfw][168000]XNorm: 22.362449\n[lfw][168000]Accuracy-Flip: 0.99717+-0.00269\ntesting verification..\n(14000, 512)\ninfer time 5.129416\n[cfp_fp][168000]XNorm: 21.610070\n[cfp_fp][168000]Accuracy-Flip: 0.98700+-0.00426\ntesting verification..\n(12000, 512)\ninfer time 4.524317999999999\n[agedb_30][168000]XNorm: 22.744910\n[agedb_30][168000]Accuracy-Flip: 0.98067+-0.00739\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[168000]Accuracy-Highest: 0.98067\nINFO:root:Iter[16] Batch [6160]\tSpeed: 243.97 samples/sec\nINFO:root:Iter[16] Batch [6180]\tSpeed: 651.98 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0.65234375\nlr-batch-epoch: 0.0010000000000000002 8143 16\ntesting verification..\n(12000, 512)\ninfer time 4.613684\n[lfw][170000]XNorm: 22.287266\n[lfw][170000]Accuracy-Flip: 0.99750+-0.00239\ntesting verification..\n(14000, 512)\ninfer time 5.279231000000001\n[cfp_fp][170000]XNorm: 21.554854\n[cfp_fp][170000]Accuracy-Flip: 0.98671+-0.00286\ntesting verification..\n(12000, 512)\ninfer time 4.576589\n[agedb_30][170000]XNorm: 22.741497\n[agedb_30][170000]Accuracy-Flip: 0.97950+-0.00633\n[170000]Accuracy-Highest: 0.98067\nINFO:root:Iter[16] Batch [8160]\tSpeed: 253.85 samples/sec\nINFO:root:Iter[16] Batch [8180]\tSpeed: 699.55 samples/sec\nINFO:root:Iter[16] Batch [8200]\tSpeed: 648.99 samples/sec\nINFO:root:Iter[16] Batch [8220]\tSpeed: 661.29 samples/sec\nINFO:root:Iter[16] Batch [8240]\tSpeed: 660.31 samples/sec\nINFO:root:Iter[16] Batch [8260]\tSpeed: 663.09 samples/sec\nINFO:root:Iter[16] Batch [8280]\tSpeed: 646.71 samples/sec\nINFO:root:Iter[16] Batch [8300]\tSpeed: 677.71 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verification..\n(12000, 512)\ninfer time 4.733766000000001\n[agedb_30][172000]XNorm: 22.667121\n[agedb_30][172000]Accuracy-Flip: 0.98117+-0.00699\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[172000]Accuracy-Highest: 0.98117\nINFO:root:Iter[17] Batch [40]\tSpeed: 238.65 samples/sec\nINFO:root:Iter[17] Batch [60]\tSpeed: 633.26 samples/sec\nINFO:root:Iter[17] Batch [80]\tSpeed: 623.28 samples/sec\nINFO:root:Iter[17] Batch [100]\tSpeed: 637.71 samples/sec\nINFO:root:Iter[17] Batch [120]\tSpeed: 674.50 samples/sec\nINFO:root:Iter[17] Batch [140]\tSpeed: 652.26 samples/sec\nINFO:root:Iter[17] Batch [160]\tSpeed: 616.79 samples/sec\nINFO:root:Iter[17] Batch [180]\tSpeed: 628.91 samples/sec\nINFO:root:Iter[17] Batch [200]\tSpeed: 639.29 samples/sec\nINFO:root:Iter[17] Batch [220]\tSpeed: 639.44 samples/sec\nINFO:root:Iter[17] Batch [240]\tSpeed: 635.07 samples/sec\nINFO:root:Iter[17] Batch [260]\tSpeed: 607.55 samples/sec\nINFO:root:Iter[17] 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0.6230469\nlr-batch-epoch: 0.0010000000000000002 4027 17\ntesting verification..\n(12000, 512)\ninfer time 4.625084\n[lfw][176000]XNorm: 22.324362\n[lfw][176000]Accuracy-Flip: 0.99750+-0.00239\ntesting verification..\n(14000, 512)\ninfer time 5.5593379999999994\n[cfp_fp][176000]XNorm: 21.744170\n[cfp_fp][176000]Accuracy-Flip: 0.98757+-0.00264\ntesting verification..\n(12000, 512)\ninfer time 4.696576000000001\n[agedb_30][176000]XNorm: 22.776666\n[agedb_30][176000]Accuracy-Flip: 0.97950+-0.00628\n[176000]Accuracy-Highest: 0.98117\nINFO:root:Iter[17] Batch [4040]\tSpeed: 235.22 samples/sec\nINFO:root:Iter[17] Batch [4060]\tSpeed: 594.11 samples/sec\nINFO:root:Iter[17] Batch [4080]\tSpeed: 593.21 samples/sec\nINFO:root:Iter[17] Batch [4100]\tSpeed: 606.52 samples/sec\nINFO:root:Iter[17] Batch [4120]\tSpeed: 574.16 samples/sec\nINFO:root:Iter[17] Batch [4140]\tSpeed: 599.71 samples/sec\nINFO:root:Iter[17] Batch [4160]\tSpeed: 594.27 samples/sec\nINFO:root:Iter[17] Batch [4180]\tSpeed: 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samples/sec\n{fc7_acc} 182000 0.6582031\nlr-batch-epoch: 0.0010000000000000002 10027 17\ntesting verification..\n(12000, 512)\ninfer time 4.629121\n[lfw][182000]XNorm: 22.344922\n[lfw][182000]Accuracy-Flip: 0.99750+-0.00239\ntesting verification..\n(14000, 512)\ninfer time 5.51079\n[cfp_fp][182000]XNorm: 21.834040\n[cfp_fp][182000]Accuracy-Flip: 0.98829+-0.00318\ntesting verification..\n(12000, 512)\ninfer time 4.641132\n[agedb_30][182000]XNorm: 22.648604\n[agedb_30][182000]Accuracy-Flip: 0.97950+-0.00707\n[182000]Accuracy-Highest: 0.98133\nINFO:root:Iter[17] Batch [10040]\tSpeed: 234.47 samples/sec\nINFO:root:Iter[17] Batch [10060]\tSpeed: 597.09 samples/sec\nINFO:root:Iter[17] Batch [10080]\tSpeed: 569.12 samples/sec\nINFO:root:Iter[17] Batch [10100]\tSpeed: 585.20 samples/sec\nINFO:root:Epoch[17] Time cost=8786.149\ncall reset()\nINFO:root:Iter[18] Batch [20]\tSpeed: 582.14 samples/sec\nINFO:root:Iter[18] Batch [40]\tSpeed: 582.11 samples/sec\nINFO:root:Iter[18] Batch [60]\tSpeed: 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4.699032000000001\n[agedb_30][184000]XNorm: 22.751367\n[agedb_30][184000]Accuracy-Flip: 0.97767+-0.00841\n[184000]Accuracy-Highest: 0.98133\nINFO:root:Iter[18] Batch [1920]\tSpeed: 234.34 samples/sec\nINFO:root:Iter[18] Batch [1940]\tSpeed: 567.01 samples/sec\nINFO:root:Iter[18] Batch [1960]\tSpeed: 565.32 samples/sec\nINFO:root:Iter[18] Batch [1980]\tSpeed: 573.61 samples/sec\nINFO:root:Iter[18] Batch [2000]\tSpeed: 563.82 samples/sec\nINFO:root:Iter[18] Batch [2020]\tSpeed: 580.88 samples/sec\nINFO:root:Iter[18] Batch [2040]\tSpeed: 575.87 samples/sec\nINFO:root:Iter[18] Batch [2060]\tSpeed: 560.19 samples/sec\nINFO:root:Iter[18] Batch [2080]\tSpeed: 562.10 samples/sec\nINFO:root:Iter[18] Batch [2100]\tSpeed: 563.22 samples/sec\nINFO:root:Iter[18] Batch [2120]\tSpeed: 569.46 samples/sec\nINFO:root:Iter[18] Batch [2140]\tSpeed: 559.35 samples/sec\nINFO:root:Iter[18] Batch [2160]\tSpeed: 553.12 samples/sec\nINFO:root:Iter[18] Batch [2180]\tSpeed: 567.73 samples/sec\nINFO:root:Iter[18] 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5.982574999999998\n[lfw][188000]XNorm: 22.435840\n[lfw][188000]Accuracy-Flip: 0.99717+-0.00279\ntesting verification..\n(14000, 512)\ninfer time 5.648836999999998\n[cfp_fp][188000]XNorm: 22.102128\n[cfp_fp][188000]Accuracy-Flip: 0.98886+-0.00413\ntesting verification..\n(12000, 512)\ninfer time 4.590596\n[agedb_30][188000]XNorm: 22.899462\n[agedb_30][188000]Accuracy-Flip: 0.97933+-0.00731\n[188000]Accuracy-Highest: 0.98133\nINFO:root:Iter[18] Batch [5920]\tSpeed: 94.78 samples/sec\nINFO:root:Iter[18] Batch [5940]\tSpeed: 572.67 samples/sec\nINFO:root:Iter[18] Batch [5960]\tSpeed: 572.47 samples/sec\nINFO:root:Iter[18] Batch [5980]\tSpeed: 575.82 samples/sec\nINFO:root:Iter[18] Batch [6000]\tSpeed: 548.46 samples/sec\nINFO:root:Iter[18] Batch [6020]\tSpeed: 545.22 samples/sec\nINFO:root:Iter[18] Batch [6040]\tSpeed: 571.43 samples/sec\nINFO:root:Iter[18] Batch [6060]\tSpeed: 550.75 samples/sec\nINFO:root:Iter[18] Batch [6080]\tSpeed: 547.77 samples/sec\nINFO:root:Iter[18] Batch 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time 5.399257999999999\n[cfp_fp][200000]XNorm: 22.015812\n[cfp_fp][200000]Accuracy-Flip: 0.98857+-0.00286\ntesting verification..\n(12000, 512)\ninfer time 4.267128\n[agedb_30][200000]XNorm: 22.860042\n[agedb_30][200000]Accuracy-Flip: 0.98067+-0.00688\n[200000]Accuracy-Highest: 0.98133\nINFO:root:Iter[19] Batch [7800]\tSpeed: 245.35 samples/sec\nINFO:root:Iter[19] Batch [7820]\tSpeed: 778.82 samples/sec\nINFO:root:Iter[19] Batch [7840]\tSpeed: 744.54 samples/sec\nINFO:root:Iter[19] Batch [7860]\tSpeed: 777.41 samples/sec\nINFO:root:Iter[19] Batch [7880]\tSpeed: 694.13 samples/sec\nINFO:root:Iter[19] Batch [7900]\tSpeed: 754.40 samples/sec\nINFO:root:Iter[19] Batch [7920]\tSpeed: 688.05 samples/sec\nINFO:root:Iter[19] Batch [7940]\tSpeed: 671.86 samples/sec\nINFO:root:Iter[19] Batch [7960]\tSpeed: 697.36 samples/sec\nINFO:root:Iter[19] Batch [7980]\tSpeed: 695.93 samples/sec\nINFO:root:Iter[19] Batch [8000]\tSpeed: 667.62 samples/sec\nINFO:root:Iter[19] Batch [8020]\tSpeed: 686.99 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0.6738281\nlr-batch-epoch: 0.0010000000000000002 1679 20\ntesting verification..\n(12000, 512)\ninfer time 4.6051090000000015\n[lfw][204000]XNorm: 22.524374\n[lfw][204000]Accuracy-Flip: 0.99750+-0.00239\ntesting verification..\n(14000, 512)\ninfer time 5.069748000000001\n[cfp_fp][204000]XNorm: 22.121963\n[cfp_fp][204000]Accuracy-Flip: 0.98929+-0.00363\ntesting verification..\n(12000, 512)\ninfer time 4.34192\n[agedb_30][204000]XNorm: 23.020326\n[agedb_30][204000]Accuracy-Flip: 0.98000+-0.00860\n[204000]Accuracy-Highest: 0.98133\nINFO:root:Iter[20] Batch [1680]\tSpeed: 244.37 samples/sec\nINFO:root:Iter[20] Batch [1700]\tSpeed: 730.35 samples/sec\nINFO:root:Iter[20] Batch [1720]\tSpeed: 759.24 samples/sec\nINFO:root:Iter[20] Batch [1740]\tSpeed: 759.28 samples/sec\nINFO:root:Iter[20] Batch [1760]\tSpeed: 712.70 samples/sec\nINFO:root:Iter[20] Batch [1780]\tSpeed: 742.00 samples/sec\nINFO:root:Iter[20] Batch [1800]\tSpeed: 675.71 samples/sec\nINFO:root:Iter[20] Batch [1820]\tSpeed: 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verification..\n(12000, 512)\ninfer time 4.460148000000001\n[lfw][210000]XNorm: 22.654044\n[lfw][210000]Accuracy-Flip: 0.99717+-0.00350\ntesting verification..\n(14000, 512)\ninfer time 5.1507819999999995\n[cfp_fp][210000]XNorm: 22.314327\n[cfp_fp][210000]Accuracy-Flip: 0.98929+-0.00374\ntesting verification..\n(12000, 512)\ninfer time 4.5117709999999995\n[agedb_30][210000]XNorm: 22.986626\n[agedb_30][210000]Accuracy-Flip: 0.97967+-0.00710\n[210000]Accuracy-Highest: 0.98133\nINFO:root:Iter[20] Batch [7680]\tSpeed: 239.79 samples/sec\nINFO:root:Iter[20] Batch [7700]\tSpeed: 694.28 samples/sec\nINFO:root:Iter[20] Batch [7720]\tSpeed: 705.27 samples/sec\nINFO:root:Iter[20] Batch [7740]\tSpeed: 708.43 samples/sec\nINFO:root:Iter[20] Batch [7760]\tSpeed: 687.46 samples/sec\nINFO:root:Iter[20] Batch [7780]\tSpeed: 671.28 samples/sec\nINFO:root:Iter[20] Batch [7800]\tSpeed: 656.26 samples/sec\nINFO:root:Iter[20] Batch [7820]\tSpeed: 655.38 samples/sec\nINFO:root:Iter[20] Batch [7840]\tSpeed: 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4.621801\n[lfw][216000]XNorm: 22.304107\n[lfw][216000]Accuracy-Flip: 0.99733+-0.00309\ntesting verification..\n(14000, 512)\ninfer time 5.508092\n[cfp_fp][216000]XNorm: 21.812644\n[cfp_fp][216000]Accuracy-Flip: 0.98900+-0.00271\ntesting verification..\n(12000, 512)\ninfer time 4.541527999999999\n[agedb_30][216000]XNorm: 22.649629\n[agedb_30][216000]Accuracy-Flip: 0.98183+-0.00669\nsaving 1\nINFO:root:Saved checkpoint to \"./models/r50-arcface-retina/model-0001.params\"\n[216000]Accuracy-Highest: 0.98183\nINFO:root:Iter[21] Batch [3580]\tSpeed: 245.02 samples/sec\nINFO:root:Iter[21] Batch [3600]\tSpeed: 727.45 samples/sec\nINFO:root:Iter[21] Batch [3620]\tSpeed: 681.04 samples/sec\nINFO:root:Iter[21] Batch [3640]\tSpeed: 698.83 samples/sec\nINFO:root:Iter[21] Batch [3660]\tSpeed: 673.84 samples/sec\nINFO:root:Iter[21] Batch [3680]\tSpeed: 642.68 samples/sec\nINFO:root:Iter[21] Batch [3700]\tSpeed: 678.11 samples/sec\nINFO:root:Iter[21] Batch [3720]\tSpeed: 645.54 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samples/sec\nINFO:root:Iter[24] Batch [1200]\tSpeed: 536.45 samples/sec\n{fc7_acc} 244000 0.7128906\nlr-batch-epoch: 0.00010000000000000003 1215 24\ntesting verification..\n(12000, 512)\ninfer time 4.521699000000001\n[lfw][244000]XNorm: 22.170035\n[lfw][244000]Accuracy-Flip: 0.99750+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 5.451293\n[cfp_fp][244000]XNorm: 22.177032\n[cfp_fp][244000]Accuracy-Flip: 0.98914+-0.00301\ntesting verification..\n(12000, 512)\ninfer time 4.076279\n[agedb_30][244000]XNorm: 22.858804\n[agedb_30][244000]Accuracy-Flip: 0.97983+-0.00697\n[244000]Accuracy-Highest: 0.98183\nINFO:root:Iter[24] Batch [1220]\tSpeed: 235.11 samples/sec\nINFO:root:Iter[24] Batch [1240]\tSpeed: 870.49 samples/sec\nINFO:root:Iter[24] Batch [1260]\tSpeed: 793.61 samples/sec\nINFO:root:Iter[24] Batch [1280]\tSpeed: 691.14 samples/sec\nINFO:root:Iter[24] Batch [1300]\tSpeed: 678.57 samples/sec\nINFO:root:Iter[24] Batch [1320]\tSpeed: 736.20 samples/sec\nINFO:root:Iter[24] 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samples/sec\nINFO:root:Iter[24] Batch [2980]\tSpeed: 496.88 samples/sec\nINFO:root:Iter[24] Batch [3000]\tSpeed: 510.31 samples/sec\nINFO:root:Iter[24] Batch [3020]\tSpeed: 503.57 samples/sec\nINFO:root:Iter[24] Batch [3040]\tSpeed: 504.98 samples/sec\nINFO:root:Iter[24] Batch [3060]\tSpeed: 501.44 samples/sec\nINFO:root:Iter[24] Batch [3080]\tSpeed: 535.49 samples/sec\nINFO:root:Iter[24] Batch [3100]\tSpeed: 499.29 samples/sec\nINFO:root:Iter[24] Batch [3120]\tSpeed: 500.15 samples/sec\nINFO:root:Iter[24] Batch [3140]\tSpeed: 498.68 samples/sec\nINFO:root:Iter[24] Batch [3160]\tSpeed: 548.64 samples/sec\nINFO:root:Iter[24] Batch [3180]\tSpeed: 527.09 samples/sec\nINFO:root:Iter[24] Batch [3200]\tSpeed: 518.27 samples/sec\n{fc7_acc} 246000 0.72265625\nlr-batch-epoch: 0.00010000000000000003 3215 24\ntesting verification..\n(12000, 512)\ninfer time 4.654249\n[lfw][246000]XNorm: 22.184556\n[lfw][246000]Accuracy-Flip: 0.99733+-0.00309\ntesting verification..\n(14000, 512)\ninfer time 5.252119999999999\n[cfp_fp][246000]XNorm: 22.141661\n[cfp_fp][246000]Accuracy-Flip: 0.98957+-0.00271\ntesting verification..\n(12000, 512)\ninfer time 3.7945889999999998\n[agedb_30][246000]XNorm: 22.773084\n[agedb_30][246000]Accuracy-Flip: 0.98083+-0.00724\n[246000]Accuracy-Highest: 0.98183\nINFO:root:Iter[24] Batch [3220]\tSpeed: 243.18 samples/sec\nINFO:root:Iter[24] Batch [3240]\tSpeed: 841.33 samples/sec\nINFO:root:Iter[24] Batch [3260]\tSpeed: 858.42 samples/sec\nINFO:root:Iter[24] Batch [3280]\tSpeed: 852.52 samples/sec\nINFO:root:Iter[24] Batch [3300]\tSpeed: 690.43 samples/sec\nINFO:root:Iter[24] Batch [3320]\tSpeed: 603.20 samples/sec\nINFO:root:Iter[24] Batch [3340]\tSpeed: 587.99 samples/sec\nINFO:root:Iter[24] Batch [3360]\tSpeed: 548.50 samples/sec\nINFO:root:Iter[24] Batch [3380]\tSpeed: 522.19 samples/sec\nINFO:root:Iter[24] Batch [3400]\tSpeed: 594.24 samples/sec\nINFO:root:Iter[24] Batch [3420]\tSpeed: 540.34 samples/sec\nINFO:root:Iter[24] Batch [3440]\tSpeed: 515.68 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22.168860\n[cfp_fp][252000]Accuracy-Flip: 0.98914+-0.00400\ntesting verification..\n(12000, 512)\ninfer time 3.824371\n[agedb_30][252000]XNorm: 22.720397\n[agedb_30][252000]Accuracy-Flip: 0.97950+-0.00746\n[252000]Accuracy-Highest: 0.98183\nINFO:root:Iter[24] Batch [9220]\tSpeed: 237.21 samples/sec\nINFO:root:Iter[24] Batch [9240]\tSpeed: 876.50 samples/sec\nINFO:root:Iter[24] Batch [9260]\tSpeed: 858.38 samples/sec\nINFO:root:Iter[24] Batch [9280]\tSpeed: 864.17 samples/sec\nINFO:root:Iter[24] Batch [9300]\tSpeed: 836.35 samples/sec\nINFO:root:Iter[24] Batch [9320]\tSpeed: 815.82 samples/sec\nINFO:root:Iter[24] Batch [9340]\tSpeed: 867.31 samples/sec\nINFO:root:Iter[24] Batch [9360]\tSpeed: 810.06 samples/sec\nINFO:root:Iter[24] Batch [9380]\tSpeed: 737.31 samples/sec\nINFO:root:Iter[24] Batch [9400]\tSpeed: 710.61 samples/sec\nINFO:root:Iter[24] Batch [9420]\tSpeed: 624.27 samples/sec\nINFO:root:Iter[24] Batch [9440]\tSpeed: 590.79 samples/sec\nINFO:root:Iter[24] Batch [9460]\tSpeed: 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verification..\n(12000, 512)\ninfer time 4.695453\n[lfw][256000]XNorm: 22.166511\n[lfw][256000]Accuracy-Flip: 0.99733+-0.00309\ntesting verification..\n(14000, 512)\ninfer time 5.504968000000001\n[cfp_fp][256000]XNorm: 22.218946\n[cfp_fp][256000]Accuracy-Flip: 0.98900+-0.00286\ntesting verification..\n(12000, 512)\ninfer time 3.993035\n[agedb_30][256000]XNorm: 22.860274\n[agedb_30][256000]Accuracy-Flip: 0.97900+-0.00731\n[256000]Accuracy-Highest: 0.98183\nINFO:root:Iter[25] Batch [3100]\tSpeed: 223.57 samples/sec\nINFO:root:Iter[25] Batch [3120]\tSpeed: 789.51 samples/sec\nINFO:root:Iter[25] Batch [3140]\tSpeed: 779.43 samples/sec\nINFO:root:Iter[25] Batch [3160]\tSpeed: 651.61 samples/sec\nINFO:root:Iter[25] Batch [3180]\tSpeed: 592.46 samples/sec\nINFO:root:Iter[25] Batch [3200]\tSpeed: 691.35 samples/sec\nINFO:root:Iter[25] Batch [3220]\tSpeed: 609.07 samples/sec\nINFO:root:Iter[25] Batch [3240]\tSpeed: 579.18 samples/sec\nINFO:root:Iter[25] Batch [3260]\tSpeed: 638.11 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verification..\n(12000, 512)\ninfer time 4.740588000000002\n[lfw][262000]XNorm: 22.190214\n[lfw][262000]Accuracy-Flip: 0.99750+-0.00318\ntesting verification..\n(14000, 512)\ninfer time 5.162668\n[cfp_fp][262000]XNorm: 22.245973\n[cfp_fp][262000]Accuracy-Flip: 0.98914+-0.00249\ntesting verification..\n(12000, 512)\ninfer time 4.211380000000001\n[agedb_30][262000]XNorm: 22.792154\n[agedb_30][262000]Accuracy-Flip: 0.98117+-0.00715\n[262000]Accuracy-Highest: 0.98183\nINFO:root:Iter[25] Batch [9100]\tSpeed: 222.79 samples/sec\nINFO:root:Iter[25] Batch [9120]\tSpeed: 847.92 samples/sec\nINFO:root:Iter[25] Batch [9140]\tSpeed: 817.06 samples/sec\nINFO:root:Iter[25] Batch [9160]\tSpeed: 839.64 samples/sec\nINFO:root:Iter[25] Batch [9180]\tSpeed: 670.40 samples/sec\nINFO:root:Iter[25] Batch [9200]\tSpeed: 708.05 samples/sec\nINFO:root:Iter[25] Batch [9220]\tSpeed: 626.71 samples/sec\nINFO:root:Iter[25] Batch [9240]\tSpeed: 583.73 samples/sec\nINFO:root:Iter[25] Batch [9260]\tSpeed: 589.35 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verification..\n(12000, 512)\ninfer time 4.649915999999999\n[lfw][268000]XNorm: 22.194020\n[lfw][268000]Accuracy-Flip: 0.99733+-0.00309\ntesting verification..\n(14000, 512)\ninfer time 4.468873\n[cfp_fp][268000]XNorm: 22.238437\n[cfp_fp][268000]Accuracy-Flip: 0.98943+-0.00265\ntesting verification..\n(12000, 512)\ninfer time 3.3612589999999996\n[agedb_30][268000]XNorm: 22.730904\n[agedb_30][268000]Accuracy-Flip: 0.97967+-0.00686\n[268000]Accuracy-Highest: 0.98183\nINFO:root:Iter[26] Batch [5000]\tSpeed: 274.63 samples/sec\nINFO:root:Iter[26] Batch [5020]\tSpeed: 907.90 samples/sec\nINFO:root:Iter[26] Batch [5040]\tSpeed: 806.27 samples/sec\nINFO:root:Iter[26] Batch [5060]\tSpeed: 746.20 samples/sec\nINFO:root:Iter[26] Batch [5080]\tSpeed: 654.04 samples/sec\nINFO:root:Iter[26] Batch [5100]\tSpeed: 701.45 samples/sec\nINFO:root:Iter[26] Batch [5120]\tSpeed: 702.15 samples/sec\nINFO:root:Iter[26] Batch [5140]\tSpeed: 641.51 samples/sec\nINFO:root:Iter[26] Batch [5160]\tSpeed: 536.76 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3.7528879999999996\n[agedb_30][270000]XNorm: 22.801659\n[agedb_30][270000]Accuracy-Flip: 0.98133+-0.00718\n[270000]Accuracy-Highest: 0.98183\nINFO:root:Iter[26] Batch [7000]\tSpeed: 259.82 samples/sec\nINFO:root:Iter[26] Batch [7020]\tSpeed: 716.54 samples/sec\nINFO:root:Iter[26] Batch [7040]\tSpeed: 723.22 samples/sec\nINFO:root:Iter[26] Batch [7060]\tSpeed: 609.26 samples/sec\nINFO:root:Iter[26] Batch [7080]\tSpeed: 616.12 samples/sec\nINFO:root:Iter[26] Batch [7100]\tSpeed: 592.40 samples/sec\nINFO:root:Iter[26] Batch [7120]\tSpeed: 575.03 samples/sec\nINFO:root:Iter[26] Batch [7140]\tSpeed: 555.45 samples/sec\nINFO:root:Iter[26] Batch [7160]\tSpeed: 548.99 samples/sec\nINFO:root:Iter[26] Batch [7180]\tSpeed: 568.99 samples/sec\nINFO:root:Iter[26] Batch [7200]\tSpeed: 580.21 samples/sec\nINFO:root:Iter[26] Batch [7220]\tSpeed: 598.11 samples/sec\nINFO:root:Iter[26] Batch [7240]\tSpeed: 577.82 samples/sec\nINFO:root:Iter[26] Batch [7260]\tSpeed: 588.53 samples/sec\nINFO:root:Iter[26] 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verification..\n(12000, 512)\ninfer time 4.658848999999998\n[lfw][280000]XNorm: 22.178855\n[lfw][280000]Accuracy-Flip: 0.99750+-0.00271\ntesting verification..\n(14000, 512)\ninfer time 5.500916000000002\n[cfp_fp][280000]XNorm: 22.228011\n[cfp_fp][280000]Accuracy-Flip: 0.98957+-0.00307\ntesting verification..\n(12000, 512)\ninfer time 4.498231\n[agedb_30][280000]XNorm: 22.730727\n[agedb_30][280000]Accuracy-Flip: 0.98133+-0.00714\n[280000]Accuracy-Highest: 0.98183\nINFO:root:Iter[27] Batch [6880]\tSpeed: 246.82 samples/sec\nINFO:root:Iter[27] Batch [6900]\tSpeed: 905.84 samples/sec\nINFO:root:Iter[27] Batch [6920]\tSpeed: 861.96 samples/sec\nINFO:root:Iter[27] Batch [6940]\tSpeed: 720.77 samples/sec\nINFO:root:Iter[27] Batch [6960]\tSpeed: 644.31 samples/sec\nINFO:root:Iter[27] Batch [6980]\tSpeed: 596.09 samples/sec\nINFO:root:Iter[27] Batch [7000]\tSpeed: 654.85 samples/sec\nINFO:root:Iter[27] Batch [7020]\tSpeed: 569.48 samples/sec\nINFO:root:Iter[27] Batch [7040]\tSpeed: 598.75 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4.608176\n[lfw][286000]XNorm: 22.150140\n[lfw][286000]Accuracy-Flip: 0.99733+-0.00309\ntesting verification..\n(14000, 512)\ninfer time 5.3960550000000005\n[cfp_fp][286000]XNorm: 22.206329\n[cfp_fp][286000]Accuracy-Flip: 0.98986+-0.00251\ntesting verification..\n(12000, 512)\ninfer time 4.261118999999999\n[agedb_30][286000]XNorm: 22.812511\n[agedb_30][286000]Accuracy-Flip: 0.97883+-0.00734\n[286000]Accuracy-Highest: 0.98183\nINFO:root:Iter[28] Batch [2760]\tSpeed: 254.60 samples/sec\nINFO:root:Iter[28] Batch [2780]\tSpeed: 871.01 samples/sec\nINFO:root:Iter[28] Batch [2800]\tSpeed: 837.81 samples/sec\nINFO:root:Iter[28] Batch [2820]\tSpeed: 863.56 samples/sec\nINFO:root:Iter[28] Batch [2840]\tSpeed: 749.99 samples/sec\nINFO:root:Iter[28] Batch [2860]\tSpeed: 712.87 samples/sec\nINFO:root:Iter[28] Batch [2880]\tSpeed: 661.59 samples/sec\nINFO:root:Iter[28] Batch [2900]\tSpeed: 744.87 samples/sec\nINFO:root:Iter[28] Batch [2920]\tSpeed: 661.05 samples/sec\nINFO:root:Iter[28] Batch 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4.575980999999999\n[lfw][292000]XNorm: 22.271433\n[lfw][292000]Accuracy-Flip: 0.99767+-0.00327\ntesting verification..\n(14000, 512)\ninfer time 5.374421000000001\n[cfp_fp][292000]XNorm: 22.251917\n[cfp_fp][292000]Accuracy-Flip: 0.98829+-0.00270\ntesting verification..\n(12000, 512)\ninfer time 3.878468999999998\n[agedb_30][292000]XNorm: 22.818390\n[agedb_30][292000]Accuracy-Flip: 0.98017+-0.00669\n[292000]Accuracy-Highest: 0.98183\nINFO:root:Iter[28] Batch [8760]\tSpeed: 256.33 samples/sec\nINFO:root:Iter[28] Batch [8780]\tSpeed: 797.56 samples/sec\nINFO:root:Iter[28] Batch [8800]\tSpeed: 664.18 samples/sec\nINFO:root:Iter[28] Batch [8820]\tSpeed: 620.28 samples/sec\nINFO:root:Iter[28] Batch [8840]\tSpeed: 622.86 samples/sec\nINFO:root:Iter[28] Batch [8860]\tSpeed: 651.41 samples/sec\nINFO:root:Iter[28] Batch [8880]\tSpeed: 648.01 samples/sec\nINFO:root:Iter[28] Batch [8900]\tSpeed: 659.30 samples/sec\nINFO:root:Iter[28] Batch [8920]\tSpeed: 624.20 samples/sec\nINFO:root:Iter[28] 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22.830463\n[agedb_30][306000]Accuracy-Flip: 0.98000+-0.00719\n[306000]Accuracy-Highest: 0.98183\nINFO:root:Iter[30] Batch [2520]\tSpeed: 246.00 samples/sec\nINFO:root:Iter[30] Batch [2540]\tSpeed: 732.93 samples/sec\nINFO:root:Iter[30] Batch [2560]\tSpeed: 808.97 samples/sec\nINFO:root:Iter[30] Batch [2580]\tSpeed: 752.62 samples/sec\nINFO:root:Iter[30] Batch [2600]\tSpeed: 682.24 samples/sec\nINFO:root:Iter[30] Batch [2620]\tSpeed: 691.25 samples/sec\nINFO:root:Iter[30] Batch [2640]\tSpeed: 660.08 samples/sec\nINFO:root:Iter[30] Batch [2660]\tSpeed: 649.10 samples/sec\nINFO:root:Iter[30] Batch [2680]\tSpeed: 608.64 samples/sec\nINFO:root:Iter[30] Batch [2700]\tSpeed: 644.08 samples/sec\nINFO:root:Iter[30] Batch [2720]\tSpeed: 612.02 samples/sec\nINFO:root:Iter[30] Batch [2740]\tSpeed: 621.23 samples/sec\nINFO:root:Iter[30] Batch [2760]\tSpeed: 608.27 samples/sec\nINFO:root:Iter[30] Batch [2780]\tSpeed: 626.78 samples/sec\nINFO:root:Iter[30] Batch [2800]\tSpeed: 643.80 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samples/sec\nINFO:root:Iter[30] Batch [9140]\tSpeed: 586.76 samples/sec\nINFO:root:Iter[30] Batch [9160]\tSpeed: 610.48 samples/sec\nINFO:root:Iter[30] Batch [9180]\tSpeed: 619.90 samples/sec\nINFO:root:Iter[30] Batch [9200]\tSpeed: 601.62 samples/sec\nINFO:root:Iter[30] Batch [9220]\tSpeed: 632.28 samples/sec\nINFO:root:Iter[30] Batch [9240]\tSpeed: 615.17 samples/sec\nTraceback (most recent call last):\n  File \"train_parall.py\", line 353, in <module>\n    main()\n  File \"train_parall.py\", line 350, in main\n    train_net(args)\n  File \"train_parall.py\", line 345, in train_net\n    epoch_end_callback = epoch_cb )\n  File \"/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py\", line 497, in fit\n    next_data_batch = next(data_iter)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py\", line 230, in __next__\n    return self.next()\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py\", line 473, in next\n    if self.iter_next():\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py\", line 451, in iter_next\n    i.wait()\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/threading.py\", line 552, in wait\n    signaled = self._cond.wait(timeout)\n  File \"/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/threading.py\", line 296, in wait\n    waiter.acquire()\nKeyboardInterrupt\n"
  },
  {
    "path": "face_recognition/models/MS1Mretina-resnet50-arcface/model-symbol.json",
    "content": "{\n  \"nodes\": [\n    {\n      \"op\": \"null\", \n      \"name\": \"data\", \n      \"inputs\": []\n    }, \n    {\n      \"op\": \"_copy\", \n      \"name\": \"id\", \n      \"inputs\": [[0, 0, 0]]\n    }, \n    {\n      \"op\": \"_minus_scalar\", \n      \"name\": \"_minusscalar0\", \n      \"attrs\": {\"scalar\": \"127.5\"}, \n      \"inputs\": [[1, 0, 0]]\n    }, \n    {\n      \"op\": \"_mul_scalar\", \n      \"name\": \"_mulscalar0\", \n      \"attrs\": {\"scalar\": \"0.0078125\"}, \n      \"inputs\": [[2, 0, 0]]\n    }, \n    {\n      \"op\": \"null\", \n      \"name\": \"conv0_weight\", \n      \"attrs\": {\n        \"kernel\": \"(3, 3)\", \n        \"no_bias\": \"True\", \n        \"num_filter\": \"64\", \n        \"pad\": \"(1, 1)\", \n        \"stride\": \"(1, 1)\", \n        \"workspace\": \"256\"\n      }, \n      \"inputs\": []\n    }, \n    {\n      \"op\": \"Convolution\", \n      \"name\": \"conv0\", \n      \"attrs\": {\n        \"kernel\": \"(3, 3)\", \n        \"no_bias\": \"True\", \n        \"num_filter\": \"64\", \n        \"pad\": \"(1, 1)\", \n        \"stride\": \"(1, 1)\", \n        \"workspace\": \"256\"\n      }, \n      \"inputs\": [[3, 0, 0], [4, 0, 0]]\n    }, \n    {\n      \"op\": \"null\", \n      \"name\": \"bn0_gamma\", \n      \"attrs\": {\n        \"eps\": \"2e-05\", \n        \"fix_gamma\": \"False\", \n        \"momentum\": \"0.9\"\n      }, \n      \"inputs\": []\n    }, \n    {\n      \"op\": \"null\", \n      \"name\": \"bn0_beta\", \n      \"attrs\": {\n        \"eps\": \"2e-05\", \n        \"fix_gamma\": \"False\", \n        \"momentum\": \"0.9\"\n      }, \n      \"inputs\": []\n    }, \n    {\n      \"op\": \"null\", \n      \"name\": \"bn0_moving_mean\", \n      \"attrs\": {\n        \"__init__\": \"[\\\"zero\\\", {}]\", \n        \"eps\": \"2e-05\", \n        \"fix_gamma\": \"False\", \n        \"momentum\": \"0.9\"\n      }, \n      \"inputs\": []\n    }, \n    {\n      \"op\": \"null\", \n      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  },
  {
    "path": "face_recognition/parall_module_local_v1.py",
    "content": "\n'''\n@author: insightface\n'''\n\nimport logging\nimport copy\nimport time\nimport os\n\nimport mxnet as mx\nimport numpy as np\nfrom mxnet import context as ctx\nfrom mxnet.initializer import Uniform\nfrom mxnet.module.base_module import BaseModule\nfrom mxnet.module.module import Module\nfrom mxnet import metric\nfrom mxnet.model import BatchEndParam\nfrom mxnet import io\nimport mxnet.ndarray as nd\nfrom config import config\n\nclass ParallModule(BaseModule):\n    def __init__(self, symbol, data_names, label_names,\n                 logger=logging, context=ctx.cpu(), work_load_list=None,\n                 asymbol = None,\n                 args = None):\n        super(ParallModule, self).__init__(logger=logger)\n        self._symbol = symbol\n        self._asymbol = asymbol\n        self._data_names = data_names\n        self._label_names = label_names\n        self._context = context\n        self._work_load_list = work_load_list\n        self._num_classes = config.num_classes\n        self._batch_size = args.batch_size\n        self._verbose = args.verbose\n        self._emb_size = config.emb_size\n        self._local_class_start = args.local_class_start\n        self._iter = 0\n\n        self._curr_module = None\n\n        self._num_workers = config.num_workers\n        self._num_ctx = len(self._context)\n        self._ctx_num_classes = args.ctx_num_classes\n        self._nd_cache = {}\n        self._ctx_cpu = mx.cpu()\n        self._ctx_single_gpu = self._context[-1]\n        self._fixed_param_names = None\n        self._curr_module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger,\n                        context=self._context, work_load_list=self._work_load_list,\n                        fixed_param_names=self._fixed_param_names)\n        self._arcface_modules = []\n        self._ctx_class_start = []\n        for i in range(len(self._context)):\n\n          args._ctxid = i\n          _module = Module(self._asymbol(args), self._data_names, self._label_names, logger=self.logger,\n                          context=mx.gpu(i), work_load_list=self._work_load_list,\n                          fixed_param_names=self._fixed_param_names)\n          self._arcface_modules.append(_module)\n          _c = args.local_class_start + i*args.ctx_num_classes\n          self._ctx_class_start.append(_c)\n        self._usekv = False\n        if self._usekv:\n          self._distkv = mx.kvstore.create('dist_sync')\n          self._kvinit = {}\n\n\n    def _reset_bind(self):\n        self.binded = False\n        self._curr_module = None\n\n    @property\n    def data_names(self):\n        return self._data_names\n\n    @property\n    def output_names(self):\n        return self._symbol.list_outputs()\n\n    @property\n    def data_shapes(self):\n        assert self.binded\n        return self._curr_module.data_shapes\n\n    @property\n    def label_shapes(self):\n        assert self.binded\n        return self._curr_module.label_shapes\n\n    @property\n    def output_shapes(self):\n        assert self.binded\n        return self._curr_module.output_shapes\n\n    def get_export_params(self):\n        assert self.binded and self.params_initialized\n        _g, _x = self._curr_module.get_params()\n        g = _g.copy()\n        x = _x.copy()\n        return g, x\n\n    def get_params(self):\n        assert self.binded and self.params_initialized\n        _g, _x = self._curr_module.get_params()\n        g = _g.copy()\n        x = _x.copy()\n        for _module in self._arcface_modules:\n          _g, _x = _module.get_params()\n          ag = _g.copy()\n          ax = _x.copy()\n          g.update(ag)\n          x.update(ax)\n        return g, x\n\n    def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,\n                   allow_extra=False):\n      g = arg_params\n      x = aux_params\n      #ag = {}\n      #ax = {}\n      rk = []\n      for k in g:\n        v = g[k]\n        if k.startswith('fc7'):\n          p1 = k.find('_')\n          p2 = k.rfind('_')\n          _ctxid = int(k[p1+1:p2])\n          self._arcface_modules[_ctxid].set_params({k:v}, {})\n          rk.append(k)\n      for k in rk:\n        del g[k]\n      self._curr_module.set_params(g, x)\n      #self._arcface_module.set_params(ag, ax)\n\n\n    def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None,\n                    allow_missing=False, force_init=False, allow_extra=False):\n        if self.params_initialized and not force_init:\n            return\n        assert self.binded, 'call bind before initializing the parameters'\n        #TODO init the same weights with all work nodes\n        self._curr_module.init_params(initializer=initializer, arg_params=None,\n                                      aux_params=None, allow_missing=allow_missing,\n                                      force_init=force_init, allow_extra=allow_extra)\n        for _module in self._arcface_modules:\n          #_initializer = initializer\n          _initializer = mx.init.Normal(0.01)\n          _module.init_params(initializer=_initializer, arg_params=None,\n                                        aux_params=None, allow_missing=allow_missing,\n                                        force_init=force_init, allow_extra=allow_extra)\n        self.params_initialized = True\n\n\n    def bind(self, data_shapes, label_shapes=None, for_training=True,\n             inputs_need_grad=False, force_rebind=False, shared_module=None):\n        print('in_bind', self.params_initialized, data_shapes, label_shapes)\n        if self.params_initialized:\n            arg_params, aux_params = self.get_params()\n\n        # force rebinding is typically used when one want to switch from\n        # training to prediction phase.\n        if force_rebind:\n            self._reset_bind()\n\n        if self.binded:\n            self.logger.warning('Already binded, ignoring bind()')\n            return\n\n        assert shared_module is None, 'shared_module for MutableModule is not supported'\n        self.for_training = for_training\n        self.inputs_need_grad = inputs_need_grad\n        self.binded = True\n        self._curr_module.bind(data_shapes, label_shapes, for_training, inputs_need_grad,\n                    force_rebind=False, shared_module=None)\n        _data_shape = data_shapes[0][1]\n        print('_data_shape', _data_shape, label_shapes)\n        for _module in self._arcface_modules:\n          _module.bind([('data', (_data_shape[0]*self._num_workers, self._emb_size))], [('softmax_label', (_data_shape[0]*self._num_workers,))], for_training, True,\n                      force_rebind=False, shared_module=None)\n        if self.params_initialized:\n            self.set_params(arg_params, aux_params)\n\n    def init_optimizer(self, kvstore='local', optimizer='sgd',\n                       optimizer_params=(('learning_rate', 0.01),), force_init=False):\n        assert self.binded and self.params_initialized\n        if self.optimizer_initialized and not force_init:\n            self.logger.warning('optimizer already initialized, ignoring.')\n            return\n\n        self._curr_module.init_optimizer(kvstore, optimizer, optimizer_params,\n                                         force_init=force_init)\n        for _module in self._arcface_modules:\n          _module.init_optimizer(kvstore, optimizer, optimizer_params,\n                                           force_init=force_init)\n        self.optimizer_initialized = True\n\n    def kv_push(self, key, value):\n      #if value.context!=mx.cpu():\n      #  value = value.as_in_context(mx.cpu())\n      if not key in self._kvinit:\n        self._distkv.init(key, nd.zeros_like(value))\n        self._kvinit[key] = 1\n      self._distkv.push(key, value)\n\n    #get fc1 and partial fc7\n    def forward(self, data_batch, is_train=None):\n        #g,x = self.get_params()\n        #print('{fc7_weight[0][0]}', self._iter, g['fc7_0_weight'].asnumpy()[0][0])\n        #print('{pre_fc1_weight[0][0]}', self._iter, g['pre_fc1_weight'].asnumpy()[0][0])\n\n\n        assert self.binded and self.params_initialized\n        self._curr_module.forward(data_batch, is_train=is_train)\n        if is_train:\n          self._iter+=1\n          fc1, label = self._curr_module.get_outputs(merge_multi_context=True)\n          global_fc1 = fc1\n          self.global_label = label.as_in_context(self._ctx_cpu)\n\n\n          for i, _module in enumerate(self._arcface_modules):\n            _label = self.global_label - self._ctx_class_start[i]\n            db_global_fc1 = io.DataBatch([global_fc1], [_label])\n            _module.forward(db_global_fc1) #fc7 with margin\n        #print('forward end')\n\n\n    def get_ndarray(self, context, name, shape):\n      key = \"%s_%s\"%(name, context)\n      #print(key)\n      if not key in self._nd_cache:\n        v = nd.zeros( shape=shape, ctx = context)\n        self._nd_cache[key] = v\n      else:\n        v = self._nd_cache[key]\n      return v\n\n    def get_ndarray2(self, context, name, arr):\n      key = \"%s_%s\"%(name, context)\n      #print(key)\n      if not key in self._nd_cache:\n        v = nd.zeros( shape=arr.shape, ctx = context)\n        self._nd_cache[key] = v\n      else:\n        v = self._nd_cache[key]\n      arr.copyto(v)\n      return v\n\n    def backward(self, out_grads=None):\n        #print('in backward')\n        assert self.binded and self.params_initialized\n        #tmp_ctx = self._ctx_cpu\n        tmp_ctx = self._ctx_single_gpu\n        fc7_outs = []\n        ctx_fc7_max = self.get_ndarray(tmp_ctx, 'ctx_fc7_max', (self._batch_size, len(self._context)))\n        #local_fc7_max = nd.zeros( (self.global_label.shape[0],1), ctx=mx.cpu())\n        for i, _module in enumerate(self._arcface_modules):\n          _fc7 = _module.get_outputs(merge_multi_context=True)[0]\n          fc7_outs.append(_fc7)\n          _fc7_max = nd.max(_fc7, axis=1).as_in_context(tmp_ctx)\n          ctx_fc7_max[:,i] = _fc7_max\n\n        local_fc7_max = self.get_ndarray(tmp_ctx, 'local_fc7_max', (self._batch_size, 1))\n        nd.max(ctx_fc7_max, axis=1, keepdims=True, out=local_fc7_max)\n        global_fc7_max = local_fc7_max\n        #local_fc7_sum = None\n        local_fc7_sum = self.get_ndarray(tmp_ctx, 'local_fc7_sum', (self._batch_size,1))\n        local_fc7_sum[:,:] = 0.0\n        for i, _module in enumerate(self._arcface_modules):\n          _max = self.get_ndarray2(fc7_outs[i].context, 'fc7_max', global_fc7_max)\n          fc7_outs[i] = nd.broadcast_sub(fc7_outs[i], _max)\n          fc7_outs[i] = nd.exp(fc7_outs[i])\n          _sum = nd.sum(fc7_outs[i], axis=1, keepdims=True).as_in_context(tmp_ctx)\n          local_fc7_sum += _sum\n        global_fc7_sum = local_fc7_sum\n\n        if self._iter%self._verbose==0:\n          #_ctx = self._context[-1]\n          _ctx = self._ctx_cpu\n          _probs = []\n          for i, _module in enumerate(self._arcface_modules):\n            _prob = self.get_ndarray2(_ctx, '_fc7_prob_%d'%i, fc7_outs[i])\n            _probs.append(_prob)\n          fc7_prob = self.get_ndarray(_ctx, 'test_fc7_prob', (self._batch_size, self._ctx_num_classes*len(self._context)))\n          nd.concat(*_probs, dim=1, out=fc7_prob)\n          fc7_pred = nd.argmax(fc7_prob, axis=1)\n          local_label = self.global_label - self._local_class_start\n          #local_label = self.get_ndarray2(_ctx, 'test_label', local_label)\n          _pred = nd.equal(fc7_pred, local_label)\n          print('{fc7_acc}', self._iter, nd.mean(_pred).asnumpy()[0])\n\n\n        #local_fc1_grad = []\n        #fc1_grad_ctx = self._ctx_cpu\n        fc1_grad_ctx = self._ctx_single_gpu\n        local_fc1_grad = self.get_ndarray(fc1_grad_ctx, 'local_fc1_grad', (self._batch_size,self._emb_size))\n        local_fc1_grad[:,:] = 0.0\n\n        for i, _module in enumerate(self._arcface_modules):\n          _sum = self.get_ndarray2(fc7_outs[i].context, 'fc7_sum', global_fc7_sum)\n          fc7_outs[i] = nd.broadcast_div(fc7_outs[i], _sum)\n          a = i*self._ctx_num_classes\n          b = (i+1)*self._ctx_num_classes\n          _label = self.global_label - self._ctx_class_start[i]\n          _label = self.get_ndarray2(fc7_outs[i].context, 'label', _label)\n          onehot_label = self.get_ndarray(fc7_outs[i].context, 'label_onehot', (self._batch_size, self._ctx_num_classes))\n          nd.one_hot(_label, depth=self._ctx_num_classes, on_value = 1.0, off_value = 0.0, out=onehot_label)\n          fc7_outs[i] -= onehot_label\n          _module.backward(out_grads = [fc7_outs[i]])\n          #ctx_fc1_grad = _module.get_input_grads()[0].as_in_context(mx.cpu())\n          ctx_fc1_grad = self.get_ndarray2(fc1_grad_ctx, 'ctx_fc1_grad_%d'%i, _module.get_input_grads()[0])\n          local_fc1_grad += ctx_fc1_grad\n\n        global_fc1_grad = local_fc1_grad\n        self._curr_module.backward(out_grads = [global_fc1_grad])\n\n\n    def update(self):\n        assert self.binded and self.params_initialized and self.optimizer_initialized\n        self._curr_module.update()\n        for i, _module in enumerate(self._arcface_modules):\n          _module.update()\n        mx.nd.waitall()\n\n\n    def get_outputs(self, merge_multi_context=True):\n        assert self.binded and self.params_initialized\n        return self._curr_module.get_outputs(merge_multi_context=merge_multi_context)\n        #return self._arcface_module.get_outputs(merge_multi_context=merge_multi_context)\n\n    def get_input_grads(self, merge_multi_context=True):\n        assert self.binded and self.params_initialized and self.inputs_need_grad\n        return self._curr_module.get_input_grads(merge_multi_context=merge_multi_context)\n\n    def update_metric(self, eval_metric, labels):\n        assert self.binded and self.params_initialized\n        #self._curr_module.update_metric(eval_metric, labels)\n        #label = labels[0]\n        #print(label.shape)\n        #self._arcface_module.update_metric(eval_metric, labels)\n\n    def install_monitor(self, mon):\n        \"\"\" Install monitor on all executors \"\"\"\n        assert self.binded\n        self._curr_module.install_monitor(mon)\n\n    def forward_backward(self, data_batch):\n        \"\"\"A convenient function that calls both ``forward`` and ``backward``.\"\"\"\n        self.forward(data_batch, is_train=True) # get fc1 and partial fc7\n        self.backward()\n\n    def fit(self, train_data, eval_data=None, eval_metric='acc',\n            epoch_end_callback=None, batch_end_callback=None, kvstore='local',\n            optimizer='sgd', optimizer_params=(('learning_rate', 0.01),),\n            eval_end_callback=None,\n            eval_batch_end_callback=None, initializer=Uniform(0.01),\n            arg_params=None, aux_params=None, allow_missing=False,\n            force_rebind=False, force_init=False, begin_epoch=0, num_epoch=None,\n            validation_metric=None, monitor=None, sparse_row_id_fn=None):\n        \"\"\"Trains the module parameters.\n\n        Checkout `Module Tutorial <http://mxnet.io/tutorials/basic/module.html>`_ to see\n        a end-to-end use-case.\n\n        Parameters\n        ----------\n        train_data : DataIter\n            Train DataIter.\n        eval_data : DataIter\n            If not ``None``, will be used as validation set and the performance\n            after each epoch will be evaluated.\n        eval_metric : str or EvalMetric\n            Defaults to 'accuracy'. The performance measure used to display during training.\n            Other possible predefined metrics are:\n            'ce' (CrossEntropy), 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy'.\n        epoch_end_callback : function or list of functions\n            Each callback will be called with the current `epoch`, `symbol`, `arg_params`\n            and `aux_params`.\n        batch_end_callback : function or list of function\n            Each callback will be called with a `BatchEndParam`.\n        kvstore : str or KVStore\n            Defaults to 'local'.\n        optimizer : str or Optimizer\n            Defaults to 'sgd'.\n        optimizer_params : dict\n            Defaults to ``(('learning_rate', 0.01),)``. The parameters for\n            the optimizer constructor.\n            The default value is not a dict, just to avoid pylint warning on dangerous\n            default values.\n        eval_end_callback : function or list of function\n            These will be called at the end of each full evaluation, with the metrics over\n            the entire evaluation set.\n        eval_batch_end_callback : function or list of function\n            These will be called at the end of each mini-batch during evaluation.\n        initializer : Initializer\n            The initializer is called to initialize the module parameters when they are\n            not already initialized.\n        arg_params : dict\n            Defaults to ``None``, if not ``None``, should be existing parameters from a trained\n            model or loaded from a checkpoint (previously saved model). In this case,\n            the value here will be used to initialize the module parameters, unless they\n            are already initialized by the user via a call to `init_params` or `fit`.\n            `arg_params` has a higher priority than `initializer`.\n        aux_params : dict\n            Defaults to ``None``. Similar to `arg_params`, except for auxiliary states.\n        allow_missing : bool\n            Defaults to ``False``. Indicates whether to allow missing parameters when `arg_params`\n            and `aux_params` are not ``None``. If this is ``True``, then the missing parameters\n            will be initialized via the `initializer`.\n        force_rebind : bool\n            Defaults to ``False``. Whether to force rebinding the executors if already bound.\n        force_init : bool\n            Defaults to ``False``. Indicates whether to force initialization even if the\n            parameters are already initialized.\n        begin_epoch : int\n            Defaults to 0. Indicates the starting epoch. Usually, if resumed from a\n            checkpoint saved at a previous training phase at epoch N, then this value should be\n            N+1.\n        num_epoch : int\n            Number of epochs for training.\n        sparse_row_id_fn : A callback function\n            The function  takes `data_batch` as an input and returns a dict of\n            str -> NDArray. The resulting dict is used for pulling row_sparse\n            parameters from the kvstore, where the str key is the name of the param,\n            and the value is the row id of the param to pull.\n\n        Examples\n        --------\n        >>> # An example of using fit for training.\n        >>> # Assume training dataIter and validation dataIter are ready\n        >>> # Assume loading a previously checkpointed model\n        >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 3)\n        >>> mod.fit(train_data=train_dataiter, eval_data=val_dataiter, optimizer='sgd',\n        ...     optimizer_params={'learning_rate':0.01, 'momentum': 0.9},\n        ...     arg_params=arg_params, aux_params=aux_params,\n        ...     eval_metric='acc', num_epoch=10, begin_epoch=3)\n        \"\"\"\n        assert num_epoch is not None, 'please specify number of epochs'\n        assert arg_params is None and aux_params is None\n\n        self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label,\n                  for_training=True, force_rebind=force_rebind)\n        if monitor is not None:\n            self.install_monitor(monitor)\n        self.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params,\n                         allow_missing=allow_missing, force_init=force_init)\n        self.init_optimizer(kvstore=kvstore, optimizer=optimizer,\n                            optimizer_params=optimizer_params)\n\n        if validation_metric is None:\n            validation_metric = eval_metric\n        if not isinstance(eval_metric, metric.EvalMetric):\n            eval_metric = metric.create(eval_metric)\n        epoch_eval_metric = copy.deepcopy(eval_metric)\n\n        ################################################################################\n        # training loop\n        ################################################################################\n        for epoch in range(begin_epoch, num_epoch):\n            tic = time.time()\n            eval_metric.reset()\n            epoch_eval_metric.reset()\n            nbatch = 0\n            data_iter = iter(train_data)\n            end_of_batch = False\n            next_data_batch = next(data_iter)\n            while not end_of_batch:\n                data_batch = next_data_batch\n                if monitor is not None:\n                    monitor.tic()\n                self.forward_backward(data_batch)\n                self.update()\n                assert not isinstance(data_batch, list)\n\n                #if isinstance(data_batch, list):\n                #    #print('XXX')\n                #    self.update_metric(eval_metric,\n                #                       [db.label for db in data_batch],\n                #                       pre_sliced=True)\n                #    self.update_metric(epoch_eval_metric,\n                #                       [db.label for db in data_batch],\n                #                       pre_sliced=True)\n                #else:\n                #    #print('before update metric')\n                #    self.update_metric(eval_metric, data_batch.label)\n                #    self.update_metric(epoch_eval_metric, data_batch.label)\n                #labels = data_batch.label\n                #labels = [self.global_label]\n                #self.update_metric(eval_metric, labels)\n                #self.update_metric(epoch_eval_metric, labels)\n\n                try:\n                    # pre fetch next batch\n                    next_data_batch = next(data_iter)\n                    self.prepare(next_data_batch, sparse_row_id_fn=sparse_row_id_fn)\n                except StopIteration:\n                    end_of_batch = True\n\n                if monitor is not None:\n                    monitor.toc_print()\n\n                #if end_of_batch:\n                #    eval_name_vals = epoch_eval_metric.get_name_value()\n\n                if batch_end_callback is not None:\n                    batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch,\n                                                     eval_metric=None,\n                                                     locals=locals())\n                    batch_end_callback(batch_end_params)\n                    #for callback in _as_list(batch_end_callback):\n                    #    callback(batch_end_params)\n                nbatch += 1\n\n            # one epoch of training is finished\n            #for name, val in eval_name_vals:\n            #    self.logger.info('Epoch[%d] Train-%s=%f', epoch, name, val)\n            toc = time.time()\n            self.logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc-tic))\n\n            # sync aux params across devices\n            arg_params, aux_params = self.get_params()\n            self.set_params(arg_params, aux_params)\n\n            # end of 1 epoch, reset the data-iter for another epoch\n            train_data.reset()\n\n"
  },
  {
    "path": "face_recognition/sample_config.py",
    "content": "import numpy as np\nimport os\nfrom easydict import EasyDict as edict\n\nconfig = edict()\n\nconfig.bn_mom = 0.9\nconfig.workspace = 256\nconfig.emb_size = 512\nconfig.ckpt_embedding = True\nconfig.net_se = 0\nconfig.net_act = 'prelu'\nconfig.net_unit = 3\nconfig.net_input = 1\nconfig.net_blocks = [1,4,6,2]\nconfig.net_output = 'E'\nconfig.net_multiplier = 1.0\nconfig.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\nconfig.ce_loss = True\nconfig.fc7_lr_mult = 1.0\nconfig.fc7_wd_mult = 1.0\nconfig.fc7_no_bias = False\nconfig.max_steps = 0\nconfig.data_rand_mirror = True\nconfig.data_cutoff = False\nconfig.data_color = 0\nconfig.data_images_filter = 0\nconfig.count_flops = True\nconfig.memonger = False #not work now\n\n\n# network settings\nnetwork = edict()\n\nnetwork.r100 = edict()\nnetwork.r100.net_name = 'fresnet'\nnetwork.r100.num_layers = 100\n\nnetwork.r100fc = edict()\nnetwork.r100fc.net_name = 'fresnet'\nnetwork.r100fc.num_layers = 100\nnetwork.r100fc.net_output = 'FC'\n\nnetwork.r50 = edict()\nnetwork.r50.net_name = 'fresnet'\nnetwork.r50.num_layers = 50\n\nnetwork.r50v1 = edict()\nnetwork.r50v1.net_name = 'fresnet'\nnetwork.r50v1.num_layers = 50\nnetwork.r50v1.net_unit = 1\n\nnetwork.d169 = edict()\nnetwork.d169.net_name = 'fdensenet'\nnetwork.d169.num_layers = 169\nnetwork.d169.per_batch_size = 64\nnetwork.d169.densenet_dropout = 0.0\n\nnetwork.d201 = edict()\nnetwork.d201.net_name = 'fdensenet'\nnetwork.d201.num_layers = 201\nnetwork.d201.per_batch_size = 64\nnetwork.d201.densenet_dropout = 0.0\n\nnetwork.y1 = edict()\nnetwork.y1.net_name = 'fmobilefacenet'\nnetwork.y1.emb_size = 128\nnetwork.y1.net_output = 'GDC'\n\nnetwork.y2 = edict()\nnetwork.y2.net_name = 'fmobilefacenet'\nnetwork.y2.emb_size = 256\nnetwork.y2.net_output = 'GDC'\nnetwork.y2.net_blocks = [2,8,16,4]\n\nnetwork.m1 = edict()\nnetwork.m1.net_name = 'fmobilenet'\nnetwork.m1.emb_size = 256\nnetwork.m1.net_output = 'GDC'\nnetwork.m1.net_multiplier = 1.0\n\nnetwork.m05 = edict()\nnetwork.m05.net_name = 'fmobilenet'\nnetwork.m05.emb_size = 256\nnetwork.m05.net_output = 'GDC'\nnetwork.m05.net_multiplier = 0.5\n\nnetwork.mnas = edict()\nnetwork.mnas.net_name = 'fmnasnet'\nnetwork.mnas.emb_size = 256\nnetwork.mnas.net_output = 'GDC'\nnetwork.mnas.net_multiplier = 1.0\n\nnetwork.mnas05 = edict()\nnetwork.mnas05.net_name = 'fmnasnet'\nnetwork.mnas05.emb_size = 256\nnetwork.mnas05.net_output = 'GDC'\nnetwork.mnas05.net_multiplier = 0.5\n\nnetwork.mnas025 = edict()\nnetwork.mnas025.net_name = 'fmnasnet'\nnetwork.mnas025.emb_size = 256\nnetwork.mnas025.net_output = 'GDC'\nnetwork.mnas025.net_multiplier = 0.25\n\nnetwork.vargfacenet = edict()\nnetwork.vargfacenet.net_name = 'vargfacenet'\nnetwork.vargfacenet.net_multiplier = 1.25\nnetwork.vargfacenet.emb_size = 512\nnetwork.vargfacenet.net_output='J'\n\n# dataset settings\ndataset = edict()\n\ndataset.emore = edict()\ndataset.emore.dataset = 'emore'\ndataset.emore.dataset_path = '../datasets/faces_emore'\ndataset.emore.num_classes = 85742\ndataset.emore.image_shape = (112,112,3)\ndataset.emore.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\n\ndataset.retina = edict()\ndataset.retina.dataset = 'retina'\ndataset.retina.dataset_path = '../datasets/ms1m-retinaface-t1'\ndataset.retina.num_classes = 93431\ndataset.retina.image_shape = (112,112,3)\ndataset.retina.val_targets = ['lfw', 'cfp_fp', 'agedb_30']\n\nloss = edict()\nloss.softmax = edict()\nloss.softmax.loss_name = 'softmax'\n\nloss.nsoftmax = edict()\nloss.nsoftmax.loss_name = 'margin_softmax'\nloss.nsoftmax.loss_s = 64.0\nloss.nsoftmax.loss_m1 = 1.0\nloss.nsoftmax.loss_m2 = 0.0\nloss.nsoftmax.loss_m3 = 0.0\n\nloss.arcface = edict()\nloss.arcface.loss_name = 'margin_softmax'\nloss.arcface.loss_s = 64.0\nloss.arcface.loss_m1 = 1.0\nloss.arcface.loss_m2 = 0.5\nloss.arcface.loss_m3 = 0.0\n\nloss.cosface = edict()\nloss.cosface.loss_name = 'margin_softmax'\nloss.cosface.loss_s = 64.0\nloss.cosface.loss_m1 = 1.0\nloss.cosface.loss_m2 = 0.0\nloss.cosface.loss_m3 = 0.35\n\nloss.combined = edict()\nloss.combined.loss_name = 'margin_softmax'\nloss.combined.loss_s = 64.0\nloss.combined.loss_m1 = 1.0\nloss.combined.loss_m2 = 0.3\nloss.combined.loss_m3 = 0.2\n\nloss.triplet = edict()\nloss.triplet.loss_name = 'triplet'\nloss.triplet.images_per_identity = 5\nloss.triplet.triplet_alpha = 0.3\nloss.triplet.triplet_bag_size = 7200\nloss.triplet.triplet_max_ap = 0.0\nloss.triplet.per_batch_size = 60\nloss.triplet.lr = 0.05\n\nloss.atriplet = edict()\nloss.atriplet.loss_name = 'atriplet'\nloss.atriplet.images_per_identity = 5\nloss.atriplet.triplet_alpha = 0.35\nloss.atriplet.triplet_bag_size = 7200\nloss.atriplet.triplet_max_ap = 0.0\nloss.atriplet.per_batch_size = 60\nloss.atriplet.lr = 0.05\n\n# default settings\ndefault = edict()\n\n# default network\ndefault.network = 'r100'\ndefault.pretrained = ''\ndefault.pretrained_epoch = 1\n# default dataset\ndefault.dataset = 'emore'\ndefault.loss = 'arcface'\ndefault.frequent = 20\ndefault.verbose = 2000\ndefault.kvstore = 'device'\n\ndefault.end_epoch = 10000\ndefault.lr = 0.1\ndefault.wd = 0.0005\ndefault.mom = 0.9\ndefault.per_batch_size = 128\ndefault.ckpt = 3\ndefault.lr_steps = '100000,160000,220000'\ndefault.models_root = './models'\n\n\ndef generate_config(_network, _dataset, _loss):\n    for k, v in loss[_loss].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    for k, v in network[_network].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    for k, v in dataset[_dataset].items():\n      config[k] = v\n      if k in default:\n        default[k] = v\n    config.loss = _loss\n    config.network = _network\n    config.dataset = _dataset\n    config.num_workers = 1\n    if 'DMLC_NUM_WORKER' in os.environ:\n      config.num_workers = int(os.environ['DMLC_NUM_WORKER'])\n\n"
  },
  {
    "path": "face_recognition/symbol/fdensenet.py",
    "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n# coding: utf-8\n# pylint: disable= arguments-differ\n\"\"\"DenseNet, implemented in Gluon.\"\"\"\n\nimport sys\nimport os\nimport mxnet as mx\nimport mxnet.ndarray as nd\nimport mxnet.gluon as gluon\nimport mxnet.gluon.nn as nn\nimport mxnet.autograd as ag\nimport symbol_utils\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\ndef Act():\n    if config.net_act=='prelu':\n      return nn.PReLU()\n    else:\n      return nn.Activation(config.net_act)\n# Helpers\ndef _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index):\n    out = nn.HybridSequential(prefix='stage%d_'%stage_index)\n    with out.name_scope():\n        for _ in range(num_layers):\n            out.add(_make_dense_layer(growth_rate, bn_size, dropout))\n    return out\n\ndef _make_dense_layer(growth_rate, bn_size, dropout):\n    new_features = nn.HybridSequential(prefix='')\n    new_features.add(nn.BatchNorm())\n    #new_features.add(nn.Activation('relu'))\n    new_features.add(Act())\n    new_features.add(nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False))\n    new_features.add(nn.BatchNorm())\n    #new_features.add(nn.Activation('relu'))\n    new_features.add(Act())\n    new_features.add(nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False))\n    if dropout:\n        new_features.add(nn.Dropout(dropout))\n\n    out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix='')\n    out.add(gluon.contrib.nn.Identity())\n    out.add(new_features)\n\n    return out\n\ndef _make_transition(num_output_features):\n    out = nn.HybridSequential(prefix='')\n    out.add(nn.BatchNorm())\n    #out.add(nn.Activation('relu'))\n    out.add(Act())\n    out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False))\n    out.add(nn.AvgPool2D(pool_size=2, strides=2))\n    return out\n\n# Net\nclass DenseNet(nn.HybridBlock):\n    r\"\"\"Densenet-BC model from the\n    `\"Densely Connected Convolutional Networks\" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper.\n\n    Parameters\n    ----------\n    num_init_features : int\n        Number of filters to learn in the first convolution layer.\n    growth_rate : int\n        Number of filters to add each layer (`k` in the paper).\n    block_config : list of int\n        List of integers for numbers of layers in each pooling block.\n    bn_size : int, default 4\n        Multiplicative factor for number of bottle neck layers.\n        (i.e. bn_size * k features in the bottleneck layer)\n    dropout : float, default 0\n        Rate of dropout after each dense layer.\n    classes : int, default 1000\n        Number of classification classes.\n    \"\"\"\n    def __init__(self, num_init_features, growth_rate, block_config,\n                 bn_size=4, dropout=0, classes=1000, **kwargs):\n\n        super(DenseNet, self).__init__(**kwargs)\n        with self.name_scope():\n            self.features = nn.HybridSequential(prefix='')\n            self.features.add(nn.Conv2D(num_init_features, kernel_size=3,\n                                        strides=1, padding=1, use_bias=False))\n            self.features.add(nn.BatchNorm())\n            self.features.add(nn.Activation('relu'))\n            self.features.add(nn.MaxPool2D(pool_size=3, strides=2, padding=1))\n            # Add dense blocks\n            num_features = num_init_features\n            for i, num_layers in enumerate(block_config):\n                self.features.add(_make_dense_block(num_layers, bn_size, growth_rate, dropout, i+1))\n                num_features = num_features + num_layers * growth_rate\n                if i != len(block_config) - 1:\n                    self.features.add(_make_transition(num_features // 2))\n                    num_features = num_features // 2\n            self.features.add(nn.BatchNorm())\n            self.features.add(nn.Activation('relu'))\n            #self.features.add(nn.AvgPool2D(pool_size=7))\n            #self.features.add(nn.Flatten())\n\n            #self.output = nn.Dense(classes)\n\n    def hybrid_forward(self, F, x):\n        x = self.features(x)\n        #x = self.output(x)\n        return x\n\n\n# Specification\ndensenet_spec = {121: (64, 32, [6, 12, 24, 16]),\n                 161: (96, 48, [6, 12, 36, 24]),\n                 169: (64, 32, [6, 12, 32, 32]),\n                 201: (64, 32, [6, 12, 48, 32])}\n\n\n# Constructor\ndef get_symbol():\n    num_layers = config.num_layers\n    num_init_features, growth_rate, block_config = densenet_spec[num_layers]\n    net = DenseNet(num_init_features, growth_rate, block_config, dropout = config.densenet_dropout)\n    data = mx.sym.Variable(name='data')\n    data = data-127.5\n    data = data*0.0078125\n    body = net(data)\n    fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output)\n    return fc1\n\n"
  },
  {
    "path": "face_recognition/symbol/fmnasnet.py",
    "content": "import sys\r\nimport os\r\nimport mxnet as mx\r\nimport mxnet.ndarray as nd\r\nimport mxnet.gluon as gluon\r\nimport mxnet.gluon.nn as nn\r\nimport mxnet.autograd as ag\r\nimport symbol_utils\r\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\r\nfrom config import config\r\n\r\ndef Act():\r\n    if config.net_act=='prelu':\r\n      return nn.PReLU()\r\n    else:\r\n      return nn.Activation(config.net_act)\r\n\r\ndef ConvBlock(channels, kernel_size, strides, **kwargs):\r\n    out = nn.HybridSequential(**kwargs)\r\n    with out.name_scope():\r\n        out.add(\r\n            nn.Conv2D(channels, kernel_size, strides=strides, padding=1, use_bias=False),\r\n            nn.BatchNorm(scale=True),\r\n            Act()\r\n            #nn.Activation('relu')\r\n        )\r\n    return out\r\n\r\ndef Conv1x1(channels, is_linear=False, **kwargs):\r\n    out = nn.HybridSequential(**kwargs)\r\n    with out.name_scope():\r\n        out.add(\r\n            nn.Conv2D(channels, 1, padding=0, use_bias=False),\r\n            nn.BatchNorm(scale=True)\r\n        )\r\n        if not is_linear:\r\n            #out.add(nn.Activation('relu'))\r\n            out.add(Act())\r\n    return out\r\n\r\ndef DWise(channels, strides, kernel_size=3, **kwargs):\r\n    out = nn.HybridSequential(**kwargs)\r\n    with out.name_scope():\r\n        out.add(\r\n            nn.Conv2D(channels, kernel_size, strides=strides, padding=kernel_size // 2, groups=channels, use_bias=False),\r\n            nn.BatchNorm(scale=True),\r\n            Act()\r\n            #nn.Activation('relu')\r\n        )\r\n    return out\r\n\r\nclass SepCONV(nn.HybridBlock):\r\n    def __init__(self, inp, output, kernel_size, depth_multiplier=1, with_bn=True, **kwargs):\r\n        super(SepCONV, self).__init__(**kwargs)\r\n        with self.name_scope():\r\n            self.net = nn.HybridSequential()\r\n            cn = int(inp*depth_multiplier)\r\n\r\n            if output is None:\r\n                self.net.add(\r\n                    nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2\r\n                        , use_bias=not with_bn)\r\n                )\r\n            else:\r\n                self.net.add(\r\n                    nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2\r\n                        , use_bias=False),\r\n                    nn.BatchNorm(),\r\n                    Act(),\r\n                    #nn.Activation('relu'),\r\n                    nn.Conv2D(in_channels=cn, channels=output, kernel_size=(1,1), strides=(1,1)\r\n                        , use_bias=not with_bn)\r\n                )\r\n\r\n            self.with_bn = with_bn\r\n            self.act = Act()\r\n            #self.act = nn.Activation('relu')\r\n            if with_bn:\r\n                self.bn = nn.BatchNorm()\r\n    def hybrid_forward(self, F ,x):\r\n        x = self.net(x)\r\n        if self.with_bn:\r\n            x = self.bn(x)\r\n        if self.act is not None:\r\n            x = self.act(x)\r\n        return x\r\n\r\nclass ExpandedConv(nn.HybridBlock):\r\n    def __init__(self, inp, oup, t, strides, kernel=3, same_shape=True, **kwargs):\r\n        super(ExpandedConv, self).__init__(**kwargs)\r\n\r\n        self.same_shape = same_shape\r\n        self.strides = strides\r\n        with self.name_scope(): \r\n            self.bottleneck = nn.HybridSequential()\r\n            self.bottleneck.add(\r\n                Conv1x1(inp*t, prefix=\"expand_\"),\r\n                DWise(inp*t, self.strides, kernel, prefix=\"dwise_\"),\r\n                Conv1x1(oup, is_linear=True, prefix=\"linear_\")\r\n            )\r\n    def hybrid_forward(self, F, x):\r\n        out = self.bottleneck(x)\r\n        if self.strides == 1 and self.same_shape:\r\n            out = F.elemwise_add(out, x)\r\n        return out\r\n\r\ndef ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs):\r\n    seq = nn.HybridSequential(**kwargs)\r\n    with seq.name_scope():\r\n        seq.add(ExpandedConv(inp, oup, t, first_strides, k, same_shape=False))\r\n        curr_inp = oup\r\n        for i in range(1, repeats):\r\n            seq.add(ExpandedConv(curr_inp, oup, t, 1))\r\n            curr_inp = oup\r\n    return seq\r\n\r\nclass MNasNet(nn.HybridBlock):\r\n    def __init__(self, m=1.0, **kwargs):\r\n        super(MNasNet, self).__init__(**kwargs)\r\n        \r\n        self.first_oup = int(32*m)\r\n        self.second_oup = int(16*m)\r\n        #self.second_oup = int(32*m)\r\n        self.interverted_residual_setting = [\r\n            # t, c,  n, s, k\r\n            [3, int(24*m),  3, 2, 3, \"stage2_\"],  # -> 56x56\r\n            [3, int(40*m),  3, 2, 5, \"stage3_\"],  # -> 28x28\r\n            [6, int(80*m),  3, 2, 5, \"stage4_1_\"],  # -> 14x14\r\n            [6, int(96*m),  2, 1, 3, \"stage4_2_\"],  # -> 14x14\r\n            [6, int(192*m), 4, 2, 5, \"stage5_1_\"], # -> 7x7\r\n            [6, int(320*m), 1, 1, 3, \"stage5_2_\"], # -> 7x7          \r\n        ]\r\n        self.last_channels = int(1024*m)\r\n\r\n        with self.name_scope():\r\n            self.features = nn.HybridSequential()\r\n            self.features.add(ConvBlock(self.first_oup, 3, 1, prefix=\"stage1_conv0_\"))\r\n            self.features.add(SepCONV(self.first_oup, self.second_oup, 3, prefix=\"stage1_sepconv0_\"))\r\n            inp = self.second_oup\r\n            for i, (t, c, n, s, k, prefix) in enumerate(self.interverted_residual_setting):\r\n                oup = c\r\n                self.features.add(ExpandedConvSequence(t, k, inp, oup, n, s, prefix=prefix))\r\n                inp = oup\r\n\r\n            self.features.add(Conv1x1(self.last_channels, prefix=\"stage5_3_\"))\r\n            #self.features.add(nn.GlobalAvgPool2D())\r\n            #self.features.add(nn.Flatten())\r\n            #self.output = nn.Dense(num_classes)\r\n    def hybrid_forward(self, F, x):\r\n        x = self.features(x)\r\n        #x = self.output(x)\r\n        return x\r\n\r\n    def num_output_channel(self):\r\n      return self.last_channels\r\n\r\ndef get_symbol():\r\n  net = MNasNet(config.net_multiplier)\r\n  data = mx.sym.Variable(name='data')\r\n  data = data-127.5\r\n  data = data*0.0078125\r\n  body = net(data)\r\n  fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output, input_channel=net.num_output_channel())\r\n  return fc1\r\n\r\n"
  },
  {
    "path": "face_recognition/symbol/fmobilefacenet.py",
    "content": "\nimport sys\nimport os\nimport mxnet as mx\nimport symbol_utils\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\n\ndef Act(data, act_type, name):\n    #ignore param act_type, set it in this function \n    if act_type=='prelu':\n      body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)\n    else:\n      body = mx.sym.Activation(data=data, act_type=act_type, name=name)\n    return body\n\ndef Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom)\n    act = Act(data=bn, act_type=config.net_act, name='%s%s_relu' %(name, suffix))\n    return act\n    \ndef Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom)    \n    return bn\n\ndef ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    return conv    \n\n    \ndef DResidual(data, num_out=1, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=1, name=None, suffix=''):\n    conv = Conv(data=data, num_filter=num_group, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_sep' %(name, suffix))\n    conv_dw = Conv(data=conv, num_filter=num_group, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name='%s%s_conv_dw' %(name, suffix))\n    proj = Linear(data=conv_dw, num_filter=num_out, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_proj' %(name, suffix))\n    return proj\n    \ndef Residual(data, num_block=1, num_out=1, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=1, name=None, suffix=''):\n    identity=data\n    for i in range(num_block):\n    \tshortcut=identity\n    \tconv=DResidual(data=identity, num_out=num_out, kernel=kernel, stride=stride, pad=pad, num_group=num_group, name='%s%s_block' %(name, suffix), suffix='%d'%i)\n    \tidentity=conv+shortcut\n    return identity\n        \n\ndef get_symbol():\n    num_classes = config.emb_size\n    print('in_network', config)\n    fc_type = config.net_output\n    data = mx.symbol.Variable(name=\"data\")\n    data = data-127.5\n    data = data*0.0078125\n    blocks = config.net_blocks\n    conv_1 = Conv(data, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_1\")\n    if blocks[0]==1:\n      conv_2_dw = Conv(conv_1, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_2_dw\")\n    else:\n      conv_2_dw = Residual(conv_1, num_block=blocks[0], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=64, name=\"res_2\")\n    conv_23 = DResidual(conv_2_dw, num_out=64, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=128, name=\"dconv_23\")\n    conv_3 = Residual(conv_23, num_block=blocks[1], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=128, name=\"res_3\")\n    conv_34 = DResidual(conv_3, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=256, name=\"dconv_34\")\n    conv_4 = Residual(conv_34, num_block=blocks[2], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name=\"res_4\")\n    conv_45 = DResidual(conv_4, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=512, name=\"dconv_45\")\n    conv_5 = Residual(conv_45, num_block=blocks[3], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name=\"res_5\")\n    conv_6_sep = Conv(conv_5, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_6sep\")\n\n    fc1 = symbol_utils.get_fc1(conv_6_sep, num_classes, fc_type)\n    return fc1\n\n"
  },
  {
    "path": "face_recognition/symbol/fmobilenet.py",
    "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\nimport sys\nimport os\nimport mxnet as mx\nimport symbol_utils\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\ndef Act(data, act_type, name):\n    #ignore param act_type, set it in this function \n    if act_type=='prelu':\n      body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)\n    else:\n      body = mx.sym.Activation(data=data, act_type=act_type, name=name)\n    return body\n\ndef Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True)\n    act = Act(data=bn, act_type=config.net_act, name='%s%s_relu' %(name, suffix))\n    return act\n\ndef ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    return conv\n\ndef get_symbol():\n    num_classes = config.emb_size\n    bn_mom = config.bn_mom\n    workspace = config.workspace\n    data = mx.symbol.Variable(name=\"data\") # 224\n    data = data-127.5\n    data = data*0.0078125\n    fc_type = config.net_output\n    bf = int(32*config.net_multiplier)\n    if config.net_input==0:\n      conv_1 = Conv(data, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_1\") # 224/112\n    else:\n      conv_1 = Conv(data, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_1\") # 224/112\n    conv_2_dw = Conv(conv_1, num_group=bf, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_2_dw\") # 112/112\n    conv_2 = Conv(conv_2_dw, num_filter=bf*2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_2\") # 112/112\n    conv_3_dw = Conv(conv_2, num_group=bf*2, num_filter=bf*2, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_3_dw\") # 112/56\n    conv_3 = Conv(conv_3_dw, num_filter=bf*4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_3\") # 56/56\n    conv_4_dw = Conv(conv_3, num_group=bf*4, num_filter=bf*4, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_4_dw\") # 56/56\n    conv_4 = Conv(conv_4_dw, num_filter=bf*4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_4\") # 56/56\n    conv_5_dw = Conv(conv_4, num_group=bf*4, num_filter=bf*4, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_5_dw\") # 56/28\n    conv_5 = Conv(conv_5_dw, num_filter=bf*8, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_5\") # 28/28\n    conv_6_dw = Conv(conv_5, num_group=bf*8, num_filter=bf*8, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_6_dw\") # 28/28\n    conv_6 = Conv(conv_6_dw, num_filter=bf*8, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_6\") # 28/28\n    conv_7_dw = Conv(conv_6, num_group=bf*8, num_filter=bf*8, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_7_dw\") # 28/14\n    conv_7 = Conv(conv_7_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_7\") # 14/14\n\n    conv_8_dw = Conv(conv_7, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_8_dw\") # 14/14\n    conv_8 = Conv(conv_8_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_8\") # 14/14\n    conv_9_dw = Conv(conv_8, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_9_dw\") # 14/14\n    conv_9 = Conv(conv_9_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_9\") # 14/14\n    conv_10_dw = Conv(conv_9, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_10_dw\") # 14/14\n    conv_10 = Conv(conv_10_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_10\") # 14/14\n    conv_11_dw = Conv(conv_10, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_11_dw\") # 14/14\n    conv_11 = Conv(conv_11_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_11\") # 14/14\n    conv_12_dw = Conv(conv_11, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_12_dw\") # 14/14\n    conv_12 = Conv(conv_12_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_12\") # 14/14\n\n    conv_13_dw = Conv(conv_12, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name=\"conv_13_dw\") # 14/7\n    conv_13 = Conv(conv_13_dw, num_filter=bf*32, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_13\") # 7/7\n    conv_14_dw = Conv(conv_13, num_group=bf*32, num_filter=bf*32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name=\"conv_14_dw\") # 7/7\n    conv_14 = Conv(conv_14_dw, num_filter=bf*32, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"conv_14\") # 7/7\n    body = conv_14\n    fc1 = symbol_utils.get_fc1(body, num_classes, fc_type)\n    return fc1\n\n"
  },
  {
    "path": "face_recognition/symbol/fresnet.py",
    "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n'''\nAdapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py\nOriginal author Wei Wu\n\nImplemented the following paper:\n\nKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. \"Identity Mappings in Deep Residual Networks\"\n'''\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nimport sys\nimport os\nimport mxnet as mx\nimport numpy as np\nimport symbol_utils\nimport memonger\nimport sklearn\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\ndef Conv(**kwargs):\n    #name = kwargs.get('name')\n    #_weight = mx.symbol.Variable(name+'_weight')\n    #_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)\n    #body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)\n    body = mx.sym.Convolution(**kwargs)\n    return body\n\n\ndef Act(data, act_type, name):\n    if act_type=='prelu':\n      body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)\n    else:\n      body = mx.symbol.Activation(data=data, act_type=act_type, name=name)\n    return body\n\ndef residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    use_se = kwargs.get('version_se', 1)\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    act_type = kwargs.get('version_act', 'prelu')\n    #print('in unit1')\n    if bottle_neck:\n        conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0),\n                                   no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),\n                                   no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')\n        conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,\n                                   workspace=workspace, name=name + '_conv3')\n        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn3 = mx.symbol.broadcast_mul(bn3, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3')\n    else:\n        conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn2 = mx.symbol.broadcast_mul(bn2, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3')\n\ndef residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    use_se = kwargs.get('version_se', 1)\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    act_type = kwargs.get('version_act', 'prelu')\n    #print('in unit1')\n    if bottle_neck:\n        conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),\n                                   no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),\n                                   no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')\n        conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,\n                                   workspace=workspace, name=name + '_conv3')\n        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn3 = mx.symbol.broadcast_mul(bn3, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3')\n    else:\n        conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn2 = mx.symbol.broadcast_mul(bn2, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3')\n\ndef residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    use_se = kwargs.get('version_se', 1)\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    act_type = kwargs.get('version_act', 'prelu')\n    #print('in unit2')\n    if bottle_neck:\n        # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper\n        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv1 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),\n                                   no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')\n        conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1),\n                                   no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n        act3 = Act(data=bn3, act_type=act_type, name=name + '_relu3')\n        conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True,\n                                   workspace=workspace, name=name + '_conv3')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=conv3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          conv3 = mx.symbol.broadcast_mul(conv3, body)\n        if dim_match:\n            shortcut = data\n        else:\n            shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return conv3 + shortcut\n    else:\n        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')\n        act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')\n        conv2 = Conv(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv2')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=conv2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          conv2 = mx.symbol.broadcast_mul(conv2, body)\n        if dim_match:\n            shortcut = data\n        else:\n            shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return conv2 + shortcut\n\ndef residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n    \n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    use_se = kwargs.get('version_se', 1)\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    act_type = kwargs.get('version_act', 'prelu')\n    #print('in unit3')\n    if bottle_neck:\n        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        conv1 = Conv(data=bn1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),\n                                   no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),\n                                   no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n        act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2')\n        conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,\n                                   workspace=workspace, name=name + '_conv3')\n        bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4')\n\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn4 = mx.symbol.broadcast_mul(bn4, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return bn4 + shortcut\n    else:\n        bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn3 = mx.symbol.broadcast_mul(bn3, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        \n        act2 = Act(data=bn3*shortcut, act_type='tanh', name=name + '_tanh1')\n        conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1, 1), stride=(1,1), pad=(0, 0),\n                     no_bias=True, workspace=workspace, name=name + '_conv3')\n        bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4')\n\n        return bn3 + shortcut + bn4\n\ndef residual_unit_v3_x(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n    \n    \"\"\"Return ResNeXt Unit symbol for building ResNeXt\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    assert(bottle_neck)\n    use_se = kwargs.get('version_se', 1)\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    act_type = kwargs.get('version_act', 'prelu')\n    num_group = 32\n    #print('in unit3')\n    bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n    conv1 = Conv(data=bn1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0),\n                               no_bias=True, workspace=workspace, name=name + '_conv1')\n    bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n    act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1')\n    conv2 = Conv(data=act1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1),\n                               no_bias=True, workspace=workspace, name=name + '_conv2')\n    bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n    act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2')\n    conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,\n                               workspace=workspace, name=name + '_conv3')\n    bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4')\n\n    if use_se:\n      #se begin\n      body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n      body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                name=name+\"_se_conv1\", workspace=workspace)\n      body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n      body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                name=name+\"_se_conv2\", workspace=workspace)\n      body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n      bn4 = mx.symbol.broadcast_mul(bn4, body)\n      #se end\n\n    if dim_match:\n        shortcut = data\n    else:\n        conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                        workspace=workspace, name=name+'_conv1sc')\n        shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')\n    if memonger:\n        shortcut._set_attr(mirror_stage='True')\n    return bn4 + shortcut\n\n\ndef residual_unit(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs):\n  uv = kwargs.get('version_unit', 3)\n  version_input = kwargs.get('version_input', 1)\n#  print(uv, version_input)\n  if uv==1:\n    if version_input==0:\n      return residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs)\n    else:\n      return residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs)\n  elif uv==2:\n    return residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs)\n  elif uv==4:\n    return residual_unit_v4(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs)\n  else:\n    return residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs)\n\ndef resnet(units, num_stages, filter_list, num_classes, bottle_neck):\n    bn_mom = config.bn_mom\n    workspace = config.workspace\n    kwargs = {'version_se' : config.net_se,\n        'version_input': config.net_input,\n        'version_output': config.net_output,\n        'version_unit': config.net_unit,\n        'version_act': config.net_act,\n        'bn_mom': bn_mom,\n        'workspace': workspace,\n        'memonger': config.memonger,\n        }\n    \"\"\"Return ResNet symbol of\n    Parameters\n    ----------\n    units : list\n        Number of units in each stage\n    num_stages : int\n        Number of stage\n    filter_list : list\n        Channel size of each stage\n    num_classes : int\n        Ouput size of symbol\n    dataset : str\n        Dataset type, only cifar10 and imagenet supports\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    version_se = kwargs.get('version_se', 1)\n    version_input = kwargs.get('version_input', 1)\n    assert version_input>=0\n    version_output = kwargs.get('version_output', 'E')\n    fc_type = version_output\n    version_unit = kwargs.get('version_unit', 3)\n    act_type = kwargs.get('version_act', 'prelu')\n    memonger = kwargs.get('memonger', False)\n    print(version_se, version_input, version_output, version_unit, act_type, memonger)\n    num_unit = len(units)\n    assert(num_unit == num_stages)\n    data = mx.sym.Variable(name='data')\n    if version_input==0:\n      #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')\n      data = mx.sym.identity(data=data, name='id')\n      data = data-127.5\n      data = data*0.0078125\n      body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3),\n                                no_bias=True, name=\"conv0\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')\n      body = Act(data=body, act_type=act_type, name='relu0')\n      #body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')\n    elif version_input==2:\n      data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')\n      body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1,1),\n                                no_bias=True, name=\"conv0\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')\n      body = Act(data=body, act_type=act_type, name='relu0')\n    else:\n      data = mx.sym.identity(data=data, name='id')\n      data = data-127.5\n      data = data*0.0078125\n      body = data\n      body = Conv(data=body, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1),\n                                no_bias=True, name=\"conv0\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')\n      body = Act(data=body, act_type=act_type, name='relu0')\n\n    for i in range(num_stages):\n      #if version_input==0:\n      #  body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False,\n      #                       name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs)\n      #else:\n      #  body = residual_unit(body, filter_list[i+1], (2, 2), False,\n      #    name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs)\n      body = residual_unit(body, filter_list[i+1], (2, 2), False,\n        name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs)\n      for j in range(units[i]-1):\n        body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i+1, j+2),\n          bottle_neck=bottle_neck, **kwargs)\n\n    if bottle_neck:\n      body = Conv(data=body, num_filter=512, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                no_bias=True, name=\"convd\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnd')\n      body = Act(data=body, act_type=act_type, name='relud')\n\n    fc1 = symbol_utils.get_fc1(body, num_classes, fc_type)\n    return fc1\n\ndef get_symbol():\n    \"\"\"\n    Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py\n    Original author Wei Wu\n    \"\"\"\n    num_classes = config.emb_size\n    num_layers = config.num_layers\n    if num_layers >= 500:\n        filter_list = [64, 256, 512, 1024, 2048]\n        bottle_neck = True\n    else:\n        filter_list = [64, 64, 128, 256, 512]\n        bottle_neck = False\n    num_stages = 4\n    if num_layers == 18:\n        units = [2, 2, 2, 2]\n    elif num_layers == 34:\n        units = [3, 4, 6, 3]\n    elif num_layers == 49:\n        units = [3, 4, 14, 3]\n    elif num_layers == 50:\n        units = [3, 4, 14, 3]\n    elif num_layers == 74:\n        units = [3, 6, 24, 3]\n    elif num_layers == 90:\n        units = [3, 8, 30, 3]\n    elif num_layers == 98:\n        units = [3, 4, 38, 3]\n    elif num_layers == 99:\n        units = [3, 8, 35, 3]\n    elif num_layers == 100:\n        units = [3, 13, 30, 3]\n    elif num_layers == 134:\n        units = [3, 10, 50, 3]\n    elif num_layers == 136:\n        units = [3, 13, 48, 3]\n    elif num_layers == 140:\n        units = [3, 15, 48, 3]\n    elif num_layers == 124:\n        units = [3, 13, 40, 5]\n    elif num_layers == 160:\n        units = [3, 24, 49, 3]\n    elif num_layers == 101:\n        units = [3, 4, 23, 3]\n    elif num_layers == 152:\n        units = [3, 8, 36, 3]\n    elif num_layers == 200:\n        units = [3, 24, 36, 3]\n    elif num_layers == 269:\n        units = [3, 30, 48, 8]\n    else:\n        raise ValueError(\"no experiments done on num_layers {}, you can do it yourself\".format(num_layers))\n\n    net = resnet(units       = units,\n                  num_stages  = num_stages,\n                  filter_list = filter_list,\n                  num_classes = num_classes,\n                  bottle_neck = bottle_neck)\n\n    if config.memonger:\n      dshape = (config.per_batch_size, config.image_shape[2], config.image_shape[0], config.image_shape[1])\n      net_mem_planned = memonger.search_plan(net, data=dshape)\n      old_cost = memonger.get_cost(net, data=dshape)\n      new_cost = memonger.get_cost(net_mem_planned, data=dshape)\n\n      print('Old feature map cost=%d MB' % old_cost)\n      print('New feature map cost=%d MB' % new_cost)\n      net = net_mem_planned\n    return net\n\n\n\n"
  },
  {
    "path": "face_recognition/symbol/memonger.py",
    "content": "import mxnet as mx\nimport math\n\ndef prod(shape):\n    \"\"\"Get product of the shape.\n    \"\"\"\n    ret = 1\n    for s in shape:\n        ret *= s\n    return ret\n\n\ndef is_param(name):\n    \"\"\"Quick script to check if name is a parameter.\n    \"\"\"\n    if name == 'data':\n        return False\n    if name.endswith('weight'):\n        return True\n    if name.endswith('bias'):\n        return True\n    if name.endswith('beta'):\n        return True\n    if name.endswith('gamma'):\n        return True\n    return False\n\n\ndef make_mirror_plan(sym, threshold, plan_info=None, **kwargs):\n    \"\"\"Memory allocation planner with a given threshold.\n\n    The user can pass in a network configuration,\n    a threshold that limits memory per block.\n    And input shape configurations.\n\n    Parameters\n    ----------\n    sym : symbol\n        Input configuration of symbols.\n        The user need to pre-mark the attribute \"mirror_stage\" on the nodes\n        that can be book-kept as stage\n\n        The algorithm will decide whether to disbale mirror on the stage nodes.\n\n    threshold: integer\n        A tuning parameter to tune the approximate size of each stage blocks\n\n    plan_info: dict, optional\n        Used to hold plan information.\n\n    **kwargs:\n        The arguments to infer shape.\n\n    Returns\n    -------\n    alloc_sym: symbol\n        A symbol with force mirror tagged on the nodes for better allocation.\n    \"\"\"\n    threshold = threshold << 20\n    sym = sym.__copy__()\n    internals = sym.get_internals()\n    _, out_shapes, _ = internals.infer_shape(**kwargs)\n    shape_dict = list(zip(internals.list_outputs(), out_shapes))\n    total_size = 0\n    param_size = 0\n    local_size = 0\n    save_size = 0\n    max_size = 0\n    last_sb = None\n    last_local = 0\n    period = 1\n    last_stage = ''\n    stage_decision = ''\n\n    for idx, item in enumerate(shape_dict):\n        sb = internals[idx]\n        name, shape = item\n        if is_param(name):\n            param_size += prod(shape) * 4\n            continue\n        else:\n            total_size += prod(shape) * 4\n            local_size += prod(shape) * 4\n            sb._set_attr(force_mirroring='True')\n\n        if sb.attr('mirror_stage') is not None:\n            stage = sb.attr('mirror_stage')\n            if stage == 'True' or stage != last_stage:\n                if local_size > threshold:\n                    save_size += prod(shape) * 4\n                    max_size = max(max_size, local_size)\n                    local_size = 0\n                    stage_decision = 'False'\n                    sb._set_attr(force_mirroring=stage_decision)\n                else:\n                    stage_decision = 'True'\n                    pass\n                last_stage = stage\n            elif stage == last_stage and stage_decision == 'False':\n                save_size += prod(shape) * 4\n                sb._set_attr(force_mirroring=stage_decision)\n\n    if plan_info is not None:\n        plan_info['max_size'] = max_size\n        plan_info['save_size'] = save_size\n    return sym\n\n\ndef get_cost(sym, type_dict=None, **kwargs):\n    \"\"\"Get the cost of the current symbolic plan by running bind on CPU.\n\n    sym : Symbolic Variable\n\n    \"\"\"\n    texec = sym.simple_bind(ctx=mx.gpu(),\n                            grad_req='write',\n                            type_dict=type_dict,\n                            **kwargs)\n    return int(texec.debug_str().split('\\n')[-3].split()[1])\n\n\ndef search_plan(sym, ntrial=6, type_dict=None, **kwargs):\n    \"\"\"Quickly heurestic search over possible plans to find good memory plan.\n\n    Parameters\n    ----------\n    sym : symbolic\n       Symbolic configurations\n\n    ntrial: integer\n       Additional grid search steps\n    \"\"\"\n    history = []\n    threshold = 0\n    min_threshold = None\n    min_cost = None\n    nbegin = 3\n\n    for k in range(nbegin):\n        info = {}\n        sym = make_mirror_plan(sym, threshold=threshold, plan_info=info, **kwargs)\n        cost = get_cost(sym, type_dict, **kwargs)\n        save_size = info['save_size'] >> 20\n        local_size = info['max_size'] >> 20\n        guess = int(math.sqrt(save_size * local_size / 2))\n        if min_cost is None or min_cost > cost:\n            min_cost = cost\n        if min_threshold is None or local_size < min_threshold:\n            min_threshold = local_size\n        print (\"Search threshold=%d MB, cost=%d MB\" % (threshold, cost))\n        history.append((cost, threshold, sym))\n        threshold = guess\n\n    max_threshold = threshold * math.sqrt(2)\n    step = int((max_threshold - min_threshold) / ntrial)\n    threshold = min_threshold + step\n    if step > 0:\n        for k in range(ntrial):\n            sym = make_mirror_plan(sym, threshold=threshold, plan_info=info, **kwargs)\n            cost = get_cost(sym, type_dict, **kwargs)\n            print (\"Search threshold=%d MB, cost=%d MB\" % (threshold, cost))\n            history.append((cost, threshold, sym))\n            threshold += step\n\n    history.sort(key = lambda x: x[0])\n    cost, threshold, sym = history[0]\n    print('Find best plan with threshold=%d, cost=%d MB' % (threshold, cost))\n    return sym\n"
  },
  {
    "path": "face_recognition/symbol/symbol_utils.py",
    "content": "import sys\nimport os\nimport mxnet as mx\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\ndef Conv(**kwargs):\n    #name = kwargs.get('name')\n    #_weight = mx.symbol.Variable(name+'_weight')\n    #_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0)\n    #body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs)\n    body = mx.sym.Convolution(**kwargs)\n    return body\n\ndef Act(data, act_type, name):\n    #ignore param act_type, set it in this function \n    if act_type=='prelu':\n      body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name)\n    else:\n      body = mx.sym.Activation(data=data, act_type=act_type, name=name)\n    return body\n\nbn_mom = config.bn_mom\ndef Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''):\n    conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix))\n    bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=bn_mom)    \n    return bn\n\ndef get_fc1(last_conv, num_classes, fc_type, input_channel=512):\n  body = last_conv\n  if fc_type=='Z':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    body = mx.symbol.Dropout(data=body, p=0.4)\n    fc1 = body\n  elif fc_type=='E':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    body = mx.symbol.Dropout(data=body, p=0.4)\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='FC':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='SFC':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    body = Conv(data=body, num_filter=input_channel, kernel=(3,3), stride=(2,2), pad=(1,1),\n                              no_bias=True, name=\"convf\", num_group = input_channel)\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnf')\n    body = Act(data=body, act_type=config.net_act, name='reluf')\n    body = Conv(data=body, num_filter=input_channel, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name=\"convf2\")\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnf2')\n    body = Act(data=body, act_type=config.net_act, name='reluf2')\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='GAP':\n    bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    relu1 = Act(data=bn1, act_type=config.net_act, name='relu1')\n    # Although kernel is not used here when global_pool=True, we should put one\n    pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1')\n    flat = mx.sym.Flatten(data=pool1)\n    fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='GNAP': #mobilefacenet++\n    filters_in = 512 # param in mobilefacenet\n    if num_classes>filters_in:\n      body = mx.sym.Convolution(data=last_conv, num_filter=num_classes, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, name='convx')\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=0.9, name='convx_bn')\n      body = Act(data=body, act_type=config.net_act, name='convx_relu')\n      filters_in = num_classes\n    else:\n      body = last_conv\n    body = mx.sym.BatchNorm(data=body, fix_gamma=True, eps=2e-5, momentum=0.9, name='bn6f')  \n\n    spatial_norm=body*body\n    spatial_norm=mx.sym.sum(data=spatial_norm, axis=1, keepdims=True)\n    spatial_sqrt=mx.sym.sqrt(spatial_norm)\n    #spatial_mean=mx.sym.mean(spatial_sqrt, axis=(1,2,3), keepdims=True)\n    spatial_mean=mx.sym.mean(spatial_sqrt)\n    spatial_div_inverse=mx.sym.broadcast_div(spatial_mean, spatial_sqrt)\n\n    spatial_attention_inverse=mx.symbol.tile(spatial_div_inverse, reps=(1,filters_in,1,1))   \n    body=body*spatial_attention_inverse\n    #body = mx.sym.broadcast_mul(body, spatial_div_inverse)\n\n    fc1 = mx.sym.Pooling(body, kernel=(7, 7), global_pool=True, pool_type='avg')\n    if num_classes<filters_in:\n      fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=0.9, name='bn6w')\n      fc1 = mx.sym.FullyConnected(data=fc1, num_hidden=num_classes, name='pre_fc1')\n    else:\n      fc1 = mx.sym.Flatten(data=fc1)\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=0.9, name='fc1')\n  elif fc_type==\"GDC\": #mobilefacenet_v1\n    conv_6_dw = Linear(last_conv, num_filter=input_channel, num_group=input_channel, kernel=(7,7), pad=(0, 0), stride=(1, 1), name=\"conv_6dw7_7\")  \n    conv_6_f = mx.sym.FullyConnected(data=conv_6_dw, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=conv_6_f, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='F':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    body = mx.symbol.Dropout(data=body, p=0.4)\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')\n  elif fc_type=='G':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')\n  elif fc_type=='H':\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='fc1')\n  elif fc_type=='I':\n    body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1')\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  elif fc_type=='J':\n    fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1')\n    fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1')\n  return fc1\n\ndef residual_unit_v3(data, num_filter, stride, dim_match, name, **kwargs):\n    \n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    workspace = kwargs.get('workspace', 256)\n    memonger = kwargs.get('memonger', False)\n    #print('in unit3')\n    bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n    conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                  no_bias=True, workspace=workspace, name=name + '_conv1')\n    bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n    act1 = Act(data=bn2, act_type=config.net_act, name=name + '_relu1')\n    conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                  no_bias=True, workspace=workspace, name=name + '_conv2')\n    bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n\n    if dim_match:\n        shortcut = data\n    else:\n        conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                        workspace=workspace, name=name+'_conv1sc')\n        shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')\n    if memonger:\n        shortcut._set_attr(mirror_stage='True')\n    return bn3 + shortcut\n\ndef residual_unit_v1l(data, num_filter, stride, dim_match, name, bottle_neck):\n    \"\"\"Return ResNet Unit symbol for building ResNet\n    Parameters\n    ----------\n    data : str\n        Input data\n    num_filter : int\n        Number of output channels\n    bnf : int\n        Bottle neck channels factor with regard to num_filter\n    stride : tuple\n        Stride used in convolution\n    dim_match : Boolean\n        True means channel number between input and output is the same, otherwise means differ\n    name : str\n        Base name of the operators\n    workspace : int\n        Workspace used in convolution operator\n    \"\"\"\n    workspace = config.workspace\n    bn_mom = config.bn_mom\n    memonger = False\n    use_se = config.net_se\n    act_type = config.net_act\n    #print('in unit1')\n    if bottle_neck:\n        conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0),\n                                   no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1),\n                                   no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2')\n        act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2')\n        conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True,\n                                   workspace=workspace, name=name + '_conv3')\n        bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3')\n\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn3 = mx.symbol.broadcast_mul(bn3, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3')\n    else:\n        conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv1')\n        bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1')\n        act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1')\n        conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1),\n                                      no_bias=True, workspace=workspace, name=name + '_conv2')\n        bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2')\n        if use_se:\n          #se begin\n          body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1')\n          body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv1\", workspace=workspace)\n          body = Act(data=body, act_type=act_type, name=name+'_se_relu1')\n          body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0),\n                                    name=name+\"_se_conv2\", workspace=workspace)\n          body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+\"_se_sigmoid\")\n          bn2 = mx.symbol.broadcast_mul(bn2, body)\n          #se end\n\n        if dim_match:\n            shortcut = data\n        else:\n            conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True,\n                                            workspace=workspace, name=name+'_conv1sc')\n            shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc')\n        if memonger:\n            shortcut._set_attr(mirror_stage='True')\n        return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3')\n\ndef get_head(data, version_input, num_filter):\n    bn_mom = config.bn_mom\n    workspace = config.workspace\n    kwargs = {'bn_mom': bn_mom, 'workspace' : workspace}\n    data = data-127.5\n    data = data*0.0078125\n    #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data')\n    if version_input==0:\n      body = Conv(data=data, num_filter=num_filter, kernel=(7, 7), stride=(2,2), pad=(3, 3),\n                                no_bias=True, name=\"conv0\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')\n      body = Act(data=body, act_type=config.net_act, name='relu0')\n      body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max')\n    else:\n      body = data\n      _num_filter = min(num_filter, 64)\n      body = Conv(data=body, num_filter=_num_filter, kernel=(3,3), stride=(1,1), pad=(1, 1),\n                                no_bias=True, name=\"conv0\", workspace=workspace)\n      body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0')\n      body = Act(data=body, act_type=config.net_act, name='relu0')\n      #body = residual_unit_v3(body, _num_filter, (2, 2), False, name='head', **kwargs)\n      body = residual_unit_v1l(body, _num_filter, (2, 2), False, name='head', bottle_neck=False)\n    return body\n\n\n"
  },
  {
    "path": "face_recognition/symbol/vargfacenet.py",
    "content": "# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n'''\nAuthor: Horizon Robotics Inc.\nThe company is committed to be the global leader of edge AI platform.\nThe model implemented in this scripts runs ~200fps on the Sunrise 2.\nSunrise 2 is the second generation of an embedded AI chip designed by Horizon Robotics,\ntargeting to empower AIoT devices by AI.\n\nImplemented the following paper:\nMengjia Yan, Mengao Zhao, Zining Xu, Qian Zhang, Guoli Wang, Zhizhong Su. \"VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition\" (https://arxiv.org/abs/1910.04985)\n\n'''\n\nimport os\nimport sys\n\nimport mxnet as mx\nimport symbol_utils\n\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\nfrom config import config\n\ndef Act(data, act_type, name):\n    if act_type == 'prelu':\n        body = mx.sym.LeakyReLU(data=data, act_type='prelu', name=name)\n    else:\n        body = mx.symbol.Activation(data=data, act_type=act_type, name=name)\n    return body\n\n\ndef get_setting_params(**kwargs):\n    # bn_params\n    bn_mom = kwargs.get('bn_mom', 0.9)\n    bn_eps = kwargs.get('bn_eps', 2e-5)\n    fix_gamma = kwargs.get('fix_gamma', False)\n    use_global_stats = kwargs.get('use_global_stats', False)\n    # net_setting param\n    workspace = kwargs.get('workspace', 512)\n    act_type = kwargs.get('act_type', 'prelu')\n    use_se = kwargs.get('use_se', True)\n    se_ratio = kwargs.get('se_ratio', 4)\n    group_base = kwargs.get('group_base', 8)\n\n    setting_params = {}\n    setting_params['bn_mom'] = bn_mom\n    setting_params['bn_eps'] = bn_eps\n    setting_params['fix_gamma'] = fix_gamma\n    setting_params['use_global_stats'] = use_global_stats\n    setting_params['workspace'] = workspace\n    setting_params['act_type'] = act_type\n    setting_params['use_se'] = use_se\n    setting_params['se_ratio'] = se_ratio\n    setting_params['group_base'] = group_base\n\n    return setting_params\n\n\ndef se_block(data, num_filter, setting_params, name):\n    se_ratio = setting_params['se_ratio']\n    act_type = setting_params['act_type']\n\n    pool1 = mx.sym.Pooling(data=data,\n                           global_pool=True,\n                           pool_type='avg',\n                           name=name + '_se_pool1')\n    conv1 = mx.sym.Convolution(data=pool1,\n                               num_filter=num_filter // se_ratio,\n                               kernel=(1, 1),\n                               stride=(1, 1),\n                               pad=(0, 0),\n                               name=name + \"_se_conv1\")\n    act1 = Act(data=conv1, act_type=act_type, name=name + '_se_act1')\n\n    conv2 = mx.sym.Convolution(data=act1,\n                               num_filter=num_filter,\n                               kernel=(1, 1),\n                               stride=(1, 1),\n                               pad=(0, 0),\n                               name=name + \"_se_conv2\")\n    act2 = mx.symbol.Activation(data=conv2,\n                                act_type='sigmoid',\n                                name=name + \"_se_sigmoid\")\n    out_data = mx.symbol.broadcast_mul(data, act2)\n    return out_data\n\n\ndef separable_conv2d(data,\n                     in_channels,\n                     out_channels,\n                     kernel,\n                     pad,\n                     setting_params,\n                     stride=(1, 1),\n                     factor=1,\n                     bias=False,\n                     bn_dw_out=True,\n                     act_dw_out=True,\n                     bn_pw_out=True,\n                     act_pw_out=True,\n                     dilate=1,\n                     name=None):\n    bn_mom = setting_params['bn_mom']\n    bn_eps = setting_params['bn_eps']\n    fix_gamma = setting_params['fix_gamma']\n    use_global_stats = setting_params['use_global_stats']\n    workspace = setting_params['workspace']\n    group_base = setting_params['group_base']\n    act_type = setting_params['act_type']\n    assert in_channels % group_base == 0\n\n    # depthwise\n    dw_out = mx.sym.Convolution(data=data,\n                                num_filter=int(in_channels * factor),\n                                kernel=kernel,\n                                pad=pad,\n                                stride=stride,\n                                no_bias=False if bias else True,\n                                num_group=int(in_channels / group_base),\n                                dilate=(dilate, dilate),\n                                workspace=workspace,\n                                name=name + '_conv2d_depthwise')\n    if bn_dw_out:\n        dw_out = mx.sym.BatchNorm(data=dw_out,\n                                  fix_gamma=fix_gamma,\n                                  eps=bn_eps,\n                                  momentum=bn_mom,\n                                  use_global_stats=use_global_stats,\n                                  name=name + '_conv2d_depthwise_bn')\n    if act_dw_out:\n        dw_out = Act(data=dw_out,\n                     act_type=act_type,\n                     name=name + '_conv2d_depthwise_act')\n    # pointwise\n    pw_out = mx.sym.Convolution(data=dw_out,\n                                num_filter=out_channels,\n                                kernel=(1, 1),\n                                stride=(1, 1),\n                                pad=(0, 0),\n                                num_group=1,\n                                no_bias=False if bias else True,\n                                workspace=workspace,\n                                name=name + '_conv2d_pointwise')\n    if bn_pw_out:\n        pw_out = mx.sym.BatchNorm(data=pw_out,\n                                  fix_gamma=fix_gamma,\n                                  eps=bn_eps,\n                                  momentum=bn_mom,\n                                  use_global_stats=use_global_stats,\n                                  name=name + '_conv2d_pointwise_bn')\n    if act_pw_out:\n        pw_out = Act(data=pw_out,\n                     act_type=act_type,\n                     name=name + '_conv2d_pointwise_act')\n    return pw_out\n\n\ndef vargnet_block(data,\n                  n_out_ch1,\n                  n_out_ch2,\n                  n_out_ch3,\n                  setting_params,\n                  factor=2,\n                  dim_match=True,\n                  multiplier=1,\n                  kernel=(3, 3),\n                  stride=(1, 1),\n                  dilate=1,\n                  with_dilate=False,\n                  name=None):\n    use_se = setting_params['use_se']\n    act_type = setting_params['act_type']\n\n    out_channels_1 = int(n_out_ch1 * multiplier)\n    out_channels_2 = int(n_out_ch2 * multiplier)\n    out_channels_3 = int(n_out_ch3 * multiplier)\n\n    pad = (((kernel[0] - 1) * dilate + 1) // 2,\n           ((kernel[1] - 1) * dilate + 1) // 2)\n\n    if with_dilate:\n        stride = (1, 1)\n    if dim_match:\n        short_cut = data\n    else:\n        short_cut = separable_conv2d(data=data,\n                                     in_channels=out_channels_1,\n                                     out_channels=out_channels_3,\n                                     kernel=kernel,\n                                     pad=pad,\n                                     setting_params=setting_params,\n                                     stride=stride,\n                                     factor=factor,\n                                     bias=False,\n                                     act_pw_out=False,\n                                     dilate=dilate,\n                                     name=name + '_shortcut')\n    sep1_data = separable_conv2d(data=data,\n                                 in_channels=out_channels_1,\n                                 out_channels=out_channels_2,\n                                 kernel=kernel,\n                                 pad=pad,\n                                 setting_params=setting_params,\n                                 stride=stride,\n                                 factor=factor,\n                                 bias=False,\n                                 dilate=dilate,\n                                 name=name + '_sep1_data')\n    sep2_data = separable_conv2d(data=sep1_data,\n                                 in_channels=out_channels_2,\n                                 out_channels=out_channels_3,\n                                 kernel=kernel,\n                                 pad=pad,\n                                 setting_params=setting_params,\n                                 stride=(1, 1),\n                                 factor=factor,\n                                 bias=False,\n                                 dilate=dilate,\n                                 act_pw_out=False,\n                                 name=name + '_sep2_data')\n\n    if use_se:\n        sep2_data = se_block(data=sep2_data,\n                             num_filter=out_channels_3,\n                             setting_params=setting_params,\n                             name=name)\n\n    out_data = sep2_data + short_cut\n    out_data = Act(data=out_data, act_type=act_type, name=name + '_out_data_act')\n    return out_data\n\n\ndef vargnet_branch_merge_block(data,\n                               n_out_ch1,\n                               n_out_ch2,\n                               n_out_ch3,\n                               setting_params,\n                               factor=2,\n                               dim_match=False,\n                               multiplier=1,\n                               kernel=(3, 3),\n                               stride=(2, 2),\n                               dilate=1,\n                               with_dilate=False,\n                               name=None):\n    act_type = setting_params['act_type']\n\n    out_channels_1 = int(n_out_ch1 * multiplier)\n    out_channels_2 = int(n_out_ch2 * multiplier)\n    out_channels_3 = int(n_out_ch3 * multiplier)\n\n    pad = (((kernel[0] - 1) * dilate + 1) // 2,\n           ((kernel[1] - 1) * dilate + 1) // 2)\n\n    if with_dilate:\n        stride = (1, 1)\n    if dim_match:\n        short_cut = data\n    else:\n        short_cut = separable_conv2d(data=data,\n                                     in_channels=out_channels_1,\n                                     out_channels=out_channels_3,\n                                     kernel=kernel,\n                                     pad=pad,\n                                     setting_params=setting_params,\n                                     stride=stride,\n                                     factor=factor,\n                                     bias=False,\n                                     act_pw_out=False,\n                                     dilate=dilate,\n                                     name=name + '_shortcut')\n    sep1_data_brach1 = separable_conv2d(data=data,\n                                        in_channels=out_channels_1,\n                                        out_channels=out_channels_2,\n                                        kernel=kernel,\n                                        pad=pad,\n                                        setting_params=setting_params,\n                                        stride=stride,\n                                        factor=factor,\n                                        bias=False,\n                                        dilate=dilate,\n                                        act_pw_out=False,\n                                        name=name + '_sep1_data_branch')\n    sep1_data_brach2 = separable_conv2d(data=data,\n                                        in_channels=out_channels_1,\n                                        out_channels=out_channels_2,\n                                        kernel=kernel,\n                                        pad=pad,\n                                        setting_params=setting_params,\n                                        stride=stride,\n                                        factor=factor,\n                                        bias=False,\n                                        dilate=dilate,\n                                        act_pw_out=False,\n                                        name=name + '_sep2_data_branch')\n    sep1_data = sep1_data_brach1 + sep1_data_brach2\n    sep1_data = Act(data=sep1_data, act_type=act_type, name=name + '_sep1_data_act')\n    sep2_data = separable_conv2d(data=sep1_data,\n                                 in_channels=out_channels_2,\n                                 out_channels=out_channels_3,\n                                 kernel=kernel,\n                                 pad=pad,\n                                 setting_params=setting_params,\n                                 stride=(1, 1),\n                                 factor=factor,\n                                 bias=False,\n                                 dilate=dilate,\n                                 act_pw_out=False,\n                                 name=name + '_sep2_data')\n    out_data = sep2_data + short_cut\n    out_data = Act(data=out_data, act_type=act_type, name=name + '_out_data_act')\n    return out_data\n\n\ndef add_vargnet_conv_block(data,\n                           stage,\n                           units,\n                           in_channels,\n                           out_channels,\n                           setting_params,\n                           kernel=(3, 3),\n                           stride=(2, 2),\n                           multiplier=1,\n                           factor=2,\n                           dilate=1,\n                           with_dilate=False,\n                           name=None):\n    assert stage >= 2, 'stage is {}, stage must be set >=2'.format(stage)\n    data = vargnet_branch_merge_block(data=data,\n                                      n_out_ch1=in_channels,\n                                      n_out_ch2=out_channels,\n                                      n_out_ch3=out_channels,\n                                      setting_params=setting_params,\n                                      factor=factor,\n                                      dim_match=False,\n                                      multiplier=multiplier,\n                                      kernel=kernel,\n                                      stride=stride,\n                                      dilate=dilate,\n                                      with_dilate=with_dilate,\n                                      name=name + '_stage_{}_unit_1'.format(stage))\n    for i in range(units - 1):\n        data = vargnet_block(data=data,\n                             n_out_ch1=out_channels,\n                             n_out_ch2=out_channels,\n                             n_out_ch3=out_channels,\n                             setting_params=setting_params,\n                             factor=factor,\n                             dim_match=True,\n                             multiplier=multiplier,\n                             kernel=kernel,\n                             stride=(1, 1),\n                             dilate=dilate,\n                             with_dilate=with_dilate,\n                             name=name + '_stage_{}_unit_{}'.format(stage, i + 2))\n    return data\n\n\ndef add_head_block(data,\n                   num_filter,\n                   setting_params,\n                   multiplier,\n                   head_pooling=False,\n                   kernel=(3, 3),\n                   stride=(2, 2),\n                   pad=(1, 1),\n                   name=None):\n    bn_mom = setting_params['bn_mom']\n    bn_eps = setting_params['bn_eps']\n    fix_gamma = setting_params['fix_gamma']\n    use_global_stats = setting_params['use_global_stats']\n    workspace = setting_params['workspace']\n    act_type = setting_params['act_type']\n    channels = int(num_filter * multiplier)\n\n    conv1 = mx.sym.Convolution(data=data,\n                               num_filter=channels,\n                               kernel=kernel,\n                               pad=pad,\n                               stride=stride,\n                               no_bias=True,\n                               num_group=1,\n                               workspace=workspace,\n                               name=name + '_conv1')\n    bn1 = mx.sym.BatchNorm(data=conv1,\n                           fix_gamma=fix_gamma,\n                           eps=bn_eps,\n                           momentum=bn_mom,\n                           use_global_stats=use_global_stats,\n                           name=name + '_conv1_bn')\n\n    act1 = Act(data=bn1, act_type=act_type, name=name + '_conv1_act')\n\n    if head_pooling:\n        head_data = mx.symbol.Pooling(data=act1,\n                                      kernel=(3, 3),\n                                      stride=(2, 2),\n                                      pad=(1, 1),\n                                      pool_type='max',\n                                      name=name + '_max_pooling')\n    else:\n        head_data = vargnet_block(data=act1,\n                                  n_out_ch1=num_filter,\n                                  n_out_ch2=num_filter,\n                                  n_out_ch3=num_filter,\n                                  setting_params=setting_params,\n                                  factor=1,\n                                  dim_match=False,\n                                  multiplier=multiplier,\n                                  kernel=kernel,\n                                  stride=(2, 2),\n                                  dilate=1,\n                                  with_dilate=False,\n                                  name=name + '_head_pooling')\n    return head_data\n\n\ndef add_emb_block(data,\n                  input_channels,\n                  last_channels,\n                  emb_size,\n                  fc_type,\n                  setting_params,\n                  bias=False,\n                  name=None):\n    bn_mom = setting_params['bn_mom']\n    bn_eps = setting_params['bn_eps']\n    fix_gamma = setting_params['fix_gamma']\n    use_global_stats = setting_params['use_global_stats']\n    workspace = setting_params['workspace']\n    act_type = setting_params['act_type']\n    group_base = setting_params['group_base']\n    # last channels\n    if input_channels != last_channels:\n        data = mx.sym.Convolution(data=data,\n                                  num_filter=last_channels,\n                                  kernel=(1, 1),\n                                  pad=(0, 0),\n                                  stride=(1, 1),\n                                  no_bias=False if bias else True,\n                                  workspace=workspace,\n                                  name=name + '_convx')\n        data = mx.sym.BatchNorm(data=data,\n                                fix_gamma=fix_gamma,\n                                eps=bn_eps,\n                                momentum=bn_mom,\n                                use_global_stats=use_global_stats,\n                                name=name + '_convx_bn')\n        data = Act(data=data, act_type=act_type, name=name + '_convx_act')\n    # depthwise\n    convx_depthwise = mx.sym.Convolution(data=data,\n                                         num_filter=last_channels,\n                                         num_group=int(last_channels / group_base),\n                                         kernel=(7, 7),\n                                         pad=(0, 0),\n                                         stride=(1, 1),\n                                         no_bias=False if bias else True,\n                                         workspace=workspace,\n                                         name=name + '_convx_depthwise')\n    convx_depthwise = mx.sym.BatchNorm(data=convx_depthwise,\n                                       fix_gamma=fix_gamma,\n                                       eps=bn_eps,\n                                       momentum=bn_mom,\n                                       use_global_stats=use_global_stats,\n                                       name=name + '_convx_depthwise_bn')\n    # pointwise\n    convx_pointwise = mx.sym.Convolution(data=convx_depthwise,\n                                         num_filter=last_channels // 2,\n                                         kernel=(1, 1),\n                                         pad=(0, 0),\n                                         stride=(1, 1),\n                                         no_bias=False if bias else True,\n                                         workspace=workspace,\n                                         name=name + '_convx_pointwise')\n    convx_pointwise = mx.sym.BatchNorm(data=convx_pointwise,\n                                       fix_gamma=fix_gamma,\n                                       eps=bn_eps,\n                                       momentum=bn_mom,\n                                       use_global_stats=use_global_stats,\n                                       name=name + '_convx_pointwise_bn')\n    convx_pointwise = Act(data=convx_pointwise,\n                          act_type=act_type,\n                          name=name + '_convx_pointwise_act')\n\n    fc1 = symbol_utils.get_fc1(convx_pointwise, emb_size, fc_type)\n    return fc1\n\n\ndef get_symbol():\n    multiplier = config.net_multiplier\n    emb_size = config.emb_size\n    fc_type = config.net_output\n\n    kwargs = {'use_se' : config.net_se,\n        'act_type': config.net_act,\n        'bn_mom': config.bn_mom,\n        'workspace': config.workspace,\n        }\n\n    setting_params = get_setting_params(**kwargs)\n\n    factor = 2\n    head_pooling = False\n    num_stage = 3\n    stage_list = [2, 3, 4]\n    units = [3, 7, 4]\n    filter_list = [32, 64, 128, 256]\n    last_channels = 1024\n    dilate_list = [1, 1, 1]\n    with_dilate_list = [False, False, False]\n\n    data = mx.sym.Variable(name='data')\n    data = mx.sym.identity(data=data, name='id')\n    data = data - 127.5\n    data = data * 0.0078125\n\n    body = add_head_block(data=data,\n                          num_filter=filter_list[0],\n                          setting_params=setting_params,\n                          multiplier=multiplier,\n                          head_pooling=head_pooling,\n                          kernel=(3, 3),\n                          stride=(1, 1),\n                          pad=(1, 1),\n                          name=\"vargface_head\")\n\n    for i in range(num_stage):\n        body = add_vargnet_conv_block(data=body,\n                                      stage=stage_list[i],\n                                      units=units[i],\n                                      in_channels=filter_list[i],\n                                      out_channels=filter_list[i + 1],\n                                      setting_params=setting_params,\n                                      kernel=(3, 3),\n                                      stride=(2, 2),\n                                      multiplier=multiplier,\n                                      factor=factor,\n                                      dilate=dilate_list[i],\n                                      with_dilate=with_dilate_list[i],\n                                      name=\"vargface\")\n    emb_feat = add_emb_block(data=body,\n                             input_channels=filter_list[3],\n                             last_channels=last_channels,\n                             emb_size=emb_size,\n                             fc_type=fc_type,\n                             setting_params=setting_params,\n                             bias=False,\n                             name='embed')\n    return emb_feat\n\n\nif __name__ == '__main__':\n    get_symbol()\n"
  },
  {
    "path": "face_recognition/train.py",
    "content": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport math\nimport random\nimport logging\nimport sklearn\nimport pickle\nimport numpy as np\nimport mxnet as mx\nfrom mxnet import ndarray as nd\nimport argparse\nimport mxnet.optimizer as optimizer\nfrom config import config, default, generate_config\nfrom metric import *\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))\nimport flops_counter\nsys.path.append(os.path.join(os.path.dirname(__file__), 'eval'))\nimport verification\nsys.path.append(os.path.join(os.path.dirname(__file__), 'symbol'))\nimport fresnet\nimport fmobilefacenet\nimport fmobilenet\nimport fmnasnet\nimport fdensenet\nimport vargfacenet\n\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\nargs = None\n\n\n\ndef parse_args():\n  parser = argparse.ArgumentParser(description='Train face network')\n  # general\n  parser.add_argument('--dataset', default=default.dataset, help='dataset config')\n  parser.add_argument('--network', default=default.network, help='network config')\n  parser.add_argument('--loss', default=default.loss, help='loss config')\n  args, rest = parser.parse_known_args()\n  generate_config(args.network, args.dataset, args.loss)\n  parser.add_argument('--models-root', default=default.models_root, help='root directory to save model.')\n  parser.add_argument('--pretrained', default=default.pretrained, help='pretrained model to load')\n  parser.add_argument('--pretrained-epoch', type=int, default=default.pretrained_epoch, help='pretrained epoch to load')\n  parser.add_argument('--ckpt', type=int, default=default.ckpt, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')\n  parser.add_argument('--verbose', type=int, default=default.verbose, help='do verification testing and model saving every verbose batches')\n  parser.add_argument('--lr', type=float, default=default.lr, help='start learning rate')\n  parser.add_argument('--lr-steps', type=str, default=default.lr_steps, help='steps of lr changing')\n  parser.add_argument('--wd', type=float, default=default.wd, help='weight decay')\n  parser.add_argument('--mom', type=float, default=default.mom, help='momentum')\n  parser.add_argument('--frequent', type=int, default=default.frequent, help='')\n  parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='batch size in each context')\n  parser.add_argument('--kvstore', type=str, default=default.kvstore, help='kvstore setting')\n  args = parser.parse_args()\n  return args\n\n\ndef get_symbol(args):\n  embedding = eval(config.net_name).get_symbol()\n  all_label = mx.symbol.Variable('softmax_label')\n  gt_label = all_label\n  is_softmax = True\n  if config.loss_name=='softmax': #softmax\n    _weight = mx.symbol.Variable(\"fc7_weight\", shape=(config.num_classes, config.emb_size), \n        lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01))\n    if config.fc7_no_bias:\n      fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=config.num_classes, name='fc7')\n    else:\n      _bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0)\n      fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=config.num_classes, name='fc7')\n  elif config.loss_name=='margin_softmax':\n    _weight = mx.symbol.Variable(\"fc7_weight\", shape=(config.num_classes, config.emb_size), \n        lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01))\n    s = config.loss_s\n    _weight = mx.symbol.L2Normalization(_weight, mode='instance')\n    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s\n    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=config.num_classes, name='fc7')\n    if config.loss_m1!=1.0 or config.loss_m2!=0.0 or config.loss_m3!=0.0:\n      if config.loss_m1==1.0 and config.loss_m2==0.0:\n        s_m = s*config.loss_m3\n        gt_one_hot = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = s_m, off_value = 0.0)\n        fc7 = fc7-gt_one_hot\n      else:\n        zy = mx.sym.pick(fc7, gt_label, axis=1)\n        cos_t = zy/s\n        t = mx.sym.arccos(cos_t)\n        if config.loss_m1!=1.0:\n          t = t*config.loss_m1\n        if config.loss_m2>0.0:\n          t = t+config.loss_m2\n        body = mx.sym.cos(t)\n        if config.loss_m3>0.0:\n          body = body - config.loss_m3\n        new_zy = body*s\n        diff = new_zy - zy\n        diff = mx.sym.expand_dims(diff, 1)\n        gt_one_hot = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = 1.0, off_value = 0.0)\n        body = mx.sym.broadcast_mul(gt_one_hot, diff)\n        fc7 = fc7+body\n  elif config.loss_name.find('triplet')>=0:\n    is_softmax = False\n    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')\n    anchor = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=args.per_batch_size//3)\n    positive = mx.symbol.slice_axis(nembedding, axis=0, begin=args.per_batch_size//3, end=2*args.per_batch_size//3)\n    negative = mx.symbol.slice_axis(nembedding, axis=0, begin=2*args.per_batch_size//3, end=args.per_batch_size)\n    if config.loss_name=='triplet':\n      ap = anchor - positive\n      an = anchor - negative\n      ap = ap*ap\n      an = an*an\n      ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)\n      an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)\n      triplet_loss = mx.symbol.Activation(data = (ap-an+config.triplet_alpha), act_type='relu')\n      triplet_loss = mx.symbol.mean(triplet_loss)\n    else:\n      ap = anchor*positive\n      an = anchor*negative\n      ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1)\n      an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1)\n      ap = mx.sym.arccos(ap)\n      an = mx.sym.arccos(an)\n      triplet_loss = mx.symbol.Activation(data = (ap-an+config.triplet_alpha), act_type='relu')\n      triplet_loss = mx.symbol.mean(triplet_loss)\n    triplet_loss = mx.symbol.MakeLoss(triplet_loss)\n  out_list = [mx.symbol.BlockGrad(embedding)]\n  if is_softmax:\n    softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')\n    out_list.append(softmax)\n    if config.ce_loss:\n      #ce_loss = mx.symbol.softmax_cross_entropy(data=fc7, label = gt_label, name='ce_loss')/args.per_batch_size\n      body = mx.symbol.SoftmaxActivation(data=fc7)\n      body = mx.symbol.log(body)\n      _label = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = -1.0, off_value = 0.0)\n      body = body*_label\n      ce_loss = mx.symbol.sum(body)/args.per_batch_size\n      out_list.append(mx.symbol.BlockGrad(ce_loss))\n  else:\n    out_list.append(mx.sym.BlockGrad(gt_label))\n    out_list.append(triplet_loss)\n  out = mx.symbol.Group(out_list)\n  return out\n\ndef train_net(args):\n    ctx = []\n    cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()\n    if len(cvd)>0:\n      for i in range(len(cvd.split(','))):\n        ctx.append(mx.gpu(i))\n    if len(ctx)==0:\n      ctx = [mx.cpu()]\n      print('use cpu')\n    else:\n      print('gpu num:', len(ctx))\n    prefix = os.path.join(args.models_root, '%s-%s-%s'%(args.network, args.loss, args.dataset), 'model')\n    prefix_dir = os.path.dirname(prefix)\n    print('prefix', prefix)\n    if not os.path.exists(prefix_dir):\n      os.makedirs(prefix_dir)\n    args.ctx_num = len(ctx)\n    args.batch_size = args.per_batch_size*args.ctx_num\n    args.rescale_threshold = 0\n    args.image_channel = config.image_shape[2]\n    config.batch_size = args.batch_size\n    config.per_batch_size = args.per_batch_size\n\n    data_dir = config.dataset_path\n    path_imgrec = None\n    path_imglist = None\n    image_size = config.image_shape[0:2]\n    assert len(image_size)==2\n    assert image_size[0]==image_size[1]\n    print('image_size', image_size)\n    print('num_classes', config.num_classes)\n    path_imgrec = os.path.join(data_dir, \"train.rec\")\n\n    print('Called with argument:', args, config)\n    data_shape = (args.image_channel,image_size[0],image_size[1])\n    mean = None\n\n    begin_epoch = 0\n    if len(args.pretrained)==0:\n      arg_params = None\n      aux_params = None\n      sym = get_symbol(args)\n      if config.net_name=='spherenet':\n        data_shape_dict = {'data' : (args.per_batch_size,)+data_shape}\n        spherenet.init_weights(sym, data_shape_dict, args.num_layers)\n    else:\n      print('loading', args.pretrained, args.pretrained_epoch)\n      _, arg_params, aux_params = mx.model.load_checkpoint(args.pretrained, args.pretrained_epoch)\n      sym = get_symbol(args)\n\n    if config.count_flops:\n      all_layers = sym.get_internals()\n      _sym = all_layers['fc1_output']\n      FLOPs = flops_counter.count_flops(_sym, data=(1,3,image_size[0],image_size[1]))\n      _str = flops_counter.flops_str(FLOPs)\n      print('Network FLOPs: %s'%_str)\n\n    #label_name = 'softmax_label'\n    #label_shape = (args.batch_size,)\n    model = mx.mod.Module(\n        context       = ctx,\n        symbol        = sym,\n    )\n    val_dataiter = None\n\n    if config.loss_name.find('triplet')>=0:\n      from triplet_image_iter import FaceImageIter\n      triplet_params = [config.triplet_bag_size, config.triplet_alpha, config.triplet_max_ap]\n      train_dataiter = FaceImageIter(\n          batch_size           = args.batch_size,\n          data_shape           = data_shape,\n          path_imgrec          = path_imgrec,\n          shuffle              = True,\n          rand_mirror          = config.data_rand_mirror,\n          mean                 = mean,\n          cutoff               = config.data_cutoff,\n          ctx_num              = args.ctx_num,\n          images_per_identity  = config.images_per_identity,\n          triplet_params       = triplet_params,\n          mx_model             = model,\n      )\n      _metric = LossValueMetric()\n      eval_metrics = [mx.metric.create(_metric)]\n    else:\n      from image_iter import FaceImageIter\n      train_dataiter = FaceImageIter(\n          batch_size           = args.batch_size,\n          data_shape           = data_shape,\n          path_imgrec          = path_imgrec,\n          shuffle              = True,\n          rand_mirror          = config.data_rand_mirror,\n          mean                 = mean,\n          cutoff               = config.data_cutoff,\n          color_jittering      = config.data_color,\n          images_filter        = config.data_images_filter,\n      )\n      metric1 = AccMetric()\n      eval_metrics = [mx.metric.create(metric1)]\n      if config.ce_loss:\n        metric2 = LossValueMetric()\n        eval_metrics.append( mx.metric.create(metric2) )\n\n    if config.net_name=='fresnet' or config.net_name=='fmobilefacenet':\n      initializer = mx.init.Xavier(rnd_type='gaussian', factor_type=\"out\", magnitude=2) #resnet style\n    else:\n      initializer = mx.init.Xavier(rnd_type='uniform', factor_type=\"in\", magnitude=2)\n    #initializer = mx.init.Xavier(rnd_type='gaussian', factor_type=\"out\", magnitude=2) #resnet style\n    _rescale = 1.0/args.ctx_num\n    opt = optimizer.SGD(learning_rate=args.lr, momentum=args.mom, wd=args.wd, rescale_grad=_rescale)\n    _cb = mx.callback.Speedometer(args.batch_size, args.frequent)\n\n    ver_list = []\n    ver_name_list = []\n    for name in config.val_targets:\n      path = os.path.join(data_dir,name+\".bin\")\n      if os.path.exists(path):\n        data_set = verification.load_bin(path, image_size)\n        ver_list.append(data_set)\n        ver_name_list.append(name)\n        print('ver', name)\n\n\n\n    def ver_test(nbatch):\n      results = []\n      for i in range(len(ver_list)):\n        acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, 10, None, None)\n        print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))\n        #print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1))\n        print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))\n        results.append(acc2)\n      return results\n\n\n\n    highest_acc = [0.0, 0.0]  #lfw and target\n    #for i in range(len(ver_list)):\n    #  highest_acc.append(0.0)\n    global_step = [0]\n    save_step = [0]\n    lr_steps = [int(x) for x in args.lr_steps.split(',')]\n    print('lr_steps', lr_steps)\n    def _batch_callback(param):\n      #global global_step\n      global_step[0]+=1\n      mbatch = global_step[0]\n      for step in lr_steps:\n        if mbatch==step:\n          opt.lr *= 0.1\n          print('lr change to', opt.lr)\n          break\n\n      _cb(param)\n      if mbatch%1000==0:\n        print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)\n\n      if mbatch>=0 and mbatch%args.verbose==0:\n        acc_list = ver_test(mbatch)\n        save_step[0]+=1\n        msave = save_step[0]\n        do_save = False\n        is_highest = False\n        if len(acc_list)>0:\n          #lfw_score = acc_list[0]\n          #if lfw_score>highest_acc[0]:\n          #  highest_acc[0] = lfw_score\n          #  if lfw_score>=0.998:\n          #    do_save = True\n          score = sum(acc_list)\n          if acc_list[-1]>=highest_acc[-1]:\n            if acc_list[-1]>highest_acc[-1]:\n              is_highest = True\n            else:\n              if score>=highest_acc[0]:\n                is_highest = True\n                highest_acc[0] = score\n            highest_acc[-1] = acc_list[-1]\n            #if lfw_score>=0.99:\n            #  do_save = True\n        if is_highest:\n          do_save = True\n        if args.ckpt==0:\n          do_save = False\n        elif args.ckpt==2:\n          do_save = True\n        elif args.ckpt==3:\n          msave = 1\n\n        if do_save:\n          print('saving', msave)\n          arg, aux = model.get_params()\n          if config.ckpt_embedding:\n            all_layers = model.symbol.get_internals()\n            _sym = all_layers['fc1_output']\n            _arg = {}\n            for k in arg:\n              if not k.startswith('fc7'):\n                _arg[k] = arg[k]\n            mx.model.save_checkpoint(prefix, msave, _sym, _arg, aux)\n          else:\n            mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux)\n        print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))\n      if config.max_steps>0 and mbatch>config.max_steps:\n        sys.exit(0)\n\n    epoch_cb = None\n    train_dataiter = mx.io.PrefetchingIter(train_dataiter)\n\n    model.fit(train_dataiter,\n        begin_epoch        = begin_epoch,\n        num_epoch          = 999999,\n        eval_data          = val_dataiter,\n        eval_metric        = eval_metrics,\n        kvstore            = args.kvstore,\n        optimizer          = opt,\n        #optimizer_params   = optimizer_params,\n        initializer        = initializer,\n        arg_params         = arg_params,\n        aux_params         = aux_params,\n        allow_missing      = True,\n        batch_end_callback = _batch_callback,\n        epoch_end_callback = epoch_cb )\n\ndef main():\n    global args\n    args = parse_args()\n    train_net(args)\n\nif __name__ == '__main__':\n    main()\n\n"
  },
  {
    "path": "face_recognition/train_parall.py",
    "content": "\n'''\n@author: insightface\n'''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport math\nimport random\nimport logging\nimport pickle\nimport sklearn\nimport numpy as np\nfrom image_iter import FaceImageIter\nimport mxnet as mx\nfrom mxnet import ndarray as nd\nimport argparse\nimport mxnet.optimizer as optimizer\nsys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common'))\nimport flops_counter\nfrom config import config, default, generate_config\nsys.path.append(os.path.join(os.path.dirname(__file__), 'eval'))\nimport verification\nsys.path.append(os.path.join(os.path.dirname(__file__), 'symbol'))\nimport fresnet\nimport fmobilefacenet\nimport fmobilenet\nimport fmnasnet\nimport fdensenet\nimport vargfacenet\n\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\nargs = None\n\n\n\ndef parse_args():\n  parser = argparse.ArgumentParser(description='Train parall face network')\n  # general\n  parser.add_argument('--dataset', default=default.dataset, help='dataset config')\n  parser.add_argument('--network', default=default.network, help='network config')\n  parser.add_argument('--loss', default=default.loss, help='loss config')\n  args, rest = parser.parse_known_args()\n  generate_config(args.network, args.dataset, args.loss)\n  parser.add_argument('--models-root', default=default.models_root, help='root directory to save model.')\n  parser.add_argument('--pretrained', default=default.pretrained, help='pretrained model to load')\n  parser.add_argument('--pretrained-epoch', type=int, default=default.pretrained_epoch, help='pretrained epoch to load')\n  parser.add_argument('--ckpt', type=int, default=default.ckpt, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save')\n  parser.add_argument('--verbose', type=int, default=default.verbose, help='do verification testing and model saving every verbose batches')\n  parser.add_argument('--lr', type=float, default=default.lr, help='start learning rate')\n  parser.add_argument('--lr-steps', type=str, default=default.lr_steps, help='steps of lr changing')\n  parser.add_argument('--wd', type=float, default=default.wd, help='weight decay')\n  parser.add_argument('--mom', type=float, default=default.mom, help='momentum')\n  parser.add_argument('--frequent', type=int, default=default.frequent, help='')\n  parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='batch size in each context')\n  parser.add_argument('--kvstore', type=str, default=default.kvstore, help='kvstore setting')\n  parser.add_argument('--worker-id', type=int, default=0, help='worker id for dist training, starts from 0')\n  parser.add_argument('--extra-model-name', type=str, default='', help='extra model name')\n  args = parser.parse_args()\n  return args\n\n\ndef get_symbol_embedding():\n  embedding = eval(config.net_name).get_symbol()\n  all_label = mx.symbol.Variable('softmax_label')\n  #embedding = mx.symbol.BlockGrad(embedding)\n  all_label = mx.symbol.BlockGrad(all_label)\n  out_list = [embedding, all_label]\n  out = mx.symbol.Group(out_list)\n  return out\n\ndef get_symbol_arcface(args):\n  embedding = mx.symbol.Variable('data')\n  all_label = mx.symbol.Variable('softmax_label')\n  gt_label = all_label\n  is_softmax = True\n  #print('call get_sym_arcface with', args, config)\n  _weight = mx.symbol.Variable(\"fc7_%d_weight\"%args._ctxid, shape=(args.ctx_num_classes, config.emb_size), \n      lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult)\n  if config.loss_name=='softmax': #softmax\n    fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=args.ctx_num_classes, name='fc7_%d'%args._ctxid)\n  elif config.loss_name=='margin_softmax':\n    _weight = mx.symbol.L2Normalization(_weight, mode='instance')\n    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n_%d'%args._ctxid)\n    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.ctx_num_classes, name='fc7_%d'%args._ctxid)\n    if config.loss_m1!=1.0 or config.loss_m2!=0.0 or config.loss_m3!=0.0:\n      gt_one_hot = mx.sym.one_hot(gt_label, depth = args.ctx_num_classes, on_value = 1.0, off_value = 0.0)\n      if config.loss_m1==1.0 and config.loss_m2==0.0:\n        _one_hot = gt_one_hot*args.margin_b\n        fc7 = fc7-_one_hot\n      else:\n        fc7_onehot = fc7 * gt_one_hot\n        cos_t = fc7_onehot\n        t = mx.sym.arccos(cos_t)\n        if config.loss_m1!=1.0:\n          t = t*config.loss_m1\n        if config.loss_m2!=0.0:\n          t = t+config.loss_m2\n        margin_cos = mx.sym.cos(t)\n        if config.loss_m3!=0.0:\n          margin_cos = margin_cos - config.loss_m3\n        margin_fc7 = margin_cos\n        margin_fc7_onehot = margin_fc7 * gt_one_hot\n        diff = margin_fc7_onehot - fc7_onehot\n        fc7 = fc7+diff\n    fc7 = fc7*config.loss_s\n\n  out_list = []\n  out_list.append(fc7)\n  if config.loss_name=='softmax': #softmax\n    out_list.append(gt_label)\n  out = mx.symbol.Group(out_list)\n  return out\n\ndef train_net(args):\n    #_seed = 727\n    #random.seed(_seed)\n    #np.random.seed(_seed)\n    #mx.random.seed(_seed)\n    ctx = []\n    cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip()\n    if len(cvd)>0:\n      for i in range(len(cvd.split(','))):\n        ctx.append(mx.gpu(i))\n    if len(ctx)==0:\n      ctx = [mx.cpu()]\n      print('use cpu')\n    else:\n      print('gpu num:', len(ctx))\n    if len(args.extra_model_name)==0:\n      prefix = os.path.join(args.models_root, '%s-%s-%s'%(args.network, args.loss, args.dataset), 'model')\n    else:\n      prefix = os.path.join(args.models_root, '%s-%s-%s-%s'%(args.network, args.loss, args.dataset, args.extra_model_name), 'model')\n    prefix_dir = os.path.dirname(prefix)\n    print('prefix', prefix)\n    if not os.path.exists(prefix_dir):\n      os.makedirs(prefix_dir)\n    args.ctx_num = len(ctx)\n    if args.per_batch_size==0:\n      args.per_batch_size = 128\n    args.batch_size = args.per_batch_size*args.ctx_num\n    args.rescale_threshold = 0\n    args.image_channel = config.image_shape[2]\n    config.batch_size = args.batch_size\n    config.per_batch_size = args.per_batch_size\n    data_dir = config.dataset_path\n    path_imgrec = None\n    path_imglist = None\n    image_size = config.image_shape[0:2]\n    assert len(image_size)==2\n    assert image_size[0]==image_size[1]\n    print('image_size', image_size)\n    print('num_classes', config.num_classes)\n    path_imgrec = os.path.join(data_dir, \"train.rec\")\n\n    data_shape = (args.image_channel,image_size[0],image_size[1])\n\n    num_workers = config.num_workers\n    global_num_ctx = num_workers * args.ctx_num\n    if config.num_classes%global_num_ctx==0:\n      args.ctx_num_classes = config.num_classes//global_num_ctx\n    else:\n      args.ctx_num_classes = config.num_classes//global_num_ctx+1\n    args.local_num_classes = args.ctx_num_classes * args.ctx_num\n    args.local_class_start = args.local_num_classes * args.worker_id\n\n    #if len(args.partial)==0:\n    #  local_classes_range = (0, args.num_classes)\n    #else:\n    #  _vec = args.partial.split(',')\n    #  local_classes_range = (int(_vec[0]), int(_vec[1]))\n\n    #args.partial_num_classes = local_classes_range[1] - local_classes_range[0]\n    #args.partial_start = local_classes_range[0]\n\n    print('Called with argument:', args, config)\n    mean = None\n\n    begin_epoch = 0\n    base_lr = args.lr\n    base_wd = args.wd\n    base_mom = args.mom\n    arg_params = None\n    aux_params = None\n    if len(args.pretrained)==0:\n      esym = get_symbol_embedding()\n      asym = get_symbol_arcface\n    else:\n      assert False\n\n    if config.count_flops:\n      all_layers = esym.get_internals()\n      _sym = all_layers['fc1_output']\n      FLOPs = flops_counter.count_flops(_sym, data=(1,3,image_size[0],image_size[1]))\n      _str = flops_counter.flops_str(FLOPs)\n      print('Network FLOPs: %s'%_str)\n\n    if config.num_workers==1:\n      from parall_module_local_v1 import ParallModule\n    else:\n      from parall_module_dist import ParallModule\n\n    model = ParallModule(\n        context       = ctx,\n        symbol        = esym,\n        data_names    = ['data'],\n        label_names    = ['softmax_label'],\n        asymbol       = asym,\n        args = args,\n    )\n    val_dataiter = None\n    train_dataiter = FaceImageIter(\n        batch_size           = args.batch_size,\n        data_shape           = data_shape,\n        path_imgrec          = path_imgrec,\n        shuffle              = True,\n        rand_mirror          = config.data_rand_mirror,\n        mean                 = mean,\n        cutoff               = config.data_cutoff,\n        color_jittering      = config.data_color,\n        images_filter        = config.data_images_filter,\n    )\n\n\n    \n    if config.net_name=='fresnet' or config.net_name=='fmobilefacenet':\n      initializer = mx.init.Xavier(rnd_type='gaussian', factor_type=\"out\", magnitude=2) #resnet style\n    else:\n      initializer = mx.init.Xavier(rnd_type='uniform', factor_type=\"in\", magnitude=2)\n\n    _rescale = 1.0/args.batch_size\n    opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale)\n    _cb = mx.callback.Speedometer(args.batch_size, args.frequent)\n\n\n    ver_list = []\n    ver_name_list = []\n    for name in config.val_targets:\n      path = os.path.join(data_dir,name+\".bin\")\n      if os.path.exists(path):\n        data_set = verification.load_bin(path, image_size)\n        ver_list.append(data_set)\n        ver_name_list.append(name)\n        print('ver', name)\n\n\n    def ver_test(nbatch):\n      results = []\n      for i in range(len(ver_list)):\n        acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, 10, None, None)\n        print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm))\n        #print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1))\n        print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2))\n        results.append(acc2)\n      return results\n\n\n    highest_acc = [0.0, 0.0]  #lfw and target\n    #for i in range(len(ver_list)):\n    #  highest_acc.append(0.0)\n    global_step = [0]\n    save_step = [0]\n    lr_steps = [int(x) for x in args.lr_steps.split(',')]\n    print('lr_steps', lr_steps)\n    def _batch_callback(param):\n      #global global_step\n      global_step[0]+=1\n      mbatch = global_step[0]\n      for step in lr_steps:\n        if mbatch==step:\n          opt.lr *= 0.1\n          print('lr change to', opt.lr)\n          break\n\n      _cb(param)\n      if mbatch%1000==0:\n        print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch)\n\n      if mbatch>=0 and mbatch%args.verbose==0:\n        acc_list = ver_test(mbatch)\n        save_step[0]+=1\n        msave = save_step[0]\n        do_save = False\n        is_highest = False\n        if len(acc_list)>0:\n          #lfw_score = acc_list[0]\n          #if lfw_score>highest_acc[0]:\n          #  highest_acc[0] = lfw_score\n          #  if lfw_score>=0.998:\n          #    do_save = True\n          score = sum(acc_list)\n          if acc_list[-1]>=highest_acc[-1]:\n            if acc_list[-1]>highest_acc[-1]:\n              is_highest = True\n            else:\n              if score>=highest_acc[0]:\n                is_highest = True\n                highest_acc[0] = score\n            highest_acc[-1] = acc_list[-1]\n            #if lfw_score>=0.99:\n            #  do_save = True\n        if is_highest:\n          do_save = True\n        if args.ckpt==0:\n          do_save = False\n        elif args.ckpt==2:\n          do_save = True\n        elif args.ckpt==3:\n          msave = 1\n\n        if do_save:\n          print('saving', msave)\n          arg, aux = model.get_export_params()\n          all_layers = model.symbol.get_internals()\n          _sym = all_layers['fc1_output']\n          mx.model.save_checkpoint(prefix, msave, _sym, arg, aux)\n        print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1]))\n      if config.max_steps>0 and mbatch>config.max_steps:\n        sys.exit(0)\n\n    epoch_cb = None\n    train_dataiter = mx.io.PrefetchingIter(train_dataiter)\n\n    model.fit(train_dataiter,\n        begin_epoch        = begin_epoch,\n        num_epoch          = 999999,\n        eval_data          = val_dataiter,\n        #eval_metric        = eval_metrics,\n        kvstore            = args.kvstore,\n        optimizer          = opt,\n        #optimizer_params   = optimizer_params,\n        initializer        = initializer,\n        arg_params         = arg_params,\n        aux_params         = aux_params,\n        allow_missing      = True,\n        batch_end_callback = _batch_callback,\n        epoch_end_callback = epoch_cb )\n\ndef main():\n    global args\n    args = parse_args()\n    train_net(args)\n\nif __name__ == '__main__':\n    main()\n\n"
  },
  {
    "path": "face_recognition/triplet_image_iter.py",
    "content": "# THIS FILE IS FOR EXPERIMENTS, USE image_iter.py FOR NORMAL IMAGE LOADING.\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport random\nimport logging\nimport sys\nimport numbers\nimport math\nimport sklearn\nimport datetime\nimport numpy as np\nimport cv2\n\nimport mxnet as mx\nfrom mxnet import ndarray as nd\n#from . import _ndarray_internal as _internal\n#from mxnet._ndarray_internal import _cvimresize as imresize\n#from ._ndarray_internal import _cvcopyMakeBorder as copyMakeBorder\nfrom mxnet import io\nfrom mxnet import recordio\nsys.path.append(os.path.join(os.path.dirname(__file__), 'common'))\nimport face_preprocess\n\nlogger = logging.getLogger()\n\nclass FaceImageIter(io.DataIter):\n\n    def __init__(self, batch_size, data_shape,\n                 path_imgrec = None,\n                 shuffle=False, aug_list=None,\n                 rand_mirror = False, cutoff = 0,\n                 ctx_num = 0, images_per_identity = 0,\n                 triplet_params = None,\n                 mx_model = None,\n                 data_name='data', label_name='softmax_label', **kwargs):\n        super(FaceImageIter, self).__init__()\n        assert path_imgrec\n        assert shuffle\n        logging.info('loading recordio %s...',\n                     path_imgrec)\n        path_imgidx = path_imgrec[0:-4]+\".idx\"\n        self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type\n        s = self.imgrec.read_idx(0)\n        header, _ = recordio.unpack(s)\n        assert header.flag>0\n        print('header0 label', header.label)\n        self.header0 = (int(header.label[0]), int(header.label[1]))\n        #assert(header.flag==1)\n        self.imgidx = range(1, int(header.label[0]))\n        self.id2range = {}\n        self.seq_identity = range(int(header.label[0]), int(header.label[1]))\n        for identity in self.seq_identity:\n          s = self.imgrec.read_idx(identity)\n          header, _ = recordio.unpack(s)\n          a,b = int(header.label[0]), int(header.label[1])\n          self.id2range[identity] = (a,b)\n\n        print('id2range', len(self.id2range))\n        self.seq = self.imgidx\n        print(len(self.seq))\n\n        self.check_data_shape(data_shape)\n        self.provide_data = [(data_name, (batch_size,) + data_shape)]\n        self.batch_size = batch_size\n        self.data_shape = data_shape\n        self.shuffle = shuffle\n        self.image_size = '%d,%d'%(data_shape[1],data_shape[2])\n        self.rand_mirror = rand_mirror\n        print('rand_mirror', rand_mirror)\n        self.cutoff = cutoff\n        #self.cast_aug = mx.image.CastAug()\n        #self.color_aug = mx.image.ColorJitterAug(0.4, 0.4, 0.4)\n        self.ctx_num = ctx_num \n        self.per_batch_size = int(self.batch_size/self.ctx_num)\n        self.images_per_identity = images_per_identity\n        if self.images_per_identity>0:\n          self.identities = int(self.per_batch_size/self.images_per_identity)\n          self.per_identities = self.identities\n          self.repeat = 3000000.0/(self.images_per_identity*len(self.id2range))\n          self.repeat = int(self.repeat)\n          print(self.images_per_identity, self.identities, self.repeat)\n        self.mx_model = mx_model\n        self.triplet_params = triplet_params\n        self.triplet_mode = False\n        #self.provide_label = None\n        self.provide_label = [(label_name, (batch_size,))]\n        if self.triplet_params is not None:\n          assert self.images_per_identity>0\n          assert self.mx_model is not None\n          self.triplet_bag_size = self.triplet_params[0]\n          self.triplet_alpha = self.triplet_params[1]\n          self.triplet_max_ap = self.triplet_params[2]\n          assert self.triplet_bag_size>0\n          assert self.triplet_alpha>=0.0\n          assert self.triplet_alpha<=1.0\n          self.triplet_mode = True\n          self.triplet_cur = 0\n          self.triplet_seq = []\n          self.triplet_reset()\n          self.seq_min_size = self.batch_size*2\n        self.cur = 0\n        self.nbatch = 0\n        self.is_init = False\n        self.times = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n        #self.reset()\n\n\n\n    def pairwise_dists(self, embeddings):\n      nd_embedding_list = []\n      for i in range(self.ctx_num):\n        nd_embedding = mx.nd.array(embeddings, mx.gpu(i))\n        nd_embedding_list.append(nd_embedding)\n      nd_pdists = []\n      pdists = []\n      for idx in range(embeddings.shape[0]):\n        emb_idx = idx%self.ctx_num\n        nd_embedding = nd_embedding_list[emb_idx]\n        a_embedding = nd_embedding[idx]\n        body = mx.nd.broadcast_sub(a_embedding, nd_embedding)\n        body = body*body\n        body = mx.nd.sum_axis(body, axis=1)\n        nd_pdists.append(body)\n        if len(nd_pdists)==self.ctx_num or idx==embeddings.shape[0]-1:\n          for x in nd_pdists:\n            pdists.append(x.asnumpy())\n          nd_pdists = []\n      return pdists\n\n    def pick_triplets(self, embeddings, nrof_images_per_class):\n      emb_start_idx = 0\n      triplets = []\n      people_per_batch = len(nrof_images_per_class)\n      #self.time_reset()\n      pdists = self.pairwise_dists(embeddings)\n      #self.times[3] += self.time_elapsed()\n\n      for i in range(people_per_batch):\n          nrof_images = int(nrof_images_per_class[i])\n          for j in range(1,nrof_images):\n              #self.time_reset()\n              a_idx = emb_start_idx + j - 1\n              #neg_dists_sqr = np.sum(np.square(embeddings[a_idx] - embeddings), 1)\n              neg_dists_sqr = pdists[a_idx]\n              #self.times[3] += self.time_elapsed()\n\n              for pair in range(j, nrof_images): # For every possible positive pair.\n                  p_idx = emb_start_idx + pair\n                  #self.time_reset()\n                  pos_dist_sqr = np.sum(np.square(embeddings[a_idx]-embeddings[p_idx]))\n                  #self.times[4] += self.time_elapsed()\n                  #self.time_reset()\n                  neg_dists_sqr[emb_start_idx:emb_start_idx+nrof_images] = np.NaN\n                  if self.triplet_max_ap>0.0:\n                    if pos_dist_sqr>self.triplet_max_ap:\n                      continue\n                  all_neg = np.where(np.logical_and(neg_dists_sqr-pos_dist_sqr<self.triplet_alpha, pos_dist_sqr<neg_dists_sqr))[0]  # FaceNet selection\n                  #self.times[5] += self.time_elapsed()\n                  #self.time_reset()\n                  #all_neg = np.where(neg_dists_sqr-pos_dist_sqr<alpha)[0] # VGG Face selecction\n                  nrof_random_negs = all_neg.shape[0]\n                  if nrof_random_negs>0:\n                      rnd_idx = np.random.randint(nrof_random_negs)\n                      n_idx = all_neg[rnd_idx]\n                      triplets.append( (a_idx, p_idx, n_idx) )\n          emb_start_idx += nrof_images\n      np.random.shuffle(triplets)\n      return triplets\n\n    def triplet_reset(self):\n      #reset self.oseq by identities seq\n      self.triplet_cur = 0\n      ids = []\n      for k in self.id2range:\n        ids.append(k)\n      random.shuffle(ids)\n      self.triplet_seq = []\n      for _id in ids:\n        v = self.id2range[_id]\n        _list = range(*v)\n        random.shuffle(_list)\n        if len(_list)>self.images_per_identity:\n          _list = _list[0:self.images_per_identity]\n        self.triplet_seq += _list\n      print('triplet_seq', len(self.triplet_seq))\n      assert len(self.triplet_seq)>=self.triplet_bag_size\n\n    def time_reset(self):\n      self.time_now = datetime.datetime.now()\n\n    def time_elapsed(self):\n      time_now = datetime.datetime.now()\n      diff = time_now - self.time_now\n      return diff.total_seconds()\n\n\n    def select_triplets(self):\n      self.seq = []\n      while len(self.seq)<self.seq_min_size:\n        self.time_reset()\n        embeddings = None\n        bag_size = self.triplet_bag_size\n        batch_size = self.batch_size\n        #data = np.zeros( (bag_size,)+self.data_shape )\n        #label = np.zeros( (bag_size,) )\n        tag = []\n        #idx = np.zeros( (bag_size,) )\n        print('eval %d images..'%bag_size, self.triplet_cur)\n        print('triplet time stat', self.times)\n        if self.triplet_cur+bag_size>len(self.triplet_seq):\n          self.triplet_reset()\n          #bag_size = min(bag_size, len(self.triplet_seq))\n          print('eval %d images..'%bag_size, self.triplet_cur)\n        self.times[0] += self.time_elapsed()\n        self.time_reset()\n        #print(data.shape)\n        data = nd.zeros( self.provide_data[0][1] )\n        label = None\n        if self.provide_label is not None:\n          label = nd.zeros( self.provide_label[0][1] )\n        ba = 0\n        while True:\n          bb = min(ba+batch_size, bag_size)\n          if ba>=bb:\n            break\n          _count = bb-ba\n          #data = nd.zeros( (_count,)+self.data_shape )\n          #_batch = self.data_iter.next()\n          #_data = _batch.data[0].asnumpy()\n          #print(_data.shape)\n          #_label = _batch.label[0].asnumpy()\n          #data[ba:bb,:,:,:] = _data\n          #label[ba:bb] = _label\n          for i in range(ba, bb):\n            #print(ba, bb, self.triplet_cur, i, len(self.triplet_seq))\n            _idx = self.triplet_seq[i+self.triplet_cur]\n            s = self.imgrec.read_idx(_idx)\n            header, img = recordio.unpack(s)\n            img = self.imdecode(img)\n            data[i-ba][:] = self.postprocess_data(img)\n            _label = header.label\n            if not isinstance(_label, numbers.Number):\n              _label = _label[0]\n            if label is not None:\n              label[i-ba][:] = _label\n            tag.append( ( int(_label), _idx) )\n            #idx[i] = _idx\n\n          db = mx.io.DataBatch(data=(data,))\n          self.mx_model.forward(db, is_train=False)\n          net_out = self.mx_model.get_outputs()\n          #print('eval for selecting triplets',ba,bb)\n          #print(net_out)\n          #print(len(net_out))\n          #print(net_out[0].asnumpy())\n          net_out = net_out[0].asnumpy()\n          #print(net_out)\n          #print('net_out', net_out.shape)\n          if embeddings is None:\n            embeddings = np.zeros( (bag_size, net_out.shape[1]))\n          embeddings[ba:bb,:] = net_out\n          ba = bb\n        assert len(tag)==bag_size\n        self.triplet_cur+=bag_size\n        embeddings = sklearn.preprocessing.normalize(embeddings)\n        self.times[1] += self.time_elapsed()\n        self.time_reset()\n        nrof_images_per_class = [1]\n        for i in range(1, bag_size):\n          if tag[i][0]==tag[i-1][0]:\n            nrof_images_per_class[-1]+=1\n          else:\n            nrof_images_per_class.append(1)\n          \n        triplets = self.pick_triplets(embeddings, nrof_images_per_class) # shape=(T,3)\n        print('found triplets', len(triplets))\n        ba = 0\n        while True:\n          bb = ba+self.per_batch_size//3\n          if bb>len(triplets):\n            break\n          _triplets = triplets[ba:bb]\n          for i in range(3):\n            for triplet in _triplets:\n              _pos = triplet[i]\n              _idx = tag[_pos][1]\n              self.seq.append(_idx)\n          ba = bb\n        self.times[2] += self.time_elapsed()\n\n    def hard_mining_reset(self):\n      #import faiss\n      from annoy import AnnoyIndex\n      data = nd.zeros( self.provide_data[0][1] )\n      label = nd.zeros( self.provide_label[0][1] )\n      #label = np.zeros( self.provide_label[0][1] )\n      X = None\n      ba = 0\n      batch_num = 0\n      while ba<len(self.oseq):\n        batch_num+=1\n        if batch_num%10==0:\n          print('loading batch',batch_num, ba)\n        bb = min(ba+self.batch_size, len(self.oseq))\n        _count = bb-ba\n        for i in range(_count):\n          idx = self.oseq[i+ba]\n          s = self.imgrec.read_idx(idx)\n          header, img = recordio.unpack(s)\n          img = self.imdecode(img)\n          data[i][:] = self.postprocess_data(img)\n          label[i][:] = header.label\n        db = mx.io.DataBatch(data=(data,self.data_extra), label=(label,))\n        self.mx_model.forward(db, is_train=False)\n        net_out = self.mx_model.get_outputs()\n        embedding = net_out[0].asnumpy()\n        nembedding = sklearn.preprocessing.normalize(embedding)\n        if _count<self.batch_size:\n          nembedding = nembedding[0:_count,:]\n        if X is None:\n          X = np.zeros( (len(self.id2range), nembedding.shape[1]), dtype=np.float32 )\n        nplabel = label.asnumpy()\n        for i in range(_count):\n          ilabel = int(nplabel[i])\n          #print(ilabel, ilabel.__class__)\n          X[ilabel] += nembedding[i]\n        ba = bb\n      X = sklearn.preprocessing.normalize(X)\n      d = X.shape[1]\n      t = AnnoyIndex(d, metric='euclidean')\n      for i in range(X.shape[0]):\n        t.add_item(i, X[i])\n      print('start to build index')\n      t.build(20)\n      print(X.shape)\n      k = self.per_identities\n      self.seq = []\n      for i in range(X.shape[0]):\n        nnlist = t.get_nns_by_item(i, k)\n        assert nnlist[0]==i\n        for _label in nnlist:\n          assert _label<len(self.id2range)\n          _id = self.header0[0]+_label\n          v = self.id2range[_id]\n          _list = range(*v)\n          if len(_list)<self.images_per_identity:\n            random.shuffle(_list)\n          else:\n            _list = np.random.choice(_list, self.images_per_identity, replace=False)\n          for i in range(self.images_per_identity):\n            _idx = _list[i%len(_list)]\n            self.seq.append(_idx)\n      #faiss_params = [20,5]\n      #quantizer = faiss.IndexFlatL2(d)  # the other index\n      #index = faiss.IndexIVFFlat(quantizer, d, faiss_params[0], faiss.METRIC_L2)\n      #assert not index.is_trained\n      #index.train(X)\n      #index.add(X)\n      #assert index.is_trained\n      #print('trained')\n      #index.nprobe = faiss_params[1]\n      #D, I = index.search(X, k)     # actual search\n      #print(I.shape)\n      #self.seq = []\n      #for i in range(I.shape[0]):\n      #  #assert I[i][0]==i\n      #  for j in range(k):\n      #    _label = I[i][j]\n      #    assert _label<len(self.id2range)\n      #    _id = self.header0[0]+_label\n      #    v = self.id2range[_id]\n      #    _list = range(*v)\n      #    if len(_list)<self.images_per_identity:\n      #      random.shuffle(_list)\n      #    else:\n      #      _list = np.random.choice(_list, self.images_per_identity, replace=False)\n      #    for i in range(self.images_per_identity):\n      #      _idx = _list[i%len(_list)]\n      #      self.seq.append(_idx)\n\n    def reset(self):\n        \"\"\"Resets the iterator to the beginning of the data.\"\"\"\n        print('call reset()')\n        self.cur = 0\n        if self.images_per_identity>0:\n          if self.triplet_mode:\n            self.select_triplets()\n          elif not self.hard_mining:\n            self.seq = []\n            idlist = []\n            for _id in self.id2range:\n              v = self.id2range[_id]\n              idlist.append((_id,range(*v)))\n            for r in range(self.repeat):\n              if r%10==0:\n                print('repeat', r)\n              if self.shuffle:\n                random.shuffle(idlist)\n              for item in idlist:\n                _id = item[0]\n                _list = item[1]\n                #random.shuffle(_list)\n                if len(_list)<self.images_per_identity:\n                  random.shuffle(_list)\n                else:\n                  _list = np.random.choice(_list, self.images_per_identity, replace=False)\n                for i in range(self.images_per_identity):\n                  _idx = _list[i%len(_list)]\n                  self.seq.append(_idx)\n          else:\n            self.hard_mining_reset()\n          print('seq len', len(self.seq))\n        else:\n          if self.shuffle:\n              random.shuffle(self.seq)\n        if self.seq is None and self.imgrec is not None:\n            self.imgrec.reset()\n\n    def num_samples(self):\n      return len(self.seq)\n\n    def next_sample(self):\n      while True:\n        if self.cur >= len(self.seq):\n            raise StopIteration\n        idx = self.seq[self.cur]\n        self.cur += 1\n        s = self.imgrec.read_idx(idx)\n        header, img = recordio.unpack(s)\n        label = header.label\n        if not isinstance(label, numbers.Number):\n          label = label[0]\n        return label, img, None, None\n\n    def brightness_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      src *= alpha\n      return src\n\n    def contrast_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      coef = np.array([[[0.299, 0.587, 0.114]]])\n      gray = src * coef\n      gray = (3.0 * (1.0 - alpha) / gray.size) * np.sum(gray)\n      src *= alpha\n      src += gray\n      return src\n\n    def saturation_aug(self, src, x):\n      alpha = 1.0 + random.uniform(-x, x)\n      coef = np.array([[[0.299, 0.587, 0.114]]])\n      gray = src * coef\n      gray = np.sum(gray, axis=2, keepdims=True)\n      gray *= (1.0 - alpha)\n      src *= alpha\n      src += gray\n      return src\n\n    def color_aug(self, img, x):\n      augs = [self.brightness_aug, self.contrast_aug, self.saturation_aug]\n      random.shuffle(augs)\n      for aug in augs:\n        #print(img.shape)\n        img = aug(img, x)\n        #print(img.shape)\n      return img\n\n    def mirror_aug(self, img):\n      _rd = random.randint(0,1)\n      if _rd==1:\n        for c in range(img.shape[2]):\n          img[:,:,c] = np.fliplr(img[:,:,c])\n      return img\n\n\n    def next(self):\n        if not self.is_init:\n          self.reset()\n          self.is_init = True\n        \"\"\"Returns the next batch of data.\"\"\"\n        #print('in next', self.cur, self.labelcur)\n        self.nbatch+=1\n        batch_size = self.batch_size\n        c, h, w = self.data_shape\n        batch_data = nd.empty((batch_size, c, h, w))\n        if self.provide_label is not None:\n          batch_label = nd.empty(self.provide_label[0][1])\n        i = 0\n        try:\n            while i < batch_size:\n                label, s, bbox, landmark = self.next_sample()\n                _data = self.imdecode(s)\n                if self.rand_mirror:\n                  _rd = random.randint(0,1)\n                  if _rd==1:\n                    _data = mx.ndarray.flip(data=_data, axis=1)\n                if self.cutoff>0:\n                  centerh = random.randint(0, _data.shape[0]-1)\n                  centerw = random.randint(0, _data.shape[1]-1)\n                  half = self.cutoff//2\n                  starth = max(0, centerh-half)\n                  endh = min(_data.shape[0], centerh+half)\n                  startw = max(0, centerw-half)\n                  endw = min(_data.shape[1], centerw+half)\n                  _data = _data.astype('float32')\n                  #print(starth, endh, startw, endw, _data.shape)\n                  _data[starth:endh, startw:endw, :] = 127.5\n                #_npdata = _data.asnumpy()\n                #if landmark is not None:\n                #  _npdata = face_preprocess.preprocess(_npdata, bbox = bbox, landmark=landmark, image_size=self.image_size)\n                #if self.rand_mirror:\n                #  _npdata = self.mirror_aug(_npdata)\n                #if self.mean is not None:\n                #  _npdata = _npdata.astype(np.float32)\n                #  _npdata -= self.mean\n                #  _npdata *= 0.0078125\n                #nimg = np.zeros(_npdata.shape, dtype=np.float32)\n                #nimg[self.patch[1]:self.patch[3],self.patch[0]:self.patch[2],:] = _npdata[self.patch[1]:self.patch[3], self.patch[0]:self.patch[2], :]\n                #_data = mx.nd.array(nimg)\n                data = [_data]\n                try:\n                    self.check_valid_image(data)\n                except RuntimeError as e:\n                    logging.debug('Invalid image, skipping:  %s', str(e))\n                    continue\n                #print('aa',data[0].shape)\n                #data = self.augmentation_transform(data)\n                #print('bb',data[0].shape)\n                for datum in data:\n                    assert i < batch_size, 'Batch size must be multiples of augmenter output length'\n                    #print(datum.shape)\n                    batch_data[i][:] = self.postprocess_data(datum)\n                    if self.provide_label is not None:\n                      batch_label[i][:] = label\n                    i += 1\n        except StopIteration:\n            if i<batch_size:\n                raise StopIteration\n\n        #print('next end', batch_size, i)\n        _label = None\n        if self.provide_label is not None:\n          _label = [batch_label]\n        return io.DataBatch([batch_data], _label, batch_size - i)\n\n    def check_data_shape(self, data_shape):\n        \"\"\"Checks if the input data shape is valid\"\"\"\n        if not len(data_shape) == 3:\n            raise ValueError('data_shape should have length 3, with dimensions CxHxW')\n        if not data_shape[0] == 3:\n            raise ValueError('This iterator expects inputs to have 3 channels.')\n\n    def check_valid_image(self, data):\n        \"\"\"Checks if the input data is valid\"\"\"\n        if len(data[0].shape) == 0:\n            raise RuntimeError('Data shape is wrong')\n\n    def imdecode(self, s):\n        \"\"\"Decodes a string or byte string to an NDArray.\n        See mx.img.imdecode for more details.\"\"\"\n        img = mx.image.imdecode(s) #mx.ndarray\n        return img\n\n    def read_image(self, fname):\n        \"\"\"Reads an input image `fname` and returns the decoded raw bytes.\n\n        Example usage:\n        ----------\n        >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes.\n        \"\"\"\n        with open(os.path.join(self.path_root, fname), 'rb') as fin:\n            img = fin.read()\n        return img\n\n    def augmentation_transform(self, data):\n        \"\"\"Transforms input data with specified augmentation.\"\"\"\n        for aug in self.auglist:\n            data = [ret for src in data for ret in aug(src)]\n        return data\n\n    def postprocess_data(self, datum):\n        \"\"\"Final postprocessing step before image is loaded into the batch.\"\"\"\n        return nd.transpose(datum, axes=(2, 0, 1))\n\nclass FaceImageIterList(io.DataIter):\n  def __init__(self, iter_list):\n    assert len(iter_list)>0\n    self.provide_data = iter_list[0].provide_data\n    self.provide_label = iter_list[0].provide_label\n    self.iter_list = iter_list\n    self.cur_iter = None\n\n  def reset(self):\n    self.cur_iter.reset()\n\n  def next(self):\n    self.cur_iter = random.choice(self.iter_list)\n    while True:\n      try:\n        ret = self.cur_iter.next()\n      except StopIteration:\n        self.cur_iter.reset()\n        continue\n      return ret\n\n\n"
  },
  {
    "path": "image_generation_chainer/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/datasets/mnist.py",
    "content": "from os.path import isdir\nimport chainer\nimport numpy as np\n\n\nclass MnistDb(chainer.dataset.DatasetMixin):\n    def __init__(self, path, ims_suff='train_ims.npy', labels_suff=None, internal_perm=False, \n                 seed=9, permute=False, n_samples=None, train=True, **kwargs):\n        # # load the images (loaded all in ram).\n        if isdir(path):\n            self.base = np.load(path + ims_suff)\n        else:\n            print('Path not found. Loading fashion-mnist by default.')\n            trainset, _ = chainer.datasets.get_fashion_mnist(withlabel=True, scale=1, ndim=3)\n            ims = np.array([np.pad(samp[0], ((0, 0), (2, 2), (2, 2)), 'constant') for samp in trainset], dtype=np.float32)\n            self.base = ims * 2 - 1\n\n        if self.base[0].ndim == 2:\n            # # extend to avoid issues. Channels in front in chainer!!!\n            sh1 = self.base.shape\n            self.base = np.reshape(self.base, (sh1[0], 1, sh1[1], sh1[2]))\n        elif self.base.shape[-1] == 3 and self.base.ndim == 4:\n            self.base = self.base.transpose((0, 3, 1, 2))\n        if n_samples is not None:\n            self.base = self.base[:n_samples]\n        # # load the labels if the appropriate npy is provided.\n        if labels_suff is not None:\n            self.labels = np.load(path + labels_suff)\n            if n_samples is not None:\n                self.labels = self.labels[:n_samples]\n        if internal_perm:\n            # # do a random permutation of the data.\n            np.random.seed(seed)\n            perm = np.random.permutation(range(self.base.shape[0]))\n            self.base = self.base[perm]\n            if labels_suff is not None:\n                self.labels = self.labels[perm]\n        self.n_classes = len(set(self.labels)) if (labels_suff is not None) else 0\n\n    def __len__(self):\n        return len(self.base)\n\n    def get_example(self, i):\n        image = self.base[i]\n        if hasattr(self, 'labels'): \n            return image, self.labels[i]\n        else: \n            return image, 1\n\n\n\n\n"
  },
  {
    "path": "image_generation_chainer/dis_models/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/dis_models/resblocks_dis.py",
    "content": "import math\nimport chainer\nfrom source.links.sn_convolution_2d import SNConvolution2D\nfrom chainer import functions as F\nfrom chainer import links as L\n\n\ndef _downsample(x):\n    # Downsample (Mean Avg Pooling with 2x2 kernel)\n    return F.average_pooling_2d(x, 2)\n\n\nclass Block(chainer.Chain):\n    def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1,\n                 activation=F.relu, downsample=False, dropout_ratio=0.0, sn=False,\n                 gate=False):\n        super(Block, self).__init__()\n        initializer = chainer.initializers.GlorotUniform(math.sqrt(2))\n        initializer_sc = chainer.initializers.GlorotUniform()\n        self.dropout_ratio = dropout_ratio\n        self.activation = activation\n        self.downsample = downsample\n        self.learnable_sc = (in_channels != out_channels) or downsample\n        self.gate = gate\n        if sn:\n            Conv = SNConvolution2D\n        else:\n            Conv = L.Convolution2D\n        hidden_channels = in_channels if hidden_channels is None else hidden_channels\n        with self.init_scope():\n            self.c1 = Conv(in_channels, hidden_channels, ksize=ksize, pad=pad, initialW=initializer)\n            self.c2 = Conv(hidden_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer)\n            if self.learnable_sc:\n                self.c_sc = Conv(in_channels, out_channels, ksize=1, pad=0, initialW=initializer_sc)\n            if gate:\n                self.a = chainer.Parameter(initializer=chainer.initializers.Zero(), shape=(1))\n\n    def residual(self, x):\n        h = x\n        h = self.activation(h)\n        h = self.c1(h)\n        h = self.activation(h)\n        h = self.c2(h)\n        if self.downsample:\n            h = _downsample(h)\n        return h\n\n    def shortcut(self, x):\n        if self.learnable_sc:\n            x = self.c_sc(x)\n            if self.downsample:\n                return _downsample(x)\n            else:\n                return x\n        else:\n            return x\n\n    def __call__(self, x):\n        if self.gate:\n            res = self.residual(x)\n            sh = self.shortcut(x)\n            sigm_a = F.broadcast_to(F.sigmoid(self.a)[:, None, None, None], res.shape)\n            return 2 * ((1 - sigm_a) * res + sigm_a * sh)\n        else:\n            return self.residual(x) + self.shortcut(x)\n\n\nclass OptimizedBlock(chainer.Chain):\n    def __init__(self, in_channels, out_channels, ksize=3, pad=1,\n                 activation=F.relu, sn=False, gate=False):\n        super(OptimizedBlock, self).__init__()\n        initializer = chainer.initializers.GlorotUniform(math.sqrt(2))\n        initializer_sc = chainer.initializers.GlorotUniform()\n        self.activation = activation\n        self.gate = gate\n        if sn:\n            Conv = SNConvolution2D\n        else:\n            Conv = L.Convolution2D\n        with self.init_scope():\n            self.c1 = Conv(in_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer)\n            self.c2 = Conv(out_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer)\n            self.c_sc = Conv(in_channels, out_channels, ksize=1, pad=0, initialW=initializer_sc)\n            if gate:\n                self.a = chainer.Parameter(initializer=chainer.initializers.Zero(), shape=(1))\n\n    def residual(self, x):\n        h = x\n        h = self.c1(h)\n        h = self.activation(h)\n        # Conv -> Pool\n        h = self.c2(h)\n        h = _downsample(h)\n        return h\n\n    def shortcut(self, x):\n        # Pool -> Conv\n        return self.c_sc(_downsample(x))\n\n    def __call__(self, x):\n        if self.gate:\n            res = self.residual(x)\n            sh = self.shortcut(x)\n            sigm_a = F.broadcast_to(F.sigmoid(self.a)[:, None, None, None], res.shape)\n            return 2 * ((1 - sigm_a) * res + sigm_a * sh)\n        else:\n            return self.residual(x) + self.shortcut(x)\n"
  },
  {
    "path": "image_generation_chainer/dis_models/snresnet_32.py",
    "content": "import chainer\nfrom chainer import functions as F\nfrom source.links.sn_embed_id import SNEmbedID\nfrom source.links.sn_linear import SNLinear\nfrom dis_models.resblocks_dis import Block, OptimizedBlock\n\n\nclass SNResNetProjectionDiscriminator(chainer.Chain):\n    def __init__(self, ch=128, n_classes=0, activation=F.relu, sn=False, ch_input=3):\n        super(SNResNetProjectionDiscriminator, self).__init__()\n        self.activation = activation\n        self.n_classes = n_classes\n        with self.init_scope():\n            self.block1 = OptimizedBlock(ch_input, ch, sn=sn)\n            self.block2 = Block(ch, ch, activation=activation, downsample=True, sn=sn)\n            self.block3 = Block(ch, ch, activation=activation, downsample=False, sn=sn)\n            self.block4 = Block(ch, ch, activation=activation, downsample=False, sn=sn)\n            self.l5 = SNLinear(ch, 1, initialW=chainer.initializers.GlorotUniform(), nobias=True)\n            if n_classes > 0:\n                self.l_y = SNEmbedID(n_classes, ch, initialW=chainer.initializers.GlorotUniform())\n\n    def __call__(self, x, y=None, return_feature=False):\n        h = x\n        h = self.block1(h)\n        h = self.block2(h)\n        h = self.block3(h)\n        h = self.block4(h)\n        h = self.activation(h)\n        # Global average pooling\n        h = F.sum(h, axis=(2, 3))\n        output = self.l5(h)\n        if return_feature:\n            return output, h\n        if y is not None and self.n_classes:\n            w_y = self.l_y(y)\n            output += F.sum(w_y * h, axis=1, keepdims=True)\n        return output\n\n"
  },
  {
    "path": "image_generation_chainer/evaluations/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/evaluations/extensions.py",
    "content": "import os\nimport sys\nimport math\n\nimport numpy as np\nfrom PIL import Image\nimport scipy.linalg\n\nimport chainer\nimport chainer.cuda\nfrom chainer import Variable\nfrom chainer import serializers\nfrom chainer import cuda\nimport chainer.functions as F\nfrom chainer.training import extensions\n\nsys.path.append(os.path.dirname(__file__))\nsys.path.append('../')\nfrom source.inception.inception_score import inception_score, Inception\nfrom source.links.sn_convolution_2d import SNConvolution2D\nfrom source.functions.max_sv import max_singular_value\nfrom numpy.linalg import svd\ntry:\n    from skl_groups.divergences import KNNDivergenceEstimator\nexcept ImportError:\n    pass\n# # even though NDB is imported, its evaluation was not used for this \n# # project; it remains here for legacy mode.\nfrom evaluations.ndb import NDB\n\n# # define as globals since the trainer does not have a memory.\nbest_min_metric, best_inception, best_fid = 100000000, 0, 1000000\nbest_min_ndb, ndb = 100000000, None\n\n\ndef gen_images(gen, n=50000, batchsize=100, seed=None, z=None):\n    ims = []\n    xp = gen.xp\n    if seed is not None:\n        xp.random.seed(seed=seed)\n    for i in range(0, n, batchsize):\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            x = gen(batchsize, z=z)\n        x.unchain_backward()\n        x_cpu = chainer.cuda.to_cpu(x.data)\n        del x\n        x_cpu = np.asarray(np.clip(x_cpu * 127.5 + 127.5, 0.0, 255.0), dtype=np.uint8)\n        ims.append(x_cpu)\n    ims = np.asarray(ims)\n    # # the line below is only for images; synthetic might differ in number of dimensions.\n    _, _, c, h, w = ims.shape\n    ims = ims.reshape((n, c, h, w))\n    return ims\n\n\ndef gen_images_with_condition(gen, c=0, n=500, batchsize=100, z=None):\n    # # used to synthesize images with condition c, i.e. pass to the generator the\n    # # variable c.\n    ims = []\n    xp = gen.xp\n    for i in range(0, n, batchsize):\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            if isinstance(c, np.ndarray) and len(c.shape) == 2 and c.shape[0] == batchsize:\n                y = xp.asarray(c, dtype=xp.int32)\n            else:\n                y = xp.asarray([c] * batchsize, dtype=xp.int32)\n            x = gen(batchsize, y=y, z=z)\n        x = chainer.cuda.to_cpu(x.data)\n        x = np.asarray(np.clip(x * 127.5 + 127.5, 0.0, 255.0), dtype=np.uint8)\n        ims.append(x)\n    ims = np.asarray(ims)\n    _, _, c, h, w = ims.shape\n    ims = ims.reshape((n, c, h, w))\n    return ims\n\n\ndef sample_generate_light(gen, dst, rows=5, cols=5, batchsize=5, seed=0):\n    @chainer.training.make_extension()\n    def make_image(trainer):\n        np.random.seed(seed)\n        n_images = rows * cols\n        x = gen_images(gen, n_images, batchsize=batchsize)\n        _, c, H, W = x.shape\n        x = x.reshape((rows, cols, c, H, W))\n        x = x.transpose(0, 3, 1, 4, 2)\n        x = x.reshape((rows * H, cols * W, c))\n        if c == 1:\n            x = np.concatenate([x, x, x], axis=2)\n        preview_dir = '{}/preview'.format(dst)\n        preview_path = preview_dir + '/image_latest.png'\n        if not os.path.exists(preview_dir):\n            os.makedirs(preview_dir)\n        Image.fromarray(x).save(preview_path)\n\n    return make_image\n\n\ndef sample_generate(gen, dst, rows=10, cols=10, batchsize=10, seed=0):\n    @chainer.training.make_extension()\n    def make_image(trainer):\n        print('in sample generation: ', trainer.updater.iteration)\n        np.random.seed(seed)\n        n_images = rows * cols\n        x = gen_images(gen, n_images, batchsize=batchsize)\n        _, c, h, w = x.shape\n        x = x.reshape((rows, cols, c, h, w))\n        x = x.transpose(0, 3, 1, 4, 2)\n        x = x.reshape((rows * h, cols * w, c))\n        if c == 1:\n            x = np.concatenate([x, x, x], axis=2)\n        if c > 3:\n            x = x[:, :, :3]\n        preview_dir = '{}/preview'.format(dst)\n        preview_path = preview_dir + '/image{:0>8}.png'.format(trainer.updater.iteration)\n        if not os.path.exists(preview_dir):\n            os.makedirs(preview_dir)\n        Image.fromarray(x).save(preview_path)\n\n    return make_image\n\n\ndef sample_generate_conditional(gen, dst, rows=10, n_classes=1000, seed=0, classes=None):\n    \"\"\"Visualization of rows*cols images randomly generated by the generator.\"\"\"\n    if classes is None:\n        classes = np.arange(10) * (n_classes / 10)\n    print('Sample visualization classes: ', classes)\n    # # update the rows if less than the clases.\n    rows = min(rows, len(classes))\n\n    @chainer.training.make_extension()\n    def make_image(trainer=None):\n        np.random.seed(seed)\n        xp = gen.xp\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            x = []\n            for c in classes:\n                x.append(gen_images_with_condition(gen, c=c, n=rows, batchsize=rows))\n            x = np.concatenate(x, 0)\n        _, c, h, w = x.shape\n        x = x.reshape((rows, len(classes), c, h, w))\n        x = x.transpose(0, 3, 1, 4, 2)\n        x = x.reshape((rows * h, len(classes) * w, c))\n        if c == 1:\n            x = np.concatenate([x, x, x], axis=2)\n        preview_dir = '{}/preview'.format(dst)\n        preview_path = preview_dir + '/image{:0>8}.png'.format(\n            trainer.updater.iteration if trainer is not None else None)\n        if not os.path.exists(preview_dir):\n            os.makedirs(preview_dir)\n        Image.fromarray(x).save(preview_path)\n\n    return make_image\n\ndef divergence_gen(gen, gt_db, batch=1000, metric='kl', normalize=False, \n                   n_bins=100, whitening=True, classes=None, **kwargs):\n    \"\"\" \n    Given a generator and the gt function (the one generator \n    tries to approximate), we measure the discrepancy of the\n    generated from the gt signals. \n    \"\"\"\n    # # generate some samples.\n    batch = gt_db.shape[0]\n    if classes is None:\n        gen_samples = gen_images(gen, n=batch, batchsize=batch)\n    else:\n        # # conditional case.\n        gen_csamples, n_ms = [], int(batch // len(classes) + 10)\n        for cl in classes:\n            x = gen_images_with_condition(gen, n=n_ms, c=cl, batchsize=n_ms)\n            gen_csamples.append(x)\n        gen_csamples = np.concatenate(gen_csamples, 0)\n        gen_samples = gen_csamples[:gt_db.shape[0]]\n    if len(gt_db.shape) != 2:\n        gt_db = gt_db.reshape((batch, -1))\n    if len(gen_samples.shape) != 2:\n        gen_samples = gen_samples.reshape((batch, -1))\n    if gen_samples.dtype == np.uint8:\n        gen_samples = gen_samples.astype(np.float32)\n    if normalize:\n        # # Given that gen_images have a range [0, 255], normalize\n        # # the images in the [-1, 1] range for the KNN.\n        gen_samples1 = gen_samples / 127.5 - 1\n    else:\n        gen_samples1 = gen_samples\n\n    if metric == 'ndb':\n        global ndb\n        if ndb is None:\n            ndb = NDB(training_data=gt_db, number_of_bins=n_bins, whitening=whitening)\n        metric_val = ndb.evaluate(gen_samples)\n        chainer.reporter.report({'ndb': metric_val['NDB']})\n        chainer.reporter.report({'JS': metric_val['JS']})\n        diver = metric_val['NDB']\n    else:\n        # # define an estimator (e.g. KL divergence).\n        est = KNNDivergenceEstimator(div_funcs=[metric], Ks=[3], clamp=False)\n        # # fit and return the result.\n        res_diver = est.fit_transform([gt_db, gen_samples])\n        try:\n            diver = res_diver[0, 1]\n        except:\n            diver = res_diver[0][0][0, 1]\n        chainer.reporter.report({'kl': diver})\n    return diver\n\n    \ndef divergence_trainer(gen, db, metric='kl', export_best=True, **kwargs):\n    \"\"\"\n    Chainer extension for evaluating the divergence of the generator from gt.\n    \"\"\"\n    @chainer.training.make_extension()\n    def evaluation(trainer=None):\n        \n        for met in metric:\n            metric_val = divergence_gen(gen, db, metric=met, **kwargs)\n            if export_best and trainer is not None:\n                # # compare with the best score (per metric).\n                global best_min_metric, best_min_ndb\n                if 'kl' in met and metric_val < best_min_metric:\n                    # # export if it has the best score so far (kl).\n                    best_min_metric = metric_val\n                    ext = extensions.snapshot_object(gen, '{}_best.npz'.format(gen.__class__.__name__))\n                    ext(trainer)\n                elif 'ndb' in met and metric_val < best_min_ndb:\n                    # # export if it has the best score so far (ndb).\n                    best_min_ndb = metric_val\n                    ext = extensions.snapshot_object(gen, '{}_best_ndb.npz'.format(gen.__class__.__name__))\n                    ext(trainer)\n\n    if isinstance(metric, str):\n        metric = [metric]\n    return evaluation\n\n\ndef load_inception_model(path=None, gpu=0):\n    path = path if path is not None else \"%s/inception/inception_score.model\" % os.path.dirname(__file__)\n    model = Inception()\n    serializers.load_hdf5(path, model)\n    if gpu >= 0:\n        model.to_gpu()\n    return model\n\n\ndef calc_inception(gen, batchsize=10, dst=None, path=None, n_ims=50000, splits=10):\n    @chainer.training.make_extension()\n    def evaluation(trainer=None):\n        model = load_inception_model(path)\n        ims = gen_images(gen, n_ims, batchsize=batchsize).astype(\"f\")\n        mean, std = inception_score(model, ims, splits=splits)\n        chainer.reporter.report({\n            'inception_mean': mean,\n            'inception_std': std\n        })\n        if dst is not None:\n            preview_dir = '{}/stats'.format(dst)\n            preview_path = preview_dir + '/inception_score_{:0>8}.txt'.format(\n                trainer.updater.iteration if trainer is not None else None)\n            np.savetxt(preview_path, np.array([mean, std]))\n        # # export the best model.\n        global best_inception\n        if trainer is not None and best_inception < mean:\n            ext = extensions.snapshot_object(gen, gen.__class__.__name__ + '_best_inception.npz')\n            ext(trainer)\n            best_inception = mean\n\n    return evaluation\n\n\ndef get_mean_cov(model, ims, batch_size=100, verbose=False):\n    n, c, w, h = ims.shape\n    n_batches = int(math.ceil(float(n) / float(batch_size)))\n    xp = model.xp\n    ys = xp.empty((n, 2048), dtype=xp.float32)\n    for i in range(n_batches):\n        if verbose:\n            print('Running batch', i + 1, '/', n_batches, '...')\n        batch_start = (i * batch_size)\n        batch_end = min((i + 1) * batch_size, n)\n        ims_batch = ims[batch_start:batch_end]\n        ims_batch = Variable(xp.asarray(ims_batch, dtype=xp.float32))\n\n        # Resize image to the shape expected by the inception module\n        if (w, h) != (299, 299):\n            ims_batch = F.resize_images(ims_batch, (299, 299))  # bilinear\n\n        # Feed images to the inception module to get the features\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            y = model(ims_batch, get_feature=True)\n        ys[batch_start:batch_end] = y.data\n\n    mean = xp.mean(ys, axis=0).get()\n    # cov = F.cross_covariance(ys, ys, reduce=\"no\").datasets.get()\n    cov = np.cov(ys.get().T)\n    return mean, cov\n\n\ndef monitor_largest_singular_values(dis, dst):\n    @chainer.training.make_extension()\n    def evaluation(trainer=None):\n        def _l2normalize(v, eps=1e-12):\n            return v / (((v ** 2).sum()) ** 0.5 + eps)\n\n        xp = dis.xp\n        links = [[name, link] for name, link in sorted(dis.namedlinks())]\n        sigmas = []\n        for name, link in links:\n            if isinstance(link, SNConvolution2D):\n                W, u = link.W, link.u\n                W_mat = W.reshape(W.shape[0], -1)\n                sigma, _, _ = max_singular_value(W_mat, u)\n                W_bar = cuda.to_cpu((W_mat.data / xp.squeeze(sigma.data)))\n                _, s, _ = svd(W_bar)\n                _sigma = s[0]\n                print(name.strip('/'), _sigma)\n                sigmas.append([name.strip('/'), _sigma])\n\n        if dst is not None:\n            preview_dir = '{}/sigmas'.format(dst)\n            if not os.path.exists(preview_dir):\n                os.makedirs(preview_dir)\n            preview_path = preview_dir + '/sigmas_{:0>8}.txt'.format(\n                trainer.updater.iteration if trainer is not None else None)\n            with open(preview_path, 'wb') as f:\n                np.savetxt(f, np.array(sigmas, dtype=np.str), delimiter=\" \", fmt=\"%s\")\n\n    return evaluation\n\n\ndef FID(m0, c0, m1, c1):\n    ret = 0\n    ret += np.sum((m0 - m1) ** 2)\n    ret += np.trace(c0 + c1 - 2.0 * scipy.linalg.sqrtm(np.dot(c0, c1)))\n    return np.real(ret)\n\n\ndef calc_FID(gen, batchsize=100, stat_file=\"%s/cifar-10-fid.npz\" % os.path.dirname(__file__), dst=None, path=None,\n             n_ims=5000):\n    \"\"\"Frechet Inception Distance proposed by https://arxiv.org/abs/1706.08500\"\"\"\n\n    @chainer.training.make_extension()\n    def evaluation(trainer=None):\n        model = load_inception_model(path)\n        stat = np.load(stat_file)\n        ims = gen_images(gen, n_ims, batchsize=batchsize).astype(\"f\")\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            mean, cov = get_mean_cov(model, ims)\n        fid = FID(stat[\"mean\"], stat[\"cov\"], mean, cov)\n        chainer.reporter.report({'FID': fid})\n        if dst is not None:\n            preview_dir = '{}/stats'.format(dst)\n            preview_path = preview_dir + '/fid_{:0>8}.txt'.format(\n                trainer.updater.iteration if trainer is not None else None)\n            np.savetxt(preview_path, np.array([fid]))\n\n        # # export the best model.\n        global best_fid\n        if trainer is not None and best_fid > fid:\n            ext = extensions.snapshot_object(gen, gen.__class__.__name__ + '_best_fid.npz')\n            ext(trainer)\n            best_fid = fid\n\n    return evaluation\n\n# For classification\n\ndef get_batch(iterator, xp):\n    batch = iterator.next()\n    batchsize = len(batch)\n    x, y = [], []\n    for j in range(batchsize):\n        _x = batch[j][0]\n        _y = batch[j][1]\n        if isinstance(_x, (list, tuple)):\n            for k in range(len(_x)):\n                x.append(np.asarray(_x[k]).astype(\"f\"))\n                y.append(np.asarray(_y[k]).astype(np.int32))\n        else:\n            x.append(np.asarray(batch[j][0]).astype(\"f\"))\n            y.append(np.asarray(batch[j][1]).astype(np.int32))\n    x = xp.asarray(x)\n    y = xp.asarray(y, dtype=xp.int32)\n    return x, y\n\n\n\ndef validation_loss_and_acc(cls, iterator, n=50000, dis=None):\n\n    @chainer.training.make_extension()\n    def _evaluate(trainer):\n        iterator.reset()\n        losses = []\n        accs = []\n        for i in range(0, n, iterator.batch_size):\n            x, y = get_batch(iterator, cls.xp)\n            with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n                if dis is not None:\n                    _, x = dis(x, return_feature=True)\n                logit = cls(x)\n                loss = F.softmax_cross_entropy(logit, y)\n                acc = F.accuracy(logit, y)\n                losses.append(chainer.cuda.to_cpu(loss.array))\n                accs.append(chainer.cuda.to_cpu(acc.array))\n        chainer.reporter.report({\n            'val_loss': np.mean(np.asarray(losses)),\n            'val_acc': np.mean(np.asarray(accs))\n        })\n\n    return _evaluate\n"
  },
  {
    "path": "image_generation_chainer/evaluations/ndb.py",
    "content": "import os\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom scipy.stats import norm\nfrom matplotlib import pyplot as plt\nimport pickle as pkl\n\nclass NDB:\n    def __init__(self, training_data=None, number_of_bins=100, significance_level=0.05, z_threshold=None,\n                 whitening=False, max_dims=None, cache_folder=None, verbose=False):\n        \"\"\"\n        Original code: https://github.com/eitanrich/gans-n-gmms/blob/master/utils/ndb.py\n        Publication: https://arxiv.org/abs/1805.12462 .\n        NDB Evaluation Class\n        :param training_data: Optional - the training samples - array of m x d floats (m samples of dimension d)\n        :param number_of_bins: Number of bins (clusters) default=100\n        :param significance_level: The statistical significance level for the two-sample test\n        :param z_threshold: Allow defining a threshold in terms of difference/SE for defining a bin as statistically different\n        :param whitening: Perform data whitening - subtract mean and divide by per-dimension std\n        :param max_dims: Max dimensions to use in K-means. By default derived automatically from d\n        :param bins_file: Optional - file to write / read-from the clusters (to avoid re-calculation)\n        \"\"\"\n        self.number_of_bins = number_of_bins\n        self.significance_level = significance_level\n        self.z_threshold = z_threshold\n        self.whitening = whitening\n        self.ndb_eps = 1e-6\n        self.training_mean = 0.0\n        self.training_std = 1.0\n        self.max_dims = max_dims\n        self.cache_folder = cache_folder\n        self.bin_centers = None\n        self.bin_proportions = None\n        self.ref_sample_size = None\n        self.used_d_indices = None\n        self.results_file = None\n        self.verbose = verbose\n        self.test_name = 'ndb_{}_bins_{}'.format(self.number_of_bins, 'whiten' if self.whitening else 'orig')\n        self.cached_results = {}\n        if self.cache_folder:\n            self.results_file = os.path.join(cache_folder, self.test_name+'_results.pkl')\n            if os.path.isfile(self.results_file):\n                # print('Loading previous results from', self.results_file, ':')\n                self.cached_results = pkl.load(open(self.results_file, 'rb'))\n                # print(self.cached_results.keys())\n        if training_data is not None or cache_folder is not None:\n                bins_file = None\n                if cache_folder:\n                    os.makedirs(cache_folder, exist_ok=True)\n                    bins_file = os.path.join(cache_folder, self.test_name+'.pkl')\n                self.construct_bins(training_data, bins_file)\n\n    def construct_bins(self, training_samples, bins_file):\n        \"\"\"\n        Performs K-means clustering of the training samples\n        :param training_samples: An array of m x d floats (m samples of dimension d)\n        \"\"\"\n\n        if self.__read_from_bins_file(bins_file):\n            return\n        n, d = training_samples.shape\n        k = self.number_of_bins\n        if self.whitening:\n            self.training_mean = np.mean(training_samples, axis=0)\n            self.training_std = np.std(training_samples, axis=0) + self.ndb_eps\n\n        if self.max_dims is None and d > 1000:\n            # To ran faster, perform binning on sampled data dimension (i.e. don't use all channels of all pixels)\n            self.max_dims = d//6\n\n        whitened_samples = (training_samples-self.training_mean)/self.training_std\n        d_used = d if self.max_dims is None else min(d, self.max_dims)\n        self.used_d_indices = np.random.choice(d, d_used, replace=False)\n\n        print('Performing K-Means clustering of {} samples in dimension {} / {} to {} clusters ...'.format(n, d_used, d, k))\n        #print('Can take a couple of minutes...')\n        if n//k > 1000:\n            print('Training data size should be ~500 times the number of bins (for reasonable speed and accuracy)')\n\n        clusters = KMeans(n_clusters=k, max_iter=100, n_jobs=-1).fit(whitened_samples[:, self.used_d_indices])\n\n        bin_centers = np.zeros([k, d])\n        for i in range(k):\n            bin_centers[i, :] = np.mean(whitened_samples[clusters.labels_ == i, :], axis=0)\n\n        # Organize bins by size\n        label_vals, label_counts = np.unique(clusters.labels_, return_counts=True)\n        bin_order = np.argsort(-label_counts)\n        self.bin_proportions = label_counts[bin_order] / np.sum(label_counts)\n        self.bin_centers = bin_centers[bin_order, :]\n        self.ref_sample_size = n\n        self.__write_to_bins_file(bins_file)\n        #print('Done.')\n\n    def evaluate(self, query_samples, model_label=None):\n        \"\"\"\n        Assign each sample to the nearest bin center (in L2). Pre-whiten if required. and calculate the NDB\n        (Number of statistically Different Bins) and JS divergence scores.\n        :param query_samples: An array of m x d floats (m samples of dimension d)\n        :param model_label: optional label string for the evaluated model, allows plotting results of multiple models\n        :return: results dictionary containing NDB and JS scores and array of labels (assigned bin for each query sample)\n        \"\"\"\n        n = query_samples.shape[0]\n        query_bin_proportions, query_bin_assignments = self.__calculate_bin_proportions(query_samples)\n        # print(query_bin_proportions)\n        different_bins = NDB.two_proportions_z_test(self.bin_proportions, self.ref_sample_size, query_bin_proportions,\n                                                    n, significance_level=self.significance_level,\n                                                    z_threshold=self.z_threshold)\n        ndb = np.count_nonzero(different_bins)\n        js = NDB.jensen_shannon_divergence(self.bin_proportions, query_bin_proportions)\n        results = {'NDB': ndb,\n                   'JS': js,\n                   'Proportions': query_bin_proportions,\n                   'N': n,\n                   'Bin-Assignment': query_bin_assignments,\n                   'Different-Bins': different_bins}\n\n        if model_label:\n            print('Results for {} samples from {}: '.format(n, model_label), end='')\n            self.cached_results[model_label] = results\n            if self.results_file:\n                # print('Storing result to', self.results_file)\n                pkl.dump(self.cached_results, open(self.results_file, 'wb'))\n\n        if self.verbose:\n            print('NDB =', ndb, 'NDB/K =', ndb/self.number_of_bins, ', JS =', js)\n        return results\n\n    def print_results(self):\n        print('NSB results (K={}{}):'.format(self.number_of_bins, ', data whitening' if self.whitening else ''))\n        for model in sorted(list(self.cached_results.keys())):\n            res = self.cached_results[model]\n            print('%s: NDB = %d, NDB/K = %.3f, JS = %.4f' % (model, res['NDB'], res['NDB']/self.number_of_bins, res['JS']))\n\n    def plot_results(self, models_to_plot=None):\n        \"\"\"\n        Plot the binning proportions of different methods\n        :param models_to_plot: optional list of model labels to plot\n        \"\"\"\n        K = self.number_of_bins\n        w = 1.0 / (len(self.cached_results)+1)\n        assert K == self.bin_proportions.size\n        assert self.cached_results\n\n        # Used for plotting only\n        def calc_se(p1, n1, p2, n2):\n            p = (p1 * n1 + p2 * n2) / (n1 + n2)\n            return np.sqrt(p * (1 - p) * (1/n1 + 1/n2))\n\n        if not models_to_plot:\n            models_to_plot = sorted(list(self.cached_results.keys()))\n\n        # Visualize the standard errors using the train proportions and size and query sample size\n        train_se = calc_se(self.bin_proportions, self.ref_sample_size,\n                           self.bin_proportions, self.cached_results[models_to_plot[0]]['N'])\n        plt.bar(np.arange(0, K)+0.5, height=train_se*2.0, bottom=self.bin_proportions-train_se,\n                width=1.0, label='Train$\\pm$SE', color='gray')\n\n        ymax = 0.0\n        for i, model in enumerate(models_to_plot):\n            results = self.cached_results[model]\n            label = '%s (%i : %.4f)' % (model, results['NDB'], results['JS'])\n            ymax = max(ymax, np.max(results['Proportions']))\n            if K <= 70:\n                plt.bar(np.arange(0, K)+(i+1.0)*w, results['Proportions'], width=w, label=label)\n            else:\n                plt.plot(np.arange(0, K)+0.5, results['Proportions'], '--*', label=label)\n        plt.legend(loc='best')\n        plt.ylim((0.0, min(ymax, np.max(self.bin_proportions)*4.0)))\n        plt.grid(True)\n        plt.title('Binning Proportions Evaluation Results for {} bins (NDB : JS)'.format(K))\n        plt.show()\n\n    def __calculate_bin_proportions(self, samples):\n        if self.bin_centers is None:\n            print('First run construct_bins on samples from the reference training data')\n        assert samples.shape[1] == self.bin_centers.shape[1]\n        n, d = samples.shape\n        k = self.bin_centers.shape[0]\n        D = np.zeros([n, k], dtype=samples.dtype)\n        if self.verbose:\n            print('Calculating bin assignments for {} samples...'.format(n))\n        whitened_samples = (samples-self.training_mean)/self.training_std\n        for i in range(k):\n            if self.verbose:\n                print('.', end='', flush=True)\n            D[:, i] = np.linalg.norm(whitened_samples[:, self.used_d_indices] - self.bin_centers[i, self.used_d_indices],\n                                     ord=2, axis=1)\n        if self.verbose:\n            print()\n        labels = np.argmin(D, axis=1)\n        probs = np.zeros([k])\n        label_vals, label_counts = np.unique(labels, return_counts=True)\n        probs[label_vals] = label_counts / n\n        return probs, labels\n\n    def __read_from_bins_file(self, bins_file):\n        if bins_file and os.path.isfile(bins_file):\n            print('Loading binning results from', bins_file)\n            bins_data = pkl.load(open(bins_file,'rb'))\n            self.bin_proportions = bins_data['proportions']\n            self.bin_centers = bins_data['centers']\n            self.ref_sample_size = bins_data['n']\n            self.training_mean = bins_data['mean']\n            self.training_std = bins_data['std']\n            self.used_d_indices = bins_data['d_indices']\n            return True\n        return False\n\n    def __write_to_bins_file(self, bins_file):\n        if bins_file:\n            print('Caching binning results to', bins_file)\n            bins_data = {'proportions': self.bin_proportions,\n                         'centers': self.bin_centers,\n                         'n': self.ref_sample_size,\n                         'mean': self.training_mean,\n                         'std': self.training_std,\n                         'd_indices': self.used_d_indices}\n            pkl.dump(bins_data, open(bins_file, 'wb'))\n\n    @staticmethod\n    def two_proportions_z_test(p1, n1, p2, n2, significance_level, z_threshold=None):\n        # Per http://stattrek.com/hypothesis-test/difference-in-proportions.aspx\n        # See also http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/binotest.htm\n        p = (p1 * n1 + p2 * n2) / (n1 + n2)\n        se = np.sqrt(p * (1 - p) * (1/n1 + 1/n2))\n        z = (p1 - p2) / se\n        # Allow defining a threshold in terms as Z (difference relative to the SE) rather than in p-values.\n        if z_threshold is not None:\n            return abs(z) > z_threshold\n        p_values = 2.0 * norm.cdf(-1.0 * np.abs(z))    # Two-tailed test\n        return p_values < significance_level\n\n    @staticmethod\n    def jensen_shannon_divergence(p, q):\n        \"\"\"\n        Calculates the symmetric Jensen–Shannon divergence between the two PDFs\n        \"\"\"\n        m = (p + q) * 0.5\n        return 0.5 * (NDB.kl_divergence(p, m) + NDB.kl_divergence(q, m))\n\n    @staticmethod\n    def kl_divergence(p, q):\n        \"\"\"\n        The Kullback–Leibler divergence.\n        Defined only if q != 0 whenever p != 0.\n        \"\"\"\n        assert np.all(np.isfinite(p))\n        assert np.all(np.isfinite(q))\n        assert not np.any(np.logical_and(p != 0, q == 0))\n\n        p_pos = (p > 0)\n        return np.sum(p[p_pos] * np.log(p[p_pos] / q[p_pos]))\n\n\nif __name__ == \"__main__\":\n    dim=100\n    k=100\n    n_train = k*100\n    n_test = k*10\n\n    train_samples = np.random.uniform(size=[n_train, dim])\n    ndb = NDB(training_data=train_samples, number_of_bins=k, whitening=True)\n\n    test_samples = np.random.uniform(high=1.0, size=[n_test, dim])\n    ndb.evaluate(test_samples, model_label='Test')\n\n    test_samples = np.random.uniform(high=0.9, size=[n_test, dim])\n    ndb.evaluate(test_samples, model_label='Good')\n\n    test_samples = np.random.uniform(high=0.75, size=[n_test, dim])\n    ndb.evaluate(test_samples, model_label='Bad')\n\n    ndb.plot_results(models_to_plot=['Test', 'Good', 'Bad'])\n\n"
  },
  {
    "path": "image_generation_chainer/gen_models/cnn_gen_custom_prodpoly.py",
    "content": "import numpy as np\nimport chainer\nimport chainer.functions as F\nimport chainer.links as L\nfrom source.links.sn_linear import SNLinear\nfrom source.links.sn_convolution_2d import SNConvolution2D\nfrom source.links.categorical_conditional_batch_normalization import CategoricalConditionalBatchNormalization\ntry:\n    from source.links.instance_normalization import InstanceNormalization\nexcept ImportError:\n    #print('The instance norm is not implemented or imported in the generator.')\n    pass\nfrom functools import partial\n\nfrom source.miscs.random_samples import sample_continuous, sample_categorical\n\n\ndef return_norm(norm):\n    if isinstance(norm, str):\n        return norm\n    if norm == 0:\n        return 'batch'\n    elif norm == 1:\n        return 'instance'\n    elif norm == 2:\n        return 'cinstance'\n\n\ndef _upsample(x):\n    h, w = x.shape[2:]\n    return F.unpooling_2d(x, 2, outsize=(h * 2, w * 2))\n\n\nclass ProdPolyConvGenerator(chainer.Chain):\n    def __init__(self, layer_d, use_bn=False, sn=False, out_ch=2, mult_lat=True,\n                 distribution='uniform', ksizes=None, strides=None, paddings=None, \n                 activ=None, bottom_width=1, use_localz=True, n_classes=0, \n                 use_out_act=False, channels=None, power_poly=[4, 3], \n                 derivfc=0, activ_prod=0, use_bias=True, dim_z=128, train=True,\n                 add_h_injection=False, add_h_poly=False, norm_after_poly=False,\n                 normalize_preinject=False, use_act_zgl=False, allow_conv=False,\n                 repeat_poly=1, share_first=False, type_norm='batch', typen_poly='batch',\n                 skip_rep=False, order_out_poly=None, use_activ=False, thresh_skip=None,\n                 use_sec_recurs=False, sign_srec=-1):\n        \"\"\"\n        Initializes the product polynomial generator.\n        :param layer_d: List of all layers' inputs/outputs channels (including \n                        the input to the last output).\n        :param use_bn: Bool, whether to use batch normalization.\n        :param sn: Bool, if True use spectral normalization.\n        :param out_ch: int, Output channels of the generator.\n        :param ksizes: List of the kernel sizes per layer.\n        :param strides: List of the stride per layer.\n        :param paddings: List of the padding per layer (int).\n        :param activ: str or None: The activation to use.\n        :param bottom_width: int, spatial resolution to reshape the init noise.\n        :param use_localz: bool, if True, then use 'local' transformations (affine)\n               to transform from the original z to the current shape.\n        :param use_out_act: bool, if True, it uses a tanh as output activation.\n        :param repeat_poly: int or list: List of how many times to repeat each \n               polynomial (original with FC layers, the last is NOT repeated).\n        :param share_first: bool. If True, it shares the first FC layer in the\n               FC polynomials. It assumes the respective FC layers are of the same size.\n        :param type_norm: str. The type of normalization to be used, i.e. 'batch' for\n               BN, 'instance' for instance norm.\n        :param typen_poly: str. The type of normalization to be used ONLY \n               for the FC layers. \n        :param skip_rep: bool. If True, it uses a skip connection before the layer to \n               the next, i.e. in practice it directly skips the convolution/fc plus hadamard \n               product of this layer. See derivation 3.\n        :param order_out_poly: int or None. If int, a polynomial of the order is used\n               in the output, i.e. before the final convolution.\n        :param use_activ: bool. If True, use activations in the main deconvolutional part.\n        :param thresh_skip: None, int or list. It represents the probability of skipping the\n               hadamard product in the deconvolution part (i.e. main polynomial). This is per\n               layer; e.g. if 0.3, this means that approx. 30% of the time this hadamard will\n               be skipped. It should have values in the [0, 1]. In inference, it should be\n               deactivated.\n        :param use_sec_recurs: bool, default False. If True, it uses the second recursive term, \n               i.e. x_(n+1)=f(x_n, x_(n-1)). This is used only in the derivation 3 setting. Also, \n               for practical reasons, use only for the FC layers when their order > 3.\n        \"\"\"\n        super(ProdPolyConvGenerator, self).__init__()\n        self.n_l = n_l = len(layer_d) - 1\n        w = chainer.initializers.GlorotUniform()\n        if sn:\n            Conv = SNConvolution2D\n            Linear = SNLinear\n            raise RuntimeError('Not implemented!')\n        else:\n            Conv = L.Deconvolution2D\n            Linear = L.Linear\n        Conv0 = L.Convolution2D\n        # # initialize args not provided.\n        if ksizes is None:\n            ksizes = [4] * n_l\n        if strides is None:\n            strides = [2] * n_l\n        if paddings is None:\n            paddings = [1] * n_l\n        # # the length of the ksizes and the strides is only for the conv layers, hence\n        # # it should be one number short of the layer_d.\n        assert len(ksizes) == len(strides) == len(paddings) == n_l\n        # # save in self, several useful properties.\n        self.use_bn = use_bn\n        self.n_channels = layer_d\n        self.mult_lat = mult_lat\n        self.distribution = distribution\n        # # Define dim_z.\n        assert isinstance(dim_z, int)\n        self.dim_z = dim_z\n        self.train = train\n        # # bottom_width: The default starting spatial resolution of the convolutions.\n        self.bottom_width = bottom_width\n        self.use_localz = use_localz\n        self.n_classes = n_classes\n        if activ is not None:\n            activ = getattr(F, activ)\n        elif use_activ:\n            activ = F.relu\n        else:\n            activ = lambda x: x\n        self.activ = self.activation = activ\n        self.out_act = F.tanh if use_out_act else lambda x: x\n        # # Set the add_one to True out of consistency with the other \n        # # implementation (resnet-based).\n        self.add_one = 1\n        self.add_h_injection = add_h_injection\n        self.normalize_preinject = normalize_preinject\n        self.use_bias = use_bias\n        self.add_h_poly = add_h_poly\n        self.power_poly = power_poly\n        if channels is None:\n            # # Initialize in the dimension of z.\n            channels = [dim_z] * len(self.power_poly)\n        self.channels = channels\n        if isinstance(activ_prod, int):\n            activ_prod = [activ_prod] * len(self.power_poly)\n        self.activ_prod = activ_prod\n        if isinstance(repeat_poly, int):\n            repeat_poly = [repeat_poly] * len(self.power_poly)\n        self.repeat_poly = repeat_poly\n        assert len(self.power_poly) == len(self.activ_prod) == len(self.channels)\n        assert len(self.power_poly) == len(self.repeat_poly)\n        # # If True, it compensates for python open interval in the end of range. \n        self.derivfc = derivfc\n        self.norm_after_poly = norm_after_poly\n        # # whether to use an activation in the global transformation.\n        self.use_act_zgl = use_act_zgl and use_activ\n        self.share_first = share_first\n        self.type_norm = return_norm(type_norm)\n        self.typen_poly = return_norm(typen_poly)\n        self.skip_rep = skip_rep\n        # # optionally adding one 'output polynomial', before making the final output convolution.\n        self.order_out_poly = order_out_poly\n        # # set the threshold for skipping the injection in the deconvolution part.\n        if isinstance(thresh_skip, float) and self.train:\n            self.thresh_skip = [0] + [thresh_skip] * (n_l - 1)\n            print('[Gen] Skip probabilities: {}.'.format(self.thresh_skip))\n        else:\n            self.thresh_skip = thresh_skip\n        self.use_sec_recurs = use_sec_recurs and skip_rep\n        self.sign_srec = sign_srec\n\n        with self.init_scope():\n            if self.n_classes == 0:\n                bn1 = partial(L.BatchNormalization, use_gamma=True, use_beta=False)\n            elif type_norm == 'batch':\n                print('[Gen] Categorical batch normalization in the generator.')\n                bn1 = partial(CategoricalConditionalBatchNormalization, n_cat=n_classes)\n            elif type_norm == 'instance':\n                print('[Gen] Instance normalization in the generator.')\n                bn1 = InstanceNormalization\n            if self.typen_poly != self.type_norm and self.typen_poly == 'instance':\n                print('[Gen] Using instance normalization for the FC layers *ONLY*.')\n                bn2 = InstanceNormalization\n            else:\n                bn2 = bn1\n            # # inpc_pol: the input size to the current polynomial; initialize on dimz.\n            inpc_pol = dim_z\n            # # iterate over all the polynomials (in the fully-connected part).\n            print('[Gen] Number of product polynomials: {}.'.format(len(self.power_poly)))\n            for id_poly in range(len(self.power_poly)):\n                chp = self.channels[id_poly]\n                m1 = '[Gen] Channels of the polynomial {}: {}, depth: {}.'\n                print(m1.format(id_poly, chp, self.power_poly[id_poly]))\n                # ensure that the current input channels match the expected.\n                setattr(self, 'has_rsz{}'.format(id_poly), inpc_pol != chp)\n                if inpc_pol != chp:\n                    setattr(self, 'resize{}'.format(id_poly), Linear(inpc_pol, chp, nobias=not use_bias, initialW=w))\n                # # now build the current polynomial (id_poly).\n                for l in range(1, self.power_poly[id_poly] + self.add_one):\n                    if l == 1 and id_poly > 0 and self.share_first:\n                        # # in this case the layer will be shared with the first polynomial's\n                        # # the first layer.\n                        continue\n                    setattr(self, 'l{}_{}'.format(id_poly, l), Linear(chp, chp, nobias=not use_bias, initialW=w))\n                # # define the activation for this polynomial.\n                actp = F.relu if self.activ_prod[id_poly] else lambda x: x\n                setattr(self, 'activ{}'.format(id_poly), actp)\n                if self.norm_after_poly:\n                    # # define different batch normalizations for each polynomial repeated.\n                    for repeat in range(self.repeat_poly[id_poly]):\n                        setattr(self, 'bnp{}_r{}'.format(id_poly, repeat), bn2(chp))\n                # # update input for the next polynomial.\n                inpc_pol = int(chp)\n\n            if bottom_width > 1:\n                # # make a linear layer to transform to this shape.\n                setattr(self, 'lin0', Linear(inpc_pol, inpc_pol * bottom_width ** 2, initialW=w))\n\n            # # iterate over all layers (till the last) and save in self.\n            for l in range(1, n_l + 1):\n                # # define the input and the output names.\n                ni, no = layer_d[l - 1], layer_d[l]\n                Conv_sel = Conv0 if strides[l - 1] == 1 and ksizes[l - 1] == 3 and allow_conv else Conv\n                conv_i = partial(Conv_sel, initialW=w, ksize=ksizes[l - 1], \n                                 stride=strides[l - 1], pad=paddings[l - 1])\n                # # save the self.layer.\n                setattr(self, 'l{}'.format(l), conv_i(ni, no))\n                if self.skip_rep:\n                    # # define some 1x1 convolutions iff the channels are not the same in\n                    # # the input and the output. Otherwise, use the identity mapping.\n                    func = (lambda x: x) if ni == no else Conv(ni, no, ksize=1)\n                    setattr(self, 'skipch{}'.format(l), func)\n\n            if self.order_out_poly is not None:\n                ch1 = layer_d[n_l]\n                for i in range(1, self.order_out_poly):\n                    setattr(self, 'oro{}'.format(i + 1), Conv0(ch1, ch1, ksize=3, stride=1, pad=1, initialW=w))\n\n            # # save the last layer.\n            # # In Deconv, we need to define a ksize (otherwise the dimensions unchanged). This \n            # # last layer leaves the spatial dimensions untouched.\n            setattr(self, 'l{}'.format(n_l + 1), Conv(layer_d[n_l], out_ch, ksize=3, \n                                                      pad=1, initialW=w))\n            self.n_channels.append(out_ch)\n            if use_bn:\n                # # set the batch norm (applied before the first layer conv).\n                setattr(self, 'bn{}'.format(1), bn1(layer_d[0]))\n                for l in range(2, self.n_l + 1):\n                    # # set the batch norm for the layer.\n                    setattr(self, 'bn{}'.format(l), bn1(layer_d[l - 1]))\n            if self.mult_lat and use_localz:\n                # # define the 'local' transformations of z (z local Linear).\n                for l in range(1, n_l + 1):\n                    ni, no = layer_d[0], layer_d[l]\n                    setattr(self, 'locz{}'.format(l), Linear(ni, no, initialW=w))\n            # # the condition to add a residual skip to the representation.\n            self.skip1 = self.add_h_injection or self.add_h_poly or self.skip_rep\n\n    def return_injected(self, h, z, n_layer, mult_until_exec=None):\n        \"\"\" Performs the Hadamard products with z. \"\"\"\n        # # check whether to skip the hadamard.\n        skip_injection = False \n        if self.thresh_skip is not None and self.thresh_skip[n_layer-1] > 0:\n            # # skip the hadamard, iff the random number is smaller than the threshold.\n            skip_injection = np.random.uniform() < self.thresh_skip[n_layer-1]\n        if not skip_injection and mult_until_exec is not None:\n            skip_injection = mult_until_exec <= n_layer\n        if self.mult_lat and not skip_injection:\n            if self.use_localz:\n                # # apply local transformation.\n                z1 = getattr(self, 'locz{}'.format(n_layer))(z)\n            else:\n                z1 = z\n            # # appropriately reshape z for the elementwise multiplication.\n            sh = h.shape\n            z1 = F.reshape(z1, (sh[0], sh[1], 1))\n            if self.normalize_preinject:\n                z1 /= F.sqrt(F.mean(z1 * z1, axis=1, keepdims=True) + 1e-8)\n            z2 = F.repeat(z1, sh[3] * sh[2], axis=2)\n            z2 = F.reshape(z2, sh)\n            ret = h * z2 + h if self.add_h_injection else h * z2\n            return ret\n        return h\n\n    def prod_poly_FC(self, z, mult_until_exec, batchsize=None, y=None):\n        \"\"\"\n        Performs the products of polynomials for the fully-connected part.\n        \"\"\"\n        # # input_poly: the input variable to each polynomial; for the \n        # # first, simply z, i.e. the noise vector.\n        input_poly = z + 0\n        # # iterate over all the polynomials (length of channels many).\n        for id_poly in range(len(self.channels)):\n            # # ensure that the channels from previous polynomial are of the\n            # # appropriate size.\n            if getattr(self, 'has_rsz{}'.format(id_poly)):\n                input_poly = getattr(self, 'resize{}'.format(id_poly))(input_poly)\n\n            # # id_first: The index of the first FC layer; if we share it, it should\n            # # be that of l0_1; otherwise l[id_poly]_1.\n            id_first = id_poly if not self.share_first else 0\n            # # order of the current polynomial.\n            order = self.power_poly[id_poly] + self.add_one\n            # # condition to use the second recursive term.\n            sec_rec = order > 2 and self.skip1 and self.use_sec_recurs\n            # # Repeat each polynomial repeat_poly times (if repeat==1, then only running once).\n            for repeat in range(self.repeat_poly[id_poly]):\n                h = getattr(self, 'l{}_1'.format(id_first))(input_poly)\n                if sec_rec:\n                    all_reps = []\n\n                # # loop over the current polynomial layers and compute the \n                # # output (for this polynomial). \n                for layer in range(2, order):\n                    if layer <= mult_until_exec:\n                        # # step 1: perform the hadamard product.\n                        if self.derivfc == 0:\n                            z1 = getattr(self, 'l{}_{}'.format(id_poly, layer))(input_poly)\n                            if self.skip1:\n                                if sec_rec:\n                                    all_reps.append(h + 0)\n                                h += z1 * h\n                            else:\n                                h = z1 * h \n                        elif self.derivfc == 1:\n                            # # In this case, we assume that both A_i, B_i of\n                            # # the original polygan derivation are identity matrices.\n                            h1 = getattr(self, 'l{}_{}'.format(id_poly, layer))(h)\n                            if self.skip1:\n                                if sec_rec:\n                                    all_reps.append(h + 0)\n                                h += h1 * input_poly\n                            else:\n                                h = h1 * input_poly\n                        if sec_rec and layer > 2:\n                            h = h + self.sign_srec * all_reps[-2]\n                    # # step 2: activation.\n                    h = getattr(self, 'activ{}'.format(id_poly))(h)\n                # # if we included the second recursive term, add back in the final representation.\n                if sec_rec:\n                    for rep in all_reps:\n                        h = h - self.sign_srec * rep\n                if self.norm_after_poly:\n                    # # apply a different BN for every repeat time of the poly.\n                    if y is None or self.typen_poly == 'instance':\n                        h = getattr(self, 'bnp{}_r{}'.format(id_poly, repeat))(h)\n                    else:\n                        h = getattr(self, 'bnp{}_r{}'.format(id_poly, repeat))(h, y)\n                # # update the input for the next polynomial.\n                input_poly = h + 0\n\n        # # use the output of the products above as z.\n        z = input_poly if not self.use_act_zgl else self.activation(input_poly)\n        return z\n\n    def __call__(self, batchsize=None, y=None, z=None, mult_until_exec=None, **kwargs):\n        if z is None:\n            z = sample_continuous(self.dim_z, batchsize, distribution=self.distribution, xp=self.xp)\n        if y is None:\n            y = sample_categorical(self.n_classes, batchsize, distribution=\"uniform\",\n                                   xp=self.xp) if self.n_classes > 0 else None\n        activ = self.activation\n\n        # # mult_until_exec: If set, we perform the multiplications until that layer.\n        # # In the product of polynomials, it applies the same rule for *every*\n        # # polynomial. E.g. if mult_until_exec == 2 it will perform the hadamard\n        # # products until second order terms in every polynomial.\n        if mult_until_exec is None:\n            mult_until_exec = 10000\n        z = self.prod_poly_FC(z, mult_until_exec, batchsize=batchsize, y=y)\n\n        h = z + 0\n        if self.bottom_width > 1:\n            h = getattr(self, 'lin0')(h)\n        h = F.reshape(h, (h.shape[0], -1, self.bottom_width, self.bottom_width))\n\n        # # loop over the layers and get the layers along with the\n        # # normalizations per layer.\n        for l in range(1, self.n_l + 1):\n            if self.skip_rep:\n                h_hold = h + 0\n            if self.use_bn and y is None:\n                h = getattr(self, 'bn{}'.format(l))(h)\n            elif self.use_bn:\n                h = getattr(self, 'bn{}'.format(l))(h, y)\n            h = activ(getattr(self, 'l{}'.format(l))(h))\n            h = self.return_injected(h, z, l, mult_until_exec=mult_until_exec)\n            if self.skip_rep:\n                # # transform the channels of h_hold if required.\n                h_hold = getattr(self, 'skipch{}'.format(l))(h_hold)\n                # # upsample if required.\n                if h_hold.shape[-1] != h.shape[-1]:\n                    h_hold = _upsample(h_hold)\n                h += h_hold\n        if self.order_out_poly is not None:\n            z0 = h + 0\n            for i in range(1, self.order_out_poly):\n                h1 = getattr(self, 'oro{}'.format(i + 1))(h)\n                if self.skip_rep:\n                    # # model3 polynomial.\n                    h += z0 * h1\n                else:\n                    h = z0 * h1\n        # # last layer (no activation).\n        output = getattr(self, 'l{}'.format(self.n_l + 1))(h)\n        out = self.out_act(output)\n        return out\n\n    def __str__(self, **kwargs):\n        m1 = 'Layers: {}.\\t Info for channels: {}.'\n        str1 = m1.format(self.n_l, self.n_channels)\n        return str1\n\n"
  },
  {
    "path": "image_generation_chainer/jobs/pinet/fashionmnist_cnn_prodpoly_linear.yml",
    "content": "batchsize: 64\niteration: 50000\niteration_decay_start: 0\nseed: 0\ndisplay_interval: 200\nsnapshot_interval: 5000\nevaluation_interval: 2000\n\nmodels:\n  generator:\n    fn: gen_models/cnn_gen_custom_prodpoly.py\n    name: ProdPolyConvGenerator\n    args:\n      dim_z: 128\n      distribution: uniform\n      out_ch: 1\n      power_poly: [1, 3]\n      activ_prod: 0\n      add_h_poly: True\n      layer_d:  [128, 128, 128, 256, 256, 256, 256, 256, 64]\n      strides:  [2, 2, 1, 2, 1, 2, 2, 1]\n      paddings: [1, 1, 1, 1, 1, 1, 1, 1]\n      ksizes:   [4, 4, 3, 4, 3, 4, 4, 3]\n      use_bn: True\n      use_act_zgl: False\n      normalize_preinject: 0\n      add_h_injection: 0\n      derivfc: 1\n      order_out_poly: 2\n      use_out_act: True\n\n\n  discriminator:\n      fn: dis_models/snresnet_32.py\n      name: SNResNetProjectionDiscriminator\n      args:\n        ch: 80\n        n_classes: 0\n        sn: True\n        ch_input: 1\n       \n\ndataset:\n  dataset_fn: datasets/mnist.py\n  dataset_name: MnistDb\n  args:\n    path: /vol/deform/grigoris/databases/fashion_mnist/\n    labels_suff: train_labels.npy\n\n\nadam:\n  alpha: 0.0002\n  beta1: 0.0\n  beta2: 0.9\n\nupdater:\n  fn: updaters/gans/updater.py\n  name: Updater\n  args:\n    n_dis: 1\n    n_gen_samples: 512\n    conditional: False\n    loss_type: hinge\n\n"
  },
  {
    "path": "image_generation_chainer/jobs/pinet/train_mn_mnist.py",
    "content": "import os, sys\nimport numpy as np\nfrom socket import gethostname\nfrom time import strftime\nimport argparse\nimport chainer\nfrom chainer import training\nfrom chainer.training import extensions, extension\nimport chainermn\nimport multiprocessing\n\nbase = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.join(base, '../../'))\nsys.path.append(base)\n\n\nfrom evaluations.extensions import divergence_trainer, sample_generate, sample_generate_conditional\nimport yaml\nimport source.yaml_utils as yaml_utils\nfrom source.miscs.model_moiving_average import ModelMovingAverage\nfrom source.misc_train_utils import create_result_dir, load_models, ensure_config_paths, plot_losses_log\n\n\ndef make_optimizer(model, comm, alpha=0.001, beta1=0.9, beta2=0.999, chmn=False, weight_decay_rate=0):\n    # # 12/2018: problem in minoas, probably related with openmpi.\n    if chmn:\n        optimizer = chainermn.create_multi_node_optimizer(\n            chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2, \n                                    weight_decay_rate=weight_decay_rate), comm)\n    else:\n        optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2,\n                                            weight_decay_rate=weight_decay_rate)\n    optimizer.setup(model)\n    return optimizer\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--config_path', type=str, default='jobs/pinet/fashionmnist_cnn_prodpoly_linear.yml')\n    parser.add_argument('--n_devices', type=int)\n    parser.add_argument('--test', action='store_true', default=False)\n    parser.add_argument('--communicator', type=str,\n                        default='hierarchical', help='Type of communicator')\n    parser.add_argument('--results_dir', type=str, default='results_polynomial')\n    parser.add_argument('--inception_model_path', type=str,\n                        default='/home/user/inception/inception.model')\n    parser.add_argument('--resume', type=str, default='')\n    parser.add_argument('--gen_snapshot', type=str, default=None, help='path to the generator snapshot')\n    parser.add_argument('--dis_snapshot', type=str, default=None, help='path to the discriminator snapshot')\n    parser.add_argument('--loaderjob', type=int,\n                        help='Number of parallel data loading processes')\n    parser.add_argument('--multiprocessing', action='store_true', default=False)\n    parser.add_argument('--label', type=str, default='synth')\n    parser.add_argument('--batch_val', type=int, default=1000)\n    args = parser.parse_args()\n    config = yaml_utils.Config(yaml.load(open(args.config_path)))\n    # # ensure that the paths of the config are correct.\n    config = ensure_config_paths(config)\n    try:\n        comm = chainermn.create_communicator(args.communicator)\n    except:\n        comm = chainermn.create_communicator()\n    device = comm.intra_rank\n    chainer.cuda.get_device_from_id(device).use()\n    # # get the pc name, e.g. for chainerui.\n    pcname = gethostname()\n    print('Init on pc: {}.'.format(pcname))\n    if comm.rank == 0:\n        print('==========================================')\n        print('Using {} communicator'.format(args.communicator))\n        print('==========================================')\n    gen, dis = load_models(config)\n    gen.to_gpu()\n    dis.to_gpu()\n    mma = ModelMovingAverage(0.999, gen)\n    models = {\"gen\": gen, \"dis\": dis}\n    if args.gen_snapshot is not None:\n        print('Loading generator: {}.'.format(args.gen_snapshot))\n        chainer.serializers.load_npz(args.gen_snapshot, gen)\n    if args.dis_snapshot is not None:\n        print('Loading discriminator: {}.'.format(args.dis_snapshot))\n        chainer.serializers.load_npz(args.dis_snapshot, dis)\n    # Optimizer\n    # # convenience function for optimizer:\n    func_opt = lambda net, alpha, wdr0=0: make_optimizer(net, comm, chmn=args.multiprocessing,\n                                          alpha=alpha, beta1=config.adam['beta1'], \n                                          beta2=config.adam['beta2'], weight_decay_rate=wdr0)\n    # Optimizer\n    wdr = 0 if 'weight_decay_rate' not in config.updater['args'] else config.updater['args']['weight_decay_rate_gener']\n    opt_gen = func_opt(gen, config.adam['alpha'], wdr0=wdr)\n    keydopt = 'alphad' if 'alphad' in config.adam.keys() else 'alpha'\n    opt_dis = func_opt(dis, config.adam[keydopt])\n    opts = {\"opt_gen\": opt_gen, \"opt_dis\": opt_dis}\n    if hasattr(dis, 'fix_last') and hasattr(dis, 'lin') and dis.fix_last:\n        # # This should be used with care. It fixes the linear layer that\n        # # makes the classification in the discriminator.\n        print('Fixing the linear layer of the discriminator!')\n        dis.disable_update()\n    # Dataset\n    if comm.rank == 0:\n        dataset = yaml_utils.load_dataset(config)\n        # # even though not new samples, use as proxy for iid validation ones.\n        if hasattr(dataset, 'n_concats') and dataset.n_concats == 1:\n            valid_samples = (dataset.base[:args.batch_val] + 1) * 127.5\n        else:\n            valid_samples = [(dataset.get_example(i)[0] + 1) * 127.5 for i in range(args.batch_val)]\n            # # convert the validation to an array as required by the kl script.\n            valid_samples = np.array(valid_samples, dtype=np.float32)\n    else:\n        _ = yaml_utils.load_dataset(config)  # Dummy, for adding path to the dataset module\n        dataset = None\n    dataset = chainermn.scatter_dataset(dataset, comm)\n    # Iterator\n    multiprocessing.set_start_method('forkserver')\n    if args.multiprocessing:\n        # # In minoas this might fail with the forkserver.py error.\n        iterator = chainer.iterators.MultiprocessIterator(dataset, config.batchsize,\n                                                          n_processes=args.loaderjob)\n    else:\n        iterator = chainer.iterators.SerialIterator(dataset, config.batchsize)\n    kwargs = config.updater['args'] if 'args' in config.updater else {}\n    kwargs.update({\n        'models': models,\n        'iterator': iterator,\n        'optimizer': opts,\n        'device': device,\n        'mma': mma,\n    })\n    updater = yaml_utils.load_updater_class(config)\n    updater = updater(**kwargs)\n    if not args.test:\n        mainf = '{}_{}'.format(strftime('%Y_%m_%d__%H_%M_%S'), args.label)\n        out = os.path.join(args.results_dir, mainf, '')\n    else:\n        out = 'results/test'\n    if comm.rank == 0:\n        create_result_dir(out, args.config_path, config)\n    trainer = training.Trainer(updater, (config.iteration, 'iteration'), out=out)\n    report_keys = ['loss_dis', 'loss_gen', 'kl', 'ndb', 'JS', 'dis_real', 'dis_fake']\n\n    if comm.rank == 0:\n        # Set up logging\n#         trainer.extend(extensions.snapshot(), trigger=(config.snapshot_interval, 'iteration'))\n        for m in models.values():\n            trainer.extend(extensions.snapshot_object(\n                m, m.__class__.__name__ + '_{.updater.iteration}.npz'), trigger=(config.snapshot_interval, 'iteration'))\n\n        trainer.extend(extensions.LogReport(trigger=(config.display_interval, 'iteration')))\n        trainer.extend(extensions.PrintReport(report_keys), trigger=(config.display_interval, 'iteration'))\n        trainer.extend(extensions.ProgressBar(update_interval=config.display_interval))\n\n        if gen.n_classes == 0:\n            trainer.extend(sample_generate(mma.avg_model, out),\n                           trigger=(config.evaluation_interval, 'iteration'),\n                           priority=extension.PRIORITY_WRITER)\n            print('unconditional image generation extension added.')\n        else:\n            trainer.extend(sample_generate_conditional(mma.avg_model, out, n_classes=gen.n_classes),\n                           trigger=(config.evaluation_interval, 'iteration'),\n                           priority=extension.PRIORITY_WRITER)\n\n        trainer.extend(divergence_trainer(gen, valid_samples, metric=['kl', 'ndb'], batch=args.batch_val),\n                       trigger=(config.evaluation_interval, 'iteration'),\n                       priority=extension.PRIORITY_WRITER)\n\n    # # convenience function for linearshift in optimizer:\n    func_opt_shift = lambda optim1: extensions.LinearShift('alpha', (config.adam['alpha'], 0.),\n                                                           (config.iteration_decay_start, \n                                                            config.iteration), optim1)\n    # # define the actual extensions (for optimizer shift).\n    trainer.extend(func_opt_shift(opt_gen))\n    trainer.extend(func_opt_shift(opt_dis))\n\n    if args.resume:\n        print(\"Resume Trainer\")\n        chainer.serializers.load_npz(args.resume, trainer)\n\n    m1 = 'Generator params: {}. Discriminator params: {}.'\n    print(m1.format(gen.count_params(), dis.count_params()))\n    # Run the training\n    print(\"start training\")\n    trainer.run()\n    print('The output dir was {}.'.format(out))\n    plot_losses_log(out, savefig=True)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "image_generation_chainer/readme.rst",
    "content": "=======================================\nΠ-nets: Deep Polynomial Neural Networks\n=======================================\n\nChainer implementation of the image generation as described on the CVPR'20 paper \"**Π-nets: Deep Polynomial Neural Networks**\".\n\nSpecifically, we include the code for image generation on fashion images.\n\n\n\nBrowsing the folders\n====================\nThe folder structure is the following:\n\n*    ``gen_models``: The folder for the generator models; this is the primary folder with the polynomial generator(s).\n\n*    ``dis_models``: The folder for the discriminator models; do not modify the optional arguments, unless you know what you are doing.\n\n*    ``updater``: The folder that contains the core code for the updater, i.e. the chunk of code that runs in every iteration to update the modules.\n\n*    ``jobs``: It contains a) the yml with the hyper-parameter setting, b) the main command to load the models and run the training (train_mn*.py).\n\n*    ``source``: It contains auxiliary code, you should probably not modify any of that code.\n\n*    ``evaluations``: It contains the code for validation (either during training or offline).\n\n*    ``datasets``: Each file in the folder describes a parser for a dataset; normally you should not modify it for provided dataset(s).\n\n\n\nTrain the network\n=================\n\nTo train the network, you can execute the following command::\n\n   python jobs/pinet/train_mn_mnist.py --label my_experiment\n\nThe yml file describes the modules/dataset to train on. The default dataset used is \n`Fashion-mnist <https://github.com/zalandoresearch/fashion-mnist>`_; other datasets can \nbe used by modifying the respective paths in the yml.\n\nThe hyper-parameters are included in the yml, no need to hardcode them in the files. \nProbably, you should modify the path of the dataset (in the yml, find the `dataset:` part) \nto match the one in your local pc.\n\nThe results are exported in the directory defined in `train_mn_mnist.py` (default `results_polynomial`). \nThe directory with the results includes folders named as [start_date][label]; inside each respective\nfolder the pretrained weights and few indicative images are exported. \n\n\n\n\n\nRequirements\n============\n\nThe code requires the chainer library.\n\nTested on a Linux machine with:\n\n* chainer=4.0.0, chainercv=0.9.0,\n\n* chainer=5.2.0, chainercv=0.12.0.\n\n* chainer=6.1.0, chainercv=0.13.1.\n\n\nThe code is highly influenced by [1]_.\n\nApart from Chainer, the code depends on Pyaml [2]_ and [3]_ (for the evaluation). \n\n\nReferences\n==========\n\n.. [1] https://github.com/grigorisg9gr/rocgan/\n\n.. [2] https://pypi.org/project/pyaml/\n\n.. [3] https://github.com/djsutherland/skl-groups\n\n"
  },
  {
    "path": "image_generation_chainer/source/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/source/functions/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/source/functions/cond_bn_wrapper.py",
    "content": "import chainer\nfrom chainer import configuration\nfrom chainer import cuda\nfrom chainer.functions.normalization import batch_normalization\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import argument\nfrom chainer import variable\nfrom chainer.links import EmbedID\nimport chainer.functions as F\n\n\ndef cond_bn_wrapper(x, c, bn, gamma, beta):\n    h = bn(x)\n    shape = h.shape\n    ndim = len(shape)\n    gamma_c = gamma(c)\n    beta_c = beta(c)\n    gamma_c = F.broadcast_to(F.reshape(gamma_c, list(gamma_c.shape) + [1] * (ndim - 2)), shape)\n    beta_c = F.broadcast_to(F.reshape(beta_c, list(beta_c.shape) + [1] * (ndim - 2)), shape)\n    return gamma_c * h + beta_c\n"
  },
  {
    "path": "image_generation_chainer/source/functions/losses.py",
    "content": "import numpy as np\n\nimport chainer\nimport chainer.functions as F\nfrom chainer import Variable\n\ndef _tensor_to_matrix(tensor, axis=0):\n    \"\"\" Reshapes a 4-dimensional tensor to 2D matrix. \"\"\"\n    if tensor.ndim == 4:\n        # # reshape to a 2D matrix.\n        matr = F.reshape(tensor, (tensor.shape[axis], -1))\n    elif tensor.ndim == 2:\n        matr = tensor\n    else:\n        matr = F.reshape(tensor, (-1, 1))\n    return matr\n\n\ndef cosine_loss(tens1, tens2, absol=True):\n    \"\"\"\n    Computes the cosine loss between two representations.\n    The cos is computed per element, i.e. assumed that \n    tens1[i] and tens2[i] correspond to the representations\n    of which we want to compute the cos.\n    Works only on chainer 5.x, because of the einsum.\n    \"\"\"\n    mat1 = _tensor_to_matrix(tens1, axis=0)\n    mat2 = _tensor_to_matrix(tens2, axis=0)\n    # # compute the inner product.\n    prod = F.einsum('ij,ij->i', mat1, mat2)\n    # # compute the norms.\n    norm1 = F.batch_l2_norm_squared(mat1)\n    norm2 = F.batch_l2_norm_squared(mat2)\n    # # compute the final cosine (per element).\n    cos = prod / F.matmul(norm1, norm2)\n    if absol:\n        # # We restrict the angles to [-90, 90] effectively.\n        # # That is, we allow only positive cos.\n        cos = F.absolute(cos)\n    return F.mean(cos)\n\n\ndef decov_loss(tensor, xp=None, axis=1):\n    \"\"\"\n    Decov loss as described in https://arxiv.org/pdf/1511.06068.pdf\n    'Reducing Overfitting In Deep Networks by Decorrelating Representation'.\n    This version implements the loss in the variable format.\n\n    ARGS:\n        axis: (int, optional) If the tensor is 4-dim, it is\n            reshaped into 2-dim; axis is the first dimension.\n    \"\"\"\n    if xp is None:\n        # # get xp module if not provided.\n        xp = chainer.cuda.get_array_module(tensor.data)\n    # # reshape to a 2D matrix.\n    matr = _tensor_to_matrix(tensor, axis=axis)\n    # # subtract the mean.\n    centered = F.bias(matr, -F.mean(matr))\n    # # compute the covariance.\n    cov = F.matmul(centered, F.transpose(centered))\n    # # compute the frombenius norm.\n    frob_norm = F.sum(F.square(cov))\n    # # get the norm of diagonal elements.\n    # # in chainer 5.x this should work.\n#     corr_diag_sqr = F.sum(F.square(F.diagonal(cov1)))\n    corr_diag_sqr = F.sum(F.square(cov * xp.eye(cov.shape[0], dtype=cov.dtype)))\n    loss = 0.5 * (frob_norm - corr_diag_sqr)\n    return loss\n\n\ndef decov_loss_matrix(tensor, xp=None, axis=1):\n    \"\"\"\n    Decov loss as described in https://arxiv.org/pdf/1511.06068.pdf\n    'Reducing Overfitting In Deep Networks by Decorrelating Representation'.\n    This version implements the matrix case, i.e. Variables are converted\n    into matrices/tensors.\n\n    ARGS:\n        axis: (int, optional) If the tensor is 4-dim, it is\n            reshaped into 2-dim; axis is the first dimension.\n    \"\"\"\n    if type(tensor, Variable):\n        tensor = tensor.data\n    if xp is None:\n        # # get xp module if not provided.\n        xp = chainer.cuda.get_array_module(tensor)\n    if tensor.ndim == 4:\n        # # reshape to a 2D matrix.\n        matr = tensor.reshape((tensor.shape[axis], -1))\n    elif tensor.ndim == 2:\n        matr = tensor.copy()\n    # # subtract the mean.\n    mean1 = matr.mean()\n    matr -= mean1\n    # # compute the covariance.\n    cov = xp.matmul(matr, matr.T)\n    # # compute the frombenius norm.\n    frob_norm = xp.sum(xp.square(cov))\n    # # get the norm of diagonal elements.\n    corr_diag_sqr = xp.sum(xp.square(xp.diag(cov)))\n    loss = 0.5 * (frob_norm - corr_diag_sqr)\n    return loss\n\n\ndef loss_revKL_dis(dis_fake, dis_real):\n    L1 = F.mean(F.softplus(-dis_real))\n    L2 = F.mean(F.softplus(dis_fake))\n    loss = L1 + L2\n    return loss\n\n\ndef loss_revKL_gen(dis_fake):\n    loss = -F.mean(dis_fake)\n    return loss\n\n\n# Classic Adversarial Loss\ndef loss_dcgan_dis(dis_fake, dis_real):\n    L1 = F.mean(F.softplus(-dis_real))\n    L2 = F.mean(F.softplus(dis_fake))\n    loss = L1 + L2\n    return loss\n\n\ndef loss_dcgan_gen(dis_fake):\n    loss = F.mean(F.softplus(-dis_fake))\n    return loss\n\n\n# Hinge Loss\ndef loss_hinge_dis(dis_fake, dis_real):\n    loss = F.mean(F.relu(1. - dis_real))\n    loss += F.mean(F.relu(1. + dis_fake))\n    return loss\n\n\ndef loss_hinge_gen(dis_fake):\n    loss = -F.mean(dis_fake)\n    return loss\n\n\n# WGAN Loss\ndef loss_wgan_dis(dis_fake, dis_real):\n    loss = - F.mean(dis_real) + F.mean(dis_fake)\n    return loss\n\n\ndef loss_wgan_gen(dis_fake):\n    loss = - F.mean(dis_fake)\n    return loss\n\n\ndef gradient_penalty(dis_output, x):\n    grad_x, = chainer.grad([dis_output], [x], enable_double_backprop=True)\n    norm_grad_x = F.mean(F.sum(grad_x * grad_x, axis=(1, 2, 3)))\n    return norm_grad_x\n\n\ndef gradient_penalty_wgangp(dis_output, x, lipnorm):\n    grad_x, = chainer.grad([dis_output], [x], enable_double_backprop=True)\n    norm_grad_x = F.sqrt(F.sum(grad_x * grad_x, axis=(1, 2, 3)))\n    xp = norm_grad_x.xp\n    return F.mean_squared_error(norm_grad_x, lipnorm * xp.ones_like(norm_grad_x.array))\n"
  },
  {
    "path": "image_generation_chainer/source/functions/max_sv.py",
    "content": "import chainer\nfrom chainer import cuda\nfrom chainer.functions.math import sum\nfrom chainer.functions.connection import linear\nfrom chainer.functions.array import transpose\nimport numpy as np\n\n\ndef _l2normalize_gpu(v, eps=1e-12):\n    norm = cuda.reduce('T x', 'T out',\n                       'x * x', 'a + b', 'out = sqrt(a)', 0,\n                       'norm_sn')\n    div = cuda.elementwise('T x, T norm, T eps',\n                           'T out',\n                           'out = x / (norm + eps)',\n                           'div_sn')\n    return div(v, norm(v), eps)\n\n\ndef _l2normalize_cpu(v, eps=1e-12):\n   return v / (((v ** 2).sum()) ** 0.5 + eps)\n\n\ndef max_singular_value(W, u=None, Ip=1):\n    \"\"\"\n    Apply power iteration for the weight parameter\n    \"\"\"\n    xp = cuda.get_array_module(W.data)\n    if u is None:\n        u = xp.random.normal(size=(1, W.shape[0])).astype(xp.float32)\n    _u = u\n    if xp == np:\n        _l2normalize = _l2normalize_cpu\n    else:\n        _l2normalize = _l2normalize_gpu\n    for _ in range(Ip):\n        _v = _l2normalize(xp.dot(_u, W.data), eps=1e-12)\n        _u = _l2normalize(xp.dot(_v, W.data.transpose()), eps=1e-12)\n    sigma = sum.sum(linear.linear(_u, transpose.transpose(W)) * _v)\n    return sigma, _u, _v\n"
  },
  {
    "path": "image_generation_chainer/source/inception/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/source/inception/download.py",
    "content": "\"\"\"\nIncluding code from the official implementation by OpenAI found at\nhttps://github.com/openai/improved-gan\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os.path\nimport sys\nimport tarfile\nimport numpy as np\nimport tensorflow as tf\nimport glob\nimport scipy.misc\nimport math\nfrom six.moves import urllib\nfrom chainer import serializers\nfrom inception_score import Inception\n\n\nMODEL_DIR = '/tmp/imagenet'\nDATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--outfile', type=str, default='inception_score.model')\n    return parser.parse_args()\n\n\ndef copy_conv(sess, tftensor, layer):\n\n    \"\"\"Copy TensorFlow convolution layer weights to Chainer model.\"\"\"\n\n    W = sess.graph.get_tensor_by_name('{}/conv2d_params:0'.format(tftensor)).eval()\n    W = W.transpose((3, 2, 0, 1))\n\n    assert W.shape == layer.W.data.shape\n\n    layer.W.data = W\n\n\ndef copy_bn(sess, tftensor, layer):\n\n    \"\"\"Copy TensorFlow batch normalization params weights to Chainer model.\"\"\"\n\n    gamma = sess.graph.get_tensor_by_name('{}/gamma:0'.format(tftensor)).eval()\n    beta = sess.graph.get_tensor_by_name('{}/beta:0'.format(tftensor)).eval()\n    avg_mean = sess.graph.get_tensor_by_name('{}/moving_mean:0'.format(tftensor)).eval()\n    avg_var = sess.graph.get_tensor_by_name('{}/moving_variance:0'.format(tftensor)).eval()\n    eps = sess.graph.get_operation_by_name(tftensor).get_attr('variance_epsilon')\n\n    assert layer.beta.data.shape == beta.shape\n    assert layer.gamma.data.shape == gamma.shape\n    assert layer.avg_mean.shape == avg_mean.shape\n    assert layer.avg_var.shape == avg_var.shape\n    assert eps > 0.0\n\n    layer.beta.data = beta\n    layer.gamma.data = gamma\n    layer.avg_mean = avg_mean\n    layer.avg_var = avg_var\n    layer.eps = eps\n\n\ndef copy_inception(sess, model):\n\n    \"\"\"Copy weights and params from the graph in the given TensorFlow session\n    to the Chainer chain.\"\"\"\n\n    print('Copying first layers ...')\n    copy_conv(sess, 'conv', model.conv)\n    copy_bn(sess, 'conv/batchnorm', model.bn_conv)\n    copy_conv(sess, 'conv_1', model.conv_1)\n    copy_bn(sess, 'conv_1/batchnorm', model.bn_conv_1)\n    copy_conv(sess, 'conv_2', model.conv_2)\n    copy_bn(sess, 'conv_2/batchnorm', model.bn_conv_2)\n    copy_conv(sess, 'conv_3', model.conv_3)\n    copy_bn(sess, 'conv_3/batchnorm', model.bn_conv_3)\n    copy_conv(sess, 'conv_4', model.conv_4)\n    copy_bn(sess, 'conv_4/batchnorm', model.bn_conv_4)\n\n    for m in ['mixed', 'mixed_1', 'mixed_2']:\n        print('Copying ', m, '...')\n        copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv)\n        copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv)\n\n        for t in ['tower', 'tower_1', 'tower_2']:\n            copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv)\n            copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv)\n\n            if t == 'tower' or t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1)\n                copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1)\n\n            if t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2)\n                copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2)\n\n    for m in ['mixed_3']:\n        print('Copying ', m, '...')\n        copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv)\n        copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv)\n\n        for t in ['tower']:\n            copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv)\n            copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv)\n            copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1)\n            copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1)\n            copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2)\n            copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2)\n\n    for m in ['mixed_4', 'mixed_5', 'mixed_6', 'mixed_7']:\n        print('Copying ', m, '...')\n        copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv)\n        copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv)\n\n        for t in ['tower', 'tower_1', 'tower_2']:\n            copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv)\n            copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv)\n\n            if t == 'tower' or t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1)\n                copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1)\n                copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2)\n                copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2)\n\n            if t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_3'.format(m, t), getattr(getattr(model, m), t).conv_3)\n                copy_bn(sess, '{}/{}/conv_3/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_3)\n                copy_conv(sess, '{}/{}/conv_4'.format(m, t), getattr(getattr(model, m), t).conv_4)\n                copy_bn(sess, '{}/{}/conv_4/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_4)\n\n    for m in ['mixed_8']:\n        print('Copying ', m, '...')\n        for t in ['tower', 'tower_1']:\n            copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv)\n            copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv)\n            copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1)\n            copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1)\n\n            if t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2)\n                copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2)\n                copy_conv(sess, '{}/{}/conv_3'.format(m, t), getattr(getattr(model, m), t).conv_3)\n                copy_bn(sess, '{}/{}/conv_3/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_3)\n\n    for m in ['mixed_9', 'mixed_10']:\n        print('Copying ', m, '...')\n        copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv)\n        copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv)\n\n        for t in ['tower', 'tower_1', 'tower_2']:\n            copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv)\n            copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv)\n\n            if t == 'tower' or t == 'tower_1':\n                copy_conv(sess, '{}/{}/mixed/conv'.format(m, t), getattr(getattr(model, m), t).mixed.conv.conv)\n                copy_bn(sess, '{}/{}/mixed/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).mixed.conv.bn_conv)\n                copy_conv(sess, '{}/{}/mixed/conv_1'.format(m, t), getattr(getattr(model, m), t).mixed.conv_1.conv_1)\n                copy_bn(sess, '{}/{}/mixed/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).mixed.conv_1.bn_conv_1)\n\n            if t == 'tower_1':\n                copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1)\n                copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1)\n\n    print('Copying logit...')\n    w = sess.graph.get_operation_by_name(\"softmax/logits/MatMul\").inputs[1].eval()\n    b = sess.graph.get_tensor_by_name(\"softmax/biases:0\").eval()\n\n    assert w.T.shape == model.logit.W.shape\n    assert b.shape == model.logit.b.shape\n\n    model.logit.W.data = w.T\n    model.logit.b.data = b\n\n\ndef download_tf_params():\n\n    \"\"\"Download and extract pretrained TensorFlow inception model params.\"\"\"\n\n    if not os.path.exists(MODEL_DIR):\n        os.makedirs(MODEL_DIR)\n    filename = DATA_URL.split('/')[-1]\n    filepath = os.path.join(MODEL_DIR, filename)\n\n    if not os.path.exists(filepath):\n        def _progress(count, block_size, total_size):\n            sys.stdout.write('\\r>> Downloading %s %.1f%%' % (\n                filename, float(count * block_size) / float(total_size) * 100.0))\n            sys.stdout.flush()\n\n        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)\n        statinfo = os.stat(filepath)\n        print()\n        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n\n    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)\n\n\ndef set_tf_params(model, write_graph=False):\n\n    \"\"\"Update the parameters of the given chainer model with the downloaded\n    TensorFlow model.\"\"\"\n\n    with tf.gfile.FastGFile(os.path.join(\n            MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\n        graph_def = tf.GraphDef()\n        graph_def.ParseFromString(f.read())\n        _ = tf.import_graph_def(graph_def, name='')\n\n        if write_graph:\n            # Write graph to file so that it can be visualized using TensorBoard\n            summary_writer = tf.summary.FileWriter('data', graph=graph_def)\n\n    config = tf.ConfigProto()\n    config.gpu_options.allow_growth = True  # Do not allocale the whole GPU mem\n    with tf.Session(config=config) as sess:\n        copy_inception(sess, model)\n\n\ndef main(args):\n    outfile = args.outfile\n\n    # Download pretrained TensorFlow model\n    download_tf_params()\n\n    # Create empty Chainer inception model\n    model = Inception()\n\n    # Update parameters of Chainer model with pretrained TensorFlow model\n    set_tf_params(model)\n\n    # TODO(hvy): Test score similarity with the original implementation\n\n    print('Saving ', outfile, '...')\n    serializers.save_hdf5(outfile, model)\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    main(args)\n"
  },
  {
    "path": "image_generation_chainer/source/inception/example.py",
    "content": "import argparse\nimport numpy as np\n\nfrom chainer import cuda\nfrom chainer import datasets\nfrom chainer import serializers\nfrom inception_score import Inception\nfrom inception_score import inception_score\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--gpu', type=int, default=0)\n    parser.add_argument('--samples', type=int, default=-1)\n    parser.add_argument('--model', type=str, default='inception_score.model')\n    return parser.parse_args()\n\n\ndef main(args):\n    # Load trained model\n    model = Inception()\n    serializers.load_hdf5(args.model, model)\n\n    if args.gpu >= 0:\n        cuda.get_device(args.gpu).use()\n        model.to_gpu()\n\n    # Load images\n    if 0:\n        train, test = datasets.get_cifar10(ndim=3, withlabel=False, scale=255.0)\n    else:\n        train, test = datasets.get_mnist(ndim=3, rgb_format=True, scale=255.0, withlabel=False)\n\n    # Use all 60000 images, unless the number of samples are specified\n    ims = np.concatenate((train, test))\n    if args.samples > 0:\n        ims = ims[:args.samples]\n\n    mean, std = inception_score(model, ims)\n\n    print('Inception score mean:', mean)\n    print('Inception score std:', std)\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    main(args)\n"
  },
  {
    "path": "image_generation_chainer/source/inception/inception_score.py",
    "content": "import math\n\nimport chainer\nfrom chainer import Chain\nfrom chainer import functions as F\nfrom chainer import links as L\nfrom chainer import Variable\n\n\ndef inception_forward(model, ims, batch_size):\n    \"\"\" Run the inception model (forward pass). \"\"\"\n    n, c, w, h = ims.shape\n    n_batches = int(math.ceil(float(n) / float(batch_size)))\n\n    xp = model.xp\n\n    # Compute the softmax predicitions for for all images, split into batches\n    # in order to fit in memory\n\n    ys = xp.empty((n, 1008), dtype=xp.float32)  # Softmax container\n\n    for i in range(n_batches):\n        batch_start = (i * batch_size)\n        batch_end = min((i + 1) * batch_size, n)\n\n        ims_batch = ims[batch_start:batch_end]\n        ims_batch = xp.asarray(ims_batch)  # To GPU if using CuPy\n        ims_batch = Variable(ims_batch)\n\n        # Resize image to the shape expected by the inception module\n        if (w, h) != (299, 299):\n            ims_batch = F.resize_images(ims_batch, (299, 299))  # bilinear\n\n        # Feed images to the inception module to get the softmax predictions\n        with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n            y = model(ims_batch)\n        ys[batch_start:batch_end] = y.data\n    # # grigoris: trained on imagenet, so 1k classes(?)\n    ys = ys[:, 1:1001] #0 and 1001-1008 are the dummies\n    return ys\n\n\ndef inception_score(model, ims, batch_size=100, splits=10, verbose=True):\n    \"\"\"Compute the inception score for given images.\n\n    Default batch_size is 100 and split size is 10. Please refer to the\n    official implementation. It is recommended to to use at least 50000\n    images to obtain a reliable score.\n\n    Reference:\n    https://github.com/openai/improved-gan/blob/master/inception_score/model.py\n\n    \"\"\"\n    n, c, w, h = ims.shape\n    n_batches = int(math.ceil(float(n) / float(batch_size)))\n    xp = model.xp\n    if c == 1:\n        ims = xp.repeat(ims, 3, axis=1)\n\n    if verbose:\n        print('Inception: batch_size: {}, n_ims: {}, n_batches: {}'.format(batch_size, n, n_batches))\n    ys = inception_forward(model, ims, batch_size)\n    # Compute the inception score based on the softmax predictions of the\n    # inception module.\n    scores = xp.empty((splits), dtype=xp.float32)  # Split inception scores\n    for i in range(splits):\n        # get n//split representations (from inception features).\n        part = ys[(i * n // splits):((i + 1) * n // splits), :]\n        # # grigoris, compute the KL: KL(p(y|x)||p(y)) where y is the classes and x the image.\n        kl = part * (xp.log(part) -\n                     xp.log(xp.expand_dims(xp.mean(part, 0), 0)))\n        kl = xp.mean(xp.sum(kl, 1))\n        scores[i] = xp.exp(kl)\n\n    return xp.mean(scores), xp.std(scores)\n\n\ndef inception_accuracy(model, ims, labels, batch_size=100, splits=10, lab_range=None):\n    \"\"\"Compute the inception score for given images.\n\n     Default batch_size is 100 and split size is 10. Please refer to the\n     official implementation. It is recommended to to use at least 50000\n     images to obtain a reliable score.\n\n     Reference:\n     https://github.com/openai/improved-gan/blob/master/inception_score/model.py\n\n     \"\"\"\n    if isinstance(ims, (list, tuple)):\n        ims_list = ims\n        ys_list = []\n        for ims in ims_list:\n            n, c, w, h = ims.shape\n            n_batches = int(math.ceil(float(n) / float(batch_size)))\n            xp = model.xp\n\n            print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches))\n            print('Calculating inception accuracy...')\n            ys = inception_forward(model, ims, batch_size)\n            ys_list.append(ys)\n        ys = sum(ys_list) / len(ys_list)\n    else:\n        n, c, w, h = ims.shape\n        n_batches = int(math.ceil(float(n) / float(batch_size)))\n        xp = model.xp\n\n        print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches))\n        print('Calculating inception accuracy...')\n        ys = inception_forward(model, ims, batch_size)\n    if lab_range is not None:\n        ys = ys[np.arange(len(ys)), lab_range]\n    return F.accuracy(ys, labels).data\n\n\nclass Mixed(Chain):\n    def __init__(self, trunk):\n        super().__init__()\n        for name, link in trunk:\n            self.add_link(name, link)\n        self.trunk = trunk\n\n    def __call__(self, x):\n        hs = []\n        for name, _ in self.trunk:\n            h = getattr(self, name)(x)\n            hs.append(h)\n        return F.concat(hs)\n\n\nclass Tower(Chain):\n    def __init__(self, trunk):\n        super().__init__()\n        for name, link in trunk:\n            if not name.startswith('_'):\n                self.add_link(name, link)\n        self.trunk = trunk\n\n    def __call__(self, x):\n        h = x\n        for name, f in self.trunk:\n            if not name.startswith('_'):  # Link\n                if 'bn' in name:\n                    h = getattr(self, name)(h)\n                else:\n                    h = getattr(self, name)(h)\n            else:  # AveragePooling2D, MaxPooling2D or ReLU\n                h = f(h)\n        return h\n\n\ndef _average_pooling_2d(h):\n    return F.average_pooling_2d(h, 3, 1, 1)\n\n\ndef _max_pooling_2d(h):\n    return F.max_pooling_2d(h, 3, 1, 1)\n\n\ndef _max_pooling_2d_320(h):\n    return F.max_pooling_2d(h, 3, 2, 0)\n\n\nclass Inception(Chain):\n    def __init__(self):\n        super().__init__(\n            conv=L.Convolution2D(3, 32, 3, stride=2, pad=0),\n            conv_1=L.Convolution2D(32, 32, 3, stride=1, pad=0),\n            conv_2=L.Convolution2D(32, 64, 3, stride=1, pad=1),\n            conv_3=L.Convolution2D(64, 80, 1, stride=1, pad=0),\n            conv_4=L.Convolution2D(80, 192, 3, stride=1, pad=0),\n            bn_conv=L.BatchNormalization(32),\n            bn_conv_1=L.BatchNormalization(32),\n            bn_conv_2=L.BatchNormalization(64),\n            bn_conv_3=L.BatchNormalization(80),\n            bn_conv_4=L.BatchNormalization(192),\n            mixed=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(192, 48, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(48)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),\n                    ('bn_conv_1', L.BatchNormalization(64)),\n                    ('_relu_1', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(96)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_2', L.BatchNormalization(96)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(192, 32, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(32)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_1=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(256, 48, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(48)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),\n                    ('bn_conv_1', L.BatchNormalization(64)),\n                    ('_relu_1', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(96)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_2', L.BatchNormalization(96)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_2=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(288, 48, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(48)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)),\n                    ('bn_conv_1', L.BatchNormalization(64)),\n                    ('_relu_1', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(96)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_2', L.BatchNormalization(96)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_3=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(288, 384, 3, stride=2, pad=0)),\n                    ('bn_conv', L.BatchNormalization(384)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(64)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(96)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(96, 96, 3, stride=2, pad=0)),\n                    ('bn_conv_2', L.BatchNormalization(96)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('pool', Tower([\n                    ('_pooling', _max_pooling_2d_320)\n                ]))\n            ]),\n            mixed_4=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(128)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(128, 128, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_1', L.BatchNormalization(128)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(128, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(128)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(128, 128, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_1', L.BatchNormalization(128)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(128, 128, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_2', L.BatchNormalization(128)),\n                    ('_relu_2', F.relu),\n                    ('conv_3', L.Convolution2D(128, 128, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_3', L.BatchNormalization(128)),\n                    ('_relu_3', F.relu),\n                    ('conv_4', L.Convolution2D(128, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_4', L.BatchNormalization(192)),\n                    ('_relu_4', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_5=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(160)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_1', L.BatchNormalization(160)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(160)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_1', L.BatchNormalization(160)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_2', L.BatchNormalization(160)),\n                    ('_relu_2', F.relu),\n                    ('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_3', L.BatchNormalization(160)),\n                    ('_relu_3', F.relu),\n                    ('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_4', L.BatchNormalization(192)),\n                    ('_relu_4', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_6=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(160)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_1', L.BatchNormalization(160)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(160)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_1', L.BatchNormalization(160)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_2', L.BatchNormalization(160)),\n                    ('_relu_2', F.relu),\n                    ('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_3', L.BatchNormalization(160)),\n                    ('_relu_3', F.relu),\n                    ('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_4', L.BatchNormalization(192)),\n                    ('_relu_4', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_7=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_1', L.BatchNormalization(192)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_1', L.BatchNormalization(192)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu),\n                    ('conv_3', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_3', L.BatchNormalization(192)),\n                    ('_relu_3', F.relu),\n                    ('conv_4', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_4', L.BatchNormalization(192)),\n                    ('_relu_4', F.relu)\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_8=Mixed([\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(192, 320, 3, stride=2, pad=0)),\n                    ('bn_conv_1', L.BatchNormalization(320)),\n                    ('_relu_1', F.relu)\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))),\n                    ('bn_conv_1', L.BatchNormalization(192)),\n                    ('_relu_1', F.relu),\n                    ('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))),\n                    ('bn_conv_2', L.BatchNormalization(192)),\n                    ('_relu_2', F.relu),\n                    ('conv_3', L.Convolution2D(192, 192, 3, stride=2, pad=0)),\n                    ('bn_conv_3', L.BatchNormalization(192)),\n                    ('_relu_3', F.relu)\n                ])),\n                ('pool', Tower([\n                    ('_pooling', _max_pooling_2d_320)\n                ]))\n            ]),\n            mixed_9=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(1280, 320, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(320)),\n                    ('_relu', F.relu),\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(1280, 384, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(384)),\n                    ('_relu', F.relu),\n                    ('mixed', Mixed([\n                        ('conv', Tower([\n                            ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))),\n                            ('bn_conv', L.BatchNormalization(384)),\n                            ('_relu', F.relu),\n                        ])),\n                        ('conv_1', Tower([\n                            ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))),\n                            ('bn_conv_1', L.BatchNormalization(384)),\n                            ('_relu_1', F.relu),\n                        ]))\n                    ]))\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(1280, 448, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(448)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(384)),\n                    ('_relu_1', F.relu),\n                    ('mixed', Mixed([\n                        ('conv', Tower([\n                            ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))),\n                            ('bn_conv', L.BatchNormalization(384)),\n                            ('_relu', F.relu),\n                        ])),\n                        ('conv_1', Tower([\n                            ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))),\n                            ('bn_conv_1', L.BatchNormalization(384)),\n                            ('_relu_1', F.relu),\n                        ]))\n                    ]))\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _average_pooling_2d),\n                    ('conv', L.Convolution2D(1280, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            mixed_10=Mixed([\n                ('conv', Tower([\n                    ('conv', L.Convolution2D(2048, 320, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(320)),\n                    ('_relu', F.relu),\n                ])),\n                ('tower', Tower([\n                    ('conv', L.Convolution2D(2048, 384, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(384)),\n                    ('_relu', F.relu),\n                    ('mixed', Mixed([\n                        ('conv', Tower([\n                            ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))),\n                            ('bn_conv', L.BatchNormalization(384)),\n                            ('_relu', F.relu),\n                        ])),\n                        ('conv_1', Tower([\n                            ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))),\n                            ('bn_conv_1', L.BatchNormalization(384)),\n                            ('_relu_1', F.relu),\n                        ]))\n                    ]))\n                ])),\n                ('tower_1', Tower([\n                    ('conv', L.Convolution2D(2048, 448, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(448)),\n                    ('_relu', F.relu),\n                    ('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)),\n                    ('bn_conv_1', L.BatchNormalization(384)),\n                    ('_relu_1', F.relu),\n                    ('mixed', Mixed([\n                        ('conv', Tower([\n                            ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))),\n                            ('bn_conv', L.BatchNormalization(384)),\n                            ('_relu', F.relu)\n                        ])),\n                        ('conv_1', Tower([\n                            ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))),\n                            ('bn_conv_1', L.BatchNormalization(384)),\n                            ('_relu_1', F.relu)\n                        ]))\n                    ]))\n                ])),\n                ('tower_2', Tower([\n                    ('_pooling', _max_pooling_2d),\n                    ('conv', L.Convolution2D(2048, 192, 1, stride=1, pad=0)),\n                    ('bn_conv', L.BatchNormalization(192)),\n                    ('_relu', F.relu)\n                ]))\n            ]),\n            logit=L.Linear(2048, 1008)\n        )\n\n    def __call__(self, x, get_feature=False):\n        \"\"\"Input dims are (batch_size, 3, 299, 299).\"\"\"\n\n        # assert x.shape[1:] == (3, 299, 299)\n\n        x -= 128.0\n        x *= 0.0078125\n\n        h = F.relu(self.bn_conv(self.conv(x)))\n        # assert h.shape[1:] == (32, 149, 149)\n\n        h = F.relu(self.bn_conv_1(self.conv_1(h)))\n        # assert h.shape[1:] == (32, 147, 147)\n\n        h = F.relu(self.bn_conv_2(self.conv_2(h)))\n        # assert h.shape[1:] == (64, 147, 147)\n\n        h = F.max_pooling_2d(h, 3, stride=2, pad=0)\n        # assert h.shape[1:] == (64, 73, 73)\n\n        h = F.relu(self.bn_conv_3(self.conv_3(h)))\n        # assert h.shape[1:] == (80, 73, 73)\n\n        h = F.relu(self.bn_conv_4(self.conv_4(h)))\n        # assert h.shape[1:] == (192, 71, 71)\n\n        h = F.max_pooling_2d(h, 3, stride=2, pad=0)\n        # assert h.shape[1:] == (192, 35, 35)\n\n        h = self.mixed(h)\n        # assert h.shape[1:] == (256, 35, 35)\n\n        h = self.mixed_1(h)\n        # assert h.shape[1:] == (288, 35, 35)\n\n        h = self.mixed_2(h)\n        # assert h.shape[1:] == (288, 35, 35)\n\n        h = self.mixed_3(h)\n        # assert h.shape[1:] == (768, 17, 17)\n\n        h = self.mixed_4(h)\n        # assert h.shape[1:] == (768, 17, 17)\n\n        h = self.mixed_5(h)\n        # assert h.shape[1:] == (768, 17, 17)\n\n        h = self.mixed_6(h)\n        # assert h.shape[1:] == (768, 17, 17)\n\n        h = self.mixed_7(h)\n        # assert h.shape[1:] == (768, 17, 17)\n\n        h = self.mixed_8(h)\n        # assert h.shape[1:] == (1280, 8, 8)\n\n        h = self.mixed_9(h)\n        # assert h.shape[1:] == (2048, 8, 8)\n\n        h = self.mixed_10(h)\n        # assert h.shape[1:] == (2048, 8, 8)\n\n        h = F.average_pooling_2d(h, 8, 1)\n        # assert h.shape[1:] == (2048, 1, 1)\n        h = F.reshape(h, (-1, 2048))\n\n        if get_feature:\n            return h\n        else:\n            h = self.logit(h)\n            h = F.softmax(h)\n\n            # assert h.shape[1:] == (1008,)\n\n            return h\n"
  },
  {
    "path": "image_generation_chainer/source/inception/inception_score_tf.py",
    "content": "# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os.path\nimport tarfile\n\nimport numpy as np\nfrom six.moves import urllib\nimport tensorflow as tf\nimport scipy.misc\nimport math\nimport sys\nfrom chainer import functions as F\n\nMODEL_DIR = '/tmp/imagenet'\nDATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'\nsoftmax = None\nlast_layer = None\n\nconfig = tf.ConfigProto()\nconfig.gpu_options.per_process_gpu_memory_fraction = 0.3\n\n\ndef inception_forward(images, layer):\n    assert (type(images[0]) == np.ndarray)\n    assert (len(images[0].shape) == 3)\n    try:\n        assert (np.max(images[0]) > 10), 'image in the [0, 1] interval (or too bad)?'\n    except AssertionError:\n        print('Either image [0, 1] interval or too bad generation.')\n    assert (np.min(images[0]) >= 0.0)\n    bs = 100\n    images = images.transpose(0, 2, 3, 1)\n    with tf.Session(config=config) as sess:\n        preds = []\n        n_batches = int(math.ceil(float(len(images)) / float(bs)))\n        for i in range(n_batches):\n            sys.stdout.write(\".\")\n            sys.stdout.flush()\n            inp = images[(i * bs):min((i + 1) * bs, len(images))]\n            pred = sess.run(layer, {'ExpandDims:0': inp})\n            preds.append(pred)\n        preds = np.concatenate(preds, 0)\n    return preds\n\n\ndef get_mean_and_cov(images):\n    before_preds = inception_forward(images, last_layer)\n    m = np.mean(before_preds, 0)\n    cov = np.cov(before_preds, rowvar=False)\n    return m, cov\n\n\ndef get_fid(images, ref_stats=None, images_ref=None, splits=10):\n    before_preds = inception_forward(images, last_layer)\n    if ref_stats is None:\n        if images_ref is None:\n            raise ValueError('images_ref should be provided if ref_stats is None')\n        m_ref, cov_ref = get_mean_and_cov(images_ref)\n    fids = []\n    for i in range(splits):\n        part = before_preds[(i * before_preds.shape[0] // splits):((i + 1) * before_preds.shape[0] // splits), :]\n        m_gen = np.mean(part, 0)\n        cov_gen = np.cov(part, rowvar=False)\n        fid = np.sum((m_ref - m_gen) ** 2) + np.trace(\n            cov_ref + cov_gen - 2 * scipy.linalg.sqrtm(np.dot(cov_ref, cov_gen)))\n        fids.append(fid)\n    return np.mean(fids), np.std(fids)\n\n\n# Call this function with list of images. Each of elements should be a\n# numpy array with values ranging from 0 to 255.\ndef get_inception_score(images, splits=10):\n    preds = inception_forward(images, softmax)\n    scores = []\n    for i in range(splits):\n        part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]\n        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))\n        kl = np.mean(np.sum(kl, 1))\n        scores.append(np.exp(kl))\n    return np.mean(scores), np.std(scores)\n\n\n# Call this function with list of images. Each of elements should be a\n# numpy array with values ranging from 0 to 255.\ndef get_inception_accuracy(images, labels, lab_range=None):\n    batch_size = 100\n    if isinstance(images, (list, tuple)):\n        ims_list = images\n        ys_list = []\n        for ims in ims_list:\n            n, _, _, _ = ims.shape\n            n_batches = int(math.ceil(float(n) / float(batch_size)))\n            print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches))\n            print('Calculating inception accuracy...')\n            ys = inception_forward(ims, softmax)[:, 1:1001]\n            ys_list.append(ys)\n        ys = sum(ys_list) / len(ys_list)\n    else:\n        n, _, _, _, = images.shape\n        n_batches = int(math.ceil(float(n) / float(batch_size)))\n\n        print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches))\n        print('Calculating inception accuracy...')\n        ys = inception_forward(images, softmax)[:, 1:1001]\n    if lab_range is not None:\n        ys = ys[:, lab_range]\n    print(ys, labels)\n    return F.accuracy(ys, labels).data\n\n\n# This function is called automatically.\ndef _init_inception():\n    global softmax\n    global last_layer\n    if not os.path.exists(MODEL_DIR):\n        os.makedirs(MODEL_DIR)\n    filename = DATA_URL.split('/')[-1]\n    filepath = os.path.join(MODEL_DIR, filename)\n    if not os.path.exists(filepath):\n        def _progress(count, block_size, total_size):\n            sys.stdout.write('\\r>> Downloading %s %.1f%%' % (\n                filename, float(count * block_size) / float(total_size) * 100.0))\n            sys.stdout.flush()\n\n        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)\n        print()\n        statinfo = os.stat(filepath)\n        print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.')\n    tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR)\n    with tf.gfile.FastGFile(os.path.join(\n            MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f:\n        graph_def = tf.GraphDef()\n        graph_def.ParseFromString(f.read())\n        _ = tf.import_graph_def(graph_def, name='')\n    # Works with an arbitrary minibatch size.\n    with tf.Session(config=config) as sess:\n        pool3 = sess.graph.get_tensor_by_name('pool_3:0')\n        ops = pool3.graph.get_operations()\n        for op_idx, op in enumerate(ops):\n            for o in op.outputs:\n                shape = o.get_shape()\n                shape = [s.value for s in shape]\n                new_shape = []\n                for j, s in enumerate(shape):\n                    if s == 1 and j == 0:\n                        new_shape.append(None)\n                    else:\n                        new_shape.append(s)\n                o._shape = tf.TensorShape(new_shape)\n        w = sess.graph.get_operation_by_name(\"softmax/logits/MatMul\").inputs[1]\n        last_layer = tf.squeeze(pool3)\n        logits = tf.matmul(last_layer, w)\n        softmax = tf.nn.softmax(logits)\n\n\nif softmax is None:\n    _init_inception()\n"
  },
  {
    "path": "image_generation_chainer/source/links/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/source/links/categorical_conditional_batch_normalization.py",
    "content": "import numpy\n\nimport chainer\nfrom chainer import configuration\nfrom chainer import cuda\nfrom chainer.functions.normalization import batch_normalization\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import argument\nfrom chainer import variable\nfrom chainer.links import EmbedID\nimport chainer.functions as F\nfrom source.links.conditional_batch_normalization import ConditionalBatchNormalization\n\n\nclass CategoricalConditionalBatchNormalization(ConditionalBatchNormalization):\n    \"\"\"\n    Conditional Batch Normalization\n    Args:\n        size (int or tuple of ints): Size (or shape) of channel\n            dimensions.\n        n_cat (int): the number of categories of categorical variable.\n        decay (float): Decay rate of moving average. It is used on training.\n        eps (float): Epsilon value for numerical stability.\n        dtype (numpy.dtype): Type to use in computing.\n        use_gamma (bool): If ``True``, use scaling parameter. Otherwise, use\n            unit(1) which makes no effect.\n        use_beta (bool): If ``True``, use shifting parameter. Otherwise, use\n            unit(0) which makes no effect.\n    See: `Batch Normalization: Accelerating Deep Network Training by Reducing\\\n          Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_\n    .. seealso::\n       :func:`~chainer.functions.batch_normalization`,\n       :func:`~chainer.functions.fixed_batch_normalization`\n    Attributes:\n        gamma (~chainer.Variable): Scaling parameter.\n        beta (~chainer.Variable): Shifting parameter.\n        avg_mean (numpy.ndarray or cupy.ndarray): Population mean.\n        avg_var (numpy.ndarray or cupy.ndarray): Population variance.\n        N (int): Count of batches given for fine-tuning.\n        decay (float): Decay rate of moving average. It is used on training.\n        eps (float): Epsilon value for numerical stability. This value is added\n            to the batch variances.\n    \"\"\"\n\n    def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float32,\n                 initial_gamma=None, initial_beta=None, comm=None):\n        super(CategoricalConditionalBatchNormalization, self).__init__(\n            size=size, n_cat=n_cat, decay=decay, eps=eps, dtype=dtype, comm=comm)\n\n        with self.init_scope():\n            if initial_gamma is None:\n                initial_gamma = 1\n            initial_gamma = initializers._get_initializer(initial_gamma)\n            initial_gamma.dtype = dtype\n            self.gammas = EmbedID(n_cat, size, initialW=initial_gamma)\n            if initial_beta is None:\n                initial_beta = 0\n            initial_beta = initializers._get_initializer(initial_beta)\n            initial_beta.dtype = dtype\n            self.betas = EmbedID(n_cat, size, initialW=initial_beta)\n\n    def __call__(self, x, c, **kwargs):\n        \"\"\"__call__(self, x, c, finetune=False)\n        Invokes the forward propagation of BatchNormalization.\n        In training mode, the BatchNormalization computes moving averages of\n        mean and variance for evaluatino during training, and normalizes the\n        input using batch statistics.\n        .. warning::\n           ``test`` argument is not supported anymore since v2.\n           Instead, use ``chainer.using_config('train', train)``.\n           See :func:`chainer.using_config`.\n        Args:\n            x (Variable): Input variable.\n            c (Variable): Input variable for conditioning gamma and beta\n            finetune (bool): If it is in the training mode and ``finetune`` is\n                ``True``, BatchNormalization runs in fine-tuning mode; it\n                accumulates the input array to compute population statistics\n                for normalization, and normalizes the input using batch\n                statistics.\n        \"\"\"\n        weights, = argument.parse_kwargs(kwargs, ('weights', None))\n        if isinstance(c, (list, tuple)):\n            gamma_cs = []\n            beta_cs = []\n            for _c, w in zip(c, weights):\n                _gamma_c = self.gammas(_c)\n                _beta_c = self.betas(_c)\n                # print(F.expand_dims(w, 1).shape, _gamma_c.shape)\n                # print(F.broadcast_to(F.expand_dims(w, 1), _gamma_c).shape)\n                gamma_cs.append(F.broadcast_to(F.expand_dims(w, 1), _gamma_c.shape) * _gamma_c)\n                beta_cs.append(F.broadcast_to(F.expand_dims(w, 1), _beta_c.shape) * _beta_c)\n            gamma_c = sum(gamma_cs)\n            beta_c = sum(beta_cs)\n        else:\n            gamma_c = self.gammas(c)\n            beta_c = self.betas(c)\n        return super(CategoricalConditionalBatchNormalization, self).__call__(x, gamma_c, beta_c)\n\n\ndef start_finetuning(self):\n    \"\"\"Resets the population count for collecting population statistics.\n    This method can be skipped if it is the first time to use the\n    fine-tuning mode. Otherwise, this method should be called before\n    starting the fine-tuning mode again.\n    \"\"\"\n    self.N = 0\n"
  },
  {
    "path": "image_generation_chainer/source/links/conditional_batch_normalization.py",
    "content": "import numpy\n\nimport chainer\nfrom chainer import configuration\nfrom chainer import cuda\nfrom chainer.functions.normalization import batch_normalization\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import argument\nfrom chainer import variable\nfrom chainer.links import EmbedID\nimport chainer.functions as F\n\n\n\nclass ConditionalBatchNormalization(chainer.Chain):\n    \"\"\"\n    Conditional Batch Normalization\n    Args:\n        size (int or tuple of ints): Size (or shape) of channel\n            dimensions.\n        n_cat (int): the number of categories of categorical variable.\n        decay (float): Decay rate of moving average. It is used on training.\n        eps (float): Epsilon value for numerical stability.\n        dtype (numpy.dtype): Type to use in computing.\n        use_gamma (bool): If ``True``, use scaling parameter. Otherwise, use\n            unit(1) which makes no effect.\n        use_beta (bool): If ``True``, use shifting parameter. Otherwise, use\n            unit(0) which makes no effect.\n    See: `Batch Normalization: Accelerating Deep Network Training by Reducing\\\n          Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_\n    .. seealso::\n       :func:`~chainer.functions.batch_normalization`,\n       :func:`~chainer.functions.fixed_batch_normalization`\n    Attributes:\n        gamma (~chainer.Variable): Scaling parameter.\n        beta (~chainer.Variable): Shifting parameter.\n        avg_mean (numpy.ndarray or cupy.ndarray): Population mean.\n        avg_var (numpy.ndarray or cupy.ndarray): Population variance.\n        N (int): Count of batches given for fine-tuning.\n        decay (float): Decay rate of moving average. It is used on training.\n        eps (float): Epsilon value for numerical stability. This value is added\n            to the batch variances.\n    \"\"\"\n\n    def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float32, comm=None):\n        super(ConditionalBatchNormalization, self).__init__()\n        self.avg_mean = numpy.zeros(size, dtype=dtype)\n        self.register_persistent('avg_mean')\n        self.avg_var = numpy.zeros(size, dtype=dtype)\n        self.register_persistent('avg_var')\n        self.N = 0\n        self.register_persistent('N')\n        self.decay = decay\n        self.eps = eps\n        self.n_cat = n_cat\n        self.comm = comm\n        if self.comm is not None:\n            # # only enable this feature in gpu machines.\n            from chainermn.functions.batch_normalization import MultiNodeBatchNormalizationFunction\n\n    def __call__(self, x, gamma, beta, **kwargs):\n        \"\"\"__call__(self, x, c, finetune=False)\n        Invokes the forward propagation of BatchNormalization.\n        In training mode, the BatchNormalization computes moving averages of\n        mean and variance for evaluatino during training, and normalizes the\n        input using batch statistics.\n        .. warning::\n           ``test`` argument is not supported anymore since v2.\n           Instead, use ``chainer.using_config('train', train)``.\n           See :func:`chainer.using_config`.\n        Args:\n            x (Variable): Input variable.\n            gamma (Variable): Input variable of gamma of shape\n            finetune (bool): If it is in the training mode and ``finetune`` is\n                ``True``, BatchNormalization runs in fine-tuning mode; it\n                accumulates the input array to compute population statistics\n                for normalization, and normalizes the input using batch\n                statistics.\n        \"\"\"\n        argument.check_unexpected_kwargs(\n            kwargs, test='test argument is not supported anymore. '\n                         'Use chainer.using_config')\n        finetune, = argument.parse_kwargs(kwargs, ('finetune', False))\n        with cuda.get_device_from_id(self._device_id):\n            _gamma = variable.Variable(self.xp.ones(\n                self.avg_mean.shape, dtype=x.dtype))\n        with cuda.get_device_from_id(self._device_id):\n            _beta = variable.Variable(self.xp.zeros(\n                self.avg_mean.shape, dtype=x.dtype))\n        if configuration.config.train:\n            if finetune:\n                self.N += 1\n                decay = 1. - 1. / self.N\n            else:\n                decay = self.decay\n            if self.comm is not None:\n                func = MultiNodeBatchNormalizationFunction(\n                    self.comm, self.eps, self.avg_mean, self.avg_var, decay)\n                ret = func(x, _gamma, _beta)\n                self.avg_mean[:] = func.running_mean\n                self.avg_var[:] = func.running_var\n            else:\n                ret = chainer.functions.batch_normalization(x, _gamma, _beta, eps=self.eps, running_mean=self.avg_mean,\n                                                            running_var=self.avg_var, decay=decay)\n        else:\n            # Use running average statistics or fine-tuned statistics.\n            mean = variable.Variable(self.avg_mean)\n            var = variable.Variable(self.avg_var)\n            ret = batch_normalization.fixed_batch_normalization(\n                x, _gamma, _beta, mean, var, self.eps)\n        shape = ret.shape\n        ndim = len(shape)\n        gamma = F.broadcast_to(F.reshape(gamma, list(gamma.shape) + [1] * (ndim - len(gamma.shape))), shape)\n        beta = F.broadcast_to(F.reshape(beta, list(beta.shape) + [1] * (ndim - len(beta.shape))), shape)\n        return gamma * ret + beta\n\n\ndef start_finetuning(self):\n    \"\"\"Resets the population count for collecting population statistics.\n    This method can be skipped if it is the first time to use the\n    fine-tuning mode. Otherwise, this method should be called before\n    starting the fine-tuning mode again.\n    \"\"\"\n    self.N = 0\n"
  },
  {
    "path": "image_generation_chainer/source/links/resnet_layer.py",
    "content": "from __future__ import print_function\nimport collections\nimport os\n\nimport numpy\n\ntry:\n    from PIL import Image\n\n    available = True\nexcept ImportError as e:\n    available = False\n    _import_error = e\n\nfrom chainer.dataset.convert import concat_examples\nfrom chainer.dataset import download\nfrom chainer import function\nfrom chainer.functions.activation.relu import relu\nfrom chainer.functions.activation.softmax import softmax\nfrom chainer.functions.array.reshape import reshape\nfrom chainer.functions.math.sum import sum\nfrom chainer.functions.pooling.average_pooling_2d import average_pooling_2d\nfrom chainer.functions.pooling.max_pooling_2d import max_pooling_2d\nfrom chainer.initializers import constant\nfrom chainer.initializers import normal\nfrom chainer import link\nfrom chainer.links.connection.convolution_2d import Convolution2D\nfrom chainer.links.connection.linear import Linear\nfrom chainer.links.normalization.batch_normalization import BatchNormalization\nfrom chainer.serializers import npz\nfrom chainer.utils import argument\nfrom chainer.utils import imgproc\nfrom chainer.variable import Variable\n\n\nclass ResNetLayers(link.Chain):\n    \"\"\"A pre-trained CNN model provided by MSRA.\n    When you specify the path of the pre-trained chainer model serialized as\n    a ``.npz`` file in the constructor, this chain model automatically\n    initializes all the parameters with it.\n    This model would be useful when you want to extract a semantic feature\n    vector per image, or fine-tune the model on a different dataset.\n    Note that unlike ``VGG16Layers``, it does not automatically download a\n    pre-trained caffemodel. This caffemodel can be downloaded at\n    `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.\n    If you want to manually convert the pre-trained caffemodel to a chainer\n    model that can be specified in the constructor,\n    please use ``convert_caffemodel_to_npz`` classmethod instead.\n    See: K. He et. al., `Deep Residual Learning for Image Recognition\n    <https://arxiv.org/abs/1512.03385>`_\n    Args:\n        pretrained_model (str): the destination of the pre-trained\n            chainer model serialized as a ``.npz`` file.\n            If this argument is specified as ``auto``,\n            it automatically loads and converts the caffemodel from\n            ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-{n-layers}-model.caffemodel``,\n            where ``$CHAINER_DATASET_ROOT`` is set as\n            ``$HOME/.chainer/dataset`` unless you specify another value\n            by modifying the environment variable and {n_layers} is replaced\n            with the specified number of layers given as the first argment to\n            this costructor. Note that in this case the converted chainer\n            model is stored on the same directory and automatically used from\n            the next time.\n            If this argument is specified as ``None``, all the parameters\n            are not initialized by the pre-trained model, but the default\n            initializer used in the original paper, i.e.,\n            ``chainer.initializers.HeNormal(scale=1.0)``.\n        n_layers (int): The number of layers of this model. It should be either\n            50, 101, or 152.\n    Attributes:\n        available_layers (list of str): The list of available layer names\n            used by ``__call__`` and ``extract`` methods.\n    \"\"\"\n\n    def __init__(self, pretrained_model, n_layers):\n        super(ResNetLayers, self).__init__()\n\n        if pretrained_model:\n            # As a sampling process is time-consuming,\n            # we employ a zero initializer for faster computation.\n            kwargs = {'initialW': constant.Zero()}\n        else:\n            # employ default initializers used in the original paper\n            kwargs = {'initialW': normal.HeNormal(scale=1.0)}\n\n        if n_layers == 50:\n            block = [3, 4, 6, 3]\n        elif n_layers == 101:\n            block = [3, 4, 23, 3]\n        elif n_layers == 152:\n            block = [3, 8, 36, 3]\n        else:\n            raise ValueError('The n_layers argument should be either 50, 101,'\n                             ' or 152, but {} was given.'.format(n_layers))\n\n        with self.init_scope():\n            self.conv1 = Convolution2D(3, 64, 7, 2, 3, **kwargs)\n            self.bn1 = BatchNormalization(64)\n            self.res2 = BuildingBlock(block[0], 64, 64, 256, 1, **kwargs)\n            self.res3 = BuildingBlock(block[1], 256, 128, 512, 2, **kwargs)\n            self.res4 = BuildingBlock(block[2], 512, 256, 1024, 2, **kwargs)\n            self.res5 = BuildingBlock(block[3], 1024, 512, 2048, 2, **kwargs)\n            self.fc6 = Linear(2048, 1000)\n\n        if pretrained_model and pretrained_model.endswith('.caffemodel'):\n            _retrieve(n_layers, 'ResNet-{}-model.npz'.format(n_layers),\n                      pretrained_model, self)\n        elif pretrained_model:\n            npz.load_npz(pretrained_model, self)\n\n    @property\n    def functions(self):\n        return collections.OrderedDict([\n            ('conv1', [self.conv1, self.bn1, relu]),\n            ('pool1', [lambda x: max_pooling_2d(x, ksize=3, stride=2)]),\n            ('res2', [self.res2]),\n            ('res3', [self.res3]),\n            ('res4', [self.res4]),\n            ('res5', [self.res5]),\n            ('pool5', [_global_average_pooling_2d]),\n            ('fc6', [self.fc6]),\n            ('prob', [softmax]),\n        ])\n\n    @property\n    def available_layers(self):\n        return list(self.functions.keys())\n\n    @classmethod\n    def convert_caffemodel_to_npz(cls, path_caffemodel, path_npz, n_layers=50):\n        \"\"\"Converts a pre-trained caffemodel to a chainer model.\n        Args:\n            path_caffemodel (str): Path of the pre-trained caffemodel.\n            path_npz (str): Path of the converted chainer model.\n        \"\"\"\n\n        # As CaffeFunction uses shortcut symbols,\n        # we import CaffeFunction here.\n        from chainer.links.caffe.caffe_function import CaffeFunction\n        caffemodel = CaffeFunction(path_caffemodel)\n        chainermodel = ResNetLayers(pretrained_model=None, n_layers=n_layers)\n        if n_layers == 50:\n            _transfer_resnet50(caffemodel, chainermodel)\n        elif n_layers == 101:\n            _transfer_resnet101(caffemodel, chainermodel)\n        elif n_layers == 152:\n            _transfer_resnet152(caffemodel, chainermodel)\n        else:\n            raise ValueError('The n_layers argument should be either 50, 101,'\n                             ' or 152, but {} was given.'.format(n_layers))\n        npz.save_npz(path_npz, chainermodel, compression=False)\n\n    def __call__(self, x, layers=['prob'], **kwargs):\n        \"\"\"__call__(self, x, layers=['prob'])\n        Computes all the feature maps specified by ``layers``.\n        .. warning::\n           ``test`` argument is not supported anymore since v2.\n           Instead, use ``chainer.using_config('train', train)``.\n           See :func:`chainer.using_config`.\n        Args:\n            x (~chainer.Variable): Input variable.\n            layers (list of str): The list of layer names you want to extract.\n        Returns:\n            Dictionary of ~chainer.Variable: A directory in which\n            the key contains the layer name and the value contains\n            the corresponding feature map variable.\n        \"\"\"\n\n        argument.check_unexpected_kwargs(\n            kwargs, test='test argument is not supported anymore. '\n                         'Use chainer.using_config')\n        argument.assert_kwargs_empty(kwargs)\n\n        h = x\n        activations = {}\n        target_layers = set(layers)\n        for key, funcs in self.functions.items():\n            if len(target_layers) == 0:\n                break\n            for func in funcs:\n                h = func(h)\n            if key in target_layers:\n                activations[key] = h\n                target_layers.remove(key)\n        return activations\n\n    def extract(self, images, layers=['pool5'], size=(224, 224), **kwargs):\n        \"\"\"extract(self, images, layers=['pool5'], size=(224, 224))\n        Extracts all the feature maps of given images.\n        The difference of directly executing ``__call__`` is that\n        it directly accepts images as an input and automatically\n        transforms them to a proper variable. That is,\n        it is also interpreted as a shortcut method that implicitly calls\n        ``prepare`` and ``__call__`` functions.\n        .. warning::\n           ``test`` and ``volatile`` arguments are not supported anymore since\n           v2.\n           Instead, use ``chainer.using_config('train', train)`` and\n           ``chainer.using_config('enable_backprop', not volatile)``\n           respectively.\n           See :func:`chainer.using_config`.\n        Args:\n            images (iterable of PIL.Image or numpy.ndarray): Input images.\n            layers (list of str): The list of layer names you want to extract.\n            size (pair of ints): The resolution of resized images used as\n                an input of CNN. All the given images are not resized\n                if this argument is ``None``, but the resolutions of\n                all the images should be the same.\n        Returns:\n            Dictionary of ~chainer.Variable: A directory in which\n            the key contains the layer name and the value contains\n            the corresponding feature map variable.\n        \"\"\"\n\n        argument.check_unexpected_kwargs(\n            kwargs, test='test argument is not supported anymore. '\n                         'Use chainer.using_config',\n            volatile='volatile argument is not supported anymore. '\n                     'Use chainer.using_config')\n        argument.assert_kwargs_empty(kwargs)\n\n        x = concat_examples([prepare(img, size=size) for img in images])\n        x = Variable(self.xp.asarray(x))\n        return self(x, layers=layers)\n\n    def predict(self, images, oversample=True):\n        \"\"\"Computes all the probabilities of given images.\n        Args:\n            images (iterable of PIL.Image or numpy.ndarray): Input images.\n            oversample (bool): If ``True``, it averages results across\n                center, corners, and mirrors. Otherwise, it uses only the\n                center.\n        Returns:\n            ~chainer.Variable: Output that contains the class probabilities\n            of given images.\n        \"\"\"\n\n        x = concat_examples([prepare(img, size=(256, 256)) for img in images])\n        if oversample:\n            x = imgproc.oversample(x, crop_dims=(224, 224))\n        else:\n            x = x[:, :, 16:240, 16:240]\n        # Use no_backprop_mode to reduce memory consumption\n        with function.no_backprop_mode():\n            x = Variable(self.xp.asarray(x))\n            y = self(x, layers=['prob'])['prob']\n            if oversample:\n                n = y.data.shape[0] // 10\n                y_shape = y.data.shape[1:]\n                y = reshape(y, (n, 10) + y_shape)\n                y = sum(y, axis=1) / 10\n        return y\n\n\nclass ResNet50Layers(ResNetLayers):\n    \"\"\"A pre-trained CNN model with 50 layers provided by MSRA.\n    When you specify the path of the pre-trained chainer model serialized as\n    a ``.npz`` file in the constructor, this chain model automatically\n    initializes all the parameters with it.\n    This model would be useful when you want to extract a semantic feature\n    vector per image, or fine-tune the model on a different dataset.\n    Note that unlike ``VGG16Layers``, it does not automatically download a\n    pre-trained caffemodel. This caffemodel can be downloaded at\n    `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.\n    If you want to manually convert the pre-trained caffemodel to a chainer\n    model that can be specified in the constructor,\n    please use ``convert_caffemodel_to_npz`` classmethod instead.\n    ResNet50 has 25,557,096 trainable parameters, and it's 58% and 43% fewer\n    than ResNet101 and ResNet152, respectively. On the other hand, the top-5\n    classification accuracy on ImageNet dataset drops only 0.7% and 1.1% from\n    ResNet101 and ResNet152, respectively. Therefore, ResNet50 may have the\n    best balance between the accuracy and the model size. It would be basically\n    just enough for many cases, but some advanced models for object detection\n    or semantic segmentation use deeper ones as their building blocks, so these\n    deeper ResNets are here for making reproduction work easier.\n    See: K. He et. al., `Deep Residual Learning for Image Recognition\n    <https://arxiv.org/abs/1512.03385>`_\n    Args:\n        pretrained_model (str): the destination of the pre-trained\n            chainer model serialized as a ``.npz`` file.\n            If this argument is specified as ``auto``,\n            it automatically loads and converts the caffemodel from\n            ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-50-model.caffemodel``,\n            where ``$CHAINER_DATASET_ROOT`` is set as\n            ``$HOME/.chainer/dataset`` unless you specify another value\n            by modifying the environment variable. Note that in this case the\n            converted chainer model is stored on the same directory and\n            automatically used from the next time.\n            If this argument is specified as ``None``, all the parameters\n            are not initialized by the pre-trained model, but the default\n            initializer used in the original paper, i.e.,\n            ``chainer.initializers.HeNormal(scale=1.0)``.\n    Attributes:\n        available_layers (list of str): The list of available layer names\n            used by ``__call__`` and ``extract`` methods.\n    \"\"\"\n\n    def __init__(self, pretrained_model='auto'):\n        if pretrained_model == 'auto':\n            pretrained_model = 'ResNet-50-model.caffemodel'\n        super(ResNet50Layers, self).__init__(pretrained_model, 50)\n\n\nclass ResNet101Layers(ResNetLayers):\n    \"\"\"A pre-trained CNN model with 101 layers provided by MSRA.\n    When you specify the path of the pre-trained chainer model serialized as\n    a ``.npz`` file in the constructor, this chain model automatically\n    initializes all the parameters with it.\n    This model would be useful when you want to extract a semantic feature\n    vector per image, or fine-tune the model on a different dataset.\n    Note that unlike ``VGG16Layers``, it does not automatically download a\n    pre-trained caffemodel. This caffemodel can be downloaded at\n    `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.\n    If you want to manually convert the pre-trained caffemodel to a chainer\n    model that can be specified in the constructor,\n    please use ``convert_caffemodel_to_npz`` classmethod instead.\n    ResNet101 has 44,549,224 trainable parameters, and it's 43% fewer than\n    ResNet152 model, while the top-5 classification accuracy on ImageNet\n    dataset drops 1.1% from ResNet152. For many cases, ResNet50 may have the\n    best balance between the accuracy and the model size.\n    See: K. He et. al., `Deep Residual Learning for Image Recognition\n    <https://arxiv.org/abs/1512.03385>`_\n    Args:\n        pretrained_model (str): the destination of the pre-trained\n            chainer model serialized as a ``.npz`` file.\n            If this argument is specified as ``auto``,\n            it automatically loads and converts the caffemodel from\n            ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-101-model.caffemodel``,\n            where ``$CHAINER_DATASET_ROOT`` is set as\n            ``$HOME/.chainer/dataset`` unless you specify another value\n            by modifying the environment variable. Note that in this case the\n            converted chainer model is stored on the same directory and\n            automatically used from the next time.\n            If this argument is specified as ``None``, all the parameters\n            are not initialized by the pre-trained model, but the default\n            initializer used in the original paper, i.e.,\n            ``chainer.initializers.HeNormal(scale=1.0)``.\n    Attributes:\n        available_layers (list of str): The list of available layer names\n            used by ``__call__`` and ``extract`` methods.\n    \"\"\"\n\n    def __init__(self, pretrained_model='auto'):\n        if pretrained_model == 'auto':\n            pretrained_model = 'ResNet-101-model.caffemodel'\n        super(ResNet101Layers, self).__init__(pretrained_model, 101)\n\n\nclass ResNet152Layers(ResNetLayers):\n    \"\"\"A pre-trained CNN model with 152 layers provided by MSRA.\n    When you specify the path of the pre-trained chainer model serialized as\n    a ``.npz`` file in the constructor, this chain model automatically\n    initializes all the parameters with it.\n    This model would be useful when you want to extract a semantic feature\n    vector per image, or fine-tune the model on a different dataset.\n    Note that unlike ``VGG16Layers``, it does not automatically download a\n    pre-trained caffemodel. This caffemodel can be downloaded at\n    `GitHub <https://github.com/KaimingHe/deep-residual-networks>`_.\n    If you want to manually convert the pre-trained caffemodel to a chainer\n    model that can be specified in the constructor,\n    please use ``convert_caffemodel_to_npz`` classmethod instead.\n    ResNet152 has 60,192,872 trainable parameters, and it's the deepest ResNet\n    model and it achieves the best result on ImageNet classification task in\n    `ILSVRC 2015 <http://image-net.org/challenges/LSVRC/2015/results#loc>`_.\n    See: K. He et. al., `Deep Residual Learning for Image Recognition\n    <https://arxiv.org/abs/1512.03385>`_\n    Args:\n        pretrained_model (str): the destination of the pre-trained\n            chainer model serialized as a ``.npz`` file.\n            If this argument is specified as ``auto``,\n            it automatically loads and converts the caffemodel from\n            ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-152-model.caffemodel``,\n            where ``$CHAINER_DATASET_ROOT`` is set as\n            ``$HOME/.chainer/dataset`` unless you specify another value\n            by modifying the environment variable. Note that in this case the\n            converted chainer model is stored on the same directory and\n            automatically used from the next time.\n            If this argument is specified as ``None``, all the parameters\n            are not initialized by the pre-trained model, but the default\n            initializer used in the original paper, i.e.,\n            ``chainer.initializers.HeNormal(scale=1.0)``.\n    Attributes:\n        available_layers (list of str): The list of available layer names\n            used by ``__call__`` and ``extract`` methods.\n    \"\"\"\n\n    def __init__(self, pretrained_model='auto'):\n        if pretrained_model == 'auto':\n            pretrained_model = 'ResNet-152-model.caffemodel'\n        super(ResNet152Layers, self).__init__(pretrained_model, 152)\n\n\ndef prepare(image, size=(224, 224)):\n    \"\"\"Converts the given image to the numpy array for ResNets.\n    Note that you have to call this method before ``__call__``\n    because the pre-trained resnet model requires to resize the given\n    image, covert the RGB to the BGR, subtract the mean,\n    and permute the dimensions before calling.\n    Args:\n        image (PIL.Image or numpy.ndarray): Input image.\n            If an input is ``numpy.ndarray``, its shape must be\n            ``(height, width)``, ``(height, width, channels)``,\n            or ``(channels, height, width)``, and\n            the order of the channels must be RGB.\n        size (pair of ints): Size of converted images.\n            If ``None``, the given image is not resized.\n    Returns:\n        numpy.ndarray: The converted output array.\n    \"\"\"\n\n    if not available:\n        raise ImportError('PIL cannot be loaded. Install Pillow!\\n'\n                          'The actual import error is as follows:\\n' +\n                          str(_import_error))\n    if isinstance(image, numpy.ndarray):\n        if image.ndim == 3:\n            if image.shape[0] == 1:\n                image = image[0, :, :]\n            elif image.shape[0] == 3:\n                image = image.transpose((1, 2, 0))\n        image = Image.fromarray(image.astype(numpy.uint8))\n    image = image.convert('RGB')\n    if size:\n        image = image.resize(size)\n    image = numpy.asarray(image, dtype=numpy.float32)\n    image = image[:, :, ::-1]\n    # NOTE: in the original paper they subtract a fixed mean image,\n    #       however, in order to support arbitrary size we instead use the\n    #       mean pixel (rather than mean image) as with VGG team. The mean\n    #       value used in ResNet is slightly different from that of VGG16.\n    image -= numpy.array(\n        [103.063, 115.903, 123.152], dtype=numpy.float32)\n    image = image.transpose((2, 0, 1))\n    return image\n\n\nclass BuildingBlock(link.Chain):\n    \"\"\"A building block that consists of several Bottleneck layers.\n    Args:\n        n_layer (int): Number of layers used in the building block.\n        in_channels (int): Number of channels of input arrays.\n        mid_channels (int): Number of channels of intermediate arrays.\n        out_channels (int): Number of channels of output arrays.\n        stride (int or tuple of ints): Stride of filter application.\n        initialW (4-D array): Initial weight value used in\n            the convolutional layers.\n    \"\"\"\n\n    def __init__(self, n_layer, in_channels, mid_channels,\n                 out_channels, stride, initialW=None):\n        super(BuildingBlock, self).__init__()\n        with self.init_scope():\n            self.a = BottleneckA(\n                in_channels, mid_channels, out_channels, stride, initialW)\n            self._forward = [\"a\"]\n            for i in range(n_layer - 1):\n                name = 'b{}'.format(i + 1)\n                bottleneck = BottleneckB(out_channels, mid_channels, initialW)\n                setattr(self, name, bottleneck)\n                self._forward.append(name)\n\n    def __call__(self, x):\n        for name in self._forward:\n            l = getattr(self, name)\n            x = l(x)\n        return x\n\n    @property\n    def forward(self):\n        return [getattr(self, name) for name in self._forward]\n\n\nclass BottleneckA(link.Chain):\n    \"\"\"A bottleneck layer that reduces the resolution of the feature map.\n    Args:\n        in_channels (int): Number of channels of input arrays.\n        mid_channels (int): Number of channels of intermediate arrays.\n        out_channels (int): Number of channels of output arrays.\n        stride (int or tuple of ints): Stride of filter application.\n        initialW (4-D array): Initial weight value used in\n            the convolutional layers.\n    \"\"\"\n\n    def __init__(self, in_channels, mid_channels, out_channels,\n                 stride=2, initialW=None):\n        super(BottleneckA, self).__init__()\n        with self.init_scope():\n            self.conv1 = Convolution2D(\n                in_channels, mid_channels, 1, stride, 0, initialW=initialW,\n                nobias=True)\n            self.bn1 = BatchNormalization(mid_channels)\n            self.conv2 = Convolution2D(\n                mid_channels, mid_channels, 3, 1, 1, initialW=initialW,\n                nobias=True)\n            self.bn2 = BatchNormalization(mid_channels)\n            self.conv3 = Convolution2D(\n                mid_channels, out_channels, 1, 1, 0, initialW=initialW,\n                nobias=True)\n            self.bn3 = BatchNormalization(out_channels)\n            self.conv4 = Convolution2D(\n                in_channels, out_channels, 1, stride, 0, initialW=initialW,\n                nobias=True)\n            self.bn4 = BatchNormalization(out_channels)\n\n    def __call__(self, x):\n        h1 = relu(self.bn1(self.conv1(x)))\n        h1 = relu(self.bn2(self.conv2(h1)))\n        h1 = self.bn3(self.conv3(h1))\n        h2 = self.bn4(self.conv4(x))\n        return relu(h1 + h2)\n\n\nclass BottleneckB(link.Chain):\n    \"\"\"A bottleneck layer that maintains the resolution of the feature map.\n    Args:\n        in_channels (int): Number of channels of input and output arrays.\n        mid_channels (int): Number of channels of intermediate arrays.\n        initialW (4-D array): Initial weight value used in\n            the convolutional layers.\n    \"\"\"\n\n    def __init__(self, in_channels, mid_channels, initialW=None):\n        super(BottleneckB, self).__init__()\n        with self.init_scope():\n            self.conv1 = Convolution2D(\n                in_channels, mid_channels, 1, 1, 0, initialW=initialW,\n                nobias=True)\n            self.bn1 = BatchNormalization(mid_channels)\n            self.conv2 = Convolution2D(\n                mid_channels, mid_channels, 3, 1, 1, initialW=initialW,\n                nobias=True)\n            self.bn2 = BatchNormalization(mid_channels)\n            self.conv3 = Convolution2D(\n                mid_channels, in_channels, 1, 1, 0, initialW=initialW,\n                nobias=True)\n            self.bn3 = BatchNormalization(in_channels)\n\n    def __call__(self, x):\n        h = relu(self.bn1(self.conv1(x)))\n        h = relu(self.bn2(self.conv2(h)))\n        h = self.bn3(self.conv3(h))\n        return relu(h + x)\n\n\ndef _global_average_pooling_2d(x):\n    n, channel, rows, cols = x.data.shape\n    h = average_pooling_2d(x, (rows, cols), stride=1)\n    h = reshape(h, (n, channel))\n    return h\n\n\ndef _transfer_components(src, dst_conv, dst_bn, bname, cname):\n    src_conv = getattr(src, 'res{}_branch{}'.format(bname, cname))\n    src_bn = getattr(src, 'bn{}_branch{}'.format(bname, cname))\n    src_scale = getattr(src, 'scale{}_branch{}'.format(bname, cname))\n    dst_conv.W.data[:] = src_conv.W.data\n    dst_bn.avg_mean[:] = src_bn.avg_mean\n    dst_bn.avg_var[:] = src_bn.avg_var\n    dst_bn.gamma.data[:] = src_scale.W.data\n    dst_bn.beta.data[:] = src_scale.bias.b.data\n\n\ndef _transfer_bottleneckA(src, dst, name):\n    _transfer_components(src, dst.conv1, dst.bn1, name, '2a')\n    _transfer_components(src, dst.conv2, dst.bn2, name, '2b')\n    _transfer_components(src, dst.conv3, dst.bn3, name, '2c')\n    _transfer_components(src, dst.conv4, dst.bn4, name, '1')\n\n\ndef _transfer_bottleneckB(src, dst, name):\n    _transfer_components(src, dst.conv1, dst.bn1, name, '2a')\n    _transfer_components(src, dst.conv2, dst.bn2, name, '2b')\n    _transfer_components(src, dst.conv3, dst.bn3, name, '2c')\n\n\ndef _transfer_block(src, dst, names):\n    _transfer_bottleneckA(src, dst.a, names[0])\n    for i, name in enumerate(names[1:]):\n        dst_bottleneckB = getattr(dst, 'b{}'.format(i + 1))\n        _transfer_bottleneckB(src, dst_bottleneckB, name)\n\n\ndef _transfer_resnet50(src, dst):\n    dst.conv1.W.data[:] = src.conv1.W.data\n    dst.conv1.b.data[:] = src.conv1.b.data\n    dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean\n    dst.bn1.avg_var[:] = src.bn_conv1.avg_var\n    dst.bn1.gamma.data[:] = src.scale_conv1.W.data\n    dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data\n\n    _transfer_block(src, dst.res2, ['2a', '2b', '2c'])\n    _transfer_block(src, dst.res3, ['3a', '3b', '3c', '3d'])\n    _transfer_block(src, dst.res4, ['4a', '4b', '4c', '4d', '4e', '4f'])\n    _transfer_block(src, dst.res5, ['5a', '5b', '5c'])\n\n    dst.fc6.W.data[:] = src.fc1000.W.data\n    dst.fc6.b.data[:] = src.fc1000.b.data\n\n\ndef _transfer_resnet101(src, dst):\n    dst.conv1.W.data[:] = src.conv1.W.data\n    dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean\n    dst.bn1.avg_var[:] = src.bn_conv1.avg_var\n    dst.bn1.gamma.data[:] = src.scale_conv1.W.data\n    dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data\n\n    _transfer_block(src, dst.res2, ['2a', '2b', '2c'])\n    _transfer_block(src, dst.res3, ['3a', '3b1', '3b2', '3b3'])\n    _transfer_block(src, dst.res4,\n                    ['4a'] + ['4b{}'.format(i) for i in range(1, 23)])\n    _transfer_block(src, dst.res5, ['5a', '5b', '5c'])\n\n    dst.fc6.W.data[:] = src.fc1000.W.data\n    dst.fc6.b.data[:] = src.fc1000.b.data\n\n\ndef _transfer_resnet152(src, dst):\n    dst.conv1.W.data[:] = src.conv1.W.data\n    dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean\n    dst.bn1.avg_var[:] = src.bn_conv1.avg_var\n    dst.bn1.gamma.data[:] = src.scale_conv1.W.data\n    dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data\n\n    _transfer_block(src, dst.res2, ['2a', '2b', '2c'])\n    _transfer_block(src, dst.res3,\n                    ['3a'] + ['3b{}'.format(i) for i in range(1, 8)])\n    _transfer_block(src, dst.res4,\n                    ['4a'] + ['4b{}'.format(i) for i in range(1, 36)])\n    _transfer_block(src, dst.res5, ['5a', '5b', '5c'])\n\n    dst.fc6.W.data[:] = src.fc1000.W.data\n    dst.fc6.b.data[:] = src.fc1000.b.data\n\n\ndef _make_npz(path_npz, path_caffemodel, model, n_layers):\n    print('Now loading caffemodel (usually it may take few minutes)')\n    if not os.path.exists(path_caffemodel):\n        raise IOError(\n            'The pre-trained caffemodel does not exist. Please download it '\n            'from \\'https://github.com/KaimingHe/deep-residual-networks\\', '\n            'and place it on {}'.format(path_caffemodel))\n\n    if n_layers == 50:\n        ResNet50Layers.convert_caffemodel_to_npz(path_caffemodel, path_npz, 50)\n    elif n_layers == 101:\n        ResNet101Layers.convert_caffemodel_to_npz(\n            path_caffemodel, path_npz, 101)\n    elif n_layers == 152:\n        ResNet152Layers.convert_caffemodel_to_npz(\n            path_caffemodel, path_npz, 152)\n    npz.load_npz(path_npz, model)\n    return model\n\n\ndef _retrieve(n_layers, name_npz, name_caffemodel, model):\n    root = download.get_dataset_directory('pfnet/chainer/models/')\n    path = os.path.join(root, name_npz)\n    path_caffemodel = os.path.join(root, name_caffemodel)\n    return download.cache_or_load_file(\n        path, lambda path: _make_npz(path, path_caffemodel, model, n_layers),\n        lambda path: npz.load_npz(path, model))\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_convolution_2d.py",
    "content": "import chainer\nimport numpy as np\nfrom chainer import cuda\nfrom chainer.functions.array.broadcast import broadcast_to\nfrom chainer.functions.connection import convolution_2d\nfrom chainer.links.connection.convolution_2d import Convolution2D\nfrom source.functions.max_sv import max_singular_value\n\n\nclass SNConvolution2D(Convolution2D):\n    \"\"\"Two-dimensional convolutional layer with spectral normalization.\n\n    This link wraps the :func:`~chainer.functions.convolution_2d` function and\n    holds the filter weight and bias vector as parameters.\n\n    Args:\n        in_channels (int): Number of channels of input arrays. If ``None``,\n            parameter initialization will be deferred until the first forward\n            datasets pass at which time the size will be determined.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or pair of ints): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k)`` are equivalent.\n        stride (int or pair of ints): Stride of filter applications.\n            ``stride=s`` and ``stride=(s, s)`` are equivalent.\n        pad (int or pair of ints): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p)`` are equivalent.\n        wscale (float): Scaling factor of the initial weight.\n        bias (float): Initial bias value.\n        nobias (bool): If ``True``, then this link does not use the bias term.\n        initialW (4-D array): Initial weight value. If ``None``, then this\n            function uses to initialize ``wscale``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        initial_bias (1-D array): Initial bias value. If ``None``, then this\n            function uses to initialize ``bias``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        use_gamma (bool): If true, apply scalar multiplication to the \n            normalized weight (i.e. reparameterize).\n        Ip (int): The number of power iteration for calculating the spcetral \n            norm of the weights.\n        factor (float) : constant factor to adjust spectral norm of W_bar.\n\n    .. seealso::\n       See :func:`chainer.functions.convolution_2d` for the definition of\n       two-dimensional convolution.\n\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        W_bar (~chainer.Variable): Spectrally normalized weight parameter.\n        b (~chainer.Variable): Bias parameter.\n        u (~numpy.array): Current estimation of the right largest singular vector of W.\n        (optional) gamma (~chainer.Variable): the multiplier parameter.\n        (optional) factor (float): constant factor to adjust spectral norm of W_bar.\n\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,\n                 nobias=False, initialW=None, initial_bias=None, use_gamma=False, Ip=1, factor=None):\n        self.Ip = Ip\n        self.use_gamma = use_gamma\n        super(SNConvolution2D, self).__init__(\n            in_channels, out_channels, ksize, stride, pad,\n            nobias, initialW, initial_bias)\n        self.u = np.random.normal(size=(1, out_channels)).astype(dtype=\"f\")\n        self.register_persistent('u')\n        self.factor = factor\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectrally Normalized Weight\n        \"\"\"\n        W_mat = self.W.reshape(self.W.shape[0], -1)\n        sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip)\n        if self.factor:\n            sigma = sigma / self.factor\n        sigma = broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape)\n        if chainer.config.train:\n            # Update estimated 1st singular vector\n            self.u[:] = _u\n        if hasattr(self, 'gamma'):\n            return broadcast_to(self.gamma, self.W.shape) * self.W / sigma\n        else:\n            return self.W / sigma\n\n    def _initialize_params(self, in_size):\n        super(SNConvolution2D, self)._initialize_params(in_size)\n        if self.use_gamma:\n            W_mat = self.W.data.reshape(self.W.shape[0], -1)\n            _, s, _ = np.linalg.svd(W_mat)\n            with self.init_scope():\n                self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1))\n\n    def __call__(self, x):\n        \"\"\"Applies the convolution layer.\n\n        Args:\n            x (~chainer.Variable): Input image.\n\n        Returns:\n            ~chainer.Variable: Output of the convolution.\n\n        \"\"\"\n        if self.W.data is None:\n            self._initialize_params(x.shape[1])\n        return convolution_2d.convolution_2d(\n            x, self.W_bar, self.b, self.stride, self.pad)\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_convolution_nd.py",
    "content": "import numpy as np\nfrom chainer.functions.connection import convolution_nd\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import conv_nd\nfrom chainer import variable\nfrom chainer.functions.array.broadcast import broadcast_to\n\nfrom source.functions.max_sv import max_singular_value\n\n\nclass SNConvolutionND(link.Link):\n    \"\"\"N-dimensional convolution layer.\n    This link wraps the :func:`~chainer.functions.convolution_nd` function and\n    holds the filter weight and bias vector as parameters.\n    Args:\n        ndim (int): Number of spatial dimensions.\n        in_channels (int): Number of channels of input arrays.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or tuple of ints): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent.\n        stride (int or tuple of ints): Stride of filter application.\n            ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent.\n        pad (int or tuple of ints): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.\n        nobias (bool): If ``True``, then this function does not use the bias.\n        initialW (array): Initial weight array. If ``None``, the default\n            initializer is used.\n            May be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        initial_bias (array): Initial bias vector. If ``None``, the bias is\n            set to zero.\n            May be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray``\n            and edits its value.\n        cover_all (bool): If ``True``, all spatial locations are convoluted\n            into some output pixels. It may make the output size larger.\n            ``cover_all`` needs to be ``False`` if you want to use cuDNN.\n        use_gamma (bool): If true, apply scalar multiplication to the\n            normalized weight (i.e. reparameterize).\n        Ip (int): The number of power iteration for calculating the spcetral\n            norm of the weights.\n        factor (float) : constant factor to adjust spectral norm of W_bar.\n    .. seealso::\n        See :func:`~chainer.functions.convolution_nd` for the definition of\n        N-dimensional convolution. See\n        :func:`~chainer.functions.convolution_2d` for the definition of\n        two-dimensional convolution.\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        W_bar (~chainer.Variable): Spectrally normalized weight parameter.\n        b (~chainer.Variable): Bias parameter. If ``initial_bias`` is ``None``,\n            set to ``None``.\n        u (~numpy.array): Current estimation of the right largest singular vector of W.\n        (optional) gamma (~chainer.Variable): the multiplier parameter.\n        (optional) factor (float): constant factor to adjust spectral norm of W_bar.\n    \"\"\"\n\n    def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0,\n                 nobias=False, initialW=None, initial_bias=None,\n                 cover_all=False, use_gamma=False, Ip=1, factor=None):\n        super(SNConvolutionND, self).__init__()\n        ksize = conv_nd.as_tuple(ksize, ndim)\n        self.stride = stride\n        self.pad = pad\n        self.cover_all = cover_all\n        self.use_gamma = use_gamma\n        self.Ip = Ip\n        self.u = np.random.normal(size=(1, out_channels)).astype(dtype=\"f\")\n        self.register_persistent('u')\n        self.factor = factor\n        with self.init_scope():\n            W_shape = (out_channels, in_channels) + ksize\n            self.W = variable.Parameter(\n                initializers._get_initializer(initialW), W_shape)\n\n            if nobias:\n                self.b = None\n            else:\n                if initial_bias is None:\n                    initial_bias = 0\n                initial_bias = initializers._get_initializer(initial_bias)\n                self.b = variable.Parameter(initial_bias, out_channels)\n\n            if self.use_gamma:\n                W_mat = self.W.data.reshape(self.W.shape[0], -1)\n                _, s, _ = np.linalg.svd(W_mat)\n                self.gamma = variable.Parameter(s[0], (1,) * len(self.W.shape))\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectral Normalized Weight\n        \"\"\"\n        W_mat = self.W.reshape(self.W.shape[0], -1)\n        sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip)\n        if self.factor:\n            sigma = sigma / self.factor\n        sigma = broadcast_to(sigma.reshape([1] * len(self.W.shape)), self.W.shape)\n        self.u[:] = _u\n\n        if hasattr(self, 'gamma'):\n            return broadcast_to(self.gamma, self.W.shape) * self.W / sigma\n        else:\n            return self.W / sigma\n\n    def __call__(self, x):\n        \"\"\"Applies N-dimensional convolution layer.\n        Args:\n            x (~chainer.Variable): Input image.\n        Returns:\n            ~chainer.Variable: Output of convolution.\n        \"\"\"\n        return convolution_nd.convolution_nd(\n            x, self.W_bar, self.b, self.stride, self.pad, cover_all=self.cover_all)\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_dilated_convolution_2d.py",
    "content": "import chainer\nimport numpy as np\nfrom chainer import cuda\nfrom chainer.functions.array.broadcast import broadcast_to\nfrom chainer.functions.connection import convolution_2d\nfrom chainer.links.connection.convolution_2d import Convolution2D\nfrom source.functions.max_sv import max_singular_value\n\nfrom chainer.functions.connection import dilated_convolution_2d\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer import variable\nfrom chainer.links.connection.dilated_convolution_2d import DilatedConvolution2D\n\n\nclass SNDilatedConvolution2D(DilatedConvolution2D):\n    \"\"\"Two-dimensional dilated convolutional layer with spectral normalization.\n    Please see the original DilatedConv implementation:\n    https://github.com/chainer/chainer/blob/v3.2.0/chainer/links/connection/dilated_convolution_2d.py\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, ksize=None, stride=1, pad=0,\n                 dilate=1, nobias=False, initialW=None, initial_bias=None, use_gamma=False, Ip=1,\n                 adjust_snorm_for_convfilter=False):\n\n        if adjust_snorm_for_convfilter is not False:\n            self.ratio = ksize // stride\n        else:\n            self.ratio = None\n\n        if ksize is None:\n            out_channels, ksize, in_channels = in_channels, out_channels, None\n\n        self.ksize = ksize\n        self.stride = _pair(stride)\n        self.pad = _pair(pad)\n        self.dilate = _pair(dilate)\n        self.out_channels = out_channels\n        self.use_gamma = use_gamma\n        self.Ip = Ip\n        self.u = None\n\n        super(SNDilatedConvolution2D, self).__init__(\n            in_channels, out_channels, ksize, stride, pad, dilate,\n            nobias, initialW, initial_bias)\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectral Normalized Weight\n        \"\"\"\n        xp = cuda.get_array_module(self.W.data)\n        W_mat = self.W.reshape(self.W.shape[0], -1)\n        sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip)\n        sigma = self.ratio * sigma if self.ratio is not None else sigma\n        sigma = broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape)\n        self.u = _u\n        if hasattr(self, 'gamma'):\n            return broadcast_to(self.gamma, self.W.shape) * self.W / sigma\n        else:\n            return self.W / sigma\n\n    def _initialize_params(self, in_channels):\n        super(SNDilatedConvolution2D, self)._initialize_params(in_channels)\n        if self.use_gamma:\n            W_mat = self.W.data.reshape(self.W.shape[0], -1)\n            _, s, _ = np.linalg.svd(W_mat)\n            with self.init_scope():\n                self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1))\n\n    def __call__(self, x):\n        \"\"\"Applies the convolution layer.\n        Args:\n            x (~chainer.Variable): Input image.\n        Returns:\n            ~chainer.Variable: Output of the convolution.\n        \"\"\"\n        if self.W.data is None:\n            self._initialize_params(x.shape[1])\n        return dilated_convolution_2d.dilated_convolution_2d(\n            x, self.W_bar, self.b, self.stride, self.pad, self.dilate)\n\n\ndef _pair(x):\n    if hasattr(x, '__getitem__'):\n        return x\n    return x, x\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_embed_id.py",
    "content": "import chainer\nfrom chainer.functions.connection import embed_id\nfrom chainer.initializers import normal\nfrom chainer import link\nfrom chainer import variable\nfrom chainer.functions.array.broadcast import broadcast_to\nfrom source.functions.max_sv import max_singular_value\nimport numpy as np\n\n\nclass SNEmbedID(link.Link):\n    \"\"\"Efficient linear layer for one-hot input.\n    This is a link that wraps the :func:`~chainer.functions.embed_id` function.\n    This link holds the ID (word) embedding matrix ``W`` as a parameter.\n    Args:\n        in_size (int): Number of different identifiers (a.k.a. vocabulary\n            size).\n        out_size (int): Size of embedding vector.\n        initialW (2-D array): Initial weight value. If ``None``, then the\n            matrix is initialized from the standard normal distribution.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        ignore_label (int or None): If ``ignore_label`` is an int value,\n            ``i``-th column of return value is filled with ``0``.\n        Ip (int): The number of power iteration for calculating the spcetral\n            norm of the weights.\n        factor (float) : constant factor to adjust spectral norm of W_bar.\n    .. seealso:: :func:`chainer.functions.embed_id`\n    Attributes:\n        W (~chainer.Variable): Embedding parameter matrix.\n        W_bar (~chainer.Variable): Spectrally normalized weight parameter.\n        u (~numpy.array): Current estimation of the right largest singular vector of W.\n        (optional) gamma (~chainer.Variable): the multiplier parameter.\n        (optional) factor (float): constant factor to adjust spectral norm of W_bar.\n    \"\"\"\n\n    ignore_label = None\n\n    def __init__(self, in_size, out_size, initialW=None, ignore_label=None, Ip=1, factor=None):\n        super(SNEmbedID, self).__init__()\n        self.ignore_label = ignore_label\n        self.Ip = Ip\n        with self.init_scope():\n            if initialW is None:\n                initialW = normal.Normal(1.0)\n            self.W = variable.Parameter(initialW, (in_size, out_size))\n        self.u = np.random.normal(size=(1, in_size)).astype(dtype=\"f\")\n        self.register_persistent('u')\n        self.factor = factor\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectral Normalized Weight\n        \"\"\"\n        sigma, _u, _ = max_singular_value(self.W, self.u, self.Ip)\n        if chainer.config.train:\n            # Update estimated 1st singular vector\n            self.u[:] = _u\n        if self.factor:\n            sigma = sigma / self.factor\n        sigma = broadcast_to(sigma.reshape((1, 1)), self.W.shape)\n        return self.W / sigma\n\n    def __call__(self, x):\n        \"\"\"Extracts the word embedding of given IDs.\n        Args:\n            x (~chainer.Variable): Batch vectors of IDs.\n        Returns:\n            ~chainer.Variable: Batch of corresponding embeddings.\n        \"\"\"\n        return embed_id.embed_id(x, self.W_bar, ignore_label=self.ignore_label)\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_linear.py",
    "content": "import chainer\nimport numpy as np\nfrom chainer.functions.array.broadcast import broadcast_to\nfrom chainer.functions.connection import linear\nfrom chainer.links.connection.linear import Linear\nfrom source.functions.max_sv import max_singular_value\n\n\nclass SNLinear(Linear):\n    \"\"\"Linear layer with Spectral Normalization.\n\n    Args:\n        in_size (int): Dimension of input vectors. If ``None``, parameter\n            initialization will be deferred until the first forward datasets pass\n            at which time the size will be determined.\n        out_size (int): Dimension of output vectors.\n        wscale (float): Scaling factor of the weight matrix.\n        bias (float): Initial bias value.\n        nobias (bool): If ``True``, then this function does not use the bias.\n        initialW (2-D array): Initial weight value. If ``None``, then this\n            function uses to initialize ``wscale``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        initial_bias (1-D array): Initial bias value. If ``None``, then this\n            function uses to initialize ``bias``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        use_gamma (bool): If true, apply scalar multiplication to the \n            normalized weight (i.e. reparameterize).\n        Ip (int): The number of power iteration for calculating the spcetral \n            norm of the weights.\n        factor (float) : constant factor to adjust spectral norm of W_bar.\n\n    .. seealso:: :func:`~chainer.functions.linear`\n\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        W_bar (~chainer.Variable): Spectrally normalized weight parameter.\n        b (~chainer.Variable): Bias parameter.\n        u (~numpy.array): Current estimation of the right largest singular vector of W.\n        (optional) gamma (~chainer.Variable): the multiplier parameter.\n        (optional) factor (float): constant factor to adjust spectral norm of W_bar.\n\n    \"\"\"\n\n    def __init__(self, in_size, out_size, use_gamma=False, nobias=False,\n                 initialW=None, initial_bias=None, Ip=1, factor=None):\n        self.Ip = Ip\n        self.use_gamma = use_gamma\n        super(SNLinear, self).__init__(\n            in_size, out_size, nobias, initialW, initial_bias\n        )\n        self.u = np.random.normal(size=(1, out_size)).astype(dtype=\"f\")\n        self.register_persistent('u')\n        self.factor = factor\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectral Normalized Weight\n        \"\"\"\n        sigma, _u, _ = max_singular_value(self.W, self.u, self.Ip)\n        if self.factor:\n            sigma = sigma / self.factor\n        sigma = broadcast_to(sigma.reshape((1, 1)), self.W.shape)\n        if chainer.config.train:\n            # Update estimated 1st singular vector\n            self.u[:] = _u\n        if hasattr(self, 'gamma'):\n            return broadcast_to(self.gamma, self.W.shape) * self.W / sigma\n        else:\n            return self.W / sigma\n\n    def _initialize_params(self, in_size):\n        super(SNLinear, self)._initialize_params(in_size)\n        if self.use_gamma:\n            _, s, _ = np.linalg.svd(self.W.data)\n            with self.init_scope():\n                self.gamma = chainer.Parameter(s[0], (1, 1))\n\n    def __call__(self, x):\n        \"\"\"Applies the linear layer.\n\n        Args:\n            x (~chainer.Variable): Batch of input vectors.\n\n        Returns:\n            ~chainer.Variable: Output of the linear layer.\n\n        \"\"\"\n        if self.W.data is None:\n            self._initialize_params(x.size // x.shape[0])\n        return linear.linear(x, self.W_bar, self.b)\n"
  },
  {
    "path": "image_generation_chainer/source/links/sn_symmetric_linear.py",
    "content": "import chainer\nimport numpy as np\nfrom chainer.functions.array.broadcast import broadcast_to\nfrom chainer.functions.connection import linear\nfrom chainer.links.connection.linear import Linear\nfrom source.functions.max_sv import max_singular_value\n\n\nclass SNSymmetricLinear(Linear):\n    \"\"\"Symmetric Linear layer with Spectral Normalization.\n\n    Args:\n        in_size (int): Dimension of input vectors. If ``None``, parameter\n            initialization will be deferred until the first forward datasets pass\n            at which time the size will be determined.\n        out_size (int): Dimension of output vectors.\n        wscale (float): Scaling factor of the weight matrix.\n        bias (float): Initial bias value.\n        nobias (bool): If ``True``, then this function does not use the bias.\n        initialW (2-D array): Initial weight value. If ``None``, then this\n            function uses to initialize ``wscale``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        initial_bias (1-D array): Initial bias value. If ``None``, then this\n            function uses to initialize ``bias``.\n            May also be a callable that takes ``numpy.ndarray`` or\n            ``cupy.ndarray`` and edits its value.\n        use_gamma (bool): If true, apply scalar multiplication to the \n            normalized weight (i.e. reparameterize).\n        Ip (int): The number of power iteration for calculating the spcetral \n            norm of the weights. \n    .. seealso:: :func:`~chainer.functions.linear`\n\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        W_bar (~chainer.Variable): Normalized (Reparametrized) weight parameter.\n        b (~chainer.Variable): Bias parameter.\n        u (~array): Current estimation of the right largest singular vector of W.\n        (optional) gamma (~chainer.Variable): the multiplier parameter.\n\n    \"\"\"\n\n    def __init__(self, in_size, out_size, use_gamma=False, nobias=False,\n                 initialW=None, initial_bias=None, Ip=1):\n        self.Ip = Ip\n        self.u = None\n        self.use_gamma = use_gamma\n        super(SNLinear, self).__init__(\n            in_size, out_size, nobias, initialW, initial_bias\n        )\n\n    @property\n    def W_sym(self):\n        return self.W + F.transpose(self.W)\n\n    @property\n    def W_bar(self):\n        \"\"\"\n        Spectral Normalized Weight\n        \"\"\"\n        sigma, _u, _ = max_singular_value(self.W_sym, self.u, self.Ip)\n        sigma = broadcast_to(sigma.reshape((1, 1)), self.W_sym.shape)\n        self.u = _u\n        if hasattr(self, 'gamma'):\n            return broadcast_to(self.gamma, self.W_sym.shape) * self.W_sym / sigma\n        else:\n            return self.W_sym / sigma\n\n    def _initialize_params(self, in_size):\n        super(SNLinear, self)._initialize_params(in_size)\n        if self.use_gamma:\n            _, s, _ = np.linalg.svd(self.W_sym.data)\n            with self.init_scope():\n                self.gamma = chainer.Parameter(s[0], (1, 1))\n\n    def __call__(self, x):\n        \"\"\"Applies the linear layer.\n\n        Args:\n            x (~chainer.Variable): Batch of input vectors.\n\n        Returns:\n            ~chainer.Variable: Output of the linear layer.\n\n        \"\"\"\n        if self.W.data is None:\n            self._initialize_params(x.size // x.shape[0])\n        return linear.linear(x, self.W_bar, self.b)\n"
  },
  {
    "path": "image_generation_chainer/source/misc_train_utils.py",
    "content": "import matplotlib as mpl\nmpl.use('Agg')\nfrom os import makedirs, getcwd\nfrom os.path import basename, exists, join, isfile, dirname, isdir\nimport numpy as np\nimport shutil\nfrom pathlib import Path\nfrom functools import reduce\nimport operator\nfrom time import strftime\nimport json\nfrom textwrap import wrap\nimport matplotlib.pyplot as plt\n\nimport source.yaml_utils as yaml_utils\n\n\ndef printtime(msg, time_format='%a %d/%m %H:%M:%S'):\n    \"\"\"\n    Wraps the typical print(msg) with a time to display\n    the time.\n    \"\"\"\n    print('[{}] {}'.format(strftime(time_format), msg))\n\n\ndef get_by_nested_path(root, items):\n    \"\"\"Access a nested object in root by item sequence.\"\"\"\n    return reduce(operator.getitem, items, root)\n\n\ndef get_config_value(config, path, default=None):\n    \"\"\"\n    Fetches the value of a nested key or returns default value. \n    Wrapper around get_by_nested_path().\n    \"\"\"\n    error = False\n    try:\n        value = get_by_nested_path(config, path)\n    except KeyError:\n        value, error = default, True\n    return value, error\n\n\ndef create_result_dir(result_dir, config_path, config):\n    if not exists(result_dir):\n        makedirs(result_dir)\n\n    def copy_to_result_dir(fn, result_dir):\n        shutil.copy(fn, '{}/{}'.format(result_dir, basename(fn)))\n\n    copy_to_result_dir(config_path, result_dir)\n    # # define the paths we want to copy from. Some of those might\n    # # not exist; copy only the existing ones.\n    pool = [['models', 'generator', 'fn'], ['models', 'encoder', 'fn'],\n            ['models', 'decoder', 'fn'], ['models', 'discriminator', 'fn']]\n    for path1 in pool:\n        val, err = get_config_value(config, path1)\n        if not err:\n            copy_to_result_dir(val, result_dir)\n        \n    copy_to_result_dir(\n        config.dataset['dataset_fn'], result_dir)\n    copy_to_result_dir(\n        config.updater['fn'], result_dir)\n\n\ndef load_models(config):\n    gen_conf = config.models['generator']\n    gen = yaml_utils.load_model(gen_conf['fn'], gen_conf['name'], gen_conf['args'])\n    dis_conf = config.models['discriminator']\n    dis = yaml_utils.load_model(dis_conf['fn'], dis_conf['name'], dis_conf['args'])\n    return gen, dis\n\n\ndef load_models_cgan(config, rocgan=False):\n    \"\"\" Load all the network models based on the yml config. \"\"\"\n    # # convenience function for loading a net:\n    loadnet = lambda net_conf: yaml_utils.load_model(net_conf['fn'], net_conf['name'], \n                                                     net_conf['args'])\n    # # load the encoder/decoder and discriminator.\n    enc = loadnet(config.models['encoder'])\n    dec = loadnet(config.models['decoder'])\n    dis = loadnet(config.models['discriminator'])\n    \n    if not rocgan:\n        return enc, dec, dis\n    else:\n        # # load the second encoder (ae pathway) in this case.\n        enc_ae = loadnet(config.models['encoder_ae'])\n        return enc, dec, dis, enc_ae\n\n\ndef ensure_config_paths(config, pb=None, verbose=True):\n    \"\"\"\n    Parses the config (i.e. a dict) and ensures the\n    paths with label 'fn' exist, or tries to replace\n    it with local paths.\n    \"\"\"\n    def _get_key(config, key):\n        try:\n            config[key]\n            return True\n        except (AttributeError, KeyError):\n            return False\n    # # set the base name (if the modules should change dir). If pb is provided,\n    # # use that, otherwise use the current working directory.\n    pbase = pb if pb is not None and isdir(pb) else getcwd()\n    # # boolean to understand if something is changed.\n    changed = False\n    if _get_key(config, 'models'):\n        # # iterate over all models and change paths.\n        for key in config['models'].keys():\n            m_conf = config['models'][key]\n            if 'fn' in m_conf.keys() and not isfile(m_conf['fn']):\n                # # modify the path (since the yml is for deepmux.)\n                modelp = Path(m_conf['fn'])\n                m_conf['fn'] = join(pbase, modelp.parts[-2], modelp.parts[-1])\n                changed = True\n    # # similarly change the dataset path.\n    if _get_key(config, 'dataset'):\n        dbp = Path(config['dataset']['dataset_fn'])\n        if not dbp.exists():\n            config['dataset']['dataset_fn'] = join(pbase, dbp.parts[-2], dbp.parts[-1])\n            changed = True\n    if _get_key(config, 'updater'):\n        updp = Path(config['updater']['fn'])\n        if not updp.exists():\n            config['updater']['fn'] = join(pbase, updp.parts[-3], updp.parts[-2], updp.parts[-1])\n            changed = True\n    if verbose and changed:\n        print('Changed the paths in config to base: {}.'.format(pbase))\n    return config\n\n\ndef moving_average(values, n=3):\n    \"\"\" Moving average in numpy (values is a list/array). \"\"\"\n    ret = np.cumsum(values, dtype=float)\n    ret[n:] = ret[n:] - ret[:-n]\n    return ret[n - 1:] / n\n\n\ndef plot_losses_log(pout, savefig=True, title_len=30, name_out='losses.png', \n                    enable_ma=False, ma_n=5, interp_short=False):\n    \"\"\"\n    Plots a host of losses (hardcoded) based on the log. The log and the\n    output dir are assumed to be in pout (convenient for running time).\n    ARGS:\n      enable_ma: (bool, optional) If True, the moving average of the saved\n          values is plotted.\n      interp_short: (bool, optional) If True, the lists shorter than the log, \n          e.g. ones not exported frequently are interpolated.\n    \"\"\"\n    # # Naming conventions: l[name] is loss of [name], vl[n] is validation\n    # # loss of [n] var, bp[var] is best position of [var]. \n    printtime('Plotting losses.')\n    # # convenience function if moving average is requested.\n    ma = lambda x: moving_average(x, n=ma_n) if enable_ma else x\n    # # convenience function to set the subtitle length to many lines.\n    set_title = lambda ax, text, length=title_len: ax.set_title('\\n'.join(wrap(text, length)))\n    # # log1: The list with the values exported per iteration. \n    # # Each element of the list is (expected to be) a dictionary.\n    log1 = json.load(open(join(pout, 'log'), 'r'))\n    total_lines = len(log1)\n    # # empty lists for the losses. \n    # # ldis: loss discriminator, lgadv: adversarial loss of generator, \n    ldis, lgadv, ll1  = [], [], []\n    vlmssim, vlmmae, vlsssim, bpssim, vfid = [], [], [], [], []\n    # # additional vars to save.\n    d_fake, d_real, real_min, fake_max = [], [], [], []\n    for cnt, dict_iter in enumerate(log1):\n        # # plot the training losses.\n        ldis.append(dict_iter['loss_dis'])\n        if 'lgen_adv' in dict_iter.keys():\n            lgadv.append(dict_iter['lgen_adv'])\n        if 'loss_l1' in dict_iter.keys():\n            ll1.append(dict_iter['loss_l1'])\n        # # plot the dis_fake/real.\n        d_fake.append(dict_iter['dis_fake'])\n        d_real.append(dict_iter['dis_real'])\n        if 'real_min' in dict_iter.keys():\n            real_min.append(dict_iter['real_min'])\n            fake_max.append(dict_iter['fake_max'])\n        # # append the validation losses.\n        if 'mssim' in dict_iter.keys():\n            vlmssim.append(dict_iter['mssim'])\n            vlsssim.append(dict_iter['sdssim'])\n        if 'mmae' in dict_iter.keys():\n            vlmmae.append(dict_iter['mmae'])\n        if 'bssim' in dict_iter.keys():\n            bpssim.append(dict_iter['bssim'])\n        if 'FID' in dict_iter.keys():\n            vfid.append(dict_iter['FID'])\n\n    # # get the export frequency from log.\n    freq = log1[1]['iteration'] - log1[0]['iteration']\n\n    pool_to_plot = [\n        (ldis, 'Discriminator adversarial loss', []),\n        (lgadv, 'Generator adversarial loss', []),\n        (ll1, 'Training loss L1', []),\n        (d_real, 'Real vs fake (red)', d_fake),\n        (vlmssim, '(Valid) Mean ssim', []),\n        (vlsssim, '(Valid) Std ssim', []),\n        (vlmmae, '(Valid) Mean MAE', []),\n        (real_min, 'Real min vs fake max (red)', fake_max),\n        (bpssim, 'Best valid (ssim) iter', []),\n        (vfid, 'Frechet Inception Distance', []),\n    ]\n    # # define the maximum rows and columns to support. \n    max_rows, max_cols = 3, 4\n    f, axarr = plt.subplots(max_rows, max_cols, sharex=True, \n                            figsize=(5 * max_cols, 7 * max_rows))\n    # # c1: counter for the plots added.\n    c1, total = 0, max_rows * max_cols\n    # # iterate over the pool above and plot them.\n    for cnt1, (list1, str1, l2) in enumerate(pool_to_plot):\n        if len(list1) > 0:\n            # # select the subplot.\n            ax = axarr[c1 // max_cols, c1 % max_cols]\n            if interp_short and len(list1) < total_lines // 4:\n                # # in this case, we interpolate (staircase) the \n                # # variable that was exported infrequently.\n                list1 = np.repeat(list1, int(total_lines // len(list1)))                \n            ax.plot(ma(list1))\n            set_title(ax, str1)\n            if len(l2) > 0:\n                # # in this case, add a second variable to plot.\n                ax.plot(ma(l2), c='r')\n            # # increase the counter for the plots made.\n            c1 += 1\n    if savefig:\n        plt.savefig(pout + name_out, bbox_inches='tight', pad_inches=0.)\n    return f\n\n\n"
  },
  {
    "path": "image_generation_chainer/source/miscs/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_chainer/source/miscs/model_moiving_average.py",
    "content": "import copy\nimport chainer\n\n\ndef copypersistents(src, dst):\n    assert isinstance(src, chainer.Link)\n    assert isinstance(dst, chainer.Link)\n\n    dst._persistent = list(src._persistent)\n    for name in dst._persistent:\n        dst.__dict__[name] = copy.deepcopy(src.__dict__[name])\n\n    if isinstance(src, chainer.Chain):\n        assert isinstance(dst, chainer.Chain)\n        for name in src._children:\n            copypersistents(src.__dict__[name], dst.__dict__[name])\n\n\ndef namedpersistents(src):\n    assert isinstance(src, chainer.Link)\n\n    for lname, link in src.namedlinks():\n        for pname in link._persistent:\n            yield lname + '/' + pname, link.__dict__[pname]\n\n\ndef apply_exponential_averaging(src_model, dst_model, alpha):\n    src_params = [\n        param for _, param in sorted(src_model.namedparams())]\n    dst_params = [\n        param for _, param in sorted(dst_model.namedparams())]\n    assert (len(src_params) == len(dst_params))\n    for p, avg_p in zip(src_params, dst_params):\n        avg_p.data[:] += float(1 - alpha) * (p.data - avg_p.data)\n\n    src_persistents = [\n        param for _, param in sorted(namedpersistents(src_model))]\n    dst_persistents = [\n        param for _, param in sorted(namedpersistents(dst_model))]\n    assert (len(src_persistents) == len(dst_persistents))\n    for src, dst in zip(src_persistents, dst_persistents):\n        dst *= alpha\n        dst += src * (1 - alpha)\n\n\nclass ModelMovingAverage(object):\n    name = \"ModelMovingAverage\"\n\n    def __init__(self, alpha, model=None):\n        self.alpha = alpha\n        self.avg_model = copy.deepcopy(model) if model is not None else None\n\n    def update(self, src_model):\n        if self.avg_model is None:\n            self.avg_model = copy.deepcopy(src_model)\n        apply_exponential_averaging(src_model, self.avg_model, self.alpha)\n\n    def copy_avg_model_to(self, model):\n        if self.avg_model is None:\n            raise Exception(\"self.avg_model is None\")\n        apply_exponential_averaging(self.avg_model, model, 0)\n\n    def set_avg_model(self, model):\n        self.avg_model = copy.deepcopy(model)\n"
  },
  {
    "path": "image_generation_chainer/source/miscs/random_samples.py",
    "content": "import numpy as np\nimport chainer\n\n\ndef sample_continuous(dim, batchsize, distribution='normal', xp=np):\n    if distribution == \"normal\":\n        return xp.random.randn(batchsize, dim) \\\n            .astype(xp.float32)\n    elif distribution == \"laplace\":\n        if xp == np:\n            return np.random.laplace(0, 1, size=(batchsize, dim)).astype(xp.float32)\n        else:\n            # TODO: cuda kernel implementation\n            return xp.asarray(np.random.laplace(0, 1, size=(batchsize, dim)).astype(np.float32))\n    elif distribution == \"uniform\":\n        return xp.random.uniform(-1, 1, (batchsize, dim)) \\\n            .astype(xp.float32)\n    else:\n        raise NotImplementedError\n\n\ndef sample_categorical(n_cat, batchsize, distribution='uniform', xp=np, a=None):\n    if a is not None:\n        return xp.random.choice(a, size=(batchsize)).astype(xp.int32)\n    if distribution == 'uniform':\n        return xp.random.randint(low=0, high=n_cat, size=(batchsize)).astype(xp.int32)\n    else:\n        raise NotImplementedError\n\n\ndef sample_from_categorical_distribution(batch_probs):\n    \"\"\"Sample a batch of actions from a batch of action probabilities.\n    Args:\n        batch_probs (ndarray): batch of action probabilities BxA\n    Returns:\n        ndarray consisting of sampled action indices\n    \"\"\"\n    xp = chainer.cuda.get_array_module(batch_probs)\n    return xp.argmax(\n        xp.log(batch_probs) + xp.random.gumbel(size=batch_probs.shape),\n        axis=1).astype(np.int32, copy=False)\n"
  },
  {
    "path": "image_generation_chainer/source/yaml_utils.py",
    "content": "# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom os.path import splitext, basename, dirname, isfile, join\nfrom os import getcwd\nimport sys\nimport time\nfrom pathlib import Path\nfrom copy import deepcopy\n\nimport yaml\n\n\n# Copy from tgans repo.\nclass Config(object):\n    def __init__(self, config_dict):\n        self.config = config_dict\n\n    def __getattr__(self, key):\n        if key in self.config:\n            return self.config[key]\n        else:\n            raise AttributeError(key)\n\n    def __getitem__(self, key):\n        return self.config[key]\n\n    def __repr__(self):\n        return yaml.dump(self.config, default_flow_style=False)\n\n\ndef load_dataset(config, validation=False, valid_path=None):\n    args = deepcopy(config.dataset['args'])\n    dataset = load_module(config.dataset['dataset_fn'],\n                          config.dataset['dataset_name'])\n    if validation:\n        # # Modify the path for the validation db.\n        if 'path' in args.keys():\n            p1 = args['path']\n            if not isfile(p1):\n                # # try one more time by appending the current path.\n                p1 = join(getcwd(), args['path'])\n            if valid_path is not None:\n                args['path'] = Path(p1).with_name(valid_path).as_posix()\n        else:\n            # # e.g. used in cifar10/svhn.\n            args['train'] = False\n    return dataset(**args)\n\n\ndef load_module(fn, name):\n    mod_name = splitext(basename(fn))[0]\n    mod_path = dirname(fn)\n    sys.path.insert(0, mod_path)\n    return getattr(__import__(mod_name), name)\n\n\ndef load_model(model_fn, model_name, args=None, GPU=0):\n    model = load_module(model_fn, model_name)\n    model1 = model(**args) if args else model()\n    if GPU:\n        model1.to_gpu()\n    return model1\n\n\ndef load_updater_class(config):\n    return load_module(config.updater['fn'], config.updater['name'])\n\n"
  },
  {
    "path": "image_generation_chainer/updaters/gans/updater.py",
    "content": "import numpy as np\n\nimport chainer\nimport chainer.functions as F\nfrom chainer import Variable\nimport source.functions.losses as losses\nfrom source.miscs.random_samples import sample_continuous, sample_categorical\n\n\nclass Updater(chainer.training.StandardUpdater):\n    def __init__(self, *args, iter_incndis=None, iter_ndis=1, start_ndisone=False, \n                 mma=None, mma_dis=None, update_gener=True, **kwargs):\n        self.mma, self.mma_dis, self.update_gener = mma, mma_dis, update_gener\n        self.iter_incndis, self.iter_ndis, self.start_ndisone = iter_incndis, iter_ndis, start_ndisone\n        self.models = kwargs.pop('models')\n        self.n_dis = kwargs.pop('n_dis')\n        self.loss_type = kwargs.pop('loss_type')\n        self.conditional = kwargs.pop('conditional')\n        self.n_gen_samples = kwargs.pop('n_gen_samples')\n        # # iter_incndis: The iteration(s) to increase n_dis, e.g. in the later stages of the training.\n        self.idx_inc, self.ndis_inc = 0, kwargs.pop('ndis_inc') if 'ndis_inc' in kwargs else self.n_dis\n        if self.iter_incndis is not None and isinstance(self.iter_incndis, int):\n            self.iter_incndis = [(self.iter_incndis, self.ndis_inc)]\n        if self.loss_type == 'softplus':\n            self.loss_gen = losses.loss_dcgan_gen\n            self.loss_dis = losses.loss_dcgan_dis\n        elif self.loss_type == 'hinge':\n            self.loss_gen = losses.loss_hinge_gen\n            self.loss_dis = losses.loss_hinge_dis\n        elif self.loss_type == 'revKL':\n            self.loss_gen = losses.loss_revKL_gen\n            self.loss_dis = losses.loss_revKL_dis\n        elif self.loss_type == 'wgan':\n            self.loss_gen = losses.loss_wgan_gen\n            self.loss_dis = losses.loss_wgan_dis\n\n        super(Updater, self).__init__(*args, **kwargs)\n\n    def _generate_samples(self, n_gen_samples=None):\n        if n_gen_samples is None:\n            n_gen_samples = self.n_gen_samples\n        gen = self.models['gen']\n        if self.conditional:\n            y = sample_categorical(gen.n_classes, n_gen_samples, xp=gen.xp)\n        else:\n            y = None\n        x_fake = gen(n_gen_samples, y=y)\n        return x_fake, y\n\n    def get_batch(self, xp):\n        batch = self.get_iterator('main').next()\n        batchsize = len(batch)\n        x, y = [], []\n        for j in range(batchsize):\n            x.append(np.asarray(batch[j][0]).astype(\"f\"))\n            y.append(np.asarray(batch[j][1]).astype(np.int32))\n        x_real = Variable(xp.asarray(x))\n        y_real = Variable(xp.asarray(y, dtype=xp.int32)) if self.conditional else None\n        return x_real, y_real\n\n    def update_core(self):\n        gen = self.models['gen']\n        dis = self.models['dis']\n        gen_optimizer = self.get_optimizer('opt_gen')\n        dis_optimizer = self.get_optimizer('opt_dis')\n        xp = gen.xp\n        if self.start_ndisone:\n            if self.iteration == 0:\n                # # save the original n_dis and make it 1 in the first few iterations.\n                self.org_ndis = int(self.n_dis)\n                self.n_dis = 1\n                print('Updater (iter={}): Setting n_dis={}.'.format(self.iteration, self.n_dis))\n            elif self.iteration == self.iter_ndis:\n                # # make the normal n_dis for the rest of the training.\n                self.n_dis = int(self.org_ndis)\n                # # set the original flag to false to avoid doing the if's.\n                self.start_ndisone = False\n                print('Updater (iter={}): Setting n_dis={}.'.format(self.iteration, self.n_dis))\n        if self.iter_incndis is not None:\n            if self.iteration < self.iter_incndis[self.idx_inc][0]:\n                self.n_dis = self.iter_incndis[self.idx_inc][1]\n            else:\n                self.idx_inc += 1\n                if len(self.iter_incndis) < self.idx_inc:\n                    # # set to None, since we do not have any more parts to change.\n                    self.iter_incndis = None\n                m1 = 'Updater (iter={}): Was n_dis={} ({}).'\n                print(m1.format(self.iteration, self.n_dis, self.iter_incndis))\n        for i in range(self.n_dis):\n            x_real, y_real = self.get_batch(xp)\n            batchsize = len(x_real)\n            dis_real = dis(x_real, y=y_real)\n            x_fake, y_fake = self._generate_samples(n_gen_samples=batchsize)\n            dis_fake = dis(x_fake, y=y_fake)\n            x_fake.unchain_backward()\n\n            fake_arr, real_arr = dis_fake.array, dis_real.array\n            chainer.reporter.report({'dis_fake': fake_arr.mean()})\n            chainer.reporter.report({'dis_real': real_arr.mean()})\n            chainer.reporter.report({'fake_max': fake_arr.max()})\n            chainer.reporter.report({'real_min': real_arr.min()})\n\n            loss_dis = self.loss_dis(dis_fake=dis_fake, dis_real=dis_real)\n            dis.cleargrads()\n            loss_dis.backward()\n            dis_optimizer.update()\n            loss_dis.unchain_backward()\n            chainer.reporter.report({'loss_dis': loss_dis.array})\n            del loss_dis\n\n            if i == 0 and self.update_gener:\n                x_fake, y_fake = self._generate_samples()\n                dis_fake = dis(x_fake, y=y_fake)\n                loss_gen = self.loss_gen(dis_fake=dis_fake)\n                assert not xp.isnan(loss_gen.data)\n                gen.cleargrads()\n                loss_gen.backward()\n                gen_optimizer.update()\n                loss_gen.unchain_backward()\n                chainer.reporter.report({'loss_gen': loss_gen.array})\n                del loss_gen\n\n        self.mma.update(gen) if self.mma is not None else None\n"
  },
  {
    "path": "image_generation_pytorch/FID/LICENSE",
    "content": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. 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  },
  {
    "path": "image_generation_pytorch/FID/README.md",
    "content": "# Fréchet Inception Distance (FID score) in PyTorch\n\nThis is a port of the official implementation of [Fréchet Inception Distance](https://arxiv.org/abs/1706.08500) to PyTorch. \nSee [https://github.com/bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR) for the original implementation using Tensorflow.\n\nFID is a measure of similarity between two datasets of images. \nIt was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks.\nFID is calculated by computing the [Fréchet distance](https://en.wikipedia.org/wiki/Fr%C3%A9chet_distance) between two Gaussians fitted to feature representations of the Inception network. \n\nFurther insights and an independent evaluation of the FID score can be found in [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337).\n\n**Note that the official implementation most likely gives slightly different scores**, as it uses pretrained weights from Tensorflow 's Inception, not PyTorch's Inception as used here.\nIf you report FID scores in your paper, and you want them to be comparable to FID scores reported in other papers, you should use [the official Tensorflow implementation](https://github.com/bioinf-jku/TTUR).\nYou can still use this version if you want a quick FID estimate without installing Tensorflow.\n\n## Usage\n\nRequirements:\n- python3\n- pytorch\n- torchvision\n- numpy\n- scipy\n\nTo compute the FID score between two datasets, where images of each dataset are contained in an individual folder:\n```\n./fid_score.py path/to/dataset1 path/to/dataset2\n```\n\nTo run the evaluation on GPU, use the flag `--gpu N`, where `N` is the index of the GPU to use. \n\n### Using different layers for feature maps\n\nIn difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default `pool3` layer. \nAs the lower layer features still have spatial extent, the features are first global average pooled to a vector before estimating mean and covariance.\n\nThis might be useful if the datasets you want to compare have less than the otherwise required 2048 images. \nNote that this changes the magnitude of the FID score and you can not compare them against scores calculated on another dimensionality. \nThe resulting scores might also no longer correlate with visual quality.\n\nYou can select the dimensionality of features to use with the flag `--dims N`, where N is the dimensionality of features. \nThe choices are:\n- 64:   first max pooling features\n- 192:  second max pooling featurs\n- 768:  pre-aux classifier features\n- 2048: final average pooling features (this is the default)\n\n## License\n\nThis implementation is licensed under the Apache License 2.0.\n\nFID was introduced by Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler and Sepp Hochreiter in \"GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium\", see [https://arxiv.org/abs/1706.08500](https://arxiv.org/abs/1706.08500)\n\nThe original implementation is by the Institute of Bioinformatics, JKU Linz, licensed under the Apache License 2.0.\nSee [https://github.com/bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR).\n"
  },
  {
    "path": "image_generation_pytorch/FID/__init__.py",
    "content": ""
  },
  {
    "path": "image_generation_pytorch/FID/fid_score.py",
    "content": "#!/usr/bin/env python3\n\"\"\"Calculates the Frechet Inception Distance (FID) to evalulate GANs\n\nThe FID metric calculates the distance between two distributions of images.\nTypically, we have summary statistics (mean & covariance matrix) of one\nof these distributions, while the 2nd distribution is given by a GAN.\n\nWhen run as a stand-alone program, it compares the distribution of\nimages that are stored as PNG/JPEG at a specified location with a\ndistribution given by summary statistics (in pickle format).\n\nThe FID is calculated by assuming that X_1 and X_2 are the activations of\nthe pool_3 layer of the inception net for generated samples and real world\nsamples respectively.\n\nSee --help to see further details.\n\nCode apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead\nof Tensorflow\n\nCopyright 2018 Institute of Bioinformatics, JKU Linz\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\nimport os\nimport pathlib\nfrom argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\nimport numpy as np\nimport torch\nimport pickle\nfrom scipy import linalg\nfrom scipy.misc import imread\nfrom torch.nn.functional import adaptive_avg_pool2d\n\ntry:\n    from tqdm import tqdm\nexcept ImportError:\n    # If not tqdm is not available, provide a mock version of it\n    def tqdm(x): return x\n\nfrom .inception import InceptionV3\n\nparser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)\nparser.add_argument('path', type=str, nargs=2,\n                    help=('Path to the generated images or '\n                          'to .npz statistic files'))\nparser.add_argument('--batch-size', type=int, default=50,\n                    help='Batch size to use')\nparser.add_argument('--dims', type=int, default=2048,\n                    choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),\n                    help=('Dimensionality of Inception features to use. '\n                          'By default, uses pool3 features'))\nparser.add_argument('-c', '--gpu', default='', type=str,\n                    help='GPU to use (leave blank for CPU only)')\n\n\ndef get_activations(files, model, batch_size=50, dims=2048,\n                    cuda=True, verbose=False):\n    \"\"\"Calculates the activations of the pool_3 layer for all images.\n\n    Params:\n    -- files       : List of image files paths\n    -- model       : Instance of inception model\n    -- batch_size  : Batch size of images for the model to process at once.\n                     Make sure that the number of samples is a multiple of\n                     the batch size, otherwise some samples are ignored. This\n                     behavior is retained to match the original FID score\n                     implementation.\n    -- dims        : Dimensionality of features returned by Inception\n    -- cuda        : If set to True, use GPU\n    -- verbose     : If set to True and parameter out_step is given, the number\n                     of calculated batches is reported.\n    Returns:\n    -- A numpy array of dimension (num images, dims) that contains the\n       activations of the given tensor when feeding inception with the\n       query tensor.\n    \"\"\"\n    model.eval()\n\n    if len(files) % batch_size != 0:\n        print(('Warning: number of images is not a multiple of the '\n               'batch size. Some samples are going to be ignored.'))\n    if batch_size > len(files):\n        print(('Warning: batch size is bigger than the data size. '\n               'Setting batch size to data size'))\n        batch_size = len(files)\n\n    n_batches = len(files) // batch_size\n    n_used_imgs = n_batches * batch_size\n\n    pred_arr = np.empty((n_used_imgs, dims))\n\n    for i in tqdm(range(n_batches)):\n        if verbose:\n            print('\\rPropagating batch %d/%d' % (i + 1, n_batches),\n                  end='', flush=True)\n        start = i * batch_size\n        end = start + batch_size\n\n        images = np.array([imread(str(f)).astype(np.float32)\n                           for f in files[start:end]])\n\n        # Reshape to (n_images, 3, height, width)\n        images = images.transpose((0, 3, 1, 2))\n        images /= 255\n\n        batch = torch.from_numpy(images).type(torch.FloatTensor)\n        if cuda:\n            batch = batch.cuda()\n\n        pred = model(batch)[0]\n\n        # If model output is not scalar, apply global spatial average pooling.\n        # This happens if you choose a dimensionality not equal 2048.\n        if pred.shape[2] != 1 or pred.shape[3] != 1:\n            pred = adaptive_avg_pool2d(pred, output_size=(1, 1))\n\n        pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1)\n\n    if verbose:\n        print(' done')\n\n    return pred_arr\n\n\ndef calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):\n    \"\"\"Numpy implementation of the Frechet Distance.\n    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)\n    and X_2 ~ N(mu_2, C_2) is\n            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).\n\n    Stable version by Dougal J. Sutherland.\n\n    Params:\n    -- mu1   : Numpy array containing the activations of a layer of the\n               inception net (like returned by the function 'get_predictions')\n               for generated samples.\n    -- mu2   : The sample mean over activations, precalculated on an\n               representative data set.\n    -- sigma1: The covariance matrix over activations for generated samples.\n    -- sigma2: The covariance matrix over activations, precalculated on an\n               representative data set.\n\n    Returns:\n    --   : The Frechet Distance.\n    \"\"\"\n\n    mu1 = np.atleast_1d(mu1)\n    mu2 = np.atleast_1d(mu2)\n\n    sigma1 = np.atleast_2d(sigma1)\n    sigma2 = np.atleast_2d(sigma2)\n\n    assert mu1.shape == mu2.shape, \\\n        'Training and test mean vectors have different lengths'\n    assert sigma1.shape == sigma2.shape, \\\n        'Training and test covariances have different dimensions'\n\n    diff = mu1 - mu2\n\n    # Product might be almost singular\n    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)\n    if not np.isfinite(covmean).all():\n        msg = ('fid calculation produces singular product; '\n               'adding %s to diagonal of cov estimates') % eps\n        print(msg)\n        offset = np.eye(sigma1.shape[0]) * eps\n        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))\n\n    # Numerical error might give slight imaginary component\n    if np.iscomplexobj(covmean):\n        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):\n            m = np.max(np.abs(covmean.imag))\n            raise ValueError('Imaginary component {}'.format(m))\n        covmean = covmean.real\n\n    tr_covmean = np.trace(covmean)\n\n    return (diff.dot(diff) + np.trace(sigma1) +\n            np.trace(sigma2) - 2 * tr_covmean)\n\n\ndef calculate_activation_statistics(files, model, batch_size=50,\n                                    dims=2048, cuda=False, verbose=False):\n    \"\"\"Calculation of the statistics used by the FID.\n    Params:\n    -- files       : List of image files paths\n    -- model       : Instance of inception model\n    -- batch_size  : The images numpy array is split into batches with\n                     batch size batch_size. A reasonable batch size\n                     depends on the hardware.\n    -- dims        : Dimensionality of features returned by Inception\n    -- cuda        : If set to True, use GPU\n    -- verbose     : If set to True and parameter out_step is given, the\n                     number of calculated batches is reported.\n    Returns:\n    -- mu    : The mean over samples of the activations of the pool_3 layer of\n               the inception model.\n    -- sigma : The covariance matrix of the activations of the pool_3 layer of\n               the inception model.\n    \"\"\"\n    act = get_activations(files, model, batch_size, dims, cuda, verbose)\n    mu = np.mean(act, axis=0)\n    sigma = np.cov(act, rowvar=False)\n    return mu, sigma\n\n\ndef _compute_statistics_of_path(path, model, batch_size, dims, cuda):\n    if path.endswith('.pkl'):\n        f = pickle.load(open(path, 'rb'))\n        m, s = f['mu'][:], f['sigma'][:]\n    else:\n        path = pathlib.Path(path)\n        files = list(path.glob('*.jpg')) + list(path.glob('*.png'))\n        m, s = calculate_activation_statistics(files, model, batch_size,\n                                               dims, cuda)\n    return m, s\n\n\ndef calculate_fid_given_paths(paths, batch_size, cuda, dims):\n    \"\"\"Calculates the FID of two paths\"\"\"\n    for p in paths:\n        if not os.path.exists(p):\n            raise RuntimeError('Invalid path: %s' % p)\n\n    block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]\n\n    model = InceptionV3([block_idx])\n    if cuda:\n        model.cuda()\n\n    m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size,\n                                         dims, cuda)\n    m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size,\n                                         dims, cuda)\n    fid_value = calculate_frechet_distance(m1, s1, m2, s2)\n\n    return fid_value\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n    # os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu\n\n    fid_value = calculate_fid_given_paths(args.path,\n                                          args.batch_size,\n                                          True,\n                                          args.dims)\n    print('FID: ', fid_value)\n"
  },
  {
    "path": "image_generation_pytorch/FID/inception.py",
    "content": "import torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import models\n\n\nclass InceptionV3(nn.Module):\n    \"\"\"Pretrained InceptionV3 network returning feature maps\"\"\"\n\n    # Index of default block of inception to return,\n    # corresponds to output of final average pooling\n    DEFAULT_BLOCK_INDEX = 3\n\n    # Maps feature dimensionality to their output blocks indices\n    BLOCK_INDEX_BY_DIM = {\n        64: 0,   # First max pooling features\n        192: 1,  # Second max pooling featurs\n        768: 2,  # Pre-aux classifier features\n        2048: 3  # Final average pooling features\n    }\n\n    def __init__(self,\n                 output_blocks=[DEFAULT_BLOCK_INDEX],\n                 resize_input=True,\n                 normalize_input=True,\n                 requires_grad=False):\n        \"\"\"Build pretrained InceptionV3\n\n        Parameters\n        ----------\n        output_blocks : list of int\n            Indices of blocks to return features of. Possible values are:\n                - 0: corresponds to output of first max pooling\n                - 1: corresponds to output of second max pooling\n                - 2: corresponds to output which is fed to aux classifier\n                - 3: corresponds to output of final average pooling\n        resize_input : bool\n            If true, bilinearly resizes input to width and height 299 before\n            feeding input to model. As the network without fully connected\n            layers is fully convolutional, it should be able to handle inputs\n            of arbitrary size, so resizing might not be strictly needed\n        normalize_input : bool\n            If true, scales the input from range (0, 1) to the range the\n            pretrained Inception network expects, namely (-1, 1)\n        requires_grad : bool\n            If true, parameters of the model require gradient. Possibly useful\n            for finetuning the network\n        \"\"\"\n        super(InceptionV3, self).__init__()\n\n        self.resize_input = resize_input\n        self.normalize_input = normalize_input\n        self.output_blocks = sorted(output_blocks)\n        self.last_needed_block = max(output_blocks)\n\n        assert self.last_needed_block <= 3, \\\n            'Last possible output block index is 3'\n\n        self.blocks = nn.ModuleList()\n\n        inception = models.inception_v3(pretrained=True)\n\n        # Block 0: input to maxpool1\n        block0 = [\n            inception.Conv2d_1a_3x3,\n            inception.Conv2d_2a_3x3,\n            inception.Conv2d_2b_3x3,\n            nn.MaxPool2d(kernel_size=3, stride=2)\n        ]\n        self.blocks.append(nn.Sequential(*block0))\n\n        # Block 1: maxpool1 to maxpool2\n        if self.last_needed_block >= 1:\n            block1 = [\n                inception.Conv2d_3b_1x1,\n                inception.Conv2d_4a_3x3,\n                nn.MaxPool2d(kernel_size=3, stride=2)\n            ]\n            self.blocks.append(nn.Sequential(*block1))\n\n        # Block 2: maxpool2 to aux classifier\n        if self.last_needed_block >= 2:\n            block2 = [\n                inception.Mixed_5b,\n                inception.Mixed_5c,\n                inception.Mixed_5d,\n                inception.Mixed_6a,\n                inception.Mixed_6b,\n                inception.Mixed_6c,\n                inception.Mixed_6d,\n                inception.Mixed_6e,\n            ]\n            self.blocks.append(nn.Sequential(*block2))\n\n        # Block 3: aux classifier to final avgpool\n        if self.last_needed_block >= 3:\n            block3 = [\n                inception.Mixed_7a,\n                inception.Mixed_7b,\n                inception.Mixed_7c,\n                nn.AdaptiveAvgPool2d(output_size=(1, 1))\n            ]\n            self.blocks.append(nn.Sequential(*block3))\n\n        for param in self.parameters():\n            param.requires_grad = requires_grad\n\n    def forward(self, inp):\n        \"\"\"Get Inception feature maps\n\n        Parameters\n        ----------\n        inp : torch.autograd.Variable\n            Input tensor of shape Bx3xHxW. Values are expected to be in\n            range (0, 1)\n\n        Returns\n        -------\n        List of torch.autograd.Variable, corresponding to the selected output\n        block, sorted ascending by index\n        \"\"\"\n        outp = []\n        x = inp\n\n        if self.resize_input:\n            x = F.interpolate(x,\n                              size=(299, 299),\n                              mode='bilinear',\n                              align_corners=False)\n\n        if self.normalize_input:\n            x = 2 * x - 1  # Scale from range (0, 1) to range (-1, 1)\n\n        for idx, block in enumerate(self.blocks):\n            x = block(x)\n            if idx in self.output_blocks:\n                outp.append(x)\n\n            if idx == self.last_needed_block:\n                break\n\n        return outp\n"
  },
  {
    "path": "image_generation_pytorch/IS/LICENSE.md",
    "content": "Copyright 2017 Shane T. Barratt\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
  },
  {
    "path": "image_generation_pytorch/IS/README.md",
    "content": "# Inception Score Pytorch\n\nPytorch was lacking code to calculate the Inception Score for GANs. This repository fills this gap.\nHowever, we do not recommend using the Inception Score to evaluate generative models, see [our note](https://arxiv.org/abs/1801.01973) for why.\n\n## Getting Started\n\nClone the repository and navigate to it:\n```\n$ git clone git@github.com:sbarratt/inception-score-pytorch.git\n$ cd inception-score-pytorch\n```\n\nTo generate random 64x64 images and calculate the inception score, do the following:\n```\n$ python inception_score.py\n```\n\nThe only function is `inception_score`. It takes a list of numpy images normalized to the range [0,1] and a set of arguments and then calculates the inception score. Please assure your images are 299x299x3 and if not (e.g. your GAN was trained on CIFAR), pass `resize=True` to the function to have it automatically resize using bilinear interpolation before passing the images to the inception network.\n\n```python\ndef inception_score(imgs, cuda=True, batch_size=32, resize=False):\n    \"\"\"Computes the inception score of the generated images imgs\n\n    imgs -- list of (HxWx3) numpy images normalized in the range [0,1]\n    cuda -- whether or not to run on GPU\n    batch_size -- batch size to feed into inception\n    \"\"\"\n```\n\n### Prerequisites\n\nYou will need [torch](http://pytorch.org/), [torchvision](https://github.com/pytorch/vision), [numpy/scipy](https://scipy.org/).\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details\n\n## Acknowledgments\n\n* Inception Score from [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498)\n"
  },
  {
    "path": "image_generation_pytorch/IS/inception_score.py",
    "content": "import torch\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nimport torch.utils.data\n\nfrom torchvision.models.inception import inception_v3\n\nimport numpy as np\nfrom scipy.stats import entropy\n\ndef inception_score(imgs, cuda=True, batch_size=1, resize=True, splits=1):\n    \"\"\"Computes the inception score of the generated images imgs\n\n    imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1]\n    cuda -- whether or not to run on GPU\n    batch_size -- batch size for feeding into Inception v3\n    splits -- number of splits\n    \"\"\"\n    N = len(imgs)\n\n    assert batch_size > 0\n    assert N > batch_size\n\n    # Set up dtype\n    if cuda:\n        dtype = torch.cuda.FloatTensor\n    else:\n        if torch.cuda.is_available():\n            print(\"WARNING: You have a CUDA device, so you should probably set cuda=True\")\n        dtype = torch.FloatTensor\n\n    # Set up dataloader\n    # dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size)\n\n    # Load inception model\n    inception_model = inception_v3(pretrained=True, transform_input=False).type(dtype)\n    inception_model.eval()\n    # up = nn.functional.interpolate(size=(299, 299), mode='bilinear', align_corners=True).type(dtype)\n    def get_pred(x):\n        if resize:\n            # x = up(x)\n            x = nn.functional.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True).type(dtype)\n        x = inception_model(x)\n        return F.softmax(x, dim=1).data.cpu().numpy()\n\n    # Get predictions\n    preds = np.zeros((N, 1000))\n\n    # for i, batch in enumerate(dataloader, 0):\n    # for i, batch in enumerate(imgs):\n    for i in range(N//batch_size):\n        batch = imgs[batch_size*i:batch_size*(i+1), :, :, :]\n        batch = torch.from_numpy(batch)\n        batch = batch.type(dtype)\n        batchv = Variable(batch)\n        # batchv = batchv.unsqueeze(0)\n        batch_size_i = batch.size()[0]\n        preds[i*batch_size:i*batch_size + batch_size_i] = get_pred(batchv)\n\n    # Now compute the mean kl-div\n    split_scores = []\n\n    for k in range(splits):\n        part = preds[k * (N // splits): (k+1) * (N // splits), :]\n        py = np.mean(part, axis=0)\n        scores = []\n        for i in range(part.shape[0]):\n            pyx = part[i, :]\n            scores.append(entropy(pyx, py))\n        split_scores.append(np.exp(np.mean(scores)))\n\n    return np.mean(split_scores), np.std(split_scores)\n"
  },
  {
    "path": "image_generation_pytorch/README.rst",
    "content": "=======================================\nΠ-nets: Deep Polynomial Neural Networks\n=======================================\n\nPytorch implementation of the image generation as described on the CVPR'20 paper \"**Π-nets: Deep Polynomial Neural Networks**\".\n\nSpecifically, we include the code for image generation on Cifar10 images.\n\n\n\nBrowsing the folders\n====================\nThe folder structure is the following:\n\n*    ``data``: The folder that contains the data for Cifar10. If it has not been downloaded and extracted already, it will be downloaded during the first run.\n\n*    ``FID``: The folder that contains the core code for computing the FID scores.\n\n*    ``IS``: The folder that contains the core code for computing the IS scores.\n\n\nTrain the network\n=================\n\nFirst download the following file:\n\nhttps://www.doc.ic.ac.uk/~sm3515/cifar_10_stats.pkl\n\nWe will need this file to calculate the FID score for Cifar10. We have pre-computed the first/second order statistics for Cifar10 database to expedite the training process (so that we do not need to process and extract statistics from the Cifar10 images each time we need to compute the FID score during the validation).\n\nTo train the network, you can execute the following command:\n\n   python main.py\n\nThe file main.py contains many hyper-parameters which can be either passed as arguments upon the execution of the command above or you can edit the file itself with the hyper-parameters of your liking. If you will be using the default hyper-parameters, the experiments will be saved in the folder dcgan_inject, in the current working directory.\n\n\n\nRequirements\n============\n\nThe code was tested on a Linux machine and requires the following packages/versions to run.\n\n* python 3.5 with cuda toolkit and cuda  10 (if you intend on running on a GPU) \n\n* tensorflow 1.15\n\n* pytorch 1.2\n\n* torchvision 0.4\n\n* scipy 1.1.0\n\n\nSnippets of the code are borrowed by the official pytorch DCGAN implementation [1].\n\nThe codes for the IS and FID scores are borrowed from [2] and [3], respectively. We should point out that the FID/IS scores that are mentioned in the paper may be slightly different to the ones calculated here since for the paper scores, after having extracted the best network, we run the official Tensorflow FID/IS scores implementations and report those, in order to be consistent throughout the whole set of experiments and frameworks (chainer, pytorch, etc.).\n\nReferences\n==========\n\n.. [1] https://github.com/pytorch/examples/tree/master/dcgan\n\n.. [2] https://github.com/sbarratt/inception-score-pytorch \n\n.. [3] https://github.com/mseitzer/pytorch-fid\n \n\n"
  },
  {
    "path": "image_generation_pytorch/data_loader.py",
    "content": "import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\n\ndef get_loader(data_path, batch_size, mode, num_workers=4):\n\n    transform = transforms.Compose(\n    [transforms.ToTensor(),\n     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n\n    if mode == 'train':\n        dataset = torchvision.datasets.CIFAR10(root=data_path, train=True,\n                                            download=True, transform=transform)\n        dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n                                              shuffle=True, num_workers=num_workers)\n\n    else:\n        dataset = torchvision.datasets.CIFAR10(root=data_path, train=False,\n                                               download=True, transform=transform)\n        dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n                                                 shuffle=False, num_workers=num_workers)\n\n    return dataloader\n"
  },
  {
    "path": "image_generation_pytorch/logger.py",
    "content": "# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514\nimport tensorflow as tf\nimport numpy as np\nimport scipy.misc \ntry:\n    from StringIO import StringIO  # Python 2.7\nexcept ImportError:\n    from io import BytesIO         # Python 3.x\n\n\nclass Logger(object):\n    \n    def __init__(self, log_dir):\n        \"\"\"Create a summary writer logging to log_dir.\"\"\"\n        self.writer = tf.summary.FileWriter(log_dir)\n\n    def scalar_summary(self, tag, value, step):\n        \"\"\"Log a scalar variable.\"\"\"\n        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])\n        self.writer.add_summary(summary, step)\n\n    def image_summary(self, tag, images, step):\n        \"\"\"Log a list of images.\"\"\"\n\n        img_summaries = []\n        for i, img in enumerate(images):\n            # Write the image to a string\n            try:\n                s = StringIO()\n            except:\n                s = BytesIO()\n            scipy.misc.toimage(img).save(s, format=\"png\")\n\n            # Create an Image object\n            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),\n                                       height=img.shape[0],\n                                       width=img.shape[1])\n            # Create a Summary value\n            img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))\n\n        # Create and write Summary\n        summary = tf.Summary(value=img_summaries)\n        self.writer.add_summary(summary, step)\n        \n    def histo_summary(self, tag, values, step, bins=1000):\n        \"\"\"Log a histogram of the tensor of values.\"\"\"\n\n        # Create a histogram using numpy\n        counts, bin_edges = np.histogram(values, bins=bins)\n\n        # Fill the fields of the histogram proto\n        hist = tf.HistogramProto()\n        hist.min = float(np.min(values))\n        hist.max = float(np.max(values))\n        hist.num = int(np.prod(values.shape))\n        hist.sum = float(np.sum(values))\n        hist.sum_squares = float(np.sum(values**2))\n\n        # Drop the start of the first bin\n        bin_edges = bin_edges[1:]\n\n        # Add bin edges and counts\n        for edge in bin_edges:\n            hist.bucket_limit.append(edge)\n        for c in counts:\n            hist.bucket.append(c)\n\n        # Create and write Summary\n        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])\n        self.writer.add_summary(summary, step)\n        self.writer.flush()\n"
  },
  {
    "path": "image_generation_pytorch/main.py",
    "content": "import argparse\nimport os\nfrom solver import Solver\nfrom data_loader import get_loader\nfrom torch.backends import cudnn\nfrom datetime import datetime\nimport json\n\ndef save_config(config, shared_path):\n    save_path = os.path.join(shared_path, \"params.json\")\n\n    with open(save_path, 'w') as fp:\n        json.dump(config.__dict__, fp, indent=4, sort_keys=True)\n\n\ndef str2bool(v):\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef get_time():\n    return datetime.now().strftime(\"%d%m_%H%M%S\")\n\n\ndef main(config):\n    cudnn.benchmark = True\n\n    config.time_now = get_time()\n\n    if config.mode == 'train':\n        data_loader = get_loader(data_path=config.data_path,\n                                 batch_size=config.batch_size,\n                                 mode=config.mode,\n                                 num_workers=config.num_workers)\n\n\n        shared_path = '{}{}'.format(config.base_path, config.time_now)\n\n        config.base_path = shared_path\n        config.model_path = \"{}/{}\".format(shared_path, config.model_path)\n        config.sample_path = \"{}/{}\".format(shared_path, config.sample_path)\n        config.logs_path = \"{}/{}\".format(shared_path, config.logs_path)\n        config.validation_path = \"{}/{}\".format(shared_path, config.validation_path)\n        \n        if not os.path.exists(config.model_path):\n            os.makedirs(config.model_path)\n\n        if not os.path.exists(config.sample_path):\n            os.makedirs(config.sample_path)\n\n        if not os.path.exists(config.logs_path):\n            os.makedirs(config.logs_path)\n\n        if not os.path.exists(config.validation_path):\n            os.makedirs(config.validation_path)\n\n        save_config(config, shared_path)\n\n    else:\n        data_loader = None\n\n\n    # for i in range(400, 705, 100):\n\n        # config.seed = i\n    solver = Solver(config, data_loader)\n\n    if config.mode == 'train':\n        solver.train()\n    elif config.mode == 'test' or config.mode == 'sample':\n        data_loader = None\n        if config.mode == 'test':\n            solver.test()\n        else:\n            solver.sample(config.n_samples)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    # training hyper-parameters\n    parser.add_argument('--num_epochs', type=int, default=100)\n    parser.add_argument('--batch_size', type=int, default=64)\n    parser.add_argument('--sample_size', type=int, default=100) \n    parser.add_argument('--num_workers', type=int, default=16)\n    parser.add_argument('--lr', type=float, default=0.0002)\n    parser.add_argument('--beta1', type=float, default=0.5)\n    parser.add_argument('--beta2', type=float, default=0.99)\n    parser.add_argument('--g_layers', type=int, nargs='+', default=[100, 512, 256, 128, 64, 3])  # network structure of the Generator. First layer (100) is the size of the noise, the last layer (3) is the output size (RGB image in our case)\n    parser.add_argument('--d_layers', type=int, nargs='+', default=[3, 64, 128, 256, 512, 1])  # network structure of the Discriminator. First layer (3) is the input size (RGB image in our case) and the last layer (1) corresponds to the output\n    parser.add_argument('--activation_fn', type=str2bool, default=True)\n    parser.add_argument('--inject_z', type=str2bool, default=True)\n    parser.add_argument('--concat_injection', type=str2bool, default=False)  # whether the injection will be concatenated or multiplied\n    parser.add_argument('--loss', type=str, default='original')  # can be changed to Wassterstein GAN with GP. Just put 'wgan-gp'\n    parser.add_argument('--gp_weight', type=float, default=10)\n    parser.add_argument('--seed', type=int, default=0)\n    parser.add_argument('--pc_name', type=str, default='neumann')\n    parser.add_argument('--spectral_norm', type=str2bool, default=False)\n    parser.add_argument('--transform_z', type=str2bool, default=False)  # whether to have an MLP before injection\n    parser.add_argument('--transform_rep', type=int, default=1)  # if transform_z is ``True'', how many MLPs to have in a row before injection\n    parser.add_argument('--norm', type=str, default='batch')  # can be changed to ``instance'' to activate the instance normalization\n\n    # misc\n    parser.add_argument('--mode', type=str, default='train', choices=['train', 'sample'])\n    parser.add_argument('--model_path', type=str, default='models')\n    parser.add_argument('--sample_path', type=str, default='samples')\n    parser.add_argument('--logs_path', type=str, default=\"logs\")\n    parser.add_argument('--data_path', type=str, default='./data')\n    parser.add_argument('--base_path', type=str, default='./dcgan_inject/')  # where to save the experiment results\n    parser.add_argument('--ckpt_gen_path', type=str, default='')\n    parser.add_argument('--log_step', type=int, default=50)\n    parser.add_argument('--sample_step', type=int, default=350)\n    parser.add_argument('--validation_step', type=int, default=350)\n    parser.add_argument('--validation_path', type=str, default=\"validation\")\n    parser.add_argument('--cifar10_path', type=str, default='./cifar_10_stats.pkl')  # the path for the cifar_10_stats.pkl file\n    parser.add_argument('--FID_images', type=int, default=2048)\n    parser.add_argument('--num_imgs_val', type=int, default=2048)\n    parser.add_argument('--max_score', type=float, default=0)\n    parser.add_argument('--save_every', type=int, default=5)\n    parser.add_argument('--n_samples', type=int, default=100000)  # number of samples to generate under the ``sample'' scenario\n    config = parser.parse_args()\n    print(config)\n    main(config)\n"
  },
  {
    "path": "image_generation_pytorch/model.py",
    "content": "import torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.utils import spectral_norm as SN\nfrom numpy import sqrt\nimport torch\nfrom torch.nn.functional import linear  \n\n\nclass Generator(nn.Module):\n    def __init__(self, g_layers=[], activation_fn=True, inject_z=True, transform_rep=6, transform_z=False, concat_injection=False, norm='instance'):\n        super(Generator, self).__init__()\n        self.activation_fn = activation_fn\n        self.g_layers = g_layers\n        self.inject_z = inject_z\n        self.num_layers = len(self.g_layers) - 1  # minus the input/output sizes \n        self.transform_rep = transform_rep\n        self.transform_z = transform_z\n        self.concat_injection = concat_injection\n        self.norm = norm\n\n        if self.transform_z:\n            for i in range(self.transform_rep):\n                setattr(self, \"global{}\".format(i), nn.Sequential(\n                                                        nn.Linear(self.g_layers[0], self.g_layers[0]),\n                                                        nn.ReLU()))\n\n        for i in range(self.num_layers):\n                if i == 0:\n                    if not self.activation_fn:\n                        if self.norm == 'instance':\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False),\n                                                                    nn.InstanceNorm2d(self.g_layers[i+1])))  \n                        else:\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False),\n                                                                    nn.BatchNorm2d(self.g_layers[i+1])))                              \n                    else:\n                        if self.norm == 'instance':\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), \n                                                                    nn.InstanceNorm2d(self.g_layers[i+1]),\n                                                                    nn.ReLU()))\n                        else:\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), \n                                                                    nn.BatchNorm2d(self.g_layers[i+1]),\n                                                                    nn.ReLU()))\n\n                elif i == self.num_layers - 1:\n                    if not self.activation_fn:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 3, 1, 1, bias=False),\n                                                                nn.Tanh()))\n                    else:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 3, 1, 1, bias=False),\n                                                                nn.Tanh())) \n                else:\n                    if self.inject_z:\n                        if not self.activation_fn:\n                            setattr(self, \"inject{}\".format(i), nn.Linear(self.g_layers[0], self.g_layers[i]))\n                        else:\n                            setattr(self, \"inject{}\".format(i), nn.Sequential(\n                                                                    nn.Linear(self.g_layers[0], self.g_layers[i]),\n                                                                    nn.ReLU()))                          \n                    if not self.activation_fn:\n                        if self.norm == 'instance':\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False),\n                                                                    nn.InstanceNorm2d(self.g_layers[i+1])))\n                        else:\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False),\n                                                                    nn.BatchNorm2d(self.g_layers[i+1])))                            \n                        if self.concat_injection:\n                            if self.norm == 'instance':\n                                setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False),\n                                                                    nn.InstanceNorm2d(self.g_layers[i+1])))\n                            else:\n                                setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False),\n                                                                    nn.BatchNorm2d(self.g_layers[i+1])))                                \n                    else:\n                        if self.norm == 'instance':\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), \n                                                                    nn.InstanceNorm2d(self.g_layers[i+1]),\n                                                                    nn.ReLU()))\n                        else:\n                            setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                    nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), \n                                                                    nn.BatchNorm2d(self.g_layers[i+1]),\n                                                                    nn.ReLU()))                            \n                        if self.concat_injection:\n                            if self.norm == 'instance':\n                                setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                        nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), \n                                                                        nn.InstanceNorm2d(self.g_layers[i+1]),\n                                                                        nn.ReLU())) \n                            else:\n                                setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                        nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), \n                                                                        nn.BatchNorm2d(self.g_layers[i+1]),\n                                                                        nn.ReLU()))                                   \n\n    def forward(self, x):\n        z = x.squeeze(3).squeeze(2)\n        if self.transform_z:\n            for i in range(self.transform_rep):\n                z = getattr(self, \"global{}\".format(i))(z)\n        x = z.unsqueeze(2).unsqueeze(3)\n        for i in range(self.num_layers):\n            if self.inject_z:\n                if i > 0 and i < self.num_layers - 1:\n                    a = getattr(self, \"inject{}\".format(i))(z)\n                    a = a.unsqueeze(2).unsqueeze(3).expand(x.size(0), x.size(1), x.size(2), x.size(3))\n                    if self.concat_injection:\n                        x = torch.cat((x, a), dim=1)\n                    else:\n                        x *= a \n            x = getattr(self, \"layer{}\".format(i))(x)\n        return x\n\n\nclass Discriminator(nn.Module):\n    def __init__(self, d_layers=[], activation_fn=True, spectral_norm=False):\n        super(Discriminator, self).__init__()\n        self.activation_fn = activation_fn\n        self.d_layers = d_layers\n        self.num_layers = len(self.d_layers) - 1  # minus the input/output sizes \n        for i in range(self.num_layers):\n            if i == 0:\n                if not spectral_norm:\n                    if not self.activation_fn:\n                        setattr(self, \"layer{}\".format(i), nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False))\n                    else:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False),\n                                                                nn.LeakyReLU(0.2, inplace=True)))\n                else:\n                    if not self.activation_fn:\n                        setattr(self, \"layer{}\".format(i), spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)))\n                    else:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)),\n                                                                nn.LeakyReLU(0.2, inplace=True)))\n            elif i == self.num_layers - 1:\n                if not spectral_norm:\n                    setattr(self, \"layer{}\".format(i), nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False))           \n                else:\n                    setattr(self, \"layer{}\".format(i), spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)))                                            \n            else:\n                if not spectral_norm:\n                    if not self.activation_fn:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False),\n                                                                nn.BatchNorm2d(self.d_layers[i+1])))     \n                    else:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False),\n                                                                nn.BatchNorm2d(self.d_layers[i+1]),\n                                                                nn.LeakyReLU(0.2, inplace=True)))                             \n                else:\n                    if not self.activation_fn:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)),\n                                                                nn.BatchNorm2d(self.d_layers[i+1])))   \n                    else:\n                        setattr(self, \"layer{}\".format(i), nn.Sequential(\n                                                                nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False),\n                                                                nn.BatchNorm2d(self.d_layers[i+1]),\n                                                                nn.LeakyReLU(0.2, inplace=True)))         \n\n    def forward(self, x):\n        for i in range(self.num_layers):\n            x = getattr(self, \"layer{}\".format(i))(x)\n        return x\n"
  },
  {
    "path": "image_generation_pytorch/solver.py",
    "content": "import torch\nimport torch.nn as nn\nimport os\nfrom torch import optim\nfrom torch.autograd import Variable\nfrom model import Discriminator\nfrom model import Generator\nfrom logger import Logger\nimport numpy as np\nfrom torchvision.utils import save_image\nfrom torch.autograd import grad as torch_grad\nfrom collections import OrderedDict\nfrom IS.inception_score import inception_score as IS \ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\nimport json\nfrom FID.fid_score import calculate_fid_given_paths as FID\n\ndef save_is(base_path, score):\n    save_file = os.path.join(base_path, \"scores.json\")\n\n    with open(save_file, 'a+') as fp:\n        json.dump(score, fp, indent=4)\n        fp.write(\"\\n\")\n        fp.close()\n\n\ndef denorm(x):\n    x = 0.5 * (x + 1)\n    x = x.clamp(0, 1)\n    return x\n\n\ndef to_cuda(x):\n    if torch.cuda.is_available():\n        x = x.cuda()\n    return x\n\n\ndef to_numpy(x):\n    if torch.cuda.is_available():\n        x = x.data.cpu()\n    return x.numpy()\n\n\nclass Solver(object):\n    def __init__(self, config, data_loader):\n        self.generator = None\n        self.discriminator = None\n        self.g_optimizer = None\n        self.d_optimizer = None\n        self.pc_name = config.pc_name\n        self.base_path = config.base_path\n        self.time_now = config.time_now\n        self.inject_z = config.inject_z\n        self.data_loader = data_loader\n        self.num_epochs = config.num_epochs\n        self.sample_size = config.sample_size\n        self.logs_path = config.logs_path\n        self.save_every = config.save_every\n        self.activation_fn = config.activation_fn\n        self.max_score = config.max_score\n        self.lr = config.lr\n        self.beta1 = config.beta1\n        self.beta2 = config.beta2\n        self.log_step = config.log_step\n        self.sample_step = config.sample_step\n        self.validation_step = config.validation_step\n        self.sample_path = config.sample_path\n        self.model_path = config.model_path\n        self.g_layers = config.g_layers\n        self.d_layers = config.d_layers\n        self.z_dim = self.g_layers[0]\n        self.num_imgs_val = config.num_imgs_val\n        self.criterion = nn.BCEWithLogitsLoss()\n        self.ckpt_gen_path = config.ckpt_gen_path\n        self.gp_weight = config.gp_weight\n        self.loss = config.loss\n        self.seed = config.seed\n        self.validation_path = config.validation_path\n        self.FID_images = config.FID_images\n        self.transform_rep = config.transform_rep\n        self.transform_z = config.transform_z\n        self.spectral_norm = config.spectral_norm\n        self.cifar10_path = config.cifar10_path\n        self.fid_score = 100000\n        self.concat_injection = config.concat_injection\n        self.norm = config.norm\n        self.build_model()\n\n    def build_model(self):\n        torch.manual_seed(self.seed)\n        self.generator = Generator(g_layers=self.g_layers, activation_fn=self.activation_fn, inject_z=self.inject_z, transform_rep=self.transform_rep, transform_z=self.transform_z, concat_injection=self.concat_injection, norm=self.norm)\n        self.discriminator = Discriminator(d_layers=self.d_layers, activation_fn=self.activation_fn, spectral_norm=self.spectral_norm)\n        self.generator.apply(self.weights_init)\n        self.discriminator.apply(self.weights_init)\n        self.g_optimizer = optim.Adam(self.generator.parameters(), self.lr, betas=(self.beta1, self.beta2))\n        self.d_optimizer = optim.Adam(self.discriminator.parameters(), self.lr, betas=(self.beta1, self.beta2))\n        self.logger = Logger(self.logs_path)\n\n\n        self.gen_params = sum(p.numel() for p in self.generator.parameters() if p.requires_grad)\n        self.disc_params = sum(p.numel() for p in self.discriminator.parameters() if p.requires_grad)\n        self.total_params = self.gen_params + self.disc_params\n\n        print(\"Generator params: {}\".format(self.gen_params))\n        print(\"Discrimintor params: {}\".format(self.disc_params))\n        print(\"Total params: {}\".format(self.total_params))\n\n        if torch.cuda.is_available():\n            self.generator.cuda()\n            self.discriminator.cuda()\n\n    def reset_grad(self):\n        self.discriminator.zero_grad()\n        self.generator.zero_grad()\n\n    # custom weights initialization called on netG and netD\n    def weights_init(self, m):\n        classname = m.__class__.__name__\n        if classname.find('Conv') != -1:\n            m.weight.data.normal_(0.0, 0.02)\n        elif classname.find('BatchNorm') != -1:\n            m.weight.data.normal_(1.0, 0.02)\n            m.bias.data.fill_(0)\n\n    def gradient_penalty(self, real_data, generated_data):\n        batch_size = real_data.size()[0]\n\n        # Calculate interpolation\n        alpha = torch.rand(batch_size, 1)\n        alpha = alpha.expand_as(real_data)\n        alpha = alpha.cuda()\n        interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data\n        interpolated = Variable(interpolated, requires_grad=True)\n        interpolated = interpolated.cuda()\n\n        # Calculate probability of interpolated examples\n        prob_interpolated = self.discriminator(interpolated)\n\n        # Calculate gradients of probabilities with respect to examples\n        gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated,\n                               grad_outputs=torch.ones(prob_interpolated.size()).cuda(),\n                               create_graph=True, retain_graph=True)[0]\n\n        # Gradients have shape (batch_size, num_channels, height, width),\n        # so flatten to easily take norm per example in batch\n        gradients = gradients.view(batch_size, -1)\n\n        # Derivatives of the gradient close to 0 can cause problems because of\n        # the square root, so manually calculate norm and add epsilon\n        gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12)\n\n        # Return gradient penalty\n        return self.gp_weight*((gradients_norm - 1) ** 2).mean()\n\n    def train(self):\n        total_step = len(self.data_loader)\n        for epoch in range(self.num_epochs):\n            for i, (data, _) in enumerate(self.data_loader):\n\n                batch_size = data.size(0)\n                # train Discriminator\n                data = data.type(torch.FloatTensor)\n                data = to_cuda(data)\n\n                real_labels = to_cuda(torch.ones(batch_size, self.d_layers[-1]))\n                fake_labels = to_cuda(torch.zeros(batch_size, self.d_layers[-1]))\n\n                outputs_real = self.discriminator(data)\n                z = to_cuda(torch.randn(batch_size, self.z_dim, 1, 1))\n                fake_data = self.generator(z)\n                outputs_fake = self.discriminator(fake_data)\n\n                if self.loss == 'original':\n                    d_loss_real = self.criterion(outputs_real.squeeze(), real_labels.squeeze())\n                    d_loss_fake = self.criterion(outputs_fake.squeeze(), fake_labels.squeeze())\n                    d_loss = d_loss_real + d_loss_fake\n\n                elif self.loss == 'wgan-gp':\n                    gradient_penalty = self.gradient_penalty(data, fake_data)\n                    d_loss = - outputs_real.mean() + outputs_fake.mean() + gradient_penalty\n\n                self.reset_grad()\n                d_loss.backward()\n                self.d_optimizer.step()\n\n                # train Generator\n                z = to_cuda(torch.randn(batch_size, self.z_dim, 1, 1))\n                fake_data = self.generator(z)\n                outputs_fake = self.discriminator(fake_data)\n\n                if self.loss == 'original':\n                    g_loss = self.criterion(outputs_fake.squeeze(), real_labels.squeeze())\n                elif self.loss == 'wgan-gp':\n                    g_loss = -outputs_fake.mean()\n\n                self.reset_grad()\n                g_loss.backward()\n                self.g_optimizer.step()\n\n                if (i+1) % self.log_step == 0:\n                    print('Epoch [{0:d}/{1:d}], Step [{2:d}/{3:d}], d_real_loss: {4:.4f}, '\n                          ' g_loss: {5:.4f}'.format(epoch+1, self.num_epochs, i+1, total_step, d_loss.item(),\n                                                   g_loss.item()))\n\n                    # log scalars in tensorboard\n                    info = {\n                        'd_real_loss': d_loss.item(),\n                        'g_loss': g_loss.item(),\n                        'inception_score': self.max_score\n                    }\n\n                    for tag, value in info.items():\n                        self.logger.scalar_summary(tag, value, epoch*total_step + i + 1)\n\n                if (i + 1) % self.sample_step == 0:\n                    save_image(denorm(fake_data).cpu(), self.sample_path + \"/epoch_{}_{}.png\".format(i + 1, epoch + 1), nrow=8)\n\n                if (i + 1) % self.validation_step == 0:\n                    fake_data_all = np.zeros((self.num_imgs_val, fake_data.size(1), fake_data.size(2), fake_data.size(3)))\n                    for j in range(self.num_imgs_val // batch_size):\n                        fake_data_all[j*batch_size:(j+1)*batch_size] = to_numpy(fake_data)\n                    npy_path = os.path.join(self.model_path, '{}_{}_val_data.pkl'.format(epoch + 1, i + 1))\n                    np.save(npy_path, fake_data_all)\n                    score, _ = IS(fake_data_all, cuda=True, batch_size=batch_size)\n                    if score > self.max_score:\n                        print(\"Found new best IS score: {}\".format(score))\n                        self.max_score = score\n                        data = \"IS \" + str(self.seed) + \" \" + str(epoch + 1) + \" \" + str(i + 1) + \" \" + str(self.max_score)\n                        save_is(self.base_path, data)\n                        g_path = os.path.join(self.model_path, 'generator-best.pkl')\n                        d_path = os.path.join(self.model_path, 'discriminator-best.pkl')\n                        torch.save(self.generator.state_dict(), g_path)\n                        torch.save(self.discriminator.state_dict(), d_path)\n                    for j in range(self.FID_images):\n                        z = to_cuda(torch.randn(1, self.z_dim, 1, 1))\n                        fake_datum = self.generator(z)\n                        save_image(denorm(fake_datum.squeeze()).cpu(), self.validation_path + \"/\" + str(j) + \".png\")\n                    fid_value = FID([self.validation_path, self.cifar10_path], 64, True, 2048)\n                    if fid_value < self.fid_score:\n                        self.fid_score = fid_value\n                        print(\"Found new best FID score: {}\".format(self.fid_score))\n                        data = \"FID \" + str(self.seed) + \" \" + str(epoch + 1) + \" \" + str(i + 1) + \" \" + str(self.fid_score)\n                        save_is(self.base_path, data)\n                        g_path = os.path.join(self.model_path, 'generator-best-fid.pkl')\n                        d_path = os.path.join(self.model_path, 'discriminator-best-fid.pkl')\n                        torch.save(self.generator.state_dict(), g_path)\n                        torch.save(self.discriminator.state_dict(), d_path)                        \n\n            if (epoch + 1) % self.save_every == 0:\n                g_path = os.path.join(self.model_path, 'generator-{}.pkl'.format(epoch+1))\n                d_path = os.path.join(self.model_path, 'discriminator-{}.pkl'.format(epoch+1))\n                torch.save(self.generator.state_dict(), g_path)\n                torch.save(self.discriminator.state_dict(), d_path)\n\n\n    def sample(self, n_samples):\n        self.n_samples = n_samples\n        self.generator = Generator(g_layers=self.g_layers, inject_z=self.inject_z)\n        self.generator.load_state_dict(torch.load(self.ckpt_gen_path))\n        if torch.cuda.is_available():\n            self.generator.cuda()\n        self.generator.eval()\n\n        z_samples = to_cuda(torch.randn(n_samples, self.z_dim, 1, 1))\n        generated_samples = self.generator(z_samples)\n        generated_samples = to_numpy(generated_samples)\n        np.save('./saved/generated_samples.npy', generated_samples)\n        z_samples = to_numpy(z_samples)\n        np.save('./saved/z_samples.npy', z_samples)\n"
  },
  {
    "path": "mesh_pi_nets/.gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n.idea/\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n*.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n#   However, in case of collaboration, if having platform-specific dependencies or dependencies\n#   having no cross-platform support, pipenv may install dependencies that don't work, or not\n#   install all needed dependencies.\n#Pipfile.lock\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule\ncelerybeat.pid\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n"
  },
  {
    "path": "mesh_pi_nets/README.md",
    "content": "Official Implementation of the experiments on mesh representation learning with polynomial networks as described in the CVPR'20 paper [Π-nets: Deep Polynomial Neural Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf) and its [extension](https://arxiv.org/abs/2006.13026).\n\nThis repository is based on Neural3DMM [[paper](https://arxiv.org/abs/1905.02876), [code](https://github.com/gbouritsas/Neural3DMM)]. Please refer to the Neural3DMM repository for technical details (e.g. regarding the local vertex orderings or the mesh downsampling operations) and for explanations of the data organisation/folder hierarchy.\n\n\n## Data\n\nWe provide the templates (mesh topology) as well as their downsampled versions for the two datasets that we experimented with, namely [COMA](https://coma.is.tue.mpg.de/) and [DFAUST](http://dfaust.is.tue.mpg.de/). Unfortunately, we cannot provide the data in this repository, but they can be easily obtained from the authors of the datasets. Then, follow the instructions in the [Neural3DMM repo](https://github.com/gbouritsas/Neural3DMM) to organise the data as expected from the code. For COMA we use the splits provided by the authors (in the paper we report the results on the 'sliced' version of the dataset - interpolation experiment). If you would like to train on the same splits for DFAUST, please contact us.\n\n## Important parameters\n\n```\n  --injection: if set to True runs the polynomial version of SpiralNet. Alternatively, plain SpiralNet is created.\n  --order: the order of the polynomial\n  --model:'full' creates the polynomial that NCP decomposition yields, \n          'simple' creates the parameter efficient polynomial (element-wise multiplications akin to polynomial activation functions)\n  --normalize: 'final' (only the highest order term is normalised),\n                '2nd' (normalises the 2nd order term of each iterate of the NCP recursive formulation), \n                'all' (normalises the output of each iterate of the NCP recursive formulation)\n  --residual: if True, it invokes the NCP-skip decomposition\n  --activation: if set to identity, no activation functions are used (linear w.r.t. weights)\n```\n\n## Training\n\nExample usage:\n\n**COMA**\n```\npython spiral_pi_nets.py --root_dir ./datasets --name sliced --dataset COMA --mode train --order 3 --model full  --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 \n```\n\n**DFAUST**\n```\npython spiral_pi_nets.py --root_dir ./datasets --dataset DFAUST --mode train --order 3 --model full  --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 \n```\n\n## Testing\n\nAssign to the _results_folder_ argument the value of the folder where the pretrained network is saved. By setting the _mm_constant_ you can obtain measurements in milimiters. For COMA and DFAUST this value is 10^3. Example usage:\n\n**COMA**\n```\npython spiral_pi_nets.py --root_dir ./datasets --name sliced --dataset COMA --mode test --order 3 --model full  --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 --mm_constant 1000\n```\n\n**DFAUST**\n```\npython spiral_pi_nets.py --root_dir ./datasets --dataset DFAUST --mode test --order 3 --model full  --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 --mm_constant 1000\n```\n\n\n## Dependencies and Installation Instructions\n\nRequirements:\n- python 3.7\n- pytorch>=1.2.0\n- cudatoolkit>=9.2\n- scipy\n- trimesh\n- sklearn\n- tqdm\n- tensorboardX\n\nRecommended installation instructions\n```\nconda create --name pi_spirals python=3.7\nconda activate pi_spirals\nconda install pytorch==1.2.0 cudatoolkit=9.2 -c pytorch\npip install scipy\npip install trimesh\npip install sklearn\npip install tqdm\npip install tensorboardX\n```\n\n"
  },
  {
    "path": "mesh_pi_nets/autoencoder_dataset.py",
    "content": "from torch.utils.data import Dataset\nimport torch\nimport numpy as np\nimport os\n\n\nclass autoencoder_dataset(Dataset):\n\n    def __init__(self, root_dir, points_dataset, shapedata, normalization=True, dummy_node=True):\n        \n        self.shapedata = shapedata\n        self.normalization = normalization\n        self.root_dir = root_dir\n        self.points_dataset = points_dataset\n        self.dummy_node = dummy_node\n        self.paths = np.load(os.path.join(root_dir, 'paths_'+points_dataset+'.npy'))\n\n    def __len__(self):\n        return len(self.paths)\n\n    def __getitem__(self, idx):\n        basename = self.paths[idx]\n        \n        verts_init = np.load(os.path.join(self.root_dir, 'points'+'_'+self.points_dataset, basename+'.npy'))\n        if self.normalization:\n            verts_init = verts_init - self.shapedata.mean\n            verts_init = verts_init/self.shapedata.std\n        verts_init[np.where(np.isnan(verts_init))]=0.0\n        \n        verts_init = verts_init.astype('float32')\n        if self.dummy_node:\n            verts = np.zeros((verts_init.shape[0]+1, verts_init.shape[1]), dtype=np.float32)\n            verts[:-1, :] = verts_init\n            verts_init = verts\n        verts = torch.Tensor(verts_init)\n        \n\n        sample = {'points': verts}\n\n        return sample\n    \n  "
  },
  {
    "path": "mesh_pi_nets/datasets/COMA/templates/COMA_downsample/downsampling_matrices.pkl",
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29\nf 36 34 33\nf 37 39 38\nf 21 23 20\nf 25 27 24\nf 41 43 42\nf 48 46 45\nf 52 50 49\nf 53 55 54\nf 60 58 57\nf 29 62 61\nf 66 64 63\nf 67 6 68\nf 72 70 69\nf 76 74 73\nf 77 79 78\nf 81 83 82\nf 59 86 85\nf 88 90 89\nf 96 94 93\nf 100 98 97\nf 101 103 102\nf 106 34 87\nf 108 115 107\nf 117 36 118\nf 124 126 125\nf 131 129 128\nf 132 134 133\nf 142 140 139\nf 148 60 147\nf 144 127 124\nf 149 151 150\nf 156 154 153\nf 159 158 157\nf 116 164 105\nf 171 169 168\nf 174 173 172\nf 179 181 180\nf 186 188 187\nf 194 196 195\nf 199 139 198\nf 206 208 207\nf 213 211 210\nf 202 214 201\nf 96 216 54\nf 92 43 44\nf 97 246 100\nf 250 229 228\nf 252 58 253\nf 258 256 255\nf 260 229 259\nf 110 112 113\nf 114 263 109\nf 265 267 266\nf 56 270 269\nf 274 249 250\nf 185 63 64\nf 280 278 276\nf 285 283 157\nf 288 211 212\nf 141 92 46\nf 289 175 176\nf 148 105 106\nf 312 170 171\nf 238 42 43\nf 318 160 317\nf 310 308 307\nf 241 325 324\nf 160 240 317\nf 47 307 308\nf 108 315 164\nf 345 86 119\nf 348 350 349\nf 356 358 357\nf 355 353 352\nf 363 32 362\nf 149 150 176\nf 364 253 365\nf 366 368 367\nf 183 371 63\nf 163 161 160\nf 209 101 208\nf 378 165 166\nf 379 381 380\nf 383 3 4\nf 284 386 385\nf 387 389 388\nf 391 229 249\nf 73 397 396\nf 405 407 406\nf 203 411 410\nf 414 413 412\nf 1 357 4\nf 277 131 275\nf 254 57 58\nf 358 415 351\nf 417 386 408\nf 401 192 420\nf 419 127 143\nf 421 182 422\nf 103 302 409\nf 428 368 174\nf 380 359 356\nf 62 266 323\nf 140 431 430\nf 357 351 348\nf 356 379 380\nf 415 403 434\nf 437 436 435\nf 438 440 439\nf 188 450 449\nf 452 454 453\nf 265 181 421\nf 224 64 225\nf 237 213 427\nf 275 128 458\nf 281 460 459\nf 50 79 461\nf 73 282 281\nf 468 466 465\nf 390 352 359\nf 482 484 483\nf 489 487 486\nf 493 495 494\nf 472 470 469\nf 502 500 499\nf 220 222 223\nf 512 143 66\nf 518 511 80\nf 513 230 286\nf 205 31 244\nf 189 451 188\nf 163 532 8\nf 533 225 251\nf 446 447 412\nf 335 540 334\nf 544 546 545\nf 329 291 292\nf 199 262 238\nf 385 416 550\nf 532 191 247\nf 363 118 243\nf 236 237 250\nf 578 580 579\nf 573 571 570\nf 561 562 272\nf 241 259 240\nf 590 592 591\nf 545 595 594\nf 503 155 504\nf 606 624 623\nf 478 480 479\nf 646 648 647\nf 590 689 684\nf 564 651 650\nf 728 184 727\nf 714 716 715\nf 692 575 693\nf 541 581 578\nf 346 248 400\nf 161 230 160\nf 128 94 95\nf 698 704 703\nf 574 576 575\nf 290 401 328\nf 630 725 726\nf 733 735 734\nf 755 496 493\nf 759 481 760\nf 757 762 761\nf 764 466 467\nf 768 3808 3809\nf 759 488 489\nf 492 475 771\nf 474 769 473\nf 773 465 772\nf 771 486 483\nf 484 775 491\nf 774 489 486\nf 495 764 756\nf 495 761 494\nf 137 784 136\nf 325 797 547\nf 165 51 52\nf 369 29 370\nf 730 568 569\nf 90 301 89\nf 243 516 242\nf 800 806 805\nf 817 808 816\nf 816 823 822\nf 306 447 807\nf 815 809 808\nf 829 828 825\nf 726 501 502\nf 274 424 423\nf 879 881 880\nf 884 879 885\nf 219 217 211\nf 906 904 897\nf 455 205 542\nf 898 830 829\nf 903 880 881\nf 906 897 898\nf 377 519 165\nf 923 948 946\nf 946 951 950\nf 3519 739 3517\nf 900 886 880\nf 225 65 144\nf 182 442 422\nf 809 947 824\nf 954 962 961\nf 927 965 966\nf 885 880 886\nf 925 966 967\nf 343 341 340\nf 965 971 968\nf 968 970 969\nf 972 969 973\nf 963 981 978\nf 336 338 339\nf 992 827 828\nf 810 996 959\nf 1007 1002 1008\nf 1012 827 1009\nf 1011 1007 1008\nf 294 292 291\nf 907 920 919\nf 1017 1019 1018\nf 1020 1022 1021\nf 1035 1020 1044\nf 1061 1047 1046\nf 1063 1046 1062\nf 1044 1021 1045\nf 1102 1097 1109\nf 1116 1118 1117\nf 1016 1014 1013\nf 881 904 903\nf 816 1127 817\nf 1094 949 1097\nf 514 167 515\nf 146 81 82\nf 1133 1130 1129\nf 1102 1126 822\nf 822 1116 816\nf 1145 1148 1136\nf 1076 1151 1069\nf 1148 1086 1087\nf 1109 1114 1136\nf 1154 1156 1155\nf 1153 967 1156\nf 3464 463 3714\nf 1144 1152 1151\nf 1156 1163 1162\nf 1164 1169 1165\nf 1145 1176 1147\nf 1114 1154 1171\nf 1183 1164 1177\nf 1184 1177 1185\nf 1069 1062 1064\nf 1023 1194 1022\nf 1162 1170 1169\nf 1195 1185 1191\nf 1183 1176 1155\nf 1201 1152 1196\nf 1061 1201 1202\nf 3825 843 3836\nf 1203 1195 1034\nf 1202 1184 1195\nf 1218 1203 1217\nf 1060 1202 1203\nf 1225 1217 1219\nf 671 694 670\nf 1219 1024 1035\nf 1217 1034 1024\nf 1226 1015 1227\nf 1243 1234 1233\nf 1231 1229 1228\nf 847 1233 843\nf 843 1244 878\nf 1233 1245 1244\nf 1923 233 216\nf 2100 499 500\nf 3914 2135 3900\nf 61 323 505\nf 210 217 457\nf 1299 1309 1300\nf 3863 1231 3862\nf 1066 1321 1322\nf 1322 1228 1229\nf 1060 1321 1047\nf 1325 1323 1310\nf 1244 1332 1330\nf 1290 1242 1288\nf 1337 1339 1338\nf 1330 1340 1339\nf 1341 1245 1294\nf 1342 1332 1341\nf 1343 1341 1344\nf 1344 1294 1293\nf 3857 1345 1346\nf 1347 1342 1343\nf 3856 1295 1345\nf 1349 1348 1347\nf 3861 1347 3853\nf 3853 1343 3857\nf 906 829 1353\nf 905 1353 1354\nf 865 1355 1357\nf 1355 829 825\nf 1357 825 826\nf 1356 1353 1355\nf 3834 1338 3833\nf 866 1356 865\nf 1358 1354 1356\nf 3828 1337 3834\nf 838 1012 841\nf 1363 1338 1339\nf 1368 1339 1340\nf 1348 1340 1342\nf 1371 3824 3829\nf 524 526 523\nf 528 530 527\nf 1372 1369 1371\nf 535 537 538\nf 1367 1348 1350\nf 1370 1359 1369\nf 1369 3833 3824\nf 1373 903 904\nf 555 557 558\nf 1374 910 1373\nf 1373 905 1375\nf 1374 1375 1376\nf 1375 1354 1370\nf 1378 908 1377\nf 376 651 1387\nf 1388 973 969\nf 586 588 585\nf 1390 1388 1391\nf 1391 969 970\nf 597 599 596\nf 600 602 603\nf 1392 970 979\nf 608 610 607\nf 612 523 611\nf 614 616 613\nf 617 25 24\nf 620 622 619\nf 1397 1391 1392\nf 1398 1392 1399\nf 1400 920 1378\nf 1402 1165 1169\nf 636 638 635\nf 930 1402 929\nf 641 643 644\nf 929 1169 1170\nf 1404 1403 1402\nf 1411 1365 1406\nf 1412 1415 1414\nf 652 654 655\nf 609 657 658\nf 659 661 662\nf 558 586 585\nf 665 667 664\nf 1413 1406 1412\nf 1418 3810 3813\nf 1417 1371 1416\nf 1416 3829 3810\nf 1419 1416 1418\nf 1386 1374 1420\nf 677 679 676\nf 1385 1420 1421\nf 1420 1376 1422\nf 685 687 688\nf 1421 1422 1423\nf 528 691 658\nf 1422 1372 1417\nf 1018 747 1425\nf 699 701 702\nf 1424 579 1017\nf 3663 1767 3660\nf 700 711 712\nf 1093 3754 1571\nf 717 530 719\nf 721 723 720\nf 704 3543 3554\nf 19 1426 1430\nf 1429 1427 1426\nf 1658 3542 3544\nf 1439 1434 1438\nf 1437 1435 1434\nf 1426 1433 1432\nf 1442 1432 1440\nf 742 744 745\nf 1432 1441 1440\nf 751 753 750\nf 1438 1403 1405\nf 1446 1444 1443\nf 1447 1446 1443\nf 3860 1443 3848\nf 1415 1445 1446\nf 1414 1446 1448\nf 2173 469 470\nf 777 779 686\nf 780 782 783\nf 1676 1806 1675\nf 3859 1349 3861\nf 789 791 788\nf 745 793 742\nf 652 795 653\nf 1455 1457 1456\nf 1454 1350 1452\nf 610 802 801\nf 3831 698 703\nf 1352 1045 1351\nf 1015 1459 1460\nf 811 813 814\nf 2118 3817 2117\nf 819 821 818\nf 1466 1194 1190\nf 1467 1465 1466\nf 1182 1468 1467\nf 832 833 831\nf 835 837 834\nf 1467 1190 1182\nf 1469 1444 1445\nf 840 712 690\nf 1453 1469 1454\nf 599 845 846\nf 1226 3849 3855\nf 851 853 850\nf 855 856 854\nf 858 860 857\nf 861 863 864\nf 1014 3855 3858\nf 867 868 869\nf 870 872 873\nf 874 876 877\nf 1310 1471 1309\nf 1476 1427 1475\nf 1461 1475 1460\nf 887 889 890\nf 788 892 891\nf 894 896 893\nf 1460 1227 1015\nf 901 524 612\nf 1465 1477 1464\nf 1865 3500 1864\nf 911 913 914\nf 916 918 915\nf 1850 3507 1851\nf 921 895 894\nf 1479 1436 1478\nf 926 667 601\nf 1481 563 1480\nf 269 84 1482\nf 931 933 916\nf 934 614 869\nf 936 688 937\nf 938 662 939\nf 1484 122 123\nf 941 835 834\nf 320 509 510\nf 1849 3505 1850\nf 84 1484 83\nf 1487 1488 539\nf 1757 3502 3509\nf 365 1491 364\nf 461 80 511\nf 117 362 1496\nf 1494 541 1495\nf 926 957 958\nf 1477 1433 1476\nf 1709 1585 1700\nf 119 1496 345\nf 271 504 156\nf 511 189 186\nf 345 1492 365\nf 255 344 254\nf 57 299 132\nf 1501 268 1500\nf 267 1499 145\nf 974 976 977\nf 1678 1693 1676\nf 3846 1807 3840\nf 557 663 558\nf 983 984 985\nf 1443 3849 3848\nf 421 268 265\nf 989 991 988\nf 1490 418 1504\nf 279 309 310\nf 997 676 679\nf 998 999 875\nf 1504 38 1505\nf 1506 1507 1486\nf 360 1508 1506\nf 1506 207 360\nf 939 1006 938\nf 270 1483 269\nf 66 546 512\nf 1010 793 935\nf 367 437 1509\nf 244 363 243\nf 1670 3534 1669\nf 209 1510 226\nf 235 1512 1511\nf 1511 234 235\nf 388 426 425\nf 427 250 237\nf 161 521 231\nf 100 45 44\nf 1025 608 1027\nf 1029 1031 1028\nf 939 1033 1032\nf 41 594 99\nf 1036 783 1037\nf 702 1038 1039\nf 1040 1042 1043\nf 362 369 1493\nf 187 477 186\nf 70 539 198\nf 1511 1521 234\nf 1049 603 1048\nf 613 869 614\nf 860 1051 857\nf 1005 1053 1006\nf 753 1056 1054\nf 1057 1059 1055\nf 293 1522 292\nf 288 1521 1522\nf 292 1523 329\nf 331 1508 1524\nf 306 453 305\nf 100 41 99\nf 462 78 79\nf 330 1524 462\nf 635 1068 636\nf 1485 1526 1528\nf 206 1528 1510\nf 1070 1072 831\nf 942 1073 1074\nf 916 1075 917\nf 440 1530 439\nf 157 1514 285\nf 1529 5 1530\nf 371 546 63\nf 790 663 791\nf 1077 857 1078\nf 1403 1435 1165\nf 1080 1082 1079\nf 531 1529 440\nf 21 1084 1085\nf 272 1531 561\nf 137 396 397\nf 690 802 691\nf 335 3567 581\nf 1090 1029 1091\nf 1092 794 1042\nf 1533 137 138\nf 661 1095 1096\nf 69 445 72\nf 1099 1101 1098\nf 38 1536 1505\nf 1104 1106 1103\nf 852 1108 853\nf 1377 909 1386\nf 291 411 294\nf 316 516 33\nf 539 1533 534\nf 1505 316 313\nf 1110 1112 1113\nf 1539 460 264\nf 522 1537 1539\nf 834 1120 1119\nf 1122 1124 1121\nf 612 1041 901\nf 935 615 614\nf 276 130 131\nf 1686 3522 1685\nf 193 1541 273\nf 231 1540 287\nf 556 982 557\nf 365 85 345\nf 190 318 1541\nf 1810 3574 3528\nf 3924 3512 3791\nf 1132 985 1131\nf 208 102 132\nf 1134 662 661\nf 1491 80 77\nf 429 1542 147\nf 1137 915 918\nf 172 428 174\nf 1139 1141 1142\nf 549 54 55\nf 1517 1518 296\nf 976 1146 938\nf 834 984 941\nf 1543 286 115\nf 1544 239 513\nf 133 1544 1542\nf 506 257 258\nf 420 328 401\nf 1158 1160 1157\nf 587 1103 588\nf 1513 1514 151\nf 607 1027 608\nf 304 1545 303\nf 364 77 506\nf 69 198 139\nf 1006 976 938\nf 700 1112 701\nf 3877 3525 3787\nf 1166 657 656\nf 1167 858 1077\nf 1300 3524 1299\nf 1172 1174 1175\nf 218 200 217\nf 1179 1181 1178\nf 372 97 98\nf 89 227 88\nf 1186 1188 1189\nf 172 1517 1549\nf 1482 81 1498\nf 1090 1193 1030\nf 394 353 1551\nf 526 1198 1197\nf 811 858 812\nf 1199 1026 1025\nf 606 697 696\nf 1204 1206 743\nf 1207 523 526\nf 1209 867 613\nf 914 685 936\nf 1478 1441 1477\nf 1029 1161 1091\nf 1211 1213 1214\nf 3858 1451 3859\nf 844 1216 845\nf 423 273 274\nf 1220 1222 1083\nf 22 1224 23\nf 882 897 881\nf 1519 173 387\nf 1189 1232 1178\nf 349 550 416\nf 1071 1236 1072\nf 1237 1239 745\nf 1221 1189 1240\nf 1510 1512 333\nf 939 1241 1005\nf 1556 5 67\nf 1553 394 1551\nf 404 67 347\nf 1247 1249 1246\nf 142 238 92\nf 1251 635 1250\nf 850 1253 851\nf 1254 1077 1255\nf 57 133 429\nf 443 552 551\nf 1187 1175 1188\nf 1256 831 833\nf 432 382 383\nf 741 747 746\nf 434 351 415\nf 349 383 348\nf 251 1481 1480\nf 395 352 353\nf 1518 1550 1551\nf 1558 378 340\nf 109 314 315\nf 285 1513 407\nf 395 393 392\nf 202 410 1558\nf 417 405 432\nf 1246 1258 1247\nf 3 297 2\nf 1259 1261 1262\nf 403 347 284\nf 955 953 952\nf 462 51 330\nf 1096 687 1263\nf 429 60 57\nf 896 1265 932\nf 1267 1268 1266\nf 423 327 420\nf 37 180 40\nf 20 1187 1186\nf 1256 1071 1070\nf 155 156 504\nf 1188 1269 1232\nf 1236 1271 1262\nf 1272 535 621\nf 565 463 464\nf 1273 745 1239\nf 876 1255 1274\nf 316 164 315\nf 437 503 1509\nf 194 1495 197\nf 1275 864 863\nf 624 631 630\nf 1277 1137 1278\nf 569 567 566\nf 1481 125 366\nf 324 547 326\nf 1279 1107 1280\nf 1525 329 1523\nf 1281 1137 918\nf 171 1553 1552\nf 1488 396 1533\nf 1557 1562 1559\nf 1282 1206 1205\nf 1053 1278 1281\nf 778 607 1283\nf 713 3566 3569\nf 251 144 124\nf 4 348 383\nf 709 707 706\nf 728 45 246\nf 261 1563 544\nf 789 1287 790\nf 74 263 282\nf 403 385 434\nf 1178 1240 1189\nf 1223 1289 1224\nf 158 150 151\nf 1220 1186 1221\nf 296 354 433\nf 784 1538 1566\nf 1181 1291 1222\nf 1269 1257 1246\nf 1566 438 399\nf 136 1566 1564\nf 1563 169 1565\nf 107 109 108\nf 1297 1298 1296\nf 1464 1476 1461\nf 1520 507 508\nf 1301 1303 1304\nf 1306 1308 1305\nf 175 1532 272\nf 1532 200 201\nf 1311 1312 1313\nf 872 1315 1316\nf 1317 1318 1319\nf 3836 878 3828\nf 1305 864 1276\nf 1040 863 1320\nf 1564 399 169\nf 592 1575 591\nf 591 1088 689\nf 1581 1583 1582\nf 1196 1147 1176\nf 710 583 713\nf 285 408 386\nf 953 961 963\nf 1594 1589 1593\nf 1585 1587 1586\nf 1592 1590 1589\nf 3885 944 3899\nf 1600 1598 1597\nf 1604 1602 1601\nf 1399 979 980\nf 1589 1596 1595\nf 1610 1608 1607\nf 298 361 360\nf 551 276 277\nf 1627 3814 3819\nf 859 818 860\nf 1078 1328 1329\nf 1555 1530 1556\nf 572 680 681\nf 772 776 774\nf 977 918 917\nf 404 392 393\nf 1054 750 753\nf 1644 1643 1642\nf 932 1082 933\nf 1067 1334 1068\nf 1540 264 114\nf 597 643 1336\nf 471 762 470\nf 1642 3831 3842\nf 1644 3842 3843\nf 923 950 927\nf 96 548 95\nf 520 522 521\nf 445 443 442\nf 849 848 842\nf 480 755 493\nf 1646 481 1639\nf 311 1567 170\nf 464 3497 3495\nf 3497 1361 3495\nf 234 236 235\nf 190 192 191\nf 153 152 149\nf 231 286 230\nf 1552 1551 1550\nf 760 1648 488\nf 3832 1649 3830\nf 828 826 825\nf 52 166 165\nf 1663 1661 1660\nf 287 115 286\nf 668 670 669\nf 634 640 639\nf 553 559 554\nf 118 33 516\nf 492 491 490\nf 3552 1651 3553\nf 582 584 583\nf 384 392 415\nf 1381 1383 1380\nf 1562 435 436\nf 952 951 947\nf 3529 583 584\nf 1660 1667 1666\nf 1394 1396 1393\nf 1660 1664 1663\nf 1671 1669 1668\nf 1624 1673 1672\nf 1407 1409 1410\nf 1677 1675 1674\nf 1613 1677 1625\nf 1679 1676 1677\nf 1626 3811 3814\nf 534 198 539\nf 176 150 159\nf 803 734 800\nf 400 191 192\nf 65 143 144\nf 3526 1656 3540\nf 402 157 283\nf 629 627 626\nf 461 49 50\nf 758 495 756\nf 796 786 785\nf 406 1513 1560\nf 342 166 167\nf 1694 1658 1695\nf 1692 1694 1696\nf 3557 1652 3552\nf 3921 1703 3923\nf 326 302 90\nf 3916 1702 1706\nf 3919 1698 1701\nf 1699 3927 3918\nf 1557 1560 1561\nf 1127 1117 1128\nf 400 289 346\nf 1706 1707 1708\nf 1707 1599 1600\nf 1709 1708 1588\nf 1714 1711 1713\nf 3919 1709 1700\nf 1704 1577 1703\nf 1716 1713 1715\nf 1559 1549 297\nf 1722 1720 1719\nf 1335 596 1489\nf 410 377 378\nf 973 1163 972\nf 168 399 398\nf 1571 1089 1093\nf 1572 682 1571\nf 749 647 754\nf 1730 1723 1729\nf 1594 1729 1592\nf 1729 1591 1592\nf 1731 1730 1583\nf 1384 1377 1379\nf 1732 1712 1714\nf 1724 1731 1732\nf 1712 1731 1580\nf 1722 1732 1733\nf 1721 1733 1734\nf 1734 1716 1717\nf 1735 1736 1697\nf 1733 1714 1716\nf 1663 1741 1668\nf 1742 1740 1743\nf 1738 1737 1735\nf 1665 1666 1740\nf 1665 1744 1664\nf 1725 1746 1745\nf 1591 1745 1590\nf 1590 1747 1596\nf 1745 1748 1747\nf 1659 3538 3542\nf 3287 3545 3282\nf 1185 1182 1190\nf 1664 1749 1741\nf 1741 1739 1738\nf 1746 1743 1748\nf 1726 1742 1746\nf 1153 924 925\nf 1115 1153 1154\nf 1654 3575 1655\nf 1896 3549 1686\nf 1720 1744 1726\nf 1721 1749 1720\nf 1750 1701 1698\nf 1697 1750 1696\nf 1151 1063 1062\nf 1751 1670 1671\nf 1695 1752 1751\nf 961 986 981\nf 1770 1768 1767\nf 1766 1593 1765\nf 1731 1579 1580\nf 1579 1774 1578\nf 3928 1771 3929\nf 1764 3928 3917\nf 1777 1775 1774\nf 3762 1699 1700\nf 1004 1128 1003\nf 1774 1771 1772\nf 987 1399 980\nf 1578 1772 1764\nf 1783 1781 1778\nf 1597 1770 1767\nf 639 3895 3884\nf 3928 1769 1770\nf 1793 1789 1788\nf 3929 1798 1769\nf 3508 3773 1806\nf 1802 3924 3922\nf 1788 1794 1793\nf 1793 1795 1776\nf 3773 1675 1806\nf 1584 1783 1789\nf 1771 3920 3929\nf 1801 1798 1800\nf 1768 1798 1799\nf 1775 1797 1771\nf 508 515 1520\nf 1796 1802 1797\nf 1800 3920 3922\nf 1794 1804 1795\nf 3532 1752 3547\nf 485 1807 775\nf 3809 1640 768\nf 3837 1810 1653\nf 485 1640 1808\nf 1690 3770 3772\nf 487 1648 1811\nf 3839 1816 3822\nf 3838 1648 3830\nf 3839 1811 3838\nf 1812 487 1811\nf 1641 767 768\nf 465 755 776\nf 767 3823 3808\nf 3822 3826 3847\nf 1809 1815 767\nf 3837 1651 3840\nf 675 3541 3496\nf 1814 1822 1821\nf 1821 3821 3820\nf 1824 1649 1825\nf 1827 1850 1805\nf 1668 1662 1663\nf 1779 1848 1828\nf 1790 1805 1794\nf 1833 1853 1852\nf 3521 693 575\nf 1614 1616 1617\nf 1852 3498 3501\nf 1620 1622 1623\nf 1855 1574 1861\nf 1847 3501 3513\nf 1766 1855 1782\nf 1629 1631 1628\nf 1633 1634 1632\nf 1635 1637 1638\nf 1848 1852 1847\nf 1855 1863 1862\nf 1803 1867 1802\nf 1862 1831 1832\nf 1782 1862 1781\nf 3922 1865 1800\nf 1873 1870 1869\nf 1874 799 804\nf 2045 3897 2044\nf 1219 1295 1225\nf 1615 1396 1395\nf 3884 1877 1876\nf 645 3786 3559\nf 1345 1035 1346\nf 634 3884 3880\nf 15 1327 14\nf 3910 1878 1893\nf 1894 605 1893\nf 3506 627 693\nf 3560 645 3559\nf 340 454 1558\nf 1897 195 1898\nf 1889 1877 1890\nf 1901 129 1900\nf 196 754 232\nf 1705 3921 3916\nf 340 166 343\nf 1570 563 561\nf 1904 194 1897\nf 1781 1832 1780\nf 1905 55 1906\nf 1000 995 996\nf 986 920 987\nf 1191 1190 1194\nf 2094 2075 2093\nf 1920 1919 1915\nf 453 1912 1911\nf 1915 1918 1916\nf 3882 2106 3901\nf 1655 3531 1868\nf 1916 1926 1925\nf 704 3561 703\nf 278 1925 1900\nf 3565 1818 1820\nf 1097 1115 1114\nf 1927 727 1923\nf 3578 3654 3758\nf 1920 307 1929\nf 1929 48 728\nf 503 1497 1509\nf 1902 787 796\nf 1816 3820 3826\nf 1925 1923 1924\nf 1535 1568 1534\nf 254 258 255\nf 1509 366 367\nf 119 35 36\nf 219 293 294\nf 141 47 431\nf 542 244 245\nf 1940 430 431\nf 1779 1827 1790\nf 1701 3915 3919\nf 1661 3515 1667\nf 270 754 647\nf 197 749 196\nf 1727 1212 1211\nf 53 232 754\nf 1465 1479 1478\nf 1284 3854 1293\nf 1951 412 413\nf 168 1554 1553\nf 1486 206 207\nf 506 78 361\nf 994 1007 993\nf 951 963 964\nf 1013 3861 1351\nf 3826 1820 1818\nf 1498 1954 1482\nf 299 543 132\nf 1754 1756 1753\nf 1681 1683 1682\nf 1696 1695 1697\nf 13 738 737\nf 473 475 474\nf 482 768 1640\nf 204 30 205\nf 1761 820 819\nf 611 1125 612\nf 1501 1908 1909\nf 1074 711 699\nf 1960 82 83\nf 120 1960 123\nf 35 87 34\nf 1773 1025 1031\nf 1965 1569 1535\nf 494 471 479\nf 1966 1969 1968\nf 446 1971 1967\nf 174 125 126\nf 1580 1578 1577\nf 1785 1787 1784\nf 1972 304 305\nf 1254 875 999\nf 1973 342 514\nf 321 1973 509\nf 1910 275 1907\nf 509 514 508\nf 1486 1527 1485\nf 1974 804 1975\nf 1977 1974 1978\nf 1980 1966 1981\nf 379 2 295\nf 1718 1710 1701\nf 884 992 883\nf 1680 1678 1679\nf 902 525 524\nf 344 256 299\nf 135 137 136\nf 236 228 333\nf 448 1967 1966\nf 589 604 584\nf 910 908 907\nf 1981 1968 1987\nf 1982 1987 1979\nf 1988 1987 1989\nf 1990 1989 1991\nf 1991 1993 1992\nf 322 320 319\nf 992 830 883\nf 1144 1086 1143\nf 1995 1978 1994\nf 1133 1118 1126\nf 1997 1994 1996\nf 870 1335 1489\nf 805 1982 1998\nf 727 2000 1999\nf 1996 1988 1990\nf 1994 1979 1988\nf 1829 792 1273\nf 1830 941 983\nf 798 800 799\nf 729 731 730\nf 2011 2023 2022\nf 1834 846 1159\nf 402 284 347\nf 2022 2024 2025\nf 1838 989 988\nf 810 952 809\nf 1823 1840 644\nf 1430 1432 1431\nf 1215 1841 1079\nf 149 176 177\nf 1316 873 872\nf 2112 1384 2106\nf 1842 1844 1845\nf 1159 845 1160\nf 837 1846 1120\nf 1971 2030 2027\nf 517 370 189\nf 858 833 812\nf 1471 2027 1888\nf 3551 626 627\nf 2174 770 490\nf 935 1854 1010\nf 1613 1611 1618\nf 1231 1225 1295\nf 1856 891 892\nf 1300 748 741\nf 1150 1858 1859\nf 1643 696 1642\nf 1215 1264 1216\nf 1320 902 901\nf 1956 1944 1960\nf 2031 669 1474\nf 2034 1310 1299\nf 1879 1656 1657\nf 40 242 39\nf 1992 2039 1456\nf 888 957 889\nf 1882 1996 1990\nf 1078 1255 1077\nf 1871 1192 1090\nf 294 218 219\nf 1323 2026 1471\nf 123 83 1484\nf 1324 2041 1323\nf 384 359 352\nf 1324 2038 2036\nf 1883 1885 1829\nf 287 114 107\nf 1096 1134 661\nf 915 931 916\nf 806 1981 1982\nf 3533 584 604\nf 1425 2042 1018\nf 1018 1948 1017\nf 3562 740 3519\nf 1948 649 646\nf 1881 1990 1991\nf 1900 130 278\nf 992 994 993\nf 923 925 924\nf 3878 2047 3876\nf 1643 625 606\nf 2050 2051 2049\nf 2045 1404 2047\nf 3876 945 3885\nf 1139 1079 1841\nf 1037 892 982\nf 2055 2057 2056\nf 1198 718 717\nf 1619 1612 1611\nf 2058 1606 2059\nf 2059 1607 2058\nf 2058 1624 1605\nf 71 179 37\nf 405 1557 382\nf 976 1006 977\nf 322 1972 1911\nf 2061 2063 2062\nf 2066 2067 1825\nf 2052 2064 2051\nf 1609 2066 1647\nf 1515 152 1516\nf 1009 993 1007\nf 1644 3815 2118\nf 279 1916 278\nf 1285 3862 1284\nf 1647 1825 1649\nf 2056 2067 2065\nf 319 1953 1952\nf 667 1913 1914\nf 2070 625 2048\nf 2052 2055 2063\nf 2038 1655 1458\nf 1606 2073 2072\nf 1235 1270 1236\nf 2072 2075 2074\nf 2059 2072 2062\nf 2074 2077 2076\nf 1601 2078 2073\nf 2049 1643 1645\nf 1000 1001 994\nf 1089 684 689\nf 1931 1306 1930\nf 1462 1021 1022\nf 1932 1934 1935\nf 875 1846 998\nf 1936 1937 1938\nf 44 46 92\nf 1534 1956 120\nf 1541 317 391\nf 2081 2084 2083\nf 146 1959 300\nf 1360 1942 1361\nf 985 1032 1033\nf 1995 1892 2043\nf 2079 2082 2081\nf 2095 2076 2077\nf 2081 2078 2079\nf 2055 2054 2053\nf 501 2095 500\nf 2097 2099 2098\nf 2098 2094 2083\nf 2140 3893 3866\nf 2141 3894 3893\nf 2051 2048 2049\nf 592 2101 1576\nf 2071 2076 2096\nf 2102 2098 2101\nf 593 2102 592\nf 1019 746 747\nf 1950 12 1327\nf 725 2096 501\nf 1385 2104 1379\nf 1379 2106 1384\nf 2107 2100 2097\nf 3908 502 3896\nf 3896 499 3901\nf 940 2109 944\nf 928 1170 973\nf 3868 684 3899\nf 1962 1964 1961\nf 3878 573 3897\nf 2039 1968 1969\nf 1389 2110 2109\nf 1398 2113 1397\nf 3757 3753 3743\nf 3911 2107 2103\nf 949 924 1115\nf 260 324 326\nf 1390 2113 2110\nf 2070 2064 2071\nf 3867 2103 593\nf 2105 1421 2114\nf 2114 1423 2115\nf 3892 2115 3903\nf 3908 2114 3892\nf 554 1902 553\nf 972 967 966\nf 1765 1573 1574\nf 1984 1986 1983\nf 2053 2069 2057\nf 3573 724 714\nf 1625 1674 1673\nf 3881 2119 2122\nf 3903 2123 3904\nf 56 1906 55\nf 626 3879 629\nf 2001 2003 2004\nf 3912 2127 3907\nf 2006 2008 2009\nf 2127 1412 2128\nf 2014 2016 2017\nf 2018 2020 2021\nf 1495 1947 197\nf 1379 1386 1385\nf 3814 1680 1618\nf 2029 1884 2028\nf 3902 2120 3887\nf 629 3914 3872\nf 682 943 1089\nf 724 2121 716\nf 1757 3821 3845\nf 765 3844 3809\nf 3851 715 3852\nf 706 3870 3872\nf 981 987 980\nf 1457 1656 1880\nf 2017 2032 2033\nf 1458 1868 1457\nf 1776 1792 1793\nf 1959 1946 1958\nf 3663 3725 3661\nf 3913 2122 3850\nf 1093 689 1088\nf 966 968 972\nf 3852 3881 3913\nf 2172 2170 2171\nf 670 2137 669\nf 3761 1903 233\nf 1429 28 1449\nf 964 978 971\nf 3841 1413 2127\nf 1473 669 2137\nf 3834 1359 1358\nf 2138 1870 2137\nf 1872 2137 1870\nf 715 3888 3881\nf 1881 1992 1880\nf 3843 739 740\nf 1837 3513 3514\nf 253 59 85\nf 1808 3840 1807\nf 1647 3818 1609\nf 3865 671 668\nf 2112 3883 2111\nf 1405 2044 1438\nf 3829 1364 1366\nf 273 391 249\nf 576 3520 3555\nf 3875 668 2031\nf 1430 2140 2139\nf 2136 3860 28\nf 1430 18 19\nf 3893 570 695\nf 1442 2046 2141\nf 1431 2141 2140\nf 3869 3850 2136\nf 3808 1817 766\nf 3866 695 671\nf 2142 798 2138\nf 571 2142 694\nf 3819 1618 1611\nf 2143 2025 2142\nf 694 2138 670\nf 681 2144 572\nf 2144 2022 2143\nf 328 457 290\nf 1572 2011 2144\nf 630 3903 624\nf 2085 2087 2088\nf 2090 2092 2089\nf 572 2143 571\nf 1875 2153 2149\nf 1873 2149 2154\nf 2103 2097 2102\nf 2157 2155 2156\nf 2128 1414 2120\nf 3874 589 582\nf 3872 628 629\nf 2149 2158 2159\nf 2159 3905 3890\nf 698 3902 705\nf 3823 3827 1817\nf 2158 3874 3905\nf 1438 2046 1439\nf 2035 2160 2161\nf 2161 3889 3886\nf 3871 605 589\nf 3873 18 3875\nf 370 61 451\nf 649 413 648\nf 2153 2156 2158\nf 1428 1449 1450\nf 1490 73 1488\nf 3890 710 633\nf 2155 3891 3871\nf 1456 1880 1992\nf 2125 2087 2124\nf 2160 3890 3889\nf 300 505 323\nf 1064 1046 1065\nf 2043 1891 2155\nf 2129 2004 2003\nf 2131 2133 2134\nf 3848 1450 1449\nf 1998 804 805\nf 3879 3864 3914\nf 474 490 770\nf 1164 1182 1177\nf 707 3909 3870\nf 708 2162 707\nf 2162 3886 3909\nf 548 1905 458\nf 1145 1114 1171\nf 705 1650 704\nf 2043 1977 1995\nf 737 554 559\nf 1034 1191 1023\nf 458 1907 275\nf 195 232 233\nf 2040 1969 1970\nf 2145 2147 2148\nf 2054 2049 1645\nf 3832 1639 3818\nf 9 11 10\nf 252 506 258\nf 472 479 471\nf 1865 3791 3503\nf 900 910 907\nf 1941 2151 1942\nf 1921 1927 1919\nf 2163 1898 2116\nf 271 153 178\nf 1904 12 9\nf 282 264 460\nf 1872 2154 2035\nf 1326 1897 2163\nf 628 693 627\nf 1724 1719 1723\nf 1549 436 172\nf 1582 1730 1729\nf 1921 1929 1928\nf 3563 10 11\nf 2152 1950 1949\nf 475 774 771\nf 769 2171 473\nf 2171 476 473\nf 476 772 475\nf 1791 464 1760\nf 2166 2168 2169\nf 595 372 98\nf 483 1641 482\nf 762 2173 470\nf 771 484 492\nf 486 1809 483\nf 297 1517 296\nf 763 2174 762\nf 776 759 489\nf 184 2176 2000\nf 412 303 414\nf 2005 185 224\nf 3811 1682 3846\nf 2164 756 764\nf 1472 2161 2162\nf 950 964 965\nf 2177 2179 2180\nf 227 1510 333\nf 764 467 2164\nf 2182 2184 2181\nf 2186 2018 2185\nf 1985 2181 1986\nf 214 729 1531\nf 1545 1546 1569\nf 865 842 866\nf 849 1357 1362\nf 452 1558 454\nf 559 3188 737\nf 16 737 3188\nf 560 498 374\nf 3193 3195 3194\nf 3200 3198 3199\nf 3211 3213 3212\nf 3215 3217 3216\nf 2187 2002 2001\nf 2169 2189 2190\nf 2192 2085 2191\nf 2193 2195 2196\nf 2198 2200 2197\nf 1998 1979 1975\nf 3664 3668 3671\nf 2116 1903 1902\nf 1859 2009 1150\nf 3904 2126 3906\nf 1135 1087 1130\nf 2201 2015 2014\nf 1149 110 1858\nf 3221 3218 3215\nf 2203 2205 2206\nf 579 1895 1019\nf 1755 2208 1756\nf 3891 1893 605\nf 1211 2150 1727\nf 868 2210 2209\nf 944 930 940\nf 2130 2202 2201\nf 233 1898 195\nf 665 750 666\nf 1167 999 618\nf 2211 2213 2214\nf 2215 2217 2218\nf 3867 590 3868\nf 2220 220 2219\nf 823 948 1094\nf 3225 3227 3226\nf 266 455 265\nf 3882 2107 3883\nf 2221 3519 3517\nf 2223 2225 2226\nf 2207 2124 2208\nf 673 3869 3864\nf 817 996 815\nf 2227 2146 2145\nf 1947 646 749\nf 3900 17 3873\nf 2229 1638 1381\nf 1679 1618 1680\nf 1637 2232 2230\nf 2233 1617 1616\nf 2231 2236 2237\nf 1381 2238 1382\nf 3586 2242 3587\nf 1630 2233 1631\nf 1681 472 469\nf 1408 2243 2244\nf 3236 3238 3237\nf 3232 3240 3235\nf 1637 2238 1638\nf 3237 3233 3234\nf 1409 1634 2245\nf 1380 2247 2246\nf 1628 2245 1621\nf 2249 1622 1633\nf 1632 1408 2244\nf 2248 1631 2250\nf 2235 1637 1636\nf 3245 3199 3198\nf 631 2071 725\nf 515 52 49\nf 782 2251 1251\nf 2253 2255 2252\nf 397 459 784\nf 1456 2040 1455\nf 2040 2036 1455\nf 1401 2111 1398\nf 2041 1970 2026\nf 1535 414 1965\nf 1586 2080 2079\nf 2256 1132 1886\nf 1700 3918 3919\nf 3815 1686 3817\nf 1263 1886 1096\nf 598 844 599\nf 2257 2259 2260\nf 2258 2262 2259\nf 2259 2263 2216\nf 2260 2216 2215\nf 1967 1970 1969\nf 2212 1963 2213\nf 922 2218 2217\nf 1312 1101 2264\nf 1307 1286 789\nf 1548 1546 1972\nf 2266 2268 2265\nf 2265 2270 2269\nf 2272 2269 2271\nf 2273 2265 2272\nf 2275 2277 2274\nf 2278 2277 2279\nf 2284 3617 3639\nf 2287 2286 2281\nf 2288 2283 2287\nf 2290 3639 3620\nf 2291 2293 2294\nf 2295 2294 2279\nf 2296 2291 2295\nf 2297 2292 2291\nf 2294 2299 2300\nf 2279 2300 2278\nf 2298 2302 2292\nf 2292 2303 2293\nf 2293 2304 2299\nf 2304 2306 2299\nf 2306 2300 2299\nf 2308 2310 2307\nf 2302 2310 2303\nf 2301 2307 2302\nf 2312 2307 2311\nf 2313 2315 2316\nf 2318 2320 2317\nf 3748 2322 2323\nf 2324 2273 2326\nf 2313 2326 2327\nf 2328 2313 2316\nf 2329 2324 2328\nf 2331 2333 2330\nf 2330 2267 2266\nf 2325 2266 2273\nf 2329 2330 2325\nf 2333 2335 2267\nf 2336 2333 2332\nf 2338 2332 2337\nf 2331 2337 2332\nf 2329 2339 2331\nf 2328 2340 2329\nf 2316 2341 2328\nf 2342 2315 2343\nf 2344 2346 2347\nf 2341 2347 2340\nf 2342 2344 2341\nf 2348 2345 2344\nf 2350 2352 2353\nf 2339 2353 2337\nf 2340 2350 2339\nf 2347 2351 2350\nf 2337 2354 2338\nf 2353 2355 2354\nf 2356 2276 2357\nf 2356 2358 2359\nf 2356 2296 2295\nf 2279 2356 2295\nf 2360 2270 2268\nf 2267 2360 2268\nf 2362 2364 2365\nf 2362 2361 2358\nf 2366 2358 2357\nf 2366 2363 2362\nf 2357 2368 2366\nf 2366 2369 2367\nf 2276 2370 2368\nf 2368 2282 2369\nf 2372 2374 2371\nf 2375 2377 2007\nf 2378 2380 2381\nf 2382 2381 2383\nf 2384 2378 2382\nf 2385 2379 2378\nf 2388 2390 2387\nf 2387 2392 2391\nf 2393 2387 2391\nf 2394 2388 2387\nf 2186 2402 2400\nf 2403 2327 2326\nf 2272 2326 2273\nf 2271 2403 2272\nf 2405 2404 2403\nf 2404 2408 2327\nf 2409 2411 2412\nf 2409 2271 2269\nf 2413 2269 2270\nf 2413 2410 2409\nf 2417 2416 2418\nf 2417 2406 2405\nf 2412 2405 2271\nf 2411 2417 2412\nf 2406 2407 2404\nf 3569 632 713\nf 2420 2397 2421\nf 2420 2423 2422\nf 2424 2422 2419\nf 2424 2398 2420\nf 2425 2392 2426\nf 2421 2426 2423\nf 2397 2425 2421\nf 2396 2391 2425\nf 2427 2315 2314\nf 2427 2408 2428\nf 2430 2428 2429\nf 2343 2427 2430\nf 2432 2430 2431\nf 2431 2429 2433\nf 2435 2433 2434\nf 2436 2431 2435\nf 2428 2407 2437\nf 2422 2407 2419\nf 2438 2422 2423\nf 2429 2437 2438\nf 2440 2433 2439\nf 2441 2433 2429\nf 2442 2439 2441\nf 2443 2440 2439\nf 2445 2444 2443\nf 2447 2443 2442\nf 2383 2445 2447\nf 2381 2446 2445\nf 2448 2423 2426\nf 2449 2426 2392\nf 2441 2449 2442\nf 2441 2438 2448\nf 2449 2390 2450\nf 2450 2389 2451\nf 2447 2451 2383\nf 2442 2450 2447\nf 2451 2453 2452\nf 2452 2455 2454\nf 2382 2454 2384\nf 2383 2452 2382\nf 2456 2373 2372\nf 2459 2461 2458\nf 2463 2465 2462\nf 2462 2285 2284\nf 2466 2284 2290\nf 2467 2462 2466\nf 2461 3703 3701\nf 2468 2470 2471\nf 2472 2471 2473\nf 2474 3701 3688\nf 2475 2477 2478\nf 2458 2453 2479\nf 2479 2389 2388\nf 2395 2479 2388\nf 2480 2458 2479\nf 2481 2483 2478\nf 2484 2485 221\nf 2471 2464 2463\nf 2473 2463 2467\nf 2487 2466 2486\nf 2486 2290 2488\nf 2490 2488 2489\nf 2491 2486 2490\nf 2492 2467 2487\nf 2493 2487 2491\nf 2494 2492 2493\nf 2495 2473 2492\nf 3690 2474 3688\nf 3691 2496 3690\nf 2498 2493 2499\nf 2500 2499 2501\nf 3700 2498 2500\nf 3687 2494 2498\nf 2306 2278 2300\nf 2305 2504 2306\nf 2504 2507 2506\nf 2506 2288 2508\nf 2274 2508 2275\nf 2278 2506 2274\nf 3633 2489 2488\nf 3620 2488 2290\nf 2507 2289 2288\nf 2505 2509 2507\nf 2508 2287 2511\nf 2511 2281 2280\nf 2370 2280 2282\nf 2275 2511 2370\nf 2512 2489 2513\nf 2514 2513 2515\nf 2517 2514 2516\nf 2517 2490 2512\nf 2499 2491 2517\nf 2499 2516 2501\nf 2510 2519 2518\nf 2518 2521 2520\nf 3616 2520 3621\nf 3632 2518 3616\nf 2522 2303 2310\nf 2523 2310 2309\nf 2305 2522 2524\nf 2524 2523 2525\nf 2519 2525 2521\nf 2505 2524 2519\nf 2308 2527 2526\nf 2526 2529 2528\nf 2531 2528 2530\nf 2309 2526 2531\nf 1214 2151 1211\nf 2419 2418 2424\nf 2533 2349 2317\nf 2534 2317 2320\nf 2535 2533 2534\nf 2536 2345 2533\nf 2537 2346 2536\nf 2538 2536 2535\nf 2539 2537 2538\nf 2540 2351 2537\nf 2312 2541 2527\nf 2527 2542 2529\nf 2543 2434 2544\nf 2545 2544 2546\nf 2319 2543 2545\nf 2318 2435 2543\nf 2514 2548 2547\nf 2547 2550 2549\nf 2552 2549 2551\nf 2552 2514 2547\nf 2520 2554 2553\nf 2553 2556 2555\nf 3590 2555 3593\nf 3621 2553 3590\nf 2523 2531 2557\nf 2557 2530 2558\nf 2525 2557 2559\nf 2559 2558 2560\nf 2554 2560 2556\nf 2521 2559 2554\nf 2561 2007 2006\nf 2376 2225 2377\nf 2564 2566 2563\nf 2563 338 2567\nf 2569 2567 2568\nf 2570 2563 2569\nf 2572 2574 2571\nf 2571 2576 2575\nf 2544 2575 2546\nf 2544 2572 2571\nf 2578 2569 2577\nf 2579 2569 2568\nf 2574 2579 2576\nf 2573 2577 2574\nf 2581 2583 2580\nf 2584 2583 2585\nf 3697 2580 2584\nf 3676 2580 3675\nf 2582 2552 2583\nf 2583 2551 2585\nf 2500 2589 2588\nf 2590 2589 2591\nf 3693 2588 2590\nf 3695 2500 2588\nf 2501 2591 2589\nf 2592 2594 2595\nf 2386 2503 2502\nf 2596 2386 2374\nf 2596 2373 2597\nf 2599 2597 2598\nf 2599 2379 2596\nf 2600 2380 2599\nf 2600 2598 2601\nf 2603 2601 2602\nf 2603 2446 2600\nf 2604 2602 2605\nf 2440 2605 2434\nf 2444 2604 2440\nf 2606 2581 2607\nf 2606 2590 2591\nf 2591 2582 2606\nf 3676 2456 3689\nf 3689 2372 3693\nf 2478 2608 2481\nf 2609 2611 2199\nf 2564 2613 2612\nf 2612 2598 2597\nf 2457 2597 2373\nf 2565 2612 2457\nf 2614 2570 2578\nf 2615 2578 2573\nf 2602 2614 2615\nf 2601 2613 2614\nf 2572 2605 2616\nf 2616 2602 2615\nf 2573 2616 2615\nf 1459 1045 1021\nf 1265 1079 1082\nf 1612 1609 1611\nf 2067 2069 2068\nf 1135 1136 1148\nf 1938 2618 2617\nf 95 458 128\nf 2058 1608 1612\nf 1454 1445 1367\nf 655 1048 2619\nf 722 2621 723\nf 1131 1033 1134\nf 562 366 1497\nf 1723 1725 1591\nf 1786 1122 1787\nf 267 323 266\nf 1325 1654 2038\nf 1468 1435 1479\nf 658 529 528\nf 8 247 7\nf 382 1559 3\nf 788 1307 789\nf 940 929 928\nf 911 2623 912\nf 2621 655 654\nf 2624 2625 2626\nf 998 27 26\nf 635 2626 2625\nf 2627 644 1840\nf 2628 1840 2629\nf 1200 2627 2628\nf 1199 641 2627\nf 1520 49 477\nf 1452 1349 1451\nf 2630 1859 1858\nf 644 1335 1823\nf 1197 717 2632\nf 2632 719 1166\nf 2628 1166 1200\nf 2629 2632 2628\nf 1762 1050 821\nf 1823 873 1839\nf 2634 794 652\nf 1028 1027 1161\nf 1042 2635 1043\nf 93 1901 1924\nf 2036 1458 1455\nf 1527 1525 1526\nf 877 1274 1277\nf 134 797 1544\nf 1328 931 1329\nf 607 1091 1161\nf 1043 2637 2636\nf 2636 2639 2638\nf 1275 2638 1276\nf 1275 1043 2636\nf 1272 2626 2640\nf 2640 638 637\nf 1110 2640 637\nf 536 2640 2641\nf 1472 1474 1473\nf 2042 1951 649\nf 1058 1105 1059\nf 2642 2643 2644\nf 1192 2644 1193\nf 912 2642 1192\nf 3898 3526 3786\nf 1049 2620 2621\nf 616 1885 613\nf 1331 666 750\nf 990 1198 525\nf 1911 321 322\nf 1298 1205 2645\nf 76 1504 75\nf 1502 421 422\nf 610 658 691\nf 1146 659 938\nf 2646 914 936\nf 1280 1238 1279\nf 975 2647 2648\nf 2647 975 974\nf 1453 1452 1451\nf 1514 158 151\nf 835 1843 1842\nf 1271 1173 1289\nf 2649 813 2650\nf 1893 1826 1894\nf 1050 1937 1051\nf 2651 814 813\nf 1006 1281 977\nf 2653 2654 2655\nf 1001 1003 1002\nf 585 679 558\nf 2624 621 620\nf 555 2028 556\nf 1207 1197 2629\nf 1626 478 472\nf 426 419 1567\nf 1030 1773 1031\nf 3898 634 3880\nf 534 138 262\nf 1125 1042 1041\nf 333 88 227\nf 569 561 730\nf 1489 871 870\nf 1857 620 891\nf 1673 3770 3763\nf 622 864 1308\nf 2647 974 2657\nf 619 891 620\nf 261 545 262\nf 393 1556 404\nf 2658 2647 2657\nf 1075 2657 917\nf 1081 2658 1075\nf 1080 2659 2658\nf 2644 1141 2660\nf 2660 1140 1336\nf 642 1336 643\nf 1193 2660 642\nf 679 555 558\nf 1503 1500 1502\nf 1318 1101 1100\nf 2661 1139 1142\nf 2661 2663 2662\nf 2662 2659 2661\nf 2662 2622 2664\nf 2664 911 2646\nf 2648 2646 1146\nf 2648 2662 2664\nf 2083 2093 2081\nf 1442 1437 1439\nf 519 330 51\nf 1885 1209 613\nf 799 805 804\nf 1946 1952 1953\nf 1302 1253 1252\nf 566 224 533\nf 1389 928 1388\nf 683 681 680\nf 836 1842 2665\nf 2665 1845 1303\nf 2666 1303 1302\nf 24 2665 2666\nf 831 2650 832\nf 1845 1304 1303\nf 2010 2012 2011\nf 1051 2667 2668\nf 1522 1511 1523\nf 531 441 522\nf 782 1251 1250\nf 1250 2625 2669\nf 2669 1857 1856\nf 783 1856 1037\nf 782 2669 783\nf 720 2671 721\nf 1055 1930 1056\nf 1333 1251 2251\nf 619 1308 1307\nf 652 2619 2634\nf 521 1539 1540\nf 1838 537 2672\nf 2672 536 2641\nf 1113 2641 1110\nf 1113 1838 2672\nf 1016 1351 1045\nf 3897 570 3894\nf 226 104 101\nf 3564 3827 1818\nf 1036 1884 1883\nf 2674 1304 2673\nf 2673 1844 2675\nf 1334 2675 1039\nf 1333 2673 1334\nf 2676 1039 2675\nf 1843 2675 1844\nf 942 2676 1843\nf 1074 702 2676\nf 811 859 858\nf 752 1055 753\nf 1200 656 1026\nf 1120 874 2677\nf 2677 877 1052\nf 1241 1052 1005\nf 1119 2677 1241\nf 1560 151 1515\nf 1208 611 523\nf 215 736 214\nf 1460 1462 1461\nf 1208 1839 873\nf 854 1282 1297\nf 72 182 179\nf 544 1565 512\nf 217 456 457\nf 2047 930 945\nf 1204 1937 2633\nf 913 686 685\nf 840 1113 1112\nf 1531 730 561\nf 2678 1283 801\nf 601 1914 602\nf 1537 459 460\nf 1819 3509 3527\nf 991 862 538\nf 2679 1073 1830\nf 2680 983 2256\nf 779 2256 1263\nf 778 2680 779\nf 2678 1830 2680\nf 180 245 40\nf 1886 1131 1134\nf 1138 1274 1255\nf 1841 1140 1139\nf 75 314 74\nf 193 420 192\nf 2681 1099 1098\nf 170 1565 169\nf 349 417 432\nf 1121 2684 2683\nf 547 409 302\nf 123 121 120\nf 2643 1142 1141\nf 1542 513 1543\nf 899 907 919\nf 1210 676 867\nf 791 982 892\nf 24 837 836\nf 893 932 931\nf 1860 863 862\nf 535 862 861\nf 832 813 812\nf 2650 813 832\nf 1569 1547 1568\nf 181 542 180\nf 660 937 688\nf 2013 568 2012\nf 887 2631 2630\nf 1524 361 78\nf 520 162 1529\nf 2653 854 2685\nf 1238 2655 2686\nf 718 527 530\nf 1492 518 1491\nf 792 615 793\nf 599 1835 596\nf 290 456 175\nf 312 1552 425\nf 1248 1179 1249\nf 153 177 178\nf 1240 1222 1221\nf 1257 1235 1071\nf 1291 1083 1222\nf 1052 1277 1053\nf 743 2618 1204\nf 444 430 443\nf 23 1172 1187\nf 2687 1180 1179\nf 102 409 134\nf 1283 610 801\nf 373 650 651\nf 1180 1084 1291\nf 855 2655 1237\nf 1173 1917 1174\nf 2689 853 2688\nf 2688 1108 1279\nf 2686 1279 1238\nf 2687 2688 2686\nf 2691 1252 2690\nf 2690 850 2689\nf 1248 2689 2687\nf 1248 2691 2690\nf 2692 24 2666\nf 2691 2666 1302\nf 1247 2692 2691\nf 1258 617 2692\nf 394 1555 393\nf 2693 1259 1223\nf 1072 1262 1261\nf 1257 1168 1258\nf 398 1555 1554\nf 871 1314 872\nf 1178 1249 1179\nf 1224 1173 1172\nf 1010 2618 742\nf 1175 1292 1269\nf 2654 2686 2655\nf 2694 1216 1264\nf 71 1487 70\nf 701 1111 1038\nf 1072 2649 2650\nf 1085 22 21\nf 895 1264 896\nf 1601 1605 1604\nf 1259 1271 1289\nf 21 1220 1083\nf 988 538 537\nf 598 1336 1140\nf 820 1762 821\nf 1050 818 821\nf 1298 1761 2652\nf 1168 618 617\nf 858 1256 833\nf 39 516 1536\nf 2652 819 814\nf 313 315 314\nf 2026 1971 2027\nf 2656 596 1835\nf 814 818 811\nf 2696 813 2649\nf 1260 2649 1261\nf 2693 2696 1260\nf 2697 2651 2696\nf 381 390 380\nf 3818 1611 1609\nf 1475 1428 1227\nf 1292 1917 1235\nf 1551 354 1518\nf 1526 1523 1511\nf 1593 1595 1573\nf 1232 1246 1249\nf 2695 1223 22\nf 2698 1296 2697\nf 2698 2693 2695\nf 2685 2695 1085\nf 2685 1297 2698\nf 562 271 272\nf 2652 1296 1298\nf 1365 1364 1363\nf 1958 1953 1957\nf 585 2210 997\nf 1561 1515 435\nf 975 1146 976\nf 347 68 346\nf 1038 636 1068\nf 2700 780 2699\nf 2699 1036 1883\nf 2701 1883 1829\nf 851 2699 2701\nf 2674 2251 2702\nf 2702 781 2700\nf 1253 2700 851\nf 1301 2702 1253\nf 1519 388 1550\nf 1273 1280 2703\nf 2703 1107 852\nf 2701 852 851\nf 1829 2703 2701\nf 91 326 90\nf 1784 2656 1785\nf 2683 2705 2704\nf 604 3499 3533\nf 1669 3548 1662\nf 2062 2060 2059\nf 621 861 622\nf 2023 732 2024\nf 686 1263 687\nf 857 2668 1328\nf 883 882 879\nf 637 1111 1110\nf 2668 893 1328\nf 2667 894 2668\nf 1039 1068 1334\nf 313 1504 1505\nf 1911 305 453\nf 1936 1964 2212\nf 2707 2401 2183\nf 2708 2183 2182\nf 738 2116 554\nf 2349 2432 2436\nf 2711 2020 2019\nf 2400 2019 2186\nf 2714 2716 2713\nf 2717 2719 2720\nf 2721 2723 2724\nf 2726 2728 2725\nf 2725 2730 2729\nf 2731 2729 2709\nf 2732 2725 2731\nf 2734 2736 2733\nf 2738 2740 2737\nf 2737 2741 2742\nf 2744 2746 2743\nf 2745 2748 2747\nf 2749 2747 2751\nf 2753 2751 2752\nf 2250 2246 2248\nf 2754 1614 2755\nf 3598 2755 3609\nf 3608 2747 3609\nf 1617 2755 1614\nf 2242 1620 2757\nf 2191 2760 2759\nf 2761 2760 2762\nf 3648 2761 3649\nf 3623 2759 3648\nf 3209 3223 3224\nf 1395 2236 2235\nf 1395 1636 1615\nf 1616 2229 2233\nf 1617 2763 2756\nf 2756 3628 3608\nf 2765 2234 1630\nf 2766 3628 3622\nf 3202 3246 3247\nf 1516 435 1515\nf 2768 3552 3553\nf 1495 578 1424\nf 2135 28 17\nf 3526 2773 2774\nf 1690 3926 3925\nf 2778 2780 2777\nf 2777 2781 2782\nf 2784 2777 2783\nf 3555 575 576\nf 1494 9 540\nf 3529 2015 3566\nf 451 505 450\nf 2786 2788 2785\nf 2779 2785 2780\nf 2789 2741 2740\nf 2790 2752 2789\nf 2792 2794 2791\nf 2793 2796 2794\nf 2796 2750 2749\nf 2794 2749 2753\nf 2798 1396 2754\nf 3591 2754 3598\nf 2798 2800 1393\nf 3591 2799 2798\nf 2797 2795 2801\nf 2791 2753 2790\nf 216 232 54\nf 3804 2807 3803\nf 3805 2804 3803\nf 2769 3557 3552\nf 183 184 97\nf 2811 2775 2776\nf 2804 2776 2805\nf 2809 2811 2804\nf 2812 2813 2814\nf 3805 2816 3795\nf 3796 2815 3795\nf 3798 2820 2821\nf 3793 2819 3798\nf 2824 2735 2819\nf 2823 2824 2819\nf 2825 2826 2724\nf 3793 2827 3792\nf 3796 2826 3792\nf 2713 2829 2828\nf 2828 2831 2830\nf 2822 2828 2830\nf 2821 2713 2828\nf 2832 2822 2830\nf 2833 2830 2831\nf 2834 2832 2833\nf 2835 2827 2832\nf 2837 2839 2836\nf 2836 2841 2840\nf 2842 2840 2727\nf 2843 2836 2842\nf 2840 2845 2844\nf 2718 2845 2719\nf 2730 2844 2718\nf 2727 2844 2728\nf 2846 2719 2845\nf 2846 2841 2847\nf 2829 2847 2831\nf 2829 2716 2846\nf 2847 2839 2848\nf 2848 2838 2849\nf 2833 2849 2834\nf 2831 2848 2833\nf 2850 2851 2852\nf 2853 2852 2854\nf 2783 2782 2855\nf 2785 2853 2856\nf 2780 2856 2781\nf 2857 2855 2782\nf 2859 2782 2781\nf 2861 2842 2860\nf 2860 2727 2726\nf 2862 2726 2732\nf 2863 2860 2862\nf 2861 2858 2857\nf 2843 2857 2859\nf 2856 2854 2864\nf 2781 2864 2859\nf 2837 2859 2864\nf 2838 2864 2854\nf 2852 2834 2849\nf 2854 2849 2838\nf 2865 2818 2835\nf 2851 2835 2834\nf 2813 2815 2865\nf 2814 2865 2851\nf 2866 2814 2850\nf 2788 2850 2853\nf 2866 2787 2867\nf 2812 2867 2775\nf 1892 1890 1891\nf 2720 2846 2870\nf 298 256 257\nf 2872 2713 2820\nf 3794 2735 2734\nf 2729 2874 2873\nf 2846 2715 2870\nf 2876 2878 2875\nf 2733 2878 2734\nf 3806 2879 2880\nf 3806 2872 3794\nf 2882 2884 2881\nf 2881 2715 2714\nf 2880 2714 2872\nf 2879 2881 2880\nf 2885 2887 2888\nf 2888 2890 2885\nf 2885 2892 2891\nf 2893 2885 2891\nf 1942 2532 2894\nf 2895 2720 2870\nf 2884 2870 2715\nf 2883 2895 2884\nf 2896 2892 2895\nf 2889 2717 2890\nf 2890 2720 2892\nf 2897 2891 2896\nf 2898 2896 2883\nf 2899 2883 2882\nf 2900 2882 2879\nf 3797 2879 3807\nf 2876 2901 2877\nf 2901 2904 2903\nf 1785 1835 1834\nf 2905 2899 2900\nf 2907 2897 2898\nf 3797 2903 3799\nf 632 577 633\nf 3532 2867 2787\nf 434 550 350\nf 2768 3640 3643\nf 2910 1623 2249\nf 2241 2758 2242\nf 3640 2913 3652\nf 1623 2757 1620\nf 3587 2757 3652\nf 2229 1380 2250\nf 2765 1629 2914\nf 2915 1629 2912\nf 3642 2914 2915\nf 3641 2765 2914\nf 2919 3642 3613\nf 2918 2912 2241\nf 3614 2241 3586\nf 3618 3613 3670\nf 3541 2180 3496\nf 3521 2188 3506\nf 3705 714 3851\nf 2925 2917 2924\nf 2924 2916 2919\nf 3610 2919 3618\nf 3611 2924 3610\nf 2926 2766 2925\nf 3658 2925 3611\nf 1715 1705 1710\nf 785 498 796\nf 1387 565 1791\nf 374 1899 560\nf 3573 2206 3556\nf 2929 2886 2893\nf 2932 2934 2931\nf 1905 1908 1907\nf 2930 2936 2937\nf 2938 2930 2929\nf 1493 517 518\nf 1603 1586 1602\nf 2939 2929 2908\nf 2940 2908 2907\nf 3665 1600 3661\nf 2886 2937 2887\nf 2906 2898 2899\nf 2908 2893 2897\nf 2942 2933 2941\nf 2941 2937 2936\nf 3501 2936 3513\nf 3498 2941 3501\nf 2943 2710 2873\nf 2943 2874 2889\nf 2945 2889 2888\nf 2946 2943 2945\nf 2932 2888 2887\nf 2931 2945 2932\nf 727 1929 728\nf 2903 2947 2948\nf 545 42 262\nf 1480 533 251\nf 2907 2949 2940\nf 2906 2950 2949\nf 1558 215 202\nf 3799 2948 3801\nf 2054 1644 2118\nf 3705 2203 3573\nf 1828 1849 1827\nf 2953 2955 2952\nf 2952 2957 2956\nf 2956 2258 2952\nf 2953 2258 2257\nf 2454 2474 2496\nf 3747 2959 3766\nf 3747 2131 3737\nf 2961 2963 2958\nf 2958 2964 2959\nf 222 2485 2965\nf 2477 2966 2608\nf 2631 2006 1859\nf 2384 2496 2497\nf 2967 2959 2964\nf 2968 2970 2969\nf 3759 2971 3784\nf 3784 2960 3766\nf 2972 2033 2032\nf 2007 2973 2008\nf 3560 2134 2133\nf 1836 1832 1831\nf 3506 2090 3551\nf 955 959 956\nf 672 674 673\nf 633 713 632\nf 2024 733 803\nf 2025 803 798\nf 425 1550 388\nf 2975 888 887\nf 1319 1157 2694\nf 1212 2977 1213\nf 889 2228 2227\nf 1978 1975 1979\nf 1138 1329 915\nf 751 2979 2978\nf 1619 1625 1624\nf 2252 1728 1727\nf 1157 845 1216\nf 2706 1938 2617\nf 2980 2254 2981\nf 2982 887 2630\nf 802 711 2679\nf 678 2029 2028\nf 527 690 528\nf 1839 2629 1840\nf 2983 602 1914\nf 2983 1913 1331\nf 2985 1331 1054\nf 2986 2983 2985\nf 2987 2635 2634\nf 2985 2639 2986\nf 2990 1318 2989\nf 2991 2993 2994\nf 2995 2994 2996\nf 1106 2991 2995\nf 1058 2992 2991\nf 1415 1365 1367\nf 1317 2217 2997\nf 1932 2255 2980\nf 926 2999 664\nf 3001 2992 3000\nf 3000 1057 2978\nf 2999 2978 2979\nf 2998 3000 2999\nf 3002 2209 3003\nf 3002 2996 2994\nf 2993 3002 2994\nf 3001 2998 3002\nf 1103 2995 3004\nf 3004 2996 3003\nf 2209 3004 3003\nf 588 3004 2210\nf 295 433 381\nf 2638 2988 3005\nf 3005 1056 1930\nf 1305 1930 1306\nf 1276 3005 1305\nf 1987 1993 1989\nf 2633 1761 2645\nf 1423 1417 1419\nf 3561 739 703\nf 2671 2704 2705\nf 1473 2035 1472\nf 922 2694 895\nf 1401 1378 1384\nf 556 1884 1037\nf 842 3825 3836\nf 1854 2617 1010\nf 933 1081 1075\nf 1440 1436 1437\nf 529 719 530\nf 1031 1027 1028\nf 777 912 1871\nf 2645 1204 2633\nf 441 1538 1537\nf 790 587 586\nf 2065 1610 1607\nf 1860 990 902\nf 60 87 59\nf 1424 1948 1947\nf 332 1507 331\nf 261 138 135\nf 187 449 507\nf 1719 1726 1725\nf 1931 1059 3006\nf 3006 1105 1104\nf 1287 1104 587\nf 1286 3006 1287\nf 3007 3009 3010\nf 3009 653 795\nf 618 26 25\nf 2029 1210 1209\nf 957 600 2228\nf 1049 2228 600\nf 2631 3011 2561\nf 890 2227 3011\nf 3011 2145 3012\nf 2561 3012 2375\nf 1026 609 608\nf 2706 1961 1964\nf 3013 2485 3014\nf 2148 1267 3015\nf 3015 1266 1311\nf 2997 1101 1317\nf 642 1773 1193\nf 186 461 511\nf 1041 1320 901\nf 1121 3017 1122\nf 2984 2987 3018\nf 3018 2634 2619\nf 1048 3018 2619\nf 603 2984 3018\nf 2679 801 802\nf 2062 2074 2061\nf 2061 2076 2064\nf 414 1545 1965\nf 3016 2683 3019\nf 3019 2704 2670\nf 3020 2670 720\nf 3007 3019 3020\nf 1871 1091 777\nf 3021 1122 3017\nf 3022 3017 3010\nf 1314 3022 1315\nf 1784 3021 1314\nf 1316 1763 611\nf 3023 3010 3009\nf 3023 795 1092\nf 1763 1092 1125\nf 1315 3023 1763\nf 3016 3010 3017\nf 752 2978 1057\nf 2986 2637 2987\nf 3024 653 3008\nf 3020 3008 3007\nf 720 3024 3020\nf 723 654 3024\nf 2681 1268 3025\nf 2671 1268 721\nf 2705 3025 2671\nf 1313 3027 1311\nf 3026 3029 3027\nf 3027 3030 3031\nf 1311 3031 3015\nf 2012 731 2023\nf 917 974 977\nf 743 856 744\nf 1888 2030 1425\nf 2682 2684 3032\nf 3032 1124 2989\nf 1100 2989 1318\nf 1099 3032 1100\nf 1123 2989 1124\nf 3033 1159 1158\nf 2990 1158 1319\nf 1123 3033 2990\nf 1786 1834 3033\nf 1054 2988 2985\nf 2979 664 2999\nf 2623 2663 2643\nf 1095 688 687\nf 3034 2261 2956\nf 3036 2956 2957\nf 3037 3034 3036\nf 3038 3035 3034\nf 3040 3026 1313\nf 2263 3041 3040\nf 3042 3043 3041\nf 3041 3028 3026\nf 3044 3043 3039\nf 3046 3045 3044\nf 3047 3049 3045\nf 3043 3049 3028\nf 3038 3051 3050\nf 3050 3053 3052\nf 3052 3044 3050\nf 3039 3050 3044\nf 3054 3039 3042\nf 3042 2262 3054\nf 2261 3054 2262\nf 2216 2997 2217\nf 2212 2667 1936\nf 3056 2209 3055\nf 934 1961 1854\nf 2013 3757 567\nf 3058 3060 3061\nf 3059 3063 3060\nf 3060 2213 1963\nf 3061 1963 1962\nf 3063 2214 2213\nf 3062 3064 3063\nf 3065 2260 3064\nf 3064 2215 2214\nf 3066 3031 3030\nf 2608 3067 3066\nf 2966 3068 3067\nf 3031 3068 3015\nf 2455 2461 2474\nf 2476 2226 2477\nf 3551 2089 3525\nf 3526 3769 3786\nf 433 355 381\nf 2115 1419 2123\nf 3509 2922 3527\nf 3769 2131 2134\nf 3069 2167 2166\nf 2971 2928 2960\nf 2961 2773 2962\nf 3070 2478 2483\nf 2610 3071 2954\nf 2502 2374 2386\nf 3072 2595 3073\nf 2964 3052 2967\nf 3048 3074 3049\nf 2593 3075 3074\nf 3075 3029 3074\nf 3074 3028 3049\nf 3072 3075 2592\nf 3076 2482 2481\nf 2481 3066 3076\nf 3076 3030 3075\nf 2967 3053 2970\nf 2482 3073 2483\nf 479 493 494\nf 706 3873 709\nf 3561 2221 3517\nf 2013 3754 3755\nf 3533 2016 3529\nf 3562 2222 2974\nf 1171 1155 1176\nf 2586 2565 2587\nf 2982 113 2484\nf 2562 3077 3068\nf 2376 3012 3077\nf 3012 2148 3077\nf 3077 3015 3068\nf 2963 3046 2964\nf 3051 2970 3053\nf 1683 491 775\nf 3850 1447 3860\nf 1986 3734 3768\nf 3079 2546 2575\nf 3081 2575 2576\nf 1983 3768 3765\nf 3082 2319 2545\nf 3080 2545 2546\nf 3734 3082 3080\nf 3732 2323 3082\nf 1309 1888 748\nf 3073 2772 2771\nf 2760 3084 2762\nf 2760 2088 3083\nf 3085 3086 3087\nf 3089 3090 3088\nf 3089 3084 3085\nf 3092 3090 3091\nf 3093 2744 2743\nf 2751 2748 3093\nf 3093 2752 2751\nf 2742 3093 3094\nf 3094 2743 3095\nf 3096 3098 3087\nf 3099 3087 3086\nf 3097 3095 3098\nf 3100 2746 3101\nf 3101 3102 3100\nf 2764 2927 3101\nf 2745 3101 2746\nf 3102 3091 3100\nf 3100 3095 2743\nf 2869 3592 3634\nf 3102 3104 3092\nf 3658 2869 3634\nf 2927 3103 3102\nf 3098 3091 3090\nf 3087 3090 3085\nf 2088 3105 3083\nf 3083 3086 3084\nf 2483 2771 3070\nf 2401 2185 2321\nf 3099 3105 3106\nf 2125 3106 3105\nf 3108 2742 3107\nf 3107 3094 3097\nf 3110 3107 3109\nf 3109 3097 3096\nf 3111 3096 3099\nf 3112 3109 3111\nf 3113 2737 3108\nf 3115 2722 3114\nf 3117 3114 3116\nf 3119 3116 3118\nf 3118 3120 3121\nf 3116 3113 3120\nf 3122 3111 3123\nf 3122 1755 1754\nf 3124 3126 3127\nf 3125 3121 3126\nf 3128 3118 3125\nf 3129 3125 3124\nf 2020 3128 3129\nf 3853 1346 1352\nf 1065 1047 1066\nf 1814 3830 1649\nf 2021 3129 3130\nf 3111 3106 3123\nf 3123 2207 1755\nf 2377 2224 2973\nf 3014 113 112\nf 1756 3782 3767\nf 3131 2530 2528\nf 3133 2528 2529\nf 1753 3767 3775\nf 3134 3127 3135\nf 2327 2314 2313\nf 2436 2317 2349\nf 2018 3711 3712\nf 2534 3137 2535\nf 2320 3136 2534\nf 2185 3712 3771\nf 3138 2542 2539\nf 3134 3744 3745\nf 3785 3134 3745\nf 3140 2539 2538\nf 2535 3140 2538\nf 3711 3130 3785\nf 2558 3132 3141\nf 3782 3141 3132\nf 2560 3141 3142\nf 3760 3142 3141\nf 958 2976 2981\nf 1933 2980 3143\nf 2399 2418 2416\nf 2348 2343 2432\nf 2322 2319 2323\nf 2718 2874 2730\nf 2709 2873 2710\nf 2894 1361 1942\nf 346 176 347\nf 1876 1882 1881\nf 2802 3607 3592\nf 3104 3088 3092\nf 2974 3649 3603\nf 3089 2761 2762\nf 3088 3145 3089\nf 2803 3603 3607\nf 3870 692 628\nf 2123 1418 2126\nf 1365 1368 1367\nf 1581 1782 1783\nf 3569 2129 3520\nf 3555 2002 3521\nf 3146 2551 3147\nf 3751 3147 3750\nf 3742 3146 3751\nf 3149 2585 3146\nf 3196 3197 3195\nf 3604 2191 3623\nf 3142 2556 2560\nf 3777 3150 3142\nf 3646 2550 3593\nf 3150 2555 2556\nf 3778 3151 3150\nf 3788 3152 3646\nf 3153 2551 2549\nf 3152 2549 2550\nf 3781 3153 3152\nf 3776 3147 3153\nf 3148 2206 2205\nf 2183 2321 2184\nf 2132 2928 2133\nf 3684 2204 2203\nf 3719 2197 3721\nf 2594 2773 2951\nf 2595 2951 2772\nf 1870 799 1869\nf 3713 2178 3706\nf 3706 3736 3790\nf 336 3774 3722\nf 339 3154 336\nf 3789 3149 3742\nf 3683 2584 3149\nf 2963 3048 3047\nf 3722 337 336\nf 339 2566 2586\nf 3156 2168 2167\nf 2610 2167 2611\nf 2954 3156 2610\nf 3157 2953 2257\nf 3707 2169 3709\nf 2567 3159 2568\nf 337 2567 338\nf 3730 2166 3707\nf 3764 1983 3765\nf 2579 3081 2576\nf 2568 3160 2579\nf 3709 2190 3764\nf 3065 3162 3161\nf 3161 1984 2189\nf 3157 2189 2168\nf 2257 3161 3157\nf 734 1922 806\nf 3059 2708 3163\nf 3163 2182 1985\nf 3162 1985 1984\nf 3062 3163 3162\nf 3203 3205 3204\nf 2122 1448 1447\nf 3817 1685 3812\nf 139 444 69\nf 1570 566 533\nf 2957 3165 3036\nf 2955 3164 2957\nf 1912 341 1973\nf 3130 3124 3134\nf 1548 319 1547\nf 1949 1327 1887\nf 2969 3078 3167\nf 3167 3169 3168\nf 3724 2017 3720\nf 3720 2033 3759\nf 3168 3165 3170\nf 3170 3164 3171\nf 3779 3171 3756\nf 3724 3170 3779\nf 3171 3166 3172\nf 3172 3071 3173\nf 3752 3173 3749\nf 3756 3172 3752\nf 3169 3036 3165\nf 2593 2962 2594\nf 3173 2609 3174\nf 3174 2199 2198\nf 3783 2198 3719\nf 3749 3174 3783\nf 2190 1984 1983\nf 2196 2767 3148\nf 2119 1414 1448\nf 1464 1466 1465\nf 2199 3069 2200\nf 2457 2587 2565\nf 2226 2562 2966\nf 481 1627 1639\nf 333 235 236\nf 3078 3037 3169\nf 3040 2997 2263\nf 2219 1933 2220\nf 2385 2497 2503\nf 221 3175 2484\nf 2220 3143 3175\nf 3175 2976 2975\nf 2484 2975 2982\nf 2954 3166 2955\nf 2630 110 2982\nf 1547 1952 1955\nf 2091 2188 2187\nf 3733 2178 2177\nf 1918 1927 1926\nf 3563 111 3511\nf 955 960 954\nf 2037 112 3563\nf 3143 2981 2976\nf 2218 2214 2215\nf 672 3787 3541\nf 2152 3014 2037\nf 1147 1143 1145\nf 2248 1409 2245\nf 1615 1635 1616\nf 1410 2246 2247\nf 3241 3242 3240\nf 3177 1383 1382\nf 2238 3176 1382\nf 3176 2230 3178\nf 1410 3179 1407\nf 3179 2243 1407\nf 1633 2800 2249\nf 1621 2758 2912\nf 1622 2245 1634\nf 2236 3181 3182\nf 2237 3182 3183\nf 2250 2233 2229\nf 2912 1628 1621\nf 135 1564 1563\nf 1567 512 1565\nf 1085 2653 2685\nf 3184 868 3056\nf 869 3057 934\nf 1266 1098 1312\nf 1267 722 721\nf 937 2646 936\nf 2146 722 2147\nf 2211 921 2667\nf 1957 507 449\nf 1313 2264 3040\nf 1496 1493 1492\nf 2681 2705 2682\nf 2887 2933 2932\nf 1687 1659 1694\nf 145 1498 81\nf 1317 1319 2694\nf 37 418 71\nf 1935 3185 2977\nf 104 301 103\nf 2101 2083 2084\nf 543 360 207\nf 1934 3186 3185\nf 234 212 237\nf 3845 1822 1824\nf 1976 1874 1974\nf 223 2219 220\nf 3655 3605 3612\nf 468 773 2165\nf 1935 1728 1932\nf 3240 3226 3235\nf 3261 3250 3260\nf 3215 3244 3264\nf 3265 3215 3264\nf 3267 3198 3196\nf 3205 3269 3255\nf 3214 3265 3270\nf 3271 3214 3192\nf 3251 3239 3236\nf 3222 3266 3272\nf 3232 3234 3233\nf 3216 3243 3244\nf 3196 3268 3267\nf 3244 3254 3255\nf 3238 3247 3246\nf 3264 3255 3269\nf 3253 3216 3217\nf 3211 3221 3265\nf 3257 3218 3256\nf 3258 3272 3259\nf 3274 3280 3273\nf 3228 3262 3263\nf 3275 3281 3280\nf 3226 3273 3280\nf 3236 3260 3251\nf 3281 3236 3237\nf 3287 3192 3189\nf 3235 3280 3234\nf 3234 3281 3237\nf 3288 3259 3272\nf 3260 3276 3251\nf 3289 3278 3284\nf 3210 3224 3283\nf 3199 3201 3200\nf 3228 3230 3229\nf 3290 3285 3286\nf 3282 3271 3287\nf 3260 3262 3261\nf 3243 3268 3254\nf 3267 3241 3232\nf 3194 3208 3193\nf 3191 3269 3206\nf 3270 3192 3214\nf 3275 3230 3231\nf 3264 3270 3265\nf 3252 3276 3291\nf 3248 3291 3223\nf 3213 3279 3277\nf 3272 3289 3288\nf 3277 3212 3213\nf 3228 3281 3231\nf 3220 3253 3219\nf 3219 3217 3257\nf 3256 3222 3258\nf 3247 3252 3248\nf 3245 3232 3233\nf 3266 3212 3289\nf 3208 3255 3254\nf 3288 3284 3285\nf 3204 3208 3207\nf 560 3187 559\nf 3209 3201 3202\nf 3189 3191 3190\nf 448 807 447\nf 647 122 270\nf 1833 1780 1832\nf 510 508 507\nf 3210 1887 3201\nf 1887 3200 3201\nf 15 3197 3200\nf 16 3195 3197\nf 3195 3187 3194\nf 3194 1899 3207\nf 3207 375 3204\nf 387 126 127\nf 3189 1760 3287\nf 3577 757 758\nf 763 2172 2175\nf 3583 3580 3581\nf 3581 3579 3578\nf 3594 3596 3595\nf 3400 3602 3401\nf 3626 3625 3627\nf 3631 3599 3601\nf 3636 3595 3596\nf 3647 3588 3645\nf 3650 3584 3647\nf 3653 3580 3651\nf 3653 3654 3579\nf 1801 3656 3655\nf 3651 3582 3650\nf 3644 3595 3637\nf 1767 3659 3660\nf 1597 3661 1600\nf 1708 3665 3664\nf 3671 3667 3666\nf 3666 3673 3672\nf 1588 3664 1587\nf 2080 3666 2082\nf 2082 3672 2084\nf 3674 3635 3631\nf 3677 3636 3635\nf 3682 3645 3681\nf 3692 3650 3686\nf 3681 3644 3680\nf 786 3692 785\nf 3694 3651 3692\nf 785 3686 497\nf 497 3682 650\nf 650 3681 564\nf 3696 3681 3680\nf 3405 3605 3402\nf 3702 3678 3677\nf 3699 3679 3678\nf 3645 3638 3644\nf 1799 3659 1768\nf 3708 3458 3460\nf 3462 3708 3460\nf 3714 564 3696\nf 3615 3602 3600\nf 3710 3696 3698\nf 3635 3596 3599\nf 3629 3627 3630\nf 3597 3599 3596\nf 3630 3715 3629\nf 3627 3597 3594\nf 3717 3723 3718\nf 3715 3660 3629\nf 3716 3725 3715\nf 3668 3661 3725\nf 3668 3717 3667\nf 3667 3718 3673\nf 3727 3673 3718\nf 3728 3672 3673\nf 3726 3718 3723\nf 1576 2084 3672\nf 3727 3731 3729\nf 3203 1387 3190\nf 3190 1791 3189\nf 3283 1949 3210\nf 317 259 391\nf 2000 3653 3694\nf 3601 3600 3602\nf 3589 3584 3585\nf 3577 756 2170\nf 3585 3582 3583\nf 3678 3637 3636\nf 3204 376 3203\nf 1801 3657 1799\nf 3729 3728 3727\nf 1575 3738 1088\nf 3735 1576 3728\nf 1776 1796 1775\nf 1293 1290 1284\nf 1326 1327 12\nf 1294 1234 1290\nf 878 1330 1337\nf 497 374 498\nf 122 413 121\nf 3699 3704 3708\nf 3606 3401 3602\nf 3708 3698 3699\nf 3464 3710 3462\nf 3704 3443 3458\nf 439 399 438\nf 1512 1526 1511\nf 1668 1738 1671\nf 839 1362 838\nf 1362 826 1012\nf 3843 1896 3815\nf 2053 2118 2117\nf 3888 2120 2119\nf 696 3816 3831\nf 1826 639 640\nf 3740 3729 3731\nf 956 886 960\nf 1980 1922 807\nf 33 105 164\nf 1343 3856 3857\nf 3907 2128 3902\nf 1766 1582 1594\nf 14 2163 738\nf 595 99 594\nf 3233 3246 3245\nf 1346 1044 1352\nf 748 1425 747\nf 3193 3254 3268\nf 3190 3206 3203\nf 3612 3606 3615\nf 3624 3615 3625\nf 3631 3443 3674\nf 3679 3644 3637\nf 3686 3647 3682\nf 3702 3674 3704\nf 3625 3600 3597\nf 3601 3415 3631\nf 3739 3735 3729\nf 3630 3594 3741\nf 3741 3716 3630\nf 1284 1288 1285\nf 3698 3680 3679\nf 1778 1780 1779\nf 1792 1584 1789\nf 695 571 694\nf 3638 3594 3595\nf 3741 3588 3589\nf 3247 3209 3202\nf 3292 3294 3295\nf 3296 3298 3299\nf 3299 3300 3301\nf 3302 3304 3305\nf 3306 3308 3309\nf 3307 3311 3308\nf 3303 3301 3300\nf 3312 3304 3313\nf 3314 3316 3317\nf 3318 3320 3321\nf 3324 3321 3325\nf 3326 3328 3323\nf 3329 3331 3332\nf 3333 3335 3336\nf 3337 3339 3340\nf 3333 3341 3342\nf 3342 3343 3344\nf 3319 3344 3343\nf 3338 3334 3339\nf 3346 3302 3347\nf 3305 3347 3302\nf 3312 3223 3349\nf 3350 3340 3351\nf 3312 3348 3305\nf 3352 3341 3326\nf 3311 3354 3308\nf 3341 3327 3326\nf 3359 3361 3362\nf 3360 3250 3249\nf 3318 3345 3319\nf 3364 3318 3324\nf 3314 3324 3365\nf 3366 3301 3346\nf 3299 3367 3296\nf 3308 3368 3309\nf 3369 3295 3294\nf 3317 3364 3314\nf 3370 3317 3316\nf 3325 3365 3324\nf 3352 3319 3343\nf 3296 3353 3311\nf 3297 3311 3310\nf 3363 3354 3345\nf 3345 3353 3344\nf 3344 3367 3342\nf 3366 3342 3367\nf 3347 3334 3346\nf 3339 3348 3340\nf 3348 3351 3340\nf 3363 3369 3368\nf 3294 3368 3369\nf 3293 3309 3294\nf 3331 3374 3332\nf 3346 3333 3366\nf 3323 3352 3326\nf 3356 3321 3320\nf 3355 3325 3321\nf 3357 3371 3325\nf 3322 3320 3352\nf 3361 3329 3362\nf 3373 3375 3374\nf 3374 3376 3332\nf 3329 3376 3362\nf 3327 3372 3328\nf 3336 3375 3327\nf 3335 3376 3375\nf 3376 3337 3362\nf 3337 3359 3362\nf 3359 3276 3250\nf 3377 3295 3370\nf 3277 3315 3278\nf 3371 3379 3365\nf 3378 3358 3380\nf 3365 3315 3314\nf 3316 3279 3370\nf 3282 3370 3279\nf 3313 3224 3312\nf 3378 3284 3379\nf 3380 3285 3378\nf 3379 3278 3315\nf 3276 3351 3291\nf 3291 3349 3223\nf 3313 2965 3013\nf 2965 3303 222\nf 222 3300 223\nf 223 3298 3186\nf 3298 3185 3186\nf 3297 2977 3185\nf 3310 1213 2977\nf 3307 1214 1213\nf 2532 3306 3293\nf 2894 3293 3292\nf 2871 3292 3377\nf 3253 3240 3242\nf 3283 3013 2152\nf 1997 1889 1892\nf 1237 744 855\nf 1535 120 121\nf 1531 201 214\nf 1119 1032 984\nf 946 824 947\nf 163 5 162\nf 3589 3723 3741\nf 1155 1162 1164\nf 427 327 424\nf 553 796 498\nf 3381 2231 2237\nf 3381 2230 2232\nf 2237 3180 3381\nf 3383 3385 3382\nf 3384 3387 3385\nf 3386 3389 3387\nf 3388 3391 3389\nf 3392 3394 3395\nf 3395 3396 3397\nf 3398 3397 3396\nf 3400 3399 3398\nf 3403 3405 3402\nf 3406 3404 3403\nf 3408 3407 3406\nf 3410 3409 3408\nf 3412 3411 3410\nf 3415 3398 3414\nf 3414 3396 3416\nf 3416 3394 3417\nf 3417 3393 3418\nf 3393 3420 3418\nf 3419 3421 3420\nf 3390 3422 3421\nf 3388 3423 3422\nf 3386 3424 3423\nf 3384 3425 3424\nf 3425 3426 3184\nf 2904 3428 2947\nf 3429 2904 2902\nf 3430 2902 2876\nf 2875 3430 2876\nf 3432 2733 2736\nf 2825 3435 2824\nf 3437 3439 3436\nf 3441 3436 3440\nf 2724 3434 2825\nf 3436 3115 3117\nf 3440 3117 3119\nf 3443 3414 3442\nf 3442 3416 3444\nf 3444 3417 3445\nf 3445 3418 3446\nf 3446 3420 3447\nf 3420 3448 3447\nf 3421 3449 3448\nf 3422 3450 3449\nf 3423 3451 3450\nf 3424 3452 3451\nf 3425 3056 3452\nf 3451 2253 2252\nf 3450 2252 1727\nf 3449 1727 2150\nf 3448 2150 1941\nf 3453 3448 1941\nf 3454 3447 3453\nf 3455 3446 3454\nf 3456 3445 3455\nf 3442 3456 3457\nf 3443 3457 3458\nf 3459 3458 3457\nf 3457 3455 3459\nf 3459 3454 3461\nf 3462 3459 3461\nf 3461 3453 3463\nf 3464 3461 3463\nf 3463 1941 1360\nf 3404 3401 3405\nf 3407 3399 3404\nf 3409 3397 3407\nf 3411 3395 3409\nf 3465 3413 3412\nf 3467 3469 3466\nf 3465 3431 3433\nf 3470 3466 3465\nf 3438 3432 3435\nf 3467 3438 3437\nf 3468 3437 3441\nf 3471 3468 3472\nf 3472 3441 3473\nf 3473 3440 2712\nf 2712 3119 3128\nf 3475 3472 3474\nf 3474 3473 3476\nf 3476 2712 2711\nf 3477 2711 2400\nf 3478 3476 3477\nf 3479 3474 3478\nf 3392 3413 3480\nf 3466 3480 3413\nf 3419 3392 3480\nf 3390 3480 3391\nf 3469 3391 3480\nf 3389 3471 3475\nf 3387 3475 3479\nf 3385 3479 3481\nf 3482 2400 2402\nf 3483 3477 3482\nf 3481 3478 3483\nf 3484 2402 2707\nf 3058 2707 2708\nf 3061 3484 3058\nf 3485 3482 3484\nf 3382 3481 3486\nf 3486 3483 3485\nf 3426 3382 3487\nf 3487 3486 1962\nf 3486 3061 1962\nf 1961 3487 1962\nf 3057 3426 3487\nf 3428 3402 3488\nf 3427 3403 3428\nf 3429 3406 3427\nf 3430 3408 3429\nf 3431 3410 3430\nf 3470 3433 3432\nf 3434 3438 3435\nf 2723 3439 3434\nf 2875 3433 3431\nf 2736 3435 3432\nf 2947 3488 3500\nf 3464 1360 3497\nf 1900 1924 1901\nf 1736 1718 1750\nf 1955 1943 1956\nf 1671 1735 1751\nf 1696 1698 1692\nf 1800 1864 1801\nf 2063 2056 2060\nf 1737 1717 1736\nf 1751 1697 1695\nf 943 3899 684\nf 3656 3402 3605\nf 1577 1764 1703\nf 3917 1599 3923\nf 1945 82 1944\nf 68 7 248\nf 147 1543 148\nf 1109 1133 1102\nf 1503 1907 1908\nf 1499 1909 1498\nf 452 735 215\nf 1824 3835 3845\nf 1825 2068 1824\nf 1463 1022 1194\nf 841 1009 1011\nf 1568 1955 1534\nf 1926 1927 1923\nf 1910 1502 1939\nf 1939 422 442\nf 309 1940 308\nf 1804 1866 1803\nf 269 1954 56\nf 2065 2060 2056\nf 3812 1758 3835\nf 280 552 1940\nf 1875 1869 1874\nf 1739 1734 1737\nf 1778 1790 1788\nf 310 1915 279\nf 1909 1906 1954\nf 1789 1778 1788\nf 978 980 979\nf 1584 1731 1583\nf 425 311 312\nf 148 115 116\nf 2044 3894 2046\nf 277 1939 551\nf 1943 1945 1944\nf 1461 1463 1464\nf 2253 3056 3055\nf 2981 3489 958\nf 2209 3489 3055\nf 2254 3055 3489\nf 2998 958 3489\nf 2826 2721 2724\nf 3490 2721 2817\nf 2816 3490 2817\nf 2334 2296 2335\nf 2298 2334 2336\nf 2301 2336 2338\nf 2311 2338 2354\nf 2312 2354 2355\nf 2359 2361 2360\nf 2335 2359 2360\nf 2414 2365 2364\nf 2413 2361 2365\nf 2355 2540 2541\nf 2541 2539 2542\nf 2722 2739 2738\nf 3114 2738 3113\nf 3110 3121 3120\nf 3108 3120 3113\nf 3122 3127 3126\nf 3112 3126 3121\nf 3127 1753 3135\nf 3139 2529 2542\nf 3744 3133 3139\nf 3490 2740 2739\nf 3491 2789 3490\nf 228 91 88\nf 245 243 242\nf 248 247 191\nf 2 296 295\nf 145 300 267\nf 327 210 328\nf 329 331 330\nf 250 228 236\nf 239 240 230\nf 373 375 374\nf 178 176 175\nf 1218 1228 1321\nf 3862 1295 3854\nf 808 824 823\nf 3858 1013 1014\nf 3813 1411 1413\nf 2154 2159 2160\nf 3845 1758 1757\nf 306 1922 452\nf 2157 1976 1977\nf 708 2031 1474\nf 1450 1227 1428\nf 1672 1605 1624\nf 1813 1821 1816\nf 1717 1715 1718\nf 176 402 347\nf 1710 3916 3915\nf 1712 1577 1711\nf 1705 1711 1704\nf 971 979 970\nf 822 1094 1102\nf 2909 2793 2792\nf 2810 2795 2793\nf 2799 2249 2800\nf 3662 2910 2799\nf 2769 2801 2795\nf 3177 3178 3179\nf 3178 3180 3179\nf 3181 1393 2800\nf 3181 1632 3182\nf 3182 2244 3183\nf 3183 2243 3180\nf 3492 2790 3491\nf 2808 3491 2816\nf 2239 3572 2240\nf 1149 3571 3570\nf 1150 3567 3571\nf 2202 3566 2015\nf 2921 3565 2923\nf 3568 2133 2928\nf 1777 1759 1792\nf 1201 1183 1184\nf 3561 2195 2194\nf 3553 2770 2768\nf 3927 1687 1692\nf 1516 154 503\nf 3549 2974 2803\nf 3550 2779 2778\nf 2771 3575 3546\nf 3070 3546 3558\nf 3547 2775 2867\nf 1376 1370 1372\nf 2767 3556 2206\nf 2195 3543 2767\nf 2775 3542 2776\nf 3377 3545 2871\nf 3804 3539 2806\nf 2776 3538 2805\nf 2240 3535 2920\nf 2920 3669 3619\nf 3548 2786 2779\nf 1805 1851 1804\nf 3534 2787 2786\nf 2951 3540 3531\nf 2772 3531 3575\nf 2973 3530 3536\nf 2224 3537 3530\nf 1957 320 510\nf 306 304 303\nf 2770 3528 2913\nf 2913 3574 2239\nf 2923 3527 2922\nf 308 431 47\nf 207 132 543\nf 1879 1881 1880\nf 3733 3525 2089\nf 2475 3558 3524\nf 2476 3524 3523\nf 2223 3523 3537\nf 3522 2803 2802\nf 2037 11 1950\nf 1959 450 505\nf 2003 3520 2129\nf 9 334 540\nf 2009 3518 3567\nf 2008 3536 3518\nf 3877 673 3879\nf 3515 2778 2784\nf 2935 3513 2936\nf 111 3570 3511\nf 3510 2935 2938\nf 683 3885 943\nf 3508 2810 2909\nf 736 732 729\nf 1795 1803 1796\nf 3505 2938 2939\nf 3507 2939 2940\nf 2950 3504 2949\nf 2940 3504 3507\nf 3801 3512 2950\nf 2868 3502 2869\nf 2869 3522 2802\nf 1024 1023 1020\nf 3500 2948 2947\nf 551 442 443\nf 2972 3568 2928\nf 2032 3499 2972\nf 216 1924 1923\nf 203 201 200\nf 1828 1847 1837\nf 1958 449 450\nf 272 178 175\nf 2871 3495 1361\nf 3619 3564 2921\nf 2801 3643 3662\nf 2005 3654 2176\nf 960 899 962\nf 3657 3612 3624\nf 3780 3646 3151\nf 3151 3593 2555\nf 2193 3623 2194\nf 3607 3145 3144\nf 3603 2761 3145\nf 3592 3144 3104\nf 2926 3634 3103\nf 3634 3104 3103\nf 3755 3746 3753\nf 566 3758 2005\nf 3488 1864 3500\nf 1602 2079 2078\nf 2868 3611 2922\nf 2922 3610 2923\nf 2923 3618 2921\nf 3670 2918 3614\nf 3619 3614 2920\nf 2921 3670 3619\nf 2920 3586 2240\nf 3613 2915 2918\nf 2917 3622 3641\nf 2916 3641 3642\nf 2239 3652 2913\nf 2911 3652 2757\nf 3643 2911 2910\nf 2797 3662 3591\nf 2797 3598 2750\nf 3622 2763 2765\nf 3608 2764 2745\nf 2194 3648 2221\nf 2221 3649 2222\nf 2756 3609 2755\nf 2750 3609 2747\nf 3669 1817 3827\nf 3763 1691 1699\nf 2515 3590 2548\nf 2548 3593 2550\nf 2513 3632 3616\nf 2513 3621 2515\nf 2509 3620 2289\nf 2510 3633 2509\nf 2289 3639 2283\nf 2283 3617 2286\nf 2240 3587 2239\nf 3659 3624 3626\nf 716 3887 3888\nf 727 3761 1923\nf 2165 2170 2164\nf 3660 3626 3629\nf 3743 3578 3757\nf 2175 769 770\nf 2100 2095 2099\nf 3758 3757 3578\nf 640 3568 1826\nf 1653 3553 1651\nf 1895 3530 746\nf 746 3537 741\nf 1653 3528 3576\nf 3762 1604 1672\nf 2093 2073 2078\nf 959 995 884\nf 962 919 986\nf 1400 1398 1399\nf 1000 1127 1004\nf 956 884 885\nf 1662 3550 1661\nf 2034 3546 1654\nf 3926 3539 3802\nf 3535 766 1817\nf 3572 765 766\nf 334 3571 335\nf 1868 3540 1656\nf 1684 1652 1678\nf 1682 775 1807\nf 1585 3762 1700\nf 3763 1672 1673\nf 2173 490 491\nf 1658 3547 1752\nf 3556 1650 724\nf 1571 2010 1572\nf 1587 3671 2080\nf 2099 2077 2094\nf 787 1999 786\nf 2000 786 1999\nf 3155 3697 3683\nf 3713 3683 3789\nf 2180 3685 3684\nf 3496 3684 3705\nf 1684 3840 1651\nf 1760 3495 3545\nf 2607 3693 2590\nf 2581 3689 2607\nf 2371 3700 3695\nf 2371 3693 2372\nf 2587 3675 2586\nf 2586 3697 339\nf 2497 3687 2503\nf 2502 3687 3700\nf 2494 3690 2495\nf 3690 2472 2495\nf 3688 2468 2472\nf 3701 2469 2468\nf 726 3892 630\nf 581 3518 580\nf 10 3570 334\nf 1837 3510 1849\nf 741 3523 1300\nf 1299 3558 2034\nf 1851 3504 1866\nf 3527 1820 1819\nf 580 3536 1895\nf 1758 3522 3502\nf 1693 3508 1806\nf 3504 1867 1866\nf 1894 3568 3499\nf 2126 3813 3841\nf 3906 3841 3912\nf 2113 3867 2110\nf 2179 3789 3685\nf 624 3904 623\nf 2192 3788 3780\nf 2177 3787 3733\nf 1364 3833 1338\nf 3858 1470 1453\nf 3135 3775 3744\nf 2069 3835 2068\nf 1351 3853 1352\nf 2108 3901 499\nf 2089 3736 3733\nf 3828 1358 866\nf 2001 3783 2187\nf 2187 3719 2091\nf 2130 3752 2004\nf 2004 3749 2001\nf 2014 3779 2201\nf 2201 3756 2130\nf 3167 3759 2969\nf 3168 3720 3167\nf 3159 3764 3160\nf 3160 3765 3081\nf 3730 3158 337\nf 3158 3709 3159\nf 2200 3730 3722\nf 3685 3742 2204\nf 3722 2197 2200\nf 3790 2092 3721\nf 3774 3721 2197\nf 3154 3790 3774\nf 3155 3706 3154\nf 2091 3721 2092\nf 2196 3750 3776\nf 2193 3776 3781\nf 3604 3781 3788\nf 2086 3780 3778\nf 2087 3778 3777\nf 2204 3751 2205\nf 2205 3750 3148\nf 3909 574 692\nf 2124 3777 3760\nf 2208 3760 3782\nf 3711 3140 3137\nf 3785 3138 3140\nf 3745 3139 3138\nf 3771 3136 2322\nf 3712 3137 3136\nf 3775 3131 3133\nf 3767 3132 3131\nf 1813 3838 1814\nf 1293 3856 1344\nf 2184 3748 3732\nf 2181 3732 3734\nf 3765 3079 3081\nf 3768 3080 3079\nf 709 3875 2031\nf 2774 3737 3769\nf 3786 2134 3559\nf 3809 766 765\nf 2968 3766 2959\nf 2969 3784 2968\nf 2958 3737 2961\nf 2132 3766 2960\nf 2321 3771 3748\nf 3860 1449 28\nf 3905 582 710\nf 3886 577 574\nf 3847 1818 3827\nf 705 3887 2121\nf 3816 3907 698\nf 2111 3911 2113\nf 3810 1366 1411\nf 1812 3822 1815\nf 675 3852 674\nf 3496 3851 675\nf 2110 3868 2109\nf 2104 3901 2106\nf 2109 3899 944\nf 3842 703 739\nf 1680 3846 1684\nf 1639 3819 3818\nf 623 3906 697\nf 2139 3866 3865\nf 1808 3844 3837\nf 3820 1819 1820\nf 3872 3900 706\nf 1646 3830 1648\nf 3889 633 577\nf 2139 3875 18\nf 2156 3871 3874\nf 674 3913 3869\nf 2117 3812 2069\nf 1815 3847 3823\nf 3855 1444 1470\nf 697 3912 3816\nf 866 3836 3828\nf 3864 2136 2135\nf 3585 3726 3589\nf 3583 3731 3585\nf 3753 3738 3739\nf 3740 3581 3743\nf 1088 3746 1093\nf 3739 3743 3753\nf 2791 3493 2792\nf 3492 2807 3493\nf 2792 3494 2909\nf 3493 2806 3494\nf 3508 3494 3516\nf 3516 2806 3539\nf 2948 3791 3801\nf 2105 3896 2104\nf 3538 3804 2805\nf 680 3876 683\nf 1891 3910 3891\nf 2905 3801 2950\nf 2900 3799 2905\nf 2901 3807 2877\nf 2734 3806 3794\nf 2877 3806 2878\nf 2820 3794 2872\nf 2818 3792 2827\nf 2823 3792 2826\nf 2822 3798 2821\nf 2819 3800 3798\nf 2817 3795 2816\nf 2809 3795 2815\nf 2808 3803 2807\nf 2805 3803 2804\nf 1702 3923 1599\nf 3922 1797 1802\nf 3915 1706 1709\nf 3918 1692 1698\nf 1657 3880 1879\nf 1890 3895 3910\nf 3772 1674 1675\nf 1689 3772 3773\nf 3926 3538 1688\nf 3516 1689 3773\nf 3917 1703 1764\nf 1598 3928 1770\nf 3880 1876 1879\nf 1691 3925 3927\nf 3925 1688 1687\nf 1117 1118 3930\nf 1066 1322 3930\nf 1243 847 3930\nf 3825 848 3930\nf 1128 1117 3930\nf 1129 1130 3930\nf 841 1011 3930\nf 1230 3863 3930\nf 1242 1243 3930\nf 1322 1229 3930\nf 1064 1065 3930\nf 1003 1128 3930\nf 1229 1230 3930\nf 3863 1285 3930\nf 1285 1288 3930\nf 848 839 3930\nf 847 3825 3930\nf 1118 1129 3930\nf 1065 1066 3930\nf 1087 1086 3930\nf 1008 1002 3930\nf 1011 1008 3930\nf 1002 1003 3930\nf 1076 1069 3930\nf 838 841 3930\nf 839 838 3930\nf 1288 1242 3930\nf 1130 1087 3930\nf 1086 1076 3930\nf 1069 1064 3930\nf 2410 2414 3931\nf 2411 2410 3931\nf 3617 2285 3931\nf 2394 2393 3931\nf 2393 2396 3931\nf 2281 2286 3931\nf 2282 2280 3931\nf 2398 2399 3931\nf 2465 2464 3931\nf 2396 2397 3931\nf 2470 2469 3931\nf 2399 2416 3931\nf 2480 2395 3931\nf 2459 2480 3931\nf 2369 2282 3931\nf 2460 2459 3931\nf 2415 2411 3931\nf 2416 2415 3931\nf 2364 2363 3931\nf 2286 3617 3931\nf 2464 2470 3931\nf 2469 3703 3931\nf 2285 2465 3931\nf 2414 2364 3931\nf 2395 2394 3931\nf 2363 2367 3931\nf 2397 2398 3931\nf 2280 2281 3931\nf 3703 2460 3931\nf 2367 2369 3931\nf 3934 3937 3938\nf 3934 3939 3935\nf 3932 3937 3933\nf 3938 3943 3939\nf 3937 3940 3941\nf 3937 3942 3938\nf 3942 3947 3943\nf 3941 3944 3945\nf 3942 3945 3946\nf 3947 3950 3951\nf 3944 3949 3945\nf 3945 3950 3946\nf 3950 3955 3951\nf 3949 3952 3953\nf 3950 3953 3954\nf 3955 3958 3959\nf 3953 3956 3957\nf 3953 3958 3954\nf 3959 3962 3963\nf 3957 3960 3961\nf 3958 3961 3962\nf 3962 3967 3963\nf 3960 3965 3961\nf 3961 3966 3962\nf 3966 3971 3967\nf 3965 3968 3969\nf 3966 3969 3970\nf 3970 3975 3971\nf 3968 3973 3969\nf 3969 3974 3970\nf 3974 3979 3975\nf 3973 3976 3977\nf 3974 3977 3978\nf 3979 3982 3983\nf 3976 3981 3977\nf 3977 3982 3978\nf 3982 3987 3983\nf 3981 3984 3985\nf 3982 3985 3986\nf 3987 3990 3991\nf 3984 3989 3985\nf 3985 3990 3986\nf 3991 3994 3995\nf 3989 3992 3993\nf 3990 3993 3994\nf 3995 3998 3999\nf 3992 3997 3993\nf 3993 3998 3994\nf 3998 4003 3999\nf 3997 4000 4001\nf 3998 4001 4002\nf 4002 4007 4003\nf 4000 4005 4001\nf 4001 4006 4002\nf 4006 4011 4007\nf 4005 4008 4009\nf 4006 4009 4010\nf 4011 4014 4015\nf 4008 4013 4009\nf 4009 4014 4010\nf 4014 4019 4015\nf 4013 4016 4017\nf 4014 4017 4018\nf 4019 4022 4023\nf 4016 4021 4017\nf 4017 4022 4018\nf 4023 4026 4027\nf 4021 4024 4025\nf 4022 4025 4026\nf 4027 4030 4031\nf 4024 4029 4025\nf 4025 4030 4026\nf 4031 4034 4035\nf 4029 4032 4033\nf 4030 4033 4034\nf 4034 4039 4035\nf 4032 4037 4033\nf 4033 4038 4034\nf 4038 4043 4039\nf 4037 4040 4041\nf 4038 4041 4042\nf 4043 4046 4047\nf 4040 4045 4041\nf 4041 4046 4042\nf 4046 4051 4047\nf 4045 4048 4049\nf 4046 4049 4050\nf 4051 4055 4056\nf 4048 4054 4049\nf 4049 4055 4050\nf 4056 4059 4060\nf 4053 4058 4054\nf 4055 4058 4059\nf 3932 4052 3936\nf 3936 4052 3940\nf 3940 4052 3944\nf 3944 4052 3948\nf 3948 4052 3952\nf 3952 4052 3956\nf 3956 4052 3960\nf 3960 4052 3964\nf 3964 4052 3968\nf 3968 4052 3972\nf 3972 4052 3976\nf 3976 4052 3980\nf 3980 4052 3984\nf 3984 4052 3988\nf 3988 4052 3992\nf 3992 4052 3996\nf 3996 4052 4000\nf 4000 4052 4004\nf 4004 4052 4008\nf 4008 4052 4012\nf 4012 4052 4016\nf 4016 4052 4020\nf 4020 4052 4024\nf 4024 4052 4028\nf 4028 4052 4032\nf 4032 4052 4036\nf 4036 4052 4040\nf 4040 4052 4044\nf 4044 4052 4048\nf 4048 4052 4053\nf 4053 4052 4057\nf 4060 3934 3935\nf 4058 3932 3933\nf 4058 3934 4059\nf 4057 4052 3932\nf 4069 4081 4082\nf 4066 4080 4067\nf 4065 4077 4078\nf 4063 4075 4076\nf 4071 4085 4072\nf 4069 4083 4070\nf 4067 4081 4068\nf 4066 4078 4079\nf 4063 4077 4064\nf 4061 4075 4062\nf 4072 4086 4073\nf 4071 4083 4084\nf 4076 4090 4077\nf 4074 4088 4075\nf 4086 4098 4099\nf 4083 4097 4084\nf 4082 4094 4095\nf 4079 4093 4080\nf 4078 4090 4091\nf 4076 4088 4089\nf 4085 4097 4098\nf 4082 4096 4083\nf 4080 4094 4081\nf 4079 4091 4092\nf 4089 4103 4090\nf 4088 4100 4101\nf 4098 4112 4099\nf 4096 4110 4097\nf 4095 4107 4108\nf 4092 4106 4093\nf 4090 4104 4091\nf 4089 4101 4102\nf 4097 4111 4098\nf 4095 4109 4096\nf 4094 4106 4107\nf 4092 4104 4105\nf 4103 4115 4116\nf 4100 4114 4101\nf 4112 4124 4125\nf 4110 4122 4123\nf 4107 4121 4108\nf 4105 4119 4106\nf 4103 4117 4104\nf 4101 4115 4102\nf 4110 4124 4111\nf 4109 4121 4122\nf 4107 4119 4120\nf 4105 4117 4118\nf 4115 4129 4116\nf 4114 4126 4127\nf 4124 4138 4125\nf 4123 4135 4136\nf 4120 4134 4121\nf 4119 4131 4132\nf 4117 4129 4130\nf 4114 4128 4115\nf 4124 4136 4137\nf 4122 4134 4135\nf 4119 4133 4120\nf 4117 4131 4118\nf 4129 4141 4142\nf 4126 4140 4127\nf 4138 4150 4151\nf 4136 4148 4149\nf 4133 4147 4134\nf 4131 4145 4132\nf 4130 4142 4143\nf 4127 4141 4128\nf 4137 4149 4150\nf 4135 4147 4148\nf 4132 4146 4133\nf 4131 4143 4144\nf 4142 4154 4155\nf 4140 4152 4153\nf 4150 4164 4151\nf 4149 4161 4162\nf 4146 4160 4147\nf 4144 4158 4145\nf 4142 4156 4143\nf 4140 4154 4141\nf 4149 4163 4150\nf 4148 4160 4161\nf 4146 4158 4159\nf 4144 4156 4157\nf 4155 4167 4168\nf 4153 4165 4166\nf 4163 4177 4164\nf 4161 4175 4162\nf 4159 4173 4160\nf 4158 4170 4171\nf 4155 4169 4156\nf 4153 4167 4154\nf 4163 4175 4176\nf 4161 4173 4174\nf 4159 4171 4172\nf 4156 4170 4157\nf 4167 4181 4168\nf 4165 4179 4166\nf 4176 4190 4177\nf 4175 4187 4188\nf 4173 4185 4186\nf 4170 4184 4171\nf 4169 4181 4182\nf 4167 4179 4180\nf 4175 4189 4176\nf 4173 4187 4174\nf 4171 4185 4172\nf 4170 4182 4183\nf 4180 4194 4181\nf 4178 4192 4179\nf 4190 4202 4203\nf 4187 4201 4188\nf 4186 4198 4199\nf 4183 4197 4184\nf 4182 4194 4195\nf 4180 4192 4193\nf 4189 4201 4202\nf 4186 4200 4187\nf 4184 4198 4185\nf 4183 4195 4196\nf 4193 4207 4194\nf 4192 4204 4205\nf 4202 4216 4203\nf 4200 4214 4201\nf 4199 4211 4212\nf 4196 4210 4197\nf 4194 4208 4195\nf 4193 4205 4206\nf 4201 4215 4202\nf 4199 4213 4200\nf 4198 4210 4211\nf 4196 4208 4209\nf 4207 4219 4220\nf 4204 4218 4205\nf 4216 4228 4229\nf 4213 4227 4214\nf 4211 4225 4212\nf 4209 4223 4210\nf 4207 4221 4208\nf 4205 4219 4206\nf 4214 4228 4215\nf 4213 4225 4226\nf 4211 4223 4224\nf 4209 4221 4222\nf 4219 4233 4220\nf 4218 4230 4231\nf 4228 4242 4229\nf 4226 4240 4227\nf 4225 4237 4238\nf 4223 4235 4236\nf 4221 4233 4234\nf 4218 4232 4219\nf 4228 4240 4241\nf 4226 4238 4239\nf 4223 4237 4224\nf 4221 4235 4222\nf 4233 4245 4246\nf 4230 4244 4231\nf 4242 4254 4255\nf 4240 4252 4253\nf 4237 4251 4238\nf 4235 4249 4236\nf 4234 4246 4247\nf 4231 4245 4232\nf 4241 4253 4254\nf 4239 4251 4252\nf 4236 4250 4237\nf 4235 4247 4248\nf 4246 4258 4259\nf 4244 4256 4257\nf 4254 4268 4255\nf 4253 4265 4266\nf 4250 4264 4251\nf 4248 4262 4249\nf 4246 4260 4247\nf 4244 4258 4245\nf 4253 4267 4254\nf 4252 4264 4265\nf 4250 4262 4263\nf 4248 4260 4261\nf 4259 4271 4272\nf 4257 4269 4270\nf 4267 4281 4268\nf 4265 4279 4266\nf 4263 4277 4264\nf 4262 4274 4275\nf 4259 4273 4260\nf 4257 4271 4258\nf 4266 4280 4267\nf 4265 4277 4278\nf 4263 4275 4276\nf 4260 4274 4261\nf 4271 4285 4272\nf 4269 4283 4270\nf 4281 4293 4294\nf 4279 4291 4292\nf 4277 4289 4290\nf 4274 4288 4275\nf 4273 4285 4286\nf 4271 4283 4284\nf 4280 4292 4293\nf 4277 4291 4278\nf 4275 4289 4276\nf 4274 4286 4287\nf 4284 4298 4285\nf 4282 4296 4283\nf 4294 4306 4307\nf 4291 4305 4292\nf 4290 4302 4303\nf 4287 4301 4288\nf 4286 4298 4299\nf 4284 4296 4297\nf 4293 4305 4306\nf 4290 4304 4291\nf 4288 4302 4289\nf 4287 4299 4300\nf 4297 4311 4298\nf 4296 4308 4309\nf 4306 4320 4307\nf 4304 4318 4305\nf 4303 4315 4316\nf 4300 4314 4301\nf 4298 4312 4299\nf 4297 4309 4310\nf 4305 4319 4306\nf 4304 4316 4317\nf 4302 4314 4315\nf 4300 4312 4313\nf 4310 4324 4311\nf 4308 4322 4309\nf 4320 4332 4333\nf 4318 4330 4331\nf 4316 4328 4329\nf 4313 4327 4314\nf 4311 4325 4312\nf 4310 4322 4323\nf 4318 4332 4319\nf 4316 4330 4317\nf 4315 4327 4328\nf 4312 4326 4313\nf 4323 4337 4324\nf 4322 4334 4335\nf 4332 4346 4333\nf 4330 4344 4331\nf 4329 4341 4342\nf 4327 4339 4340\nf 4325 4337 4338\nf 4322 4336 4323\nf 4332 4344 4345\nf 4330 4342 4343\nf 4327 4341 4328\nf 4325 4339 4326\nf 4337 4350 4351\nf 4334 4349 4335\nf 4346 4359 4360\nf 4344 4357 4358\nf 4341 4356 4342\nf 4340 4353 4354\nf 4338 4351 4352\nf 4335 4350 4336\nf 4345 4358 4359\nf 4343 4356 4357\nf 4340 4355 4341\nf 4338 4353 4339\nf 4351 4363 4364\nf 4349 4361 4362\nf 4359 4373 4360\nf 4358 4370 4371\nf 4355 4369 4356\nf 4354 4366 4367\nf 4351 4365 4352\nf 4349 4363 4350\nf 4358 4372 4359\nf 4357 4369 4370\nf 4355 4367 4368\nf 4352 4366 4353\nf 4364 4376 4377\nf 4362 4374 4375\nf 4373 4385 4386\nf 4370 4384 4371\nf 4368 4382 4369\nf 4367 4379 4380\nf 4364 4378 4365\nf 4362 4376 4363\nf 4372 4384 4385\nf 4370 4382 4383\nf 4368 4380 4381\nf 4365 4379 4366\nf 4376 4390 4377\nf 4374 4388 4375\nf 4385 4399 4386\nf 4384 4396 4397\nf 4382 4394 4395\nf 4379 4393 4380\nf 4378 4390 4391\nf 4376 4388 4389\nf 4384 4398 4385\nf 4382 4396 4383\nf 4380 4394 4381\nf 4379 4391 4392\nf 4389 4403 4390\nf 4387 4401 4388\nf 4399 4411 4412\nf 4396 4410 4397\nf 4395 4407 4408\nf 4393 4405 4406\nf 4391 4403 4404\nf 4389 4401 4402\nf 4398 4410 4411\nf 4395 4409 4396\nf 4393 4407 4394\nf 4391 4405 4392\nf 4402 4416 4403\nf 4401 4413 4414\nf 4411 4425 4412\nf 4409 4423 4410\nf 4408 4420 4421\nf 4406 4418 4419\nf 4403 4417 4404\nf 4402 4414 4415\nf 4410 4424 4411\nf 4408 4422 4409\nf 4407 4419 4420\nf 4404 4418 4405\nf 4416 4428 4429\nf 4413 4427 4414\nf 4425 4437 4438\nf 4423 4435 4436\nf 4421 4433 4434\nf 4418 4432 4419\nf 4416 4430 4417\nf 4415 4427 4428\nf 4423 4437 4424\nf 4421 4435 4422\nf 4420 4432 4433\nf 4418 4430 4431\nf 4428 4442 4429\nf 4426 4440 4427\nf 4437 4451 4438\nf 4435 4449 4436\nf 4434 4446 4447\nf 4432 4444 4445\nf 4430 4442 4443\nf 4427 4441 4428\nf 4437 4449 4450\nf 4435 4447 4448\nf 4432 4446 4433\nf 4430 4444 4431\nf 4442 4454 4455\nf 4439 4453 4440\nf 4451 4463 4464\nf 4449 4461 4462\nf 4446 4460 4447\nf 4444 4458 4445\nf 4443 4455 4456\nf 4440 4454 4441\nf 4450 4462 4463\nf 4447 4461 4448\nf 4445 4459 4446\nf 4444 4456 4457\nf 4455 4467 4468\nf 4453 4465 4466\nf 4463 4477 4464\nf 4462 4474 4475\nf 4459 4473 4460\nf 4457 4471 4458\nf 4455 4469 4456\nf 4453 4467 4454\nf 4462 4476 4463\nf 4461 4473 4474\nf 4459 4471 4472\nf 4457 4469 4470\nf 4347 4073 4086\nf 4347 4086 4099\nf 4347 4099 4112\nf 4347 4112 4125\nf 4347 4125 4138\nf 4347 4138 4151\nf 4347 4151 4164\nf 4347 4164 4177\nf 4347 4177 4190\nf 4347 4190 4203\nf 4347 4203 4216\nf 4347 4216 4229\nf 4347 4229 4242\nf 4347 4242 4255\nf 4347 4255 4268\nf 4347 4268 4281\nf 4347 4281 4294\nf 4347 4294 4307\nf 4347 4307 4320\nf 4347 4320 4333\nf 4347 4333 4346\nf 4347 4346 4360\nf 4347 4360 4373\nf 4347 4373 4386\nf 4347 4386 4399\nf 4347 4399 4412\nf 4347 4412 4425\nf 4347 4425 4438\nf 4347 4438 4451\nf 4347 4451 4464\nf 4347 4464 4477\nf 4468 4063 4064\nf 4466 4061 4062\nf 4477 4072 4073\nf 4474 4071 4475\nf 4472 4069 4473\nf 4471 4066 4067\nf 4468 4065 4469\nf 4466 4063 4467\nf 4347 4477 4073\nf 4476 4071 4072\nf 4474 4069 4070\nf 4472 4067 4068\nf 4469 4066 4470\nf 3935 4465 4060\nf 4465 4056 4060\nf 4452 4051 4056\nf 4439 4047 4051\nf 4047 4413 4043\nf 4043 4400 4039\nf 4039 4387 4035\nf 4387 4031 4035\nf 4031 4361 4027\nf 4361 4023 4027\nf 4348 4019 4023\nf 4334 4015 4019\nf 4015 4308 4011\nf 4011 4295 4007\nf 4007 4282 4003\nf 4282 3999 4003\nf 3999 4256 3995\nf 4256 3991 3995\nf 4243 3987 3991\nf 4230 3983 3987\nf 3983 4204 3979\nf 3979 4191 3975\nf 3975 4178 3971\nf 4178 3967 3971\nf 3967 4152 3963\nf 4152 3959 3963\nf 4139 3955 3959\nf 4126 3951 3955\nf 3951 4100 3947\nf 3947 4087 3943\nf 3943 4074 3939\nf 4074 3935 3939\nf 4480 4483 4484\nf 4480 4485 4481\nf 4479 4482 4483\nf 4484 4489 4485\nf 4482 4487 4483\nf 4483 4488 4484\nf 4488 4493 4489\nf 4487 4490 4491\nf 4488 4491 4492\nf 4493 4496 4497\nf 4490 4495 4491\nf 4491 4496 4492\nf 4496 4501 4497\nf 4495 4498 4499\nf 4496 4499 4500\nf 4501 4504 4505\nf 4498 4503 4499\nf 4499 4504 4500\nf 4505 4508 4509\nf 4503 4506 4507\nf 4504 4507 4508\nf 4509 4512 4513\nf 4506 4511 4507\nf 4507 4512 4508\nf 4513 4516 4517\nf 4511 4514 4515\nf 4512 4515 4516\nf 4516 4521 4517\nf 4514 4519 4515\nf 4515 4520 4516\nf 4520 4525 4521\nf 4518 4523 4519\nf 4520 4523 4524\nf 4525 4528 4529\nf 4522 4527 4523\nf 4523 4528 4524\nf 4528 4533 4529\nf 4527 4530 4531\nf 4528 4531 4532\nf 4533 4536 4537\nf 4530 4535 4531\nf 4531 4536 4532\nf 4537 4540 4541\nf 4534 4539 4535\nf 4536 4539 4540\nf 4541 4544 4545\nf 4539 4542 4543\nf 4539 4544 4540\nf 4544 4549 4545\nf 4542 4547 4543\nf 4544 4547 4548\nf 4548 4553 4549\nf 4547 4550 4551\nf 4547 4552 4548\nf 4552 4557 4553\nf 4551 4554 4555\nf 4552 4555 4556\nf 4557 4560 4561\nf 4554 4559 4555\nf 4555 4560 4556\nf 4560 4565 4561\nf 4559 4562 4563\nf 4560 4563 4564\nf 4565 4568 4569\nf 4562 4567 4563\nf 4563 4568 4564\nf 4569 4572 4573\nf 4567 4570 4571\nf 4568 4571 4572\nf 4572 4577 4573\nf 4570 4575 4571\nf 4571 4576 4572\nf 4576 4581 4577\nf 4575 4578 4579\nf 4576 4579 4580\nf 4580 4585 4581\nf 4578 4583 4579\nf 4579 4584 4580\nf 4584 4589 4585\nf 4583 4586 4587\nf 4584 4587 4588\nf 4589 4592 4593\nf 4586 4591 4587\nf 4587 4592 4588\nf 4592 4597 4593\nf 4591 4594 4595\nf 4592 4595 4596\nf 4597 4601 4602\nf 4594 4600 4595\nf 4595 4601 4596\nf 4602 4605 4606\nf 4600 4603 4604\nf 4601 4604 4605\nf 4478 4598 4482\nf 4482 4598 4486\nf 4486 4598 4490\nf 4490 4598 4494\nf 4494 4598 4498\nf 4498 4598 4502\nf 4502 4598 4506\nf 4506 4598 4510\nf 4510 4598 4514\nf 4514 4598 4518\nf 4518 4598 4522\nf 4522 4598 4526\nf 4526 4598 4530\nf 4530 4598 4534\nf 4534 4598 4538\nf 4538 4598 4542\nf 4542 4598 4546\nf 4546 4598 4550\nf 4550 4598 4554\nf 4554 4598 4558\nf 4558 4598 4562\nf 4562 4598 4566\nf 4566 4598 4570\nf 4570 4598 4574\nf 4574 4598 4578\nf 4578 4598 4582\nf 4582 4598 4586\nf 4586 4598 4590\nf 4590 4598 4594\nf 4594 4598 4599\nf 4599 4598 4603\nf 4606 4480 4481\nf 4603 4479 4604\nf 4604 4480 4605\nf 4603 4598 4478\nf 4615 4627 4628\nf 4612 4626 4613\nf 4611 4623 4624\nf 4609 4621 4622\nf 4618 4630 4631\nf 4615 4629 4616\nf 4613 4627 4614\nf 4612 4624 4625\nf 4609 4623 4610\nf 4607 4621 4608\nf 4619 4631 4632\nf 4617 4629 4630\nf 4622 4636 4623\nf 4620 4634 4621\nf 4632 4644 4645\nf 4629 4643 4630\nf 4628 4640 4641\nf 4626 4638 4639\nf 4624 4636 4637\nf 4622 4634 4635\nf 4631 4643 4644\nf 4628 4642 4629\nf 4626 4640 4627\nf 4624 4638 4625\nf 4635 4649 4636\nf 4634 4646 4647\nf 4644 4658 4645\nf 4642 4656 4643\nf 4641 4653 4654\nf 4639 4651 4652\nf 4636 4650 4637\nf 4635 4647 4648\nf 4643 4657 4644\nf 4642 4654 4655\nf 4640 4652 4653\nf 4637 4651 4638\nf 4648 4662 4649\nf 4646 4660 4647\nf 4658 4670 4671\nf 4656 4668 4669\nf 4653 4667 4654\nf 4651 4665 4652\nf 4649 4663 4650\nf 4648 4660 4661\nf 4656 4670 4657\nf 4654 4668 4655\nf 4653 4665 4666\nf 4651 4663 4664\nf 4661 4675 4662\nf 4660 4672 4673\nf 4670 4684 4671\nf 4668 4682 4669\nf 4667 4679 4680\nf 4665 4677 4678\nf 4663 4675 4676\nf 4661 4673 4674\nf 4670 4682 4683\nf 4667 4681 4668\nf 4665 4679 4666\nf 4663 4677 4664\nf 4675 4687 4688\nf 4672 4686 4673\nf 4684 4696 4697\nf 4682 4694 4695\nf 4679 4693 4680\nf 4678 4690 4691\nf 4676 4688 4689\nf 4673 4687 4674\nf 4683 4695 4696\nf 4681 4693 4694\nf 4678 4692 4679\nf 4676 4690 4677\nf 4688 4700 4701\nf 4686 4698 4699\nf 4696 4710 4697\nf 4695 4707 4708\nf 4692 4706 4693\nf 4691 4703 4704\nf 4688 4702 4689\nf 4686 4700 4687\nf 4695 4709 4696\nf 4694 4706 4707\nf 4692 4704 4705\nf 4689 4703 4690\nf 4701 4713 4714\nf 4699 4711 4712\nf 4710 4722 4723\nf 4707 4721 4708\nf 4705 4719 4706\nf 4704 4716 4717\nf 4701 4715 4702\nf 4699 4713 4700\nf 4709 4721 4722\nf 4707 4719 4720\nf 4705 4717 4718\nf 4702 4716 4703\nf 4713 4727 4714\nf 4711 4725 4712\nf 4723 4735 4736\nf 4721 4733 4734\nf 4719 4731 4732\nf 4716 4730 4717\nf 4715 4727 4728\nf 4713 4725 4726\nf 4721 4735 4722\nf 4719 4733 4720\nf 4717 4731 4718\nf 4716 4728 4729\nf 4726 4740 4727\nf 4724 4738 4725\nf 4736 4748 4749\nf 4733 4747 4734\nf 4732 4744 4745\nf 4730 4742 4743\nf 4728 4740 4741\nf 4726 4738 4739\nf 4735 4747 4748\nf 4732 4746 4733\nf 4730 4744 4731\nf 4728 4742 4729\nf 4739 4753 4740\nf 4738 4750 4751\nf 4748 4762 4749\nf 4746 4760 4747\nf 4745 4757 4758\nf 4743 4755 4756\nf 4740 4754 4741\nf 4739 4751 4752\nf 4747 4761 4748\nf 4745 4759 4746\nf 4744 4756 4757\nf 4741 4755 4742\nf 4753 4765 4766\nf 4750 4764 4751\nf 4762 4774 4775\nf 4759 4773 4760\nf 4758 4770 4771\nf 4755 4769 4756\nf 4753 4767 4754\nf 4752 4764 4765\nf 4760 4774 4761\nf 4758 4772 4759\nf 4757 4769 4770\nf 4755 4767 4768\nf 4765 4779 4766\nf 4764 4776 4777\nf 4774 4788 4775\nf 4772 4786 4773\nf 4771 4783 4784\nf 4769 4781 4782\nf 4767 4779 4780\nf 4765 4777 4778\nf 4774 4786 4787\nf 4771 4785 4772\nf 4769 4783 4770\nf 4767 4781 4768\nf 4779 4791 4792\nf 4776 4790 4777\nf 4788 4800 4801\nf 4786 4798 4799\nf 4783 4797 4784\nf 4782 4794 4795\nf 4780 4792 4793\nf 4777 4791 4778\nf 4787 4799 4800\nf 4785 4797 4798\nf 4782 4796 4783\nf 4780 4794 4781\nf 4792 4804 4805\nf 4790 4802 4803\nf 4800 4814 4801\nf 4799 4811 4812\nf 4796 4810 4797\nf 4795 4807 4808\nf 4792 4806 4793\nf 4790 4804 4791\nf 4799 4813 4800\nf 4798 4810 4811\nf 4796 4808 4809\nf 4793 4807 4794\nf 4805 4817 4818\nf 4803 4815 4816\nf 4814 4826 4827\nf 4811 4825 4812\nf 4809 4823 4810\nf 4808 4820 4821\nf 4805 4819 4806\nf 4803 4817 4804\nf 4813 4825 4826\nf 4811 4823 4824\nf 4809 4821 4822\nf 4806 4820 4807\nf 4817 4831 4818\nf 4815 4829 4816\nf 4826 4840 4827\nf 4825 4837 4838\nf 4823 4835 4836\nf 4820 4834 4821\nf 4819 4831 4832\nf 4817 4829 4830\nf 4825 4839 4826\nf 4823 4837 4824\nf 4821 4835 4822\nf 4820 4832 4833\nf 4830 4844 4831\nf 4828 4842 4829\nf 4840 4852 4853\nf 4837 4851 4838\nf 4836 4848 4849\nf 4834 4846 4847\nf 4832 4844 4845\nf 4830 4842 4843\nf 4839 4851 4852\nf 4836 4850 4837\nf 4834 4848 4835\nf 4832 4846 4833\nf 4843 4857 4844\nf 4842 4854 4855\nf 4852 4866 4853\nf 4850 4864 4851\nf 4849 4861 4862\nf 4847 4859 4860\nf 4844 4858 4845\nf 4843 4855 4856\nf 4851 4865 4852\nf 4849 4863 4850\nf 4848 4860 4861\nf 4845 4859 4846\nf 4857 4869 4870\nf 4855 4867 4868\nf 4866 4878 4879\nf 4864 4876 4877\nf 4862 4874 4875\nf 4859 4873 4860\nf 4857 4871 4858\nf 4856 4868 4869\nf 4864 4878 4865\nf 4862 4876 4863\nf 4861 4873 4874\nf 4859 4871 4872\nf 4869 4883 4870\nf 4868 4880 4881\nf 4878 4892 4879\nf 4877 4889 4890\nf 4874 4888 4875\nf 4873 4885 4886\nf 4871 4883 4884\nf 4868 4882 4869\nf 4878 4890 4891\nf 4876 4888 4889\nf 4873 4887 4874\nf 4871 4885 4872\nf 4883 4896 4897\nf 4880 4895 4881\nf 4892 4905 4906\nf 4890 4903 4904\nf 4887 4902 4888\nf 4885 4900 4886\nf 4884 4897 4898\nf 4881 4896 4882\nf 4891 4904 4905\nf 4889 4902 4903\nf 4886 4901 4887\nf 4885 4898 4899\nf 4897 4909 4910\nf 4895 4907 4908\nf 4905 4919 4906\nf 4904 4916 4917\nf 4901 4915 4902\nf 4899 4913 4900\nf 4897 4911 4898\nf 4895 4909 4896\nf 4904 4918 4905\nf 4903 4915 4916\nf 4901 4913 4914\nf 4899 4911 4912\nf 4910 4922 4923\nf 4908 4920 4921\nf 4919 4931 4932\nf 4916 4930 4917\nf 4914 4928 4915\nf 4913 4925 4926\nf 4910 4924 4911\nf 4908 4922 4909\nf 4918 4930 4931\nf 4916 4928 4929\nf 4914 4926 4927\nf 4911 4925 4912\nf 4922 4936 4923\nf 4920 4934 4921\nf 4931 4945 4932\nf 4930 4942 4943\nf 4928 4940 4941\nf 4925 4939 4926\nf 4924 4936 4937\nf 4922 4934 4935\nf 4930 4944 4931\nf 4928 4942 4929\nf 4926 4940 4927\nf 4925 4937 4938\nf 4935 4949 4936\nf 4933 4947 4934\nf 4945 4957 4958\nf 4942 4956 4943\nf 4941 4953 4954\nf 4938 4952 4939\nf 4937 4949 4950\nf 4935 4947 4948\nf 4944 4956 4957\nf 4941 4955 4942\nf 4939 4953 4940\nf 4938 4950 4951\nf 4948 4962 4949\nf 4947 4959 4960\nf 4957 4971 4958\nf 4955 4969 4956\nf 4954 4966 4967\nf 4951 4965 4952\nf 4949 4963 4950\nf 4948 4960 4961\nf 4956 4970 4957\nf 4954 4968 4955\nf 4953 4965 4966\nf 4951 4963 4964\nf 4962 4974 4975\nf 4959 4973 4960\nf 4971 4983 4984\nf 4968 4982 4969\nf 4966 4980 4967\nf 4964 4978 4965\nf 4962 4976 4963\nf 4960 4974 4961\nf 4969 4983 4970\nf 4967 4981 4968\nf 4966 4978 4979\nf 4964 4976 4977\nf 4975 4987 4988\nf 4973 4985 4986\nf 4983 4997 4984\nf 4981 4995 4982\nf 4979 4993 4980\nf 4978 4990 4991\nf 4976 4988 4989\nf 4973 4987 4974\nf 4983 4995 4996\nf 4981 4993 4994\nf 4978 4992 4979\nf 4976 4990 4977\nf 4988 5000 5001\nf 4985 4999 4986\nf 4997 5009 5010\nf 4995 5007 5008\nf 4992 5006 4993\nf 4991 5003 5004\nf 4989 5001 5002\nf 4986 5000 4987\nf 4996 5008 5009\nf 4994 5006 5007\nf 4991 5005 4992\nf 4989 5003 4990\nf 5001 5013 5014\nf 4999 5011 5012\nf 5009 5023 5010\nf 5008 5020 5021\nf 5005 5019 5006\nf 5004 5016 5017\nf 5001 5015 5002\nf 4999 5013 5000\nf 5008 5022 5009\nf 5007 5019 5020\nf 5005 5017 5018\nf 5002 5016 5003\nf 4893 4619 4632\nf 4893 4632 4645\nf 4893 4645 4658\nf 4893 4658 4671\nf 4893 4671 4684\nf 4893 4684 4697\nf 4893 4697 4710\nf 4893 4710 4723\nf 4893 4723 4736\nf 4893 4736 4749\nf 4893 4749 4762\nf 4893 4762 4775\nf 4893 4775 4788\nf 4893 4788 4801\nf 4893 4801 4814\nf 4893 4814 4827\nf 4893 4827 4840\nf 4893 4840 4853\nf 4893 4853 4866\nf 4893 4866 4879\nf 4893 4879 4892\nf 4893 4892 4906\nf 4893 4906 4919\nf 4893 4919 4932\nf 4893 4932 4945\nf 4893 4945 4958\nf 4893 4958 4971\nf 4893 4971 4984\nf 4893 4984 4997\nf 4893 4997 5010\nf 4893 5010 5023\nf 5014 4609 4610\nf 5012 4607 4608\nf 5022 4619 5023\nf 5020 4617 5021\nf 5018 4615 5019\nf 5017 4612 4613\nf 5014 4611 5015\nf 5012 4609 5013\nf 4893 5023 4619\nf 5021 4618 5022\nf 5020 4615 4616\nf 5018 4613 4614\nf 5015 4612 5016\nf 4481 5011 4606\nf 5011 4602 4606\nf 4998 4597 4602\nf 4985 4593 4597\nf 4593 4959 4589\nf 4589 4946 4585\nf 4585 4933 4581\nf 4933 4577 4581\nf 4577 4907 4573\nf 4907 4569 4573\nf 4894 4565 4569\nf 4880 4561 4565\nf 4561 4854 4557\nf 4557 4841 4553\nf 4553 4828 4549\nf 4828 4545 4549\nf 4545 4802 4541\nf 4802 4537 4541\nf 4789 4533 4537\nf 4776 4529 4533\nf 4529 4750 4525\nf 4525 4737 4521\nf 4521 4724 4517\nf 4724 4513 4517\nf 4513 4698 4509\nf 4698 4505 4509\nf 4685 4501 4505\nf 4672 4497 4501\nf 4497 4646 4493\nf 4493 4633 4489\nf 4489 4620 4485\nf 4620 4481 4485\nf 4 3 2\nf 8 7 6\nf 13 16 15\nf 17 28 19\nf 32 31 30\nf 36 35 34\nf 37 40 39\nf 21 22 23\nf 25 26 27\nf 41 44 43\nf 48 47 46\nf 52 51 50\nf 53 56 55\nf 60 59 58\nf 29 30 62\nf 66 65 64\nf 67 5 6\nf 72 71 70\nf 76 75 74\nf 77 80 79\nf 81 84 83\nf 59 87 86\nf 88 91 90\nf 96 95 94\nf 100 99 98\nf 101 104 103\nf 106 105 34\nf 108 116 115\nf 117 119 36\nf 124 127 126\nf 131 130 129\nf 132 102 134\nf 142 141 140\nf 148 106 60\nf 144 143 127\nf 149 152 151\nf 156 155 154\nf 159 150 158\nf 116 108 164\nf 171 170 169\nf 174 126 173\nf 179 182 181\nf 186 189 188\nf 194 197 196\nf 199 142 139\nf 206 209 208\nf 213 212 211\nf 202 215 214\nf 96 93 216\nf 92 238 43\nf 97 184 246\nf 250 249 229\nf 252 254 58\nf 258 257 256\nf 260 91 229\nf 110 111 112\nf 114 264 263\nf 265 268 267\nf 56 53 270\nf 274 273 249\nf 185 183 63\nf 280 279 278\nf 285 284 283\nf 288 219 211\nf 141 142 92\nf 289 290 175\nf 148 116 105\nf 312 311 170\nf 238 262 42\nf 318 163 160\nf 310 309 308\nf 241 239 325\nf 160 230 240\nf 47 48 307\nf 108 109 315\nf 345 85 86\nf 348 351 350\nf 356 359 358\nf 355 354 353\nf 363 31 32\nf 364 252 253\nf 366 125 368\nf 183 372 371\nf 163 162 161\nf 209 226 101\nf 378 377 165\nf 379 295 381\nf 383 382 3\nf 284 285 386\nf 387 127 389\nf 391 259 229\nf 73 281 397\nf 405 408 407\nf 203 218 411\nf 414 121 413\nf 1 356 357\nf 277 276 131\nf 254 344 57\nf 358 384 415\nf 417 416 386\nf 401 400 192\nf 419 389 127\nf 421 181 182\nf 103 301 302\nf 428 367 368\nf 380 390 359\nf 62 204 266\nf 140 141 431\nf 357 358 351\nf 356 1 379\nf 415 392 403\nf 437 428 436\nf 438 441 440\nf 188 451 450\nf 265 455 181\nf 224 185 64\nf 237 212 213\nf 275 131 128\nf 281 282 460\nf 50 462 79\nf 73 74 282\nf 468 467 466\nf 390 355 352\nf 482 485 484\nf 489 488 487\nf 493 496 495\nf 472 471 470\nf 502 501 500\nf 220 221 222\nf 512 419 143\nf 518 517 511\nf 513 239 230\nf 205 30 31\nf 189 370 451\nf 163 318 532\nf 533 224 225\nf 446 448 447\nf 335 541 540\nf 544 512 546\nf 329 519 291\nf 199 534 262\nf 385 386 416\nf 532 190 191\nf 363 117 118\nf 578 581 580\nf 573 572 571\nf 561 563 562\nf 241 260 259\nf 590 593 592\nf 545 546 595\nf 503 154 155\nf 606 625 624\nf 478 481 480\nf 646 649 648\nf 590 591 689\nf 564 565 651\nf 728 246 184\nf 714 724 716\nf 692 574 575\nf 541 335 581\nf 346 68 248\nf 161 231 230\nf 128 129 94\nf 698 705 704\nf 574 577 576\nf 290 289 401\nf 630 631 725\nf 733 736 735\nf 755 466 496\nf 759 480 481\nf 757 763 762\nf 764 496 466\nf 768 767 3808\nf 759 760 488\nf 492 474 475\nf 474 770 769\nf 773 468 465\nf 771 774 486\nf 484 485 775\nf 774 776 489\nf 495 496 764\nf 495 757 761\nf 137 397 784\nf 325 239 797\nf 165 519 51\nf 369 32 29\nf 730 731 568\nf 90 302 301\nf 243 118 516\nf 800 734 806\nf 817 815 808\nf 816 808 823\nf 306 303 447\nf 815 810 809\nf 829 830 828\nf 726 725 501\nf 274 427 424\nf 879 882 881\nf 884 883 879\nf 219 218 217\nf 906 905 904\nf 455 204 205\nf 898 882 830\nf 903 900 880\nf 377 291 519\nf 923 949 948\nf 946 947 951\nf 3519 740 739\nf 900 899 886\nf 225 64 65\nf 182 445 442\nf 809 952 947\nf 954 960 962\nf 927 950 965\nf 885 879 880\nf 925 927 966\nf 343 342 341\nf 965 964 971\nf 968 971 970\nf 972 968 969\nf 963 961 981\nf 336 337 338\nf 992 993 827\nf 810 815 996\nf 1007 1001 1002\nf 1012 826 827\nf 1011 1009 1007\nf 294 293 292\nf 907 908 920\nf 1017 579 1019\nf 1020 1023 1022\nf 1035 1024 1020\nf 1061 1060 1047\nf 1063 1061 1046\nf 1044 1020 1021\nf 1102 1094 1097\nf 1116 1126 1118\nf 1016 1015 1014\nf 881 897 904\nf 816 1116 1127\nf 1094 948 949\nf 514 342 167\nf 146 145 81\nf 1133 1135 1130\nf 1102 1133 1126\nf 822 1126 1116\nf 1145 1143 1148\nf 1076 1144 1151\nf 1148 1143 1086\nf 1109 1097 1114\nf 1154 1153 1156\nf 1153 925 967\nf 3464 3497 463\nf 1144 1147 1152\nf 1156 967 1163\nf 1164 1162 1169\nf 1145 1171 1176\nf 1114 1115 1154\nf 1183 1155 1164\nf 1184 1183 1177\nf 1069 1151 1062\nf 1023 1191 1194\nf 1162 1163 1170\nf 1195 1184 1185\nf 1183 1196 1176\nf 1201 1063 1152\nf 1061 1063 1201\nf 3825 847 843\nf 1203 1202 1195\nf 1202 1201 1184\nf 1218 1060 1203\nf 1060 1061 1202\nf 1225 1218 1217\nf 671 695 694\nf 1219 1217 1024\nf 1217 1203 1034\nf 1226 1014 1015\nf 1243 1242 1234\nf 1231 1230 1229\nf 847 1243 1233\nf 843 1233 1244\nf 1233 1234 1245\nf 1923 3761 233\nf 2100 2108 499\nf 3914 3864 2135\nf 61 62 323\nf 210 211 217\nf 1299 1310 1309\nf 3863 1230 1231\nf 1066 1047 1321\nf 1322 1321 1228\nf 1060 1218 1321\nf 1325 1324 1323\nf 1244 1245 1332\nf 1290 1234 1242\nf 1337 1330 1339\nf 1330 1332 1340\nf 1341 1332 1245\nf 1342 1340 1332\nf 1343 1342 1341\nf 1344 1341 1294\nf 3857 3856 1345\nf 1347 1348 1342\nf 3856 3854 1295\nf 1349 1350 1348\nf 3861 1349 1347\nf 3853 1347 1343\nf 906 898 829\nf 905 906 1353\nf 865 1356 1355\nf 1355 1353 829\nf 1357 1355 825\nf 1356 1354 1353\nf 3834 1337 1338\nf 866 1358 1356\nf 1358 1359 1354\nf 3828 878 1337\nf 838 1362 1012\nf 1363 1364 1338\nf 1368 1363 1339\nf 1348 1368 1340\nf 1371 1369 3824\nf 524 525 526\nf 528 529 530\nf 1372 1370 1369\nf 535 536 537\nf 1367 1368 1348\nf 1370 1354 1359\nf 1369 1359 3833\nf 1373 910 903\nf 555 556 557\nf 1374 909 910\nf 1373 904 905\nf 1374 1373 1375\nf 1375 905 1354\nf 1378 920 908\nf 376 373 651\nf 1388 928 973\nf 586 587 588\nf 1390 1389 1388\nf 1391 1388 969\nf 597 598 599\nf 600 601 602\nf 1392 1391 970\nf 608 609 610\nf 612 524 523\nf 614 615 616\nf 617 618 25\nf 620 621 622\nf 1397 1390 1391\nf 1398 1397 1392\nf 1400 987 920\nf 1402 1403 1165\nf 636 637 638\nf 930 1404 1402\nf 641 642 643\nf 929 1402 1169\nf 1404 1405 1403\nf 1411 1366 1365\nf 1412 1406 1415\nf 652 653 654\nf 609 656 657\nf 659 660 661\nf 558 663 586\nf 665 666 667\nf 1413 1411 1406\nf 1418 1416 3810\nf 1417 1372 1371\nf 1416 1371 3829\nf 1419 1417 1416\nf 1386 909 1374\nf 677 678 679\nf 1385 1386 1420\nf 1420 1374 1376\nf 685 686 687\nf 1421 1420 1422\nf 528 690 691\nf 1422 1376 1372\nf 1018 1019 747\nf 699 700 701\nf 1424 578 579\nf 3663 1597 1767\nf 700 699 711\nf 1093 3746 3754\nf 717 718 530\nf 721 722 723\nf 704 1650 3543\nf 19 1429 1426\nf 1429 1428 1427\nf 1658 1659 3542\nf 1439 1437 1434\nf 1437 1436 1435\nf 1426 1427 1433\nf 1442 1431 1432\nf 742 743 744\nf 1432 1433 1441\nf 751 752 753\nf 1438 1434 1403\nf 1446 1445 1444\nf 1447 1448 1446\nf 3860 1447 1443\nf 1415 1367 1445\nf 1414 1415 1446\nf 2173 1683 469\nf 777 778 779\nf 780 781 782\nf 1676 1693 1806\nf 3859 1451 1349\nf 789 790 791\nf 745 792 793\nf 652 794 795\nf 1455 1458 1457\nf 1454 1367 1350\nf 610 691 802\nf 3831 3816 698\nf 1352 1044 1045\nf 1015 1016 1459\nf 811 812 813\nf 2118 3815 3817\nf 819 820 821\nf 1466 1463 1194\nf 1467 1468 1465\nf 1182 1165 1468\nf 832 812 833\nf 835 836 837\nf 1467 1466 1190\nf 1469 1470 1444\nf 840 700 712\nf 1453 1470 1469\nf 599 844 845\nf 1226 1450 3849\nf 851 852 853\nf 855 744 856\nf 858 859 860\nf 861 862 863\nf 1014 1226 3855\nf 867 676 868\nf 870 871 872\nf 874 875 876\nf 1310 1323 1471\nf 1476 1433 1427\nf 1461 1476 1475\nf 887 888 889\nf 788 791 892\nf 894 895 896\nf 1460 1475 1227\nf 901 902 524\nf 1465 1478 1477\nf 1865 3503 3500\nf 911 912 913\nf 916 917 918\nf 1850 3505 3507\nf 921 922 895\nf 1479 1435 1436\nf 926 664 667\nf 1481 562 563\nf 269 1483 84\nf 931 932 933\nf 934 935 614\nf 936 685 688\nf 938 659 662\nf 1484 270 122\nf 941 942 835\nf 320 321 509\nf 1849 3510 3505\nf 84 1483 1484\nf 1487 1490 1488\nf 1757 1758 3502\nf 365 1492 1491\nf 461 79 80\nf 117 363 362\nf 1494 540 541\nf 926 601 957\nf 1477 1441 1433\nf 1709 1588 1585\nf 119 117 1496\nf 271 1497 504\nf 511 517 189\nf 345 1496 1492\nf 57 344 299\nf 1501 1499 268\nf 267 268 1499\nf 974 975 976\nf 1678 1652 1693\nf 3846 1682 1807\nf 557 982 663\nf 983 941 984\nf 1443 1444 3849\nf 421 1500 268\nf 989 990 991\nf 1490 1487 418\nf 279 280 309\nf 997 868 676\nf 998 26 999\nf 1504 418 38\nf 1506 1508 1507\nf 360 361 1508\nf 1506 1486 207\nf 939 1005 1006\nf 270 1484 1483\nf 66 63 546\nf 1010 742 793\nf 367 428 437\nf 244 31 363\nf 1670 3532 3534\nf 209 206 1510\nf 388 389 426\nf 427 274 250\nf 161 520 521\nf 100 246 45\nf 1025 1026 608\nf 1029 1030 1031\nf 939 662 1033\nf 41 42 594\nf 1036 780 783\nf 702 701 1038\nf 1040 1041 1042\nf 362 32 369\nf 187 1520 477\nf 70 1487 539\nf 1049 600 603\nf 613 867 869\nf 860 1050 1051\nf 1005 1052 1053\nf 753 1055 1056\nf 1057 1058 1059\nf 293 288 1522\nf 288 212 1521\nf 292 1522 1523\nf 331 1507 1508\nf 306 452 453\nf 100 44 41\nf 462 1524 78\nf 330 331 1524\nf 635 1067 1068\nf 1485 1527 1526\nf 206 1485 1528\nf 1070 1071 1072\nf 942 941 1073\nf 916 933 1075\nf 440 1529 1530\nf 157 158 1514\nf 1529 162 5\nf 371 595 546\nf 790 586 663\nf 1077 858 857\nf 1403 1434 1435\nf 1080 1081 1082\nf 531 520 1529\nf 21 1083 1084\nf 272 1532 1531\nf 690 712 802\nf 335 3571 3567\nf 1090 1030 1029\nf 1092 795 794\nf 1533 396 137\nf 661 660 1095\nf 69 444 445\nf 1099 1100 1101\nf 38 39 1536\nf 1104 1105 1106\nf 852 1107 1108\nf 1377 908 909\nf 291 377 411\nf 316 1536 516\nf 539 1488 1533\nf 1505 1536 316\nf 1110 1111 1112\nf 1539 1537 460\nf 522 441 1537\nf 834 837 1120\nf 1122 1123 1124\nf 612 1125 1041\nf 935 793 615\nf 276 278 130\nf 1686 3549 3522\nf 193 190 1541\nf 231 521 1540\nf 556 1037 982\nf 365 253 85\nf 190 532 318\nf 1810 765 3574\nf 3924 1867 3512\nf 1132 983 985\nf 208 101 102\nf 1134 1033 662\nf 1491 518 80\nf 429 133 1542\nf 1137 1138 915\nf 172 436 428\nf 1139 1140 1141\nf 549 96 54\nf 1517 1519 1518\nf 834 1119 984\nf 1543 513 286\nf 1544 797 239\nf 133 134 1544\nf 506 361 257\nf 420 327 328\nf 1158 1159 1160\nf 587 1104 1103\nf 607 1161 1027\nf 304 1546 1545\nf 364 1491 77\nf 69 70 198\nf 700 840 1112\nf 3877 626 3525\nf 1166 719 657\nf 1167 1168 858\nf 1300 3523 3524\nf 1172 1173 1174\nf 218 203 200\nf 1179 1180 1181\nf 372 183 97\nf 89 104 227\nf 1186 1187 1188\nf 172 173 1517\nf 1482 84 81\nf 1090 1192 1193\nf 394 395 353\nf 526 525 1198\nf 1199 1200 1026\nf 606 623 697\nf 1204 1205 1206\nf 1207 1208 523\nf 1209 1210 867\nf 914 913 685\nf 1478 1436 1441\nf 1029 1028 1161\nf 1211 1212 1213\nf 3858 1453 1451\nf 844 1215 1216\nf 423 193 273\nf 1220 1221 1222\nf 22 1223 1224\nf 882 898 897\nf 1519 1517 173\nf 1189 1188 1232\nf 349 350 550\nf 1071 1235 1236\nf 1237 1238 1239\nf 1221 1186 1189\nf 1510 1528 1512\nf 939 1032 1241\nf 1556 1530 5\nf 1553 1554 394\nf 404 1556 67\nf 1247 1248 1249\nf 142 199 238\nf 1251 1067 635\nf 850 1252 1253\nf 1254 1167 1077\nf 57 132 133\nf 443 430 552\nf 1187 1172 1175\nf 1256 1070 831\nf 432 405 382\nf 741 748 747\nf 434 350 351\nf 349 432 383\nf 251 124 1481\nf 395 384 352\nf 1518 1519 1550\nf 1558 410 378\nf 109 263 314\nf 285 1514 1513\nf 395 394 393\nf 202 203 410\nf 417 408 405\nf 1246 1257 1258\nf 3 1559 297\nf 1259 1260 1261\nf 403 404 347\nf 955 954 953\nf 462 50 51\nf 1096 1095 687\nf 429 147 60\nf 896 1264 1265\nf 1267 721 1268\nf 423 424 327\nf 37 179 180\nf 20 23 1187\nf 1256 1168 1071\nf 1188 1175 1269\nf 1236 1270 1271\nf 1272 536 535\nf 565 564 463\nf 1273 792 745\nf 876 1254 1255\nf 316 33 164\nf 437 1516 503\nf 194 1494 1495\nf 1275 1276 864\nf 624 625 631\nf 1277 1274 1137\nf 569 568 567\nf 1481 124 125\nf 324 325 547\nf 1279 1108 1107\nf 1525 332 329\nf 1281 1278 1137\nf 171 168 1553\nf 1488 73 396\nf 1557 1561 1562\nf 1282 856 1206\nf 1053 1277 1278\nf 778 777 607\nf 713 583 3566\nf 251 225 144\nf 4 357 348\nf 709 708 707\nf 728 48 45\nf 261 135 1563\nf 789 1286 1287\nf 74 314 263\nf 403 284 385\nf 1178 1181 1240\nf 1223 1259 1289\nf 1220 20 1186\nf 296 1518 354\nf 784 459 1538\nf 1181 1180 1291\nf 1269 1292 1257\nf 1566 1538 438\nf 136 784 1566\nf 1563 1564 169\nf 107 114 109\nf 1297 1282 1298\nf 1464 1477 1476\nf 1520 187 507\nf 1301 1302 1303\nf 1306 1307 1308\nf 175 456 1532\nf 1532 456 200\nf 1311 1266 1312\nf 872 1314 1315\nf 3836 843 878\nf 1305 1308 864\nf 1040 1275 863\nf 1564 1566 399\nf 592 1576 1575\nf 591 1575 1088\nf 1581 1584 1583\nf 1196 1152 1147\nf 710 582 583\nf 285 407 408\nf 953 954 961\nf 1594 1592 1589\nf 1585 1588 1587\nf 1592 1591 1590\nf 3885 945 944\nf 1600 1599 1598\nf 1604 1603 1602\nf 1399 1392 979\nf 1589 1590 1596\nf 1610 1609 1608\nf 298 257 361\nf 551 552 276\nf 1627 1626 3814\nf 859 811 818\nf 1078 857 1328\nf 1555 439 1530\nf 572 573 680\nf 772 465 776\nf 977 1281 918\nf 404 403 392\nf 1054 1331 750\nf 1644 1645 1643\nf 932 1265 1082\nf 1067 1333 1334\nf 1540 1539 264\nf 597 1335 643\nf 471 761 762\nf 1642 696 3831\nf 1644 1642 3842\nf 923 946 950\nf 96 549 548\nf 520 531 522\nf 445 444 443\nf 849 839 848\nf 480 759 755\nf 1646 760 481\nf 311 426 1567\nf 464 463 3497\nf 3497 1360 1361\nf 234 237 236\nf 190 193 192\nf 153 154 152\nf 231 287 286\nf 1552 1553 1551\nf 760 1646 1648\nf 3832 1647 1649\nf 828 827 826\nf 52 167 166\nf 1663 1662 1661\nf 287 107 115\nf 668 671 670\nf 634 645 640\nf 553 560 559\nf 118 36 33\nf 492 484 491\nf 3552 1652 1651\nf 582 589 584\nf 384 395 392\nf 1381 1382 1383\nf 1562 1561 435\nf 952 953 951\nf 3529 3566 583\nf 1660 1661 1667\nf 1394 1395 1396\nf 1660 1665 1664\nf 1671 1670 1669\nf 1624 1625 1673\nf 1407 1408 1409\nf 1677 1676 1675\nf 1613 1679 1677\nf 1679 1678 1676\nf 1626 1681 3811\nf 534 199 198\nf 803 733 734\nf 400 248 191\nf 65 66 143\nf 3526 1657 1656\nf 402 159 157\nf 629 628 627\nf 461 477 49\nf 758 757 495\nf 796 787 786\nf 406 407 1513\nf 342 343 166\nf 1694 1659 1658\nf 1692 1687 1694\nf 3557 1693 1652\nf 3921 1704 1703\nf 326 547 302\nf 3916 3921 1702\nf 3919 3918 1698\nf 1699 1691 3927\nf 1557 406 1560\nf 1127 1116 1117\nf 400 401 289\nf 1706 1702 1707\nf 1707 1702 1599\nf 1709 1706 1708\nf 1714 1712 1711\nf 3919 3915 1709\nf 1704 1711 1577\nf 1716 1714 1713\nf 1559 1562 1549\nf 1722 1721 1720\nf 1335 597 596\nf 410 411 377\nf 973 1170 1163\nf 168 169 399\nf 1571 682 1089\nf 1572 681 682\nf 749 646 647\nf 1730 1724 1723\nf 1594 1582 1729\nf 1729 1723 1591\nf 1384 1378 1377\nf 1732 1731 1712\nf 1724 1730 1731\nf 1722 1724 1732\nf 1721 1722 1733\nf 1734 1733 1716\nf 1735 1737 1736\nf 1733 1732 1714\nf 1663 1664 1741\nf 1742 1665 1740\nf 1738 1739 1737\nf 1665 1660 1666\nf 1665 1742 1744\nf 1725 1726 1746\nf 1591 1725 1745\nf 1590 1745 1747\nf 1745 1746 1748\nf 1659 1688 3538\nf 3287 1760 3545\nf 1185 1177 1182\nf 1664 1744 1749\nf 1741 1749 1739\nf 1746 1742 1743\nf 1726 1744 1742\nf 1115 924 1153\nf 1654 3546 3575\nf 1896 3562 3549\nf 1720 1749 1744\nf 1721 1739 1749\nf 1750 1718 1701\nf 1697 1736 1750\nf 1151 1152 1063\nf 1751 1752 1670\nf 1695 1658 1752\nf 961 962 986\nf 1770 1769 1768\nf 1766 1594 1593\nf 1731 1759 1579\nf 1579 1777 1774\nf 3928 1772 1771\nf 1764 1772 3928\nf 1777 1776 1775\nf 3762 3763 1699\nf 1004 1127 1128\nf 1774 1775 1771\nf 987 1400 1399\nf 1578 1774 1772\nf 1783 1782 1781\nf 1597 1598 1770\nf 639 1878 3895\nf 3928 3929 1769\nf 1793 1792 1789\nf 3929 3920 1798\nf 3508 3516 3773\nf 1802 1867 3924\nf 1788 1790 1794\nf 1793 1794 1795\nf 3773 3772 1675\nf 1584 1581 1783\nf 1771 1797 3920\nf 1801 1799 1798\nf 1768 1769 1798\nf 1775 1796 1797\nf 508 514 515\nf 1796 1803 1802\nf 1800 1798 3920\nf 1794 1805 1804\nf 3532 1670 1752\nf 485 1808 1807\nf 3809 3844 1640\nf 3837 3844 1810\nf 485 482 1640\nf 1690 1691 3770\nf 487 488 1648\nf 3839 1813 1816\nf 3838 1811 1648\nf 3839 1812 1811\nf 1812 1809 487\nf 1641 1809 767\nf 465 466 755\nf 767 1815 3823\nf 3822 1816 3826\nf 1809 1812 1815\nf 3837 1653 1651\nf 675 672 3541\nf 1814 1649 1822\nf 1821 1822 3821\nf 1824 1822 1649\nf 1827 1849 1850\nf 1668 1669 1662\nf 1779 1780 1848\nf 1790 1827 1805\nf 1833 1836 1853\nf 3521 3506 693\nf 1614 1615 1616\nf 1852 1853 3498\nf 1620 1621 1622\nf 1855 1765 1574\nf 1847 1852 3501\nf 1766 1765 1855\nf 1629 1630 1631\nf 1633 1622 1634\nf 1635 1636 1637\nf 1848 1833 1852\nf 1855 1861 1863\nf 1803 1866 1867\nf 1862 1863 1831\nf 1782 1855 1862\nf 3922 3924 1865\nf 1873 1872 1870\nf 1874 1869 799\nf 2045 3878 3897\nf 1219 1345 1295\nf 1615 1614 1396\nf 3884 3895 1877\nf 645 3898 3786\nf 1345 1219 1035\nf 634 639 3884\nf 15 1887 1327\nf 3910 3895 1878\nf 1894 604 605\nf 3506 3551 627\nf 3560 640 645\nf 340 341 454\nf 1897 194 195\nf 1889 1882 1877\nf 1901 94 129\nf 196 749 754\nf 1705 1704 3921\nf 340 378 166\nf 1570 1480 563\nf 1904 1494 194\nf 1781 1862 1832\nf 1905 549 55\nf 1000 994 995\nf 986 919 920\nf 1191 1185 1190\nf 2094 2077 2075\nf 1920 1921 1919\nf 453 454 1912\nf 1915 1919 1918\nf 3882 2112 2106\nf 1655 3575 3531\nf 1916 1918 1926\nf 704 3554 3561\nf 278 1916 1925\nf 3565 3564 1818\nf 1097 949 1115\nf 1927 1928 727\nf 3578 3579 3654\nf 1920 310 307\nf 1929 307 48\nf 503 504 1497\nf 1902 1903 787\nf 1816 1821 3820\nf 1925 1926 1923\nf 1535 1569 1568\nf 254 252 258\nf 1509 1497 366\nf 119 86 35\nf 219 288 293\nf 141 46 47\nf 542 205 244\nf 1940 552 430\nf 1779 1828 1827\nf 1701 1710 3915\nf 1661 3550 3515\nf 270 53 754\nf 197 1947 749\nf 1727 1728 1212\nf 53 54 232\nf 1465 1468 1479\nf 1284 3862 3854\nf 1951 446 412\nf 168 398 1554\nf 1486 1485 206\nf 506 77 78\nf 994 1001 1007\nf 951 953 963\nf 1013 3859 3861\nf 3826 3820 1820\nf 1498 1909 1954\nf 299 298 543\nf 1754 1755 1756\nf 1681 469 1683\nf 1696 1694 1695\nf 13 14 738\nf 473 476 475\nf 482 1641 768\nf 204 62 30\nf 1761 1762 820\nf 611 1763 1125\nf 1501 1503 1908\nf 1074 1073 711\nf 1960 1944 82\nf 120 1956 1960\nf 35 86 87\nf 1773 1199 1025\nf 1965 1545 1569\nf 494 761 471\nf 1966 1967 1969\nf 446 1951 1971\nf 174 368 125\nf 1580 1579 1578\nf 1785 1786 1787\nf 1972 1546 304\nf 1254 876 875\nf 1973 341 342\nf 321 1912 1973\nf 1910 277 275\nf 509 1973 514\nf 1486 1507 1527\nf 1974 1874 804\nf 1977 1976 1974\nf 1980 448 1966\nf 379 1 2\nf 1718 1715 1710\nf 884 995 992\nf 1680 1684 1678\nf 902 990 525\nf 344 255 256\nf 135 138 137\nf 448 446 1967\nf 589 605 604\nf 910 909 908\nf 1981 1966 1968\nf 1982 1981 1987\nf 1988 1979 1987\nf 1990 1988 1989\nf 1991 1989 1993\nf 322 321 320\nf 992 828 830\nf 1144 1076 1086\nf 1995 1977 1978\nf 1133 1129 1118\nf 1997 1995 1994\nf 870 1823 1335\nf 805 806 1982\nf 727 184 2000\nf 1996 1994 1988\nf 1994 1978 1979\nf 1829 616 792\nf 1830 1073 941\nf 798 803 800\nf 729 732 731\nf 2011 2012 2023\nf 1834 1835 846\nf 402 283 284\nf 2022 2023 2024\nf 1838 718 989\nf 810 955 952\nf 1823 1839 1840\nf 1430 1426 1432\nf 1215 844 1841\nf 1316 1208 873\nf 2112 1401 1384\nf 1842 1843 1844\nf 1159 846 845\nf 837 27 1846\nf 1971 1951 2030\nf 517 369 370\nf 1471 2026 2027\nf 3551 3525 626\nf 2174 2175 770\nf 935 934 1854\nf 1613 1619 1611\nf 1231 1228 1225\nf 1856 1857 891\nf 1300 1309 748\nf 1150 1149 1858\nf 1643 606 696\nf 1215 1265 1264\nf 1320 1860 902\nf 1956 1943 1944\nf 2031 668 669\nf 2034 1325 1310\nf 1879 1880 1656\nf 40 245 242\nf 1992 1993 2039\nf 888 958 957\nf 1882 1889 1996\nf 1078 1138 1255\nf 1871 912 1192\nf 294 411 218\nf 1323 2041 2026\nf 123 1960 83\nf 1324 2036 2041\nf 384 358 359\nf 1324 1325 2038\nf 1883 1884 1885\nf 287 1540 114\nf 1096 1886 1134\nf 915 1329 931\nf 806 1980 1981\nf 3533 3529 584\nf 1425 2030 2042\nf 1018 2042 1948\nf 3562 1896 740\nf 1948 2042 649\nf 1881 1882 1990\nf 1900 129 130\nf 992 995 994\nf 923 927 925\nf 3878 2045 2047\nf 1643 2048 625\nf 2050 2052 2051\nf 2045 1405 1404\nf 3876 2047 945\nf 1139 1080 1079\nf 1037 1856 892\nf 2055 2053 2057\nf 1198 989 718\nf 1619 2058 1612\nf 2058 1605 1606\nf 2059 2060 1607\nf 2058 1619 1624\nf 71 72 179\nf 405 406 1557\nf 322 1548 1972\nf 2061 2052 2063\nf 2066 2065 2067\nf 2052 2061 2064\nf 1609 1610 2066\nf 1515 151 152\nf 1009 827 993\nf 1644 3843 3815\nf 279 1915 1916\nf 1285 3863 3862\nf 1647 2066 1825\nf 2056 2057 2067\nf 319 320 1953\nf 667 666 1913\nf 2070 631 625\nf 2052 2050 2055\nf 2038 1654 1655\nf 1606 1601 2073\nf 1235 1917 1270\nf 2072 2073 2075\nf 2059 1606 2072\nf 2074 2075 2077\nf 1601 1602 2078\nf 2049 2048 1643\nf 1000 1004 1001\nf 1089 943 684\nf 1931 1286 1306\nf 1462 1459 1021\nf 1932 1933 1934\nf 875 874 1846\nf 1936 1051 1937\nf 44 45 46\nf 1534 1955 1956\nf 1541 318 317\nf 2081 2082 2084\nf 146 1945 1959\nf 1360 1941 1942\nf 985 984 1032\nf 1995 1997 1892\nf 2079 2080 2082\nf 2095 2096 2076\nf 2081 2093 2078\nf 2055 2050 2054\nf 501 2096 2095\nf 2097 2100 2099\nf 2098 2099 2094\nf 2140 2141 3893\nf 2141 2046 3894\nf 2051 2070 2048\nf 592 2102 2101\nf 2071 2064 2076\nf 2102 2097 2098\nf 593 2103 2102\nf 1019 1895 746\nf 1950 11 12\nf 725 2071 2096\nf 1385 2105 2104\nf 1379 2104 2106\nf 2107 2108 2100\nf 3908 726 502\nf 3896 502 499\nf 940 1389 2109\nf 928 929 1170\nf 3868 590 684\nf 1962 1963 1964\nf 3878 680 573\nf 2039 1993 1968\nf 1389 1390 2110\nf 1398 2111 2113\nf 3757 3755 3753\nf 3911 3883 2107\nf 949 923 924\nf 260 241 324\nf 1390 1397 2113\nf 2070 2051 2064\nf 3867 3911 2103\nf 2105 1385 1421\nf 2114 1421 1423\nf 3892 2114 2115\nf 3908 2105 2114\nf 554 2116 1902\nf 972 1163 967\nf 1765 1593 1573\nf 1984 1985 1986\nf 2053 2117 2069\nf 3573 3556 724\nf 1625 1677 1674\nf 3881 3888 2119\nf 3903 2115 2123\nf 56 1954 1906\nf 626 3877 3879\nf 2001 2002 2003\nf 3912 3841 2127\nf 2006 2007 2008\nf 2127 1413 1412\nf 2014 2015 2016\nf 2018 2019 2020\nf 1495 1424 1947\nf 1379 1377 1386\nf 3814 3811 1680\nf 2029 1885 1884\nf 3902 2128 2120\nf 629 3879 3914\nf 682 683 943\nf 724 1650 2121\nf 1757 1819 3821\nf 765 1810 3844\nf 3851 714 715\nf 706 707 3870\nf 981 986 987\nf 1457 1868 1656\nf 2017 2016 2032\nf 1458 1655 1868\nf 1776 1777 1792\nf 1959 1945 1946\nf 3663 3715 3725\nf 3913 3881 2122\nf 1093 1089 689\nf 966 965 968\nf 3852 715 3881\nf 2172 3577 2170\nf 670 2138 2137\nf 3761 787 1903\nf 1429 19 28\nf 964 963 978\nf 3841 3813 1413\nf 1473 1474 669\nf 3834 3833 1359\nf 2138 798 1870\nf 1872 1473 2137\nf 715 716 3888\nf 1881 1991 1992\nf 3843 3842 739\nf 1837 1847 3513\nf 253 58 59\nf 1808 3837 3840\nf 1647 3832 3818\nf 3865 3866 671\nf 2112 3882 3883\nf 1405 2045 2044\nf 3829 3824 1364\nf 273 1541 391\nf 576 632 3520\nf 3875 3865 668\nf 1430 1431 2140\nf 2136 3850 3860\nf 1430 2139 18\nf 3893 3894 570\nf 1442 1439 2046\nf 1431 1442 2141\nf 3869 3913 3850\nf 3808 3823 1817\nf 3866 3893 695\nf 2142 2025 798\nf 571 2143 2142\nf 3819 3814 1618\nf 2143 2022 2025\nf 694 2142 2138\nf 681 1572 2144\nf 2144 2011 2022\nf 328 210 457\nf 1572 2010 2011\nf 630 3892 3903\nf 2085 2086 2087\nf 2090 2091 2092\nf 572 2144 2143\nf 1875 1976 2153\nf 1873 1875 2149\nf 2103 2107 2097\nf 2157 2043 2155\nf 2128 1412 1414\nf 3874 3871 589\nf 3872 3870 628\nf 2149 2153 2158\nf 2159 2158 3905\nf 698 3907 3902\nf 3823 3847 3827\nf 2158 2156 3874\nf 1438 2044 2046\nf 2035 2154 2160\nf 2161 2160 3889\nf 3871 3891 605\nf 3873 17 18\nf 370 29 61\nf 649 1951 413\nf 2153 2157 2156\nf 1428 1429 1449\nf 1490 76 73\nf 3890 3905 710\nf 2155 1891 3891\nf 1456 1457 1880\nf 2125 2088 2087\nf 2160 2159 3890\nf 300 1959 505\nf 1064 1062 1046\nf 2043 1892 1891\nf 2129 2130 2004\nf 2131 2132 2133\nf 3848 3849 1450\nf 1998 1975 804\nf 3879 673 3864\nf 474 492 490\nf 1164 1165 1182\nf 707 2162 3909\nf 708 1472 2162\nf 2162 2161 3886\nf 548 549 1905\nf 1145 1136 1114\nf 705 2121 1650\nf 2043 2157 1977\nf 737 738 554\nf 1034 1195 1191\nf 458 1905 1907\nf 195 196 232\nf 2040 2039 1969\nf 2145 2146 2147\nf 2054 2050 2049\nf 3832 1646 1639\nf 9 12 11\nf 252 364 506\nf 472 478 479\nf 1865 3924 3791\nf 900 903 910\nf 1941 2150 2151\nf 1921 1928 1927\nf 2163 1897 1898\nf 271 156 153\nf 1904 1326 12\nf 282 263 264\nf 1872 1873 2154\nf 1326 1904 1897\nf 628 692 693\nf 1724 1722 1719\nf 1549 1562 436\nf 1582 1583 1730\nf 1921 1920 1929\nf 3563 3511 10\nf 2152 2037 1950\nf 475 772 774\nf 769 2172 2171\nf 2171 2165 476\nf 476 773 772\nf 1791 565 464\nf 2166 2167 2168\nf 595 371 372\nf 483 1809 1641\nf 762 2174 2173\nf 771 483 484\nf 486 487 1809\nf 297 1549 1517\nf 763 2175 2174\nf 776 755 759\nf 184 185 2176\nf 412 447 303\nf 2005 2176 185\nf 3811 1681 1682\nf 2164 2170 756\nf 1472 2035 2161\nf 950 951 964\nf 2177 2178 2179\nf 227 226 1510\nf 2182 2183 2184\nf 2186 2019 2018\nf 1985 2182 2181\nf 214 736 729\nf 865 849 842\nf 849 865 1357\nf 559 3187 3188\nf 16 13 737\nf 560 553 498\nf 3193 3196 3195\nf 3200 3197 3198\nf 3211 3214 3213\nf 3215 3218 3217\nf 2187 2188 2002\nf 2169 2168 2189\nf 2192 2086 2085\nf 2193 2194 2195\nf 2198 2199 2200\nf 1998 1982 1979\nf 3664 3665 3668\nf 2116 1898 1903\nf 1859 2006 2009\nf 3904 2123 2126\nf 1135 1148 1087\nf 2201 2202 2015\nf 1149 111 110\nf 3221 3222 3218\nf 2203 2204 2205\nf 579 580 1895\nf 1755 2207 2208\nf 3891 3910 1893\nf 1211 2151 2150\nf 868 997 2210\nf 944 945 930\nf 2130 2129 2202\nf 233 1903 1898\nf 665 751 750\nf 1167 1254 999\nf 2211 2212 2213\nf 2215 2216 2217\nf 3867 593 590\nf 2220 221 220\nf 823 824 948\nf 3225 3220 3227\nf 266 204 455\nf 3882 2108 2107\nf 2221 2222 3519\nf 2223 2224 2225\nf 2207 2125 2124\nf 673 674 3869\nf 817 1000 996\nf 2227 2228 2146\nf 1947 1948 646\nf 3900 2135 17\nf 2229 1635 1638\nf 1679 1613 1618\nf 1637 2231 2232\nf 2233 2234 1617\nf 2231 2235 2236\nf 1381 1638 2238\nf 3586 2241 2242\nf 1630 2234 2233\nf 1681 1626 472\nf 1408 1407 2243\nf 3236 3239 3238\nf 3232 3241 3240\nf 1637 2230 2238\nf 3237 3238 3233\nf 1409 1408 1634\nf 1380 1383 2247\nf 1628 2248 2245\nf 2249 1623 1622\nf 1632 1634 1408\nf 2248 1628 1631\nf 2235 2231 1637\nf 3245 3246 3199\nf 631 2070 2071\nf 515 167 52\nf 782 781 2251\nf 2253 2254 2255\nf 397 281 459\nf 1456 2039 2040\nf 2040 2041 2036\nf 1401 2112 2111\nf 2041 2040 1970\nf 1535 121 414\nf 1586 1587 2080\nf 2256 983 1132\nf 1700 1699 3918\nf 3815 1896 1686\nf 1263 2256 1886\nf 598 1841 844\nf 2257 2258 2259\nf 2258 2261 2262\nf 2259 2262 2263\nf 2260 2259 2216\nf 1967 1971 1970\nf 2212 1964 1963\nf 922 921 2218\nf 1312 1098 1101\nf 1307 1306 1286\nf 1548 1569 1546\nf 2266 2267 2268\nf 2265 2268 2270\nf 2272 2265 2269\nf 2273 2266 2265\nf 2275 2276 2277\nf 2278 2274 2277\nf 2284 2285 3617\nf 2287 2283 2286\nf 2288 2289 2283\nf 2290 2284 3639\nf 2291 2292 2293\nf 2295 2291 2294\nf 2296 2297 2291\nf 2297 2298 2292\nf 2294 2293 2299\nf 2279 2294 2300\nf 2298 2301 2302\nf 2292 2302 2303\nf 2293 2303 2304\nf 2304 2305 2306\nf 2308 2309 2310\nf 2302 2307 2310\nf 2301 2311 2307\nf 2312 2308 2307\nf 2313 2314 2315\nf 2318 2319 2320\nf 3748 3771 2322\nf 2324 2325 2273\nf 2313 2324 2326\nf 2328 2324 2313\nf 2329 2325 2324\nf 2331 2332 2333\nf 2330 2333 2267\nf 2325 2330 2266\nf 2329 2331 2330\nf 2333 2334 2335\nf 2336 2334 2333\nf 2338 2336 2332\nf 2331 2339 2337\nf 2329 2340 2339\nf 2328 2341 2340\nf 2316 2342 2341\nf 2342 2316 2315\nf 2344 2345 2346\nf 2341 2344 2347\nf 2342 2348 2344\nf 2348 2349 2345\nf 2350 2351 2352\nf 2339 2350 2353\nf 2340 2347 2350\nf 2347 2346 2351\nf 2337 2353 2354\nf 2353 2352 2355\nf 2356 2277 2276\nf 2356 2357 2358\nf 2356 2359 2296\nf 2279 2277 2356\nf 2360 2361 2270\nf 2267 2335 2360\nf 2362 2363 2364\nf 2362 2365 2361\nf 2366 2362 2358\nf 2366 2367 2363\nf 2357 2276 2368\nf 2366 2368 2369\nf 2276 2275 2370\nf 2368 2370 2282\nf 2372 2373 2374\nf 2375 2376 2377\nf 2378 2379 2380\nf 2382 2378 2381\nf 2384 2385 2378\nf 2385 2386 2379\nf 2388 2389 2390\nf 2387 2390 2392\nf 2393 2394 2387\nf 2394 2395 2388\nf 2186 2401 2402\nf 2403 2404 2327\nf 2272 2403 2326\nf 2271 2405 2403\nf 2405 2406 2404\nf 2404 2407 2408\nf 2409 2410 2411\nf 2409 2412 2271\nf 2413 2409 2269\nf 2413 2414 2410\nf 2417 2415 2416\nf 2417 2418 2406\nf 2412 2417 2405\nf 2411 2415 2417\nf 2406 2419 2407\nf 3569 3520 632\nf 2420 2398 2397\nf 2420 2421 2423\nf 2424 2420 2422\nf 2424 2399 2398\nf 2425 2391 2392\nf 2421 2425 2426\nf 2397 2396 2425\nf 2396 2393 2391\nf 2427 2314 2408\nf 2430 2427 2428\nf 2343 2315 2427\nf 2432 2343 2430\nf 2431 2430 2429\nf 2435 2431 2433\nf 2436 2432 2431\nf 2428 2408 2407\nf 2422 2437 2407\nf 2438 2437 2422\nf 2429 2428 2437\nf 2440 2434 2433\nf 2441 2439 2433\nf 2442 2443 2439\nf 2443 2444 2440\nf 2445 2446 2444\nf 2447 2445 2443\nf 2383 2381 2445\nf 2381 2380 2446\nf 2448 2438 2423\nf 2449 2448 2426\nf 2441 2448 2449\nf 2441 2429 2438\nf 2449 2392 2390\nf 2450 2390 2389\nf 2447 2450 2451\nf 2442 2449 2450\nf 2451 2389 2453\nf 2452 2453 2455\nf 2382 2452 2454\nf 2383 2451 2452\nf 2456 2457 2373\nf 2459 2460 2461\nf 2463 2464 2465\nf 2462 2465 2285\nf 2466 2462 2284\nf 2467 2463 2462\nf 2461 2460 3703\nf 2468 2469 2470\nf 2472 2468 2471\nf 2474 2461 3701\nf 2475 2476 2477\nf 2458 2455 2453\nf 2479 2453 2389\nf 2395 2480 2479\nf 2480 2459 2458\nf 2481 2482 2483\nf 2484 113 2485\nf 2471 2470 2464\nf 2473 2471 2463\nf 2487 2467 2466\nf 2486 2466 2290\nf 2490 2486 2488\nf 2491 2487 2486\nf 2492 2473 2467\nf 2493 2492 2487\nf 2494 2495 2492\nf 2495 2472 2473\nf 3690 2496 2474\nf 3691 2497 2496\nf 2498 2494 2493\nf 2500 2498 2499\nf 3700 3687 2498\nf 3687 3691 2494\nf 2306 2504 2278\nf 2305 2505 2504\nf 2504 2505 2507\nf 2506 2507 2288\nf 2274 2506 2508\nf 2278 2504 2506\nf 3633 3632 2489\nf 3620 3633 2488\nf 2507 2509 2289\nf 2505 2510 2509\nf 2508 2288 2287\nf 2511 2287 2281\nf 2370 2511 2280\nf 2275 2508 2511\nf 2512 2490 2489\nf 2514 2512 2513\nf 2517 2512 2514\nf 2517 2491 2490\nf 2499 2493 2491\nf 2499 2517 2516\nf 2510 2505 2519\nf 2518 2519 2521\nf 3616 2518 2520\nf 3632 2510 2518\nf 2522 2304 2303\nf 2523 2522 2310\nf 2305 2304 2522\nf 2524 2522 2523\nf 2519 2524 2525\nf 2505 2305 2524\nf 2308 2312 2527\nf 2526 2527 2529\nf 2531 2526 2528\nf 2309 2308 2526\nf 1214 2532 2151\nf 2419 2406 2418\nf 2533 2345 2349\nf 2534 2533 2317\nf 2535 2536 2533\nf 2536 2346 2345\nf 2537 2351 2346\nf 2538 2537 2536\nf 2539 2540 2537\nf 2540 2352 2351\nf 2312 2355 2541\nf 2527 2541 2542\nf 2543 2435 2434\nf 2545 2543 2544\nf 2319 2318 2543\nf 2318 2436 2435\nf 2514 2515 2548\nf 2547 2548 2550\nf 2552 2547 2549\nf 2552 2516 2514\nf 2520 2521 2554\nf 2553 2554 2556\nf 3590 2553 2555\nf 3621 2520 2553\nf 2523 2309 2531\nf 2557 2531 2530\nf 2525 2523 2557\nf 2559 2557 2558\nf 2554 2559 2560\nf 2521 2525 2559\nf 2561 2375 2007\nf 2376 2562 2225\nf 2564 2565 2566\nf 2563 2566 338\nf 2569 2563 2567\nf 2570 2564 2563\nf 2572 2573 2574\nf 2571 2574 2576\nf 2544 2571 2575\nf 2544 2434 2572\nf 2578 2570 2569\nf 2579 2577 2569\nf 2574 2577 2579\nf 2573 2578 2577\nf 2581 2582 2583\nf 2584 2580 2583\nf 3697 3675 2580\nf 3676 2581 2580\nf 2582 2516 2552\nf 2583 2552 2551\nf 2500 2501 2589\nf 2590 2588 2589\nf 3693 3695 2588\nf 3695 3700 2500\nf 2501 2516 2591\nf 2592 2593 2594\nf 2386 2385 2503\nf 2596 2379 2386\nf 2596 2374 2373\nf 2599 2596 2597\nf 2599 2380 2379\nf 2600 2446 2380\nf 2600 2599 2598\nf 2603 2600 2601\nf 2603 2444 2446\nf 2604 2603 2602\nf 2440 2604 2605\nf 2444 2603 2604\nf 2606 2582 2581\nf 2606 2607 2590\nf 2591 2516 2582\nf 3676 2587 2456\nf 3689 2456 2372\nf 2478 2477 2608\nf 2609 2610 2611\nf 2564 2570 2613\nf 2612 2613 2598\nf 2457 2612 2597\nf 2565 2564 2612\nf 2614 2613 2570\nf 2615 2614 2578\nf 2602 2601 2614\nf 2601 2598 2613\nf 2572 2434 2605\nf 2616 2605 2602\nf 2573 2572 2616\nf 1459 1016 1045\nf 1265 1215 1079\nf 1612 1608 1609\nf 2067 2057 2069\nf 1135 1109 1136\nf 1938 1937 2618\nf 95 548 458\nf 2058 1607 1608\nf 1454 1469 1445\nf 655 2620 1048\nf 722 1049 2621\nf 1131 985 1033\nf 562 1481 366\nf 1723 1719 1725\nf 1786 1123 1122\nf 267 300 323\nf 1325 2034 1654\nf 1468 1165 1435\nf 658 657 529\nf 8 532 247\nf 382 1557 1559\nf 788 619 1307\nf 940 930 929\nf 911 2622 2623\nf 2621 2620 655\nf 2624 1857 2625\nf 998 1846 27\nf 635 638 2626\nf 2627 641 644\nf 2628 2627 1840\nf 1200 1199 2627\nf 1199 1773 641\nf 1520 515 49\nf 1452 1350 1349\nf 2630 2631 1859\nf 644 643 1335\nf 1197 1198 717\nf 2632 717 719\nf 2628 2632 1166\nf 2629 1197 2632\nf 1762 2633 1050\nf 1823 870 873\nf 2634 2635 794\nf 1042 794 2635\nf 93 94 1901\nf 2036 2038 1458\nf 1527 332 1525\nf 877 876 1274\nf 134 409 797\nf 1328 893 931\nf 607 777 1091\nf 1043 2635 2637\nf 2636 2637 2639\nf 1275 2636 2638\nf 1275 1040 1043\nf 1272 2624 2626\nf 2640 2626 638\nf 1110 2641 2640\nf 536 1272 2640\nf 1472 708 1474\nf 2042 2030 1951\nf 1058 1106 1105\nf 2642 2623 2643\nf 1192 2642 2644\nf 912 2623 2642\nf 3898 1657 3526\nf 1049 1048 2620\nf 616 1829 1885\nf 1331 1913 666\nf 990 989 1198\nf 1911 1912 321\nf 1298 1282 1205\nf 76 1490 1504\nf 1502 1500 421\nf 610 609 658\nf 1146 937 659\nf 2646 911 914\nf 1280 1239 1238\nf 1453 1454 1452\nf 835 942 1843\nf 1271 1270 1173\nf 1893 1878 1826\nf 1050 2633 1937\nf 2651 2652 814\nf 1006 1053 1281\nf 2653 1084 2654\nf 1001 1004 1003\nf 585 997 679\nf 2624 1272 621\nf 555 678 2028\nf 1207 526 1197\nf 1626 1627 478\nf 426 389 419\nf 1030 1193 1773\nf 3898 645 634\nf 534 1533 138\nf 1125 1092 1042\nf 333 228 88\nf 569 1570 561\nf 1489 2656 871\nf 1857 2624 620\nf 1673 1674 3770\nf 622 861 864\nf 619 788 891\nf 261 544 545\nf 393 1555 1556\nf 2658 2659 2647\nf 1075 2658 2657\nf 1081 1080 2658\nf 1080 1139 2659\nf 2644 2643 1141\nf 2660 1141 1140\nf 642 2660 1336\nf 1193 2644 2660\nf 679 678 555\nf 1503 1501 1500\nf 1318 1317 1101\nf 2661 2659 1139\nf 2661 1142 2663\nf 2662 2647 2659\nf 2662 2663 2622\nf 2664 2622 911\nf 2648 2664 2646\nf 2648 2647 2662\nf 2083 2094 2093\nf 1442 1440 1437\nf 519 329 330\nf 1885 2029 1209\nf 799 800 805\nf 1946 1943 1952\nf 1302 1301 1253\nf 566 2005 224\nf 1389 940 928\nf 683 682 681\nf 836 835 1842\nf 2665 1842 1845\nf 2666 2665 1303\nf 24 836 2665\nf 831 1072 2650\nf 1845 1844 1304\nf 2010 2013 2012\nf 1051 1936 2667\nf 1522 1521 1511\nf 531 440 441\nf 1250 635 2625\nf 2669 2625 1857\nf 783 2669 1856\nf 782 1250 2669\nf 720 2670 2671\nf 1055 1931 1930\nf 1333 1067 1251\nf 619 622 1308\nf 652 655 2619\nf 521 522 1539\nf 1838 988 537\nf 2672 537 536\nf 1113 2672 2641\nf 1113 527 1838\nf 1016 1013 1351\nf 3897 573 570\nf 226 227 104\nf 3564 3669 3827\nf 1036 1037 1884\nf 2674 1301 1304\nf 2673 1304 1844\nf 1334 2673 2675\nf 1333 2674 2673\nf 2676 702 1039\nf 1843 2676 2675\nf 942 1074 2676\nf 1074 699 702\nf 752 1057 1055\nf 1200 1166 656\nf 1120 1846 874\nf 2677 874 877\nf 1241 2677 1052\nf 1119 1120 2677\nf 1560 1513 151\nf 1208 1316 611\nf 215 735 736\nf 1460 1459 1462\nf 1208 1207 1839\nf 854 856 1282\nf 72 445 182\nf 544 1563 1565\nf 217 200 456\nf 2047 1404 930\nf 1204 2618 1937\nf 913 777 686\nf 840 527 1113\nf 1531 729 730\nf 2678 778 1283\nf 601 667 1914\nf 1537 1538 459\nf 1819 1757 3509\nf 991 1860 862\nf 2679 711 1073\nf 2680 1830 983\nf 779 2680 2256\nf 778 2678 2680\nf 2678 2679 1830\nf 180 542 245\nf 1886 1132 1131\nf 1138 1137 1274\nf 1841 598 1140\nf 75 313 314\nf 193 423 420\nf 2681 2682 1099\nf 170 1567 1565\nf 349 416 417\nf 1121 1124 2684\nf 547 797 409\nf 123 122 121\nf 2643 2663 1142\nf 1542 1544 513\nf 899 900 907\nf 1210 677 676\nf 791 663 982\nf 24 27 837\nf 893 896 932\nf 1860 1320 863\nf 535 538 862\nf 1569 1548 1547\nf 181 455 542\nf 660 659 937\nf 2013 567 568\nf 887 890 2631\nf 1524 1508 361\nf 520 161 162\nf 2653 855 854\nf 1238 1237 2655\nf 718 1838 527\nf 1492 1493 518\nf 792 616 615\nf 599 846 1835\nf 290 457 456\nf 312 171 1552\nf 1248 2687 1179\nf 153 149 177\nf 1240 1181 1222\nf 1257 1292 1235\nf 1291 1084 1083\nf 1052 877 1277\nf 743 742 2618\nf 444 140 430\nf 23 1224 1172\nf 2687 2654 1180\nf 102 103 409\nf 1283 607 610\nf 373 497 650\nf 1180 2654 1084\nf 855 2653 2655\nf 1173 1270 1917\nf 2689 850 853\nf 2688 853 1108\nf 2686 2688 1279\nf 2687 2689 2688\nf 2691 1302 1252\nf 2690 1252 850\nf 1248 2690 2689\nf 1248 1247 2691\nf 2692 617 24\nf 2691 2692 2666\nf 1247 1258 2692\nf 1258 1168 617\nf 394 1554 1555\nf 2693 1260 1259\nf 1072 1236 1262\nf 1257 1071 1168\nf 398 439 1555\nf 871 1784 1314\nf 1178 1232 1249\nf 1224 1289 1173\nf 1010 2617 2618\nf 1175 1174 1292\nf 2654 2687 2686\nf 2694 1157 1216\nf 71 418 1487\nf 701 1112 1111\nf 1072 1261 2649\nf 1085 2695 22\nf 895 2694 1264\nf 1601 1606 1605\nf 1259 1262 1271\nf 21 20 1220\nf 988 991 538\nf 598 597 1336\nf 1050 860 818\nf 1298 2645 1761\nf 1168 1167 618\nf 858 1168 1256\nf 39 242 516\nf 2652 1761 819\nf 313 316 315\nf 2026 1970 1971\nf 2656 1489 596\nf 814 819 818\nf 2696 2651 813\nf 1260 2696 2649\nf 2693 2697 2696\nf 2697 1296 2651\nf 381 355 390\nf 3818 3819 1611\nf 1475 1427 1428\nf 1292 1174 1917\nf 1551 353 354\nf 1526 1525 1523\nf 1593 1589 1595\nf 1232 1269 1246\nf 2695 2693 1223\nf 2698 1297 1296\nf 2698 2697 2693\nf 2685 2698 2695\nf 2685 854 1297\nf 562 1497 271\nf 2652 2651 1296\nf 1365 1366 1364\nf 1958 1946 1953\nf 585 588 2210\nf 1561 1560 1515\nf 975 2648 1146\nf 347 67 68\nf 1038 1111 636\nf 2700 781 780\nf 2699 780 1036\nf 2701 2699 1883\nf 851 2700 2699\nf 2674 1333 2251\nf 2702 2251 781\nf 1253 2702 2700\nf 1301 2674 2702\nf 1519 387 388\nf 1273 1239 1280\nf 2703 1280 1107\nf 2701 2703 852\nf 1829 1273 2703\nf 91 260 326\nf 1784 871 2656\nf 2683 2684 2705\nf 604 1894 3499\nf 1669 3534 3548\nf 2062 2063 2060\nf 621 535 861\nf 2023 731 732\nf 686 779 1263\nf 857 1051 2668\nf 883 830 882\nf 637 636 1111\nf 2668 894 893\nf 2667 921 894\nf 1039 1038 1068\nf 313 75 1504\nf 1911 1972 305\nf 1936 2706 1964\nf 2707 2402 2401\nf 2708 2707 2183\nf 738 2163 2116\nf 2349 2348 2432\nf 2711 2712 2020\nf 2400 2711 2019\nf 2714 2715 2716\nf 2717 2718 2719\nf 2721 2722 2723\nf 2726 2727 2728\nf 2725 2728 2730\nf 2731 2725 2729\nf 2732 2726 2725\nf 2734 2735 2736\nf 2738 2739 2740\nf 2737 2740 2741\nf 2744 2745 2746\nf 2745 2744 2748\nf 2749 2750 2747\nf 2753 2749 2751\nf 2250 1380 2246\nf 2754 1396 1614\nf 3598 2754 2755\nf 3608 2745 2747\nf 1617 2756 2755\nf 2242 2758 1620\nf 2191 2085 2760\nf 2761 2759 2760\nf 3648 2759 2761\nf 3623 2191 2759\nf 3209 3248 3223\nf 1395 1394 2236\nf 1395 2235 1636\nf 1616 1635 2229\nf 1617 2234 2763\nf 2756 2763 3628\nf 2765 2763 2234\nf 2766 2764 3628\nf 3202 3199 3246\nf 1516 437 435\nf 2768 2769 3552\nf 1495 541 578\nf 2135 2136 28\nf 3526 3540 2773\nf 1690 1689 3926\nf 2778 2779 2780\nf 2777 2780 2781\nf 2784 2778 2777\nf 3555 3521 575\nf 1494 1904 9\nf 3529 2016 2015\nf 451 61 505\nf 2786 2787 2788\nf 2779 2786 2785\nf 2789 2752 2741\nf 2790 2753 2752\nf 2792 2793 2794\nf 2793 2795 2796\nf 2796 2797 2750\nf 2794 2796 2749\nf 2798 1393 1396\nf 3591 2798 2754\nf 2798 2799 2800\nf 3591 3662 2799\nf 2797 2796 2795\nf 2791 2794 2753\nf 216 233 232\nf 3804 2806 2807\nf 3805 2809 2804\nf 2769 2810 3557\nf 183 185 184\nf 2811 2812 2775\nf 2804 2811 2776\nf 2809 2813 2811\nf 2812 2811 2813\nf 3805 2808 2816\nf 3796 2818 2815\nf 3798 3800 2820\nf 3793 2823 2819\nf 2824 2736 2735\nf 2823 2825 2824\nf 2825 2823 2826\nf 3793 2822 2827\nf 3796 2817 2826\nf 2713 2716 2829\nf 2828 2829 2831\nf 2822 2821 2828\nf 2821 2820 2713\nf 2832 2827 2822\nf 2833 2832 2830\nf 2834 2835 2832\nf 2835 2818 2827\nf 2837 2838 2839\nf 2836 2839 2841\nf 2842 2836 2840\nf 2843 2837 2836\nf 2840 2841 2845\nf 2718 2844 2845\nf 2730 2728 2844\nf 2727 2840 2844\nf 2846 2845 2841\nf 2829 2846 2847\nf 2847 2841 2839\nf 2848 2839 2838\nf 2833 2848 2849\nf 2831 2847 2848\nf 2850 2814 2851\nf 2853 2850 2852\nf 2783 2777 2782\nf 2785 2788 2853\nf 2780 2785 2856\nf 2857 2858 2855\nf 2859 2857 2782\nf 2861 2843 2842\nf 2860 2842 2727\nf 2862 2860 2726\nf 2863 2861 2860\nf 2861 2863 2858\nf 2843 2861 2857\nf 2856 2853 2854\nf 2781 2856 2864\nf 2837 2843 2859\nf 2838 2837 2864\nf 2852 2851 2834\nf 2854 2852 2849\nf 2865 2815 2818\nf 2851 2865 2835\nf 2813 2809 2815\nf 2814 2813 2865\nf 2866 2812 2814\nf 2788 2866 2850\nf 2866 2788 2787\nf 2812 2866 2867\nf 1892 1889 1890\nf 2720 2719 2846\nf 298 299 256\nf 2872 2714 2713\nf 3794 3800 2735\nf 2729 2730 2874\nf 2846 2716 2715\nf 2876 2877 2878\nf 2733 2875 2878\nf 3806 3807 2879\nf 3806 2880 2872\nf 2882 2883 2884\nf 2881 2884 2715\nf 2880 2881 2714\nf 2879 2882 2881\nf 2885 2886 2887\nf 2888 2889 2890\nf 2885 2890 2892\nf 2893 2886 2885\nf 1942 2151 2532\nf 2895 2892 2720\nf 2884 2895 2870\nf 2883 2896 2895\nf 2896 2891 2892\nf 2889 2874 2717\nf 2890 2717 2720\nf 2897 2893 2891\nf 2898 2897 2896\nf 2899 2898 2883\nf 2900 2899 2882\nf 3797 2900 2879\nf 2876 2902 2901\nf 2901 2902 2904\nf 1785 2656 1835\nf 2905 2906 2899\nf 2907 2908 2897\nf 3797 2901 2903\nf 632 576 577\nf 3532 3547 2867\nf 434 385 550\nf 2768 2770 3640\nf 2910 2911 1623\nf 2241 2912 2758\nf 3640 2770 2913\nf 1623 2911 2757\nf 3587 2242 2757\nf 2229 1381 1380\nf 2765 1630 1629\nf 2915 2914 1629\nf 3642 3641 2914\nf 3641 3622 2765\nf 2919 2916 3642\nf 2918 2915 2912\nf 3614 2918 2241\nf 3618 2919 3613\nf 3541 2177 2180\nf 3521 2002 2188\nf 3705 3573 714\nf 2925 2766 2917\nf 2924 2917 2916\nf 3610 2924 2919\nf 3611 2925 2924\nf 2926 2927 2766\nf 3658 2926 2925\nf 1715 1713 1705\nf 785 497 498\nf 1387 651 565\nf 374 375 1899\nf 3573 2203 2206\nf 2929 2930 2886\nf 2932 2933 2934\nf 1905 1906 1908\nf 2930 2935 2936\nf 2938 2935 2930\nf 1493 369 517\nf 1603 1585 1586\nf 2939 2938 2929\nf 2940 2939 2908\nf 3665 1707 1600\nf 2886 2930 2937\nf 2906 2907 2898\nf 2908 2929 2893\nf 2942 2934 2933\nf 2941 2933 2937\nf 3501 2941 2936\nf 3498 2942 2941\nf 2943 2944 2710\nf 2943 2873 2874\nf 2945 2943 2889\nf 2946 2944 2943\nf 2932 2945 2888\nf 2931 2946 2945\nf 727 1928 1929\nf 2903 2904 2947\nf 545 594 42\nf 1480 1570 533\nf 2907 2906 2949\nf 2906 2905 2950\nf 1558 452 215\nf 3799 2903 2948\nf 2054 1645 1644\nf 3705 3684 2203\nf 1828 1837 1849\nf 2953 2954 2955\nf 2952 2955 2957\nf 2956 2261 2258\nf 2953 2952 2258\nf 2454 2455 2474\nf 3747 2958 2959\nf 3747 2132 2131\nf 2961 2962 2963\nf 2958 2963 2964\nf 222 221 2485\nf 2477 2226 2966\nf 2631 2561 2006\nf 2384 2454 2496\nf 2967 2968 2959\nf 2968 2967 2970\nf 3759 2033 2971\nf 3784 2971 2960\nf 2972 2971 2033\nf 2007 2377 2973\nf 3560 3559 2134\nf 1836 1833 1832\nf 3506 2188 2090\nf 955 810 959\nf 672 675 674\nf 633 710 713\nf 2024 732 733\nf 2025 2024 803\nf 425 1552 1550\nf 2975 2976 888\nf 1319 1158 1157\nf 1212 1935 2977\nf 889 957 2228\nf 1978 1974 1975\nf 1138 1078 1329\nf 751 665 2979\nf 1619 1613 1625\nf 2252 2255 1728\nf 1157 1160 845\nf 2706 1936 1938\nf 2980 2255 2254\nf 2982 2975 887\nf 802 712 711\nf 678 677 2029\nf 527 840 690\nf 1839 1207 2629\nf 2983 2984 602\nf 2983 1914 1913\nf 2985 2983 1331\nf 2986 2984 2983\nf 2987 2637 2635\nf 2985 2988 2639\nf 2990 1319 1318\nf 2991 2992 2993\nf 2995 2991 2994\nf 1106 1058 2991\nf 1058 1057 2992\nf 1415 1406 1365\nf 1317 922 2217\nf 1932 1728 2255\nf 926 2998 2999\nf 3001 2993 2992\nf 3000 2992 1057\nf 2999 3000 2978\nf 2998 3001 3000\nf 3002 2998 2209\nf 3002 3003 2996\nf 2993 3001 3002\nf 1103 1106 2995\nf 3004 2995 2996\nf 2209 2210 3004\nf 588 1103 3004\nf 295 296 433\nf 2638 2639 2988\nf 3005 2988 1056\nf 1305 3005 1930\nf 1276 2638 3005\nf 1987 1968 1993\nf 2633 1762 1761\nf 1423 1422 1417\nf 3561 3517 739\nf 2671 2670 2704\nf 1473 1872 2035\nf 922 1317 2694\nf 1401 1400 1378\nf 556 2028 1884\nf 842 848 3825\nf 1854 2706 2617\nf 933 1082 1081\nf 1440 1441 1436\nf 529 657 719\nf 1031 1025 1027\nf 777 913 912\nf 2645 1205 1204\nf 441 438 1538\nf 790 1287 587\nf 2065 2066 1610\nf 1860 991 990\nf 60 106 87\nf 1424 1017 1948\nf 332 1527 1507\nf 261 262 138\nf 187 188 449\nf 1719 1720 1726\nf 1931 1055 1059\nf 3006 1059 1105\nf 1287 3006 1104\nf 1286 1931 3006\nf 3007 3008 3009\nf 3009 3008 653\nf 618 999 26\nf 2029 677 1210\nf 957 601 600\nf 1049 2146 2228\nf 2631 890 3011\nf 890 889 2227\nf 3011 2227 2145\nf 2561 3011 3012\nf 1026 656 609\nf 2706 1854 1961\nf 3013 2965 2485\nf 2148 2147 1267\nf 3015 1267 1266\nf 2997 2264 1101\nf 642 641 1773\nf 186 477 461\nf 1041 1040 1320\nf 1121 3016 3017\nf 2984 2986 2987\nf 3018 2987 2634\nf 1048 603 3018\nf 603 602 2984\nf 2679 2678 801\nf 2062 2072 2074\nf 2061 2074 2076\nf 414 303 1545\nf 3016 1121 2683\nf 3019 2683 2704\nf 3020 3019 2670\nf 3007 3016 3019\nf 1871 1090 1091\nf 3021 1787 1122\nf 3022 3021 3017\nf 1314 3021 3022\nf 1784 1787 3021\nf 1316 1315 1763\nf 3023 3022 3010\nf 3023 3009 795\nf 1763 3023 1092\nf 1315 3022 3023\nf 3016 3007 3010\nf 752 751 2978\nf 2986 2639 2637\nf 3024 654 653\nf 3020 3024 3008\nf 720 723 3024\nf 723 2621 654\nf 2681 1098 1268\nf 2671 3025 1268\nf 2705 2681 3025\nf 1313 3026 3027\nf 3026 3028 3029\nf 3027 3029 3030\nf 1311 3027 3031\nf 2012 568 731\nf 917 2657 974\nf 743 1206 856\nf 1888 2027 2030\nf 2682 2705 2684\nf 3032 2684 1124\nf 1100 3032 2989\nf 1099 2682 3032\nf 1123 2990 2989\nf 3033 1834 1159\nf 2990 3033 1158\nf 1123 1786 3033\nf 1786 1785 1834\nf 1054 1056 2988\nf 2979 665 664\nf 2623 2622 2663\nf 1095 660 688\nf 3034 3035 2261\nf 3036 3034 2956\nf 3037 3038 3034\nf 3038 3039 3035\nf 3040 3041 3026\nf 2263 3042 3041\nf 3042 3039 3043\nf 3041 3043 3028\nf 3044 3045 3043\nf 3046 3047 3045\nf 3047 3048 3049\nf 3043 3045 3049\nf 3038 3037 3051\nf 3050 3051 3053\nf 3052 3046 3044\nf 3039 3038 3050\nf 3054 3035 3039\nf 3042 2263 2262\nf 2261 3035 3054\nf 2216 2263 2997\nf 2212 2211 2667\nf 3056 868 2209\nf 934 3057 1961\nf 2013 3755 3757\nf 3058 3059 3060\nf 3059 3062 3063\nf 3060 3063 2213\nf 3061 3060 1963\nf 3063 3064 2214\nf 3062 3065 3064\nf 3065 2257 2260\nf 3064 2260 2215\nf 3066 3067 3031\nf 2608 2966 3067\nf 2966 2562 3068\nf 3031 3067 3068\nf 2455 2458 2461\nf 2476 2223 2226\nf 3551 2090 2089\nf 3526 2774 3769\nf 433 354 355\nf 2115 1423 1419\nf 3509 2868 2922\nf 3769 3737 2131\nf 3069 2611 2167\nf 2971 2972 2928\nf 2961 2774 2773\nf 3070 2475 2478\nf 2610 2609 3071\nf 2502 2371 2374\nf 3072 2592 2595\nf 2964 3046 3052\nf 3048 2593 3074\nf 2593 2592 3075\nf 3075 3030 3029\nf 3074 3029 3028\nf 3072 3076 3075\nf 3076 3072 2482\nf 2481 2608 3066\nf 3076 3066 3030\nf 2967 3052 3053\nf 2482 3072 3073\nf 479 480 493\nf 706 3900 3873\nf 3561 2194 2221\nf 2013 2010 3754\nf 3533 2032 2016\nf 3562 3519 2222\nf 1171 1154 1155\nf 2586 2566 2565\nf 2982 110 113\nf 2562 2376 3077\nf 2376 2375 3012\nf 3012 2145 2148\nf 3077 2148 3015\nf 2963 3047 3046\nf 3051 3078 2970\nf 1683 2173 491\nf 3850 2122 1447\nf 1986 2181 3734\nf 3079 3080 2546\nf 3081 3079 2575\nf 1983 1986 3768\nf 3082 2323 2319\nf 3080 3082 2545\nf 3734 3732 3082\nf 3732 3748 2323\nf 1309 1471 1888\nf 3073 2595 2772\nf 2760 3083 3084\nf 2760 2085 2088\nf 3085 3084 3086\nf 3089 3085 3090\nf 3089 2762 3084\nf 3092 3088 3090\nf 3093 2748 2744\nf 2751 2747 2748\nf 3093 2741 2752\nf 2742 2741 3093\nf 3094 3093 2743\nf 3096 3097 3098\nf 3099 3096 3087\nf 3097 3094 3095\nf 3100 2743 2746\nf 3101 2927 3102\nf 2764 2766 2927\nf 2745 2764 3101\nf 3102 3092 3091\nf 3100 3091 3095\nf 2869 2802 3592\nf 3102 3103 3104\nf 3658 2868 2869\nf 2927 2926 3103\nf 3098 3095 3091\nf 3087 3098 3090\nf 2088 2125 3105\nf 3083 3105 3086\nf 2483 3073 2771\nf 2401 2186 2185\nf 3099 3086 3105\nf 2125 2207 3106\nf 3108 2737 2742\nf 3107 2742 3094\nf 3110 3108 3107\nf 3109 3107 3097\nf 3111 3109 3096\nf 3112 3110 3109\nf 3113 2738 2737\nf 3115 2723 2722\nf 3117 3115 3114\nf 3119 3117 3116\nf 3118 3116 3120\nf 3116 3114 3113\nf 3122 3112 3111\nf 3122 3123 1755\nf 3124 3125 3126\nf 3125 3118 3121\nf 3128 3119 3118\nf 3129 3128 3125\nf 2020 2712 3128\nf 3853 3857 1346\nf 1065 1046 1047\nf 1814 3838 3830\nf 2021 2020 3129\nf 3111 3099 3106\nf 3123 3106 2207\nf 2377 2225 2224\nf 3014 2485 113\nf 1756 2208 3782\nf 3131 3132 2530\nf 3133 3131 2528\nf 1753 1756 3767\nf 3134 3124 3127\nf 2327 2408 2314\nf 2436 2318 2317\nf 2018 2021 3711\nf 2534 3136 3137\nf 2320 2322 3136\nf 2185 2018 3712\nf 3138 3139 2542\nf 3134 3135 3744\nf 3785 3130 3134\nf 3140 3138 2539\nf 2535 3137 3140\nf 3711 2021 3130\nf 2558 2530 3132\nf 3782 3760 3141\nf 2560 2558 3141\nf 3760 3777 3142\nf 958 888 2976\nf 1933 1932 2980\nf 2399 2424 2418\nf 2348 2342 2343\nf 2322 2320 2319\nf 2718 2717 2874\nf 2709 2729 2873\nf 2894 2871 1361\nf 346 289 176\nf 1876 1877 1882\nf 2802 2803 3607\nf 3104 3144 3088\nf 2974 2222 3649\nf 3089 3145 2761\nf 3088 3144 3145\nf 2803 2974 3603\nf 3870 3909 692\nf 2123 1419 1418\nf 1365 1363 1368\nf 1581 1766 1782\nf 3569 2202 2129\nf 3555 2003 2002\nf 3146 2585 2551\nf 3751 3146 3147\nf 3742 3149 3146\nf 3149 2584 2585\nf 3196 3198 3197\nf 3604 2192 2191\nf 3142 3150 2556\nf 3777 3778 3150\nf 3646 3152 2550\nf 3150 3151 2555\nf 3778 3780 3151\nf 3788 3781 3152\nf 3153 3147 2551\nf 3152 3153 2549\nf 3781 3776 3153\nf 3776 3750 3147\nf 3148 2767 2206\nf 2183 2401 2321\nf 2132 2960 2928\nf 3684 3685 2204\nf 3719 2198 2197\nf 2594 2962 2773\nf 2595 2594 2951\nf 1870 798 799\nf 3713 2179 2178\nf 3706 2178 3736\nf 336 3154 3774\nf 339 3155 3154\nf 3789 3683 3149\nf 3683 3697 2584\nf 2963 2962 3048\nf 3722 3730 337\nf 339 338 2566\nf 3156 3157 2168\nf 2610 3156 2167\nf 2954 2953 3156\nf 3157 3156 2953\nf 3707 2166 2169\nf 2567 3158 3159\nf 337 3158 2567\nf 3730 3069 2166\nf 3764 2190 1983\nf 2579 3160 3081\nf 2568 3159 3160\nf 3709 2169 2190\nf 3065 3062 3162\nf 3161 3162 1984\nf 3157 3161 2189\nf 2257 3065 3161\nf 734 735 1922\nf 3059 3058 2708\nf 3163 2708 2182\nf 3162 3163 1985\nf 3062 3059 3163\nf 3203 3206 3205\nf 2122 2119 1448\nf 3817 1686 1685\nf 139 140 444\nf 1570 569 566\nf 2957 3164 3165\nf 2955 3166 3164\nf 1912 454 341\nf 3130 3129 3124\nf 1548 322 319\nf 1949 1950 1327\nf 2969 2970 3078\nf 3167 3078 3169\nf 3724 2014 2017\nf 3720 2017 2033\nf 3168 3169 3165\nf 3170 3165 3164\nf 3779 3170 3171\nf 3724 3168 3170\nf 3171 3164 3166\nf 3172 3166 3071\nf 3752 3172 3173\nf 3756 3171 3172\nf 3169 3037 3036\nf 2593 3048 2962\nf 3173 3071 2609\nf 3174 2609 2199\nf 3783 3174 2198\nf 3749 3173 3174\nf 2190 2189 1984\nf 2196 2195 2767\nf 2119 2120 1414\nf 1464 1463 1466\nf 2199 2611 3069\nf 2457 2456 2587\nf 2226 2225 2562\nf 481 478 1627\nf 333 1512 235\nf 3078 3051 3037\nf 3040 2264 2997\nf 2219 1934 1933\nf 2385 2384 2497\nf 221 2220 3175\nf 2220 1933 3143\nf 3175 3143 2976\nf 2484 3175 2975\nf 2954 3071 3166\nf 2630 1858 110\nf 1547 319 1952\nf 2091 2090 2188\nf 3733 3736 2178\nf 1918 1919 1927\nf 3563 112 111\nf 955 956 960\nf 2037 3014 112\nf 3143 2980 2981\nf 2218 2211 2214\nf 672 3877 3787\nf 2152 3013 3014\nf 1147 1144 1143\nf 2248 2246 1409\nf 1615 1636 1635\nf 1410 1409 2246\nf 3241 3243 3242\nf 1382 3176 3177\nf 3177 1410 2247\nf 2247 1383 3177\nf 3176 2238 2230\nf 1410 3177 3179\nf 3179 3180 2243\nf 1633 3181 2800\nf 1621 1620 2758\nf 1622 1621 2245\nf 2236 1394 3181\nf 2237 2236 3182\nf 2250 1631 2233\nf 2912 1629 1628\nf 135 136 1564\nf 1567 419 512\nf 1085 1084 2653\nf 3184 869 868\nf 869 3184 3057\nf 1266 1268 1098\nf 1267 2147 722\nf 937 1146 2646\nf 2146 1049 722\nf 2211 2218 921\nf 1957 510 507\nf 1313 1312 2264\nf 1496 362 1493\nf 2887 2937 2933\nf 1687 1688 1659\nf 145 1499 1498\nf 37 38 418\nf 1935 1934 3185\nf 104 89 301\nf 2101 2098 2083\nf 543 298 360\nf 1934 2219 3186\nf 234 1521 212\nf 3845 3821 1822\nf 1976 1875 1874\nf 223 3186 2219\nf 3655 3656 3605\nf 2165 2164 467\nf 467 468 2165\nf 773 476 2165\nf 1935 1212 1728\nf 3240 3225 3226\nf 3261 3249 3250\nf 3215 3216 3244\nf 3265 3221 3215\nf 3267 3245 3198\nf 3205 3206 3269\nf 3214 3211 3265\nf 3271 3213 3214\nf 3251 3252 3239\nf 3222 3221 3266\nf 3232 3235 3234\nf 3216 3242 3243\nf 3196 3193 3268\nf 3244 3243 3254\nf 3238 3239 3247\nf 3264 3244 3255\nf 3253 3242 3216\nf 3211 3266 3221\nf 3257 3217 3218\nf 3258 3222 3272\nf 3274 3275 3280\nf 3228 3229 3262\nf 3275 3231 3281\nf 3226 3227 3273\nf 3236 3263 3260\nf 3281 3263 3236\nf 3287 3271 3192\nf 3235 3226 3280\nf 3234 3280 3281\nf 3288 3290 3259\nf 3260 3250 3276\nf 3289 3212 3278\nf 3210 3209 3224\nf 3199 3202 3201\nf 3228 3231 3230\nf 3290 3288 3285\nf 3282 3279 3271\nf 3260 3263 3262\nf 3243 3241 3268\nf 3267 3268 3241\nf 3194 3207 3208\nf 3191 3270 3269\nf 3270 3191 3192\nf 3275 3274 3230\nf 3264 3269 3270\nf 3252 3251 3276\nf 3248 3252 3291\nf 3213 3271 3279\nf 3272 3266 3289\nf 3277 3278 3212\nf 3228 3263 3281\nf 3220 3225 3253\nf 3219 3253 3217\nf 3256 3218 3222\nf 3247 3239 3252\nf 3245 3267 3232\nf 3266 3211 3212\nf 3208 3205 3255\nf 3288 3289 3284\nf 3204 3205 3208\nf 560 1899 3187\nf 3209 3210 3201\nf 3189 3192 3191\nf 448 1980 807\nf 647 648 122\nf 1833 1848 1780\nf 510 509 508\nf 3210 1949 1887\nf 1887 15 3200\nf 15 16 3197\nf 16 3188 3195\nf 3195 3188 3187\nf 3194 3187 1899\nf 3207 1899 375\nf 387 173 126\nf 3189 1791 1760\nf 3577 763 757\nf 763 3577 2172\nf 3583 3582 3580\nf 3581 3580 3579\nf 3594 3597 3596\nf 3400 3601 3602\nf 3626 3624 3625\nf 3631 3635 3599\nf 3636 3637 3595\nf 3647 3584 3588\nf 3650 3582 3584\nf 3653 3579 3580\nf 3653 2176 3654\nf 1801 1864 3656\nf 3651 3580 3582\nf 3644 3638 3595\nf 1767 1768 3659\nf 1597 3663 3661\nf 1708 1707 3665\nf 3671 3668 3667\nf 3666 3667 3673\nf 1588 1708 3664\nf 2080 3671 3666\nf 2082 3666 3672\nf 3674 3677 3635\nf 3677 3678 3636\nf 3682 3647 3645\nf 3692 3651 3650\nf 3681 3645 3644\nf 786 3694 3692\nf 3694 3653 3651\nf 785 3692 3686\nf 497 3686 3682\nf 650 3682 3681\nf 3696 564 3681\nf 3405 3606 3605\nf 3702 3699 3678\nf 3699 3698 3679\nf 3645 3588 3638\nf 1799 3657 3659\nf 3708 3704 3458\nf 3462 3710 3708\nf 3714 463 564\nf 3615 3606 3602\nf 3710 3714 3696\nf 3635 3636 3596\nf 3629 3626 3627\nf 3597 3600 3599\nf 3630 3716 3715\nf 3627 3625 3597\nf 3717 3716 3723\nf 3715 3663 3660\nf 3716 3717 3725\nf 3668 3665 3661\nf 3668 3725 3717\nf 3667 3717 3718\nf 3727 3728 3673\nf 3728 1576 3672\nf 3726 3727 3718\nf 1576 2101 2084\nf 3727 3726 3731\nf 3203 376 1387\nf 3190 1387 1791\nf 3283 2152 1949\nf 317 240 259\nf 2000 2176 3653\nf 3601 3599 3600\nf 3589 3588 3584\nf 3577 758 756\nf 3585 3584 3582\nf 3678 3679 3637\nf 3204 375 376\nf 1801 3655 3657\nf 3729 3735 3728\nf 1575 3735 3738\nf 3735 1575 1576\nf 1776 1795 1796\nf 1293 1294 1290\nf 1326 14 1327\nf 1294 1245 1234\nf 878 1244 1330\nf 497 373 374\nf 122 648 413\nf 3699 3702 3704\nf 3606 3405 3401\nf 3708 3710 3698\nf 3464 3714 3710\nf 3704 3674 3443\nf 439 398 399\nf 1512 1528 1526\nf 1668 1741 1738\nf 839 849 1362\nf 1362 1357 826\nf 3843 740 1896\nf 2053 2054 2118\nf 3888 3887 2120\nf 696 697 3816\nf 1826 1878 639\nf 3740 3739 3729\nf 956 885 886\nf 1980 806 1922\nf 33 34 105\nf 1343 1344 3856\nf 3907 2127 2128\nf 1766 1581 1582\nf 14 1326 2163\nf 595 98 99\nf 3233 3238 3246\nf 1346 1035 1044\nf 748 1888 1425\nf 3193 3208 3254\nf 3190 3191 3206\nf 3612 3605 3606\nf 3624 3612 3615\nf 3631 3415 3443\nf 3679 3680 3644\nf 3686 3650 3647\nf 3702 3677 3674\nf 3625 3615 3600\nf 3601 3400 3415\nf 3739 3738 3735\nf 3630 3627 3594\nf 3741 3723 3716\nf 1284 1290 1288\nf 3698 3696 3680\nf 1778 1781 1780\nf 1792 1759 1584\nf 695 570 571\nf 3638 3741 3594\nf 3741 3638 3588\nf 3247 3248 3209\nf 3292 3293 3294\nf 3296 3297 3298\nf 3299 3298 3300\nf 3302 3303 3304\nf 3306 3307 3308\nf 3307 3310 3311\nf 3303 3302 3301\nf 3312 3305 3304\nf 3314 3315 3316\nf 3318 3319 3320\nf 3324 3318 3321\nf 3326 3327 3328\nf 3329 3330 3331\nf 3333 3334 3335\nf 3337 3338 3339\nf 3333 3336 3341\nf 3342 3341 3343\nf 3319 3345 3344\nf 3338 3335 3334\nf 3346 3301 3302\nf 3305 3348 3347\nf 3312 3224 3223\nf 3350 3337 3340\nf 3312 3349 3348\nf 3352 3343 3341\nf 3311 3353 3354\nf 3341 3336 3327\nf 3359 3360 3361\nf 3360 3359 3250\nf 3318 3363 3345\nf 3364 3363 3318\nf 3314 3364 3324\nf 3366 3299 3301\nf 3299 3366 3367\nf 3308 3354 3368\nf 3369 3317 3295\nf 3317 3369 3364\nf 3370 3295 3317\nf 3325 3371 3365\nf 3352 3320 3319\nf 3296 3367 3353\nf 3297 3296 3311\nf 3363 3368 3354\nf 3345 3354 3353\nf 3344 3353 3367\nf 3366 3333 3342\nf 3347 3339 3334\nf 3339 3347 3348\nf 3348 3349 3351\nf 3363 3364 3369\nf 3294 3309 3368\nf 3293 3306 3309\nf 3331 3373 3374\nf 3346 3334 3333\nf 3323 3322 3352\nf 3356 3355 3321\nf 3355 3357 3325\nf 3357 3358 3371\nf 3322 3356 3320\nf 3361 3330 3329\nf 3373 3372 3375\nf 3374 3375 3376\nf 3329 3332 3376\nf 3327 3375 3372\nf 3336 3335 3375\nf 3335 3338 3376\nf 3376 3338 3337\nf 3337 3350 3359\nf 3359 3350 3276\nf 3377 3292 3295\nf 3277 3316 3315\nf 3371 3378 3379\nf 3378 3371 3358\nf 3365 3379 3315\nf 3316 3277 3279\nf 3282 3377 3370\nf 3313 3283 3224\nf 3378 3285 3284\nf 3380 3286 3285\nf 3379 3284 3278\nf 3276 3350 3351\nf 3291 3351 3349\nf 3313 3304 2965\nf 2965 3304 3303\nf 222 3303 3300\nf 223 3300 3298\nf 3298 3297 3185\nf 3297 3310 2977\nf 3310 3307 1213\nf 3307 3306 1214\nf 2532 1214 3306\nf 2894 2532 3293\nf 2871 2894 3292\nf 3253 3225 3240\nf 3283 3313 3013\nf 1997 1996 1889\nf 1237 745 744\nf 1535 1534 120\nf 1531 1532 201\nf 1119 1241 1032\nf 946 948 824\nf 163 8 5\nf 3589 3726 3723\nf 1155 1156 1162\nf 427 213 327\nf 553 1902 796\nf 3381 2232 2231\nf 3381 3178 2230\nf 2237 3183 3180\nf 3383 3384 3385\nf 3384 3386 3387\nf 3386 3388 3389\nf 3388 3390 3391\nf 3392 3393 3394\nf 3395 3394 3396\nf 3398 3399 3397\nf 3400 3401 3399\nf 3403 3404 3405\nf 3406 3407 3404\nf 3408 3409 3407\nf 3410 3411 3409\nf 3412 3413 3411\nf 3415 3400 3398\nf 3414 3398 3396\nf 3416 3396 3394\nf 3417 3394 3393\nf 3393 3419 3420\nf 3419 3390 3421\nf 3390 3388 3422\nf 3388 3386 3423\nf 3386 3384 3424\nf 3384 3383 3425\nf 3425 3383 3426\nf 2904 3427 3428\nf 3429 3427 2904\nf 3430 3429 2902\nf 2875 3431 3430\nf 3432 3433 2733\nf 2825 3434 3435\nf 3437 3438 3439\nf 3441 3437 3436\nf 2724 2723 3434\nf 3436 3439 3115\nf 3440 3436 3117\nf 3443 3415 3414\nf 3442 3414 3416\nf 3444 3416 3417\nf 3445 3417 3418\nf 3446 3418 3420\nf 3420 3421 3448\nf 3421 3422 3449\nf 3422 3423 3450\nf 3423 3424 3451\nf 3424 3425 3452\nf 3425 3184 3056\nf 3451 3452 2253\nf 3450 3451 2252\nf 3449 3450 1727\nf 3448 3449 2150\nf 3453 3447 3448\nf 3454 3446 3447\nf 3455 3445 3446\nf 3456 3444 3445\nf 3442 3444 3456\nf 3443 3442 3457\nf 3459 3460 3458\nf 3457 3456 3455\nf 3459 3455 3454\nf 3462 3460 3459\nf 3461 3454 3453\nf 3464 3462 3461\nf 3463 3453 1941\nf 3404 3399 3401\nf 3407 3397 3399\nf 3409 3395 3397\nf 3411 3392 3395\nf 3465 3466 3413\nf 3467 3468 3469\nf 3465 3412 3431\nf 3470 3467 3466\nf 3438 3470 3432\nf 3467 3470 3438\nf 3468 3467 3437\nf 3471 3469 3468\nf 3472 3468 3441\nf 3473 3441 3440\nf 2712 3440 3119\nf 3475 3471 3472\nf 3474 3472 3473\nf 3476 3473 2712\nf 3477 3476 2711\nf 3478 3474 3476\nf 3479 3475 3474\nf 3392 3411 3413\nf 3466 3469 3480\nf 3419 3393 3392\nf 3390 3419 3480\nf 3469 3471 3391\nf 3389 3391 3471\nf 3387 3389 3475\nf 3385 3387 3479\nf 3482 3477 2400\nf 3483 3478 3477\nf 3481 3479 3478\nf 3484 3482 2402\nf 3058 3484 2707\nf 3061 3485 3484\nf 3485 3483 3482\nf 3382 3385 3481\nf 3486 3481 3483\nf 3426 3383 3382\nf 3487 3382 3486\nf 3486 3485 3061\nf 1961 3057 3487\nf 3057 3184 3426\nf 3428 3403 3402\nf 3427 3406 3403\nf 3429 3408 3406\nf 3430 3410 3408\nf 3431 3412 3410\nf 3470 3465 3433\nf 3434 3439 3438\nf 2723 3115 3439\nf 2875 2733 3433\nf 2736 2824 3435\nf 2947 3428 3488\nf 3464 3463 1360\nf 1900 1925 1924\nf 1736 1717 1718\nf 1955 1952 1943\nf 1671 1738 1735\nf 1696 1750 1698\nf 1800 1865 1864\nf 2063 2055 2056\nf 1737 1734 1717\nf 1751 1735 1697\nf 943 3885 3899\nf 3656 3488 3402\nf 1577 1578 1764\nf 3917 1598 1599\nf 1945 146 82\nf 68 6 7\nf 147 1542 1543\nf 1109 1135 1133\nf 1503 1910 1907\nf 1499 1501 1909\nf 452 1922 735\nf 1824 2068 3835\nf 1825 2067 2068\nf 1463 1462 1022\nf 841 1012 1009\nf 1568 1547 1955\nf 1910 1503 1502\nf 1939 1502 422\nf 309 280 1940\nf 1804 1851 1866\nf 269 1482 1954\nf 2065 1607 2060\nf 3812 1685 1758\nf 280 276 552\nf 1875 1873 1869\nf 1739 1721 1734\nf 1778 1779 1790\nf 310 1920 1915\nf 1909 1908 1906\nf 1789 1783 1778\nf 978 981 980\nf 1584 1759 1731\nf 425 426 311\nf 148 1543 115\nf 2044 3897 3894\nf 277 1910 1939\nf 1943 1946 1945\nf 1461 1462 1463\nf 2253 3452 3056\nf 2981 2254 3489\nf 2209 2998 3489\nf 2254 2253 3055\nf 2998 926 958\nf 2826 2817 2721\nf 3490 2739 2721\nf 2816 3491 3490\nf 2334 2297 2296\nf 2298 2297 2334\nf 2301 2298 2336\nf 2311 2301 2338\nf 2312 2311 2354\nf 2359 2358 2361\nf 2335 2296 2359\nf 2414 2413 2365\nf 2413 2270 2361\nf 2355 2352 2540\nf 2541 2540 2539\nf 2722 2721 2739\nf 3114 2722 2738\nf 3110 3112 3121\nf 3108 3110 3120\nf 3122 1754 3127\nf 3112 3122 3126\nf 3127 1754 1753\nf 3139 3133 2529\nf 3744 3775 3133\nf 3490 2789 2740\nf 3491 2790 2789\nf 228 229 91\nf 245 244 243\nf 248 7 247\nf 2 297 296\nf 145 146 300\nf 327 213 210\nf 329 332 331\nf 239 241 240\nf 373 376 375\nf 178 177 176\nf 1218 1225 1228\nf 3862 1231 1295\nf 808 809 824\nf 3858 3859 1013\nf 3813 3810 1411\nf 2154 2149 2159\nf 3845 3835 1758\nf 306 807 1922\nf 2157 2153 1976\nf 708 709 2031\nf 1450 1226 1227\nf 1672 1604 1605\nf 1813 1814 1821\nf 1717 1716 1715\nf 176 159 402\nf 1710 1705 3916\nf 1712 1580 1577\nf 1705 1713 1711\nf 971 978 979\nf 822 823 1094\nf 2909 2810 2793\nf 2810 2769 2795\nf 2799 2910 2249\nf 3662 3643 2910\nf 2769 2768 2801\nf 3177 3176 3178\nf 3178 3381 3180\nf 3181 1394 1393\nf 3181 1633 1632\nf 3182 1632 2244\nf 3183 2244 2243\nf 3492 2791 2790\nf 2808 3492 3491\nf 2239 3574 3572\nf 1149 1150 3571\nf 1150 2009 3567\nf 2202 3569 3566\nf 2921 3564 3565\nf 3568 3560 2133\nf 1777 1579 1759\nf 1201 1196 1183\nf 3561 3554 2195\nf 3553 3576 2770\nf 3927 3925 1687\nf 1516 152 154\nf 3549 3562 2974\nf 3550 3548 2779\nf 2771 2772 3575\nf 3070 2771 3546\nf 3547 3544 2775\nf 1376 1375 1370\nf 2767 3543 3556\nf 2195 3554 3543\nf 2775 3544 3542\nf 3377 3282 3545\nf 3804 3802 3539\nf 2776 3542 3538\nf 2240 3572 3535\nf 2920 3535 3669\nf 3548 3534 2786\nf 1805 1850 1851\nf 3534 3532 2787\nf 2951 2773 3540\nf 2772 2951 3531\nf 2973 2224 3530\nf 2224 2223 3537\nf 1957 1953 320\nf 306 305 304\nf 2770 3576 3528\nf 2913 3528 3574\nf 2923 3565 3527\nf 308 1940 431\nf 207 208 132\nf 1879 1876 1881\nf 3733 3787 3525\nf 2475 3070 3558\nf 2476 2475 3524\nf 2223 2476 3523\nf 3522 3549 2803\nf 2037 3563 11\nf 1959 1958 450\nf 2003 3555 3520\nf 9 10 334\nf 2009 2008 3518\nf 2008 2973 3536\nf 3877 672 673\nf 3515 3550 2778\nf 2935 3514 3513\nf 111 1149 3570\nf 3510 3514 2935\nf 683 3876 3885\nf 3508 3557 2810\nf 736 733 732\nf 1795 1804 1803\nf 3505 3510 2938\nf 3507 3505 2939\nf 2950 3512 3504\nf 2940 2949 3504\nf 3801 3791 3512\nf 2868 3509 3502\nf 2869 3502 3522\nf 1024 1034 1023\nf 3500 3503 2948\nf 551 1939 442\nf 2972 3499 3568\nf 2032 3533 3499\nf 216 93 1924\nf 203 202 201\nf 1828 1848 1847\nf 1958 1957 449\nf 272 271 178\nf 2871 3545 3495\nf 3619 3669 3564\nf 2801 2768 3643\nf 2005 3758 3654\nf 960 886 899\nf 3657 3655 3612\nf 3780 3788 3646\nf 3151 3646 3593\nf 2193 3604 3623\nf 3607 3603 3145\nf 3603 3649 2761\nf 3592 3607 3144\nf 2926 3658 3634\nf 3634 3592 3104\nf 3755 3754 3746\nf 566 567 3758\nf 3488 3656 1864\nf 1602 1586 2079\nf 2868 3658 3611\nf 2922 3611 3610\nf 2923 3610 3618\nf 3670 3613 2918\nf 3619 3670 3614\nf 2921 3618 3670\nf 2920 3614 3586\nf 3613 3642 2915\nf 2917 2766 3622\nf 2916 2917 3641\nf 2239 3587 3652\nf 2911 3640 3652\nf 3643 3640 2911\nf 2797 2801 3662\nf 2797 3591 3598\nf 3622 3628 2763\nf 3608 3628 2764\nf 2194 3623 3648\nf 2221 3648 3649\nf 2756 3608 3609\nf 2750 3598 3609\nf 3669 3535 1817\nf 3763 3770 1691\nf 2515 3621 3590\nf 2548 3590 3593\nf 2513 2489 3632\nf 2513 3616 3621\nf 2509 3633 3620\nf 2510 3632 3633\nf 2289 3620 3639\nf 2283 3639 3617\nf 2240 3586 3587\nf 3659 3657 3624\nf 716 2121 3887\nf 727 1999 3761\nf 2165 2171 2170\nf 3660 3659 3626\nf 3743 3581 3578\nf 2175 2172 769\nf 2100 500 2095\nf 3758 567 3757\nf 640 3560 3568\nf 1653 3576 3553\nf 1895 3536 3530\nf 746 3530 3537\nf 1653 1810 3528\nf 3762 1603 1604\nf 2093 2075 2073\nf 959 996 995\nf 962 899 919\nf 1400 1401 1398\nf 1000 817 1127\nf 956 959 884\nf 1662 3548 3550\nf 2034 3558 3546\nf 3926 1689 3539\nf 3535 3572 766\nf 3572 3574 765\nf 334 3570 3571\nf 1868 3531 3540\nf 1684 1651 1652\nf 1682 1683 775\nf 1585 1603 3762\nf 3763 3762 1672\nf 2173 2174 490\nf 1658 3544 3547\nf 3556 3543 1650\nf 1571 3754 2010\nf 1587 3664 3671\nf 2099 2095 2077\nf 787 3761 1999\nf 2000 3694 786\nf 3155 339 3697\nf 3713 3155 3683\nf 2180 2179 3685\nf 3496 2180 3684\nf 1684 3846 3840\nf 1760 464 3495\nf 2607 3689 3693\nf 2581 3676 3689\nf 2371 2502 3700\nf 2371 3695 3693\nf 2587 3676 3675\nf 2586 3675 3697\nf 2497 3691 3687\nf 2502 2503 3687\nf 2494 3691 3690\nf 3690 3688 2472\nf 3688 3701 2468\nf 3701 3703 2469\nf 726 3908 3892\nf 581 3567 3518\nf 10 3511 3570\nf 1837 3514 3510\nf 741 3537 3523\nf 1299 3524 3558\nf 1851 3507 3504\nf 3527 3565 1820\nf 580 3518 3536\nf 1758 1685 3522\nf 1693 3557 3508\nf 3504 3512 1867\nf 1894 1826 3568\nf 2126 1418 3813\nf 3906 2126 3841\nf 2113 3911 3867\nf 2179 3713 3789\nf 624 3903 3904\nf 2192 3604 3788\nf 2177 3541 3787\nf 1364 3824 3833\nf 3858 3855 1470\nf 3135 1753 3775\nf 2069 3812 3835\nf 1351 3861 3853\nf 2108 3882 3901\nf 2089 2092 3736\nf 3828 3834 1358\nf 2001 3749 3783\nf 2187 3783 3719\nf 2130 3756 3752\nf 2004 3752 3749\nf 2014 3724 3779\nf 2201 3779 3756\nf 3167 3720 3759\nf 3168 3724 3720\nf 3159 3709 3764\nf 3160 3764 3765\nf 3730 3707 3158\nf 3158 3707 3709\nf 2200 3069 3730\nf 3685 3789 3742\nf 3722 3774 2197\nf 3790 3736 2092\nf 3774 3790 3721\nf 3154 3706 3790\nf 3155 3713 3706\nf 2091 3719 3721\nf 2196 3148 3750\nf 2193 2196 3776\nf 3604 2193 3781\nf 2086 2192 3780\nf 2087 2086 3778\nf 2204 3742 3751\nf 2205 3751 3750\nf 3909 3886 574\nf 2124 2087 3777\nf 2208 2124 3760\nf 3711 3785 3140\nf 3785 3745 3138\nf 3745 3744 3139\nf 3771 3712 3136\nf 3712 3711 3137\nf 3775 3767 3131\nf 3767 3782 3132\nf 1813 3839 3838\nf 1293 3854 3856\nf 2184 2321 3748\nf 2181 2184 3732\nf 3765 3768 3079\nf 3768 3734 3080\nf 709 3873 3875\nf 2774 2961 3737\nf 3786 3769 2134\nf 3809 3808 766\nf 2968 3784 3766\nf 2969 3759 3784\nf 2958 3747 3737\nf 2132 3747 3766\nf 2321 2185 3771\nf 3860 3848 1449\nf 3905 3874 582\nf 3886 3889 577\nf 3847 3826 1818\nf 705 3902 3887\nf 3816 3912 3907\nf 2111 3883 3911\nf 3810 3829 1366\nf 1812 3839 3822\nf 675 3851 3852\nf 3496 3705 3851\nf 2110 3867 3868\nf 2104 3896 3901\nf 2109 3868 3899\nf 3842 3831 703\nf 1680 3811 3846\nf 1639 1627 3819\nf 623 3904 3906\nf 2139 2140 3866\nf 1808 1640 3844\nf 3820 3821 1819\nf 3872 3914 3900\nf 1646 3832 3830\nf 3889 3890 633\nf 2139 3865 3875\nf 2156 2155 3871\nf 674 3852 3913\nf 2117 3817 3812\nf 1815 3822 3847\nf 3855 3849 1444\nf 697 3906 3912\nf 866 842 3836\nf 3864 3869 2136\nf 3585 3731 3726\nf 3583 3740 3731\nf 3753 3746 3738\nf 3740 3583 3581\nf 1088 3738 3746\nf 3739 3740 3743\nf 2791 3492 3493\nf 3492 2808 2807\nf 2792 3493 3494\nf 3493 2807 2806\nf 3508 2909 3494\nf 3516 3494 2806\nf 2948 3503 3791\nf 2105 3908 3896\nf 3538 3802 3804\nf 680 3878 3876\nf 1891 1890 3910\nf 2905 3799 3801\nf 2900 3797 3799\nf 2901 3797 3807\nf 2734 2878 3806\nf 2877 3807 3806\nf 2820 3800 3794\nf 2818 3796 3792\nf 2823 3793 3792\nf 2822 3793 3798\nf 2819 2735 3800\nf 2817 3796 3795\nf 2809 3805 3795\nf 2808 3805 3803\nf 2805 3804 3803\nf 1702 3921 3923\nf 3922 3920 1797\nf 3915 3916 1706\nf 3918 3927 1692\nf 1657 3898 3880\nf 1890 1877 3895\nf 3772 3770 1674\nf 1689 1690 3772\nf 3926 3802 3538\nf 3516 3539 1689\nf 3917 3923 1703\nf 1598 3917 3928\nf 3880 3884 1876\nf 1691 1690 3925\nf 3925 3926 1688\nf 3934 3933 3937\nf 3934 3938 3939\nf 3932 3936 3937\nf 3938 3942 3943\nf 3937 3936 3940\nf 3937 3941 3942\nf 3942 3946 3947\nf 3941 3940 3944\nf 3942 3941 3945\nf 3947 3946 3950\nf 3944 3948 3949\nf 3945 3949 3950\nf 3950 3954 3955\nf 3949 3948 3952\nf 3950 3949 3953\nf 3955 3954 3958\nf 3953 3952 3956\nf 3953 3957 3958\nf 3959 3958 3962\nf 3957 3956 3960\nf 3958 3957 3961\nf 3962 3966 3967\nf 3960 3964 3965\nf 3961 3965 3966\nf 3966 3970 3971\nf 3965 3964 3968\nf 3966 3965 3969\nf 3970 3974 3975\nf 3968 3972 3973\nf 3969 3973 3974\nf 3974 3978 3979\nf 3973 3972 3976\nf 3974 3973 3977\nf 3979 3978 3982\nf 3976 3980 3981\nf 3977 3981 3982\nf 3982 3986 3987\nf 3981 3980 3984\nf 3982 3981 3985\nf 3987 3986 3990\nf 3984 3988 3989\nf 3985 3989 3990\nf 3991 3990 3994\nf 3989 3988 3992\nf 3990 3989 3993\nf 3995 3994 3998\nf 3992 3996 3997\nf 3993 3997 3998\nf 3998 4002 4003\nf 3997 3996 4000\nf 3998 3997 4001\nf 4002 4006 4007\nf 4000 4004 4005\nf 4001 4005 4006\nf 4006 4010 4011\nf 4005 4004 4008\nf 4006 4005 4009\nf 4011 4010 4014\nf 4008 4012 4013\nf 4009 4013 4014\nf 4014 4018 4019\nf 4013 4012 4016\nf 4014 4013 4017\nf 4019 4018 4022\nf 4016 4020 4021\nf 4017 4021 4022\nf 4023 4022 4026\nf 4021 4020 4024\nf 4022 4021 4025\nf 4027 4026 4030\nf 4024 4028 4029\nf 4025 4029 4030\nf 4031 4030 4034\nf 4029 4028 4032\nf 4030 4029 4033\nf 4034 4038 4039\nf 4032 4036 4037\nf 4033 4037 4038\nf 4038 4042 4043\nf 4037 4036 4040\nf 4038 4037 4041\nf 4043 4042 4046\nf 4040 4044 4045\nf 4041 4045 4046\nf 4046 4050 4051\nf 4045 4044 4048\nf 4046 4045 4049\nf 4051 4050 4055\nf 4048 4053 4054\nf 4049 4054 4055\nf 4056 4055 4059\nf 4053 4057 4058\nf 4055 4054 4058\nf 4060 4059 3934\nf 4058 4057 3932\nf 4058 3933 3934\nf 4069 4068 4081\nf 4066 4079 4080\nf 4065 4064 4077\nf 4063 4062 4075\nf 4071 4084 4085\nf 4069 4082 4083\nf 4067 4080 4081\nf 4066 4065 4078\nf 4063 4076 4077\nf 4061 4074 4075\nf 4072 4085 4086\nf 4071 4070 4083\nf 4076 4089 4090\nf 4074 4087 4088\nf 4086 4085 4098\nf 4083 4096 4097\nf 4082 4081 4094\nf 4079 4092 4093\nf 4078 4077 4090\nf 4076 4075 4088\nf 4085 4084 4097\nf 4082 4095 4096\nf 4080 4093 4094\nf 4079 4078 4091\nf 4089 4102 4103\nf 4088 4087 4100\nf 4098 4111 4112\nf 4096 4109 4110\nf 4095 4094 4107\nf 4092 4105 4106\nf 4090 4103 4104\nf 4089 4088 4101\nf 4097 4110 4111\nf 4095 4108 4109\nf 4094 4093 4106\nf 4092 4091 4104\nf 4103 4102 4115\nf 4100 4113 4114\nf 4112 4111 4124\nf 4110 4109 4122\nf 4107 4120 4121\nf 4105 4118 4119\nf 4103 4116 4117\nf 4101 4114 4115\nf 4110 4123 4124\nf 4109 4108 4121\nf 4107 4106 4119\nf 4105 4104 4117\nf 4115 4128 4129\nf 4114 4113 4126\nf 4124 4137 4138\nf 4123 4122 4135\nf 4120 4133 4134\nf 4119 4118 4131\nf 4117 4116 4129\nf 4114 4127 4128\nf 4124 4123 4136\nf 4122 4121 4134\nf 4119 4132 4133\nf 4117 4130 4131\nf 4129 4128 4141\nf 4126 4139 4140\nf 4138 4137 4150\nf 4136 4135 4148\nf 4133 4146 4147\nf 4131 4144 4145\nf 4130 4129 4142\nf 4127 4140 4141\nf 4137 4136 4149\nf 4135 4134 4147\nf 4132 4145 4146\nf 4131 4130 4143\nf 4142 4141 4154\nf 4140 4139 4152\nf 4150 4163 4164\nf 4149 4148 4161\nf 4146 4159 4160\nf 4144 4157 4158\nf 4142 4155 4156\nf 4140 4153 4154\nf 4149 4162 4163\nf 4148 4147 4160\nf 4146 4145 4158\nf 4144 4143 4156\nf 4155 4154 4167\nf 4153 4152 4165\nf 4163 4176 4177\nf 4161 4174 4175\nf 4159 4172 4173\nf 4158 4157 4170\nf 4155 4168 4169\nf 4153 4166 4167\nf 4163 4162 4175\nf 4161 4160 4173\nf 4159 4158 4171\nf 4156 4169 4170\nf 4167 4180 4181\nf 4165 4178 4179\nf 4176 4189 4190\nf 4175 4174 4187\nf 4173 4172 4185\nf 4170 4183 4184\nf 4169 4168 4181\nf 4167 4166 4179\nf 4175 4188 4189\nf 4173 4186 4187\nf 4171 4184 4185\nf 4170 4169 4182\nf 4180 4193 4194\nf 4178 4191 4192\nf 4190 4189 4202\nf 4187 4200 4201\nf 4186 4185 4198\nf 4183 4196 4197\nf 4182 4181 4194\nf 4180 4179 4192\nf 4189 4188 4201\nf 4186 4199 4200\nf 4184 4197 4198\nf 4183 4182 4195\nf 4193 4206 4207\nf 4192 4191 4204\nf 4202 4215 4216\nf 4200 4213 4214\nf 4199 4198 4211\nf 4196 4209 4210\nf 4194 4207 4208\nf 4193 4192 4205\nf 4201 4214 4215\nf 4199 4212 4213\nf 4198 4197 4210\nf 4196 4195 4208\nf 4207 4206 4219\nf 4204 4217 4218\nf 4216 4215 4228\nf 4213 4226 4227\nf 4211 4224 4225\nf 4209 4222 4223\nf 4207 4220 4221\nf 4205 4218 4219\nf 4214 4227 4228\nf 4213 4212 4225\nf 4211 4210 4223\nf 4209 4208 4221\nf 4219 4232 4233\nf 4218 4217 4230\nf 4228 4241 4242\nf 4226 4239 4240\nf 4225 4224 4237\nf 4223 4222 4235\nf 4221 4220 4233\nf 4218 4231 4232\nf 4228 4227 4240\nf 4226 4225 4238\nf 4223 4236 4237\nf 4221 4234 4235\nf 4233 4232 4245\nf 4230 4243 4244\nf 4242 4241 4254\nf 4240 4239 4252\nf 4237 4250 4251\nf 4235 4248 4249\nf 4234 4233 4246\nf 4231 4244 4245\nf 4241 4240 4253\nf 4239 4238 4251\nf 4236 4249 4250\nf 4235 4234 4247\nf 4246 4245 4258\nf 4244 4243 4256\nf 4254 4267 4268\nf 4253 4252 4265\nf 4250 4263 4264\nf 4248 4261 4262\nf 4246 4259 4260\nf 4244 4257 4258\nf 4253 4266 4267\nf 4252 4251 4264\nf 4250 4249 4262\nf 4248 4247 4260\nf 4259 4258 4271\nf 4257 4256 4269\nf 4267 4280 4281\nf 4265 4278 4279\nf 4263 4276 4277\nf 4262 4261 4274\nf 4259 4272 4273\nf 4257 4270 4271\nf 4266 4279 4280\nf 4265 4264 4277\nf 4263 4262 4275\nf 4260 4273 4274\nf 4271 4284 4285\nf 4269 4282 4283\nf 4281 4280 4293\nf 4279 4278 4291\nf 4277 4276 4289\nf 4274 4287 4288\nf 4273 4272 4285\nf 4271 4270 4283\nf 4280 4279 4292\nf 4277 4290 4291\nf 4275 4288 4289\nf 4274 4273 4286\nf 4284 4297 4298\nf 4282 4295 4296\nf 4294 4293 4306\nf 4291 4304 4305\nf 4290 4289 4302\nf 4287 4300 4301\nf 4286 4285 4298\nf 4284 4283 4296\nf 4293 4292 4305\nf 4290 4303 4304\nf 4288 4301 4302\nf 4287 4286 4299\nf 4297 4310 4311\nf 4296 4295 4308\nf 4306 4319 4320\nf 4304 4317 4318\nf 4303 4302 4315\nf 4300 4313 4314\nf 4298 4311 4312\nf 4297 4296 4309\nf 4305 4318 4319\nf 4304 4303 4316\nf 4302 4301 4314\nf 4300 4299 4312\nf 4310 4323 4324\nf 4308 4321 4322\nf 4320 4319 4332\nf 4318 4317 4330\nf 4316 4315 4328\nf 4313 4326 4327\nf 4311 4324 4325\nf 4310 4309 4322\nf 4318 4331 4332\nf 4316 4329 4330\nf 4315 4314 4327\nf 4312 4325 4326\nf 4323 4336 4337\nf 4322 4321 4334\nf 4332 4345 4346\nf 4330 4343 4344\nf 4329 4328 4341\nf 4327 4326 4339\nf 4325 4324 4337\nf 4322 4335 4336\nf 4332 4331 4344\nf 4330 4329 4342\nf 4327 4340 4341\nf 4325 4338 4339\nf 4337 4336 4350\nf 4334 4348 4349\nf 4346 4345 4359\nf 4344 4343 4357\nf 4341 4355 4356\nf 4340 4339 4353\nf 4338 4337 4351\nf 4335 4349 4350\nf 4345 4344 4358\nf 4343 4342 4356\nf 4340 4354 4355\nf 4338 4352 4353\nf 4351 4350 4363\nf 4349 4348 4361\nf 4359 4372 4373\nf 4358 4357 4370\nf 4355 4368 4369\nf 4354 4353 4366\nf 4351 4364 4365\nf 4349 4362 4363\nf 4358 4371 4372\nf 4357 4356 4369\nf 4355 4354 4367\nf 4352 4365 4366\nf 4364 4363 4376\nf 4362 4361 4374\nf 4373 4372 4385\nf 4370 4383 4384\nf 4368 4381 4382\nf 4367 4366 4379\nf 4364 4377 4378\nf 4362 4375 4376\nf 4372 4371 4384\nf 4370 4369 4382\nf 4368 4367 4380\nf 4365 4378 4379\nf 4376 4389 4390\nf 4374 4387 4388\nf 4385 4398 4399\nf 4384 4383 4396\nf 4382 4381 4394\nf 4379 4392 4393\nf 4378 4377 4390\nf 4376 4375 4388\nf 4384 4397 4398\nf 4382 4395 4396\nf 4380 4393 4394\nf 4379 4378 4391\nf 4389 4402 4403\nf 4387 4400 4401\nf 4399 4398 4411\nf 4396 4409 4410\nf 4395 4394 4407\nf 4393 4392 4405\nf 4391 4390 4403\nf 4389 4388 4401\nf 4398 4397 4410\nf 4395 4408 4409\nf 4393 4406 4407\nf 4391 4404 4405\nf 4402 4415 4416\nf 4401 4400 4413\nf 4411 4424 4425\nf 4409 4422 4423\nf 4408 4407 4420\nf 4406 4405 4418\nf 4403 4416 4417\nf 4402 4401 4414\nf 4410 4423 4424\nf 4408 4421 4422\nf 4407 4406 4419\nf 4404 4417 4418\nf 4416 4415 4428\nf 4413 4426 4427\nf 4425 4424 4437\nf 4423 4422 4435\nf 4421 4420 4433\nf 4418 4431 4432\nf 4416 4429 4430\nf 4415 4414 4427\nf 4423 4436 4437\nf 4421 4434 4435\nf 4420 4419 4432\nf 4418 4417 4430\nf 4428 4441 4442\nf 4426 4439 4440\nf 4437 4450 4451\nf 4435 4448 4449\nf 4434 4433 4446\nf 4432 4431 4444\nf 4430 4429 4442\nf 4427 4440 4441\nf 4437 4436 4449\nf 4435 4434 4447\nf 4432 4445 4446\nf 4430 4443 4444\nf 4442 4441 4454\nf 4439 4452 4453\nf 4451 4450 4463\nf 4449 4448 4461\nf 4446 4459 4460\nf 4444 4457 4458\nf 4443 4442 4455\nf 4440 4453 4454\nf 4450 4449 4462\nf 4447 4460 4461\nf 4445 4458 4459\nf 4444 4443 4456\nf 4455 4454 4467\nf 4453 4452 4465\nf 4463 4476 4477\nf 4462 4461 4474\nf 4459 4472 4473\nf 4457 4470 4471\nf 4455 4468 4469\nf 4453 4466 4467\nf 4462 4475 4476\nf 4461 4460 4473\nf 4459 4458 4471\nf 4457 4456 4469\nf 4468 4467 4063\nf 4466 4465 4061\nf 4477 4476 4072\nf 4474 4070 4071\nf 4472 4068 4069\nf 4471 4470 4066\nf 4468 4064 4065\nf 4466 4062 4063\nf 4476 4475 4071\nf 4474 4473 4069\nf 4472 4471 4067\nf 4469 4065 4066\nf 3935 4061 4465\nf 4465 4452 4056\nf 4452 4439 4051\nf 4439 4426 4047\nf 4047 4426 4413\nf 4043 4413 4400\nf 4039 4400 4387\nf 4387 4374 4031\nf 4031 4374 4361\nf 4361 4348 4023\nf 4348 4334 4019\nf 4334 4321 4015\nf 4015 4321 4308\nf 4011 4308 4295\nf 4007 4295 4282\nf 4282 4269 3999\nf 3999 4269 4256\nf 4256 4243 3991\nf 4243 4230 3987\nf 4230 4217 3983\nf 3983 4217 4204\nf 3979 4204 4191\nf 3975 4191 4178\nf 4178 4165 3967\nf 3967 4165 4152\nf 4152 4139 3959\nf 4139 4126 3955\nf 4126 4113 3951\nf 3951 4113 4100\nf 3947 4100 4087\nf 3943 4087 4074\nf 4074 4061 3935\nf 4480 4479 4483\nf 4480 4484 4485\nf 4479 4478 4482\nf 4484 4488 4489\nf 4482 4486 4487\nf 4483 4487 4488\nf 4488 4492 4493\nf 4487 4486 4490\nf 4488 4487 4491\nf 4493 4492 4496\nf 4490 4494 4495\nf 4491 4495 4496\nf 4496 4500 4501\nf 4495 4494 4498\nf 4496 4495 4499\nf 4501 4500 4504\nf 4498 4502 4503\nf 4499 4503 4504\nf 4505 4504 4508\nf 4503 4502 4506\nf 4504 4503 4507\nf 4509 4508 4512\nf 4506 4510 4511\nf 4507 4511 4512\nf 4513 4512 4516\nf 4511 4510 4514\nf 4512 4511 4515\nf 4516 4520 4521\nf 4514 4518 4519\nf 4515 4519 4520\nf 4520 4524 4525\nf 4518 4522 4523\nf 4520 4519 4523\nf 4525 4524 4528\nf 4522 4526 4527\nf 4523 4527 4528\nf 4528 4532 4533\nf 4527 4526 4530\nf 4528 4527 4531\nf 4533 4532 4536\nf 4530 4534 4535\nf 4531 4535 4536\nf 4537 4536 4540\nf 4534 4538 4539\nf 4536 4535 4539\nf 4541 4540 4544\nf 4539 4538 4542\nf 4539 4543 4544\nf 4544 4548 4549\nf 4542 4546 4547\nf 4544 4543 4547\nf 4548 4552 4553\nf 4547 4546 4550\nf 4547 4551 4552\nf 4552 4556 4557\nf 4551 4550 4554\nf 4552 4551 4555\nf 4557 4556 4560\nf 4554 4558 4559\nf 4555 4559 4560\nf 4560 4564 4565\nf 4559 4558 4562\nf 4560 4559 4563\nf 4565 4564 4568\nf 4562 4566 4567\nf 4563 4567 4568\nf 4569 4568 4572\nf 4567 4566 4570\nf 4568 4567 4571\nf 4572 4576 4577\nf 4570 4574 4575\nf 4571 4575 4576\nf 4576 4580 4581\nf 4575 4574 4578\nf 4576 4575 4579\nf 4580 4584 4585\nf 4578 4582 4583\nf 4579 4583 4584\nf 4584 4588 4589\nf 4583 4582 4586\nf 4584 4583 4587\nf 4589 4588 4592\nf 4586 4590 4591\nf 4587 4591 4592\nf 4592 4596 4597\nf 4591 4590 4594\nf 4592 4591 4595\nf 4597 4596 4601\nf 4594 4599 4600\nf 4595 4600 4601\nf 4602 4601 4605\nf 4600 4599 4603\nf 4601 4600 4604\nf 4606 4605 4480\nf 4603 4478 4479\nf 4604 4479 4480\nf 4615 4614 4627\nf 4612 4625 4626\nf 4611 4610 4623\nf 4609 4608 4621\nf 4618 4617 4630\nf 4615 4628 4629\nf 4613 4626 4627\nf 4612 4611 4624\nf 4609 4622 4623\nf 4607 4620 4621\nf 4619 4618 4631\nf 4617 4616 4629\nf 4622 4635 4636\nf 4620 4633 4634\nf 4632 4631 4644\nf 4629 4642 4643\nf 4628 4627 4640\nf 4626 4625 4638\nf 4624 4623 4636\nf 4622 4621 4634\nf 4631 4630 4643\nf 4628 4641 4642\nf 4626 4639 4640\nf 4624 4637 4638\nf 4635 4648 4649\nf 4634 4633 4646\nf 4644 4657 4658\nf 4642 4655 4656\nf 4641 4640 4653\nf 4639 4638 4651\nf 4636 4649 4650\nf 4635 4634 4647\nf 4643 4656 4657\nf 4642 4641 4654\nf 4640 4639 4652\nf 4637 4650 4651\nf 4648 4661 4662\nf 4646 4659 4660\nf 4658 4657 4670\nf 4656 4655 4668\nf 4653 4666 4667\nf 4651 4664 4665\nf 4649 4662 4663\nf 4648 4647 4660\nf 4656 4669 4670\nf 4654 4667 4668\nf 4653 4652 4665\nf 4651 4650 4663\nf 4661 4674 4675\nf 4660 4659 4672\nf 4670 4683 4684\nf 4668 4681 4682\nf 4667 4666 4679\nf 4665 4664 4677\nf 4663 4662 4675\nf 4661 4660 4673\nf 4670 4669 4682\nf 4667 4680 4681\nf 4665 4678 4679\nf 4663 4676 4677\nf 4675 4674 4687\nf 4672 4685 4686\nf 4684 4683 4696\nf 4682 4681 4694\nf 4679 4692 4693\nf 4678 4677 4690\nf 4676 4675 4688\nf 4673 4686 4687\nf 4683 4682 4695\nf 4681 4680 4693\nf 4678 4691 4692\nf 4676 4689 4690\nf 4688 4687 4700\nf 4686 4685 4698\nf 4696 4709 4710\nf 4695 4694 4707\nf 4692 4705 4706\nf 4691 4690 4703\nf 4688 4701 4702\nf 4686 4699 4700\nf 4695 4708 4709\nf 4694 4693 4706\nf 4692 4691 4704\nf 4689 4702 4703\nf 4701 4700 4713\nf 4699 4698 4711\nf 4710 4709 4722\nf 4707 4720 4721\nf 4705 4718 4719\nf 4704 4703 4716\nf 4701 4714 4715\nf 4699 4712 4713\nf 4709 4708 4721\nf 4707 4706 4719\nf 4705 4704 4717\nf 4702 4715 4716\nf 4713 4726 4727\nf 4711 4724 4725\nf 4723 4722 4735\nf 4721 4720 4733\nf 4719 4718 4731\nf 4716 4729 4730\nf 4715 4714 4727\nf 4713 4712 4725\nf 4721 4734 4735\nf 4719 4732 4733\nf 4717 4730 4731\nf 4716 4715 4728\nf 4726 4739 4740\nf 4724 4737 4738\nf 4736 4735 4748\nf 4733 4746 4747\nf 4732 4731 4744\nf 4730 4729 4742\nf 4728 4727 4740\nf 4726 4725 4738\nf 4735 4734 4747\nf 4732 4745 4746\nf 4730 4743 4744\nf 4728 4741 4742\nf 4739 4752 4753\nf 4738 4737 4750\nf 4748 4761 4762\nf 4746 4759 4760\nf 4745 4744 4757\nf 4743 4742 4755\nf 4740 4753 4754\nf 4739 4738 4751\nf 4747 4760 4761\nf 4745 4758 4759\nf 4744 4743 4756\nf 4741 4754 4755\nf 4753 4752 4765\nf 4750 4763 4764\nf 4762 4761 4774\nf 4759 4772 4773\nf 4758 4757 4770\nf 4755 4768 4769\nf 4753 4766 4767\nf 4752 4751 4764\nf 4760 4773 4774\nf 4758 4771 4772\nf 4757 4756 4769\nf 4755 4754 4767\nf 4765 4778 4779\nf 4764 4763 4776\nf 4774 4787 4788\nf 4772 4785 4786\nf 4771 4770 4783\nf 4769 4768 4781\nf 4767 4766 4779\nf 4765 4764 4777\nf 4774 4773 4786\nf 4771 4784 4785\nf 4769 4782 4783\nf 4767 4780 4781\nf 4779 4778 4791\nf 4776 4789 4790\nf 4788 4787 4800\nf 4786 4785 4798\nf 4783 4796 4797\nf 4782 4781 4794\nf 4780 4779 4792\nf 4777 4790 4791\nf 4787 4786 4799\nf 4785 4784 4797\nf 4782 4795 4796\nf 4780 4793 4794\nf 4792 4791 4804\nf 4790 4789 4802\nf 4800 4813 4814\nf 4799 4798 4811\nf 4796 4809 4810\nf 4795 4794 4807\nf 4792 4805 4806\nf 4790 4803 4804\nf 4799 4812 4813\nf 4798 4797 4810\nf 4796 4795 4808\nf 4793 4806 4807\nf 4805 4804 4817\nf 4803 4802 4815\nf 4814 4813 4826\nf 4811 4824 4825\nf 4809 4822 4823\nf 4808 4807 4820\nf 4805 4818 4819\nf 4803 4816 4817\nf 4813 4812 4825\nf 4811 4810 4823\nf 4809 4808 4821\nf 4806 4819 4820\nf 4817 4830 4831\nf 4815 4828 4829\nf 4826 4839 4840\nf 4825 4824 4837\nf 4823 4822 4835\nf 4820 4833 4834\nf 4819 4818 4831\nf 4817 4816 4829\nf 4825 4838 4839\nf 4823 4836 4837\nf 4821 4834 4835\nf 4820 4819 4832\nf 4830 4843 4844\nf 4828 4841 4842\nf 4840 4839 4852\nf 4837 4850 4851\nf 4836 4835 4848\nf 4834 4833 4846\nf 4832 4831 4844\nf 4830 4829 4842\nf 4839 4838 4851\nf 4836 4849 4850\nf 4834 4847 4848\nf 4832 4845 4846\nf 4843 4856 4857\nf 4842 4841 4854\nf 4852 4865 4866\nf 4850 4863 4864\nf 4849 4848 4861\nf 4847 4846 4859\nf 4844 4857 4858\nf 4843 4842 4855\nf 4851 4864 4865\nf 4849 4862 4863\nf 4848 4847 4860\nf 4845 4858 4859\nf 4857 4856 4869\nf 4855 4854 4867\nf 4866 4865 4878\nf 4864 4863 4876\nf 4862 4861 4874\nf 4859 4872 4873\nf 4857 4870 4871\nf 4856 4855 4868\nf 4864 4877 4878\nf 4862 4875 4876\nf 4861 4860 4873\nf 4859 4858 4871\nf 4869 4882 4883\nf 4868 4867 4880\nf 4878 4891 4892\nf 4877 4876 4889\nf 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4937\nf 4935 4948 4949\nf 4933 4946 4947\nf 4945 4944 4957\nf 4942 4955 4956\nf 4941 4940 4953\nf 4938 4951 4952\nf 4937 4936 4949\nf 4935 4934 4947\nf 4944 4943 4956\nf 4941 4954 4955\nf 4939 4952 4953\nf 4938 4937 4950\nf 4948 4961 4962\nf 4947 4946 4959\nf 4957 4970 4971\nf 4955 4968 4969\nf 4954 4953 4966\nf 4951 4964 4965\nf 4949 4962 4963\nf 4948 4947 4960\nf 4956 4969 4970\nf 4954 4967 4968\nf 4953 4952 4965\nf 4951 4950 4963\nf 4962 4961 4974\nf 4959 4972 4973\nf 4971 4970 4983\nf 4968 4981 4982\nf 4966 4979 4980\nf 4964 4977 4978\nf 4962 4975 4976\nf 4960 4973 4974\nf 4969 4982 4983\nf 4967 4980 4981\nf 4966 4965 4978\nf 4964 4963 4976\nf 4975 4974 4987\nf 4973 4972 4985\nf 4983 4996 4997\nf 4981 4994 4995\nf 4979 4992 4993\nf 4978 4977 4990\nf 4976 4975 4988\nf 4973 4986 4987\nf 4983 4982 4995\nf 4981 4980 4993\nf 4978 4991 4992\nf 4976 4989 4990\nf 4988 4987 5000\nf 4985 4998 4999\nf 4997 4996 5009\nf 4995 4994 5007\nf 4992 5005 5006\nf 4991 4990 5003\nf 4989 4988 5001\nf 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4501\nf 4672 4659 4497\nf 4497 4659 4646\nf 4493 4646 4633\nf 4489 4633 4620\nf 4620 4607 4481\n# 9976 faces, 0 coords texture\n\n# End of File\n"
  },
  {
    "path": "mesh_pi_nets/datasets/DFAUST/templates/COMA_downsample/downsampling_matrices.pkl",
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-0.232226\nv -0.244448 -0.598588 -0.222915\nv -0.259946 -0.593666 -0.258205\nv -0.240478 -0.594747 -0.262240\nv -0.251009 -0.593799 -0.262548\nv -0.234231 -0.596833 -0.256358\nv -0.235319 -0.598247 -0.244896\nv -0.239345 -0.598820 -0.233960\nv -0.245632 -0.597723 -0.240578\nv -0.241671 -0.597147 -0.247783\nv -0.254216 -0.595153 -0.248789\nv -0.248595 -0.595285 -0.254213\nv -0.239358 -0.596587 -0.253824\nv -0.242901 -0.595703 -0.257099\nv -0.293767 -0.512449 -0.202127\nv -0.180401 -0.175315 -0.143887\nv -0.155881 -0.178411 -0.145534\nv -0.204283 -0.166637 -0.149437\nv -0.190931 0.228758 -0.080753\nv -0.123621 0.437604 0.006145\nv -0.121940 0.503788 0.009621\nv -0.122556 0.469200 0.010086\nv -0.197999 0.200493 -0.080174\nv -0.177110 0.547423 -0.076178\nv -0.109717 0.733025 -0.019474\nv -0.074374 1.046437 -0.153729\nv -0.072542 1.043081 -0.154339\nv -0.071076 1.041303 -0.156053\nv -0.071955 1.038845 -0.158227\nv -0.072874 1.037536 -0.161110\nv -0.065756 1.040272 -0.168517\nv -0.073662 1.042163 -0.157723\nv -0.063711 1.049882 -0.165861\nv -0.065260 1.051087 -0.163440\nv -0.073821 1.044009 -0.156493\nf 2 3 1\nf 1 3 4\nf 3 2 5\nf 5 2 6\nf 3 7 4\nf 4 7 8\nf 10 11 9\nf 9 11 12\nf 14 15 13\nf 13 15 16\nf 18 19 17\nf 17 19 20\nf 22 23 21\nf 21 23 24\nf 26 18 25\nf 25 18 17\nf 27 28 17\nf 17 28 25\nf 30 31 29\nf 29 31 32\nf 34 35 33\nf 33 35 36\nf 21 24 37\nf 37 24 38\nf 40 31 39\nf 39 31 38\nf 34 33 28\nf 28 33 25\nf 41 42 44\nf 44 42 43\nf 36 45 33\nf 33 45 25\nf 46 47 49\nf 49 47 48\nf 51 52 50\nf 50 52 53\nf 55 53 54\nf 54 53 52\nf 57 58 56\nf 56 58 59\nf 61 62 60\nf 60 62 63\nf 65 66 64\nf 64 66 67\nf 69 70 68\nf 68 70 71\nf 65 73 72\nf 72 73 74\nf 64 75 65\nf 65 75 73\nf 77 78 76\nf 76 78 79\nf 80 81 83\nf 83 81 82\nf 81 85 84\nf 84 85 86\nf 88 89 87\nf 87 89 69\nf 88 87 90\nf 90 87 91\nf 93 94 92\nf 92 94 95\nf 96 84 43\nf 43 84 44\nf 51 98 97\nf 97 98 86\nf 97 99 51\nf 51 99 52\nf 54 52 100\nf 100 52 99\nf 58 101 59\nf 59 101 102\nf 62 103 63\nf 63 103 104\nf 105 106 6\nf 6 106 5\nf 108 109 107\nf 107 109 110\nf 112 113 111\nf 111 113 114\nf 116 117 115\nf 115 117 118\nf 120 121 119\nf 119 121 122\nf 124 125 123\nf 123 125 126\nf 128 129 127\nf 127 129 130\nf 130 131 127\nf 127 131 132\nf 134 1 133\nf 133 1 135\nf 1 4 135\nf 135 4 136\nf 4 8 136\nf 136 8 137\nf 138 139 15\nf 15 139 140\nf 140 139 141\nf 141 139 142\nf 144 57 143\nf 143 57 56\nf 145 146 141\nf 141 146 147\nf 146 149 148\nf 148 149 150\nf 152 153 151\nf 151 153 154\nf 141 147 140\nf 140 147 155\nf 157 11 156\nf 156 11 158\nf 160 161 159\nf 159 161 162\nf 164 165 163\nf 163 165 166\nf 165 160 166\nf 166 160 159\nf 168 169 167\nf 167 169 170\nf 169 171 170\nf 170 171 172\nf 149 173 150\nf 150 173 174\nf 175 176 149\nf 149 176 173\nf 178 172 177\nf 177 172 171\nf 180 181 179\nf 179 181 182\nf 184 180 183\nf 183 180 179\nf 186 187 185\nf 185 187 188\nf 190 191 189\nf 189 191 192\nf 7 193 8\nf 8 193 194\nf 196 162 195\nf 195 162 161\nf 197 168 198\nf 198 168 167\nf 181 199 182\nf 182 199 200\nf 202 203 201\nf 201 203 204\nf 187 186 205\nf 205 186 206\nf 208 209 207\nf 207 209 210\nf 212 213 211\nf 211 213 214\nf 215 216 218\nf 218 216 217\nf 220 209 219\nf 219 209 208\nf 204 186 221\nf 221 186 185\nf 201 204 222\nf 222 204 221\nf 223 211 220\nf 220 211 209\nf 224 225 214\nf 214 225 210\nf 203 206 204\nf 204 206 186\nf 227 187 226\nf 226 187 205\nf 27 228 21\nf 21 228 22\nf 230 231 229\nf 229 231 232\nf 234 235 233\nf 233 235 236\nf 238 239 237\nf 237 239 240\nf 119 122 118\nf 118 122 115\nf 112 242 241\nf 241 242 243\nf 98 51 244\nf 244 51 50\nf 245 138 14\nf 14 138 15\nf 11 10 158\nf 158 10 246\nf 89 85 69\nf 69 85 70\nf 11 157 12\nf 12 157 247\nf 249 164 248\nf 248 164 163\nf 177 250 178\nf 178 250 251\nf 253 254 252\nf 252 254 255\nf 187 227 188\nf 188 227 256\nf 212 257 213\nf 213 257 258\nf 260 134 259\nf 259 134 133\nf 165 164 135\nf 135 164 133\nf 160 165 136\nf 136 165 135\nf 136 137 160\nf 160 137 161\nf 137 261 161\nf 161 261 195\nf 263 264 262\nf 262 264 265\nf 13 16 266\nf 266 16 267\nf 268 267 155\nf 155 267 16\nf 147 269 155\nf 155 269 268\nf 162 196 167\nf 167 196 198\nf 167 170 162\nf 162 170 159\nf 170 172 159\nf 159 172 166\nf 172 178 166\nf 166 178 163\nf 178 251 163\nf 163 251 248\nf 168 270 169\nf 169 270 271\nf 272 273 171\nf 171 273 177\nf 271 272 169\nf 169 272 171\nf 168 197 270\nf 270 197 274\nf 250 177 275\nf 275 177 273\nf 277 45 276\nf 276 45 278\nf 280 281 279\nf 279 281 282\nf 284 104 283\nf 283 104 103\nf 285 215 286\nf 286 215 218\nf 288 289 287\nf 287 289 290\nf 291 80 66\nf 66 80 83\nf 293 294 292\nf 292 294 295\nf 73 91 74\nf 74 91 87\nf 91 73 92\nf 92 73 75\nf 279 77 280\nf 280 77 76\nf 80 291 70\nf 70 291 71\nf 277 47 96\nf 96 47 82\nf 297 175 296\nf 296 175 149\nf 299 154 298\nf 298 154 153\nf 225 300 210\nf 210 300 207\nf 224 301 206\nf 206 301 205\nf 210 209 214\nf 214 209 211\nf 226 205 302\nf 302 205 301\nf 304 305 303\nf 303 305 306\nf 308 309 307\nf 307 309 310\nf 72 291 65\nf 65 291 66\nf 66 83 67\nf 67 83 46\nf 312 313 311\nf 311 313 314\nf 316 317 315\nf 315 317 36\nf 29 318 35\nf 35 318 319\nf 321 32 320\nf 320 32 322\nf 311 314 323\nf 323 314 324\nf 315 325 316\nf 316 325 326\nf 318 287 319\nf 319 287 290\nf 328 321 327\nf 327 321 320\nf 329 330 39\nf 39 330 331\nf 332 333 40\nf 40 333 322\nf 309 334 285\nf 285 334 335\nf 236 2 134\nf 134 2 1\nf 260 336 233\nf 233 336 337\nf 237 14 265\nf 265 14 13\nf 84 86 44\nf 44 86 98\nf 189 192 7\nf 7 192 193\nf 44 98 41\nf 41 98 244\nf 237 240 14\nf 14 240 245\nf 7 3 189\nf 189 3 5\nf 338 71 72\nf 72 71 291\nf 262 265 266\nf 266 265 13\nf 124 123 339\nf 339 123 105\nf 123 109 105\nf 105 109 106\nf 189 5 190\nf 190 5 106\nf 111 114 12\nf 12 114 9\nf 264 340 265\nf 265 340 237\nf 342 343 341\nf 341 343 344\nf 6 235 105\nf 105 235 339\nf 234 346 345\nf 345 346 347\nf 349 350 348\nf 348 350 351\nf 107 352 108\nf 108 352 353\nf 355 356 354\nf 354 356 357\nf 237 340 238\nf 238 340 358\nf 12 247 111\nf 111 247 359\nf 264 263 116\nf 116 263 360\nf 361 362 222\nf 222 362 201\nf 364 365 363\nf 363 365 366\nf 368 369 367\nf 367 369 370\nf 372 373 371\nf 371 373 374\nf 375 376 372\nf 372 376 377\nf 378 379 381\nf 381 379 380\nf 382 383 269\nf 269 383 268\nf 150 174 365\nf 365 174 384\nf 380 377 381\nf 381 377 376\nf 271 270 180\nf 180 270 181\nf 273 272 252\nf 252 272 184\nf 272 271 184\nf 184 271 180\nf 181 270 274\nf 252 255 273\nf 273 255 275\nf 254 385 255\nf 255 385 386\nf 387 388 179\nf 179 388 183\nf 389 387 182\nf 182 387 179\nf 182 200 389\nf 389 200 390\nf 388 391 183\nf 183 391 253\nf 391 392 253\nf 253 392 254\nf 394 379 393\nf 393 379 378\nf 185 188 395\nf 395 188 396\nf 221 185 397\nf 397 185 395\nf 222 221 398\nf 398 221 397\nf 188 256 396\nf 396 256 399\nf 400 401 256\nf 256 401 399\nf 402 403 405\nf 405 403 404\nf 148 150 364\nf 364 150 365\nf 393 406 394\nf 394 406 407\nf 158 246 393\nf 393 246 406\nf 342 19 238\nf 238 19 239\nf 23 22 350\nf 350 22 354\nf 244 50 245\nf 245 50 138\nf 55 142 53\nf 53 142 139\nf 145 144 296\nf 296 144 143\nf 355 228 341\nf 341 228 20\nf 41 240 42\nf 42 240 239\nf 283 308 284\nf 284 308 307\nf 349 409 408\nf 408 409 410\nf 244 245 41\nf 41 245 240\nf 268 383 406\nf 406 383 407\nf 346 411 349\nf 349 411 409\nf 251 412 248\nf 248 412 413\nf 275 414 250\nf 250 414 415\nf 25 45 26\nf 26 45 277\nf 43 26 96\nf 96 26 277\nf 416 417 61\nf 61 417 62\nf 329 24 408\nf 408 24 23\nf 31 40 32\nf 32 40 322\nf 39 38 329\nf 329 38 24\nf 37 38 30\nf 30 38 31\nf 418 419 421\nf 421 419 420\nf 78 295 79\nf 79 295 294\nf 124 339 422\nf 422 339 345\nf 232 131 357\nf 357 131 351\nf 2 236 6\nf 6 236 235\nf 252 184 253\nf 253 184 183\nf 227 423 256\nf 256 423 400\nf 423 227 305\nf 305 227 226\nf 424 258 425\nf 425 258 257\nf 426 427 212\nf 212 427 257\nf 259 133 249\nf 249 133 164\nf 419 418 428\nf 428 418 429\nf 327 430 328\nf 328 430 288\nf 49 431 46\nf 46 431 67\nf 306 302 424\nf 424 302 258\nf 313 432 314\nf 314 432 433\nf 32 321 29\nf 29 321 318\nf 324 314 433\nf 287 318 328\nf 328 318 321\nf 134 260 236\nf 236 260 233\nf 345 339 234\nf 234 339 235\nf 357 351 354\nf 354 351 350\nf 408 23 349\nf 349 23 350\nf 317 278 36\nf 36 278 45\nf 432 313 429\nf 429 313 428\nf 435 428 434\nf 434 428 313\nf 42 239 18\nf 18 239 19\nf 43 42 26\nf 26 42 18\nf 121 120 436\nf 436 120 437\nf 302 301 258\nf 258 301 213\nf 110 109 126\nf 126 109 123\nf 238 358 342\nf 342 358 343\nf 341 20 342\nf 342 20 19\nf 322 438 320\nf 320 438 439\nf 320 439 327\nf 327 439 430\nf 333 440 322\nf 322 440 438\nf 289 288 323\nf 323 288 430\nf 115 340 116\nf 116 340 264\nf 146 145 149\nf 149 145 296\nf 15 140 16\nf 16 140 155\nf 441 442 152\nf 152 442 153\nf 225 224 203\nf 203 224 206\nf 175 297 154\nf 154 297 151\nf 293 443 294\nf 294 443 444\nf 248 413 249\nf 249 413 445\nf 446 447 400\nf 400 447 401\nf 388 387 396\nf 396 387 395\nf 387 389 395\nf 395 389 397\nf 391 388 399\nf 399 388 396\nf 401 392 399\nf 399 392 391\nf 255 386 275\nf 275 386 414\nf 449 450 448\nf 448 450 451\nf 335 452 285\nf 285 452 215\nf 453 298 442\nf 442 298 153\nf 211 223 212\nf 212 223 426\nf 425 257 454\nf 454 257 427\nf 266 10 262\nf 262 10 9\nf 360 113 456\nf 456 113 455\nf 8 194 137\nf 137 194 261\nf 10 266 246\nf 246 266 267\nf 203 202 225\nf 225 202 300\nf 262 9 263\nf 263 9 114\nf 108 353 190\nf 190 353 191\nf 389 390 397\nf 397 390 398\nf 457 422 128\nf 128 422 129\nf 156 158 378\nf 378 158 393\nf 347 346 348\nf 348 346 349\nf 447 458 401\nf 401 458 392\nf 364 382 148\nf 148 382 269\nf 55 54 57\nf 57 54 58\nf 75 64 459\nf 459 64 460\nf 100 90 101\nf 101 90 95\nf 101 58 100\nf 100 58 54\nf 142 55 144\nf 144 55 57\nf 147 146 269\nf 269 146 148\nf 459 93 75\nf 75 93 92\nf 144 145 142\nf 142 145 141\nf 228 27 20\nf 20 27 17\nf 154 299 175\nf 175 299 176\nf 214 213 224\nf 224 213 301\nf 424 461 306\nf 306 461 303\nf 285 286 309\nf 309 286 310\nf 462 334 308\nf 308 334 309\nf 302 306 226\nf 226 306 305\nf 37 28 21\nf 21 28 27\nf 29 35 30\nf 30 35 34\nf 30 34 37\nf 37 34 28\nf 344 436 356\nf 356 436 229\nf 325 290 326\nf 326 290 289\nf 311 439 312\nf 312 439 438\nf 35 319 36\nf 36 319 315\nf 439 311 430\nf 430 311 323\nf 315 319 325\nf 325 319 290\nf 440 463 438\nf 438 463 312\nf 341 344 355\nf 355 344 356\nf 360 456 116\nf 116 456 117\nf 354 22 355\nf 355 22 228\nf 326 289 324\nf 324 289 323\nf 74 87 68\nf 68 87 69\nf 50 53 138\nf 138 53 139\nf 268 406 267\nf 267 406 246\nf 190 106 108\nf 108 106 109\nf 241 455 112\nf 112 455 113\nf 358 122 343\nf 343 122 121\nf 129 347 130\nf 130 347 348\nf 348 351 130\nf 130 351 131\nf 229 232 356\nf 356 232 357\nf 340 115 358\nf 358 115 122\nf 111 359 112\nf 112 359 242\nf 125 124 457\nf 457 124 422\nf 231 132 232\nf 232 132 131\nf 343 121 344\nf 344 121 436\nf 263 114 360\nf 360 114 113\nf 347 129 345\nf 345 129 422\nf 230 229 437\nf 437 229 436\nf 464 446 423\nf 423 446 400\nf 465 466 468\nf 468 466 467\nf 470 471 469\nf 469 471 472\nf 469 472 373\nf 373 472 374\nf 474 469 473\nf 473 469 373\nf 471 475 472\nf 472 475 476\nf 472 477 374\nf 374 477 478\nf 477 472 476\nf 479 480 476\nf 476 480 477\nf 473 373 377\nf 377 373 372\nf 481 482 479\nf 479 482 480\nf 483 484 481\nf 481 484 482\nf 403 402 485\nf 485 402 486\nf 487 488 483\nf 483 488 484\nf 487 489 488\nf 488 489 490\nf 489 491 490\nf 490 491 492\nf 491 493 492\nf 492 493 494\nf 493 495 494\nf 494 495 496\nf 497 498 481\nf 481 498 483\nf 484 499 482\nf 482 499 367\nf 370 480 367\nf 367 480 482\nf 480 370 477\nf 477 370 478\nf 500 497 479\nf 479 497 481\nf 451 473 380\nf 380 473 377\nf 448 451 379\nf 379 451 380\nf 501 502 504\nf 504 502 503\nf 449 505 507\nf 507 505 506\nf 486 508 505\nf 505 508 506\nf 508 509 506\nf 506 509 510\nf 511 507 510\nf 510 507 506\nf 509 508 512\nf 512 508 513\nf 515 509 514\nf 514 509 512\nf 509 515 510\nf 510 515 516\nf 510 516 511\nf 511 516 517\nf 486 505 485\nf 485 505 518\nf 505 449 518\nf 518 449 448\nf 383 382 403\nf 403 382 404\nf 363 404 364\nf 364 404 382\nf 403 485 383\nf 383 485 407\nf 519 520 361\nf 361 520 362\nf 522 496 521\nf 521 496 495\nf 450 474 451\nf 451 474 473\nf 363 366 523\nf 523 366 524\nf 525 499 488\nf 488 499 484\nf 488 490 525\nf 525 490 526\nf 496 522 520\nf 520 522 527\nf 494 496 519\nf 519 496 520\nf 492 494 528\nf 528 494 519\nf 529 530 495\nf 495 530 521\nf 490 492 526\nf 526 492 528\nf 529 495 531\nf 531 495 493\nf 533 534 532\nf 532 534 535\nf 523 524 533\nf 533 524 534\nf 486 402 532\nf 532 402 533\nf 529 536 530\nf 530 536 537\nf 536 538 537\nf 537 538 539\nf 540 541 539\nf 539 541 537\nf 530 537 535\nf 535 537 541\nf 542 539 543\nf 543 539 538\nf 539 542 540\nf 540 542 544\nf 541 540 513\nf 513 540 512\nf 540 544 512\nf 512 544 514\nf 546 547 545\nf 545 547 548\nf 549 550 531\nf 531 550 551\nf 550 549 552\nf 552 549 553\nf 546 550 554\nf 554 550 552\nf 547 546 555\nf 555 546 554\nf 498 556 558\nf 558 556 557\nf 559 557 560\nf 560 557 556\nf 561 562 560\nf 560 562 559\nf 557 559 552\nf 552 559 554\nf 559 562 554\nf 554 562 555\nf 558 557 553\nf 553 557 552\nf 475 471 563\nf 563 471 564\nf 565 563 567\nf 567 563 566\nf 471 470 564\nf 564 470 466\nf 564 466 568\nf 568 466 465\nf 568 465 569\nf 569 465 570\nf 572 573 571\nf 571 573 574\nf 569 575 567\nf 567 575 576\nf 577 565 576\nf 576 565 567\nf 578 579 581\nf 581 579 580\nf 575 561 576\nf 576 561 560\nf 556 577 560\nf 560 577 576\nf 577 556 497\nf 497 556 498\nf 565 577 500\nf 500 577 497\nf 563 565 475\nf 475 565 500\nf 553 489 558\nf 558 489 487\nf 531 493 549\nf 549 493 491\nf 489 553 491\nf 491 553 549\nf 394 407 518\nf 518 407 485\nf 518 448 394\nf 394 448 379\nf 545 548 538\nf 538 548 543\nf 551 545 536\nf 536 545 538\nf 536 529 551\nf 551 529 531\nf 507 582 449\nf 449 582 450\nf 511 583 507\nf 507 583 582\nf 511 517 583\nf 583 517 584\nf 585 586 588\nf 588 586 587\nf 590 591 589\nf 589 591 592\nf 591 590 593\nf 593 590 594\nf 596 597 595\nf 595 597 598\nf 599 600 602\nf 602 600 601\nf 604 605 603\nf 603 605 606\nf 608 609 607\nf 607 609 610\nf 612 613 611\nf 611 613 614\nf 615 616 618\nf 618 616 617\nf 620 621 619\nf 619 621 622\nf 624 625 623\nf 623 625 626\nf 628 629 627\nf 627 629 630\nf 632 633 631\nf 631 633 634\nf 635 636 638\nf 638 636 637\nf 639 640 642\nf 642 640 641\nf 644 645 643\nf 643 645 646\nf 619 622 647\nf 647 622 648\nf 650 651 649\nf 649 651 652\nf 601 653 602\nf 602 653 654\nf 656 657 655\nf 655 657 658\nf 660 661 659\nf 659 661 662\nf 664 665 663\nf 663 665 666\nf 593 594 667\nf 667 594 668\nf 669 670 672\nf 672 670 671\nf 673 674 676\nf 676 674 675\nf 633 632 677\nf 677 632 678\nf 663 666 679\nf 679 666 680\nf 681 682 684\nf 684 682 683\nf 686 687 685\nf 685 687 688\nf 690 661 689\nf 689 661 691\nf 693 686 692\nf 692 686 685\nf 695 597 694\nf 694 597 668\nf 594 696 668\nf 668 696 694\nf 697 698 696\nf 696 698 699\nf 700 701 697\nf 697 701 698\nf 702 585 703\nf 703 585 588\nf 671 690 672\nf 672 690 689\nf 640 639 704\nf 704 639 705\nf 679 706 663\nf 663 706 658\nf 708 709 707\nf 707 709 710\nf 711 712 610\nf 610 712 607\nf 713 714 716\nf 716 714 715\nf 717 718 720\nf 720 718 719\nf 722 634 721\nf 721 634 633\nf 724 725 723\nf 723 725 726\nf 728 729 727\nf 727 729 730\nf 732 733 731\nf 731 733 734\nf 707 710 735\nf 735 710 736\nf 643 737 644\nf 644 737 738\nf 740 741 739\nf 739 741 742\nf 608 744 743\nf 743 744 745\nf 747 748 746\nf 746 748 749\nf 751 752 750\nf 750 752 753\nf 754 755 757\nf 757 755 756\nf 758 759 761\nf 761 759 760\nf 763 764 762\nf 762 764 765\nf 767 768 766\nf 766 768 769\nf 771 772 770\nf 770 772 773\nf 775 776 774\nf 774 776 777\nf 779 780 778\nf 778 780 781\nf 783 712 782\nf 782 712 784\nf 785 786 788\nf 788 786 787\nf 790 628 789\nf 789 628 627\nf 792 793 791\nf 791 793 794\nf 795 796 726\nf 726 796 723\nf 798 799 797\nf 797 799 800\nf 721 633 801\nf 801 633 677\nf 797 800 802\nf 802 800 803\nf 805 806 804\nf 804 806 807\nf 809 810 808\nf 808 810 811\nf 813 814 812\nf 812 814 815\nf 809 808 817\nf 817 808 816\nf 818 819 804\nf 804 819 805\nf 820 821 818\nf 818 821 819\nf 821 820 587\nf 587 820 822\nf 809 823 810\nf 810 823 824\nf 814 807 815\nf 815 807 806\nf 826 426 825\nf 825 426 223\nf 426 826 427\nf 427 826 827\nf 828 829 454\nf 454 829 830\nf 831 832 677\nf 677 832 801\nf 834 835 833\nf 833 835 836\nf 838 839 837\nf 837 839 840\nf 841 842 844\nf 844 842 843\nf 845 846 803\nf 803 846 802\nf 847 831 678\nf 678 831 677\nf 848 849 851\nf 851 849 850\nf 853 854 852\nf 852 854 855\nf 857 858 856\nf 856 858 680\nf 860 861 859\nf 859 861 862\nf 864 865 863\nf 863 865 866\nf 868 869 867\nf 867 869 870\nf 839 871 840\nf 840 871 872\nf 849 848 874\nf 874 848 873\nf 876 877 875\nf 875 877 878\nf 880 881 879\nf 879 881 882\nf 883 884 886\nf 886 884 885\nf 846 887 802\nf 802 887 888\nf 890 891 889\nf 889 891 888\nf 893 894 892\nf 892 894 895\nf 896 897 728\nf 728 897 729\nf 899 900 898\nf 898 900 901\nf 902 852 904\nf 904 852 903\nf 849 905 850\nf 850 905 906\nf 875 878 907\nf 907 878 908\nf 910 911 909\nf 909 911 912\nf 914 915 913\nf 913 915 916\nf 917 918 862\nf 862 918 859\nf 919 920 856\nf 856 920 921\nf 923 924 922\nf 922 924 925\nf 926 927 925\nf 925 927 922\nf 929 930 928\nf 928 930 693\nf 932 695 931\nf 931 695 694\nf 823 867 824\nf 824 867 933\nf 935 936 934\nf 934 936 937\nf 938 868 823\nf 823 868 867\nf 940 847 939\nf 939 847 678\nf 632 655 678\nf 678 655 939\nf 941 942 619\nf 619 942 620\nf 687 943 688\nf 688 943 944\nf 851 850 945\nf 945 850 903\nf 850 906 903\nf 903 906 904\nf 946 947 949\nf 949 947 948\nf 951 952 950\nf 950 952 953\nf 955 956 954\nf 954 956 957\nf 907 958 875\nf 875 958 874\nf 960 961 959\nf 959 961 962\nf 963 853 902\nf 902 853 852\nf 909 964 910\nf 910 964 965\nf 967 968 966\nf 966 968 969\nf 971 972 970\nf 970 972 973\nf 974 960 975\nf 975 960 959\nf 977 978 976\nf 976 978 979\nf 981 977 980\nf 980 977 976\nf 980 982 981\nf 981 982 983\nf 985 983 984\nf 984 983 982\nf 984 986 985\nf 985 986 987\nf 989 990 988\nf 988 990 991\nf 993 994 992\nf 992 994 995\nf 997 998 996\nf 996 998 999\nf 1001 1002 1000\nf 1000 1002 1003\nf 1005 1006 1004\nf 1004 1006 1007\nf 1004 1008 1005\nf 1005 1008 1009\nf 1008 1010 1009\nf 1009 1010 1011\nf 1013 1011 1012\nf 1012 1011 1010\nf 1014 1015 1017\nf 1017 1015 1016\nf 1019 1020 1018\nf 1018 1020 1021\nf 1022 1023 976\nf 976 1023 980\nf 1024 1025 982\nf 982 1025 984\nf 992 995 1026\nf 1026 995 1027\nf 956 955 1028\nf 1028 955 1029\nf 1030 1031 1033\nf 1033 1031 1032\nf 1010 1008 1034\nf 1034 1008 1035\nf 1008 1004 1035\nf 1035 1004 1036\nf 1037 1038 1002\nf 1002 1038 1007\nf 1002 1001 1037\nf 1037 1001 1039\nf 1040 1041 997\nf 997 1041 998\nf 1015 1014 1043\nf 1043 1014 1042\nf 1044 1045 1047\nf 1047 1045 1046\nf 1044 1020 1048\nf 1048 1020 1049\nf 1050 1051 1003\nf 1003 1051 1000\nf 1052 1050 1006\nf 1006 1050 1003\nf 1053 1054 1011\nf 1011 1054 1009\nf 1013 1045 1055\nf 1055 1045 1056\nf 1058 1059 1057\nf 1057 1059 1060\nf 1062 1063 1061\nf 1061 1063 1064\nf 1065 1066 1050\nf 1050 1066 1051\nf 1067 1065 1052\nf 1052 1065 1050\nf 1068 1069 1054\nf 1054 1069 1070\nf 1071 1068 1053\nf 1053 1068 1054\nf 1073 1071 1072\nf 1072 1071 1074\nf 1074 1071 1055\nf 1055 1071 1053\nf 1076 1077 1075\nf 1075 1077 1078\nf 1079 1080 1057\nf 1057 1080 1058\nf 1081 1082 1064\nf 1064 1082 1061\nf 1084 1085 1083\nf 1083 1085 1086\nf 1088 1089 1087\nf 1087 1089 1090\nf 1092 1093 1091\nf 1091 1093 1094\nf 1096 1097 1095\nf 1095 1097 1098\nf 1099 1100 1102\nf 1102 1100 1101\nf 1103 1104 1099\nf 1099 1104 1100\nf 1106 1107 1105\nf 1105 1107 1108\nf 1110 1111 1109\nf 1109 1111 1112\nf 1114 1115 1113\nf 1113 1115 1116\nf 1118 1119 1117\nf 1117 1119 1077\nf 1117 1120 1122\nf 1122 1120 1121\nf 1105 1123 1106\nf 1106 1123 1124\nf 1125 1126 1128\nf 1128 1126 1127\nf 1130 1131 1129\nf 1129 1131 1132\nf 1134 1135 1133\nf 1133 1135 1114\nf 1113 1136 1114\nf 1114 1136 1133\nf 1118 1137 1113\nf 1113 1137 1136\nf 1118 1117 1137\nf 1137 1117 1122\nf 1139 1140 1138\nf 1138 1140 1141\nf 1143 953 1142\nf 1142 953 952\nf 1145 957 1144\nf 1144 957 956\nf 1028 1146 956\nf 956 1146 1144\nf 1014 1147 1042\nf 1042 1147 1148\nf 1014 1017 1147\nf 1147 1017 1149\nf 1059 1058 1150\nf 1150 1058 1151\nf 1153 1080 1152\nf 1152 1080 1154\nf 1156 1088 1155\nf 1155 1088 1087\nf 1157 1158 1106\nf 1106 1158 1107\nf 1106 1124 1157\nf 1157 1124 1159\nf 1161 1162 1160\nf 1160 1162 1163\nf 1165 1166 1164\nf 1164 1166 1167\nf 1168 1031 1012\nf 1012 1031 1046\nf 1024 1023 1035\nf 1035 1023 1034\nf 1036 1038 1025\nf 1025 1038 1169\nf 1170 1171 1039\nf 1039 1171 1037\nf 1028 1029 1042\nf 1042 1029 1043\nf 1172 1173 1175\nf 1175 1173 1174\nf 1055 1053 1013\nf 1013 1053 1011\nf 1005 1009 1070\nf 1070 1009 1054\nf 996 999 1062\nf 1062 999 1063\nf 1016 1060 1017\nf 1017 1060 1059\nf 1017 1059 1149\nf 1149 1059 1150\nf 1177 1178 1176\nf 1176 1178 1103\nf 1176 1072 1179\nf 1179 1072 1074\nf 1073 1102 1086\nf 1086 1102 1083\nf 1180 1181 1067\nf 1067 1181 1065\nf 1182 1183 1184\nf 1184 1183 1181\nf 1186 1187 1185\nf 1185 1187 1188\nf 1189 1190 798\nf 798 1190 799\nf 1192 1193 1191\nf 1191 1193 1194\nf 1195 1196 1194\nf 1194 1196 1191\nf 1196 1195 1198\nf 1198 1195 1197\nf 1200 674 1199\nf 1199 674 673\nf 928 1201 1203\nf 1203 1201 1202\nf 861 1204 862\nf 862 1204 921\nf 1205 1206 913\nf 913 1206 1207\nf 882 1208 879\nf 879 1208 1209\nf 1210 1211 1167\nf 1167 1211 1164\nf 1212 769 1213\nf 1213 769 768\nf 727 1214 728\nf 728 1214 1215\nf 1217 1218 1216\nf 1216 1218 1219\nf 1220 813 1221\nf 1221 813 812\nf 827 828 427\nf 427 828 454\nf 1223 1224 1222\nf 1222 1224 1165\nf 1226 1227 1225\nf 1225 1227 1228\nf 851 1229 848\nf 848 1229 1230\nf 834 854 835\nf 835 854 1231\nf 1233 1234 1232\nf 1232 1234 1235\nf 1237 751 1236\nf 1236 751 750\nf 743 819 1238\nf 1238 819 821\nf 1240 1241 1239\nf 1239 1241 1242\nf 1244 1245 1243\nf 1243 1245 1246\nf 844 1247 841\nf 841 1247 1248\nf 1250 1251 1249\nf 1249 1251 1252\nf 1254 893 1253\nf 1253 893 1237\nf 1255 1256 1258\nf 1258 1256 1257\nf 1260 1261 1259\nf 1259 1261 1262\nf 945 903 855\nf 855 903 852\nf 855 1263 945\nf 945 1263 1264\nf 832 845 801\nf 801 845 803\nf 800 721 803\nf 803 721 801\nf 721 800 722\nf 722 800 799\nf 1266 1267 1265\nf 1265 1267 1268\nf 1270 795 1269\nf 1269 795 726\nf 725 1271 726\nf 726 1271 1269\nf 737 765 738\nf 738 765 764\nf 759 740 760\nf 760 740 739\nf 717 1272 1274\nf 1274 1272 1273\nf 1163 1226 1275\nf 1275 1226 1225\nf 1277 882 1276\nf 1276 882 881\nf 1041 1040 1027\nf 1027 1040 1026\nf 900 935 901\nf 901 935 934\nf 1164 1278 1165\nf 1165 1278 1222\nf 1211 1279 1164\nf 1164 1279 1278\nf 442 1280 453\nf 453 1280 1281\nf 788 1282 785\nf 785 1282 1283\nf 1285 1286 1284\nf 1284 1286 1287\nf 1284 1288 1285\nf 1285 1288 1289\nf 757 1289 754\nf 754 1289 1288\nf 605 604 675\nf 675 604 676\nf 1290 1291 1293\nf 1293 1291 1292\nf 1295 1296 1294\nf 1294 1296 1297\nf 696 699 694\nf 694 699 931\nf 1298 1299 592\nf 592 1299 589\nf 1299 1300 589\nf 589 1300 700\nf 1300 1301 700\nf 700 1301 701\nf 743 745 819\nf 819 745 805\nf 1303 1304 1302\nf 1302 1304 1305\nf 1306 1307 1305\nf 1305 1307 1302\nf 609 1298 610\nf 610 1298 592\nf 587 822 588\nf 588 822 1308\nf 703 588 1309\nf 1309 588 1308\nf 1310 703 571\nf 571 703 1309\nf 1311 1312 789\nf 789 1312 790\nf 825 223 1313\nf 1313 223 220\nf 219 1314 220\nf 220 1314 1313\nf 1315 1316 1311\nf 1311 1316 1312\nf 1317 1318 1319\nf 1319 1318 1241\nf 441 1320 442\nf 442 1320 1280\nf 1321 217 572\nf 572 217 216\nf 1323 1230 1322\nf 1322 1230 1229\nf 1324 1325 631\nf 631 1325 656\nf 1034 1168 1010\nf 1010 1168 1012\nf 1327 1328 1326\nf 1326 1328 1329\nf 1331 1202 1330\nf 1330 1202 1201\nf 571 1309 572\nf 572 1309 1321\nf 573 572 1332\nf 1332 572 216\nf 1334 1335 1333\nf 1333 1335 1336\nf 1337 1338 918\nf 918 1338 859\nf 1339 863 1340\nf 1340 863 866\nf 1341 1342 1344\nf 1344 1342 1343\nf 1345 1346 919\nf 919 1346 920\nf 1347 1348 673\nf 673 1348 1199\nf 693 1349 928\nf 928 1349 1201\nf 1351 1257 1350\nf 1350 1257 1256\nf 1350 1352 1351\nf 1351 1352 1353\nf 653 930 654\nf 654 930 662\nf 1279 1354 1278\nf 1278 1354 1355\nf 1357 1358 1356\nf 1356 1358 1359\nf 937 1360 1140\nf 1140 1360 1361\nf 1225 1228 909\nf 909 1228 964\nf 901 934 1362\nf 1362 934 1139\nf 1364 1365 1363\nf 1363 1365 1362\nf 1367 912 1366\nf 1366 912 911\nf 1143 1142 1368\nf 1368 1142 1369\nf 1144 1186 1145\nf 1145 1186 1185\nf 1146 1175 1144\nf 1144 1175 1186\nf 1175 1146 1172\nf 1172 1146 1148\nf 1147 1018 1148\nf 1148 1018 1172\nf 1018 1147 1019\nf 1019 1147 1149\nf 1149 1150 1019\nf 1019 1150 1370\nf 1150 1151 1370\nf 1370 1151 1371\nf 1151 1153 1371\nf 1371 1153 1177\nf 1152 1156 1178\nf 1178 1156 1372\nf 1155 1158 1373\nf 1373 1158 1374\nf 1157 1159 1376\nf 1376 1159 1375\nf 1378 1379 1377\nf 1377 1379 1380\nf 1382 1383 1381\nf 1381 1383 1384\nf 1382 1386 1385\nf 1385 1386 1387\nf 1386 1233 1387\nf 1387 1233 1232\nf 1389 1390 1388\nf 1388 1390 1391\nf 1393 1394 1392\nf 1392 1394 1395\nf 1397 1398 1396\nf 1396 1398 1399\nf 629 1397 630\nf 630 1397 1396\nf 1342 1400 1343\nf 1343 1400 1401\nf 1403 1404 1402\nf 1402 1404 1405\nf 1407 1408 1406\nf 1406 1408 1409\nf 1411 1412 1410\nf 1410 1412 1413\nf 793 1414 794\nf 794 1414 1415\nf 1417 1341 1416\nf 1416 1341 1344\nf 704 705 1347\nf 1347 705 1418\nf 741 1419 742\nf 742 1419 1420\nf 1422 669 1421\nf 1421 669 672\nf 645 1423 646\nf 646 1423 1424\nf 1196 1198 614\nf 614 1198 611\nf 613 1425 614\nf 614 1425 1426\nf 1427 616 659\nf 659 616 660\nf 614 1426 1196\nf 1196 1426 1191\nf 696 594 697\nf 697 594 590\nf 697 590 700\nf 700 590 589\nf 703 1310 702\nf 702 1310 1428\nf 1421 672 1192\nf 1192 672 689\nf 1271 648 1269\nf 1269 648 622\nf 621 1270 622\nf 622 1270 1269\nf 1429 1430 1417\nf 1417 1430 1341\nf 1431 1432 1434\nf 1434 1432 1433\nf 1436 1437 1435\nf 1435 1437 1438\nf 1440 1441 1439\nf 1439 1441 1442\nf 1443 1444 1446\nf 1446 1444 1445\nf 823 809 938\nf 938 809 817\nf 810 1447 811\nf 811 1447 1448\nf 1449 1450 679\nf 679 1450 706\nf 913 916 1205\nf 1205 916 1451\nf 1275 1225 912\nf 912 1225 909\nf 1452 1453 880\nf 880 1453 881\nf 1444 1401 1445\nf 1445 1401 1400\nf 936 1454 937\nf 937 1454 1360\nf 824 1455 810\nf 810 1455 1447\nf 631 656 632\nf 632 656 655\nf 966 1456 967\nf 967 1456 1457\nf 1459 1460 1458\nf 1458 1460 1461\nf 1460 1462 1461\nf 1461 1462 1463\nf 972 974 973\nf 973 974 975\nf 990 995 991\nf 991 995 994\nf 1464 1187 1033\nf 1033 1187 1174\nf 1018 1021 1172\nf 1172 1021 1173\nf 1000 999 1001\nf 1001 999 998\nf 976 979 1022\nf 1022 979 1032\nf 1023 1024 980\nf 980 1024 982\nf 989 986 1171\nf 1171 986 1169\nf 1171 1170 989\nf 989 1170 990\nf 1170 1027 990\nf 990 1027 995\nf 1041 1039 998\nf 998 1039 1001\nf 1051 1063 1000\nf 1000 1063 999\nf 1066 1064 1051\nf 1051 1064 1063\nf 1183 1081 1066\nf 1066 1081 1064\nf 1178 1372 1103\nf 1103 1372 1104\nf 1092 1091 1465\nf 1465 1091 1182\nf 1467 1465 1466\nf 1466 1465 1468\nf 1469 1470 1184\nf 1184 1470 1468\nf 1095 1098 1471\nf 1471 1098 1101\nf 1076 1374 1120\nf 1120 1374 1376\nf 1110 1472 1127\nf 1127 1472 1132\nf 1472 1473 1132\nf 1132 1473 1129\nf 1474 1475 1466\nf 1466 1475 1473\nf 1119 1118 1116\nf 1116 1118 1113\nf 1375 1121 1376\nf 1376 1121 1120\nf 1127 1132 1128\nf 1128 1132 1131\nf 1227 1476 1228\nf 1228 1476 1361\nf 1365 1477 1362\nf 1362 1477 872\nf 1478 1479 1323\nf 1323 1479 1230\nf 1480 1481 914\nf 914 1481 915\nf 913 1277 914\nf 914 1277 1276\nf 1168 1034 1022\nf 1022 1034 1023\nf 1035 1036 1024\nf 1024 1036 1025\nf 1039 1041 1170\nf 1170 1041 1027\nf 1042 1148 1028\nf 1028 1148 1146\nf 968 1459 969\nf 969 1459 1458\nf 1482 1483 664\nf 664 1483 665\nf 1485 1486 1484\nf 1484 1486 1487\nf 1489 1490 1488\nf 1488 1490 1491\nf 1492 1493 722\nf 722 1493 634\nf 1495 1496 1494\nf 1494 1496 1497\nf 1498 1207 1339\nf 1339 1207 863\nf 1224 1499 1165\nf 1165 1499 1166\nf 1160 1163 1500\nf 1500 1163 1275\nf 1500 877 1160\nf 1160 877 1501\nf 876 873 1502\nf 1502 873 1479\nf 945 1264 851\nf 851 1264 1229\nf 835 1231 839\nf 839 1231 871\nf 1503 1283 778\nf 778 1283 1282\nf 716 715 1505\nf 1505 715 1504\nf 1446 1445 1507\nf 1507 1445 1506\nf 779 792 1381\nf 1381 792 791\nf 1430 1409 1341\nf 1341 1409 1342\nf 1411 1429 1508\nf 1508 1429 1417\nf 635 1510 1509\nf 1509 1510 1511\nf 1377 1380 1315\nf 1315 1380 1316\nf 1445 1400 1506\nf 1506 1400 1408\nf 918 917 1336\nf 1336 917 1333\nf 1513 1514 1512\nf 1512 1514 870\nf 1515 1516 1363\nf 1363 1516 1364\nf 1453 1517 881\nf 881 1517 1276\nf 992 1518 993\nf 993 1518 1519\nf 1520 1029 1518\nf 1518 1029 955\nf 1520 1026 1521\nf 1521 1026 1040\nf 1522 1015 1521\nf 1521 1015 1043\nf 996 1523 997\nf 997 1523 1522\nf 1060 1016 1524\nf 1524 1016 1523\nf 1061 1525 1062\nf 1062 1525 1524\nf 1079 1527 1526\nf 1526 1527 1528\nf 1093 1529 1094\nf 1094 1529 1528\nf 1111 1530 1112\nf 1112 1530 1531\nf 1532 1533 1105\nf 1105 1533 1123\nf 934 937 1139\nf 1139 937 1140\nf 1138 1363 1139\nf 1139 1363 1362\nf 1534 1222 1355\nf 1355 1222 1278\nf 1535 1223 1534\nf 1534 1223 1222\nf 821 587 1238\nf 1238 587 586\nf 1319 711 1317\nf 1317 711 1536\nf 1538 1200 1537\nf 1537 1200 1199\nf 1508 1417 1539\nf 1539 1417 1416\nf 1540 1541 840\nf 840 1541 837\nf 1231 898 871\nf 871 898 901\nf 1500 1275 1367\nf 1367 1275 912\nf 1543 1379 1542\nf 1542 1379 1395\nf 1539 772 1508\nf 1508 772 1544\nf 1546 1403 1545\nf 1545 1403 1402\nf 1548 1549 1547\nf 1547 1549 1550\nf 1552 1548 1551\nf 1551 1548 1547\nf 1554 1555 1553\nf 1553 1555 1556\nf 1558 1559 1557\nf 1557 1559 1560\nf 1562 1563 1561\nf 1561 1563 1564\nf 1566 1567 1565\nf 1565 1567 1568\nf 1570 1571 1569\nf 1569 1571 1572\nf 1574 1575 1573\nf 1573 1575 1576\nf 1577 1578 1572\nf 1572 1578 1569\nf 1580 1581 1579\nf 1579 1581 1582\nf 1584 1585 1583\nf 1583 1585 1586\nf 1588 1555 1587\nf 1587 1555 1589\nf 1591 1592 1590\nf 1590 1592 1593\nf 1595 1596 1594\nf 1594 1596 1597\nf 1596 1581 1597\nf 1597 1581 1576\nf 1599 1570 1598\nf 1598 1570 1569\nf 1601 1602 1600\nf 1600 1602 1603\nf 1605 1606 1604\nf 1604 1606 1607\nf 1608 1609 1568\nf 1568 1609 1565\nf 1611 1612 1610\nf 1610 1612 1613\nf 1615 1616 1614\nf 1614 1616 1617\nf 1619 1617 1618\nf 1618 1617 1616\nf 1621 1622 1620\nf 1620 1622 1623\nf 1625 1626 1624\nf 1624 1626 1627\nf 1629 1630 1628\nf 1628 1630 1631\nf 1633 1634 1632\nf 1632 1634 1635\nf 1636 1637 1639\nf 1639 1637 1638\nf 1641 1642 1640\nf 1640 1642 1643\nf 1624 1627 1644\nf 1644 1627 1645\nf 1647 1648 1646\nf 1646 1648 1649\nf 1651 1652 1650\nf 1650 1652 1653\nf 1655 1656 1654\nf 1654 1656 1657\nf 1659 1660 1658\nf 1658 1660 1661\nf 1662 1663 1631\nf 1631 1663 1628\nf 1651 1665 1664\nf 1664 1665 1666\nf 1667 1656 1619\nf 1619 1656 1617\nf 1669 1670 1668\nf 1668 1670 1671\nf 1632 1635 1672\nf 1672 1635 1673\nf 1674 1675 1638\nf 1638 1675 1639\nf 1677 1678 1676\nf 1676 1678 1679\nf 1680 1640 1681\nf 1681 1640 1643\nf 1682 1683 1643\nf 1643 1683 1681\nf 1670 1669 1235\nf 1235 1669 1232\nf 1396 1399 1674\nf 1674 1399 1675\nf 627 630 1679\nf 1679 630 1676\nf 1681 1315 1680\nf 1680 1315 1311\nf 1683 1377 1681\nf 1681 1377 1315\nf 1672 1673 1387\nf 1387 1673 1385\nf 1636 1684 1646\nf 1646 1684 1685\nf 1687 1688 1686\nf 1686 1688 1689\nf 1585 1690 1586\nf 1586 1690 1691\nf 1599 1692 1551\nf 1551 1692 1693\nf 1552 1558 1694\nf 1694 1558 1557\nf 1696 1615 1695\nf 1695 1615 1614\nf 1698 1699 1697\nf 1697 1699 1623\nf 1700 1701 1703\nf 1703 1701 1702\nf 1705 1706 1704\nf 1704 1706 1707\nf 1708 1709 1640\nf 1640 1709 1641\nf 1710 1711 1713\nf 1713 1711 1712\nf 1652 1655 1653\nf 1653 1655 1654\nf 1714 1699 1712\nf 1712 1699 1713\nf 1715 1710 1716\nf 1716 1710 1713\nf 1717 1708 1680\nf 1680 1708 1640\nf 1393 1392 1715\nf 1715 1392 1710\nf 1719 1720 1718\nf 1718 1720 1721\nf 1680 1311 1717\nf 1717 1311 789\nf 1378 1711 1392\nf 1392 1711 1710\nf 1388 1391 1719\nf 1719 1391 1720\nf 1723 1606 1722\nf 1722 1606 1689\nf 1725 1568 1724\nf 1724 1568 1567\nf 1726 1647 1685\nf 1685 1647 1646\nf 1728 1554 1727\nf 1727 1554 1553\nf 1591 1687 1600\nf 1600 1687 1686\nf 1730 1595 1729\nf 1729 1595 1594\nf 1601 1605 1731\nf 1731 1605 1604\nf 1733 1663 1732\nf 1732 1663 1734\nf 1736 1639 1735\nf 1735 1639 1675\nf 1658 1644 1737\nf 1737 1644 1645\nf 1726 1662 1724\nf 1724 1662 1631\nf 1399 1738 1675\nf 1675 1738 1735\nf 1621 1667 1739\nf 1739 1667 1619\nf 1714 1740 1620\nf 1620 1740 1741\nf 1592 1549 1593\nf 1593 1549 1742\nf 1602 1571 1603\nf 1603 1571 1550\nf 1688 1690 1689\nf 1689 1690 1722\nf 1588 1559 1582\nf 1582 1559 1579\nf 1673 1735 1385\nf 1385 1735 1738\nf 1387 1232 1672\nf 1672 1232 1669\nf 1580 1692 1573\nf 1573 1692 1743\nf 1377 1683 1378\nf 1378 1683 1711\nf 1664 1666 1744\nf 1744 1666 1745\nf 1744 1696 1664\nf 1664 1696 1695\nf 1746 1747 1732\nf 1732 1747 1748\nf 1749 1750 1677\nf 1677 1750 1678\nf 1709 1708 1750\nf 1750 1708 1678\nf 1668 1632 1669\nf 1669 1632 1672\nf 1633 1746 1734\nf 1734 1746 1732\nf 1679 1678 1717\nf 1717 1678 1708\nf 789 627 1717\nf 1717 627 1679\nf 1563 1723 1564\nf 1564 1723 1722\nf 1728 1584 1751\nf 1751 1584 1583\nf 1674 1676 1396\nf 1396 1676 630\nf 1666 1566 1745\nf 1745 1566 1565\nf 1627 1629 1645\nf 1645 1629 1628\nf 1637 1749 1638\nf 1638 1749 1677\nf 1648 1665 1649\nf 1649 1665 1752\nf 1740 1642 1741\nf 1741 1642 1657\nf 1676 1674 1677\nf 1677 1674 1638\nf 1683 1682 1711\nf 1711 1682 1712\nf 1618 1753 1619\nf 1619 1753 1739\nf 1701 1739 1702\nf 1702 1739 1753\nf 1754 1214 1756\nf 1756 1214 1755\nf 647 1757 619\nf 619 1757 941\nf 1759 1193 1758\nf 1758 1193 691\nf 1419 1760 1420\nf 1420 1760 1761\nf 682 1762 683\nf 683 1762 1763\nf 1412 780 1413\nf 1413 780 1384\nf 1383 1398 1384\nf 1384 1398 1413\nf 1197 1424 1764\nf 1764 1424 1423\nf 1537 1287 1538\nf 1538 1287 1286\nf 1634 1684 1635\nf 1635 1684 1736\nf 1724 1631 1725\nf 1725 1631 1630\nf 1562 1730 1610\nf 1610 1730 1729\nf 1735 1673 1736\nf 1736 1673 1635\nf 1757 1195 941\nf 941 1195 1194\nf 1766 200 1765\nf 1765 200 199\nf 475 500 476\nf 476 500 479\nf 498 558 483\nf 483 558 487\nf 545 551 546\nf 546 551 550\nf 453 1281 219\nf 219 1281 1314\nf 298 453 208\nf 208 453 219\nf 368 1765 1767\nf 1767 1765 199\nf 523 405 363\nf 363 405 404\nf 523 533 405\nf 405 533 402\nf 298 208 299\nf 299 208 207\nf 299 207 176\nf 176 207 300\nf 176 300 173\nf 173 300 202\nf 173 202 174\nf 174 202 201\nf 1337 918 1768\nf 1768 918 1336\nf 1769 1770 1338\nf 1338 1770 859\nf 581 570 578\nf 578 570 1771\nf 555 562 580\nf 580 562 561\nf 504 1772 501\nf 501 1772 548\nf 517 542 584\nf 584 542 543\nf 515 514 516\nf 516 514 544\nf 542 517 544\nf 544 517 516\nf 561 575 580\nf 580 575 581\nf 575 569 581\nf 581 569 570\nf 501 548 1773\nf 1773 548 547\nf 1774 1775 570\nf 570 1775 1771\nf 369 368 1776\nf 1776 368 1767\nf 372 369 375\nf 375 369 1776\nf 822 1280 1308\nf 1308 1280 1320\nf 820 1281 822\nf 822 1281 1280\nf 818 1314 820\nf 820 1314 1281\nf 1314 818 1313\nf 1313 818 804\nf 807 814 825\nf 825 814 826\nf 814 813 826\nf 826 813 827\nf 813 1220 827\nf 827 1220 828\nf 580 579 555\nf 555 579 547\nf 1477 1540 872\nf 872 1540 840\nf 367 499 368\nf 368 499 1765\nf 1766 1765 525\nf 525 1765 499\nf 1777 1766 526\nf 526 1766 525\nf 520 527 362\nf 362 527 384\nf 361 1778 519\nf 519 1778 528\nf 371 1779 372\nf 372 1779 369\nf 369 1779 370\nf 370 1779 478\nf 1779 371 478\nf 478 371 374\nf 1778 1777 528\nf 528 1777 526\nf 1781 1782 1780\nf 1780 1782 1783\nf 431 460 67\nf 67 460 64\nf 68 338 74\nf 74 338 72\nf 1785 1782 1784\nf 1784 1782 1786\nf 1787 1788 1790\nf 1790 1788 1789\nf 1792 1789 1791\nf 1791 1789 1793\nf 1791 1794 1792\nf 1792 1794 1795\nf 1794 1796 1795\nf 1795 1796 1797\nf 1797 1798 938\nf 938 1798 868\nf 1798 1799 868\nf 868 1799 869\nf 1801 1802 1800\nf 1800 1802 1451\nf 1803 1804 1205\nf 1205 1804 1206\nf 1805 1806 864\nf 864 1806 865\nf 1806 1807 865\nf 865 1807 1808\nf 68 71 338\nf 287 328 288\nf 1320 1321 1308\nf 1308 1321 1309\nf 1320 441 1321\nf 1321 441 217\nf 441 152 217\nf 217 152 218\nf 151 286 152\nf 152 286 218\nf 297 310 151\nf 151 310 286\nf 296 307 297\nf 297 307 310\nf 296 143 307\nf 307 143 284\nf 143 56 284\nf 284 56 104\nf 56 59 104\nf 104 59 63\nf 59 102 63\nf 63 102 60\nf 102 94 60\nf 60 94 282\nf 93 459 279\nf 279 459 77\nf 459 460 77\nf 77 460 78\nf 460 431 78\nf 78 431 295\nf 431 49 295\nf 295 49 292\nf 48 421 49\nf 49 421 292\nf 276 278 418\nf 418 278 429\nf 317 432 278\nf 278 432 429\nf 432 317 433\nf 433 317 316\nf 326 324 316\nf 316 324 433\nf 1536 592 1809\nf 1809 592 591\nf 1810 744 1812\nf 1812 744 1811\nf 1814 1815 1813\nf 1813 1815 1816\nf 1817 748 1818\nf 1818 748 747\nf 1812 1819 1810\nf 1810 1819 1817\nf 1820 1821 748\nf 748 1821 749\nf 1823 1824 1822\nf 1822 1824 1825\nf 1827 1828 1826\nf 1826 1828 1829\nf 1831 1832 1830\nf 1830 1832 1833\nf 1834 1827 1243\nf 1243 1827 1826\nf 1313 804 825\nf 825 804 807\nf 1835 1253 1236\nf 1236 1253 1237\nf 1836 1837 1438\nf 1438 1837 1435\nf 1838 1505 1840\nf 1840 1505 1839\nf 1841 1842 716\nf 716 1842 713\nf 1843 1844 1510\nf 1510 1844 1845\nf 1847 1848 1846\nf 1846 1848 1849\nf 1850 1240 1822\nf 1822 1240 1239\nf 636 1831 637\nf 637 1831 1830\nf 1852 1853 1851\nf 1851 1853 1854\nf 1855 1856 1858\nf 1858 1856 1857\nf 1846 1849 1859\nf 1859 1849 1860\nf 1343 1401 1509\nf 1509 1401 1861\nf 1509 1861 635\nf 635 1861 636\nf 1863 1864 1862\nf 1862 1864 1865\nf 1283 1503 1867\nf 1867 1503 1866\nf 1868 1867 681\nf 681 1867 682\nf 1286 1285 683\nf 683 1285 684\nf 757 756 1274\nf 1274 756 1869\nf 1274 1869 717\nf 717 1869 718\nf 1434 1433 719\nf 719 1433 720\nf 1200 1538 1545\nf 1545 1538 1763\nf 1285 1289 684\nf 684 1289 1273\nf 684 1273 681\nf 681 1273 1272\nf 785 1283 1868\nf 1868 1283 1867\nf 786 785 1870\nf 1870 785 1868\nf 1289 757 1273\nf 1273 757 1274\nf 1539 1416 1871\nf 1871 1416 1872\nf 773 1871 1403\nf 1403 1871 1404\nf 675 674 1405\nf 1405 674 1402\nf 1864 1873 1865\nf 1865 1873 1874\nf 638 1843 635\nf 635 1843 1510\nf 1344 1343 1511\nf 1511 1343 1509\nf 772 1539 773\nf 773 1539 1871\nf 674 1200 1402\nf 1402 1200 1545\nf 1538 1286 1763\nf 1763 1286 683\nf 1444 1443 1875\nf 1875 1443 1876\nf 1875 1876 1831\nf 1831 1876 1832\nf 1416 1344 1872\nf 1872 1344 1511\nf 1877 1878 774\nf 774 1878 1879\nf 1878 1847 1879\nf 1879 1847 1846\nf 1880 714 1881\nf 1881 714 1848\nf 1401 1444 1861\nf 1861 1444 1875\nf 1861 1875 636\nf 636 1875 1831\nf 1823 1863 1882\nf 1882 1863 1862\nf 1441 1440 1884\nf 1884 1440 1883\nf 1883 1852 1884\nf 1884 1852 1851\nf 1886 1887 1885\nf 1885 1887 1888\nf 1358 1889 1359\nf 1359 1889 1890\nf 1891 1886 1892\nf 1892 1886 1885\nf 774 1879 775\nf 775 1879 1893\nf 1859 1893 1846\nf 1846 1893 1879\nf 713 1842 1849\nf 1849 1842 1860\nf 1318 1895 1894\nf 1894 1895 1896\nf 1873 1897 1874\nf 1874 1897 1898\nf 1898 605 1899\nf 1899 605 675\nf 1405 1844 675\nf 675 1844 1899\nf 1404 1845 1405\nf 1405 1845 1844\nf 593 1296 591\nf 591 1296 1809\nf 1295 1292 1900\nf 1900 1292 1895\nf 606 1897 1291\nf 1291 1897 1896\nf 1510 1845 1511\nf 1511 1845 1872\nf 1871 1872 1404\nf 1404 1872 1845\nf 1866 770 1762\nf 1762 770 1546\nf 1762 682 1866\nf 1866 682 1867\nf 1870 1868 1272\nf 1272 1868 681\nf 1272 717 1870\nf 1870 717 720\nf 1870 720 786\nf 786 720 1433\nf 786 1433 787\nf 787 1433 1432\nf 1261 1414 1432\nf 1432 1414 787\nf 1389 1234 1260\nf 1260 1234 1415\nf 1719 1670 1388\nf 1388 1670 1235\nf 1718 1671 1719\nf 1719 1671 1670\nf 1705 1747 1718\nf 1718 1747 1671\nf 1659 1901 1704\nf 1704 1901 1748\nf 1877 1903 1902\nf 1902 1903 1904\nf 1259 1905 1390\nf 1390 1905 1394\nf 1715 1720 1393\nf 1393 1720 1391\nf 1716 1721 1715\nf 1715 1721 1720\nf 1698 1706 1716\nf 1716 1706 1721\nf 277 276 47\nf 47 276 48\nf 84 96 81\nf 81 96 82\nf 80 70 81\nf 81 70 85\nf 99 97 88\nf 88 97 89\nf 90 100 88\nf 88 100 99\nf 101 95 102\nf 102 95 94\nf 1906 1907 280\nf 280 1907 281\nf 97 86 89\nf 89 86 85\nf 60 282 61\nf 61 282 281\nf 83 82 46\nf 46 82 47\nf 293 292 420\nf 420 292 421\nf 419 1908 420\nf 420 1908 1909\nf 92 95 91\nf 91 95 90\nf 279 282 93\nf 93 282 94\nf 276 418 48\nf 48 418 421\nf 1910 1660 1697\nf 1697 1660 1707\nf 1703 1624 1700\nf 1700 1624 1644\nf 1622 1910 1623\nf 1623 1910 1697\nf 1701 1700 1911\nf 1911 1700 1661\nf 1700 1644 1661\nf 1661 1644 1658\nf 1901 1737 1733\nf 1733 1737 1645\nf 1625 1912 1604\nf 1604 1912 1731\nf 1912 1913 1731\nf 1731 1913 1914\nf 1913 1577 1914\nf 1914 1577 1572\nf 1915 1578 1616\nf 1616 1578 1618\nf 1915 1916 1598\nf 1598 1916 1743\nf 1916 1574 1743\nf 1743 1574 1573\nf 1575 1917 1576\nf 1576 1917 1597\nf 1917 1609 1597\nf 1597 1609 1594\nf 1608 1611 1729\nf 1729 1611 1610\nf 1918 1612 1629\nf 1629 1612 1630\nf 1626 1918 1627\nf 1627 1918 1629\nf 1920 1921 1919\nf 1919 1921 1922\nf 1920 1924 1923\nf 1923 1924 1925\nf 1926 1927 1924\nf 1924 1927 1925\nf 1929 1930 1928\nf 1928 1930 1931\nf 1932 1933 1935\nf 1935 1933 1934\nf 1937 1938 1936\nf 1936 1938 1939\nf 1941 1942 1940\nf 1940 1942 1943\nf 1941 1945 1944\nf 1944 1945 1946\nf 1947 1948 1694\nf 1694 1948 1949\nf 1950 1951 1953\nf 1953 1951 1952\nf 1955 1956 1954\nf 1954 1956 1589\nf 1958 1956 1957\nf 1957 1956 1932\nf 1960 1934 1959\nf 1959 1934 1933\nf 1962 1963 1961\nf 1961 1963 1964\nf 1965 1955 1751\nf 1751 1955 1954\nf 1935 1930 1932\nf 1932 1930 1957\nf 1951 1950 1926\nf 1926 1950 1929\nf 1966 1967 1593\nf 1593 1967 1590\nf 1969 1967 1968\nf 1968 1967 1964\nf 1962 1921 1970\nf 1970 1921 1971\nf 1963 1945 1964\nf 1964 1945 1968\nf 1972 1948 1922\nf 1922 1948 1919\nf 1966 1972 1961\nf 1961 1972 1922\nf 1958 1953 1587\nf 1587 1953 1560\nf 1974 1975 1973\nf 1973 1975 1976\nf 1978 1979 1977\nf 1977 1979 1691\nf 1979 1975 1691\nf 1691 1975 1586\nf 1938 1942 1939\nf 1939 1942 1980\nf 1952 1947 1557\nf 1557 1947 1694\nf 1969 1978 1981\nf 1981 1978 1977\nf 1983 1984 1982\nf 1982 1984 1985\nf 1986 1987 1989\nf 1989 1987 1988\nf 1990 1991 1983\nf 1983 1991 1984\nf 1990 1993 1992\nf 1992 1993 1994\nf 1995 1996 1994\nf 1994 1996 1997\nf 1999 2000 1998\nf 1998 2000 2001\nf 2003 2004 2002\nf 2002 2004 2005\nf 2007 2008 2006\nf 2006 2008 2009\nf 2011 2012 2010\nf 2010 2012 2013\nf 2014 2015 2017\nf 2017 2015 2016\nf 2018 2019 2021\nf 2021 2019 2020\nf 2022 1989 2024\nf 2024 1989 2023\nf 1989 1988 2023\nf 2023 1988 2025\nf 2026 2027 2017\nf 2017 2027 2014\nf 2029 2030 2028\nf 2028 2030 2031\nf 2033 2034 2032\nf 2032 2034 2035\nf 2032 2035 2036\nf 2036 2035 2037\nf 2039 2040 2038\nf 2038 2040 2041\nf 1982 2042 1983\nf 1983 2042 2043\nf 2045 2046 2044\nf 2044 2046 2047\nf 2049 2050 2048\nf 2048 2050 2051\nf 2053 2054 2052\nf 2052 2054 2055\nf 2057 2058 2056\nf 2056 2058 2059\nf 2060 2061 2033\nf 2033 2061 2034\nf 2063 2064 2062\nf 2062 2064 2065\nf 2066 2035 2067\nf 2067 2035 2034\nf 2069 2039 2068\nf 2068 2039 2038\nf 2025 2070 2023\nf 2023 2070 2071\nf 2072 2024 2071\nf 2071 2024 2023\nf 2074 2020 2073\nf 2073 2020 2019\nf 2050 2049 2047\nf 2047 2049 2075\nf 1983 2043 1990\nf 1990 2043 1993\nf 2006 2050 2007\nf 2007 2050 2076\nf 2050 2006 2051\nf 2051 2006 2077\nf 2079 2080 2078\nf 2078 2080 2081\nf 2082 2083 2085\nf 2085 2083 2084\nf 2010 2013 2086\nf 2086 2013 2087\nf 2089 2033 2088\nf 2088 2033 2032\nf 2090 2060 2089\nf 2089 2060 2033\nf 2092 2093 2091\nf 2091 2093 2094\nf 2096 1986 2095\nf 2095 1986 2097\nf 2097 1986 2022\nf 2022 1986 1989\nf 2015 2094 2016\nf 2016 2094 2098\nf 2016 2098 2100\nf 2100 2098 2099\nf 2017 2016 2101\nf 2101 2016 2100\nf 2017 2101 2026\nf 2026 2101 2102\nf 2100 2099 2104\nf 2104 2099 2103\nf 2104 2105 2100\nf 2100 2105 2101\nf 2105 2106 2101\nf 2101 2106 2102\nf 2108 2061 2107\nf 2107 2061 2109\nf 2111 2112 2110\nf 2110 2112 2020\nf 2020 2074 2110\nf 2110 2074 2113\nf 2115 2116 2114\nf 2114 2116 2117\nf 2118 2119 1995\nf 1995 2119 2120\nf 2121 2122 2027\nf 2027 2122 2014\nf 2047 2075 2044\nf 2044 2075 2123\nf 2124 2125 2127\nf 2127 2125 2126\nf 2129 2130 2128\nf 2128 2130 2131\nf 2132 2133 2057\nf 2057 2133 2058\nf 2134 1999 2136\nf 2136 1999 2135\nf 2055 2138 2137\nf 2137 2138 2139\nf 2141 2142 2140\nf 2140 2142 2143\nf 2083 2082 2064\nf 2064 2082 2065\nf 2082 2144 2065\nf 2065 2144 2145\nf 2040 2146 2041\nf 2041 2146 2147\nf 2060 2090 2148\nf 2148 2090 2149\nf 2048 2051 2137\nf 2137 2051 2150\nf 2151 1997 2135\nf 2135 1997 1996\nf 2152 2150 2077\nf 2077 2150 2051\nf 2046 2045 2118\nf 2118 2045 2119\nf 2154 2155 2153\nf 2153 2155 2156\nf 2158 2159 2157\nf 2157 2159 2160\nf 2162 2163 2161\nf 2161 2163 2164\nf 2164 2163 2165\nf 2165 2163 2166\nf 2168 2169 2167\nf 2167 2169 2170\nf 2003 2172 2171\nf 2171 2172 2173\nf 2174 2175 2002\nf 2002 2175 2176\nf 2145 2177 2065\nf 2065 2177 2062\nf 2178 2179 2180\nf 2180 2179 2134\nf 2181 2052 2152\nf 2152 2052 2150\nf 2040 2127 2146\nf 2146 2127 2182\nf 2130 2162 2131\nf 2131 2162 2161\nf 2052 2055 2150\nf 2150 2055 2137\nf 2184 2185 2183\nf 2183 2185 2186\nf 2159 2030 2160\nf 2160 2030 2187\nf 2146 2182 2029\nf 2029 2182 2188\nf 2189 2190 2192\nf 2192 2190 2191\nf 2172 2003 2176\nf 2176 2003 2002\nf 1998 2151 1999\nf 1999 2151 2135\nf 2177 2172 2062\nf 2062 2172 2176\nf 2172 2177 2173\nf 2173 2177 2193\nf 2063 2194 2064\nf 2064 2194 2195\nf 2066 2197 2196\nf 2196 2197 2198\nf 2002 2005 2174\nf 2174 2005 2199\nf 2004 2003 2200\nf 2200 2003 2171\nf 2198 2201 2171\nf 2171 2201 2200\nf 2197 2202 2198\nf 2198 2202 2201\nf 2202 2197 2203\nf 2203 2197 2204\nf 2196 2198 2173\nf 2173 2198 2171\nf 2205 2069 2170\nf 2170 2069 2206\nf 2197 2066 2204\nf 2204 2066 2067\nf 2107 2207 2108\nf 2108 2207 2208\nf 2210 2208 2209\nf 2209 2208 2207\nf 2185 2211 2212\nf 2213 2214 2211\nf 2211 2214 2212\nf 1998 2001 2213\nf 2213 2001 2214\nf 2134 2215 1999\nf 1999 2215 2000\nf 2216 2217 2219\nf 2219 2217 2218\nf 2206 2220 2170\nf 2170 2220 2167\nf 2069 2068 2206\nf 2206 2068 2221\nf 2151 1998 2222\nf 2222 1998 2213\nf 2124 2039 2205\nf 2205 2039 2069\nf 2142 2223 2143\nf 2143 2223 2128\nf 2203 2186 2202\nf 2202 2186 2185\nf 2185 2184 2211\nf 2211 2184 2224\nf 2224 2225 2222\nf 2222 2225 2226\nf 2071 2227 2072\nf 2072 2227 2228\nf 2229 2074 2230\nf 2230 2074 2073\nf 2074 2229 2113\nf 2113 2229 2231\nf 2232 2225 2227\nf 2227 2225 2224\nf 2233 2234 2031\nf 2031 2234 2028\nf 2236 2231 2235\nf 2235 2231 2229\nf 2230 2183 2229\nf 2229 2183 2235\nf 2237 1987 2096\nf 2096 1987 1986\nf 2152 2238 2181\nf 2181 2238 2239\nf 2080 2240 2081\nf 2081 2240 2241\nf 2236 2242 1971\nf 1971 2242 1970\nf 1925 2113 1923\nf 1923 2113 2231\nf 2110 1927 2111\nf 2111 1927 1928\nf 2103 2111 1931\nf 1931 2111 1928\nf 1931 2243 2103\nf 2103 2243 2104\nf 2105 2104 2244\nf 2244 2104 2243\nf 1939 1980 2148\nf 2148 1980 2109\nf 1980 1944 2109\nf 2109 1944 2107\nf 1946 2245 2207\nf 2207 2245 2209\nf 2245 1970 2209\nf 2209 1970 2242\nf 2231 2236 1923\nf 1923 2236 1971\nf 2113 1925 2110\nf 2110 1925 1927\nf 2234 2246 2028\nf 2028 2246 2147\nf 2247 2248 2058\nf 2058 2248 2059\nf 2114 2117 2249\nf 2249 2117 2250\nf 2252 2253 2251\nf 2251 2253 2254\nf 2094 2015 2091\nf 2091 2015 2255\nf 2256 2255 2014\nf 2014 2255 2015\nf 2257 2145 2258\nf 2258 2145 2144\nf 2131 2161 2123\nf 2123 2161 2044\nf 2045 2044 2164\nf 2164 2044 2161\nf 2045 2164 2119\nf 2119 2164 2165\nf 2259 2120 2165\nf 2165 2120 2119\nf 2260 2261 2259\nf 2259 2261 2120\nf 2205 2262 2124\nf 2124 2262 2125\nf 2180 2263 2178\nf 2178 2263 2264\nf 2265 2266 2092\nf 2092 2266 2093\nf 1992 2267 2268\nf 2184 2228 2224\nf 2224 2228 2227\nf 2048 2269 2049\nf 2049 2269 2141\nf 2129 2168 2157\nf 2157 2168 2167\nf 2049 2141 2075\nf 2075 2141 2140\nf 2199 2215 2270\nf 2270 2215 2271\nf 2142 2141 2272\nf 2272 2141 2269\nf 2089 2026 2090\nf 2090 2026 2102\nf 2146 2029 2147\nf 2147 2029 2028\nf 2111 2103 2112\nf 2112 2103 2099\nf 2061 2060 2109\nf 2109 2060 2148\nf 2273 2274 2041\nf 2041 2274 2038\nf 2260 2275 2261\nf 2261 2275 2136\nf 2057 2138 2132\nf 2132 2138 2276\nf 2272 2269 2056\nf 2056 2269 2139\nf 2277 2278 2270\nf 2270 2278 2199\nf 2029 2188 2030\nf 2030 2188 2187\nf 2040 2039 2127\nf 2127 2039 2124\nf 2064 2195 2083\nf 2083 2195 2279\nf 2056 2139 2057\nf 2057 2139 2138\nf 2278 2277 2247\nf 2247 2277 2248\nf 2062 2176 2063\nf 2063 2176 2175\nf 2280 2210 2242\nf 2242 2210 2209\nf 2282 2233 2281\nf 2281 2233 2031\nf 2216 2219 2283\nf 2283 2219 2221\nf 2158 2220 2218\nf 2218 2220 2219\nf 2217 2282 2218\nf 2218 2282 2281\nf 2220 2158 2167\nf 2167 2158 2157\nf 2223 2264 2128\nf 2128 2264 2263\nf 2261 1996 2120\nf 2120 1996 1995\nf 2213 2211 2222\nf 2222 2211 2224\nf 2283 2221 2284\nf 2284 2221 2068\nf 2135 1996 2136\nf 2136 1996 2261\nf 2144 2081 2258\nf 2258 2081 2241\nf 2235 2280 2236\nf 2236 2280 2242\nf 2246 2273 2147\nf 2147 2273 2041\nf 2036 2285 2032\nf 2032 2285 2088\nf 2077 2286 2152\nf 2152 2286 2238\nf 1937 1960 1973\nf 1973 1960 1959\nf 2070 2232 2071\nf 2071 2232 2227\nf 2244 2287 2105\nf 2105 2287 2106\nf 1936 1939 2149\nf 2149 1939 2148\nf 1944 1946 2107\nf 2107 1946 2207\nf 2285 2121 2088\nf 2088 2121 2027\nf 2253 2249 2254\nf 2254 2249 2250\nf 2006 2009 2077\nf 2077 2009 2286\nf 2289 2086 2288\nf 2288 2086 2087\nf 2223 2142 2290\nf 2290 2142 2272\nf 2128 2131 2143\nf 2143 2131 2123\nf 2156 2291 2153\nf 2153 2291 2175\nf 2292 2293 2278\nf 2278 2293 2294\nf 2075 2140 2123\nf 2123 2140 2143\nf 2262 2169 2275\nf 2275 2169 2180\nf 2275 2180 2136\nf 2136 2180 2134\nf 2274 2284 2038\nf 2038 2284 2068\nf 2127 2126 2182\nf 2182 2126 2166\nf 2094 2093 2098\nf 2098 2093 2021\nf 2098 2021 2099\nf 2099 2021 2112\nf 2018 2021 2266\nf 2266 2021 2093\nf 2251 2289 2252\nf 2252 2289 2288\nf 1997 2151 2226\nf 2226 2151 2222\nf 2206 2221 2220\nf 2220 2221 2219\nf 2269 2048 2139\nf 2139 2048 2137\nf 2159 2158 2281\nf 2281 2158 2218\nf 2156 2155 2190\nf 2190 2155 2191\nf 1974 1965 1583\nf 1583 1965 1751\nf 2088 2027 2089\nf 2089 2027 2026\nf 2030 2159 2031\nf 2031 2159 2281\nf 2106 2149 2102\nf 2102 2149 2090\nf 2287 1936 2106\nf 2106 1936 2149\nf 2293 2154 2294\nf 2294 2154 2153\nf 2021 2020 2112\nf 2247 2295 2278\nf 2278 2295 2292\nf 2284 2296 2283\nf 2283 2296 2297\nf 2298 2296 2274\nf 2274 2296 2284\nf 2273 2299 2274\nf 2274 2299 2298\nf 2246 2300 2273\nf 2273 2300 2299\nf 2283 2297 2216\nf 2216 2297 2301\nf 2216 2301 2217\nf 2217 2301 2302\nf 2302 2303 2217\nf 2217 2303 2282\nf 2304 2301 2305\nf 2305 2301 2297\nf 2307 2308 2306\nf 2306 2308 2309\nf 2306 2310 2307\nf 2307 2310 2311\nf 2307 2312 2308\nf 2308 2312 2313\nf 2314 2315 2312\nf 2312 2315 2316\nf 2316 2317 2312\nf 2312 2317 2313\nf 2317 2305 2313\nf 2313 2305 2318\nf 2314 2312 2311\nf 2311 2312 2307\nf 2319 2320 2322\nf 2322 2320 2321\nf 2322 2311 2319\nf 2319 2311 2310\nf 2313 2318 2308\nf 2308 2318 2309\nf 2296 2298 2318\nf 2318 2298 2309\nf 2309 2298 2323\nf 2323 2298 2299\nf 2306 2309 2324\nf 2324 2309 2323\nf 2310 2306 2325\nf 2325 2306 2324\nf 2296 2318 2297\nf 2297 2318 2305\nf 2319 2310 2326\nf 2326 2310 2325\nf 2328 2325 2327\nf 2327 2325 2324\nf 2325 2328 2326\nf 2326 2328 2329\nf 2331 2328 2330\nf 2330 2328 2327\nf 2332 2333 2327\nf 2327 2333 2330\nf 2328 2331 2329\nf 2329 2331 2334\nf 2324 2323 2327\nf 2327 2323 2332\nf 2300 2332 2299\nf 2299 2332 2323\nf 2326 2335 2319\nf 2319 2335 2320\nf 2305 2317 2336\nf 2336 2317 2316\nf 2336 2316 2337\nf 2337 2316 2315\nf 2338 2304 2336\nf 2336 2304 2305\nf 2337 2339 2336\nf 2336 2339 2338\nf 2337 2315 2341\nf 2341 2315 2340\nf 2342 2300 2234\nf 2234 2300 2246\nf 2303 2343 2282\nf 2282 2343 2233\nf 2234 2233 2342\nf 2342 2233 2343\nf 2342 2343 2333\nf 2333 2343 2344\nf 2333 2344 2330\nf 2330 2344 2345\nf 2346 2344 2303\nf 2303 2344 2343\nf 2332 2300 2333\nf 2333 2300 2342\nf 2347 2346 2302\nf 2302 2346 2303\nf 2301 2304 2302\nf 2302 2304 2347\nf 2304 2338 2347\nf 2347 2338 2348\nf 2348 2349 2347\nf 2347 2349 2346\nf 2350 2351 2348\nf 2348 2351 2349\nf 2349 2345 2346\nf 2346 2345 2344\nf 2345 2349 2352\nf 2352 2349 2351\nf 2338 2339 2348\nf 2348 2339 2350\nf 2350 2339 2353\nf 2353 2339 2354\nf 2356 2357 2355\nf 2355 2357 2353\nf 2351 2350 2357\nf 2357 2350 2353\nf 2353 2354 2355\nf 2355 2354 2335\nf 2320 2335 2341\nf 2341 2335 2354\nf 2339 2337 2354\nf 2354 2337 2341\nf 2352 2331 2345\nf 2345 2331 2330\nf 2356 2334 2352\nf 2352 2334 2331\nf 2356 2352 2357\nf 2357 2352 2351\nf 2355 2329 2356\nf 2356 2329 2334\nf 2335 2326 2355\nf 2355 2326 2329\nf 2321 2314 2322\nf 2322 2314 2311\nf 2321 2320 2340\nf 2340 2320 2341\nf 2315 2314 2340\nf 2340 2314 2321\nf 2125 2260 2126\nf 2126 2260 2259\nf 2157 2160 2129\nf 2129 2160 2130\nf 2188 2163 2187\nf 2187 2163 2162\nf 2182 2166 2188\nf 2188 2166 2163\nf 2180 2169 2263\nf 2263 2169 2168\nf 2160 2187 2130\nf 2130 2187 2162\nf 2275 2260 2262\nf 2262 2260 2125\nf 2128 2263 2129\nf 2129 2263 2168\nf 2262 2205 2169\nf 2169 2205 2170\nf 2165 2166 2259\nf 2259 2166 2126\nf 2359 2360 2358\nf 2358 2360 2361\nf 2363 2364 2362\nf 2362 2364 2365\nf 2367 2363 2366\nf 2366 2363 2362\nf 2368 2369 2371\nf 2371 2369 2370\nf 2372 2373 2248\nf 2248 2373 2059\nf 2364 2374 2365\nf 2365 2374 2375\nf 2377 2378 2376\nf 2376 2378 2379\nf 2290 2380 2223\nf 2223 2380 2381\nf 2223 2381 2264\nf 2264 2381 2382\nf 2384 2385 2383\nf 2383 2385 2386\nf 2364 2371 2374\nf 2374 2371 2387\nf 2059 2373 2290\nf 2290 2373 2380\nf 2378 2360 2379\nf 2379 2360 2388\nf 2374 2387 2359\nf 2359 2387 2389\nf 2390 2367 2386\nf 2386 2367 2391\nf 2393 2394 2392\nf 2392 2394 2395\nf 2386 2391 2383\nf 2383 2391 2396\nf 2367 2366 2391\nf 2391 2366 2397\nf 2363 2367 2368\nf 2368 2367 2390\nf 2398 2399 2361\nf 2361 2399 2358\nf 2399 2400 2358\nf 2358 2400 2375\nf 2390 2401 2368\nf 2368 2401 2369\nf 2372 2384 2376\nf 2376 2384 2383\nf 2374 2359 2375\nf 2375 2359 2358\nf 2402 2403 2365\nf 2365 2403 2362\nf 2359 2389 2360\nf 2360 2389 2388\nf 2364 2363 2371\nf 2371 2363 2368\nf 2405 2398 2404\nf 2404 2398 2361\nf 2392 2395 2406\nf 2406 2395 2397\nf 2377 2396 2394\nf 2394 2396 2395\nf 2394 2393 2404\nf 2404 2393 2405\nf 2396 2377 2383\nf 2383 2377 2376\nf 2406 2397 2407\nf 2407 2397 2366\nf 2400 2402 2375\nf 2375 2402 2365\nf 2385 2270 2401\nf 2401 2270 2271\nf 2403 2407 2362\nf 2362 2407 2366\nf 2371 2370 2387\nf 2387 2370 2382\nf 2391 2397 2396\nf 2396 2397 2395\nf 2378 2377 2404\nf 2404 2377 2394\nf 2360 2378 2361\nf 2361 2378 2404\nf 2407 2408 2406\nf 2406 2408 2409\nf 2410 2408 2403\nf 2403 2408 2407\nf 2411 2410 2402\nf 2402 2410 2403\nf 2412 2411 2400\nf 2400 2411 2402\nf 2406 2409 2392\nf 2392 2409 2413\nf 2392 2413 2393\nf 2393 2413 2414\nf 2415 2405 2414\nf 2414 2405 2393\nf 2416 2413 2417\nf 2417 2413 2409\nf 2419 2420 2418\nf 2418 2420 2421\nf 2420 2422 2421\nf 2421 2422 2423\nf 2420 2424 2422\nf 2422 2424 2425\nf 2426 2427 2424\nf 2424 2427 2428\nf 2428 2429 2424\nf 2424 2429 2425\nf 2426 2424 2419\nf 2419 2424 2420\nf 2419 2418 2431\nf 2431 2418 2430\nf 2425 2432 2422\nf 2422 2432 2423\nf 2408 2410 2432\nf 2432 2410 2423\nf 2423 2410 2433\nf 2433 2410 2411\nf 2421 2423 2434\nf 2434 2423 2433\nf 2435 2418 2434\nf 2434 2418 2421\nf 2429 2417 2425\nf 2425 2417 2432\nf 2408 2432 2409\nf 2409 2432 2417\nf 2418 2435 2430\nf 2430 2435 2436\nf 2438 2435 2437\nf 2437 2435 2434\nf 2435 2438 2436\nf 2436 2438 2439\nf 2441 2438 2440\nf 2440 2438 2437\nf 2442 2443 2437\nf 2437 2443 2440\nf 2439 2438 2444\nf 2444 2438 2441\nf 2433 2442 2434\nf 2434 2442 2437\nf 2412 2442 2411\nf 2411 2442 2433\nf 2436 2445 2430\nf 2430 2445 2446\nf 2430 2446 2431\nf 2431 2446 2447\nf 2417 2429 2448\nf 2448 2429 2428\nf 2427 2449 2428\nf 2428 2449 2448\nf 2450 2416 2448\nf 2448 2416 2417\nf 2451 2450 2449\nf 2449 2450 2448\nf 2427 2452 2449\nf 2449 2452 2453\nf 2447 2446 2452\nf 2452 2446 2453\nf 2454 2412 2399\nf 2399 2412 2400\nf 2415 2455 2405\nf 2405 2455 2398\nf 2399 2398 2454\nf 2454 2398 2455\nf 2454 2455 2443\nf 2443 2455 2456\nf 2443 2456 2440\nf 2440 2456 2457\nf 2458 2456 2415\nf 2415 2456 2455\nf 2443 2442 2454\nf 2454 2442 2412\nf 2459 2458 2414\nf 2414 2458 2415\nf 2413 2416 2414\nf 2414 2416 2459\nf 2459 2416 2460\nf 2460 2416 2450\nf 2460 2461 2459\nf 2459 2461 2458\nf 2462 2463 2460\nf 2460 2463 2461\nf 2461 2457 2458\nf 2458 2457 2456\nf 2457 2461 2464\nf 2464 2461 2463\nf 2451 2462 2450\nf 2450 2462 2460\nf 2462 2451 2465\nf 2465 2451 2466\nf 2468 2469 2467\nf 2467 2469 2465\nf 2463 2462 2469\nf 2469 2462 2465\nf 2465 2466 2467\nf 2467 2466 2445\nf 2446 2445 2453\nf 2453 2445 2466\nf 2451 2449 2466\nf 2466 2449 2453\nf 2464 2441 2457\nf 2457 2441 2440\nf 2468 2444 2464\nf 2464 2444 2441\nf 2468 2464 2469\nf 2469 2464 2463\nf 2467 2439 2468\nf 2468 2439 2444\nf 2445 2436 2467\nf 2467 2436 2439\nf 2447 2426 2431\nf 2431 2426 2419\nf 2427 2426 2452\nf 2452 2426 2447\nf 2369 2179 2370\nf 2370 2179 2178\nf 2376 2379 2372\nf 2372 2379 2373\nf 2389 2381 2388\nf 2388 2381 2380\nf 2387 2382 2389\nf 2389 2382 2381\nf 2270 2385 2277\nf 2277 2385 2384\nf 2379 2388 2373\nf 2373 2388 2380\nf 2401 2271 2369\nf 2369 2271 2179\nf 2248 2277 2372\nf 2372 2277 2384\nf 2401 2390 2385\nf 2385 2390 2386\nf 2264 2382 2178\nf 2178 2382 2370\nf 2215 2134 2271\nf 2271 2134 2179\nf 2005 2000 2199\nf 2199 2000 2215\nf 2004 2001 2005\nf 2005 2001 2000\nf 2200 2214 2004\nf 2004 2214 2001\nf 2470 2471 2473\nf 2473 2471 2472\nf 2475 2476 2474\nf 2474 2476 2477\nf 2479 2475 2478\nf 2478 2475 2474\nf 2481 2482 2480\nf 2480 2482 2483\nf 2476 2484 2477\nf 2477 2484 2485\nf 2487 2488 2486\nf 2486 2488 2489\nf 2133 2490 2058\nf 2058 2490 2491\nf 2058 2491 2247\nf 2247 2491 2492\nf 2494 2495 2493\nf 2493 2495 2496\nf 2476 2483 2484\nf 2484 2483 2497\nf 2472 2498 2488\nf 2488 2498 2489\nf 2484 2497 2471\nf 2471 2497 2499\nf 2500 2479 2496\nf 2496 2479 2501\nf 2502 2503 2505\nf 2505 2503 2504\nf 2496 2501 2493\nf 2493 2501 2506\nf 2479 2478 2501\nf 2501 2478 2507\nf 2475 2479 2480\nf 2480 2479 2500\nf 2508 2509 2473\nf 2473 2509 2470\nf 2509 2510 2470\nf 2470 2510 2485\nf 2511 2481 2500\nf 2500 2481 2480\nf 2512 2494 2486\nf 2486 2494 2493\nf 2485 2484 2470\nf 2470 2484 2471\nf 2513 2514 2477\nf 2477 2514 2474\nf 2471 2499 2472\nf 2472 2499 2498\nf 2476 2475 2483\nf 2483 2475 2480\nf 2516 2508 2515\nf 2515 2508 2473\nf 2502 2505 2517\nf 2517 2505 2507\nf 2504 2487 2505\nf 2505 2487 2506\nf 2503 2516 2504\nf 2504 2516 2515\nf 2506 2487 2493\nf 2493 2487 2486\nf 2517 2507 2518\nf 2518 2507 2478\nf 2510 2513 2485\nf 2485 2513 2477\nf 2511 2495 2293\nf 2293 2495 2154\nf 2514 2518 2474\nf 2474 2518 2478\nf 2483 2482 2497\nf 2497 2482 2492\nf 2501 2507 2506\nf 2506 2507 2505\nf 2488 2487 2515\nf 2515 2487 2504\nf 2472 2488 2473\nf 2473 2488 2515\nf 2518 2519 2517\nf 2517 2519 2520\nf 2521 2519 2514\nf 2514 2519 2518\nf 2522 2521 2513\nf 2513 2521 2514\nf 2523 2522 2510\nf 2510 2522 2513\nf 2517 2520 2502\nf 2502 2520 2524\nf 2502 2524 2503\nf 2503 2524 2525\nf 2525 2526 2503\nf 2503 2526 2516\nf 2527 2524 2528\nf 2528 2524 2520\nf 2530 2531 2529\nf 2529 2531 2532\nf 2531 2533 2532\nf 2532 2533 2534\nf 2531 2535 2533\nf 2533 2535 2536\nf 2537 2538 2535\nf 2535 2538 2539\nf 2539 2540 2535\nf 2535 2540 2536\nf 2537 2535 2530\nf 2530 2535 2531\nf 2529 2541 2530\nf 2530 2541 2542\nf 2543 2534 2536\nf 2536 2534 2533\nf 2519 2521 2543\nf 2543 2521 2534\nf 2534 2521 2544\nf 2544 2521 2522\nf 2532 2534 2545\nf 2545 2534 2544\nf 2529 2532 2546\nf 2546 2532 2545\nf 2536 2540 2543\nf 2543 2540 2528\nf 2519 2543 2520\nf 2520 2543 2528\nf 2541 2529 2547\nf 2547 2529 2546\nf 2548 2549 2545\nf 2545 2549 2546\nf 2549 2550 2546\nf 2546 2550 2547\nf 2551 2552 2548\nf 2548 2552 2549\nf 2548 2553 2551\nf 2551 2553 2554\nf 2550 2549 2555\nf 2555 2549 2552\nf 2545 2544 2548\nf 2548 2544 2553\nf 2523 2553 2522\nf 2522 2553 2544\nf 2547 2556 2541\nf 2541 2556 2557\nf 2541 2557 2542\nf 2542 2557 2558\nf 2528 2540 2559\nf 2559 2540 2539\nf 2538 2560 2539\nf 2539 2560 2559\nf 2561 2527 2559\nf 2559 2527 2528\nf 2560 2562 2559\nf 2559 2562 2561\nf 2560 2538 2564\nf 2564 2538 2563\nf 2558 2557 2563\nf 2563 2557 2564\nf 2565 2523 2509\nf 2509 2523 2510\nf 2526 2566 2516\nf 2516 2566 2508\nf 2509 2508 2565\nf 2565 2508 2566\nf 2565 2566 2554\nf 2554 2566 2567\nf 2554 2567 2551\nf 2551 2567 2568\nf 2569 2567 2526\nf 2526 2567 2566\nf 2553 2523 2554\nf 2554 2523 2565\nf 2570 2569 2525\nf 2525 2569 2526\nf 2524 2527 2525\nf 2525 2527 2570\nf 2527 2561 2570\nf 2570 2561 2571\nf 2571 2572 2570\nf 2570 2572 2569\nf 2573 2574 2571\nf 2571 2574 2572\nf 2572 2568 2569\nf 2569 2568 2567\nf 2568 2572 2575\nf 2575 2572 2574\nf 2562 2573 2561\nf 2561 2573 2571\nf 2573 2562 2576\nf 2576 2562 2577\nf 2579 2580 2578\nf 2578 2580 2576\nf 2574 2573 2580\nf 2580 2573 2576\nf 2576 2577 2578\nf 2578 2577 2556\nf 2557 2556 2564\nf 2564 2556 2577\nf 2562 2560 2577\nf 2577 2560 2564\nf 2575 2552 2568\nf 2568 2552 2551\nf 2579 2555 2575\nf 2575 2555 2552\nf 2579 2575 2580\nf 2580 2575 2574\nf 2550 2555 2578\nf 2578 2555 2579\nf 2556 2547 2578\nf 2578 2547 2550\nf 2558 2537 2542\nf 2542 2537 2530\nf 2538 2537 2563\nf 2563 2537 2558\nf 2292 2295 2481\nf 2481 2295 2482\nf 2486 2489 2512\nf 2512 2489 2581\nf 2499 2491 2498\nf 2498 2491 2490\nf 2497 2492 2499\nf 2499 2492 2491\nf 2155 2154 2494\nf 2494 2154 2495\nf 2581 2489 2490\nf 2490 2489 2498\nf 2293 2292 2511\nf 2511 2292 2481\nf 2191 2155 2512\nf 2512 2155 2494\nf 2500 2496 2511\nf 2511 2496 2495\nf 2247 2492 2295\nf 2295 2492 2482\nf 2583 2584 2582\nf 2582 2584 2585\nf 2587 2588 2586\nf 2586 2588 2589\nf 2591 2587 2590\nf 2590 2587 2586\nf 2593 2594 2592\nf 2592 2594 2595\nf 2596 2597 2599\nf 2599 2597 2598\nf 2588 2600 2589\nf 2589 2600 2601\nf 2602 2603 2605\nf 2605 2603 2604\nf 2606 2607 2609\nf 2609 2607 2608\nf 2609 2608 2190\nf 2190 2608 2610\nf 2611 2612 2614\nf 2614 2612 2613\nf 2600 2588 2615\nf 2615 2588 2595\nf 2599 2598 2606\nf 2606 2598 2607\nf 2584 2616 2604\nf 2604 2616 2605\nf 2600 2615 2583\nf 2583 2615 2617\nf 2591 2619 2618\nf 2618 2619 2614\nf 2621 2622 2620\nf 2620 2622 2623\nf 2619 2624 2614\nf 2614 2624 2611\nf 2591 2590 2619\nf 2619 2590 2625\nf 2587 2591 2592\nf 2592 2591 2618\nf 2626 2627 2585\nf 2585 2627 2582\nf 2627 2628 2582\nf 2582 2628 2601\nf 2629 2593 2618\nf 2618 2593 2592\nf 2602 2597 2611\nf 2611 2597 2612\nf 2600 2583 2601\nf 2601 2583 2582\nf 2630 2631 2589\nf 2589 2631 2586\nf 2583 2617 2584\nf 2584 2617 2616\nf 2588 2587 2595\nf 2595 2587 2592\nf 2633 2626 2632\nf 2632 2626 2585\nf 2620 2623 2634\nf 2634 2623 2625\nf 2622 2603 2623\nf 2623 2603 2624\nf 2621 2633 2622\nf 2622 2633 2632\nf 2611 2624 2602\nf 2602 2624 2603\nf 2634 2625 2635\nf 2635 2625 2590\nf 2628 2630 2601\nf 2601 2630 2589\nf 2613 2195 2629\nf 2629 2195 2194\nf 2631 2635 2586\nf 2586 2635 2590\nf 2595 2594 2615\nf 2615 2594 2610\nf 2619 2625 2624\nf 2624 2625 2623\nf 2632 2604 2622\nf 2622 2604 2603\nf 2585 2584 2632\nf 2632 2584 2604\nf 2635 2636 2634\nf 2634 2636 2637\nf 2638 2636 2631\nf 2631 2636 2635\nf 2639 2638 2630\nf 2630 2638 2631\nf 2640 2639 2628\nf 2628 2639 2630\nf 2634 2637 2620\nf 2620 2637 2641\nf 2620 2641 2621\nf 2621 2641 2642\nf 2642 2643 2621\nf 2621 2643 2633\nf 2641 2637 2644\nf 2644 2637 2645\nf 2646 2647 2649\nf 2649 2647 2648\nf 2648 2650 2649\nf 2649 2650 2651\nf 2648 2652 2650\nf 2650 2652 2653\nf 2652 2654 2656\nf 2656 2654 2655\nf 2656 2657 2652\nf 2652 2657 2653\nf 2654 2652 2647\nf 2647 2652 2648\nf 2647 2646 2659\nf 2659 2646 2658\nf 2660 2651 2653\nf 2653 2651 2650\nf 2636 2638 2660\nf 2660 2638 2651\nf 2651 2638 2661\nf 2661 2638 2639\nf 2651 2661 2649\nf 2649 2661 2662\nf 2663 2646 2662\nf 2662 2646 2649\nf 2653 2657 2660\nf 2660 2657 2645\nf 2636 2660 2637\nf 2637 2660 2645\nf 2646 2663 2658\nf 2658 2663 2664\nf 2665 2666 2662\nf 2662 2666 2663\nf 2666 2667 2663\nf 2663 2667 2664\nf 2668 2669 2665\nf 2665 2669 2666\nf 2665 2670 2668\nf 2668 2670 2671\nf 2667 2666 2672\nf 2672 2666 2669\nf 2662 2661 2665\nf 2665 2661 2670\nf 2640 2670 2639\nf 2639 2670 2661\nf 2673 2674 2664\nf 2664 2674 2658\nf 2658 2674 2659\nf 2659 2674 2675\nf 2645 2657 2676\nf 2676 2657 2656\nf 2677 2676 2655\nf 2655 2676 2656\nf 2676 2678 2645\nf 2645 2678 2644\nf 2677 2679 2676\nf 2676 2679 2678\nf 2655 2680 2677\nf 2677 2680 2681\nf 2675 2674 2680\nf 2680 2674 2681\nf 2682 2640 2627\nf 2627 2640 2628\nf 2643 2683 2633\nf 2633 2683 2626\nf 2682 2627 2683\nf 2683 2627 2626\nf 2683 2684 2682\nf 2682 2684 2671\nf 2684 2685 2671\nf 2671 2685 2668\nf 2686 2684 2643\nf 2643 2684 2683\nf 2670 2640 2671\nf 2671 2640 2682\nf 2687 2686 2642\nf 2642 2686 2643\nf 2642 2641 2687\nf 2687 2641 2644\nf 2687 2644 2688\nf 2688 2644 2678\nf 2688 2689 2687\nf 2687 2689 2686\nf 2690 2691 2688\nf 2688 2691 2689\nf 2689 2685 2686\nf 2686 2685 2684\nf 2685 2689 2692\nf 2692 2689 2691\nf 2679 2690 2678\nf 2678 2690 2688\nf 2690 2679 2693\nf 2693 2679 2694\nf 2696 2697 2695\nf 2695 2697 2693\nf 2691 2690 2697\nf 2697 2690 2693\nf 2693 2694 2695\nf 2695 2694 2673\nf 2674 2673 2681\nf 2681 2673 2694\nf 2679 2677 2694\nf 2694 2677 2681\nf 2685 2692 2668\nf 2668 2692 2669\nf 2696 2672 2692\nf 2692 2672 2669\nf 2696 2692 2697\nf 2697 2692 2691\nf 2695 2667 2696\nf 2696 2667 2672\nf 2673 2664 2695\nf 2695 2664 2667\nf 2675 2654 2659\nf 2659 2654 2647\nf 2655 2654 2680\nf 2680 2654 2675\nf 2291 2156 2593\nf 2593 2156 2594\nf 2597 2602 2598\nf 2598 2602 2605\nf 2616 2617 2607\nf 2607 2617 2608\nf 2615 2610 2617\nf 2617 2610 2608\nf 2597 2596 2612\nf 2612 2596 2698\nf 2598 2605 2607\nf 2607 2605 2616\nf 2194 2291 2629\nf 2629 2291 2593\nf 2629 2618 2613\nf 2613 2618 2614\nf 2190 2610 2156\nf 2156 2610 2594\nf 2700 2010 2699\nf 2699 2010 2086\nf 2267 2701 2116\nf 2116 2701 2702\nf 2116 2702 2117\nf 2117 2702 2703\nf 2704 2705 2250\nf 2250 2705 2254\nf 2011 2706 2267\nf 2267 2706 2701\nf 2699 2086 2707\nf 2707 2086 2289\nf 2010 2700 2011\nf 2011 2700 2706\nf 2708 2707 2251\nf 2251 2707 2289\nf 2703 2704 2117\nf 2117 2704 2250\nf 2705 2708 2254\nf 2254 2708 2251\nf 2709 2710 2708\nf 2708 2710 2707\nf 2711 2709 2705\nf 2705 2709 2708\nf 2712 2711 2704\nf 2704 2711 2705\nf 2713 2712 2703\nf 2703 2712 2704\nf 2707 2710 2699\nf 2699 2710 2714\nf 2699 2714 2700\nf 2700 2714 2715\nf 2716 2706 2715\nf 2715 2706 2700\nf 2714 2710 2717\nf 2717 2710 2718\nf 2720 2721 2719\nf 2719 2721 2722\nf 2721 2723 2722\nf 2722 2723 2724\nf 2721 2725 2723\nf 2723 2725 2726\nf 2727 2728 2725\nf 2725 2728 2726\nf 2729 2725 2720\nf 2720 2725 2721\nf 2731 2729 2730\nf 2730 2729 2720\nf 2719 2732 2720\nf 2720 2732 2730\nf 2726 2733 2723\nf 2723 2733 2724\nf 2709 2711 2733\nf 2733 2711 2724\nf 2724 2711 2734\nf 2734 2711 2712\nf 2722 2724 2735\nf 2735 2724 2734\nf 2719 2722 2736\nf 2736 2722 2735\nf 2726 2728 2733\nf 2733 2728 2718\nf 2709 2733 2710\nf 2710 2733 2718\nf 2732 2719 2737\nf 2737 2719 2736\nf 2738 2739 2735\nf 2735 2739 2736\nf 2739 2740 2736\nf 2736 2740 2737\nf 2741 2742 2738\nf 2738 2742 2739\nf 2738 2743 2741\nf 2741 2743 2744\nf 2740 2739 2745\nf 2745 2739 2742\nf 2735 2734 2738\nf 2738 2734 2743\nf 2713 2743 2712\nf 2712 2743 2734\nf 2737 2746 2732\nf 2732 2746 2747\nf 2732 2747 2730\nf 2730 2747 2731\nf 2718 2728 2748\nf 2748 2728 2727\nf 2749 2750 2727\nf 2727 2750 2748\nf 2748 2751 2718\nf 2718 2751 2717\nf 2750 2752 2748\nf 2748 2752 2751\nf 2750 2749 2754\nf 2754 2749 2753\nf 2731 2747 2753\nf 2753 2747 2754\nf 2727 2725 2749\nf 2749 2725 2729\nf 2749 2729 2753\nf 2753 2729 2731\nf 2702 2755 2703\nf 2703 2755 2713\nf 2706 2716 2701\nf 2701 2716 2756\nf 2755 2702 2756\nf 2756 2702 2701\nf 2756 2757 2755\nf 2755 2757 2744\nf 2757 2758 2744\nf 2744 2758 2741\nf 2759 2757 2716\nf 2716 2757 2756\nf 2743 2713 2744\nf 2744 2713 2755\nf 2760 2759 2715\nf 2715 2759 2716\nf 2715 2714 2760\nf 2760 2714 2717\nf 2760 2717 2761\nf 2761 2717 2751\nf 2761 2762 2760\nf 2760 2762 2759\nf 2763 2764 2761\nf 2761 2764 2762\nf 2762 2758 2759\nf 2759 2758 2757\nf 2758 2762 2765\nf 2765 2762 2764\nf 2752 2763 2751\nf 2751 2763 2761\nf 2763 2752 2766\nf 2766 2752 2767\nf 2768 2769 2766\nf 2766 2769 2770\nf 2764 2763 2770\nf 2770 2763 2766\nf 2766 2767 2768\nf 2768 2767 2746\nf 2747 2746 2754\nf 2754 2746 2767\nf 2752 2750 2767\nf 2767 2750 2754\nf 2758 2765 2741\nf 2741 2765 2742\nf 2769 2745 2765\nf 2765 2745 2742\nf 2769 2765 2770\nf 2770 2765 2764\nf 2768 2740 2769\nf 2769 2740 2745\nf 2746 2737 2768\nf 2768 2737 2740\nf 2201 2212 2200\nf 2200 2212 2214\nf 2201 2202 2212\nf 2212 2202 2185\nf 2512 2581 2191\nf 2191 2581 2192\nf 2059 2290 2056\nf 2056 2290 2272\nf 2174 2199 2294\nf 2294 2199 2278\nf 2063 2175 2194\nf 2194 2175 2291\nf 2192 2581 2133\nf 2133 2581 2490\nf 2133 2132 2192\nf 2192 2132 2189\nf 2054 2276 2055\nf 2055 2276 2138\nf 2083 2279 2084\nf 2084 2279 2771\nf 2771 2279 2599\nf 2599 2279 2596\nf 2606 2772 2599\nf 2599 2772 2771\nf 2772 2773 2771\nf 2771 2773 2084\nf 2773 2774 2084\nf 2084 2774 2085\nf 2085 2775 2082\nf 2082 2775 2144\nf 2774 2078 2085\nf 2085 2078 2775\nf 2078 2774 2079\nf 2079 2774 2776\nf 2774 2773 2776\nf 2776 2773 2777\nf 2773 2772 2777\nf 2777 2772 2778\nf 2190 2189 2609\nf 2609 2189 2778\nf 2053 2052 2779\nf 2779 2052 2181\nf 2144 2775 2081\nf 2081 2775 2078\nf 2294 2153 2174\nf 2174 2153 2175\nf 2132 2276 2189\nf 2189 2276 2778\nf 2606 2609 2772\nf 2772 2609 2778\nf 2195 2613 2698\nf 2698 2613 2612\nf 2596 2279 2698\nf 2698 2279 2195\nf 2054 2777 2276\nf 2276 2777 2778\nf 2777 2054 2776\nf 2776 2054 2053\nf 2053 2779 2776\nf 2776 2779 2079\nf 2779 2181 2079\nf 2079 2181 2080\nf 2181 2239 2080\nf 2080 2239 2240\nf 2122 2256 2014\nf 2042 1982 2076\nf 2076 1982 2007\nf 1982 1985 2007\nf 2007 1985 2008\nf 2095 2097 2265\nf 2265 2097 2266\nf 2076 2050 2042\nf 2042 2050 2047\nf 2046 2043 2047\nf 2047 2043 2042\nf 2118 1993 2046\nf 2046 1993 2043\nf 1995 1994 2118\nf 2118 1994 1993\nf 2267 1992 2011\nf 2011 1992 2012\nf 1997 2012 1994\nf 1994 2012 1992\nf 2226 2013 1997\nf 1997 2013 2012\nf 2225 2087 2226\nf 2226 2087 2013\nf 2225 2232 2087\nf 2087 2232 2288\nf 2232 2070 2288\nf 2288 2070 2252\nf 2070 2025 2252\nf 2252 2025 2253\nf 1988 2249 2025\nf 2025 2249 2253\nf 2116 2115 2267\nf 2267 2115 2268\nf 2096 1984 2237\nf 2237 1984 1991\nf 2095 1985 2096\nf 2096 1985 1984\nf 2265 2008 2095\nf 2095 2008 1985\nf 2092 2009 2265\nf 2265 2009 2008\nf 2091 2286 2092\nf 2092 2286 2009\nf 2255 2238 2091\nf 2091 2238 2286\nf 2256 2239 2255\nf 2255 2239 2238\nf 2122 2240 2256\nf 2256 2240 2239\nf 2121 2241 2122\nf 2122 2241 2240\nf 2022 2018 2097\nf 2097 2018 2266\nf 2022 2024 2018\nf 2018 2024 2019\nf 2024 2072 2019\nf 2019 2072 2073\nf 2072 2228 2073\nf 2073 2228 2230\nf 2228 2184 2230\nf 2230 2184 2183\nf 2121 2285 2241\nf 2241 2285 2258\nf 2285 2036 2258\nf 2258 2036 2257\nf 2177 2145 2193\nf 2193 2145 2257\nf 2173 2193 2196\nf 2196 2193 2037\nf 2036 2037 2257\nf 2257 2037 2193\nf 2196 2037 2066\nf 2066 2037 2035\nf 2061 2108 2034\nf 2034 2108 2067\nf 2108 2208 2067\nf 2067 2208 2204\nf 2208 2210 2204\nf 2204 2210 2203\nf 2210 2280 2203\nf 2203 2280 2186\nf 2183 2186 2235\nf 2235 2186 2280\nf 1987 2114 1988\nf 1988 2114 2249\nf 2237 2115 1987\nf 1987 2115 2114\nf 1991 2268 2237\nf 2237 2268 2115\nf 1991 1990 2268\nf 2268 1990 1992\nf 107 110 2781\nf 2781 110 2780\nf 117 2782 118\nf 118 2782 2783\nf 119 2784 120\nf 120 2784 2785\nf 125 2786 126\nf 126 2786 2787\nf 127 2788 128\nf 128 2788 2789\nf 127 132 2788\nf 2788 132 2790\nf 231 230 2792\nf 2792 230 2791\nf 118 2783 119\nf 119 2783 2784\nf 241 243 2794\nf 2794 243 2793\nf 2781 2795 107\nf 107 2795 352\nf 120 2785 437\nf 437 2785 2796\nf 126 2787 110\nf 110 2787 2780\nf 455 2797 456\nf 456 2797 2798\nf 128 2789 457\nf 457 2789 2799\nf 456 2798 117\nf 117 2798 2782\nf 2794 2797 241\nf 241 2797 455\nf 457 2799 125\nf 125 2799 2786\nf 132 231 2790\nf 2790 231 2792\nf 230 437 2791\nf 2791 437 2796\nf 2792 2789 2790\nf 2790 2789 2788\nf 2785 2784 2801\nf 2801 2784 2800\nf 2786 2802 2787\nf 2787 2802 2803\nf 2792 2791 2805\nf 2805 2791 2804\nf 2784 2783 2800\nf 2800 2783 2806\nf 2785 2801 2796\nf 2796 2801 2807\nf 2787 2803 2780\nf 2780 2803 2808\nf 2789 2809 2799\nf 2799 2809 2810\nf 2799 2810 2786\nf 2786 2810 2802\nf 2791 2796 2804\nf 2804 2796 2807\nf 2789 2792 2809\nf 2809 2792 2805\nf 2812 2797 2811\nf 2811 2797 2794\nf 2809 2805 2810\nf 2810 2805 2804\nf 2810 2804 2802\nf 2802 2804 2807\nf 2802 2807 2803\nf 2803 2807 2801\nf 2808 2803 2800\nf 2800 2803 2801\nf 2814 699 2813\nf 2813 699 698\nf 2808 2815 2780\nf 2780 2815 2781\nf 2817 2782 2816\nf 2816 2782 2798\nf 2815 2818 2781\nf 2781 2818 2795\nf 2797 2812 2798\nf 2798 2812 2816\nf 2783 2782 2806\nf 2806 2782 2817\nf 2800 2806 2808\nf 2808 2806 2815\nf 2794 2793 2811\nf 2811 2793 2819\nf 174 201 384\nf 384 201 362\nf 200 1766 390\nf 390 1766 1777\nf 1777 1778 390\nf 390 1778 398\nf 1778 361 398\nf 398 361 222\nf 2820 2821 628\nf 628 2821 629\nf 1406 2820 790\nf 790 2820 628\nf 794 1415 1233\nf 1233 1415 1234\nf 1406 790 1407\nf 1407 790 1312\nf 1407 1312 2822\nf 2822 1312 1316\nf 2823 1380 1543\nf 1543 1380 1379\nf 1385 1738 1382\nf 1382 1738 1383\nf 1381 791 1382\nf 1382 791 1386\nf 791 794 1386\nf 1386 794 1233\nf 1259 1390 1260\nf 1260 1390 1389\nf 1905 1542 1394\nf 1394 1542 1395\nf 1410 1413 1397\nf 1397 1413 1398\nf 2821 1410 629\nf 629 1410 1397\nf 2823 2822 1380\nf 1380 2822 1316\nf 1392 1395 1378\nf 1378 1395 1379\nf 1738 1399 1383\nf 1383 1399 1398\nf 1388 1235 1389\nf 1389 1235 1234\nf 1393 1391 1394\nf 1394 1391 1390\nf 1381 1384 779\nf 779 1384 780\nf 1282 788 792\nf 792 788 793\nf 1431 1262 1432\nf 1432 1262 1261\nf 1400 1342 1408\nf 1408 1342 1409\nf 1508 1544 1411\nf 1411 1544 1412\nf 788 787 793\nf 793 787 1414\nf 2821 2820 1429\nf 1429 2820 1430\nf 2822 2823 1506\nf 1506 2823 1507\nf 778 1282 779\nf 779 1282 792\nf 2820 1406 1430\nf 1430 1406 1409\nf 1410 2821 1411\nf 1411 2821 1429\nf 1407 2822 1408\nf 1408 2822 1506\nf 1544 781 1412\nf 1412 781 780\nf 1260 1415 1261\nf 1261 1415 1414\nf 1334 2824 923\nf 923 2824 924\nf 2826 1268 2825\nf 2825 1268 1267\nf 769 1212 2827\nf 2827 1212 2828\nf 1492 1190 1266\nf 1266 1190 1267\nf 1488 2829 1489\nf 1489 2829 2830\nf 922 1329 923\nf 923 1329 1328\nf 1484 2831 1485\nf 1485 2831 2832\nf 1482 2833 1485\nf 1485 2833 1486\nf 1324 1493 1489\nf 1489 1493 1490\nf 1265 1491 1266\nf 1266 1491 1490\nf 2835 2836 2834\nf 2834 2836 2837\nf 1250 2838 2827\nf 2827 2838 2839\nf 1488 1491 2840\nf 2840 1491 2841\nf 1268 2826 2842\nf 2842 2826 2843\nf 2844 2845 1495\nf 1495 2845 1496\nf 1427 2847 2846\nf 2846 2847 926\nf 2829 1488 2848\nf 2848 1488 2840\nf 1329 922 2849\nf 2849 922 927\nf 2850 2851 768\nf 768 2851 1213\nf 1265 1268 2852\nf 2852 1268 2842\nf 1491 1265 2841\nf 2841 1265 2852\nf 2854 2855 2853\nf 2853 2855 2856\nf 2855 2857 2856\nf 2856 2857 598\nf 2857 2858 598\nf 598 2858 595\nf 2858 2859 595\nf 595 2859 626\nf 649 652 2860\nf 2860 652 2861\nf 639 642 2862\nf 2862 642 2863\nf 2864 705 2862\nf 2862 705 639\nf 2865 1418 2864\nf 2864 1418 705\nf 2866 2867 2865\nf 2865 2867 1418\nf 1760 1348 1761\nf 1761 1348 2867\nf 1764 2868 1197\nf 1197 2868 1198\nf 2869 611 2868\nf 2868 611 1198\nf 2870 612 2869\nf 2869 612 611\nf 1352 1350 944\nf 944 1350 688\nf 1350 1256 688\nf 688 1256 685\nf 1256 1255 685\nf 685 1255 692\nf 1255 2871 692\nf 692 2871 2872\nf 701 2873 698\nf 698 2873 2813\nf 2872 1349 692\nf 692 1349 693\nf 1696 1744 1574\nf 1574 1744 1575\nf 1753 1618 1577\nf 1577 1618 1578\nf 1729 1594 1608\nf 1608 1594 1609\nf 1725 1630 1611\nf 1611 1630 1612\nf 1604 1607 1625\nf 1625 1607 1626\nf 1624 1703 1625\nf 1625 1703 1912\nf 1703 1702 1912\nf 1912 1702 1913\nf 1702 1753 1913\nf 1913 1753 1577\nf 1598 1569 1915\nf 1915 1569 1578\nf 1616 1615 1915\nf 1915 1615 1916\nf 1615 1696 1916\nf 1916 1696 1574\nf 1744 1745 1575\nf 1575 1745 1917\nf 1745 1565 1917\nf 1917 1565 1609\nf 1568 1725 1608\nf 1608 1725 1611\nf 2874 1613 1918\nf 1918 1613 1612\nf 1607 2874 1626\nf 1626 2874 1918\nf 1610 1613 1562\nf 1562 1613 1563\nf 1547 1550 1570\nf 1570 1550 1571\nf 1573 1576 1580\nf 1580 1576 1581\nf 1553 1556 1595\nf 1595 1556 1596\nf 1556 1582 1596\nf 1596 1582 1581\nf 1551 1547 1599\nf 1599 1547 1570\nf 1731 1914 1601\nf 1601 1914 1602\nf 1686 1689 1605\nf 1605 1689 1606\nf 1598 1743 1599\nf 1599 1743 1692\nf 2874 1607 1723\nf 1723 1607 1606\nf 1727 1553 1730\nf 1730 1553 1595\nf 1600 1686 1601\nf 1601 1686 1605\nf 1914 1572 1602\nf 1602 1572 1571\nf 1579 1693 1580\nf 1580 1693 1692\nf 1613 2874 1563\nf 1563 2874 1723\nf 1561 1727 1562\nf 1562 1727 1730\nf 1949 1742 1548\nf 1548 1742 1549\nf 1694 1949 1552\nf 1552 1949 1548\nf 1954 1589 1554\nf 1554 1589 1555\nf 1693 1579 1558\nf 1558 1579 1559\nf 1561 1564 1584\nf 1584 1564 1585\nf 1582 1556 1588\nf 1588 1556 1555\nf 1600 1603 1591\nf 1591 1603 1592\nf 1981 1977 1687\nf 1687 1977 1688\nf 1564 1722 1585\nf 1585 1722 1690\nf 1551 1693 1552\nf 1552 1693 1558\nf 1751 1954 1728\nf 1728 1954 1554\nf 1590 1981 1591\nf 1591 1981 1687\nf 1603 1550 1592\nf 1592 1550 1549\nf 1977 1691 1688\nf 1688 1691 1690\nf 1587 1560 1588\nf 1588 1560 1559\nf 1727 1561 1728\nf 1728 1561 1584\nf 2875 1919 1947\nf 1947 1919 1948\nf 1557 1560 1952\nf 1952 1560 1953\nf 1933 1932 1955\nf 1955 1932 1956\nf 1587 1589 1958\nf 1958 1589 1956\nf 1959 1933 1965\nf 1965 1933 1955\nf 1961 1964 1966\nf 1966 1964 1967\nf 1981 1590 1969\nf 1969 1590 1967\nf 1742 1949 1972\nf 1972 1949 1948\nf 1593 1742 1966\nf 1966 1742 1972\nf 1957 1950 1958\nf 1958 1950 1953\nf 1583 1586 1974\nf 1974 1586 1975\nf 1940 1943 1978\nf 1978 1943 1979\nf 1943 1976 1979\nf 1979 1976 1975\nf 1951 2875 1952\nf 1952 2875 1947\nf 1968 1940 1969\nf 1969 1940 1978\nf 1973 1959 1974\nf 1974 1959 1965\nf 1923 1971 1920\nf 1920 1971 1921\nf 1919 2875 1920\nf 1920 2875 1924\nf 2875 1951 1924\nf 1924 1951 1926\nf 1950 1957 1929\nf 1929 1957 1930\nf 2244 2243 1934\nf 1934 2243 1935\nf 1973 1976 1937\nf 1937 1976 1938\nf 1944 1980 1941\nf 1941 1980 1942\nf 1940 1968 1941\nf 1941 1968 1945\nf 2287 2244 1960\nf 1960 2244 1934\nf 1970 2245 1962\nf 1962 2245 1963\nf 2243 1931 1935\nf 1935 1931 1930\nf 1927 1926 1928\nf 1928 1926 1929\nf 1961 1922 1962\nf 1962 1922 1921\nf 2245 1946 1963\nf 1963 1946 1945\nf 1976 1943 1938\nf 1938 1943 1942\nf 1936 2287 1937\nf 1937 2287 1960\nf 743 1238 608\nf 608 1238 609\nf 607 1811 608\nf 608 1811 744\nf 1304 2876 1305\nf 1305 2876 2877\nf 586 585 1298\nf 1298 585 1299\nf 585 702 1299\nf 1299 702 1300\nf 702 1428 1300\nf 1300 1428 1301\nf 1238 586 609\nf 609 586 1298\nf 2878 2879 1218\nf 1218 2879 2877\nf 2880 2881 1812\nf 1812 2881 1819\nf 2883 1821 2882\nf 2882 1821 1820\nf 2840 2841 620\nf 620 2841 621\nf 2842 2843 795\nf 795 2843 796\nf 892 895 2844\nf 2844 895 2845\nf 2848 2840 942\nf 942 2840 620\nf 2849 927 1202\nf 1202 927 1203\nf 2852 2842 1270\nf 1270 2842 795\nf 2841 2852 621\nf 621 2852 1270\nf 941 1759 942\nf 942 1759 2884\nf 659 929 1427\nf 1427 929 2847\nf 1237 2851 751\nf 751 2851 2850\nf 1216 2885 1217\nf 1217 2885 1754\nf 2887 734 2886\nf 2886 734 733\nf 2889 1816 2888\nf 2888 1816 1815\nf 2890 1245 2889\nf 2889 1245 1816\nf 1824 2891 1825\nf 1825 2891 1246\nf 2892 2893 2895\nf 2895 2893 2894\nf 1854 1833 1851\nf 1851 1833 1832\nf 1851 1832 1884\nf 1884 1832 1876\nf 1884 1876 1441\nf 1441 1876 1443\nf 1441 1443 1442\nf 1442 1443 1446\nf 1442 1446 2896\nf 2896 1446 1507\nf 2897 2823 2898\nf 2898 2823 1543\nf 1542 2899 1543\nf 1543 2899 2898\nf 1542 1905 2899\nf 2899 1905 2900\nf 1905 1259 2900\nf 2900 1259 2901\nf 1262 1431 2902\nf 2902 1431 1904\nf 1431 1434 1904\nf 1904 1434 1902\nf 1902 1434 2903\nf 2903 1434 719\nf 2903 719 2904\nf 2904 719 718\nf 1869 1881 718\nf 718 1881 2904\nf 1881 1869 1880\nf 1880 1869 756\nf 1880 756 2905\nf 2905 756 755\nf 2905 755 2906\nf 2906 755 758\nf 762 2907 763\nf 763 2907 2908\nf 724 723 2909\nf 2909 723 894\nf 796 895 723\nf 723 895 894\nf 2843 2845 796\nf 796 2845 895\nf 2826 1496 2843\nf 2843 1496 2845\nf 2825 1497 2826\nf 2826 1497 1496\nf 1189 2836 2825\nf 2825 2836 1497\nf 797 891 798\nf 798 891 2837\nf 802 888 797\nf 797 888 891\nf 2910 2911 2913\nf 2913 2911 2912\nf 1780 1783 2915\nf 2915 1783 2914\nf 2916 2917 831\nf 831 2917 832\nf 2918 2919 845\nf 845 2919 846\nf 2920 2916 847\nf 847 2916 831\nf 2921 2922 857\nf 857 2922 858\nf 2923 2924 860\nf 860 2924 861\nf 2919 2912 846\nf 846 2912 887\nf 2925 2920 940\nf 940 2920 847\nf 2924 2926 861\nf 861 2926 1204\nf 2917 2918 832\nf 832 2918 845\nf 2927 2928 1449\nf 1449 2928 1450\nf 2929 2930 889\nf 889 2930 2931\nf 2896 1507 2897\nf 2897 1507 2823\nf 2902 2901 1262\nf 1262 2901 1259\nf 2932 2933 750\nf 750 2933 1236\nf 2933 2934 1236\nf 1236 2934 1835\nf 2936 2937 2935\nf 2935 2937 2938\nf 2940 2941 2939\nf 2939 2941 2942\nf 2943 2944 1841\nf 1841 2944 1842\nf 2945 1860 2944\nf 2944 1860 1842\nf 2945 2946 1860\nf 1860 2946 1859\nf 1889 2947 1890\nf 1890 2947 1856\nf 1856 2947 1857\nf 1857 2947 2948\nf 2950 709 2949\nf 2949 709 1829\nf 2952 730 2951\nf 2951 730 729\nf 2866 2953 2867\nf 2867 2953 1761\nf 2953 2954 1761\nf 1761 2954 1420\nf 2954 2955 1420\nf 1420 2955 742\nf 2955 2956 742\nf 742 2956 739\nf 2956 2957 739\nf 739 2957 760\nf 2957 2958 760\nf 760 2958 761\nf 2958 2959 761\nf 761 2959 2960\nf 2959 2961 2960\nf 2960 2961 2962\nf 2963 2964 1839\nf 1839 2964 1840\nf 2964 2965 1840\nf 1840 2965 2942\nf 2966 2950 2939\nf 2939 2950 2949\nf 2935 2967 2936\nf 2936 2967 2968\nf 2969 2970 736\nf 736 2970 735\nf 734 2887 1815\nf 1815 2887 2888\nf 2887 2882 2888\nf 2888 2882 2881\nf 1819 1820 1817\nf 1817 1820 748\nf 1195 1757 1197\nf 1197 1757 1424\nf 1757 647 1424\nf 1424 647 646\nf 647 648 646\nf 646 648 643\nf 648 1271 643\nf 643 1271 737\nf 1271 725 737\nf 737 725 765\nf 725 724 765\nf 765 724 762\nf 762 724 2907\nf 2907 724 2909\nf 1254 1437 2909\nf 2909 1437 2907\nf 1438 1253 1836\nf 1836 1253 1835\nf 1836 1835 2938\nf 2938 1835 2934\nf 2971 2972 2935\nf 2935 2972 2967\nf 897 896 2969\nf 2969 896 2970\nf 1815 1814 734\nf 734 1814 731\nf 728 1215 896\nf 896 1215 732\nf 2885 1216 733\nf 733 1216 2886\nf 1216 1219 2886\nf 2886 1219 2883\nf 2876 1304 1821\nf 1821 1304 749\nf 1304 1303 749\nf 749 1303 746\nf 1818 2973 1817\nf 1817 2973 1810\nf 747 2974 1818\nf 1818 2974 2973\nf 747 746 2974\nf 2974 746 1303\nf 2976 2977 2975\nf 2975 2977 2978\nf 2976 2979 2901\nf 2901 2979 2900\nf 2980 2981 2982\nf 2982 2981 2979\nf 2981 2980 2983\nf 2983 2980 2984\nf 2985 2897 2983\nf 2983 2897 2898\nf 2987 2988 2986\nf 2986 2988 2985\nf 2896 2988 1442\nf 1442 2988 1439\nf 1903 2977 1904\nf 1904 2977 2902\nf 1856 1855 1890\nf 1890 1855 2989\nf 2989 2990 1890\nf 1890 2990 1359\nf 2990 2991 1359\nf 1359 2991 1356\nf 2946 2992 1859\nf 1859 2992 1893\nf 2992 2993 1893\nf 1893 2993 775\nf 2993 2994 775\nf 775 2994 776\nf 2986 1356 2987\nf 2987 1356 2991\nf 2975 2978 776\nf 776 2978 777\nf 2982 2994 2980\nf 2980 2994 2995\nf 1357 2984 2995\nf 2995 2984 2980\nf 2995 2996 1357\nf 1357 2996 1358\nf 2996 2997 1358\nf 1358 2997 1889\nf 2998 2999 2932\nf 2932 2999 2933\nf 2999 2971 2933\nf 2933 2971 2934\nf 1837 1836 2937\nf 2937 1836 2938\nf 2943 1841 2941\nf 2941 1841 1838\nf 3001 2944 3000\nf 3000 2944 2943\nf 3001 3002 2944\nf 2944 3002 2945\nf 3002 3003 2945\nf 2945 3003 2946\nf 2997 3003 1889\nf 1889 3003 2947\nf 2947 3003 2948\nf 2948 3003 3002\nf 3001 3004 3002\nf 3002 3004 2948\nf 3000 3005 3001\nf 3001 3005 3004\nf 2940 3006 3000\nf 3000 3006 3005\nf 2999 3007 2971\nf 2971 3007 2972\nf 2998 2951 2999\nf 2999 2951 3007\nf 3008 2952 2998\nf 2998 2952 2951\nf 2966 2965 2936\nf 2936 2965 2937\nf 2935 2938 2971\nf 2971 2938 2934\nf 3003 2997 2946\nf 2946 2997 2992\nf 2997 2996 2992\nf 2992 2996 2993\nf 2996 2995 2993\nf 2993 2995 2994\nf 1356 2986 1357\nf 1357 2986 2984\nf 2982 2975 2994\nf 2994 2975 776\nf 1844 1843 1899\nf 1899 1843 3009\nf 1843 638 3009\nf 3009 638 3010\nf 638 637 3010\nf 3010 637 3011\nf 637 1830 3011\nf 3011 1830 3012\nf 1830 1833 3012\nf 3012 1833 2894\nf 1833 1854 2894\nf 2894 1854 2895\nf 1854 1853 2895\nf 2895 1853 1888\nf 1858 1857 1885\nf 1885 1857 1892\nf 1899 3009 1898\nf 1898 3009 1874\nf 3009 3010 1874\nf 1874 3010 1865\nf 3010 3011 1865\nf 1865 3011 1862\nf 3011 3012 1862\nf 1862 3012 1882\nf 3012 2894 1882\nf 1882 2894 2893\nf 1850 2890 782\nf 782 2890 2889\nf 783 2880 1811\nf 1811 2880 1812\nf 744 1810 745\nf 745 1810 2973\nf 745 2973 805\nf 805 2973 806\nf 2973 2974 806\nf 806 2974 815\nf 1303 812 2974\nf 2974 812 815\nf 1303 1302 812\nf 812 1302 1221\nf 1307 3013 1302\nf 1302 3013 1221\nf 1734 3014 1633\nf 1633 3014 1634\nf 1646 1649 1636\nf 1636 1649 1637\nf 1654 1657 1641\nf 1641 1657 1642\nf 1639 1736 1636\nf 1636 1736 1684\nf 1716 1713 1698\nf 1698 1713 1699\nf 1718 1721 1705\nf 1705 1721 1706\nf 1653 1654 1709\nf 1709 1654 1641\nf 1620 1623 1714\nf 1714 1623 1699\nf 1712 1682 1714\nf 1714 1682 1740\nf 1668 1671 1746\nf 1746 1671 1747\nf 1752 1650 1749\nf 1749 1650 1750\nf 1750 1650 1709\nf 1709 1650 1653\nf 1632 1668 1633\nf 1633 1668 1746\nf 1649 1752 1637\nf 1637 1752 1749\nf 1682 1643 1740\nf 1740 1643 1642\nf 3014 1685 1634\nf 1634 1685 1684\nf 1704 1748 1705\nf 1705 1748 1747\nf 1697 1707 1698\nf 1698 1707 1706\nf 1739 1701 1621\nf 1621 1701 1622\nf 1567 1566 1647\nf 1647 1566 1648\nf 1664 1695 1651\nf 1651 1695 1652\nf 1614 1617 1655\nf 1655 1617 1656\nf 1704 1707 1659\nf 1659 1707 1660\nf 3014 1734 1662\nf 1662 1734 1663\nf 1650 1752 1651\nf 1651 1752 1665\nf 1741 1657 1667\nf 1667 1657 1656\nf 1695 1614 1652\nf 1652 1614 1655\nf 1724 1567 1726\nf 1726 1567 1647\nf 1645 1628 1733\nf 1733 1628 1663\nf 1685 3014 1726\nf 1726 3014 1662\nf 1620 1741 1621\nf 1621 1741 1667\nf 1566 1666 1648\nf 1648 1666 1665\nf 1658 1737 1659\nf 1659 1737 1901\nf 1911 1661 1910\nf 1910 1661 1660\nf 1701 1911 1622\nf 1622 1911 1910\nf 1732 1748 1733\nf 1733 1748 1901\nf 1857 2948 1892\nf 1892 2948 3004\nf 1892 3004 1891\nf 1891 3004 3005\nf 1828 3006 1829\nf 1829 3006 2949\nf 2967 736 2968\nf 2968 736 710\nf 2972 2969 2967\nf 2967 2969 736\nf 2969 2972 897\nf 897 2972 3007\nf 897 3007 729\nf 729 3007 2951\nf 3016 753 3015\nf 3015 753 752\nf 3018 767 3017\nf 3017 767 766\nf 3019 3020 1249\nf 1249 3020 3021\nf 2915 2914 3019\nf 3019 2914 3020\nf 3023 2914 3022\nf 3022 2914 1783\nf 2827 2839 769\nf 769 2839 766\nf 3025 2838 3024\nf 3024 2838 3021\nf 768 767 2850\nf 2850 767 3026\nf 2850 3026 751\nf 751 3026 752\nf 750 753 2932\nf 2932 753 3027\nf 3008 2998 3027\nf 3027 2998 2932\nf 3028 3029 3008\nf 3008 3029 2952\nf 3029 3030 2952\nf 2952 3030 730\nf 3032 3033 3031\nf 3031 3033 3034\nf 3036 3037 3035\nf 3035 3037 3038\nf 2870 3039 612\nf 612 3039 3040\nf 3041 3042 3031\nf 3031 3042 3038\nf 3034 1352 3043\nf 3043 1352 944\nf 612 3040 613\nf 613 3040 3044\nf 613 3044 1425\nf 1425 3044 1422\nf 3045 3042 669\nf 669 3042 670\nf 3041 3043 599\nf 599 3043 600\nf 772 771 1544\nf 1544 771 781\nf 1503 771 1866\nf 1866 771 770\nf 771 1503 781\nf 781 1503 778\nf 61 281 416\nf 416 281 1907\nf 283 103 3046\nf 3046 103 3047\nf 76 79 3048\nf 3048 79 3049\nf 280 76 1906\nf 1906 76 3048\nf 3051 335 3050\nf 3050 335 334\nf 3053 417 3052\nf 3052 417 416\nf 3055 435 3054\nf 3054 435 434\nf 428 435 419\nf 419 435 1908\nf 420 1909 293\nf 293 1909 443\nf 79 294 3049\nf 3049 294 444\nf 3057 444 3056\nf 3056 444 443\nf 3051 3058 335\nf 335 3058 452\nf 216 215 1332\nf 1332 215 452\nf 308 283 462\nf 462 283 3046\nf 3050 334 3059\nf 3059 334 462\nf 103 62 3047\nf 3047 62 417\nf 313 312 434\nf 434 312 463\nf 3061 574 3060\nf 3060 574 573\nf 1310 3062 1428\nf 1428 3062 3063\nf 1258 3064 1255\nf 1255 3064 2871\nf 932 3065 2853\nf 2853 3065 3066\nf 699 2814 931\nf 931 2814 3067\nf 3068 1301 3063\nf 3063 1301 1428\nf 571 574 1310\nf 1310 574 3062\nf 3060 573 3069\nf 3069 573 1332\nf 3070 3071 1906\nf 1906 3071 1907\nf 3072 3073 1908\nf 1908 3073 1909\nf 3075 2814 3074\nf 3074 2814 2813\nf 2854 3076 1258\nf 1258 3076 3064\nf 2871 3077 2872\nf 2872 3077 3078\nf 3074 2813 3079\nf 3079 2813 2873\nf 2872 3078 1349\nf 1349 3078 3080\nf 2873 701 3068\nf 3068 701 1301\nf 1330 1201 3080\nf 3080 1201 1349\nf 1264 1263 3081\nf 3081 1263 3082\nf 3083 3084 3082\nf 3082 3084 3081\nf 811 1448 3086\nf 3086 1448 3085\nf 808 811 3087\nf 3087 811 3086\nf 3088 3083 833\nf 833 3083 3082\nf 884 3089 885\nf 885 3089 3090\nf 3087 1790 808\nf 808 1790 816\nf 1790 3087 1787\nf 1787 3087 3091\nf 3092 3091 3086\nf 3086 3091 3087\nf 3093 3094 3084\nf 3084 3094 3095\nf 3089 3096 3090\nf 3090 3096 3088\nf 3097 3093 3083\nf 3083 3093 3084\nf 3099 3100 3098\nf 3098 3100 1786\nf 3101 1788 2913\nf 2913 1788 3102\nf 838 837 885\nf 885 837 886\nf 886 837 3103\nf 3103 837 1541\nf 3094 3093 3104\nf 3104 3093 3097\nf 3105 3106 3089\nf 3089 3106 3096\nf 3108 3109 3107\nf 3107 3109 3110\nf 3111 3112 3091\nf 3091 3112 1787\nf 3114 3108 3113\nf 3113 3108 3107\nf 3114 3115 3112\nf 3112 3115 3102\nf 3091 3092 3111\nf 3111 3092 3116\nf 839 838 835\nf 835 838 836\nf 885 3090 838\nf 838 3090 836\nf 3110 3117 1247\nf 1247 3117 3118\nf 1247 3118 1248\nf 1248 3118 3119\nf 3120 3121 3119\nf 3119 3121 1248\nf 1247 844 3110\nf 3110 844 3107\nf 844 843 3107\nf 3107 843 3113\nf 843 3099 3113\nf 3113 3099 3122\nf 1781 3123 3098\nf 3098 3123 3124\nf 2915 2931 1780\nf 1780 2931 2930\nf 3019 3125 2915\nf 2915 3125 2931\nf 890 3125 2834\nf 2834 3125 1252\nf 3125 3019 1252\nf 1252 3019 1249\nf 907 908 946\nf 946 908 947\nf 1185 1188 1369\nf 1369 1188 948\nf 954 957 951\nf 951 957 952\nf 910 965 953\nf 953 965 950\nf 905 958 960\nf 960 958 961\nf 3126 978 949\nf 949 978 962\nf 936 935 967\nf 967 935 968\nf 994 993 969\nf 969 993 966\nf 902 904 971\nf 971 904 972\nf 983 985 973\nf 973 985 970\nf 906 905 974\nf 974 905 960\nf 977 981 959\nf 959 981 975\nf 911 910 1143\nf 1143 910 953\nf 952 957 1142\nf 1142 957 1145\nf 911 1143 1366\nf 1366 1143 1368\nf 1185 1369 1145\nf 1145 1369 1142\nf 993 1519 966\nf 966 1519 1456\nf 965 1454 950\nf 950 1454 1457\nf 900 899 1459\nf 1459 899 1460\nf 988 991 1461\nf 1461 991 1458\nf 899 963 1460\nf 1460 963 1462\nf 985 987 970\nf 970 987 1463\nf 904 906 972\nf 972 906 974\nf 981 983 975\nf 975 983 973\nf 935 900 968\nf 968 900 1459\nf 991 994 1458\nf 1458 994 969\nf 1154 1526 1088\nf 1088 1526 1089\nf 1109 1112 1092\nf 1092 1112 1093\nf 1083 1098 1084\nf 1084 1098 1097\nf 1078 1471 1100\nf 1100 1471 1101\nf 1075 1078 1104\nf 1104 1078 1100\nf 1152 1154 1156\nf 1156 1154 1088\nf 1156 1155 1372\nf 1372 1155 1373\nf 1372 1373 1104\nf 1104 1373 1075\nf 1467 1109 1465\nf 1465 1109 1092\nf 1465 1182 1468\nf 1468 1182 1184\nf 1474 1470 1096\nf 1096 1470 1097\nf 1102 1101 1083\nf 1083 1101 1098\nf 1112 1531 1093\nf 1093 1531 1529\nf 1102 1073 1099\nf 1099 1073 1072\nf 1526 1154 1079\nf 1079 1154 1080\nf 1081 1091 1082\nf 1082 1091 1094\nf 1069 1068 1085\nf 1085 1068 1086\nf 1153 1151 1080\nf 1080 1151 1058\nf 1371 1177 1179\nf 1179 1177 1176\nf 1099 1072 1103\nf 1103 1072 1176\nf 1068 1071 1086\nf 1086 1071 1073\nf 1085 1084 1180\nf 1180 1084 1469\nf 1066 1065 1183\nf 1183 1065 1181\nf 1153 1152 1177\nf 1177 1152 1178\nf 1091 1081 1182\nf 1182 1081 1183\nf 1057 1525 1079\nf 1079 1525 1527\nf 1120 1117 1076\nf 1076 1117 1077\nf 1087 1090 1107\nf 1107 1090 1108\nf 1127 1126 1110\nf 1110 1126 1111\nf 1095 1116 1096\nf 1096 1116 1115\nf 1471 1078 1119\nf 1119 1078 1077\nf 1087 1107 1155\nf 1155 1107 1158\nf 1376 1374 1157\nf 1157 1374 1158\nf 1076 1075 1374\nf 1374 1075 1373\nf 1109 1467 1110\nf 1110 1467 1472\nf 1467 1466 1472\nf 1472 1466 1473\nf 1135 1475 1114\nf 1114 1475 1115\nf 1471 1119 1095\nf 1095 1119 1116\nf 1126 1532 1111\nf 1111 1532 1530\nf 914 1276 1480\nf 1480 1276 1517\nf 3127 3128 1223\nf 1223 3128 1224\nf 824 933 1455\nf 1455 933 3129\nf 3131 1448 3130\nf 3130 1448 1447\nf 3132 3133 1452\nf 1452 3133 1453\nf 3130 1447 1322\nf 1322 1447 1455\nf 933 1514 3129\nf 3129 1514 3134\nf 1161 3135 1480\nf 1480 3135 1481\nf 3128 3136 1224\nf 1224 3136 1499\nf 3133 1162 1453\nf 1453 1162 1517\nf 1515 3127 1535\nf 1535 3127 1223\nf 3095 3085 3131\nf 3131 3085 1448\nf 3137 3092 3085\nf 3085 3092 3086\nf 3138 3116 3137\nf 3137 3116 3092\nf 3110 3109 3117\nf 3117 3109 3139\nf 3105 3140 3117\nf 3117 3140 3118\nf 3140 3141 3118\nf 3118 3141 3119\nf 3142 3120 3141\nf 3141 3120 3119\nf 1498 3143 1277\nf 1277 3143 882\nf 3144 3145 1340\nf 1340 3145 1339\nf 1340 3146 3144\nf 3144 3146 3147\nf 3145 3144 3143\nf 3143 3144 3148\nf 871 901 872\nf 872 901 1362\nf 3149 3148 3147\nf 3147 3148 3144\nf 1208 3150 1209\nf 3098 1786 1781\nf 1781 1786 1782\nf 2930 2929 3123\nf 3123 2929 2911\nf 1794 1791 2916\nf 2916 1791 2917\nf 1793 3101 2918\nf 2918 3101 2919\nf 1796 1794 2920\nf 2920 1794 2916\nf 3151 3152 2921\nf 2921 3152 2922\nf 3153 3154 2923\nf 2923 3154 2924\nf 2919 3101 2912\nf 2912 3101 2913\nf 3155 1796 2925\nf 2925 1796 2920\nf 3154 3156 2924\nf 2924 3156 2926\nf 1791 1793 2917\nf 2917 1793 2918\nf 3157 3158 2927\nf 2927 3158 2928\nf 1780 2930 1781\nf 1781 2930 3123\nf 2825 1267 1189\nf 1189 1267 1190\nf 2827 2828 1250\nf 1250 2828 1251\nf 1489 2830 1324\nf 1324 2830 1325\nf 923 1328 1334\nf 1334 1328 1335\nf 1485 2832 1482\nf 1482 2832 1483\nf 1266 1490 1492\nf 1492 1490 1493\nf 1333 1346 1334\nf 1334 1346 2824\nf 722 799 1492\nf 1492 799 1190\nf 664 657 1482\nf 1482 657 2833\nf 631 634 1324\nf 1324 634 1493\nf 2828 1494 1251\nf 1251 1494 2835\nf 1249 3021 1250\nf 1250 3021 2838\nf 798 2837 1189\nf 1189 2837 2836\nf 3100 842 3160\nf 3160 842 3159\nf 1788 3101 1789\nf 1789 3101 1793\nf 1792 816 1789\nf 1789 816 1790\nf 817 816 1795\nf 1795 816 1792\nf 938 817 1797\nf 1797 817 1795\nf 1796 3155 1797\nf 1797 3155 1798\nf 3155 3158 1798\nf 1798 3158 1799\nf 3157 3152 1801\nf 1801 3152 1802\nf 3151 3156 1803\nf 1803 3156 1804\nf 3154 3153 1805\nf 1805 3153 1806\nf 3153 3161 1806\nf 1806 3161 1807\nf 3099 843 3100\nf 3100 843 842\nf 1788 1787 3102\nf 3102 1787 3112\nf 2910 3115 3124\nf 3124 3115 3122\nf 3099 3098 3122\nf 3122 3098 3124\nf 416 1907 3052\nf 3052 1907 3071\nf 3046 3047 3163\nf 3163 3047 3162\nf 249 445 259\nf 259 445 3164\nf 425 3165 424\nf 424 3165 461\nf 408 410 329\nf 329 410 330\nf 39 331 40\nf 40 331 332\nf 305 304 423\nf 423 304 464\nf 385 254 458\nf 458 254 392\nf 250 415 251\nf 251 415 412\nf 233 337 234\nf 234 337 3166\nf 435 3055 1908\nf 1908 3055 3072\nf 443 1909 3056\nf 3056 1909 3073\nf 3167 3049 3057\nf 3057 3049 444\nf 452 3058 1332\nf 1332 3058 3069\nf 234 3166 346\nf 346 3166 411\nf 3046 3163 462\nf 462 3163 3059\nf 259 3164 260\nf 260 3164 336\nf 3047 417 3162\nf 3162 417 3053\nf 463 3168 434\nf 434 3168 3054\nf 454 830 425\nf 425 830 3165\nf 3062 3169 3063\nf 3063 3169 3170\nf 1355 1354 1534\nf 1534 1354 3171\nf 3063 3170 3068\nf 3068 3170 3172\nf 574 3061 3062\nf 3062 3061 3169\nf 1534 3171 1535\nf 1535 3171 3173\nf 3079 2873 3172\nf 3172 2873 3068\nf 2838 3025 2839\nf 2839 3025 3174\nf 3176 3177 3175\nf 3175 3177 3178\nf 3180 1125 3179\nf 3179 1125 1128\nf 3182 3183 3181\nf 3181 3183 3184\nf 1134 3185 1130\nf 1130 3185 3182\nf 3186 3187 3189\nf 3189 3187 3188\nf 3191 3189 3190\nf 3190 3189 3188\nf 1137 1122 3190\nf 3190 1122 3192\nf 3175 3178 3193\nf 3193 3178 3194\nf 3181 3184 3179\nf 3179 3184 3195\nf 3197 3198 3196\nf 3196 3198 3180\nf 3200 3178 3199\nf 3199 3178 3177\nf 3202 3196 3201\nf 3201 3196 3195\nf 3204 3184 3203\nf 3203 3184 3183\nf 3206 3207 3205\nf 3205 3207 3186\nf 3205 3186 3208\nf 3208 3186 3189\nf 3209 3208 3191\nf 3191 3208 3189\nf 3210 3194 3200\nf 3200 3194 3178\nf 3201 3195 3204\nf 3204 3195 3184\nf 3197 3211 3177\nf 3177 3211 3199\nf 3213 3214 3212\nf 3212 3214 3215\nf 3217 3218 3216\nf 3216 3218 3219\nf 3221 3222 3220\nf 3220 3222 3223\nf 3225 3226 3224\nf 3224 3226 3227\nf 3229 3230 3228\nf 3228 3230 3231\nf 3233 3234 3232\nf 3232 3234 3235\nf 3237 3238 3236\nf 3236 3238 3239\nf 3238 3240 3239\nf 3239 3240 3241\nf 3232 3235 3242\nf 3242 3235 3243\nf 3245 3246 3244\nf 3244 3246 3247\nf 3248 3249 3245\nf 3245 3249 3246\nf 3251 3252 3250\nf 3250 3252 3253\nf 3255 3252 3254\nf 3254 3252 3251\nf 3256 3257 3259\nf 3259 3257 3258\nf 3261 3262 3260\nf 3260 3262 3263\nf 3265 3263 3264\nf 3264 3263 3262\nf 3256 3259 3266\nf 3266 3259 3267\nf 3269 3270 3268\nf 3268 3270 3271\nf 3269 3272 3270\nf 3270 3272 3273\nf 3274 3275 3277\nf 3277 3275 3276\nf 3278 3279 3276\nf 3276 3279 3277\nf 3281 3282 3280\nf 3280 3282 3283\nf 3280 3284 3281\nf 3281 3284 3285\nf 3287 3288 3286\nf 3286 3288 3289\nf 3290 3291 3288\nf 3288 3291 3289\nf 3234 3218 3235\nf 3235 3218 3217\nf 3232 3292 3233\nf 3233 3292 3293\nf 3239 3294 3236\nf 3236 3294 3219\nf 3233 3293 3237\nf 3237 3293 3295\nf 3296 3294 3241\nf 3241 3294 3239\nf 3240 3238 3297\nf 3297 3238 3298\nf 3217 3216 3235\nf 3235 3216 3243\nf 3297 3299 3240\nf 3240 3299 3242\nf 3246 3300 3247\nf 3247 3300 3223\nf 3301 3302 3254\nf 3254 3302 3244\nf 3249 3303 3246\nf 3246 3303 3300\nf 3245 3304 3248\nf 3248 3304 3292\nf 3252 3221 3253\nf 3253 3221 3220\nf 3250 3248 3299\nf 3299 3248 3292\nf 3222 3221 3255\nf 3255 3221 3252\nf 3297 3301 3251\nf 3251 3301 3254\nf 3258 3214 3259\nf 3259 3214 3213\nf 3257 3256 3304\nf 3304 3256 3305\nf 3263 3306 3260\nf 3260 3306 3215\nf 3261 3257 3302\nf 3302 3257 3304\nf 3307 3306 3265\nf 3265 3306 3263\nf 3262 3301 3264\nf 3264 3301 3308\nf 3259 3213 3267\nf 3267 3213 3212\nf 3308 3309 3264\nf 3264 3309 3266\nf 3270 3310 3271\nf 3271 3310 3231\nf 3311 3312 3278\nf 3278 3312 3268\nf 3270 3273 3310\nf 3310 3273 3313\nf 3272 3269 3305\nf 3305 3269 3314\nf 3277 3229 3274\nf 3274 3229 3228\nf 3275 3272 3309\nf 3309 3272 3305\nf 3279 3230 3277\nf 3277 3230 3229\nf 3276 3308 3278\nf 3278 3308 3311\nf 3281 3315 3282\nf 3282 3315 3227\nf 3290 3316 3283\nf 3283 3316 3317\nf 3281 3285 3315\nf 3315 3285 3318\nf 3284 3280 3314\nf 3314 3280 3319\nf 3289 3225 3286\nf 3286 3225 3224\nf 3287 3284 3312\nf 3284 3314 3312\nf 3291 3226 3289\nf 3289 3226 3225\nf 3288 3311 3290\nf 3290 3311 3316\nf 3212 3215 3307\nf 3307 3215 3306\nf 3294 3296 3219\nf 3219 3296 3216\nf 3303 3220 3300\nf 3300 3220 3223\nf 3224 3227 3318\nf 3318 3227 3315\nf 3228 3231 3313\nf 3313 3231 3310\nf 3234 3233 3236\nf 3236 3233 3237\nf 3243 3241 3242\nf 3242 3241 3240\nf 3247 3255 3244\nf 3244 3255 3254\nf 3253 3249 3250\nf 3250 3249 3248\nf 3260 3258 3261\nf 3261 3258 3257\nf 3266 3267 3264\nf 3264 3267 3265\nf 3268 3271 3278\nf 3278 3271 3279\nf 3274 3273 3275\nf 3275 3273 3272\nf 3283 3282 3290\nf 3290 3282 3291\nf 3286 3285 3287\nf 3287 3285 3284\nf 3218 3234 3219\nf 3219 3234 3236\nf 3295 3298 3237\nf 3237 3298 3238\nf 3216 3296 3243\nf 3243 3296 3241\nf 3242 3299 3232\nf 3232 3299 3292\nf 3223 3222 3247\nf 3247 3222 3255\nf 3244 3302 3245\nf 3245 3302 3304\nf 3303 3249 3220\nf 3220 3249 3253\nf 3297 3251 3299\nf 3299 3251 3250\nf 3215 3214 3260\nf 3260 3214 3258\nf 3301 3262 3302\nf 3302 3262 3261\nf 3212 3307 3267\nf 3267 3307 3265\nf 3266 3309 3256\nf 3256 3309 3305\nf 3271 3231 3279\nf 3279 3231 3230\nf 3268 3312 3269\nf 3269 3312 3314\nf 3228 3313 3274\nf 3274 3313 3273\nf 3308 3276 3309\nf 3309 3276 3275\nf 3282 3227 3291\nf 3291 3227 3226\nf 3317 3319 3283\nf 3283 3319 3280\nf 3224 3318 3286\nf 3286 3318 3285\nf 3312 3311 3287\nf 3311 3288 3287\nf 3320 3321 3194\nf 3194 3321 3193\nf 1375 3321 1121\nf 1121 3321 3322\nf 3323 3324 3322\nf 3322 3324 3192\nf 3175 1124 3176\nf 3176 1124 1123\nf 3179 3195 3180\nf 3180 3195 3196\nf 3181 1131 3182\nf 3182 1131 1130\nf 3207 3185 3186\nf 3186 3185 3187\nf 1136 3188 1133\nf 1133 3188 3187\nf 3190 3188 1137\nf 1137 3188 1136\nf 3193 1159 3175\nf 3175 1159 1124\nf 3179 1128 3181\nf 3181 1128 1131\nf 1533 3198 1123\nf 1123 3198 3176\nf 3321 1375 3193\nf 3193 1375 1159\nf 3321 3320 3322\nf 3322 3320 3323\nf 1122 1121 3192\nf 3192 1121 3322\nf 3194 3210 3320\nf 3320 3210 3325\nf 3324 3323 3327\nf 3327 3323 3326\nf 3320 3325 3323\nf 3323 3325 3326\nf 3208 3328 3205\nf 3205 3328 3329\nf 3330 3206 3329\nf 3329 3206 3205\nf 3203 3331 3204\nf 3204 3331 3332\nf 3210 3333 3325\nf 3325 3333 3334\nf 3325 3334 3326\nf 3326 3334 3335\nf 3327 3326 3336\nf 3336 3326 3335\nf 3327 3209 3324\nf 3324 3209 3191\nf 3190 3192 3191\nf 3191 3192 3324\nf 3338 3339 3337\nf 3337 3339 3340\nf 3338 3341 3339\nf 3339 3341 3342\nf 3341 3343 3342\nf 3342 3343 3344\nf 3343 3345 3344\nf 3344 3345 3346\nf 3345 3347 3346\nf 3346 3347 3348\nf 3347 3349 3348\nf 3348 3349 3350\nf 3337 3340 3351\nf 3351 3340 3352\nf 3351 3352 3353\nf 3353 3352 3354\nf 3353 3354 3355\nf 3355 3354 3356\nf 3350 3349 3357\nf 3357 3349 3358\nf 3355 3356 3359\nf 3359 3356 3360\nf 3359 3360 3361\nf 3361 3360 3362\nf 3361 3362 3363\nf 3363 3362 3364\nf 3363 3364 3358\nf 3358 3364 3357\nf 3339 3342 3365\nf 3365 3342 3366\nf 3342 3344 3366\nf 3366 3344 3367\nf 3344 3346 3367\nf 3367 3346 3368\nf 3346 3348 3368\nf 3368 3348 3369\nf 3348 3350 3369\nf 3369 3350 3370\nf 3368 3369 3371\nf 3371 3369 3372\nf 3369 3370 3372\nf 3372 3370 3373\nf 3373 3374 3372\nf 3372 3374 3375\nf 3377 3378 3376\nf 3376 3378 3379\nf 3377 3375 3378\nf 3378 3375 3374\nf 3375 3380 3372\nf 3372 3380 3371\nf 3377 3381 3375\nf 3375 3381 3380\nf 3376 3382 3377\nf 3377 3382 3381\nf 3384 3385 3383\nf 3383 3385 3386\nf 3384 3383 3388\nf 3388 3383 3387\nf 3388 3387 3389\nf 3389 3387 3390\nf 3392 3385 3391\nf 3391 3385 3384\nf 3388 3393 3384\nf 3384 3393 3391\nf 3389 3394 3388\nf 3388 3394 3393\nf 3389 3395 3394\nf 3394 3395 3396\nf 3395 3389 3397\nf 3397 3389 3390\nf 3339 3365 3340\nf 3340 3365 3398\nf 3340 3398 3352\nf 3352 3398 3399\nf 3371 3400 3368\nf 3368 3400 3367\nf 3400 3401 3367\nf 3367 3401 3366\nf 3366 3401 3365\nf 3365 3401 3402\nf 3398 3365 3403\nf 3403 3365 3402\nf 3398 3403 3399\nf 3399 3403 3404\nf 3403 3405 3404\nf 3404 3405 3406\nf 3352 3399 3354\nf 3354 3399 3407\nf 3399 3404 3407\nf 3407 3404 3408\nf 3404 3406 3408\nf 3408 3406 3409\nf 3405 3410 3406\nf 3406 3410 3411\nf 3410 3412 3411\nf 3411 3412 3413\nf 3397 3414 3395\nf 3395 3414 3415\nf 3417 3415 3416\nf 3416 3415 3412\nf 3410 3418 3412\nf 3412 3418 3416\nf 3410 3405 3418\nf 3418 3405 3419\nf 3405 3403 3419\nf 3419 3403 3402\nf 3370 3350 3420\nf 3420 3350 3357\nf 3373 3370 3421\nf 3421 3370 3420\nf 3374 3373 3422\nf 3422 3373 3421\nf 3378 3374 3423\nf 3423 3374 3422\nf 3378 3423 3379\nf 3379 3423 3424\nf 3424 3425 3379\nf 3379 3425 3386\nf 3383 3426 3387\nf 3387 3426 3427\nf 3387 3427 3390\nf 3390 3427 3428\nf 3390 3428 3397\nf 3397 3428 3429\nf 3429 3430 3397\nf 3397 3430 3414\nf 3411 3413 3431\nf 3431 3413 3432\nf 3406 3411 3409\nf 3409 3411 3431\nf 3401 3400 3335\nf 3335 3400 3336\nf 3401 3335 3402\nf 3402 3335 3334\nf 3402 3334 3419\nf 3419 3334 3333\nf 3419 3333 3418\nf 3418 3333 3433\nf 3418 3433 3416\nf 3416 3433 3434\nf 3435 3417 3434\nf 3434 3417 3416\nf 3394 3396 3436\nf 3436 3396 3437\nf 3393 3394 3332\nf 3332 3394 3436\nf 3393 3332 3391\nf 3391 3332 3331\nf 3330 3392 3331\nf 3331 3392 3391\nf 3382 3329 3381\nf 3381 3329 3328\nf 3354 3407 3356\nf 3356 3407 3438\nf 3356 3438 3360\nf 3360 3438 3439\nf 3360 3439 3362\nf 3362 3439 3440\nf 3362 3440 3364\nf 3364 3440 3441\nf 3364 3441 3357\nf 3357 3441 3420\nf 3421 3420 3442\nf 3442 3420 3441\nf 3442 3441 3443\nf 3443 3441 3440\nf 3443 3440 3444\nf 3444 3440 3439\nf 3444 3439 3445\nf 3445 3439 3438\nf 3445 3438 3408\nf 3408 3438 3407\nf 3422 3421 3446\nf 3446 3421 3442\nf 3446 3442 3447\nf 3447 3442 3443\nf 3447 3443 3448\nf 3448 3443 3444\nf 3448 3444 3449\nf 3449 3444 3445\nf 3449 3445 3409\nf 3409 3445 3408\nf 3409 3431 3449\nf 3449 3431 3450\nf 3449 3450 3448\nf 3448 3450 3451\nf 3451 3452 3448\nf 3448 3452 3447\nf 3447 3452 3446\nf 3446 3452 3453\nf 3446 3453 3422\nf 3422 3453 3423\nf 3424 3423 3454\nf 3454 3423 3453\nf 3452 3455 3453\nf 3453 3455 3454\nf 3452 3451 3455\nf 3455 3451 3456\nf 3450 3457 3451\nf 3451 3457 3456\nf 3431 3432 3450\nf 3450 3432 3457\nf 3454 3458 3424\nf 3424 3458 3425\nf 3427 3426 3459\nf 3459 3426 3460\nf 3428 3427 3461\nf 3461 3427 3459\nf 3428 3461 3429\nf 3429 3461 3462\nf 3462 3463 3429\nf 3429 3463 3430\nf 3464 3465 3462\nf 3462 3465 3463\nf 3455 3466 3454\nf 3454 3466 3458\nf 3465 3464 3466\nf 3466 3464 3467\nf 3461 3459 3468\nf 3468 3459 3469\nf 3468 3469 3464\nf 3464 3469 3467\nf 3464 3462 3468\nf 3468 3462 3461\nf 3459 3460 3469\nf 3469 3460 3467\nf 3295 3351 3298\nf 3298 3351 3353\nf 3353 3355 3298\nf 3298 3355 3297\nf 3297 3355 3301\nf 3301 3355 3359\nf 3301 3359 3308\nf 3308 3359 3361\nf 3308 3361 3311\nf 3311 3361 3363\nf 3311 3363 3316\nf 3316 3363 3358\nf 3316 3358 3317\nf 3317 3358 3349\nf 3349 3347 3317\nf 3317 3347 3319\nf 3347 3345 3319\nf 3319 3345 3314\nf 3345 3343 3314\nf 3314 3343 3305\nf 3343 3341 3305\nf 3305 3341 3304\nf 3304 3341 3292\nf 3292 3341 3338\nf 3292 3338 3293\nf 3293 3338 3337\nf 3293 3337 3295\nf 3295 3337 3351\nf 3437 3202 3436\nf 3436 3202 3201\nf 3436 3201 3332\nf 3332 3201 3204\nf 3433 3200 3434\nf 3434 3200 3199\nf 3211 3435 3199\nf 3199 3435 3434\nf 3333 3210 3433\nf 3433 3210 3200\nf 3380 3470 3371\nf 3400 3371 3336\nf 3336 3371 3470\nf 3381 3328 3380\nf 3380 3328 3470\nf 3470 3328 3209\nf 3209 3328 3208\nf 3336 3470 3327\nf 3327 3470 3209\nf 1545 1763 1546\nf 1546 1763 1762\nf 1403 1546 773\nf 773 1546 770\nf 1221 3013 1220\nf 1220 3013 3471\nf 1305 2877 1306\nf 1306 2877 2879\nf 1220 3471 828\nf 828 3471 829\nf 1217 1754 3472\nf 3472 1754 1756\nf 1049 3473 1048\nf 1048 3473 3474\nf 1371 1179 3473\nf 3473 1179 3474\nf 1074 3475 1179\nf 1179 3475 3474\nf 3474 3475 1048\nf 1048 3475 1056\nf 919 666 1345\nf 1345 666 665\nf 856 680 919\nf 919 680 666\nf 679 680 1449\nf 1449 680 858\nf 1367 878 1500\nf 1500 878 877\nf 1366 908 1367\nf 1367 908 878\nf 1512 916 1513\nf 1513 916 915\nf 947 1368 948\nf 948 1368 1369\nf 1047 1021 1044\nf 1044 1021 1020\nf 1370 1049 1019\nf 1019 1049 1020\nf 1173 1030 1174\nf 1174 1030 1033\nf 3126 1188 1464\nf 1464 1188 1187\nf 1800 1451 1512\nf 1512 1451 916\nf 1175 1174 1186\nf 1186 1174 1187\nf 1030 1173 1047\nf 1047 1173 1021\nf 1513 915 3476\nf 3476 915 1481\nf 1345 665 3477\nf 3477 665 1483\nf 1502 1501 876\nf 876 1501 877\nf 1205 1451 1803\nf 1803 1451 1802\nf 3479 2832 3478\nf 3478 2832 2831\nf 1449 858 2927\nf 2927 858 2922\nf 908 1366 947\nf 947 1366 1368\nf 949 948 3126\nf 3126 948 1188\nf 3480 3135 1502\nf 1502 3135 1501\nf 2927 2922 3157\nf 3157 2922 3152\nf 3477 1483 3479\nf 3479 1483 2832\nf 1803 1802 3151\nf 3151 1802 3152\nf 663 658 664\nf 664 658 657\nf 655 658 939\nf 939 658 706\nf 1512 870 1800\nf 1800 870 869\nf 875 874 876\nf 876 874 873\nf 849 874 905\nf 905 874 958\nf 949 962 946\nf 946 962 961\nf 978 3126 979\nf 979 3126 1464\nf 1031 1168 1032\nf 1032 1168 1022\nf 1045 1013 1046\nf 1046 1013 1012\nf 1045 1044 1056\nf 1056 1044 1048\nf 1047 1046 1030\nf 1030 1046 1031\nf 939 706 940\nf 940 706 1450\nf 979 1464 1032\nf 1032 1464 1033\nf 3480 1478 3476\nf 3476 1478 3134\nf 2829 1487 2830\nf 2830 1487 1486\nf 848 1230 873\nf 873 1230 1479\nf 867 870 933\nf 933 870 1514\nf 1800 869 1801\nf 1801 869 1799\nf 2830 1486 1325\nf 1325 1486 2833\nf 940 1450 2925\nf 2925 1450 2928\nf 946 961 907\nf 907 961 958\nf 959 962 977\nf 977 962 978\nf 3476 3134 1513\nf 1513 3134 1514\nf 2925 2928 3155\nf 3155 2928 3158\nf 1325 2833 656\nf 656 2833 657\nf 1801 1799 3157\nf 3157 1799 3158\nf 1074 1055 3475\nf 3475 1055 1056\nf 1371 3473 1370\nf 1370 3473 1049\nf 2884 615 3481\nf 3481 615 618\nf 3478 2831 2846\nf 2846 2831 617\nf 2831 1484 617\nf 617 1484 618\nf 1484 1487 618\nf 618 1487 3481\nf 2848 3481 2829\nf 2829 3481 1487\nf 1480 1517 1161\nf 1161 1517 1162\nf 3129 1323 1455\nf 1455 1323 1322\nf 3129 3134 1323\nf 1323 3134 1478\nf 1535 3173 1515\nf 1515 3173 1516\nf 3085 3095 3137\nf 3137 3095 3094\nf 3137 3094 3138\nf 3138 3094 3104\nf 3117 3139 3105\nf 3105 3139 3106\nf 1138 1141 3127\nf 3127 1141 3128\nf 3081 3131 1264\nf 1264 3131 3130\nf 1227 1226 3132\nf 3132 1226 3133\nf 1264 3130 1229\nf 1229 3130 1322\nf 1160 1501 1161\nf 1161 1501 3135\nf 1141 1476 3128\nf 3128 1476 3136\nf 1226 1163 3133\nf 3133 1163 1162\nf 1363 1138 1515\nf 1515 1138 3127\nf 3084 3095 3081\nf 3081 3095 3131\nf 3089 884 3105\nf 3105 884 3140\nf 884 883 3140\nf 3140 883 3141\nf 883 3482 3141\nf 3141 3482 3142\nf 3476 1481 3480\nf 3480 1481 3135\nf 1502 1479 3480\nf 3480 1479 1478\nf 3168 463 440\nf 714 1880 715\nf 715 1880 2905\nf 896 732 2970\nf 2970 732 731\nf 1263 834 3082\nf 3082 834 833\nf 898 1231 853\nf 853 1231 854\nf 3125 890 2931\nf 2931 890 889\nf 1237 893 2851\nf 2851 893 892\nf 898 853 899\nf 899 853 963\nf 988 987 989\nf 989 987 986\nf 1002 1007 1003\nf 1003 1007 1006\nf 1038 1036 1007\nf 1007 1036 1004\nf 1070 1052 1005\nf 1005 1052 1006\nf 1069 1067 1070\nf 1070 1067 1052\nf 1130 1129 1134\nf 1134 1129 1135\nf 879 1167 880\nf 880 1167 1166\nf 1171 1169 1037\nf 1037 1169 1038\nf 1085 1180 1069\nf 1069 1180 1067\nf 1209 1210 879\nf 879 1210 1167\nf 2885 1215 1754\nf 1754 1215 1214\nf 1263 855 834\nf 834 855 854\nf 708 1244 1826\nf 1826 1244 1243\nf 2909 894 1254\nf 1254 894 893\nf 1228 1361 964\nf 964 1361 1360\nf 2908 2907 1436\nf 1436 2907 1437\nf 2990 1440 2991\nf 2991 1440 1439\nf 964 1360 965\nf 965 1360 1454\nf 950 1457 951\nf 951 1457 1456\nf 970 1463 971\nf 971 1463 1462\nf 1025 1169 984\nf 984 1169 986\nf 1084 1097 1469\nf 1469 1097 1470\nf 1096 1115 1474\nf 1474 1115 1475\nf 1140 1361 1141\nf 1141 1361 1476\nf 1212 1495 2828\nf 2828 1495 1494\nf 880 1166 1452\nf 1452 1166 1499\nf 715 2905 1504\nf 1504 2905 2906\nf 954 1519 955\nf 955 1519 1518\nf 1026 1520 992\nf 992 1520 1518\nf 1029 1520 1043\nf 1043 1520 1521\nf 997 1522 1040\nf 1040 1522 1521\nf 1015 1522 1016\nf 1016 1522 1523\nf 1523 996 1524\nf 1524 996 1062\nf 1057 1060 1525\nf 1525 1060 1524\nf 1094 1528 1082\nf 1082 1528 1527\nf 1526 1528 1089\nf 1089 1528 1529\nf 1090 1531 1108\nf 1108 1531 1530\nf 1125 1533 1126\nf 1126 1533 1532\nf 3483 1755 727\nf 727 1755 1214\nf 735 1814 707\nf 707 1814 1813\nf 2891 1834 1246\nf 1246 1834 1243\nf 2904 1881 1847\nf 1847 1881 1848\nf 1852 1855 1853\nf 1853 1855 1858\nf 1902 2903 1877\nf 1877 2903 1878\nf 2903 2904 1878\nf 1878 2904 1847\nf 714 713 1848\nf 1848 713 1849\nf 1440 2990 1883\nf 1883 2990 2989\nf 1883 2989 1852\nf 1852 2989 1855\nf 2895 1888 2892\nf 2892 1888 1887\nf 774 777 1877\nf 1877 777 1903\nf 1251 2835 1252\nf 1252 2835 2834\nf 1213 2844 1212\nf 1212 2844 1495\nf 2851 892 1213\nf 1213 892 2844\nf 3123 2911 3124\nf 3124 2911 2910\nf 887 2929 888\nf 888 2929 889\nf 1435 2963 1436\nf 1436 2963 2961\nf 1253 1438 1254\nf 1254 1438 1437\nf 2970 731 735\nf 735 731 1814\nf 733 732 2885\nf 2885 732 1215\nf 2901 2902 2976\nf 2976 2902 2977\nf 2975 2982 2976\nf 2976 2982 2979\nf 2899 2900 2981\nf 2981 2900 2979\nf 2899 2981 2898\nf 2898 2981 2983\nf 2986 2985 2984\nf 2984 2985 2983\nf 2897 2985 2896\nf 2896 2985 2988\nf 2991 1439 2987\nf 2987 1439 2988\nf 777 2978 1903\nf 1903 2978 2977\nf 1885 1888 1858\nf 1858 1888 1853\nf 3090 3088 836\nf 836 3088 833\nf 3083 3088 3097\nf 3097 3088 3096\nf 3097 3096 3104\nf 3104 3096 3106\nf 3111 3116 3108\nf 3108 3116 3109\nf 3112 3111 3114\nf 3114 3111 3108\nf 3113 3122 3114\nf 3114 3122 3115\nf 891 890 2837\nf 2837 890 2834\nf 951 1456 954\nf 954 1456 1519\nf 967 1457 936\nf 936 1457 1454\nf 971 1462 902\nf 902 1462 963\nf 1461 1463 988\nf 988 1463 987\nf 1466 1468 1474\nf 1474 1468 1470\nf 1089 1529 1090\nf 1090 1529 1531\nf 1184 1181 1469\nf 1469 1181 1180\nf 1082 1527 1061\nf 1061 1527 1525\nf 1129 1473 1135\nf 1135 1473 1475\nf 1108 1530 1105\nf 1105 1530 1532\nf 1452 1499 3132\nf 3132 1499 3136\nf 3109 3116 3139\nf 3139 3116 3138\nf 2912 2911 887\nf 887 2911 2929\nf 2836 2835 1497\nf 1497 2835 1494\nf 2913 3102 2910\nf 2910 3102 3115\nf 1133 3187 1134\nf 1134 3187 3185\nf 3177 3176 3197\nf 3197 3176 3198\nf 3203 3183 3206\nf 3206 3183 3207\nf 3196 3202 3197\nf 3197 3202 3211\nf 3183 3182 3207\nf 3207 3182 3185\nf 1125 3180 1533\nf 1533 3180 3198\nf 3331 3203 3330\nf 3330 3203 3206\nf 3376 3379 3385\nf 3385 3379 3386\nf 3382 3376 3392\nf 3392 3376 3385\nf 3412 3415 3413\nf 3413 3415 3414\nf 3417 3396 3415\nf 3415 3396 3395\nf 3383 3386 3426\nf 3426 3386 3425\nf 3413 3414 3432\nf 3432 3414 3430\nf 3437 3396 3435\nf 3435 3396 3417\nf 3329 3382 3330\nf 3330 3382 3392\nf 3426 3425 3460\nf 3460 3425 3458\nf 3432 3430 3457\nf 3457 3430 3463\nf 3457 3463 3456\nf 3456 3463 3465\nf 3460 3458 3467\nf 3467 3458 3466\nf 3466 3455 3465\nf 3465 3455 3456\nf 3202 3437 3211\nf 3211 3437 3435\nf 3104 3106 3138\nf 3138 3106 3139\nf 3132 3136 1227\nf 1227 3136 1476\nf 943 687 600\nf 600 687 601\nf 596 625 667\nf 667 625 1297\nf 687 686 601\nf 601 686 653\nf 1319 784 711\nf 711 784 712\nf 1811 607 783\nf 783 607 712\nf 862 921 917\nf 917 921 920\nf 3478 925 3479\nf 3479 925 924\nf 2846 926 3478\nf 3478 926 925\nf 659 662 929\nf 929 662 930\nf 856 921 857\nf 857 921 1204\nf 1206 864 1207\nf 1207 864 863\nf 2876 1219 2877\nf 2877 1219 1218\nf 667 1297 593\nf 593 1297 1296\nf 917 920 1333\nf 1333 920 1346\nf 686 693 653\nf 653 693 930\nf 1277 913 1498\nf 1498 913 1207\nf 592 1536 610\nf 610 1536 711\nf 864 1206 1805\nf 1805 1206 1804\nf 1317 1536 1900\nf 1900 1536 1809\nf 1900 1809 1295\nf 1295 1809 1296\nf 3479 924 3477\nf 3477 924 2824\nf 927 926 1203\nf 1203 926 2847\nf 3484 2859 3032\nf 3032 2859 3033\nf 3472 2878 1217\nf 1217 2878 1218\nf 2889 2888 2880\nf 2880 2888 2881\nf 2886 2883 2887\nf 2887 2883 2882\nf 929 928 2847\nf 2847 928 1203\nf 857 1204 2921\nf 2921 1204 2926\nf 1820 1819 2882\nf 2882 1819 2881\nf 1821 2883 2876\nf 2876 2883 1219\nf 782 2889 783\nf 783 2889 2880\nf 1352 3034 1353\nf 1353 3034 3033\nf 3031 3034 3041\nf 3041 3034 3043\nf 944 943 3043\nf 3043 943 600\nf 1339 3145 1498\nf 1498 3145 3143\nf 1208 882 3148\nf 3148 882 3143\nf 2921 2926 3151\nf 3151 2926 3156\nf 3477 2824 1345\nf 1345 2824 1346\nf 1805 1804 3154\nf 3154 1804 3156\nf 886 3103 883\nf 883 3103 3482\nf 841 1248 3485\nf 3485 1248 3121\nf 2811 2819 2812\nf 2812 2819 2816\nf 2818 2815 2817\nf 2817 2815 2806\nf 2817 2816 2818\nf 2818 2816 2819\nf 2793 2795 2819\nf 2819 2795 2818\nf 243 352 2793\nf 2793 352 2795\nf 242 353 243\nf 243 353 352\nf 359 191 242\nf 242 191 353\nf 247 192 359\nf 359 192 191\nf 157 193 247\nf 247 193 192\nf 156 194 157\nf 157 194 193\nf 378 261 156\nf 156 261 194\nf 378 381 261\nf 261 381 195\nf 195 381 196\nf 196 381 376\nf 196 376 198\nf 198 376 375\nf 375 1776 198\nf 198 1776 197\nf 1776 1767 197\nf 197 1767 274\nf 181 274 199\nf 199 274 1767\nf 568 566 564\nf 564 566 563\nf 568 569 566\nf 566 569 567\nf 501 1773 1771\nf 1771 1773 578\nf 547 579 1773\nf 1773 579 578\nf 3486 470 474\nf 474 470 469\nf 450 582 3487\nf 3487 582 3488\nf 582 583 3488\nf 3488 583 3489\nf 583 584 3489\nf 3489 584 3490\nf 3490 584 3491\nf 3491 584 543\nf 470 3486 466\nf 466 3486 467\nf 3489 3490 503\nf 503 3490 504\nf 503 3492 3489\nf 3489 3492 3488\nf 3492 503 3493\nf 3493 503 502\nf 3495 3492 3494\nf 3494 3492 3493\nf 3487 3488 3495\nf 3495 3488 3492\nf 468 1774 465\nf 465 1774 570\nf 3494 3493 1775\nf 1775 3493 1771\nf 3490 3491 504\nf 504 3491 1772\nf 3491 543 1772\nf 1772 543 548\nf 450 3487 474\nf 474 3487 3486\nf 3495 467 3487\nf 3487 467 3486\nf 3494 468 3495\nf 3495 468 467\nf 468 3494 1774\nf 1774 3494 1775\nf 501 1771 502\nf 502 1771 3493\nf 527 366 384\nf 384 366 365\nf 522 524 527\nf 527 524 366\nf 521 534 522\nf 522 534 524\nf 530 535 521\nf 521 535 534\nf 508 486 513\nf 513 486 532\nf 513 532 541\nf 541 532 535\nf 667 668 596\nf 596 668 597\nf 1294 1297 624\nf 624 1297 625\nf 603 641 604\nf 604 641 640\nf 1290 1293 650\nf 650 1293 651\nf 1758 691 660\nf 660 691 661\nf 602 671 599\nf 599 671 670\nf 654 662 690\nf 690 662 661\nf 2856 598 695\nf 695 598 597\nf 602 654 671\nf 671 654 690\nf 604 640 676\nf 676 640 704\nf 1826 1829 708\nf 708 1829 709\nf 1288 1284 740\nf 740 1284 741\nf 755 754 758\nf 758 754 759\nf 2853 2856 932\nf 932 2856 695\nf 689 691 1192\nf 1192 691 1193\nf 784 1319 1240\nf 1240 1319 1241\nf 1813 1816 1244\nf 1244 1816 1245\nf 754 1288 759\nf 759 1288 740\nf 1894 1242 1318\nf 1318 1242 1241\nf 2867 1348 1418\nf 1418 1348 1347\nf 676 704 673\nf 673 704 1347\nf 1284 1287 741\nf 741 1287 1419\nf 1421 1426 1422\nf 1422 1426 1425\nf 1758 660 615\nf 615 660 616\nf 1192 1191 1421\nf 1421 1191 1426\nf 2962 1839 1504\nf 1504 1839 1505\nf 941 1194 1759\nf 1759 1194 1193\nf 1287 1537 1419\nf 1419 1537 1760\nf 1882 2893 1823\nf 1823 2893 1824\nf 1886 1891 1827\nf 1827 1891 1828\nf 1887 1886 1834\nf 1834 1886 1827\nf 1841 716 1838\nf 1838 716 1505\nf 782 784 1850\nf 1850 784 1240\nf 1239 1242 1863\nf 1863 1242 1864\nf 1242 1894 1864\nf 1864 1894 1873\nf 1822 1239 1823\nf 1823 1239 1863\nf 1318 1317 1895\nf 1895 1317 1900\nf 1894 1896 1873\nf 1873 1896 1897\nf 1291 1896 1292\nf 1292 1896 1895\nf 605 1898 606\nf 606 1898 1897\nf 1258 1257 2854\nf 2854 1257 2855\nf 1257 1351 2855\nf 2855 1351 2857\nf 1351 1353 2857\nf 2857 1353 2858\nf 1353 3033 2858\nf 2858 3033 2859\nf 2860 2861 3036\nf 3036 2861 3037\nf 2862 2863 2870\nf 2870 2863 3039\nf 2870 2869 2862\nf 2862 2869 2864\nf 2868 2865 2869\nf 2869 2865 2864\nf 1764 2866 2868\nf 2868 2866 2865\nf 1537 1199 1760\nf 1760 1199 1348\nf 615 2884 1758\nf 1758 2884 1759\nf 1825 1246 2890\nf 2890 1246 1245\nf 2893 2892 1824\nf 1824 2892 2891\nf 761 2960 758\nf 758 2960 2906\nf 3000 2943 2940\nf 2940 2943 2941\nf 2968 710 2950\nf 2950 710 709\nf 1764 1423 2866\nf 2866 1423 2953\nf 1423 645 2953\nf 2953 645 2954\nf 645 644 2954\nf 2954 644 2955\nf 644 738 2955\nf 2955 738 2956\nf 738 764 2956\nf 2956 764 2957\nf 764 763 2957\nf 2957 763 2958\nf 763 2908 2958\nf 2958 2908 2959\nf 2908 1436 2959\nf 2959 1436 2961\nf 1837 2964 1435\nf 1435 2964 2963\nf 1837 2937 2964\nf 2964 2937 2965\nf 2936 2968 2966\nf 2966 2968 2950\nf 1840 2942 1838\nf 1838 2942 2941\nf 2939 2949 2940\nf 2940 2949 3006\nf 2939 2942 2966\nf 2966 2942 2965\nf 1822 1825 1850\nf 1850 1825 2890\nf 1891 3005 1828\nf 1828 3005 3006\nf 3035 3038 3045\nf 3045 3038 3042\nf 3045 3044 3035\nf 3035 3044 3040\nf 669 1422 3045\nf 3045 1422 3044\nf 599 670 3041\nf 3041 670 3042\nf 931 3067 932\nf 932 3067 3065\nf 2853 3066 2854\nf 2854 3066 3076\nf 3067 3496 3065\nf 3065 3496 3497\nf 3066 3498 3076\nf 3076 3498 3499\nf 707 1813 708\nf 708 1813 1244\nf 2960 2962 2906\nf 2906 2962 1504\nf 2892 1887 2891\nf 2891 1887 1834\nf 1839 2962 2963\nf 2963 2962 2961\nf 595 626 596\nf 596 626 625\nf 623 626 3484\nf 3484 626 2859\nf 1291 1290 606\nf 606 1290 603\nf 1290 650 603\nf 603 650 641\nf 641 650 642\nf 642 650 649\nf 2860 2863 649\nf 649 2863 642\nf 3036 3039 2860\nf 2860 3039 2863\nf 3035 3040 3036\nf 3036 3040 3039\nf 1294 1293 1295\nf 1295 1293 1292\nf 624 651 1294\nf 1294 651 1293\nf 623 652 624\nf 624 652 651\nf 3484 2861 623\nf 623 2861 652\nf 3032 3037 3484\nf 3484 3037 2861\nf 3031 3038 3032\nf 3032 3038 3037\nf 3481 2848 2884\nf 2884 2848 942\nf 2846 617 1427\nf 1427 617 616\nf 3049 3167 3048\nf 3048 3167 3500\nf 1906 3048 3070\nf 3070 3048 3500\nf 1337 1768 3501\nf 3501 1768 3502\nf 3020 3503 3021\nf 3021 3503 3024\nf 2914 3023 3020\nf 3020 3023 3503\nf 859 1770 860\nf 860 1770 3504\nf 1336 3505 1768\nf 1768 3505 3502\nf 3065 3497 3066\nf 3066 3497 3498\nf 2814 3075 3067\nf 3067 3075 3496\nf 2839 3174 766\nf 766 3174 3017\nf 1337 3501 1338\nf 1338 3501 1769\nf 767 3018 3026\nf 3026 3018 3506\nf 3076 3499 3064\nf 3064 3499 3507\nf 727 730 3483\nf 3483 730 3030\nf 3064 3507 2871\nf 2871 3507 3077\nf 3100 3160 1786\nf 1786 3160 1784\nf 3026 3506 752\nf 752 3506 3015\nf 860 3504 2923\nf 2923 3504 3508\nf 753 3016 3027\nf 3027 3016 3509\nf 3027 3509 3008\nf 3008 3509 3028\nf 1782 1785 1783\nf 1783 1785 3022\nf 2923 3508 3153\nf 3153 3508 3161\nf 3148 3149 1208\nf 1208 3149 3150\nf 1335 1328 3510\nf 3510 1328 1327\nf 1336 1335 3505\nf 3505 1335 3510\nf 866 3511 1340\nf 1340 3511 3146\nf 865 1808 866\nf 866 1808 3511\nf 842 841 3159\nf 3159 841 3485\nf 2849 3512 1329\nf 1329 3512 1326\nf 1202 1331 2849\nf 2849 1331 3512\nf 3514 3515 3513\nf 3513 3515 3516\nf 3518 3516 3517\nf 3517 3516 3515\nf 3519 3520 3514\nf 3514 3520 3515\nf 3522 3523 3521\nf 3521 3523 3524\nf 3526 3527 3525\nf 3525 3527 3528\nf 3530 3531 3529\nf 3529 3531 3532\nf 3534 3535 3533\nf 3533 3535 3536\nf 3529 3532 3537\nf 3537 3532 3538\nf 3537 3539 3529\nf 3529 3539 3540\nf 3542 3543 3541\nf 3541 3543 3544\nf 3546 3547 3545\nf 3545 3547 3548\nf 3550 3534 3549\nf 3549 3534 3533\nf 3550 3543 3551\nf 3551 3543 3552\nf 3537 3545 3539\nf 3539 3545 3548\nf 3553 3554 3556\nf 3556 3554 3555\nf 3537 3557 3545\nf 3545 3557 3546\nf 3558 3559 3561\nf 3561 3559 3560\nf 3563 3564 3562\nf 3562 3564 3565\nf 3564 3563 3566\nf 3566 3563 3567\nf 3569 3570 3568\nf 3568 3570 3571\nf 3573 3574 3572\nf 3572 3574 3575\nf 3577 3578 3576\nf 3576 3578 3579\nf 3581 3582 3580\nf 3580 3582 3583\nf 3585 3586 3584\nf 3584 3586 3579\nf 3586 3587 3579\nf 3579 3587 3576\nf 3589 3590 3588\nf 3588 3590 3591\nf 3592 3593 3595\nf 3595 3593 3594\nf 3597 3598 3596\nf 3596 3598 3595\nf 3583 3600 3599\nf 3599 3600 3601\nf 3603 3599 3602\nf 3602 3599 3601\nf 3605 3606 3604\nf 3604 3606 3607\nf 3608 3555 3596\nf 3596 3555 3554\nf 3597 3610 3609\nf 3609 3610 3565\nf 3564 3611 3565\nf 3565 3611 3609\nf 3611 3564 3612\nf 3612 3564 3566\nf 3613 3614 3569\nf 3569 3614 3570\nf 3615 3616 3573\nf 3573 3616 3574\nf 3517 3617 3518\nf 3518 3617 3618\nf 3620 3621 3619\nf 3619 3621 3622\nf 3624 3625 3623\nf 3623 3625 3626\nf 3628 3629 3627\nf 3627 3629 3630\nf 3632 3633 3631\nf 3631 3633 3634\nf 3636 3637 3635\nf 3635 3637 3638\nf 3640 3641 3639\nf 3639 3641 3642\nf 3643 3644 3639\nf 3639 3644 3640\nf 3646 3513 3645\nf 3645 3513 3647\nf 3648 3514 3646\nf 3646 3514 3513\nf 3649 3519 3648\nf 3648 3519 3514\nf 3650 3651 3527\nf 3527 3651 3652\nf 3654 3651 3653\nf 3653 3651 3650\nf 3568 3571 3655\nf 3655 3571 3656\nf 3657 3658 3653\nf 3653 3658 3659\nf 3661 3662 3660\nf 3660 3662 3658\nf 3664 3665 3663\nf 3663 3665 3666\nf 3667 3657 3650\nf 3650 3657 3653\nf 3669 3523 3668\nf 3668 3523 3670\nf 3672 3673 3671\nf 3671 3673 3674\nf 3676 3677 3675\nf 3675 3677 3678\nf 3671 3674 3676\nf 3676 3674 3677\nf 3680 3681 3679\nf 3679 3681 3682\nf 3683 3684 3680\nf 3680 3684 3681\nf 3685 3686 3661\nf 3661 3686 3662\nf 3686 3687 3662\nf 3662 3687 3688\nf 3684 3683 3689\nf 3689 3683 3690\nf 3692 3693 3691\nf 3691 3693 3694\nf 3691 3694 3695\nf 3695 3694 3696\nf 3698 3699 3697\nf 3697 3699 3700\nf 3702 3703 3701\nf 3701 3703 3704\nf 3705 3706 3519\nf 3519 3706 3520\nf 3673 3672 3707\nf 3707 3672 3708\nf 3709 3710 3682\nf 3682 3710 3679\nf 3711 3712 3692\nf 3692 3712 3693\nf 3714 3715 3713\nf 3713 3715 3716\nf 3718 3700 3717\nf 3717 3700 3699\nf 3720 3721 3719\nf 3719 3721 3722\nf 3724 3725 3723\nf 3723 3725 3726\nf 3727 3728 3730\nf 3730 3728 3729\nf 3722 3721 3731\nf 3731 3721 3732\nf 3697 3700 3733\nf 3733 3700 3714\nf 3733 3714 3734\nf 3734 3714 3713\nf 3721 3723 3732\nf 3732 3723 3735\nf 3720 3736 3724\nf 3724 3736 3737\nf 3700 3718 3714\nf 3714 3718 3715\nf 3717 3699 3738\nf 3738 3699 3739\nf 3536 3740 3533\nf 3533 3740 3540\nf 3742 3743 3741\nf 3741 3743 3744\nf 3746 3747 3745\nf 3745 3747 3748\nf 3750 3751 3749\nf 3749 3751 3752\nf 3627 3632 3628\nf 3628 3632 3631\nf 3754 3755 3753\nf 3753 3755 3626\nf 3562 3565 3756\nf 3756 3565 3610\nf 3527 3652 3528\nf 3528 3652 3757\nf 3758 3524 3669\nf 3669 3524 3523\nf 3582 3598 3583\nf 3583 3598 3600\nf 3759 3670 3522\nf 3522 3670 3523\nf 3675 3678 3760\nf 3760 3678 3761\nf 3762 3763 3690\nf 3690 3763 3689\nf 3765 3766 3764\nf 3764 3766 3767\nf 3768 3739 3698\nf 3698 3739 3699\nf 3726 3725 3770\nf 3770 3725 3769\nf 3645 3647 3771\nf 3771 3647 3772\nf 3645 3678 3646\nf 3646 3678 3677\nf 3646 3677 3648\nf 3648 3677 3674\nf 3673 3649 3674\nf 3674 3649 3648\nf 3707 3773 3673\nf 3673 3773 3649\nf 3775 3776 3774\nf 3774 3776 3777\nf 3779 3526 3778\nf 3778 3526 3525\nf 3526 3779 3667\nf 3667 3779 3780\nf 3780 3781 3667\nf 3667 3781 3657\nf 3672 3679 3708\nf 3708 3679 3710\nf 3671 3680 3672\nf 3672 3680 3679\nf 3676 3683 3671\nf 3671 3683 3680\nf 3675 3690 3676\nf 3676 3690 3683\nf 3760 3762 3675\nf 3675 3762 3690\nf 3682 3681 3783\nf 3783 3681 3782\nf 3689 3784 3684\nf 3684 3784 3785\nf 3684 3785 3681\nf 3681 3785 3782\nf 3786 3709 3783\nf 3783 3709 3682\nf 3784 3689 3787\nf 3787 3689 3763\nf 3789 3557 3788\nf 3788 3557 3790\nf 3792 3793 3791\nf 3791 3793 3794\nf 3616 3615 3795\nf 3795 3615 3796\nf 3797 3798 3727\nf 3727 3798 3728\nf 3800 3801 3799\nf 3799 3801 3802\nf 3803 3578 3592\nf 3592 3578 3593\nf 3805 3806 3804\nf 3804 3806 3807\nf 3599 3603 3585\nf 3585 3603 3586\nf 3587 3586 3604\nf 3604 3586 3603\nf 3588 3591 3794\nf 3794 3591 3791\nf 3581 3803 3582\nf 3582 3803 3592\nf 3594 3561 3608\nf 3608 3561 3790\nf 3662 3688 3808\nf 3808 3688 3809\nf 3665 3664 3810\nf 3810 3664 3811\nf 3719 3812 3720\nf 3720 3812 3736\nf 3717 3813 3718\nf 3718 3813 3737\nf 3723 3721 3724\nf 3724 3721 3720\nf 3813 3717 3814\nf 3814 3717 3738\nf 3815 3816 303\nf 303 3816 304\nf 3818 3819 3817\nf 3817 3819 3820\nf 3578 3803 3579\nf 3579 3803 3584\nf 3558 3593 3577\nf 3577 3593 3578\nf 3822 3823 3821\nf 3821 3823 3824\nf 3546 3826 3825\nf 3825 3826 3827\nf 3828 3829 3547\nf 3547 3829 3541\nf 3831 3542 3830\nf 3830 3542 3832\nf 3834 3822 3833\nf 3833 3822 3821\nf 3835 3836 3827\nf 3827 3836 3825\nf 3800 3799 3828\nf 3828 3799 3829\nf 3830 3832 3837\nf 3837 3832 3838\nf 331 330 3551\nf 3551 330 3839\nf 3831 333 3552\nf 3552 333 332\nf 3819 3797 3841\nf 3841 3797 3840\nf 3513 3516 3647\nf 3647 3516 3746\nf 337 336 3745\nf 3745 336 3772\nf 3525 3528 3775\nf 3775 3528 3749\nf 3610 3597 3554\nf 3554 3597 3596\nf 3706 3702 3520\nf 3520 3702 3701\nf 3756 3610 3553\nf 3553 3610 3554\nf 3757 3750 3528\nf 3528 3750 3749\nf 3517 3515 3701\nf 3701 3515 3520\nf 3842 3584 3581\nf 3581 3584 3803\nf 3525 3775 3778\nf 3778 3775 3774\nf 3638 3843 3635\nf 3635 3843 3618\nf 3617 3621 3618\nf 3618 3621 3635\nf 3617 3517 3704\nf 3704 3517 3701\nf 3521 3624 3522\nf 3522 3624 3623\nf 3749 3844 3775\nf 3775 3844 3776\nf 3846 3847 3845\nf 3845 3847 3848\nf 3843 3747 3618\nf 3618 3747 3518\nf 3850 3851 3849\nf 3849 3851 3748\nf 3853 3854 3852\nf 3852 3854 3855\nf 3856 3857 3622\nf 3622 3857 3619\nf 3859 3860 3858\nf 3858 3860 3861\nf 3862 3844 3752\nf 3752 3844 3749\nf 3863 3759 3623\nf 3623 3759 3522\nf 3864 3777 3630\nf 3630 3777 3776\nf 3713 3865 3734\nf 3734 3865 3866\nf 3868 3869 3867\nf 3867 3869 3870\nf 3872 3873 3871\nf 3871 3873 3874\nf 3876 3877 3875\nf 3875 3877 3878\nf 3879 3880 3878\nf 3878 3880 3881\nf 3882 3883 3885\nf 3885 3883 3884\nf 3780 3886 3781\nf 3781 3886 3887\nf 3888 3685 3869\nf 3869 3685 3661\nf 3880 3879 3883\nf 3883 3879 3884\nf 3693 3783 3694\nf 3694 3783 3782\nf 3696 3785 3764\nf 3764 3785 3784\nf 3694 3782 3696\nf 3696 3782 3785\nf 3693 3786 3783\nf 3787 3765 3784\nf 3784 3765 3764\nf 386 385 3765\nf 3765 385 3766\nf 3695 3889 3691\nf 3691 3889 3890\nf 3691 3890 3692\nf 3692 3890 3891\nf 3892 3711 3891\nf 3891 3711 3692\nf 3767 3893 3695\nf 3695 3893 3889\nf 3766 3894 3767\nf 3767 3894 3893\nf 3882 3885 3895\nf 3895 3885 3896\nf 3898 3698 3897\nf 3897 3698 3697\nf 3897 3697 3899\nf 3899 3697 3733\nf 3899 3733 3900\nf 3900 3733 3734\nf 3901 3768 3898\nf 3898 3768 3698\nf 3901 3902 3768\nf 3768 3902 3903\nf 3904 3905 3907\nf 3907 3905 3906\nf 3869 3661 3870\nf 3870 3661 3660\nf 3908 3909 3896\nf 3896 3909 3895\nf 3909 3758 3895\nf 3895 3758 3669\nf 3751 3531 3752\nf 3752 3531 3848\nf 3858 3536 3854\nf 3854 3536 3535\nf 3652 3562 3757\nf 3757 3562 3756\nf 3651 3654 3563\nf 3563 3654 3567\nf 3655 3656 3808\nf 3808 3656 3659\nf 3530 3740 3845\nf 3845 3740 3861\nf 3751 3750 3556\nf 3556 3750 3553\nf 3817 3820 3796\nf 3796 3820 3795\nf 410 409 3910\nf 3910 409 3855\nf 3750 3757 3553\nf 3553 3757 3756\nf 3908 3886 3909\nf 3909 3886 3780\nf 409 411 3855\nf 3855 411 3851\nf 413 412 3760\nf 3760 412 3762\nf 415 414 3763\nf 3763 414 3787\nf 3790 3557 3538\nf 3538 3557 3537\nf 3555 3608 3538\nf 3538 3608 3790\nf 3574 3911 3575\nf 3575 3911 3912\nf 3535 3534 3910\nf 3910 3534 3839\nf 3831 3552 3542\nf 3542 3552 3543\nf 3534 3550 3839\nf 3839 3550 3551\nf 3543 3550 3544\nf 3544 3550 3549\nf 3913 3914 3916\nf 3916 3914 3915\nf 3806 3805 3589\nf 3589 3805 3590\nf 3849 3843 3917\nf 3917 3843 3638\nf 3853 3644 3859\nf 3859 3644 3742\nf 3747 3746 3518\nf 3518 3746 3516\nf 3695 3696 3767\nf 3767 3696 3764\nf 3903 3918 3768\nf 3768 3918 3739\nf 3738 3739 3816\nf 3816 3739 3918\nf 3919 3920 3769\nf 3769 3920 3770\nf 3921 3726 3922\nf 3922 3726 3770\nf 3678 3645 3761\nf 3761 3645 3771\nf 3924 3913 3923\nf 3923 3913 3916\nf 3802 3925 3838\nf 3838 3925 3837\nf 3577 3926 3558\nf 3558 3926 3559\nf 3815 3919 3814\nf 3814 3919 3769\nf 3927 3928 3822\nf 3822 3928 3823\nf 3829 3832 3541\nf 3541 3832 3542\nf 3834 3927 3822\nf 3832 3829 3838\nf 3838 3829 3799\nf 3745 3772 3746\nf 3746 3772 3647\nf 3849 3748 3843\nf 3843 3748 3747\nf 3854 3853 3858\nf 3858 3853 3859\nf 3854 3535 3855\nf 3855 3535 3910\nf 3557 3789 3546\nf 3546 3789 3826\nf 3923 3823 3924\nf 3924 3823 3928\nf 3823 3923 3929\nf 3929 3923 3930\nf 3531 3751 3532\nf 3532 3751 3556\nf 3532 3556 3538\nf 3538 3556 3555\nf 3932 3634 3931\nf 3931 3634 3633\nf 3814 3769 3813\nf 3813 3769 3725\nf 3620 3636 3621\nf 3621 3636 3635\nf 3847 3862 3848\nf 3848 3862 3752\nf 3531 3530 3848\nf 3848 3530 3845\nf 3933 3934 3830\nf 3830 3934 3831\nf 3925 3933 3837\nf 3837 3933 3830\nf 3934 440 3831\nf 3831 440 333\nf 3925 3802 3833\nf 3833 3802 3801\nf 3776 3844 3630\nf 3630 3844 3627\nf 3808 3659 3662\nf 3662 3659 3658\nf 3667 3650 3526\nf 3526 3650 3527\nf 3935 3666 3936\nf 3936 3666 3665\nf 3718 3737 3715\nf 3715 3737 3736\nf 3663 3809 3664\nf 3664 3809 3688\nf 3807 3806 3938\nf 3938 3806 3937\nf 445 413 3761\nf 3761 413 3760\nf 3902 447 3903\nf 3903 447 446\nf 3897 3890 3898\nf 3898 3890 3889\nf 3899 3891 3897\nf 3897 3891 3890\nf 3898 3889 3901\nf 3901 3889 3893\nf 3893 3894 3901\nf 3901 3894 3902\nf 414 386 3787\nf 3787 386 3765\nf 3940 3941 3939\nf 3939 3941 3942\nf 3840 3797 3943\nf 3943 3797 3727\nf 3665 3810 3936\nf 3936 3810 3944\nf 3921 3735 3726\nf 3726 3735 3723\nf 3920 3945 3770\nf 3770 3945 3922\nf 3521 3524 3774\nf 3774 3524 3778\nf 3864 3946 3625\nf 3625 3946 3947\nf 3773 3705 3649\nf 3649 3705 3519\nf 3779 3778 3758\nf 3758 3778 3524\nf 3812 3716 3736\nf 3736 3716 3715\nf 3624 3521 3777\nf 3777 3521 3774\nf 3703 3856 3704\nf 3704 3856 3622\nf 3900 3892 3899\nf 3899 3892 3891\nf 3641 3917 3642\nf 3642 3917 3948\nf 3895 3669 3882\nf 3882 3669 3668\nf 3855 3851 3852\nf 3852 3851 3850\nf 3894 458 3902\nf 3902 458 447\nf 3781 3887 3660\nf 3660 3887 3870\nf 3570 3566 3571\nf 3571 3566 3567\nf 3950 3576 3949\nf 3949 3576 3587\nf 3605 3602 3614\nf 3614 3602 3612\nf 3614 3612 3570\nf 3570 3612 3566\nf 3571 3567 3656\nf 3656 3567 3654\nf 3660 3658 3781\nf 3781 3658 3657\nf 3604 3607 3587\nf 3587 3607 3949\nf 3653 3659 3654\nf 3654 3659 3656\nf 3529 3540 3530\nf 3530 3540 3740\nf 3687 3811 3688\nf 3688 3811 3664\nf 3813 3725 3737\nf 3737 3725 3724\nf 3919 3815 461\nf 461 3815 303\nf 3818 3798 3819\nf 3819 3798 3797\nf 3819 3841 3820\nf 3820 3841 3951\nf 3816 3815 3738\nf 3738 3815 3814\nf 3540 3539 3533\nf 3533 3539 3549\nf 3548 3547 3544\nf 3544 3547 3541\nf 3539 3548 3549\nf 3549 3548 3544\nf 3741 3931 3860\nf 3860 3931 3846\nf 3801 3800 3835\nf 3835 3800 3836\nf 3934 3933 3824\nf 3824 3933 3821\nf 3825 3828 3546\nf 3546 3828 3547\nf 3833 3821 3925\nf 3925 3821 3933\nf 3800 3828 3836\nf 3836 3828 3825\nf 3824 3952 3934\nf 3934 3952 440\nf 3860 3846 3861\nf 3861 3846 3845\nf 3629 3946 3630\nf 3630 3946 3864\nf 3740 3536 3861\nf 3861 3536 3858\nf 3833 3801 3834\nf 3834 3801 3835\nf 3583 3599 3580\nf 3580 3599 3585\nf 3651 3563 3652\nf 3652 3563 3562\nf 3758 3909 3779\nf 3779 3909 3780\nf 3621 3617 3622\nf 3622 3617 3704\nf 3625 3947 3626\nf 3626 3947 3753\nf 3633 3632 3847\nf 3847 3632 3862\nf 3852 3850 3640\nf 3640 3850 3641\nf 3644 3853 3640\nf 3640 3853 3852\nf 3859 3742 3860\nf 3860 3742 3741\nf 3632 3627 3862\nf 3862 3627 3844\nf 3755 3863 3626\nf 3626 3863 3623\nf 3917 3638 3948\nf 3948 3638 3637\nf 3644 3643 3742\nf 3742 3643 3743\nf 3931 3633 3846\nf 3846 3633 3847\nf 3625 3624 3864\nf 3864 3624 3777\nf 3917 3641 3849\nf 3849 3641 3850\nf 3744 3932 3741\nf 3741 3932 3931\nf 3903 446 3918\nf 3918 446 464\nf 3953 3954 3956\nf 3956 3954 3955\nf 3958 3959 3957\nf 3957 3959 3960\nf 3876 3958 3877\nf 3877 3958 3957\nf 3877 3957 3961\nf 3961 3957 3962\nf 3963 3964 3958\nf 3958 3964 3959\nf 3965 3966 3876\nf 3876 3966 3958\nf 3966 3963 3958\nf 3966 3967 3963\nf 3963 3967 3968\nf 3878 3877 3879\nf 3879 3877 3961\nf 3967 3969 3968\nf 3968 3969 3970\nf 3969 3971 3970\nf 3970 3971 3972\nf 3974 3904 3973\nf 3973 3904 3907\nf 3971 3975 3972\nf 3972 3975 3976\nf 3976 3975 3978\nf 3978 3975 3977\nf 3979 3980 3977\nf 3977 3980 3978\nf 3981 3982 3979\nf 3979 3982 3980\nf 3983 3984 3981\nf 3981 3984 3982\nf 3972 3985 3970\nf 3970 3985 3986\nf 3871 3987 3969\nf 3969 3987 3971\nf 3872 3871 3967\nf 3967 3871 3969\nf 3965 3872 3966\nf 3966 3872 3967\nf 3970 3986 3968\nf 3968 3986 3988\nf 3879 3961 3884\nf 3884 3961 3940\nf 3884 3940 3885\nf 3885 3940 3939\nf 3989 3990 3992\nf 3992 3990 3991\nf 3942 3993 3995\nf 3995 3993 3994\nf 3994 3996 3995\nf 3995 3996 3974\nf 3997 3998 3994\nf 3994 3998 3996\nf 3999 3997 3993\nf 3993 3997 3994\nf 4001 3996 4000\nf 4000 3996 3998\nf 4000 3998 4002\nf 4002 3998 4003\nf 4004 4003 3997\nf 3997 4003 3998\nf 3997 3999 4004\nf 4004 3999 4005\nf 4006 3995 3973\nf 3973 3995 3974\nf 3939 3942 4006\nf 4006 3942 3995\nf 3906 3887 3907\nf 3907 3887 3886\nf 3887 3906 3870\nf 3870 3906 3867\nf 3908 3973 3886\nf 3886 3973 3907\nf 3865 4007 3866\nf 3866 4007 4008\nf 3984 3983 4009\nf 4009 3983 4010\nf 3961 3962 3940\nf 3940 3962 3941\nf 4012 3868 4011\nf 4011 3868 3867\nf 4013 3975 3987\nf 3987 3975 3971\nf 4014 3977 4013\nf 4013 3977 3975\nf 4015 4010 4007\nf 4007 4010 3983\nf 4007 3983 4008\nf 4008 3983 3981\nf 4008 3981 4016\nf 4016 3981 3979\nf 4017 3984 4018\nf 4018 3984 4009\nf 3977 4014 3979\nf 3979 4014 4016\nf 3982 3984 4019\nf 4019 3984 4017\nf 4021 4022 4020\nf 4020 4022 4023\nf 4022 4012 4023\nf 4023 4012 4011\nf 4023 3904 4020\nf 4020 3904 3974\nf 4024 4025 4018\nf 4018 4025 4017\nf 4026 4027 4024\nf 4024 4027 4025\nf 4028 4026 4029\nf 4029 4026 4024\nf 4018 4021 4024\nf 4024 4021 4029\nf 4030 4031 4026\nf 4026 4031 4027\nf 4032 4030 4028\nf 4028 4030 4026\nf 4029 4001 4028\nf 4028 4001 4000\nf 4028 4000 4032\nf 4032 4000 4002\nf 4034 4035 4033\nf 4033 4035 4036\nf 4037 4038 4019\nf 4019 4038 4039\nf 4041 4039 4040\nf 4040 4039 4038\nf 4040 4038 4042\nf 4042 4038 4036\nf 4042 4036 4043\nf 4043 4036 4035\nf 3985 4044 4046\nf 4046 4044 4045\nf 4047 4048 4045\nf 4045 4048 4046\nf 4047 4049 4048\nf 4048 4049 4050\nf 4042 4047 4040\nf 4040 4047 4045\nf 4043 4049 4042\nf 4042 4049 4047\nf 4040 4045 4041\nf 4041 4045 4044\nf 4052 3959 4051\nf 4051 3959 3964\nf 4053 4054 4051\nf 4051 4054 4055\nf 3956 3960 4052\nf 4052 3960 3959\nf 3953 3956 4056\nf 4056 3956 4052\nf 4058 3953 4057\nf 4057 3953 4056\nf 4060 4061 4059\nf 4059 4061 4062\nf 4063 4064 4054\nf 4054 4064 4057\nf 4054 4053 4063\nf 4063 4053 4065\nf 4066 4067 4069\nf 4069 4067 4068\nf 4048 4050 4063\nf 4063 4050 4064\nf 4063 4065 4048\nf 4048 4065 4046\nf 3985 4046 3986\nf 3986 4046 4065\nf 3986 4065 3988\nf 3988 4065 4053\nf 3988 4053 3964\nf 3964 4053 4051\nf 3976 3978 4044\nf 4044 3978 4041\nf 3980 3982 4039\nf 4039 3982 4019\nf 4039 4041 3980\nf 3980 4041 3978\nf 3973 3908 4006\nf 4006 3908 3896\nf 3885 3939 3896\nf 3896 3939 4006\nf 4031 4034 4027\nf 4027 4034 4033\nf 4027 4033 4025\nf 4025 4033 4037\nf 4019 4017 4037\nf 4037 4017 4025\nf 3993 3942 4070\nf 4070 3942 3941\nf 3999 3993 4071\nf 4071 3993 4070\nf 3999 4071 4005\nf 4005 4071 4072\nf 4073 4074 4076\nf 4076 4074 4075\nf 4078 4079 4077\nf 4077 4079 4080\nf 4082 4080 4081\nf 4081 4080 4079\nf 4084 4085 4083\nf 4083 4085 4086\nf 4087 4088 4090\nf 4090 4088 4089\nf 4092 4093 4091\nf 4091 4093 4094\nf 4096 4097 4095\nf 4095 4097 4098\nf 4100 4101 4099\nf 4099 4101 4102\nf 4103 4104 4106\nf 4106 4104 4105\nf 4108 4109 4107\nf 4107 4109 4110\nf 4112 4113 4111\nf 4111 4113 4114\nf 4116 4117 4115\nf 4115 4117 4118\nf 4120 4121 4119\nf 4119 4121 4122\nf 4123 4124 4126\nf 4126 4124 4125\nf 4127 4128 4130\nf 4130 4128 4129\nf 4132 4133 4131\nf 4131 4133 4134\nf 4136 4108 4135\nf 4135 4108 4107\nf 4138 4139 4137\nf 4137 4139 4140\nf 4141 4142 4088\nf 4088 4142 4089\nf 4144 4145 4143\nf 4143 4145 4146\nf 4148 4149 4147\nf 4147 4149 4150\nf 4152 4153 4151\nf 4151 4153 4154\nf 4156 4082 4155\nf 4155 4082 4081\nf 4157 4158 4160\nf 4160 4158 4159\nf 4161 4162 4164\nf 4164 4162 4163\nf 4166 4122 4165\nf 4165 4122 4121\nf 4168 4152 4167\nf 4167 4152 4151\nf 4169 4170 4172\nf 4172 4170 4171\nf 4174 4175 4173\nf 4173 4175 4176\nf 4178 4149 4177\nf 4177 4149 4179\nf 4173 4176 4180\nf 4180 4176 4181\nf 4156 4085 4182\nf 4182 4085 4183\nf 4182 4184 4156\nf 4156 4184 4082\nf 4185 4186 4184\nf 4184 4186 4187\nf 4186 4188 4187\nf 4187 4188 4189\nf 4190 4191 4073\nf 4073 4191 4074\nf 4177 4179 4158\nf 4158 4179 4159\nf 4193 4127 4192\nf 4192 4127 4130\nf 4144 4194 4151\nf 4151 4194 4167\nf 4196 4197 4195\nf 4195 4197 4198\nf 4095 4199 4096\nf 4096 4199 4200\nf 4201 4202 4204\nf 4204 4202 4203\nf 4205 4206 4208\nf 4208 4206 4207\nf 4121 4120 4209\nf 4209 4120 4210\nf 4212 4213 4211\nf 4211 4213 4214\nf 4216 4217 4215\nf 4215 4217 4218\nf 4220 4221 4219\nf 4219 4221 4222\nf 4224 4196 4223\nf 4223 4196 4195\nf 4225 4226 4134\nf 4134 4226 4131\nf 4228 4229 4227\nf 4227 4229 4230\nf 4232 4233 4231\nf 4231 4233 4098\nf 4235 4236 4234\nf 4234 4236 4237\nf 4239 4240 4238\nf 4238 4240 4241\nf 4242 4243 4245\nf 4245 4243 4244\nf 4246 4247 4249\nf 4249 4247 4248\nf 4251 4252 4250\nf 4250 4252 4253\nf 4255 4256 4254\nf 4254 4256 4257\nf 4259 4260 4258\nf 4258 4260 4261\nf 4263 4264 4262\nf 4262 4264 4265\nf 4267 4268 4266\nf 4266 4268 4269\nf 4271 4199 4270\nf 4270 4199 4272\nf 4273 4274 4276\nf 4276 4274 4275\nf 4115 4118 4277\nf 4277 4118 4278\nf 4280 4281 4279\nf 4279 4281 4282\nf 4211 4283 4212\nf 4212 4283 4284\nf 4286 4287 4285\nf 4285 4287 4288\nf 4165 4121 4289\nf 4289 4121 4209\nf 4291 4286 4290\nf 4290 4286 4285\nf 4293 4294 4292\nf 4292 4294 4295\nf 4297 4298 4296\nf 4296 4298 4299\nf 4301 4302 4300\nf 4300 4302 4303\nf 4299 4304 4296\nf 4296 4304 4305\nf 4306 4292 4307\nf 4307 4292 4295\nf 4307 4308 4306\nf 4306 4308 4309\nf 4310 4309 4075\nf 4075 4309 4308\nf 4299 4298 4312\nf 4312 4298 4311\nf 4302 4301 4293\nf 4293 4301 4294\nf 3735 3921 4313\nf 4313 3921 4314\nf 4315 4314 3922\nf 3922 4314 3921\nf 4316 3945 829\nf 829 3945 830\nf 4289 4317 4165\nf 4165 4317 4318\nf 4320 4321 4319\nf 4319 4321 4322\nf 4324 4325 4323\nf 4323 4325 4326\nf 4327 4328 4330\nf 4330 4328 4329\nf 4290 4331 4291\nf 4291 4331 4332\nf 4165 4318 4166\nf 4166 4318 4333\nf 4334 4335 4337\nf 4337 4335 4336\nf 4339 4340 4338\nf 4338 4340 4341\nf 4168 4343 4342\nf 4342 4343 4344\nf 4346 4347 4345\nf 4345 4347 4348\nf 4350 4351 4349\nf 4349 4351 4352\nf 4354 4355 4353\nf 4353 4355 4356\nf 4357 4358 4324\nf 4324 4358 4325\nf 4337 4359 4334\nf 4334 4359 4360\nf 4362 4363 4361\nf 4361 4363 4364\nf 4366 4367 4365\nf 4365 4367 4368\nf 4369 4370 4372\nf 4372 4370 4371\nf 4331 4290 4374\nf 4374 4290 4373\nf 4373 4376 4375\nf 4375 4376 4377\nf 4379 4380 4378\nf 4378 4380 4381\nf 4217 4382 4218\nf 4218 4382 4383\nf 4385 4386 4384\nf 4384 4386 4387\nf 4388 4389 4338\nf 4338 4389 4390\nf 4337 4336 4392\nf 4392 4336 4391\nf 4394 4362 4393\nf 4393 4362 4361\nf 4396 4397 4395\nf 4395 4397 4398\nf 4400 4401 4399\nf 4399 4401 4402\nf 4345 4403 4346\nf 4346 4403 4404\nf 4405 4406 4342\nf 4342 4406 4407\nf 4409 4410 4408\nf 4408 4410 4411\nf 4408 4412 4409\nf 4409 4412 4413\nf 4181 4415 4414\nf 4414 4415 4416\nf 4182 4183 4417\nf 4417 4183 4418\nf 4419 4353 4311\nf 4311 4353 4312\nf 4421 4422 4420\nf 4420 4422 4423\nf 4353 4356 4312\nf 4312 4356 4424\nf 4166 4333 4425\nf 4425 4333 4426\nf 4425 4143 4166\nf 4166 4143 4122\nf 4427 4107 4428\nf 4428 4107 4110\nf 4429 4430 4174\nf 4174 4430 4175\nf 4335 4431 4336\nf 4336 4431 4390\nf 4336 4390 4391\nf 4391 4390 4389\nf 4432 4433 4435\nf 4435 4433 4434\nf 4437 4438 4436\nf 4436 4438 4439\nf 4441 4442 4440\nf 4440 4442 4443\nf 4359 4444 4361\nf 4361 4444 4393\nf 4446 4447 4445\nf 4445 4447 4448\nf 4338 4341 4388\nf 4388 4341 4449\nf 4450 4451 4398\nf 4398 4451 4395\nf 4453 4454 4452\nf 4452 4454 4455\nf 4457 4458 4456\nf 4456 4458 4459\nf 4460 4461 4448\nf 4448 4461 4445\nf 4463 4464 4462\nf 4462 4464 4465\nf 4462 4465 4466\nf 4466 4465 4467\nf 4468 4469 4467\nf 4467 4469 4466\nf 4469 4468 4470\nf 4470 4468 4471\nf 4472 4473 4471\nf 4471 4473 4470\nf 4475 4476 4474\nf 4474 4476 4477\nf 4479 4480 4478\nf 4478 4480 4481\nf 4483 4484 4482\nf 4482 4484 4485\nf 4487 4488 4486\nf 4486 4488 4489\nf 4491 4492 4490\nf 4490 4492 4493\nf 4494 4495 4493\nf 4493 4495 4490\nf 4496 4497 4494\nf 4494 4497 4495\nf 4497 4496 4498\nf 4498 4496 4499\nf 4500 4501 4503\nf 4503 4501 4502\nf 4505 4506 4504\nf 4504 4506 4507\nf 4466 4508 4462\nf 4462 4508 4509\nf 4470 4510 4469\nf 4469 4510 4511\nf 4513 4479 4512\nf 4512 4479 4478\nf 4515 4443 4514\nf 4514 4443 4442\nf 4516 4517 4519\nf 4519 4517 4518\nf 4521 4495 4520\nf 4520 4495 4497\nf 4522 4490 4521\nf 4521 4490 4495\nf 4491 4523 4488\nf 4488 4523 4524\nf 4525 4489 4524\nf 4524 4489 4488\nf 4484 4526 4485\nf 4485 4526 4527\nf 4503 4528 4500\nf 4500 4528 4529\nf 4530 4531 4533\nf 4533 4531 4532\nf 4535 4506 4534\nf 4534 4506 4530\nf 4486 4536 4487\nf 4487 4536 4537\nf 4487 4537 4492\nf 4492 4537 4538\nf 4494 4539 4496\nf 4496 4539 4540\nf 4542 4533 4541\nf 4541 4533 4499\nf 4544 4545 4543\nf 4543 4545 4546\nf 4548 4549 4547\nf 4547 4549 4550\nf 4536 4551 4537\nf 4537 4551 4552\nf 4537 4552 4538\nf 4538 4552 4553\nf 4554 4539 4556\nf 4556 4539 4555\nf 4557 4540 4554\nf 4554 4540 4539\nf 4559 4557 4558\nf 4558 4557 4560\nf 4540 4557 4541\nf 4541 4557 4559\nf 4562 4563 4561\nf 4561 4563 4564\nf 4546 4565 4543\nf 4543 4565 4566\nf 4547 4567 4548\nf 4548 4567 4568\nf 4570 4571 4569\nf 4569 4571 4572\nf 4574 4575 4573\nf 4573 4575 4576\nf 4578 4579 4577\nf 4577 4579 4580\nf 4582 4583 4581\nf 4581 4583 4584\nf 4585 4586 4588\nf 4588 4586 4587\nf 4589 4585 4590\nf 4590 4585 4588\nf 4592 4593 4591\nf 4591 4593 4594\nf 4596 4597 4595\nf 4595 4597 4598\nf 4600 4601 4599\nf 4599 4601 4602\nf 4563 4604 4603\nf 4603 4604 4605\nf 4603 4606 4608\nf 4608 4606 4607\nf 4609 4610 4594\nf 4594 4610 4591\nf 4611 4612 4614\nf 4614 4612 4613\nf 4616 4617 4615\nf 4615 4617 4618\nf 4602 4620 4619\nf 4619 4620 4621\nf 4619 4622 4602\nf 4602 4622 4599\nf 4605 4599 4623\nf 4623 4599 4622\nf 4605 4623 4603\nf 4603 4623 4606\nf 4625 4626 4624\nf 4624 4626 4627\nf 4438 4437 4628\nf 4628 4437 4629\nf 4442 4441 4630\nf 4630 4441 4631\nf 4630 4632 4442\nf 4442 4632 4514\nf 4633 4634 4529\nf 4529 4634 4500\nf 4500 4634 4501\nf 4501 4634 4635\nf 4637 4546 4636\nf 4636 4546 4545\nf 4639 4565 4638\nf 4638 4565 4640\nf 4573 4576 4641\nf 4641 4576 4642\nf 4593 4643 4594\nf 4594 4643 4644\nf 4645 4609 4644\nf 4644 4609 4594\nf 4647 4648 4646\nf 4646 4648 4649\nf 4651 4652 4650\nf 4650 4652 4653\nf 4654 4498 4519\nf 4519 4498 4532\nf 4520 4508 4521\nf 4521 4508 4511\nf 4522 4510 4523\nf 4523 4510 4655\nf 4524 4656 4525\nf 4525 4656 4657\nf 4514 4529 4515\nf 4515 4529 4528\nf 4658 4659 4661\nf 4661 4659 4660\nf 4496 4540 4499\nf 4499 4540 4541\nf 4493 4555 4494\nf 4494 4555 4539\nf 4549 4483 4550\nf 4550 4483 4482\nf 4545 4544 4501\nf 4501 4544 4502\nf 4636 4545 4635\nf 4635 4545 4501\nf 4589 4663 4662\nf 4662 4663 4664\nf 4662 4665 4558\nf 4558 4665 4559\nf 4569 4586 4570\nf 4570 4586 4560\nf 4552 4666 4553\nf 4553 4666 4667\nf 4668 4669 4670\nf 4670 4669 4666\nf 4672 4673 4671\nf 4671 4673 4674\nf 4287 4675 4288\nf 4288 4675 4676\nf 4678 4679 4677\nf 4677 4679 4680\nf 4681 4678 4682\nf 4682 4678 4677\nf 4682 4683 4681\nf 4681 4683 4684\nf 4161 4164 4685\nf 4685 4164 4686\nf 4414 4687 4689\nf 4689 4687 4688\nf 4405 4690 4346\nf 4346 4690 4347\nf 4691 4399 4693\nf 4693 4399 4692\nf 1209 4694 4365\nf 4365 4694 4366\nf 4650 1211 4651\nf 4651 1211 1210\nf 4695 4696 4255\nf 4255 4696 4256\nf 4697 4698 4218\nf 4218 4698 4215\nf 4700 4701 4699\nf 4699 4701 4702\nf 4703 4704 4303\nf 4303 4704 4300\nf 4315 3922 4316\nf 4316 3922 3945\nf 4653 4706 4705\nf 4705 4706 4707\nf 4709 4710 4708\nf 4708 4710 4711\nf 4335 4334 4713\nf 4713 4334 4712\nf 4714 4340 4321\nf 4321 4340 4322\nf 4716 4717 4715\nf 4715 4717 4718\nf 4238 4241 4719\nf 4719 4241 4720\nf 4231 4721 4307\nf 4307 4721 4308\nf 4723 4724 4722\nf 4722 4724 4725\nf 4727 4728 4726\nf 4726 4728 4729\nf 4730 4731 4327\nf 4327 4731 4328\nf 4733 4734 4732\nf 4732 4734 4735\nf 4720 4381 4736\nf 4736 4381 4737\nf 4738 4739 4741\nf 4741 4739 4740\nf 4743 4744 4742\nf 4742 4744 4745\nf 4338 4390 4339\nf 4339 4390 4431\nf 4746 4747 4431\nf 4431 4747 4339\nf 4291 4332 4289\nf 4289 4332 4317\nf 4289 4209 4291\nf 4291 4209 4286\nf 4287 4286 4210\nf 4210 4286 4209\nf 4749 4750 4748\nf 4748 4750 4751\nf 4212 4284 4752\nf 4752 4284 4753\nf 4752 4754 4212\nf 4212 4754 4213\nf 4252 4251 4225\nf 4225 4251 4226\nf 4227 4230 4248\nf 4248 4230 4249\nf 4205 4755 4757\nf 4757 4755 4756\nf 4708 4711 4758\nf 4758 4711 4647\nf 4367 4366 4759\nf 4759 4366 4760\nf 4526 4513 4527\nf 4527 4513 4512\nf 4420 4423 4385\nf 4385 4423 4386\nf 4705 4761 4653\nf 4653 4761 4650\nf 4761 1279 4650\nf 4650 1279 1211\nf 4762 4763 3944\nf 3944 4763 3936\nf 4274 4273 4765\nf 4765 4273 4764\nf 4767 4768 4766\nf 4766 4768 4769\nf 4770 4771 4769\nf 4769 4771 4766\nf 4771 4770 4242\nf 4242 4770 4243\nf 4093 4163 4094\nf 4094 4163 4162\nf 4772 4773 4775\nf 4775 4773 4774\nf 4777 4778 4776\nf 4776 4778 4779\nf 4417 4185 4182\nf 4182 4185 4184\nf 4780 4078 4781\nf 4781 4078 4077\nf 4781 4077 4782\nf 4782 4077 4189\nf 4188 4783 4189\nf 4189 4783 4782\nf 4295 4232 4307\nf 4307 4232 4231\nf 4785 4786 4784\nf 4784 4786 4787\nf 4784 1307 4785\nf 4785 1307 1306\nf 4078 4780 4096\nf 4096 4780 4097\nf 4075 4074 4310\nf 4310 4074 4788\nf 4788 4074 4789\nf 4789 4074 4191\nf 4789 4191 4059\nf 4059 4191 4790\nf 4791 4277 4792\nf 4792 4277 4278\nf 3732 3735 4793\nf 4793 3735 4313\nf 4793 4794 3732\nf 3732 4794 3731\nf 4792 4795 4791\nf 4791 4795 4796\nf 4797 4798 4799\nf 4799 4798 4724\nf 4763 4800 3936\nf 3936 4800 3935\nf 4801 4062 3729\nf 3729 4062 3730\nf 4713 4712 4802\nf 4802 4712 4803\nf 4146 4804 4119\nf 4119 4804 4805\nf 4498 4654 4497\nf 4497 4654 4520\nf 4806 4807 1326\nf 1326 4807 1327\nf 4689 4688 1330\nf 1330 4688 1331\nf 4801 4789 4062\nf 4062 4789 4059\nf 3730 4062 4808\nf 4808 4062 4061\nf 4810 4811 4809\nf 4809 4811 4812\nf 4813 4403 4814\nf 4814 4403 4345\nf 4815 4816 4349\nf 4349 4816 4350\nf 4817 4818 4820\nf 4820 4818 4819\nf 4406 4821 4407\nf 4407 4821 4822\nf 4823 4161 4824\nf 4824 4161 4685\nf 4181 4414 4825\nf 4825 4414 4689\nf 4741 4740 4826\nf 4826 4740 4827\nf 4828 4829 4827\nf 4827 4829 4826\nf 4142 4141 4415\nf 4415 4141 4148\nf 4830 1354 4761\nf 4761 1354 1279\nf 4832 4833 4831\nf 4831 4833 4834\nf 4835 4836 4626\nf 4626 4836 4421\nf 4451 4709 4395\nf 4395 4709 4708\nf 4627 4420 4837\nf 4837 4420 4385\nf 4837 1365 4838\nf 4838 1365 1364\nf 4397 4396 4839\nf 4839 4396 4840\nf 4842 4628 4841\nf 4841 4628 4629\nf 4671 4674 4631\nf 4631 4674 4630\nf 4674 4659 4630\nf 4630 4659 4632\nf 4633 4632 4658\nf 4658 4632 4659\nf 4658 4504 4633\nf 4633 4504 4634\nf 4635 4634 4507\nf 4507 4634 4504\nf 4843 4636 4507\nf 4507 4636 4635\nf 4844 4637 4843\nf 4843 4637 4636\nf 4637 4844 4640\nf 4640 4844 4664\nf 4638 4663 4642\nf 4642 4663 4845\nf 4847 4643 4846\nf 4846 4643 4641\nf 4644 4848 4645\nf 4645 4848 4849\nf 4851 4852 4850\nf 4850 4852 4853\nf 4855 4856 4854\nf 4854 4856 4857\nf 4859 4860 4858\nf 4858 4860 4857\nf 4715 4718 4859\nf 4859 4718 4860\nf 4862 4863 4861\nf 4861 4863 4864\nf 4866 4867 4865\nf 4865 4867 4868\nf 4870 4871 4869\nf 4869 4871 4872\nf 4869 4872 4116\nf 4116 4872 4117\nf 4820 4819 4874\nf 4874 4819 4873\nf 4876 4877 4875\nf 4875 4877 4878\nf 4880 4881 4879\nf 4879 4881 4882\nf 4884 4885 4883\nf 4883 4885 4886\nf 4887 4888 4280\nf 4280 4888 4281\nf 4818 4817 4889\nf 4889 4817 4890\nf 4891 4193 4823\nf 4823 4193 4192\nf 4892 4893 4228\nf 4228 4893 4229\nf 4158 4157 4894\nf 4894 4157 4895\nf 4896 4897 4132\nf 4132 4897 4133\nf 4099 4683 4100\nf 4100 4683 4682\nf 4101 4100 4899\nf 4899 4100 4898\nf 4150 4106 4147\nf 4147 4106 4900\nf 4100 4682 4898\nf 4898 4682 4677\nf 4080 4082 4187\nf 4187 4082 4184\nf 4077 4080 4189\nf 4189 4080 4187\nf 4191 4190 4790\nf 4790 4190 4901\nf 4177 4158 4680\nf 4680 4158 4894\nf 4108 4136 4752\nf 4752 4136 4754\nf 4752 4753 4108\nf 4108 4753 4109\nf 4817 4902 4890\nf 4890 4902 4903\nf 4904 4905 4907\nf 4907 4905 4906\nf 4909 4910 4908\nf 4908 4910 4911\nf 4913 4914 4912\nf 4912 4914 4915\nf 4916 4917 4919\nf 4919 4917 4918\nf 4312 4424 4299\nf 4299 4424 4304\nf 4920 4921 4297\nf 4297 4921 4298\nf 4194 4922 4167\nf 4167 4922 4923\nf 4924 4400 4691\nf 4691 4400 4399\nf 4395 4708 4396\nf 4396 4708 4758\nf 4367 4925 4368\nf 4368 4925 4926\nf 4919 4918 4873\nf 4873 4918 4874\nf 4836 4927 4421\nf 4421 4927 4422\nf 4921 4928 4298\nf 4298 4928 4311\nf 4143 4146 4122\nf 4122 4146 4119\nf 4929 4930 4455\nf 4455 4930 4452\nf 4932 4933 4931\nf 4931 4933 4934\nf 4935 4936 4932\nf 4932 4936 4933\nf 4461 4460 4457\nf 4457 4460 4458\nf 4480 4479 4475\nf 4475 4479 4476\nf 4660 4673 4517\nf 4517 4673 4937\nf 4661 4505 4658\nf 4658 4505 4504\nf 4484 4483 4489\nf 4489 4483 4486\nf 4462 4509 4463\nf 4463 4509 4518\nf 4469 4511 4466\nf 4466 4511 4508\nf 4655 4473 4656\nf 4656 4473 4477\nf 4476 4657 4477\nf 4477 4657 4656\nf 4479 4513 4476\nf 4476 4513 4657\nf 4489 4525 4484\nf 4484 4525 4526\nf 4483 4549 4486\nf 4486 4549 4536\nf 4549 4548 4536\nf 4536 4548 4551\nf 4548 4568 4551\nf 4551 4568 4670\nf 4663 4589 4845\nf 4845 4589 4590\nf 4668 4577 4938\nf 4938 4577 4580\nf 4940 4938 4939\nf 4939 4938 4941\nf 4940 4942 4669\nf 4669 4942 4943\nf 4587 4582 4944\nf 4944 4582 4581\nf 4848 4847 4608\nf 4608 4847 4564\nf 4616 4945 4613\nf 4613 4945 4598\nf 4615 4946 4616\nf 4616 4946 4945\nf 4947 4939 4948\nf 4948 4939 4946\nf 4599 4605 4600\nf 4600 4605 4604\nf 4608 4607 4848\nf 4848 4607 4849\nf 4613 4612 4616\nf 4616 4612 4617\nf 4835 4949 4709\nf 4709 4949 4710\nf 4357 1477 4837\nf 4837 1477 1365\nf 4712 4950 4803\nf 4803 4950 4951\nf 4401 4952 4402\nf 4402 4952 4953\nf 4759 4760 4402\nf 4402 4760 4399\nf 4508 4520 4509\nf 4509 4520 4654\nf 4510 4522 4511\nf 4511 4522 4521\nf 4513 4526 4657\nf 4657 4526 4525\nf 4632 4633 4514\nf 4514 4633 4529\nf 4931 4934 4453\nf 4453 4934 4454\nf 4153 4954 4154\nf 4154 4954 4955\nf 4957 4958 4956\nf 4956 4958 4959\nf 4961 4962 4960\nf 4960 4962 4963\nf 4120 4964 4210\nf 4210 4964 4965\nf 4967 4968 4966\nf 4966 4968 4969\nf 4349 4692 4815\nf 4815 4692 4970\nf 4652 4971 4653\nf 4653 4971 4706\nf 4758 4647 4972\nf 4972 4647 4646\nf 4973 4363 4646\nf 4646 4363 4972\nf 4950 4360 4974\nf 4974 4360 4364\nf 4431 4335 4746\nf 4746 4335 4713\nf 4358 4714 4325\nf 4325 4714 4321\nf 4765 4764 4266\nf 4266 4764 4975\nf 4202 4976 4203\nf 4203 4976 4977\nf 4917 4978 4918\nf 4918 4978 4979\nf 4279 4282 4854\nf 4854 4282 4269\nf 4820 4880 4817\nf 4817 4880 4902\nf 4890 4903 4980\nf 4980 4903 4886\nf 4982 4983 4981\nf 4981 4983 4123\nf 4795 4851 4796\nf 4796 4851 4850\nf 4918 4979 4874\nf 4874 4979 4881\nf 4809 4404 4810\nf 4810 4404 4403\nf 4354 4985 4984\nf 4984 4985 4986\nf 1364 1516 4838\nf 4838 1516 4987\nf 4759 4988 4367\nf 4367 4988 4925\nf 4989 4990 4481\nf 4481 4990 4478\nf 4443 4515 4990\nf 4990 4515 4991\nf 4527 4512 4992\nf 4992 4512 4991\nf 4528 4503 4992\nf 4992 4503 4993\nf 4993 4994 4485\nf 4485 4994 4482\nf 4544 4995 4502\nf 4502 4995 4994\nf 4995 4996 4550\nf 4550 4996 4547\nf 4998 4999 4997\nf 4997 4999 4566\nf 4998 5000 4578\nf 4578 5000 4579\nf 5001 5002 4596\nf 4596 5002 4597\nf 5003 4591 5004\nf 5004 4591 4610\nf 4626 4421 4627\nf 4627 4421 4420\nf 4837 4838 4627\nf 4627 4838 4624\nf 4761 4705 4830\nf 4830 4705 5005\nf 4705 4707 5005\nf 5005 4707 5006\nf 4308 4721 4075\nf 4075 4721 4076\nf 5007 4200 4797\nf 4797 4200 4798\nf 4685 4686 5008\nf 5008 4686 5009\nf 4889 4890 5010\nf 5010 4890 4980\nf 4323 1541 4324\nf 4324 1541 1540\nf 4385 4384 4358\nf 4358 4384 4714\nf 4396 4758 4840\nf 4840 4758 4972\nf 4866 4852 5011\nf 5011 4852 5012\nf 5013 4260 4980\nf 4980 4260 5010\nf 4875 4878 5014\nf 5014 4878 5015\nf 5017 5018 5016\nf 5016 5018 5019\nf 5016 5019 5020\nf 5020 5019 5021\nf 5023 5024 5022\nf 5022 5024 5025\nf 5027 5028 5026\nf 5026 5028 5029\nf 5031 5032 5030\nf 5030 5032 5033\nf 5035 5036 5034\nf 5034 5036 5037\nf 5039 5040 5038\nf 5038 5040 5041\nf 5043 5044 5042\nf 5042 5044 5045\nf 5038 5046 5039\nf 5039 5046 5047\nf 5049 5050 5048\nf 5048 5050 5051\nf 5053 5054 5052\nf 5052 5054 5055\nf 5057 5024 5056\nf 5056 5024 5058\nf 5060 5061 5059\nf 5059 5061 5062\nf 5064 5065 5063\nf 5063 5065 5066\nf 5043 5050 5064\nf 5064 5050 5065\nf 5038 5041 5067\nf 5067 5041 5068\nf 5070 5071 5069\nf 5069 5071 5072\nf 5074 5075 5073\nf 5073 5075 5076\nf 5034 5077 5035\nf 5035 5077 5078\nf 5080 5081 5079\nf 5079 5081 5082\nf 5084 5085 5083\nf 5083 5085 5086\nf 5085 5084 5087\nf 5087 5084 5088\nf 5090 5091 5089\nf 5089 5091 5092\nf 5094 5095 5093\nf 5093 5095 5096\nf 5098 5099 5097\nf 5097 5099 5100\nf 5102 5103 5101\nf 5101 5103 5104\nf 5105 5106 5108\nf 5108 5106 5107\nf 5110 5111 5109\nf 5109 5111 5112\nf 5114 5094 5113\nf 5113 5094 5093\nf 5116 5117 5115\nf 5115 5117 5118\nf 5120 5121 5119\nf 5119 5121 5122\nf 5124 5125 5123\nf 5123 5125 5126\nf 5128 5129 5127\nf 5127 5129 5130\nf 5097 5131 5098\nf 5098 5131 5132\nf 5134 5135 5133\nf 5133 5135 5122\nf 5084 5125 5088\nf 5088 5125 5136\nf 5138 5139 5137\nf 5137 5139 5140\nf 5142 5102 5141\nf 5141 5102 5101\nf 5106 5143 5107\nf 5107 5143 5144\nf 5146 5147 5145\nf 5145 5147 5148\nf 5149 5150 5109\nf 5109 5150 5110\nf 5151 5110 5152\nf 5152 5110 5150\nf 4715 5140 4716\nf 4716 5140 5139\nf 5143 4870 5144\nf 5144 4870 4869\nf 4115 5146 4116\nf 4116 5146 5145\nf 4791 4796 5149\nf 5149 4796 5150\nf 4796 4850 5150\nf 5150 4850 5152\nf 4858 5142 4859\nf 4859 5142 5141\nf 5153 5154 5115\nf 5115 5154 5105\nf 5156 5157 5155\nf 5155 5157 5158\nf 5159 5160 5053\nf 5053 5160 5054\nf 5161 5162 5020\nf 5020 5162 5068\nf 5026 5029 5163\nf 5163 5029 5021\nf 5083 5086 5164\nf 5164 5086 5165\nf 5090 5167 5166\nf 5166 5167 5168\nf 5169 5170 5172\nf 5172 5170 5171\nf 5174 5175 5173\nf 5173 5175 5176\nf 5177 5109 5178\nf 5178 5109 5112\nf 5179 5180 5182\nf 5182 5180 5181\nf 5123 5126 5120\nf 5120 5126 5121\nf 5180 5167 5181\nf 5181 5167 5183\nf 5184 5185 5179\nf 5179 5185 5180\nf 5186 5149 5177\nf 5177 5149 5109\nf 4868 5184 4865\nf 4865 5184 5179\nf 5188 5189 5187\nf 5187 5189 5190\nf 4277 4791 5186\nf 5186 4791 5149\nf 5179 5182 4865\nf 4865 5182 4853\nf 5189 4862 5190\nf 5190 4862 4861\nf 5156 5075 5191\nf 5191 5075 5192\nf 5036 5035 5193\nf 5193 5035 5194\nf 5115 5118 5153\nf 5153 5118 5195\nf 5022 5025 5196\nf 5196 5025 5197\nf 5155 5158 5069\nf 5069 5158 5062\nf 5063 5066 5198\nf 5198 5066 5199\nf 5073 5076 5200\nf 5200 5076 5072\nf 5202 5131 5201\nf 5201 5131 5203\nf 5143 5106 5204\nf 5204 5106 5205\nf 5114 5113 5206\nf 5206 5113 5127\nf 5098 5132 5193\nf 5193 5132 5195\nf 5204 5207 5143\nf 5143 5207 4870\nf 5088 5136 5208\nf 5208 5136 5092\nf 5209 5210 5089\nf 5089 5210 5183\nf 5211 5018 5060\nf 5060 5018 5061\nf 5017 5040 5070\nf 5070 5040 5071\nf 5191 5160 5156\nf 5156 5160 5157\nf 5048 5028 5049\nf 5049 5028 5058\nf 5207 5204 4858\nf 4858 5204 5142\nf 4859 5141 4715\nf 4715 5141 5140\nf 5212 5162 5042\nf 5042 5162 5051\nf 5182 5152 4853\nf 4853 5152 4850\nf 5214 5134 5213\nf 5213 5134 5133\nf 5164 5165 5133\nf 5133 5165 5213\nf 5215 5216 5201\nf 5201 5216 5217\nf 5147 5218 5148\nf 5148 5218 5219\nf 5147 5177 5218\nf 5218 5177 5178\nf 5141 5101 5140\nf 5140 5101 5137\nf 5201 5217 5202\nf 5202 5217 5104\nf 5146 5186 5147\nf 5147 5186 5177\nf 4277 5186 4115\nf 4115 5186 5146\nf 5191 5192 5031\nf 5031 5192 5032\nf 5052 5055 5220\nf 5220 5055 5197\nf 4116 5145 4869\nf 4869 5145 5144\nf 5034 5037 5214\nf 5214 5037 5134\nf 5097 5100 5114\nf 5114 5100 5094\nf 5108 5107 5219\nf 5219 5107 5148\nf 5221 5135 5116\nf 5116 5135 5117\nf 5124 5111 5209\nf 5209 5111 5210\nf 5107 5144 5148\nf 5148 5144 5145\nf 5152 5182 5151\nf 5151 5182 5181\nf 5208 5222 5088\nf 5088 5222 5087\nf 5222 5208 5171\nf 5171 5208 5172\nf 5223 1756 4698\nf 4698 1756 1755\nf 4427 5224 4107\nf 4107 5224 4135\nf 4178 4679 5225\nf 5225 4679 5226\nf 5227 5228 4892\nf 4892 5228 4893\nf 5229 5230 4171\nf 4171 5230 4172\nf 4855 4268 4884\nf 4884 4268 4885\nf 4884 4871 4855\nf 4855 4871 4856\nf 4684 5231 4896\nf 4896 5231 4897\nf 4768 4767 5009\nf 5009 4767 5008\nf 5205 5154 5102\nf 5102 5154 5103\nf 5099 5098 5194\nf 5194 5098 5193\nf 5198 5199 5079\nf 5079 5199 5033\nf 5102 5142 5205\nf 5205 5142 5204\nf 5224 4427 4681\nf 4681 4427 4678\nf 3712 3711 5232\nf 5232 3711 5233\nf 3968 3988 3963\nf 3963 3988 3964\nf 3976 4044 3972\nf 3972 4044 3985\nf 4038 4037 4036\nf 4036 4037 4033\nf 4794 4762 3731\nf 3731 4762 3944\nf 3731 3944 3722\nf 3722 3944 3810\nf 3874 5234 5232\nf 5232 5234 3712\nf 3906 3905 3867\nf 3867 3905 4011\nf 3904 4023 3905\nf 3905 4023 4011\nf 3719 3722 3811\nf 3811 3722 3810\nf 3812 3719 3687\nf 3687 3719 3811\nf 3716 3812 3686\nf 3686 3812 3687\nf 3713 3716 3685\nf 3685 3716 3686\nf 4810 4403 5235\nf 5235 4403 4813\nf 4345 1770 4814\nf 4814 1770 1769\nf 4067 4066 4058\nf 4058 4066 5236\nf 4043 4068 4049\nf 4049 4068 4050\nf 4034 5237 3989\nf 3989 5237 3990\nf 4031 4030 4072\nf 4072 4030 4005\nf 4003 4004 4002\nf 4002 4004 4032\nf 4004 4005 4032\nf 4032 4005 4030\nf 4067 4064 4068\nf 4068 4064 4050\nf 4058 4057 4067\nf 4067 4057 4064\nf 4035 4034 5238\nf 5238 4034 3989\nf 5236 5239 4058\nf 4058 5239 5240\nf 5234 3874 5241\nf 5241 3874 3873\nf 5241 3873 3881\nf 3881 3873 3878\nf 4800 4763 4788\nf 4788 4763 4310\nf 4763 4762 4310\nf 4310 4762 4309\nf 4762 4794 4309\nf 4309 4794 4306\nf 4794 4793 4306\nf 4306 4793 4292\nf 4293 4313 4302\nf 4302 4313 4314\nf 4302 4314 4303\nf 4303 4314 4315\nf 4303 4315 4703\nf 4703 4315 4316\nf 4035 4069 4043\nf 4043 4069 4068\nf 4324 1540 4357\nf 4357 1540 1477\nf 5232 3987 3874\nf 3874 3987 3871\nf 3987 5232 4013\nf 4013 5232 5233\nf 4013 5233 4014\nf 4014 5233 5242\nf 3888 4015 3865\nf 3865 4015 4007\nf 3866 4008 5243\nf 5243 4008 4016\nf 3873 5244 3878\nf 3878 5244 3875\nf 3965 5244 3872\nf 3872 5244 3873\nf 3876 3875 3965\nf 3965 3875 5244\nf 5243 4016 5242\nf 5242 4016 4014\nf 5246 5247 5245\nf 5245 5247 5248\nf 3576 3950 3577\nf 3577 3950 3926\nf 3584 3842 3585\nf 3585 3842 3580\nf 5249 5247 1784\nf 1784 5247 1785\nf 5250 5251 5253\nf 5253 5251 5252\nf 5255 5252 5254\nf 5254 5252 5256\nf 5254 5256 5258\nf 5258 5256 5257\nf 5258 5257 5260\nf 5260 5257 5259\nf 4356 5261 4424\nf 4424 5261 5259\nf 4355 5262 4356\nf 4356 5262 5261\nf 4924 5264 5263\nf 5263 5264 5265\nf 4693 5266 4691\nf 4691 5266 5267\nf 4351 5268 4352\nf 4352 5268 5269\nf 1808 1807 4351\nf 4351 1807 5268\nf 3580 3842 3581\nf 3799 3802 3838\nf 4800 4788 4801\nf 4801 4788 4789\nf 3729 3935 4801\nf 4801 3935 4800\nf 3728 3666 3729\nf 3729 3666 3935\nf 3728 3798 3666\nf 3666 3798 3663\nf 3798 3818 3663\nf 3663 3818 3809\nf 3818 3817 3809\nf 3809 3817 3808\nf 3796 3655 3817\nf 3817 3655 3808\nf 3615 3568 3796\nf 3796 3568 3655\nf 3573 3569 3615\nf 3615 3569 3568\nf 3572 3613 3573\nf 3573 3613 3569\nf 3792 3606 3572\nf 3572 3606 3613\nf 3607 3791 3949\nf 3949 3791 3591\nf 3590 3950 3591\nf 3591 3950 3949\nf 3805 3926 3590\nf 3590 3926 3950\nf 3804 3559 3805\nf 3805 3559 3926\nf 3804 3914 3559\nf 3559 3914 3560\nf 3924 3789 3913\nf 3913 3789 3788\nf 3924 3928 3789\nf 3789 3928 3826\nf 3928 3927 3826\nf 3826 3927 3827\nf 3927 3834 3827\nf 3827 3834 3835\nf 4079 4078 5270\nf 5270 4078 5007\nf 5271 5272 4233\nf 4233 5272 5273\nf 5275 5276 5274\nf 5274 5276 5277\nf 5278 5279 4236\nf 4236 5279 4237\nf 5278 5280 5271\nf 5271 5280 5272\nf 4235 5281 4236\nf 4236 5281 5282\nf 5284 5285 5283\nf 5283 5285 5286\nf 5288 5289 5287\nf 5287 5289 5290\nf 5292 5293 5291\nf 5291 5293 5294\nf 5287 5290 4726\nf 4726 5290 5295\nf 4293 4292 4313\nf 4313 4292 4793\nf 4720 4736 4719\nf 4719 4736 5296\nf 5297 4909 5298\nf 5298 4909 4908\nf 5299 5300 4976\nf 4976 5300 5301\nf 5302 4202 5303\nf 5303 4202 4201\nf 5304 5305 4983\nf 4983 5305 5306\nf 5308 5309 5307\nf 5307 5309 5310\nf 4722 4725 5283\nf 5283 4725 5311\nf 5291 5294 4125\nf 4125 5294 4126\nf 5313 5314 5312\nf 5312 5314 5315\nf 5316 5317 5319\nf 5319 5317 5318\nf 5321 5308 5320\nf 5320 5308 5307\nf 4819 4981 4873\nf 4873 4981 5322\nf 4981 4123 5322\nf 5322 4123 4126\nf 5324 5325 5323\nf 5323 5325 5326\nf 4764 5327 4975\nf 4975 5327 5328\nf 4172 5327 4169\nf 4169 5327 5329\nf 4768 4171 4769\nf 4769 4171 4170\nf 4243 4755 4244\nf 4244 4755 5330\nf 4755 4205 5330\nf 5330 4205 4208\nf 4206 4906 4207\nf 4207 4906 4905\nf 4686 5014 5009\nf 5009 5014 5229\nf 4756 4770 4170\nf 4170 4770 4769\nf 4170 4169 4756\nf 4756 4169 4757\nf 4273 5329 4764\nf 4764 5329 5327\nf 4276 5331 4273\nf 4273 5331 5329\nf 4770 4756 4243\nf 4243 4756 4755\nf 5333 4889 5332\nf 5332 4889 5010\nf 4877 5332 4878\nf 4878 5332 4259\nf 4875 4164 4876\nf 4876 4164 4163\nf 5334 5335 5324\nf 5324 5335 5325\nf 4983 5306 4123\nf 4123 5306 4124\nf 4818 4982 4819\nf 4819 4982 4981\nf 5332 5010 4259\nf 4259 5010 4260\nf 5014 4686 4875\nf 4875 4686 4164\nf 4171 4768 5229\nf 5229 4768 5009\nf 5337 4916 5336\nf 5336 4916 4919\nf 5293 5337 5294\nf 5294 5337 5336\nf 4982 4818 5333\nf 5333 4818 4889\nf 5338 5339 4262\nf 4262 5339 5340\nf 5307 5310 5338\nf 5338 5310 5339\nf 5341 5342 4204\nf 4204 5342 5309\nf 5336 4919 5322\nf 5322 4919 4873\nf 5294 5336 4126\nf 4126 5336 5322\nf 5323 5326 5343\nf 5343 5326 5286\nf 4914 5344 4915\nf 4915 5344 5345\nf 5312 5315 5344\nf 5344 5315 5345\nf 5347 5348 5346\nf 5346 5348 5349\nf 5350 5351 4832\nf 4832 5351 4833\nf 5352 5353 5349\nf 5349 5353 5346\nf 5354 5338 4265\nf 4265 5338 4262\nf 5320 5307 5354\nf 5354 5307 5338\nf 4201 5308 5303\nf 5303 5308 5321\nf 5356 5357 5355\nf 5355 5357 4799\nf 5358 5359 5334\nf 5334 5359 5335\nf 5358 5360 4093\nf 4093 5360 4163\nf 5360 5305 4163\nf 4163 5305 4876\nf 5305 5304 4876\nf 4876 5304 4877\nf 5270 4778 4079\nf 4079 4778 4081\nf 4779 5361 4774\nf 4774 5361 5357\nf 5356 5359 4775\nf 4775 5359 4092\nf 5333 5304 4982\nf 4982 5304 4983\nf 5332 4877 5333\nf 5333 4877 5304\nf 5015 4258 5230\nf 5230 4258 5328\nf 5327 4172 5328\nf 5328 4172 5230\nf 4169 5329 4757\nf 4757 5329 5331\nf 4757 5331 4205\nf 4205 5331 4206\nf 5331 4276 4206\nf 4206 4276 4906\nf 4276 4275 4906\nf 4906 4275 4907\nf 4275 4888 4907\nf 4907 4888 4744\nf 4887 4717 4745\nf 4745 4717 4864\nf 4716 5139 4861\nf 4861 5139 5190\nf 5139 5138 5190\nf 5190 5138 5187\nf 5138 5216 5187\nf 5187 5216 5176\nf 5215 5362 5173\nf 5173 5362 5130\nf 5364 5365 5363\nf 5363 5365 5340\nf 4742 4863 5366\nf 5366 4863 4867\nf 4862 5189 4868\nf 4868 5189 5184\nf 5189 5188 5184\nf 5184 5188 5185\nf 5188 5175 5185\nf 5185 5175 5168\nf 3560 3788 3561\nf 3561 3788 3790\nf 3596 3595 3608\nf 3608 3595 3594\nf 3598 3582 3595\nf 3595 3582 3592\nf 3600 3609 3601\nf 3601 3609 3611\nf 3602 3601 3612\nf 3612 3601 3611\nf 3606 3605 3613\nf 3613 3605 3614\nf 3793 5367 3794\nf 3794 5367 5368\nf 3598 3597 3600\nf 3600 3597 3609\nf 3793 3792 3575\nf 3575 3792 3572\nf 3561 3594 3558\nf 3558 3594 3593\nf 3807 3915 3804\nf 3804 3915 3914\nf 3916 3915 5370\nf 5370 3915 5369\nf 3602 3605 3603\nf 3603 3605 3604\nf 3606 3792 3607\nf 3607 3792 3791\nf 3914 3913 3560\nf 3560 3913 3788\nf 5174 5129 5166\nf 5166 5129 5371\nf 5170 5169 5093\nf 5093 5169 5113\nf 5166 5371 5090\nf 5090 5371 5091\nf 5128 5169 5372\nf 5372 5169 5172\nf 5127 5113 5128\nf 5128 5113 5169\nf 5114 5206 5203\nf 5203 5206 5362\nf 5200 5373 5073\nf 5073 5373 5096\nf 5374 5375 5200\nf 5200 5375 5373\nf 5039 5047 5374\nf 5374 5047 5375\nf 5087 5046 5085\nf 5085 5046 5376\nf 5212 5377 5067\nf 5067 5377 5376\nf 5042 5045 5212\nf 5212 5045 5377\nf 5064 5378 5043\nf 5043 5378 5044\nf 5063 5077 5064\nf 5064 5077 5378\nf 5079 5082 5198\nf 5198 5082 5078\nf 5099 5081 5100\nf 5100 5081 5379\nf 5100 5379 5094\nf 5094 5379 5095\nf 5381 5382 5380\nf 5380 5382 5383\nf 5385 5386 5384\nf 5384 5386 5383\nf 5385 5387 5386\nf 5386 5387 5388\nf 5390 5391 5389\nf 5389 5391 5392\nf 5393 5394 5396\nf 5396 5394 5395\nf 5398 5399 5397\nf 5397 5399 5400\nf 5402 5403 5401\nf 5401 5403 5404\nf 5406 5407 5405\nf 5405 5407 5404\nf 5408 5409 5163\nf 5163 5409 5410\nf 5411 5412 5414\nf 5414 5412 5413\nf 5057 5416 5415\nf 5415 5416 5417\nf 5393 5416 5418\nf 5418 5416 5419\nf 5396 5395 5420\nf 5420 5395 5421\nf 5423 5424 5422\nf 5422 5424 5425\nf 5415 5417 5220\nf 5220 5417 5426\nf 5418 5391 5393\nf 5393 5391 5394\nf 5414 5388 5411\nf 5411 5388 5392\nf 5059 5427 5060\nf 5060 5427 5428\nf 5423 5427 5429\nf 5429 5427 5430\nf 5432 5382 5431\nf 5431 5382 5425\nf 5429 5407 5423\nf 5423 5407 5424\nf 5380 5409 5381\nf 5381 5409 5433\nf 5381 5433 5422\nf 5422 5433 5428\nf 5027 5412 5056\nf 5056 5412 5419\nf 5435 5436 5434\nf 5434 5436 5437\nf 5159 5439 5438\nf 5438 5439 5440\nf 5053 5436 5159\nf 5159 5436 5439\nf 5441 5403 5398\nf 5398 5403 5399\nf 5163 5410 5026\nf 5026 5410 5413\nf 5438 5440 5442\nf 5442 5440 5430\nf 5444 5445 5443\nf 5443 5445 5446\nf 5447 5448 5450\nf 5450 5448 5449\nf 5451 5446 5452\nf 5452 5446 5445\nf 5454 5455 5453\nf 5453 5455 5451\nf 5456 5454 5458\nf 5458 5454 5457\nf 5460 5461 5459\nf 5459 5461 5462\nf 5464 5465 5463\nf 5463 5465 5466\nf 5468 5469 5467\nf 5467 5469 5470\nf 5472 5473 5471\nf 5471 5473 5474\nf 5475 5476 5478\nf 5478 5476 5477\nf 5479 5480 5482\nf 5482 5480 5481\nf 5483 5484 5448\nf 5448 5484 5485\nf 5448 5485 5449\nf 5449 5485 5486\nf 5487 5476 5488\nf 5488 5476 5475\nf 5490 5491 5489\nf 5489 5491 5492\nf 5494 5495 5493\nf 5493 5495 5496\nf 5498 5494 5497\nf 5497 5494 5493\nf 5500 5501 5499\nf 5499 5501 5502\nf 5503 5504 5446\nf 5446 5504 5443\nf 5506 5507 5505\nf 5505 5507 5508\nf 5510 5511 5509\nf 5509 5511 5512\nf 5514 5515 5513\nf 5513 5515 5516\nf 5518 5519 5517\nf 5517 5519 5520\nf 5495 5521 5496\nf 5496 5521 5522\nf 5524 5525 5523\nf 5523 5525 5526\nf 5527 5528 5494\nf 5494 5528 5495\nf 5499 5502 5529\nf 5529 5502 5530\nf 5531 5532 5485\nf 5485 5532 5486\nf 5485 5484 5531\nf 5531 5484 5533\nf 5482 5481 5534\nf 5534 5481 5535\nf 5536 5512 5506\nf 5506 5512 5511\nf 5455 5503 5451\nf 5451 5503 5446\nf 5467 5470 5511\nf 5511 5470 5537\nf 5538 5467 5510\nf 5510 5467 5511\nf 5540 5541 5539\nf 5539 5541 5542\nf 5543 5544 5546\nf 5546 5544 5545\nf 5548 5472 5547\nf 5547 5472 5471\nf 5493 5496 5549\nf 5549 5496 5550\nf 5496 5522 5550\nf 5550 5522 5551\nf 5553 5554 5552\nf 5552 5554 5555\nf 5557 5447 5556\nf 5556 5447 5558\nf 5557 5483 5447\nf 5447 5483 5448\nf 5478 5477 5553\nf 5553 5477 5559\nf 5477 5560 5559\nf 5559 5560 5561\nf 5476 5562 5477\nf 5477 5562 5560\nf 5476 5487 5562\nf 5562 5487 5563\nf 5560 5564 5561\nf 5561 5564 5565\nf 5564 5560 5566\nf 5566 5560 5562\nf 5563 5567 5562\nf 5562 5567 5566\nf 5569 5521 5568\nf 5568 5521 5570\nf 5481 5572 5571\nf 5571 5572 5573\nf 5574 5535 5571\nf 5571 5535 5481\nf 5576 5577 5575\nf 5575 5577 5578\nf 5579 5580 5456\nf 5456 5580 5581\nf 5475 5582 5488\nf 5488 5582 5583\nf 5584 5536 5505\nf 5505 5536 5506\nf 5585 5586 5588\nf 5588 5586 5587\nf 5590 5591 5589\nf 5589 5591 5592\nf 5519 5593 5520\nf 5520 5593 5594\nf 5595 5596 5462\nf 5462 5596 5597\nf 5599 5600 5598\nf 5598 5600 5514\nf 5602 5603 5601\nf 5601 5603 5604\nf 5524 5543 5525\nf 5525 5543 5546\nf 5543 5524 5606\nf 5606 5524 5605\nf 5607 5608 5500\nf 5500 5608 5501\nf 5610 5551 5609\nf 5609 5551 5522\nf 5611 5510 5598\nf 5598 5510 5509\nf 5612 5597 5457\nf 5457 5597 5458\nf 5510 5611 5538\nf 5538 5611 5613\nf 5580 5508 5581\nf 5581 5508 5507\nf 5615 5616 5614\nf 5614 5616 5617\nf 5619 5620 5618\nf 5618 5620 5621\nf 5623 5624 5622\nf 5622 5624 5625\nf 5627 5624 5626\nf 5626 5624 5623\nf 5629 5630 5628\nf 5628 5630 5631\nf 5633 5634 5632\nf 5632 5634 5466\nf 5635 5636 5463\nf 5463 5636 5637\nf 5523 5638 5524\nf 5524 5638 5605\nf 5639 5640 5641\nf 5641 5640 5595\nf 5611 5513 5613\nf 5613 5513 5642\nf 5643 5586 5608\nf 5608 5586 5501\nf 5622 5625 5590\nf 5590 5625 5591\nf 5598 5514 5611\nf 5611 5514 5513\nf 5645 5646 5644\nf 5644 5646 5647\nf 5620 5619 5491\nf 5491 5619 5648\nf 5649 5643 5492\nf 5492 5643 5608\nf 5650 5651 5653\nf 5653 5651 5652\nf 5463 5466 5635\nf 5635 5466 5634\nf 5597 5612 5462\nf 5462 5612 5459\nf 5635 5634 5523\nf 5523 5634 5638\nf 5654 5638 5633\nf 5633 5638 5634\nf 5655 5656 5525\nf 5525 5656 5526\nf 5658 5659 5657\nf 5657 5659 5527\nf 5660 5464 5637\nf 5637 5464 5463\nf 5632 5466 5661\nf 5661 5466 5465\nf 5661 5662 5632\nf 5632 5662 5658\nf 5662 5663 5658\nf 5658 5663 5659\nf 5665 5659 5664\nf 5664 5659 5663\nf 5632 5658 5633\nf 5633 5658 5657\nf 5666 5629 5530\nf 5530 5629 5667\nf 5528 5527 5665\nf 5665 5527 5659\nf 5668 5669 5570\nf 5570 5669 5568\nf 5669 5668 5670\nf 5670 5668 5671\nf 5646 5672 5673\nf 5672 5674 5673\nf 5673 5674 5675\nf 5674 5460 5675\nf 5675 5460 5459\nf 5461 5676 5462\nf 5462 5676 5595\nf 5677 5678 5680\nf 5680 5678 5679\nf 5628 5681 5629\nf 5629 5681 5667\nf 5682 5529 5667\nf 5667 5529 5530\nf 5675 5459 5683\nf 5683 5459 5612\nf 5585 5666 5502\nf 5502 5666 5530\nf 5589 5684 5602\nf 5602 5684 5603\nf 5646 5645 5663\nf 5663 5645 5664\nf 5685 5647 5673\nf 5673 5647 5646\nf 5686 5687 5683\nf 5683 5687 5685\nf 5688 5689 5533\nf 5533 5689 5531\nf 5690 5691 5535\nf 5535 5691 5534\nf 5692 5690 5574\nf 5574 5690 5535\nf 5685 5687 5689\nf 5689 5687 5693\nf 5489 5694 5490\nf 5490 5694 5695\nf 5690 5692 5696\nf 5696 5692 5697\nf 5696 5644 5690\nf 5690 5644 5691\nf 5447 5450 5558\nf 5558 5450 5698\nf 5699 5700 5642\nf 5642 5700 5613\nf 5701 5702 5540\nf 5540 5702 5541\nf 5431 5703 5432\nf 5432 5703 5697\nf 5385 5384 5574\nf 5574 5384 5692\nf 5389 5387 5573\nf 5573 5387 5571\nf 5565 5390 5573\nf 5573 5390 5389\nf 5390 5565 5704\nf 5704 5565 5564\nf 5566 5705 5564\nf 5564 5705 5704\nf 5569 5441 5609\nf 5609 5441 5398\nf 5568 5405 5569\nf 5569 5405 5441\nf 5670 5706 5669\nf 5669 5706 5406\nf 5703 5431 5670\nf 5670 5431 5706\nf 5432 5697 5384\nf 5384 5697 5692\nf 5574 5571 5385\nf 5385 5571 5387\nf 5607 5707 5489\nf 5489 5707 5694\nf 5518 5708 5519\nf 5519 5708 5709\nf 5711 5576 5710\nf 5710 5576 5575\nf 5713 5714 5712\nf 5712 5714 5715\nf 5553 5552 5478\nf 5478 5552 5716\nf 5717 5475 5716\nf 5716 5475 5478\nf 5718 5719 5605\nf 5605 5719 5606\nf 5505 5622 5584\nf 5584 5622 5590\nf 5508 5623 5505\nf 5505 5623 5622\nf 5626 5623 5580\nf 5580 5623 5508\nf 5580 5579 5626\nf 5626 5579 5720\nf 5579 5721 5720\nf 5720 5721 5722\nf 5666 5585 5723\nf 5723 5585 5588\nf 5724 5725 5639\nf 5639 5725 5640\nf 5554 5726 5555\nf 5555 5726 5727\nf 5453 5728 5729\nf 5689 5688 5685\nf 5685 5688 5647\nf 5604 5730 5512\nf 5512 5730 5509\nf 5628 5631 5618\nf 5618 5631 5592\nf 5601 5604 5536\nf 5536 5604 5512\nf 5732 5676 5731\nf 5731 5676 5660\nf 5730 5604 5733\nf 5733 5604 5603\nf 5563 5487 5551\nf 5551 5487 5550\nf 5489 5492 5607\nf 5607 5492 5608\nf 5573 5572 5565\nf 5565 5572 5561\nf 5609 5522 5569\nf 5569 5522 5521\nf 5499 5734 5500\nf 5500 5734 5735\nf 5596 5736 5721\nf 5721 5736 5722\nf 5737 5600 5594\nf 5594 5600 5520\nf 5599 5730 5517\nf 5517 5730 5733\nf 5660 5738 5731\nf 5731 5738 5739\nf 5648 5649 5491\nf 5491 5649 5492\nf 5501 5586 5502\nf 5502 5586 5585\nf 5525 5546 5655\nf 5655 5546 5740\nf 5600 5599 5520\nf 5520 5599 5517\nf 5708 5739 5709\nf 5709 5739 5738\nf 5636 5635 5526\nf 5526 5635 5523\nf 5670 5671 5703\nf 5703 5671 5741\nf 5490 5695 5742\nf 5742 5695 5743\nf 5682 5678 5744\nf 5744 5678 5677\nf 5678 5681 5679\nf 5679 5681 5621\nf 5742 5743 5679\nf 5679 5743 5680\nf 5618 5621 5628\nf 5628 5621 5681\nf 5725 5724 5589\nf 5589 5724 5684\nf 5456 5458 5579\nf 5579 5458 5721\nf 5675 5683 5673\nf 5673 5683 5685\nf 5529 5682 5745\nf 5745 5682 5744\nf 5721 5458 5596\nf 5596 5458 5597\nf 5701 5540 5719\nf 5719 5540 5606\nf 5703 5741 5697\nf 5697 5741 5696\nf 5500 5735 5607\nf 5607 5735 5707\nf 5549 5746 5493\nf 5493 5746 5497\nf 5700 5747 5613\nf 5613 5747 5538\nf 5420 5421 5434\nf 5434 5421 5400\nf 5689 5693 5531\nf 5531 5693 5532\nf 5705 5566 5748\nf 5748 5566 5567\nf 5609 5398 5610\nf 5610 5398 5397\nf 5669 5406 5568\nf 5568 5406 5405\nf 5488 5583 5549\nf 5549 5583 5746\nf 5714 5713 5710\nf 5710 5713 5711\nf 5747 5468 5538\nf 5538 5468 5467\nf 5548 5547 5749\nf 5749 5547 5750\nf 5733 5603 5751\nf 5751 5603 5684\nf 5589 5602 5590\nf 5590 5602 5584\nf 5636 5752 5614\nf 5614 5752 5615\nf 5753 5754 5738\nf 5738 5754 5755\nf 5602 5601 5584\nf 5584 5601 5536\nf 5723 5736 5630\nf 5630 5736 5640\nf 5595 5640 5596\nf 5596 5640 5736\nf 5734 5499 5745\nf 5745 5499 5529\nf 5627 5587 5643\nf 5643 5587 5586\nf 5553 5559 5554\nf 5554 5559 5480\nf 5559 5561 5480\nf 5480 5561 5572\nf 5554 5480 5726\nf 5726 5480 5479\nf 5749 5750 5715\nf 5715 5750 5712\nf 5683 5612 5686\nf 5686 5612 5457\nf 5678 5682 5681\nf 5681 5682 5667\nf 5598 5509 5599\nf 5599 5509 5730\nf 5679 5621 5742\nf 5742 5621 5620\nf 5652 5616 5653\nf 5653 5616 5615\nf 5220 5426 5052\nf 5052 5426 5437\nf 5487 5488 5550\nf 5550 5488 5549\nf 5742 5620 5490\nf 5490 5620 5491\nf 5551 5610 5563\nf 5563 5610 5567\nf 5610 5397 5567\nf 5567 5397 5748\nf 5614 5617 5753\nf 5753 5617 5754\nf 5480 5572 5481\nf 5755 5756 5738\nf 5738 5756 5709\nf 5745 5744 5758\nf 5758 5744 5757\nf 5745 5758 5734\nf 5734 5758 5759\nf 5735 5734 5760\nf 5760 5734 5759\nf 5707 5735 5761\nf 5761 5735 5760\nf 5744 5677 5757\nf 5757 5677 5762\nf 5677 5680 5762\nf 5762 5680 5763\nf 5763 5680 5764\nf 5764 5680 5743\nf 5765 5766 5762\nf 5762 5766 5757\nf 5768 5769 5767\nf 5767 5769 5770\nf 5771 5772 5770\nf 5770 5772 5767\nf 5770 5769 5774\nf 5774 5769 5773\nf 5775 5776 5774\nf 5774 5776 5777\nf 5773 5778 5774\nf 5774 5778 5775\nf 5779 5766 5773\nf 5773 5766 5778\nf 5777 5771 5774\nf 5774 5771 5770\nf 5780 5781 5783\nf 5783 5781 5782\nf 5781 5780 5771\nf 5771 5780 5772\nf 5773 5769 5779\nf 5779 5769 5768\nf 5768 5759 5779\nf 5779 5759 5758\nf 5768 5784 5759\nf 5759 5784 5760\nf 5767 5785 5768\nf 5768 5785 5784\nf 5785 5767 5786\nf 5786 5767 5772\nf 5766 5779 5757\nf 5757 5779 5758\nf 5780 5787 5772\nf 5772 5787 5786\nf 5785 5786 5788\nf 5788 5786 5789\nf 5786 5787 5789\nf 5789 5787 5790\nf 5788 5789 5791\nf 5791 5789 5792\nf 5791 5793 5788\nf 5788 5793 5794\nf 5795 5792 5790\nf 5790 5792 5789\nf 5785 5788 5784\nf 5784 5788 5794\nf 5784 5794 5760\nf 5760 5794 5761\nf 5787 5780 5796\nf 5796 5780 5783\nf 5775 5778 5797\nf 5797 5778 5766\nf 5797 5798 5775\nf 5775 5798 5776\nf 5766 5765 5797\nf 5797 5765 5799\nf 5798 5797 5800\nf 5800 5797 5799\nf 5798 5801 5776\nf 5776 5801 5802\nf 5707 5761 5694\nf 5694 5761 5803\nf 5695 5804 5743\nf 5743 5804 5764\nf 5804 5695 5803\nf 5803 5695 5694\nf 5803 5793 5804\nf 5804 5793 5805\nf 5793 5791 5805\nf 5805 5791 5806\nf 5804 5805 5764\nf 5764 5805 5807\nf 5803 5761 5793\nf 5793 5761 5794\nf 5808 5763 5807\nf 5807 5763 5764\nf 5808 5765 5763\nf 5763 5765 5762\nf 5809 5799 5808\nf 5808 5799 5765\nf 5809 5808 5810\nf 5810 5808 5807\nf 5811 5809 5812\nf 5812 5809 5810\nf 5805 5806 5807\nf 5807 5806 5810\nf 5806 5813 5810\nf 5810 5813 5812\nf 5799 5809 5800\nf 5800 5809 5811\nf 5815 5800 5814\nf 5814 5800 5811\nf 5814 5817 5816\nf 5816 5817 5818\nf 5814 5811 5817\nf 5817 5811 5812\nf 5814 5816 5815\nf 5815 5816 5796\nf 5815 5796 5801\nf 5801 5796 5783\nf 5801 5798 5815\nf 5815 5798 5800\nf 5791 5792 5806\nf 5806 5792 5813\nf 5818 5813 5795\nf 5795 5813 5792\nf 5818 5817 5813\nf 5813 5817 5812\nf 5816 5818 5790\nf 5790 5818 5795\nf 5790 5787 5816\nf 5816 5787 5796\nf 5771 5777 5781\nf 5781 5777 5782\nf 5782 5802 5783\nf 5783 5802 5801\nf 5782 5777 5802\nf 5802 5777 5776\nf 5588 5587 5722\nf 5722 5587 5720\nf 5591 5619 5592\nf 5592 5619 5618\nf 5625 5624 5648\nf 5648 5624 5649\nf 5624 5627 5649\nf 5649 5627 5643\nf 5631 5630 5725\nf 5725 5630 5640\nf 5625 5648 5591\nf 5591 5648 5619\nf 5588 5722 5723\nf 5723 5722 5736\nf 5631 5725 5592\nf 5592 5725 5589\nf 5723 5630 5666\nf 5666 5630 5629\nf 5587 5627 5720\nf 5720 5627 5626\nf 5820 5821 5819\nf 5819 5821 5822\nf 5824 5825 5823\nf 5823 5825 5826\nf 5823 5826 5827\nf 5827 5826 5828\nf 5829 5830 5832\nf 5832 5830 5831\nf 5518 5833 5708\nf 5708 5833 5834\nf 5835 5836 5824\nf 5824 5836 5825\nf 5838 5839 5837\nf 5837 5839 5840\nf 5751 5684 5842\nf 5842 5684 5841\nf 5843 5841 5724\nf 5724 5841 5684\nf 5845 5846 5844\nf 5844 5846 5847\nf 5848 5830 5836\nf 5836 5830 5825\nf 5518 5751 5833\nf 5833 5751 5842\nf 5839 5838 5821\nf 5821 5838 5849\nf 5850 5848 5822\nf 5822 5848 5836\nf 5851 5845 5828\nf 5828 5845 5852\nf 5854 5855 5853\nf 5853 5855 5856\nf 5845 5844 5852\nf 5852 5844 5857\nf 5858 5827 5852\nf 5852 5827 5828\nf 5851 5828 5829\nf 5829 5828 5826\nf 5819 5859 5820\nf 5820 5859 5860\nf 5835 5861 5819\nf 5819 5861 5859\nf 5851 5829 5862\nf 5862 5829 5832\nf 5844 5847 5837\nf 5837 5847 5834\nf 5819 5822 5835\nf 5835 5822 5836\nf 5823 5863 5824\nf 5824 5863 5864\nf 5849 5850 5821\nf 5821 5850 5822\nf 5825 5830 5826\nf 5826 5830 5829\nf 5820 5860 5865\nf 5865 5860 5866\nf 5858 5854 5867\nf 5867 5854 5853\nf 5854 5857 5855\nf 5855 5857 5840\nf 5855 5865 5856\nf 5856 5865 5866\nf 5837 5840 5844\nf 5844 5840 5857\nf 5827 5858 5868\nf 5868 5858 5867\nf 5824 5864 5835\nf 5835 5864 5861\nf 5732 5731 5862\nf 5862 5731 5846\nf 5863 5823 5868\nf 5868 5823 5827\nf 5843 5831 5848\nf 5848 5831 5830\nf 5854 5858 5857\nf 5857 5858 5852\nf 5855 5840 5865\nf 5865 5840 5839\nf 5865 5839 5820\nf 5820 5839 5821\nf 5868 5867 5870\nf 5870 5867 5869\nf 5868 5870 5863\nf 5863 5870 5871\nf 5863 5871 5864\nf 5864 5871 5872\nf 5864 5872 5861\nf 5861 5872 5873\nf 5867 5853 5869\nf 5869 5853 5874\nf 5853 5856 5874\nf 5874 5856 5875\nf 5856 5866 5875\nf 5875 5866 5876\nf 5877 5878 5874\nf 5874 5878 5869\nf 5880 5881 5879\nf 5879 5881 5882\nf 5883 5884 5880\nf 5880 5884 5881\nf 5881 5884 5886\nf 5886 5884 5885\nf 5887 5888 5886\nf 5886 5888 5889\nf 5885 5890 5886\nf 5886 5890 5887\nf 5889 5882 5886\nf 5886 5882 5881\nf 5882 5891 5879\nf 5879 5891 5892\nf 5885 5884 5893\nf 5893 5884 5883\nf 5883 5871 5893\nf 5893 5871 5870\nf 5883 5894 5871\nf 5871 5894 5872\nf 5880 5895 5883\nf 5883 5895 5894\nf 5896 5895 5879\nf 5879 5895 5880\nf 5893 5878 5885\nf 5885 5878 5890\nf 5878 5893 5869\nf 5869 5893 5870\nf 5897 5896 5892\nf 5892 5896 5879\nf 5895 5896 5898\nf 5898 5896 5899\nf 5896 5897 5899\nf 5899 5897 5900\nf 5898 5899 5901\nf 5901 5899 5902\nf 5901 5903 5898\nf 5898 5903 5904\nf 5900 5905 5899\nf 5899 5905 5902\nf 5898 5904 5895\nf 5895 5904 5894\nf 5894 5904 5872\nf 5872 5904 5873\nf 5897 5892 5907\nf 5907 5892 5906\nf 5892 5891 5906\nf 5906 5891 5908\nf 5887 5890 5909\nf 5909 5890 5878\nf 5888 5887 5910\nf 5910 5887 5909\nf 5878 5877 5909\nf 5909 5877 5911\nf 5909 5911 5910\nf 5910 5911 5912\nf 5913 5914 5910\nf 5910 5914 5888\nf 5908 5914 5906\nf 5906 5914 5913\nf 5861 5873 5859\nf 5859 5873 5915\nf 5860 5916 5866\nf 5866 5916 5876\nf 5916 5860 5915\nf 5915 5860 5859\nf 5915 5903 5916\nf 5916 5903 5917\nf 5903 5901 5917\nf 5917 5901 5918\nf 5916 5917 5876\nf 5876 5917 5919\nf 5903 5915 5904\nf 5904 5915 5873\nf 5920 5875 5919\nf 5919 5875 5876\nf 5920 5877 5875\nf 5875 5877 5874\nf 5920 5921 5877\nf 5877 5921 5911\nf 5921 5920 5922\nf 5922 5920 5919\nf 5923 5921 5924\nf 5924 5921 5922\nf 5917 5918 5919\nf 5919 5918 5922\nf 5918 5925 5922\nf 5922 5925 5924\nf 5921 5923 5911\nf 5911 5923 5912\nf 5927 5912 5926\nf 5926 5912 5923\nf 5926 5929 5928\nf 5928 5929 5930\nf 5926 5923 5929\nf 5929 5923 5924\nf 5926 5928 5927\nf 5927 5928 5907\nf 5927 5907 5913\nf 5913 5907 5906\nf 5913 5910 5927\nf 5927 5910 5912\nf 5901 5902 5918\nf 5918 5902 5925\nf 5930 5925 5905\nf 5905 5925 5902\nf 5930 5929 5925\nf 5925 5929 5924\nf 5928 5930 5900\nf 5900 5930 5905\nf 5900 5897 5928\nf 5928 5897 5907\nf 5882 5889 5891\nf 5891 5889 5908\nf 5908 5889 5914\nf 5914 5889 5888\nf 5832 5831 5641\nf 5641 5831 5639\nf 5833 5838 5834\nf 5834 5838 5837\nf 5842 5841 5849\nf 5849 5841 5850\nf 5841 5843 5850\nf 5850 5843 5848\nf 5847 5846 5739\nf 5739 5846 5731\nf 5842 5849 5833\nf 5833 5849 5838\nf 5862 5832 5732\nf 5732 5832 5641\nf 5847 5739 5834\nf 5834 5739 5708\nf 5862 5846 5851\nf 5851 5846 5845\nf 5831 5843 5639\nf 5639 5843 5724\nf 5676 5732 5595\nf 5595 5732 5641\nf 5676 5461 5660\nf 5660 5461 5464\nf 5461 5460 5464\nf 5464 5460 5465\nf 5460 5674 5465\nf 5465 5674 5661\nf 5931 5932 5934\nf 5934 5932 5933\nf 5936 5937 5935\nf 5935 5937 5938\nf 5935 5938 5939\nf 5939 5938 5940\nf 5942 5943 5941\nf 5941 5943 5944\nf 5945 5946 5936\nf 5936 5946 5937\nf 5948 5949 5947\nf 5947 5949 5950\nf 5951 5952 5519\nf 5519 5952 5593\nf 5953 5951 5709\nf 5709 5951 5519\nf 5955 5956 5954\nf 5954 5956 5957\nf 5958 5942 5946\nf 5946 5942 5937\nf 5948 5959 5949\nf 5949 5959 5933\nf 5960 5958 5934\nf 5934 5958 5946\nf 5961 5955 5940\nf 5940 5955 5962\nf 5963 5964 5966\nf 5966 5964 5965\nf 5955 5954 5962\nf 5962 5954 5967\nf 5968 5939 5962\nf 5962 5939 5940\nf 5961 5940 5941\nf 5941 5940 5938\nf 5931 5969 5932\nf 5932 5969 5970\nf 5945 5971 5931\nf 5931 5971 5969\nf 5941 5944 5961\nf 5961 5944 5972\nf 5954 5957 5947\nf 5947 5957 5973\nf 5945 5931 5946\nf 5946 5931 5934\nf 5935 5974 5936\nf 5936 5974 5975\nf 5959 5960 5933\nf 5933 5960 5934\nf 5937 5942 5938\nf 5938 5942 5941\nf 5932 5970 5976\nf 5976 5970 5977\nf 5968 5964 5978\nf 5978 5964 5963\nf 5965 5964 5950\nf 5950 5964 5967\nf 5976 5977 5965\nf 5965 5977 5966\nf 5947 5950 5954\nf 5954 5950 5967\nf 5939 5968 5979\nf 5979 5968 5978\nf 5936 5975 5945\nf 5945 5975 5971\nf 5972 5754 5956\nf 5956 5754 5617\nf 5974 5935 5979\nf 5979 5935 5939\nf 5953 5943 5958\nf 5958 5943 5942\nf 5964 5968 5967\nf 5967 5968 5962\nf 5965 5950 5976\nf 5976 5950 5949\nf 5976 5949 5932\nf 5932 5949 5933\nf 5979 5978 5981\nf 5981 5978 5980\nf 5979 5981 5974\nf 5974 5981 5982\nf 5974 5982 5975\nf 5975 5982 5983\nf 5975 5983 5971\nf 5971 5983 5984\nf 5978 5963 5980\nf 5980 5963 5985\nf 5963 5966 5985\nf 5985 5966 5986\nf 5986 5966 5987\nf 5987 5966 5977\nf 5988 5989 5985\nf 5985 5989 5980\nf 5991 5992 5990\nf 5990 5992 5993\nf 5994 5995 5991\nf 5991 5995 5992\nf 5992 5995 5997\nf 5997 5995 5996\nf 5998 5999 5997\nf 5997 5999 6000\nf 5996 6001 5997\nf 5997 6001 5998\nf 6000 5993 5997\nf 5997 5993 5992\nf 6002 6003 5993\nf 5993 6003 5990\nf 5995 5994 5996\nf 5996 5994 6004\nf 5994 5982 6004\nf 6004 5982 5981\nf 5994 6005 5982\nf 5982 6005 5983\nf 5991 6006 5994\nf 5994 6006 6005\nf 6006 5991 6007\nf 6007 5991 5990\nf 5996 6004 6001\nf 6001 6004 5989\nf 5989 6004 5980\nf 5980 6004 5981\nf 6003 6008 5990\nf 5990 6008 6007\nf 6009 6006 6010\nf 6010 6006 6007\nf 6008 6011 6007\nf 6007 6011 6010\nf 6012 6009 6013\nf 6013 6009 6010\nf 6009 6012 6015\nf 6015 6012 6014\nf 6011 6016 6010\nf 6010 6016 6013\nf 6006 6009 6005\nf 6005 6009 6015\nf 6005 6015 5983\nf 5983 6015 5984\nf 6008 6003 6018\nf 6018 6003 6017\nf 6003 6002 6017\nf 6017 6002 6019\nf 5998 6001 6020\nf 6020 6001 5989\nf 5999 5998 6021\nf 6021 5998 6020\nf 5989 5988 6020\nf 6020 5988 6022\nf 6021 6020 6023\nf 6023 6020 6022\nf 6021 6024 5999\nf 5999 6024 6025\nf 6019 6025 6017\nf 6017 6025 6024\nf 5971 5984 5969\nf 5969 5984 6026\nf 5970 6027 5977\nf 5977 6027 5987\nf 6027 5970 6026\nf 6026 5970 5969\nf 6026 6014 6027\nf 6027 6014 6028\nf 6014 6012 6028\nf 6028 6012 6029\nf 6027 6028 5987\nf 5987 6028 6030\nf 6026 5984 6014\nf 6014 5984 6015\nf 6031 5986 6030\nf 6030 5986 5987\nf 6031 5988 5986\nf 5986 5988 5985\nf 6032 6022 6031\nf 6031 6022 5988\nf 6032 6031 6033\nf 6033 6031 6030\nf 6034 6032 6035\nf 6035 6032 6033\nf 6028 6029 6030\nf 6030 6029 6033\nf 6029 6036 6033\nf 6033 6036 6035\nf 6032 6034 6022\nf 6022 6034 6023\nf 6038 6023 6037\nf 6037 6023 6034\nf 6037 6040 6039\nf 6039 6040 6041\nf 6037 6034 6040\nf 6040 6034 6035\nf 6037 6039 6038\nf 6038 6039 6018\nf 6038 6018 6024\nf 6024 6018 6017\nf 6024 6021 6038\nf 6038 6021 6023\nf 6012 6013 6029\nf 6029 6013 6036\nf 6041 6036 6016\nf 6016 6036 6013\nf 6041 6040 6036\nf 6036 6040 6035\nf 6041 6016 6039\nf 6039 6016 6011\nf 6011 6008 6039\nf 6039 6008 6018\nf 5993 6000 6002\nf 6002 6000 6019\nf 6019 6000 6025\nf 6025 6000 5999\nf 5943 5756 5944\nf 5944 5756 5755\nf 6042 5948 5973\nf 5973 5948 5947\nf 5952 5951 5959\nf 5959 5951 5960\nf 5951 5953 5960\nf 5960 5953 5958\nf 5616 5957 5617\nf 5617 5957 5956\nf 6042 5952 5948\nf 5948 5952 5959\nf 5944 5755 5972\nf 5972 5755 5754\nf 5957 5616 5973\nf 5973 5616 5652\nf 5956 5955 5972\nf 5972 5955 5961\nf 5943 5953 5756\nf 5756 5953 5709\nf 6044 6045 6043\nf 6043 6045 6046\nf 6048 6049 6047\nf 6047 6049 6050\nf 6047 6050 6051\nf 6051 6050 6052\nf 6054 6055 6053\nf 6053 6055 6056\nf 6057 6058 6060\nf 6060 6058 6059\nf 6061 6062 6048\nf 6048 6062 6049\nf 6063 6064 6066\nf 6066 6064 6065\nf 6067 6068 6070\nf 6070 6068 6069\nf 6071 6069 5653\nf 5653 6069 6068\nf 6072 6073 6075\nf 6075 6073 6074\nf 6062 6076 6049\nf 6049 6076 6054\nf 6058 6067 6059\nf 6059 6067 6070\nf 6064 6077 6065\nf 6065 6077 6045\nf 6078 6076 6046\nf 6046 6076 6062\nf 6073 6080 6079\nf 6079 6080 6052\nf 6082 6083 6081\nf 6081 6083 6084\nf 6072 6085 6073\nf 6073 6085 6080\nf 6086 6051 6080\nf 6080 6051 6052\nf 6079 6052 6053\nf 6053 6052 6050\nf 6043 6087 6044\nf 6044 6087 6088\nf 6061 6089 6043\nf 6043 6089 6087\nf 6053 6056 6079\nf 6079 6056 6090\nf 6063 6072 6060\nf 6060 6072 6075\nf 6043 6046 6061\nf 6061 6046 6062\nf 6047 6091 6048\nf 6048 6091 6092\nf 6077 6078 6045\nf 6045 6078 6046\nf 6049 6054 6050\nf 6050 6054 6053\nf 6044 6088 6093\nf 6093 6088 6094\nf 6086 6082 6095\nf 6095 6082 6081\nf 6083 6082 6066\nf 6066 6082 6085\nf 6093 6094 6083\nf 6083 6094 6084\nf 6072 6063 6085\nf 6085 6063 6066\nf 6051 6086 6096\nf 6096 6086 6095\nf 6048 6092 6061\nf 6061 6092 6089\nf 5656 5655 6090\nf 6090 5655 6074\nf 6091 6047 6096\nf 6096 6047 6051\nf 6071 6055 6076\nf 6076 6055 6054\nf 6082 6086 6085\nf 6085 6086 6080\nf 6093 6083 6065\nf 6065 6083 6066\nf 6044 6093 6045\nf 6045 6093 6065\nf 6096 6095 6098\nf 6098 6095 6097\nf 6096 6098 6091\nf 6091 6098 6099\nf 6091 6099 6092\nf 6092 6099 6100\nf 6092 6100 6089\nf 6089 6100 6101\nf 6095 6081 6097\nf 6097 6081 6102\nf 6081 6084 6102\nf 6102 6084 6103\nf 6103 6084 6104\nf 6104 6084 6094\nf 6106 6097 6105\nf 6105 6097 6102\nf 6107 6108 6110\nf 6110 6108 6109\nf 6111 6112 6108\nf 6108 6112 6109\nf 6109 6112 6114\nf 6114 6112 6113\nf 6114 6115 6117\nf 6117 6115 6116\nf 6113 6118 6114\nf 6114 6118 6115\nf 6117 6110 6114\nf 6114 6110 6109\nf 6110 6119 6107\nf 6107 6119 6120\nf 6112 6111 6113\nf 6113 6111 6121\nf 6111 6099 6121\nf 6121 6099 6098\nf 6111 6122 6099\nf 6099 6122 6100\nf 6123 6122 6108\nf 6108 6122 6111\nf 6124 6123 6107\nf 6107 6123 6108\nf 6113 6121 6118\nf 6118 6121 6106\nf 6106 6121 6097\nf 6097 6121 6098\nf 6125 6124 6120\nf 6120 6124 6107\nf 6126 6123 6127\nf 6127 6123 6124\nf 6125 6128 6124\nf 6124 6128 6127\nf 6129 6126 6130\nf 6130 6126 6127\nf 6126 6129 6132\nf 6132 6129 6131\nf 6128 6133 6127\nf 6127 6133 6130\nf 6123 6126 6122\nf 6122 6126 6132\nf 6122 6132 6100\nf 6100 6132 6101\nf 6120 6134 6125\nf 6125 6134 6135\nf 6120 6119 6134\nf 6134 6119 6136\nf 6115 6118 6137\nf 6137 6118 6106\nf 6115 6137 6116\nf 6116 6137 6138\nf 6137 6106 6139\nf 6139 6106 6105\nf 6138 6137 6140\nf 6140 6137 6139\nf 6141 6142 6138\nf 6138 6142 6116\nf 6136 6142 6134\nf 6134 6142 6141\nf 6089 6101 6087\nf 6087 6101 6143\nf 6088 6144 6094\nf 6094 6144 6104\nf 6143 6144 6087\nf 6087 6144 6088\nf 6131 6145 6143\nf 6143 6145 6144\nf 6129 6146 6131\nf 6131 6146 6145\nf 6144 6145 6104\nf 6104 6145 6147\nf 6143 6101 6131\nf 6131 6101 6132\nf 6148 6103 6147\nf 6147 6103 6104\nf 6103 6148 6102\nf 6102 6148 6105\nf 6148 6149 6105\nf 6105 6149 6139\nf 6149 6148 6150\nf 6150 6148 6147\nf 6151 6149 6152\nf 6152 6149 6150\nf 6145 6146 6147\nf 6147 6146 6150\nf 6146 6153 6150\nf 6150 6153 6152\nf 6149 6151 6139\nf 6139 6151 6140\nf 6155 6140 6154\nf 6154 6140 6151\nf 6154 6157 6156\nf 6156 6157 6158\nf 6154 6151 6157\nf 6157 6151 6152\nf 6154 6156 6155\nf 6155 6156 6135\nf 6155 6135 6141\nf 6141 6135 6134\nf 6141 6138 6155\nf 6155 6138 6140\nf 6146 6129 6153\nf 6153 6129 6130\nf 6158 6153 6133\nf 6133 6153 6130\nf 6158 6157 6153\nf 6153 6157 6152\nf 6156 6158 6128\nf 6128 6158 6133\nf 6128 6125 6156\nf 6156 6125 6135\nf 6110 6117 6119\nf 6119 6117 6136\nf 6136 6117 6142\nf 6142 6117 6116\nf 6055 5615 6056\nf 6056 5615 5752\nf 6060 6059 6063\nf 6063 6059 6064\nf 6077 6070 6078\nf 6078 6070 6069\nf 6069 6071 6078\nf 6078 6071 6076\nf 6159 6057 6075\nf 6075 6057 6060\nf 6059 6070 6064\nf 6064 6070 6077\nf 6056 5752 6090\nf 6090 5752 5656\nf 6090 6074 6079\nf 6079 6074 6073\nf 6055 6071 5615\nf 5615 6071 5653\nf 5547 5471 6160\nf 6160 5471 6161\nf 5729 5577 6163\nf 6163 5577 6162\nf 5577 5576 6162\nf 6162 5576 6164\nf 5713 6165 5711\nf 5711 6165 6166\nf 5474 5729 6167\nf 6167 5729 6163\nf 5750 5547 6168\nf 6168 5547 6160\nf 5471 5474 6161\nf 6161 5474 6167\nf 6169 5712 6168\nf 6168 5712 5750\nf 5711 6166 5576\nf 5576 6166 6164\nf 6165 5713 6169\nf 6169 5713 5712\nf 6168 6170 6169\nf 6169 6170 6171\nf 6169 6171 6165\nf 6165 6171 6172\nf 6165 6172 6166\nf 6166 6172 6173\nf 6166 6173 6164\nf 6164 6173 6174\nf 6168 6160 6170\nf 6170 6160 6175\nf 6160 6161 6175\nf 6175 6161 6176\nf 6161 6167 6176\nf 6176 6167 6177\nf 6179 6170 6178\nf 6178 6170 6175\nf 6181 6182 6180\nf 6180 6182 6183\nf 6184 6185 6181\nf 6181 6185 6182\nf 6186 6187 6185\nf 6185 6187 6182\nf 6186 6188 6187\nf 6187 6188 6189\nf 6190 6183 6187\nf 6187 6183 6182\nf 6183 6190 6191\nf 6191 6190 6192\nf 6191 6193 6183\nf 6183 6193 6180\nf 6186 6185 6194\nf 6194 6185 6184\nf 6184 6172 6194\nf 6194 6172 6171\nf 6184 6195 6172\nf 6172 6195 6173\nf 6181 6196 6184\nf 6184 6196 6195\nf 6196 6181 6197\nf 6197 6181 6180\nf 6186 6194 6188\nf 6188 6194 6179\nf 6179 6194 6170\nf 6170 6194 6171\nf 6193 6198 6180\nf 6180 6198 6197\nf 6199 6196 6200\nf 6200 6196 6197\nf 6198 6201 6197\nf 6197 6201 6200\nf 6202 6199 6203\nf 6203 6199 6200\nf 6199 6202 6205\nf 6205 6202 6204\nf 6201 6206 6200\nf 6200 6206 6203\nf 6196 6199 6195\nf 6195 6199 6205\nf 6195 6205 6173\nf 6173 6205 6174\nf 6198 6193 6208\nf 6208 6193 6207\nf 6193 6191 6207\nf 6207 6191 6192\nf 6189 6188 6209\nf 6209 6188 6179\nf 6210 6189 6211\nf 6211 6189 6209\nf 6209 6179 6212\nf 6212 6179 6178\nf 6211 6209 6213\nf 6213 6209 6212\nf 6211 6214 6210\nf 6210 6214 6215\nf 6192 6215 6207\nf 6207 6215 6214\nf 6190 6187 6210\nf 6210 6187 6189\nf 6192 6190 6215\nf 6215 6190 6210\nf 6162 6164 6216\nf 6216 6164 6174\nf 6167 6163 6177\nf 6177 6163 6217\nf 6216 6217 6162\nf 6162 6217 6163\nf 6204 6218 6216\nf 6216 6218 6217\nf 6202 6219 6204\nf 6204 6219 6218\nf 6217 6218 6177\nf 6177 6218 6220\nf 6216 6174 6204\nf 6204 6174 6205\nf 6221 6176 6220\nf 6220 6176 6177\nf 6176 6221 6175\nf 6175 6221 6178\nf 6221 6222 6178\nf 6178 6222 6212\nf 6222 6221 6223\nf 6223 6221 6220\nf 6224 6222 6225\nf 6225 6222 6223\nf 6218 6219 6220\nf 6220 6219 6223\nf 6219 6226 6223\nf 6223 6226 6225\nf 6222 6224 6212\nf 6212 6224 6213\nf 6228 6213 6227\nf 6227 6213 6224\nf 6229 6227 6231\nf 6231 6227 6230\nf 6227 6224 6230\nf 6230 6224 6225\nf 6227 6229 6228\nf 6228 6229 6208\nf 6228 6208 6214\nf 6214 6208 6207\nf 6214 6211 6228\nf 6228 6211 6213\nf 6219 6202 6226\nf 6226 6202 6203\nf 6231 6226 6206\nf 6206 6226 6203\nf 6231 6230 6226\nf 6226 6230 6225\nf 6229 6231 6201\nf 6201 6231 6206\nf 6201 6198 6229\nf 6229 6198 6208\nf 5674 5672 5661\nf 5661 5672 5662\nf 5662 5672 5663\nf 5663 5672 5646\nf 5651 6042 5652\nf 5652 6042 5973\nf 5518 5517 5751\nf 5751 5517 5733\nf 5738 5660 5753\nf 5753 5660 5637\nf 5752 5636 5656\nf 5656 5636 5526\nf 5952 6042 5593\nf 5593 6042 5651\nf 5593 5651 5594\nf 5594 5651 5650\nf 5600 5737 5514\nf 5514 5737 5515\nf 5546 5545 5740\nf 5740 5545 6232\nf 6232 6058 5740\nf 5740 6058 6057\nf 6067 6058 6233\nf 6233 6058 6232\nf 6233 6232 6234\nf 6234 6232 5545\nf 6234 5545 6235\nf 6235 5545 5544\nf 5606 6236 5543\nf 5543 6236 5544\nf 6235 5544 5539\nf 5539 5544 6236\nf 6237 6235 5542\nf 5542 6235 5539\nf 6235 6237 6234\nf 6234 6237 6238\nf 6234 6238 6233\nf 6233 6238 6239\nf 5653 6068 5650\nf 5650 6068 6239\nf 5642 5513 6240\nf 6240 5513 5516\nf 5539 6236 5540\nf 5540 6236 5606\nf 5636 5614 5637\nf 5637 5614 5753\nf 6239 5737 5650\nf 5650 5737 5594\nf 6239 6068 6233\nf 6233 6068 6067\nf 6075 6074 6159\nf 6159 6074 5655\nf 6057 6159 5740\nf 5740 6159 5655\nf 5515 5737 6238\nf 6238 5737 6239\nf 6238 6237 5515\nf 5515 6237 5516\nf 5542 6240 6237\nf 6237 6240 5516\nf 6240 5542 5642\nf 5642 5542 5541\nf 5702 5699 5541\nf 5541 5699 5642\nf 5582 5475 5717\nf 5470 5443 5537\nf 5537 5443 5504\nf 5469 5444 5470\nf 5470 5444 5443\nf 5726 5557 5727\nf 5727 5557 5556\nf 5537 5504 5511\nf 5511 5504 5506\nf 5504 5503 5506\nf 5506 5503 5507\nf 5503 5455 5507\nf 5507 5455 5581\nf 5455 5454 5581\nf 5581 5454 5456\nf 5473 5453 5474\nf 5474 5453 5729\nf 5457 5454 5473\nf 5473 5454 5453\nf 5473 5472 5457\nf 5457 5472 5686\nf 5472 5548 5686\nf 5686 5548 5687\nf 5749 5693 5548\nf 5548 5693 5687\nf 5715 5532 5749\nf 5749 5532 5693\nf 5714 5486 5715\nf 5715 5486 5532\nf 5714 5710 5486\nf 5486 5710 5449\nf 5728 5578 5729\nf 5729 5578 5577\nf 5452 5445 5698\nf 5698 5445 5558\nf 5445 5444 5558\nf 5558 5444 5556\nf 5444 5469 5556\nf 5556 5469 5727\nf 5469 5468 5727\nf 5727 5468 5555\nf 5468 5747 5555\nf 5555 5747 5552\nf 5747 5700 5552\nf 5552 5700 5716\nf 5700 5699 5716\nf 5716 5699 5717\nf 5699 5702 5717\nf 5717 5702 5582\nf 5702 5701 5582\nf 5582 5701 5583\nf 5726 5479 5557\nf 5557 5479 5483\nf 5483 5479 5484\nf 5484 5479 5482\nf 5534 5533 5482\nf 5482 5533 5484\nf 5691 5688 5534\nf 5534 5688 5533\nf 5644 5647 5691\nf 5691 5647 5688\nf 5719 5746 5701\nf 5701 5746 5583\nf 5718 5497 5719\nf 5719 5497 5746\nf 5718 5605 5654\nf 5654 5605 5638\nf 5498 5654 5657\nf 5657 5654 5633\nf 5497 5718 5498\nf 5498 5718 5654\nf 5657 5527 5498\nf 5498 5527 5494\nf 5528 5570 5495\nf 5495 5570 5521\nf 5665 5668 5528\nf 5528 5668 5570\nf 5664 5671 5665\nf 5665 5671 5668\nf 5645 5741 5664\nf 5664 5741 5671\nf 5644 5696 5645\nf 5645 5696 5741\nf 5710 5575 5449\nf 5449 5575 5450\nf 5575 5578 5450\nf 5450 5578 5698\nf 5578 5728 5698\nf 5698 5728 5452\nf 5452 5728 5451\nf 5451 5728 5453\nf 3619 6241 3620\nf 3620 6241 6242\nf 6243 6244 3628\nf 3628 6244 3629\nf 6245 6246 3634\nf 3634 6246 3631\nf 6247 6248 3636\nf 3636 6248 3637\nf 6249 6250 3642\nf 3642 6250 3639\nf 3639 6250 3643\nf 3643 6250 6251\nf 3743 6252 3744\nf 3744 6252 6253\nf 6246 6243 3631\nf 3631 6243 3628\nf 3753 6254 3754\nf 3754 6254 6255\nf 3857 6256 3619\nf 3619 6256 6241\nf 6257 6245 3932\nf 3932 6245 3634\nf 6242 6247 3620\nf 3620 6247 3636\nf 6258 6259 3946\nf 3946 6259 3947\nf 6260 6249 3948\nf 3948 6249 3642\nf 6244 6258 3629\nf 3629 6258 3946\nf 3947 6259 3753\nf 3753 6259 6254\nf 6248 6260 3637\nf 3637 6260 3948\nf 3643 6251 3743\nf 3743 6251 6252\nf 3744 6253 3932\nf 3932 6253 6257\nf 6250 6249 6251\nf 6251 6249 6252\nf 6245 6261 6246\nf 6246 6261 6262\nf 6263 6264 6247\nf 6247 6264 6248\nf 6252 6265 6253\nf 6253 6265 6266\nf 6246 6262 6243\nf 6243 6262 6267\nf 6268 6261 6257\nf 6257 6261 6245\nf 6269 6263 6242\nf 6242 6263 6247\nf 6270 6271 6260\nf 6260 6271 6249\nf 6264 6270 6248\nf 6248 6270 6260\nf 6253 6266 6257\nf 6257 6266 6268\nf 6249 6271 6252\nf 6252 6271 6265\nf 6254 6259 6272\nf 6272 6259 6273\nf 6266 6265 6270\nf 6270 6265 6271\nf 6268 6266 6264\nf 6264 6266 6270\nf 6261 6268 6263\nf 6263 6268 6264\nf 6269 6262 6263\nf 6263 6262 6261\nf 4186 4185 6274\nf 6274 4185 6275\nf 6241 6276 6242\nf 6242 6276 6269\nf 6258 6244 6277\nf 6277 6244 6278\nf 6256 6279 6241\nf 6241 6279 6276\nf 6259 6258 6273\nf 6273 6258 6277\nf 6243 6267 6244\nf 6244 6267 6278\nf 6276 6267 6269\nf 6269 6267 6262\nf 6254 6272 6255\nf 6255 6272 6280\nf 3865 3713 3888\nf 3888 3713 3685\nf 5242 5233 3892\nf 3892 5233 3711\nf 3900 5243 3892\nf 3892 5243 5242\nf 3734 3866 3900\nf 3900 3866 5243\nf 4117 6281 4118\nf 4118 6281 6282\nf 4118 6282 4278\nf 4278 6282 4879\nf 4717 4887 4718\nf 4718 4887 4280\nf 4792 4278 4882\nf 4882 4278 4879\nf 4795 4792 6283\nf 6283 4792 4882\nf 4852 4851 5012\nf 5012 4851 6284\nf 4856 5207 4857\nf 4857 5207 4858\nf 4860 4279 4857\nf 4857 4279 4854\nf 4718 4280 4860\nf 4860 4280 4279\nf 4864 4863 4745\nf 4745 4863 4742\nf 5366 4867 5011\nf 5011 4867 4866\nf 4871 4884 4872\nf 4872 4884 4883\nf 4872 4883 4117\nf 4117 4883 6281\nf 6284 4851 6283\nf 6283 4851 4795\nf 4852 4866 4853\nf 4853 4866 4865\nf 4871 4870 4856\nf 4856 4870 5207\nf 4717 4716 4864\nf 4864 4716 4861\nf 4868 4867 4862\nf 4862 4867 4863\nf 4268 4855 4269\nf 4269 4855 4854\nf 4281 4274 4282\nf 4282 4274 4765\nf 4744 4743 4907\nf 4907 4743 4904\nf 4874 4881 4820\nf 4820 4881 4880\nf 4885 5013 4886\nf 4886 5013 4980\nf 4888 4275 4281\nf 4281 4275 4274\nf 4902 6282 4903\nf 4903 6282 6281\nf 6283 4979 6284\nf 6284 4979 4978\nf 4282 4765 4269\nf 4269 4765 4266\nf 4880 4879 4902\nf 4902 4879 6282\nf 4903 6281 4886\nf 4886 6281 4883\nf 4882 4881 6283\nf 6283 4881 4979\nf 5013 4885 4267\nf 4267 4885 4268\nf 4888 4887 4744\nf 4744 4887 4745\nf 4410 6285 4411\nf 4411 6285 4812\nf 4750 4749 6286\nf 6286 4749 6287\nf 6289 4695 6288\nf 6288 4695 4255\nf 4750 4675 4751\nf 4751 4675 4965\nf 6290 6291 4963\nf 4963 6291 4960\nf 4807 4806 4411\nf 4411 4806 4408\nf 6292 6293 4959\nf 4959 6293 4956\nf 4958 6294 4959\nf 4959 6294 4955\nf 4962 4964 4963\nf 4963 4964 4805\nf 4962 4961 4751\nf 4751 4961 4748\nf 6296 6297 6295\nf 6295 6297 6298\nf 6299 6300 6288\nf 6288 6300 4735\nf 6302 4961 6301\nf 6301 4961 4960\nf 6304 6287 6303\nf 6303 6287 4749\nf 4968 6305 4969\nf 4969 6305 6306\nf 4413 6308 6307\nf 6307 6308 4900\nf 6301 4960 6309\nf 6309 4960 6291\nf 4412 4408 6310\nf 6310 4408 4806\nf 4696 6311 4256\nf 4256 6311 6312\nf 6303 4749 6313\nf 6313 4749 4748\nf 6313 4748 6302\nf 6302 4748 4961\nf 6315 6316 6314\nf 6314 6316 6317\nf 4084 6318 6315\nf 6315 6318 6316\nf 4083 6319 4084\nf 4084 6319 6318\nf 4112 6320 4083\nf 4083 6320 6319\nf 6322 4138 6321\nf 6321 4138 4137\nf 6324 4128 6323\nf 6323 4128 4127\nf 4127 4193 6323\nf 6323 4193 6325\nf 4193 4891 6325\nf 6325 4891 6326\nf 4891 6327 6326\nf 6326 6327 6328\nf 6327 4824 5227\nf 5227 4824 5228\nf 5231 4684 6329\nf 6329 4684 4683\nf 4683 4099 6329\nf 6329 4099 6330\nf 4099 4102 6330\nf 6330 4102 6331\nf 4174 4826 4429\nf 4429 4826 4829\nf 4173 4741 4174\nf 4174 4741 4826\nf 4180 4738 4173\nf 4173 4738 4741\nf 6332 6333 4180\nf 4180 6333 4738\nf 6274 6334 4186\nf 4186 6334 4188\nf 4181 4825 4180\nf 4180 4825 6332\nf 5044 5213 5045\nf 5045 5213 5165\nf 5046 5087 5047\nf 5047 5087 5222\nf 5077 5063 5078\nf 5078 5063 5198\nf 5081 5099 5082\nf 5082 5099 5194\nf 5095 5074 5096\nf 5096 5074 5073\nf 5373 5170 5096\nf 5096 5170 5093\nf 5375 5171 5373\nf 5373 5171 5170\nf 5047 5222 5375\nf 5375 5222 5171\nf 5046 5038 5376\nf 5376 5038 5067\nf 5377 5086 5376\nf 5376 5086 5085\nf 5045 5165 5377\nf 5377 5165 5086\nf 5378 5214 5044\nf 5044 5214 5213\nf 5077 5034 5378\nf 5378 5034 5214\nf 5082 5194 5078\nf 5078 5194 5035\nf 5081 5080 5379\nf 5379 5080 6335\nf 5379 6335 5095\nf 5095 6335 5074\nf 5032 5080 5033\nf 5033 5080 5079\nf 5040 5017 5041\nf 5041 5017 5016\nf 5050 5043 5051\nf 5051 5043 5042\nf 5065 5023 5066\nf 5066 5023 5022\nf 5050 5049 5065\nf 5065 5049 5023\nf 5041 5016 5068\nf 5068 5016 5020\nf 5071 5374 5072\nf 5072 5374 5200\nf 5075 5156 5076\nf 5076 5156 5155\nf 5162 5212 5068\nf 5068 5212 5067\nf 5075 5074 5192\nf 5192 5074 6335\nf 5066 5022 5199\nf 5199 5022 5196\nf 5076 5155 5072\nf 5072 5155 5069\nf 5040 5039 5071\nf 5071 5039 5374\nf 5162 5161 5051\nf 5051 5161 5048\nf 5192 6335 5032\nf 5032 6335 5080\nf 5199 5196 5033\nf 5033 5196 5030\nf 5018 5211 5019\nf 5019 5211 5408\nf 5019 5408 5021\nf 5021 5408 5163\nf 5024 5057 5025\nf 5025 5057 5415\nf 5028 5048 5029\nf 5029 5048 5161\nf 5054 5031 5055\nf 5055 5031 5030\nf 5024 5023 5058\nf 5058 5023 5049\nf 5061 5070 5062\nf 5062 5070 5069\nf 5157 5438 5158\nf 5158 5438 5442\nf 5160 5191 5054\nf 5054 5191 5031\nf 5029 5161 5021\nf 5021 5161 5020\nf 5025 5415 5197\nf 5197 5415 5220\nf 5158 5442 5062\nf 5062 5442 5059\nf 5018 5017 5061\nf 5061 5017 5070\nf 5160 5159 5157\nf 5157 5159 5438\nf 5028 5027 5058\nf 5058 5027 5056\nf 5055 5030 5197\nf 5197 5030 5196\nf 5409 5380 5410\nf 5410 5380 6336\nf 5026 5413 5027\nf 5027 5413 5412\nf 5396 5417 5393\nf 5393 5417 5416\nf 5416 5057 5419\nf 5419 5057 5056\nf 5417 5396 5426\nf 5426 5396 5420\nf 5427 5423 5428\nf 5428 5423 5422\nf 5427 5059 5430\nf 5430 5059 5442\nf 5409 5408 5433\nf 5433 5408 5211\nf 5433 5211 5428\nf 5428 5211 5060\nf 5412 5411 5419\nf 5419 5411 5418\nf 5436 5053 5437\nf 5437 5053 5052\nf 5439 5402 5440\nf 5440 5402 5401\nf 5436 5435 5439\nf 5439 5435 5402\nf 5410 6336 5413\nf 5413 6336 5414\nf 5440 5401 5430\nf 5430 5401 5429\nf 5426 5420 5437\nf 5437 5420 5434\nf 5382 5432 5383\nf 5383 5432 5384\nf 5386 6336 5383\nf 5383 6336 5380\nf 6336 5386 5414\nf 5414 5386 5388\nf 5391 5418 5392\nf 5392 5418 5411\nf 5705 5395 5704\nf 5704 5395 5394\nf 5399 5435 5400\nf 5400 5435 5434\nf 5403 5441 5404\nf 5404 5441 5405\nf 5407 5429 5404\nf 5404 5429 5401\nf 5395 5705 5421\nf 5421 5705 5748\nf 5424 5706 5425\nf 5425 5706 5431\nf 5391 5390 5394\nf 5394 5390 5704\nf 5392 5388 5389\nf 5389 5388 5387\nf 5382 5381 5425\nf 5425 5381 5422\nf 5407 5406 5424\nf 5424 5406 5706\nf 5403 5402 5399\nf 5399 5402 5435\nf 5421 5748 5400\nf 5400 5748 5397\nf 4097 4721 4098\nf 4098 4721 4231\nf 4233 5273 4098\nf 4098 5273 4095\nf 6337 6338 4785\nf 4785 6338 4786\nf 4076 4780 4073\nf 4073 4780 4781\nf 4782 4190 4781\nf 4781 4190 4073\nf 4783 4901 4782\nf 4782 4901 4190\nf 4780 4076 4097\nf 4097 4076 4721\nf 6337 2879 4701\nf 4701 2879 2878\nf 5280 6339 5272\nf 5272 6339 6340\nf 5282 5281 6341\nf 6341 5281 6342\nf 4109 6302 4110\nf 4110 6302 6301\nf 4283 6304 4284\nf 4284 6304 6303\nf 6305 4379 6306\nf 6306 4379 4378\nf 4110 6301 4428\nf 4428 6301 6309\nf 6310 4688 4412\nf 4412 4688 4687\nf 4284 6303 4753\nf 4753 6303 6313\nf 4753 6313 4109\nf 4109 6313 6302\nf 6343 5226 4428\nf 4428 5226 4427\nf 4147 4900 4416\nf 4416 4900 6308\nf 6312 6311 4241\nf 4241 6311 4720\nf 5223 6344 4702\nf 4702 6344 4699\nf 4221 4220 6345\nf 6345 4220 6346\nf 5276 5275 6347\nf 6347 5275 6348\nf 5275 4728 6348\nf 6348 4728 6349\nf 4727 6350 5284\nf 5284 6350 5285\nf 6351 6352 6354\nf 6354 6352 6353\nf 5313 5312 5292\nf 5292 5312 5293\nf 5312 5344 5293\nf 5293 5344 5337\nf 5344 4914 5337\nf 5337 4914 4916\nf 4914 4913 4916\nf 4916 4913 4917\nf 4913 6355 4917\nf 4917 6355 4978\nf 6356 6357 6284\nf 6284 6357 5012\nf 6357 6358 5012\nf 5012 6358 5011\nf 5011 6358 5366\nf 5366 6358 6359\nf 6360 4742 6359\nf 6359 4742 5366\nf 4743 6361 4904\nf 4904 6361 5364\nf 5363 4905 5364\nf 5364 4905 4904\nf 5363 6362 4905\nf 4905 6362 4207\nf 6362 6363 4207\nf 4207 6363 4208\nf 6363 5342 4208\nf 4208 5342 5330\nf 5342 5341 5330\nf 5330 5341 4244\nf 5341 6364 4244\nf 4244 6364 4245\nf 6364 6365 4245\nf 4245 6365 4246\nf 6366 6367 4253\nf 4253 6367 4250\nf 4214 6368 4211\nf 4211 6368 4380\nf 4380 4379 4211\nf 4211 4379 4283\nf 4379 6305 4283\nf 4283 6305 6304\nf 6305 4968 6304\nf 6304 4968 6287\nf 4968 4967 6287\nf 6287 4967 6286\nf 4967 6297 6286\nf 6286 6297 4676\nf 6296 4376 4288\nf 4288 4376 4285\nf 4376 4373 4285\nf 4285 4373 4290\nf 6369 6370 6372\nf 6372 6370 6371\nf 5245 6373 5246\nf 5246 6373 6374\nf 4317 6375 4318\nf 4318 6375 6376\nf 4331 6377 4332\nf 4332 6377 6378\nf 6379 4333 6376\nf 6376 4333 4318\nf 4343 6380 4344\nf 4344 6380 6381\nf 4347 6382 4348\nf 4348 6382 6383\nf 4374 6371 4331\nf 4331 6371 6377\nf 4333 6379 4426\nf 4426 6379 6384\nf 4690 6385 4347\nf 4347 6385 6382\nf 4332 6378 4317\nf 4317 6378 6375\nf 4922 6386 4923\nf 4923 6386 6387\nf 6388 6389 4375\nf 4375 6389 6390\nf 6355 6356 4978\nf 4978 6356 6284\nf 4742 6360 4743\nf 4743 6360 6361\nf 6391 4238 6392\nf 6392 4238 4719\nf 6392 4719 6393\nf 6393 4719 5296\nf 6395 6396 6394\nf 6394 6396 6397\nf 6399 6400 6398\nf 6398 6400 6401\nf 6402 5302 6403\nf 6403 5302 5303\nf 5303 5321 6403\nf 6403 5321 6404\nf 5320 6405 5321\nf 5321 6405 6404\nf 5319 6406 5350\nf 5350 6406 5351\nf 5319 5318 6406\nf 6406 5318 6407\nf 5288 4197 6408\nf 6408 4197 6409\nf 4217 4216 6410\nf 6410 4216 6411\nf 5227 6412 6327\nf 6327 6412 6328\nf 4892 6413 5227\nf 5227 6413 6412\nf 4228 6414 4892\nf 4892 6414 6413\nf 4227 6415 4228\nf 4228 6415 6414\nf 4248 6416 4227\nf 4227 6416 6415\nf 4247 6417 4248\nf 4248 6417 6416\nf 6417 4247 6419\nf 6419 4247 6418\nf 6419 6418 6421\nf 6421 6418 6420\nf 6422 5301 6423\nf 6423 5301 5300\nf 6423 5300 6424\nf 6424 5300 6399\nf 6408 6409 6398\nf 6398 6409 6425\nf 6426 6427 6397\nf 6397 6427 6394\nf 6428 4224 6429\nf 6429 4224 4223\nf 6347 6346 5276\nf 5276 6346 4220\nf 6346 6347 6341\nf 6341 6347 6339\nf 5280 5278 5282\nf 5282 5278 4236\nf 4896 5224 4684\nf 4684 5224 4681\nf 4132 4135 4896\nf 4896 4135 5224\nf 4131 4136 4132\nf 4132 4136 4135\nf 4226 4754 4131\nf 4131 4754 4136\nf 4251 4213 4226\nf 4226 4213 4754\nf 4250 4214 4251\nf 4251 4214 4213\nf 4250 6367 4214\nf 4214 6367 6368\nf 6367 4910 6368\nf 6368 4910 4737\nf 4909 5297 4736\nf 4736 5297 5296\nf 5297 6395 5296\nf 5296 6395 6393\nf 6427 6430 6394\nf 6394 6430 6431\nf 6429 4383 6428\nf 6428 4383 4382\nf 4219 5277 4220\nf 4220 5277 5276\nf 4222 4697 4383\nf 4383 4697 4218\nf 6345 4699 4221\nf 4221 4699 6344\nf 6342 4700 6345\nf 6345 4700 4699\nf 4235 4786 5281\nf 5281 4786 6338\nf 4234 4787 4235\nf 4235 4787 4786\nf 5279 5278 6432\nf 6432 5278 5271\nf 4237 5279 6433\nf 6433 5279 6432\nf 4787 4234 6433\nf 6433 4234 4237\nf 6435 6436 6434\nf 6434 6436 6437\nf 6359 6438 6360\nf 6360 6438 6437\nf 6439 6440 6441\nf 6441 6440 6438\nf 6443 6439 6442\nf 6442 6439 6441\nf 6357 6356 6442\nf 6442 6356 6444\nf 6444 6446 6445\nf 6445 6446 6447\nf 4912 6446 4913\nf 4913 6446 6355\nf 6361 6436 5364\nf 5364 6436 5365\nf 5319 5350 5316\nf 5316 5350 6448\nf 4832 6449 5350\nf 5350 6449 6448\nf 4831 6450 4832\nf 4832 6450 6449\nf 5354 6451 5320\nf 5320 6451 6405\nf 4265 6452 5354\nf 5354 6452 6451\nf 4264 6453 4265\nf 4265 6453 6452\nf 6450 4831 6447\nf 6447 4831 6445\nf 4263 6435 4264\nf 4264 6435 6434\nf 6454 6453 6439\nf 6439 6453 6440\nf 6439 6443 6454\nf 6454 6443 4834\nf 4833 6455 4834\nf 4834 6455 6454\nf 5351 6456 4833\nf 4833 6456 6455\nf 6457 6391 6458\nf 6458 6391 6392\nf 6458 6392 6431\nf 6431 6392 6393\nf 5298 6396 5297\nf 5297 6396 6395\nf 6402 6400 5302\nf 5302 6400 5299\nf 6402 6403 6459\nf 6459 6403 6460\nf 6460 6403 6461\nf 6461 6403 6404\nf 6405 6462 6404\nf 6404 6462 6461\nf 6406 6462 5351\nf 5351 6462 6456\nf 6406 6407 6462\nf 6462 6407 6461\nf 6407 6463 6461\nf 6461 6463 6460\nf 6463 6464 6460\nf 6460 6464 6459\nf 6464 6465 6459\nf 6459 6465 6401\nf 6430 6466 6431\nf 6431 6466 6458\nf 6466 6410 6458\nf 6458 6410 6457\nf 6410 6411 6457\nf 6457 6411 6467\nf 6396 6424 6397\nf 6397 6424 6425\nf 6393 6395 6431\nf 6431 6395 6394\nf 6451 6456 6405\nf 6405 6456 6462\nf 6452 6455 6451\nf 6451 6455 6456\nf 6453 6454 6452\nf 6452 6454 6455\nf 6443 6445 4834\nf 4834 6445 4831\nf 6440 6453 6434\nf 6434 6453 4264\nf 6468 5306 5360\nf 5360 5306 5305\nf 6469 4124 6468\nf 6468 4124 5306\nf 6470 4125 6469\nf 6469 4125 4124\nf 6471 5291 6470\nf 6470 5291 4125\nf 6353 5292 6471\nf 6471 5292 5291\nf 5292 6353 5313\nf 5313 6353 6352\nf 5347 5314 6352\nf 6352 5314 5313\nf 5353 5318 5346\nf 5346 5318 5317\nf 5334 6468 5358\nf 5358 6468 5360\nf 5324 6469 5334\nf 5334 6469 6468\nf 5323 6470 5324\nf 5324 6470 6469\nf 5343 6471 5323\nf 5323 6471 6470\nf 6354 6353 5343\nf 5343 6353 6471\nf 6348 6349 4270\nf 4270 6349 5311\nf 5272 6340 5273\nf 5273 6340 4272\nf 6432 5271 4232\nf 4232 5271 4233\nf 4294 6432 4295\nf 4295 6432 4232\nf 6432 4294 6433\nf 6433 4294 4301\nf 4301 4300 6433\nf 6433 4300 4787\nf 4704 4784 4300\nf 4300 4784 4787\nf 4704 3013 4784\nf 4784 3013 1307\nf 5103 6472 5104\nf 5104 6472 5202\nf 5108 5116 5105\nf 5105 5116 5115\nf 5111 5124 5112\nf 5112 5124 5123\nf 5154 5205 5105\nf 5105 5205 5106\nf 5167 5180 5168\nf 5168 5180 5185\nf 5175 5188 5176\nf 5176 5188 5187\nf 5112 5123 5178\nf 5178 5123 5120\nf 5167 5090 5183\nf 5183 5090 5089\nf 5210 5151 5183\nf 5183 5151 5181\nf 5216 5138 5217\nf 5217 5138 5137\nf 5218 5119 5219\nf 5219 5119 5221\nf 5218 5178 5119\nf 5119 5178 5120\nf 5217 5137 5104\nf 5104 5137 5101\nf 5116 5108 5221\nf 5221 5108 5219\nf 5111 5110 5210\nf 5210 5110 5151\nf 5154 5153 5103\nf 5103 5153 6472\nf 5216 5215 5176\nf 5176 5215 5173\nf 5175 5174 5168\nf 5168 5174 5166\nf 5091 5172 5092\nf 5092 5172 5208\nf 5117 5037 5118\nf 5118 5037 5036\nf 5121 5164 5122\nf 5122 5164 5133\nf 5125 5084 5126\nf 5126 5084 5083\nf 5129 5174 5130\nf 5130 5174 5173\nf 5131 5202 5132\nf 5132 5202 6472\nf 5135 5221 5122\nf 5122 5221 5119\nf 5125 5124 5136\nf 5136 5124 5209\nf 5126 5083 5121\nf 5121 5083 5164\nf 5118 5036 5195\nf 5195 5036 5193\nf 5131 5097 5203\nf 5203 5097 5114\nf 5132 6472 5195\nf 5195 6472 5153\nf 5136 5209 5092\nf 5092 5209 5089\nf 5135 5134 5117\nf 5117 5134 5037\nf 5362 5206 5130\nf 5130 5206 5127\nf 5372 5371 5128\nf 5128 5371 5129\nf 5371 5372 5091\nf 5091 5372 5172\nf 5362 5215 5203\nf 5203 5215 5201\nf 6463 6407 5353\nf 5353 6407 5318\nf 6464 6463 5352\nf 5352 6463 5353\nf 6408 6465 5288\nf 5288 6465 5289\nf 4196 4224 6426\nf 6426 4224 6427\nf 6430 6427 6428\nf 6428 6427 4224\nf 6466 6430 4382\nf 4382 6430 6428\nf 6410 6466 4217\nf 4217 6466 4382\nf 4240 4239 3015\nf 3015 4239 3016\nf 4254 4257 3017\nf 3017 4257 3018\nf 6473 6474 4732\nf 4732 6474 6475\nf 6474 6374 6475\nf 6475 6374 6373\nf 5246 6374 3022\nf 3022 6374 3023\nf 4254 6299 4255\nf 4255 6299 6288\nf 6473 6300 3024\nf 3024 6300 3025\nf 6476 4257 6312\nf 6312 4257 4256\nf 4240 6476 4241\nf 4241 6476 6312\nf 6477 4239 6391\nf 6391 4239 4238\nf 6467 6477 6457\nf 6457 6477 6391\nf 6411 3029 6467\nf 6467 3029 3028\nf 4216 3030 6411\nf 6411 3030 3029\nf 6479 6480 6478\nf 6478 6480 6481\nf 6483 6484 6482\nf 6482 6484 6485\nf 6486 6487 4102\nf 4102 6487 6331\nf 6483 6488 6478\nf 6478 6488 6489\nf 4429 4829 6490\nf 6490 4829 6479\nf 6491 6486 4101\nf 4101 6486 4102\nf 4895 6491 4899\nf 4899 6491 4101\nf 4160 6488 4157\nf 4157 6488 6492\nf 6489 4087 6490\nf 6490 4087 4090\nf 4267 4261 5013\nf 5013 4261 4260\nf 4258 4261 5328\nf 5328 4261 4975\nf 4261 4267 4975\nf 4975 4267 4266\nf 5367 3793 3912\nf 3912 3793 3575\nf 6494 3616 6493\nf 6493 3616 3795\nf 6496 3589 6495\nf 6495 3589 3588\nf 6495 3588 5368\nf 5368 3588 3794\nf 3841 3840 3050\nf 3050 3840 3051\nf 3912 3911 3052\nf 3052 3911 3053\nf 3929 3930 3054\nf 3054 3930 3055\nf 5370 3930 3916\nf 3916 3930 3923\nf 3938 5369 3807\nf 3807 5369 3915\nf 3589 6496 3806\nf 3806 6496 3937\nf 3938 3937 3056\nf 3056 3937 3057\nf 3051 3840 3058\nf 3058 3840 3943\nf 3730 4808 3727\nf 3727 4808 3943\nf 3820 3951 3795\nf 3795 3951 6493\nf 3951 3841 3059\nf 3059 3841 3050\nf 3911 3574 6494\nf 6494 3574 3616\nf 3823 3929 3824\nf 3824 3929 3952\nf 4061 4060 3060\nf 3060 4060 3061\nf 6497 6498 4901\nf 4901 6498 4790\nf 6333 6499 4738\nf 4738 6499 4739\nf 6500 6501 6314\nf 6314 6501 4418\nf 6502 6275 4417\nf 4417 6275 4185\nf 6503 6497 4783\nf 4783 6497 4901\nf 6498 4060 4790\nf 4790 4060 4059\nf 4808 4061 3069\nf 3069 4061 3060\nf 5367 3071 5368\nf 5368 3071 3070\nf 5369 3073 5370\nf 5370 3073 3072\nf 6274 6275 3074\nf 3074 6275 3075\nf 6499 6504 4739\nf 4739 6504 6317\nf 6333 6332 3077\nf 3077 6332 3078\nf 6334 6274 3079\nf 3079 6274 3074\nf 6332 4825 3078\nf 3078 4825 3080\nf 6334 6503 4188\nf 4188 6503 4783\nf 4825 4689 3080\nf 3080 4689 1330\nf 6506 4747 6505\nf 6505 4747 4746\nf 6505 6507 6506\nf 6506 6507 6508\nf 4297 6509 4920\nf 4920 6509 6510\nf 4296 6511 4297\nf 4297 6511 6509\nf 6506 6508 4319\nf 4319 6508 6512\nf 4372 4371 6514\nf 6514 4371 6513\nf 6511 4296 5251\nf 5251 4296 4305\nf 5251 5250 6511\nf 6511 5250 6515\nf 6516 6509 6515\nf 6515 6509 6511\nf 6517 6507 6519\nf 6519 6507 6518\nf 6512 6520 6513\nf 6513 6520 6514\nf 6507 6517 6508\nf 6508 6517 6521\nf 5249 6523 6522\nf 6522 6523 6524\nf 6525 6370 5253\nf 5253 6370 6526\nf 4370 4323 4371\nf 4371 4323 4326\nf 4370 3103 4323\nf 4323 3103 1541\nf 6521 6517 6527\nf 6527 6517 6519\nf 6520 6528 6514\nf 6514 6528 6529\nf 6531 6532 6530\nf 6530 6532 6533\nf 5250 6534 6515\nf 6515 6534 6535\nf 6530 6533 6536\nf 6536 6533 6537\nf 6526 6538 6534\nf 6534 6538 6537\nf 6515 6535 6516\nf 6516 6535 6539\nf 4325 4321 4326\nf 4326 4321 4320\nf 4371 4326 6513\nf 6513 4326 4320\nf 6531 4731 6541\nf 6541 4731 6540\nf 4731 4730 6540\nf 6540 4730 6542\nf 4730 3121 6542\nf 6542 3121 3120\nf 6530 4328 6531\nf 6531 4328 4731\nf 4328 6530 4329\nf 4329 6530 6536\nf 4329 6536 6524\nf 6524 6536 6543\nf 6544 6545 6522\nf 6522 6545 5248\nf 6389 6388 5245\nf 5245 6388 6373\nf 6388 6546 6373\nf 6373 6546 6475\nf 4733 6546 6295\nf 6295 6546 4377\nf 4732 6475 4733\nf 4733 6475 6546\nf 4393 4432 4394\nf 4394 4432 4435\nf 4434 4672 4842\nf 4842 4672 4671\nf 4440 4439 4441\nf 4441 4439 4438\nf 4436 4450 4437\nf 4437 4450 4398\nf 4447 4444 4448\nf 4448 4444 4392\nf 4446 4464 4433\nf 4433 4464 6547\nf 4454 4423 4455\nf 4455 4423 4422\nf 4452 4481 4453\nf 4453 4481 4480\nf 4458 4389 4459\nf 4459 4389 4388\nf 4456 4471 4457\nf 4457 4471 4468\nf 4391 4460 4392\nf 4392 4460 4448\nf 4461 4467 4445\nf 4445 4467 4465\nf 4437 4398 4629\nf 4629 4398 4397\nf 4438 4628 4441\nf 4441 4628 4631\nf 4841 4629 4839\nf 4839 4629 4397\nf 4628 4842 4631\nf 4631 4842 4671\nf 4930 4989 4452\nf 4452 4989 4481\nf 4929 4927 4436\nf 4436 4927 4450\nf 4933 4387 4934\nf 4934 4387 4386\nf 4931 4475 4932\nf 4932 4475 4474\nf 4936 4449 4933\nf 4933 4449 4387\nf 4935 4472 4456\nf 4456 4472 4471\nf 4389 4458 4391\nf 4391 4458 4460\nf 4457 4468 4461\nf 4461 4468 4467\nf 4934 4386 4454\nf 4454 4386 4423\nf 4453 4480 4931\nf 4931 4480 4475\nf 4575 4997 4576\nf 4576 4997 4639\nf 4579 4596 4580\nf 4580 4596 4595\nf 4583 4582 4572\nf 4572 4582 4569\nf 4587 4944 4588\nf 4588 4944 4562\nf 4588 4562 4590\nf 4590 4562 4561\nf 4576 4639 4642\nf 4642 4639 4638\nf 4642 4845 4641\nf 4641 4845 4846\nf 4845 4590 4846\nf 4846 4590 4561\nf 4941 4938 4595\nf 4595 4938 4580\nf 4938 4940 4668\nf 4668 4940 4669\nf 4583 4942 4584\nf 4584 4942 4947\nf 4582 4587 4569\nf 4569 4587 4586\nf 5000 5001 4579\nf 4579 5001 4596\nf 4558 4560 4585\nf 4585 4560 4586\nf 4565 4639 4566\nf 4566 4639 4997\nf 4568 4567 4577\nf 4577 4567 4578\nf 4570 4554 4571\nf 4571 4554 4556\nf 4640 4565 4637\nf 4637 4565 4546\nf 4662 4664 4665\nf 4665 4664 4844\nf 4662 4558 4589\nf 4589 4558 4585\nf 4554 4570 4557\nf 4557 4570 4560\nf 4571 4667 4572\nf 4572 4667 4943\nf 4551 4670 4552\nf 4552 4670 4666\nf 4640 4664 4638\nf 4638 4664 4663\nf 4670 4568 4668\nf 4668 4568 4577\nf 4999 4996 4566\nf 4566 4996 4543\nf 4608 4564 4603\nf 4603 4564 4563\nf 4573 4593 4574\nf 4574 4593 4592\nf 4597 4614 4598\nf 4598 4614 4613\nf 4601 4600 4584\nf 4584 4600 4581\nf 4563 4562 4604\nf 4604 4562 4944\nf 4643 4593 4641\nf 4641 4593 4573\nf 4643 4847 4644\nf 4644 4847 4848\nf 4564 4847 4561\nf 4561 4847 4846\nf 4945 4941 4598\nf 4598 4941 4595\nf 4941 4945 4939\nf 4939 4945 4946\nf 4601 4948 4602\nf 4602 4948 4620\nf 4600 4604 4581\nf 4581 4604 4944\nf 5002 5003 4597\nf 4597 5003 4614\nf 4988 4759 4953\nf 4953 4759 4402\nf 4706 6548 4707\nf 4707 6548 6549\nf 4311 4928 4419\nf 4419 4928 6550\nf 4921 4920 6551\nf 6551 4920 6552\nf 4925 6553 4926\nf 4926 6553 6554\nf 4928 4921 4802\nf 4802 4921 6551\nf 6555 4985 6550\nf 6550 4985 4419\nf 4952 6556 4953\nf 4953 6556 4649\nf 4971 6557 4706\nf 4706 6557 6548\nf 4988 4648 4925\nf 4925 4648 6553\nf 4707 6549 5006\nf 5006 6549 4987\nf 4920 6510 6552\nf 6552 6510 6518\nf 6558 6510 6516\nf 6516 6510 6509\nf 6559 6558 6539\nf 6539 6558 6516\nf 6531 6541 6532\nf 6532 6541 6560\nf 6540 6561 6541\nf 6541 6561 6529\nf 6542 6562 6540\nf 6540 6562 6561\nf 6542 3120 6562\nf 6562 3120 3142\nf 4366 6563 4760\nf 4760 6563 4970\nf 4815 6564 4816\nf 4816 6564 6565\nf 3147 3146 6565\nf 6565 3146 4816\nf 6564 6563 6565\nf 6565 6563 6566\nf 4837 4385 4357\nf 4357 4385 4358\nf 6565 6566 3147\nf 3147 6566 3149\nf 4694 1209 3150\nf 5247 5249 5248\nf 5248 5249 6522\nf 6372 6390 6545\nf 6545 6390 6389\nf 6375 5254 6376\nf 6376 5254 5258\nf 6377 6525 6378\nf 6378 6525 5255\nf 5260 6379 5258\nf 5258 6379 6376\nf 6380 6567 6381\nf 6381 6567 6568\nf 6382 6569 6383\nf 6383 6569 6570\nf 6377 6371 6525\nf 6525 6371 6370\nf 6379 5260 6384\nf 6384 5260 6571\nf 6385 6572 6382\nf 6382 6572 6569\nf 6378 5255 6375\nf 6375 5255 5254\nf 6386 6573 6387\nf 6387 6573 6574\nf 6545 6389 5248\nf 5248 6389 5245\nf 4675 4750 4676\nf 4676 4750 6286\nf 4734 6289 4735\nf 4735 6289 6288\nf 4804 6290 4805\nf 4805 6290 4963\nf 4811 4807 4812\nf 4812 4807 4411\nf 4954 6292 4955\nf 4955 6292 4959\nf 4964 4962 4965\nf 4965 4962 4751\nf 6285 4821 4812\nf 4812 4821 4809\nf 4675 4287 4965\nf 4965 4287 4210\nf 6294 4145 4955\nf 4955 4145 4154\nf 4964 4120 4805\nf 4805 4120 4119\nf 6298 4966 4734\nf 4734 4966 6289\nf 6300 6473 4735\nf 4735 6473 4732\nf 6297 6296 4676\nf 4676 6296 4288\nf 6523 3160 4330\nf 4330 3160 3159\nf 5253 5252 6525\nf 6525 5252 5255\nf 5256 5252 4305\nf 4305 5252 5251\nf 4304 5257 4305\nf 4305 5257 5256\nf 4424 5259 4304\nf 4304 5259 5257\nf 5261 6571 5259\nf 5259 6571 5260\nf 5262 6573 5261\nf 5261 6573 6571\nf 5264 6567 5265\nf 5265 6567 6574\nf 5266 6572 5267\nf 5267 6572 6568\nf 5268 6570 5269\nf 5269 6570 6569\nf 1807 3161 5268\nf 5268 3161 6570\nf 6524 6523 4329\nf 4329 6523 4330\nf 5253 6526 5250\nf 5250 6526 6534\nf 6543 6538 6544\nf 6544 6538 6369\nf 6524 6543 6522\nf 6522 6543 6544\nf 3071 5367 3052\nf 3052 5367 3912\nf 6493 3163 6494\nf 6494 3163 3162\nf 3164 445 3771\nf 3771 445 3761\nf 461 3165 3919\nf 3919 3165 3920\nf 330 410 3839\nf 3839 410 3910\nf 332 331 3552\nf 3552 331 3551\nf 464 304 3918\nf 3918 304 3816\nf 385 458 3766\nf 3766 458 3894\nf 412 415 3762\nf 3762 415 3763\nf 3166 337 3748\nf 3748 337 3745\nf 3072 3055 5370\nf 5370 3055 3930\nf 3073 5369 3056\nf 3056 5369 3938\nf 3167 3057 6496\nf 6496 3057 3937\nf 3069 3058 4808\nf 4808 3058 3943\nf 411 3166 3851\nf 3851 3166 3748\nf 3059 3163 3951\nf 3951 3163 6493\nf 336 3164 3772\nf 3772 3164 3771\nf 3053 3911 3162\nf 3162 3911 6494\nf 3054 3168 3929\nf 3929 3168 3952\nf 3165 830 3920\nf 3920 830 3945\nf 3170 3169 6497\nf 6497 3169 6498\nf 3171 1354 5005\nf 5005 1354 4830\nf 3172 3170 6503\nf 6503 3170 6497\nf 3169 3061 6498\nf 6498 3061 4060\nf 3173 3171 5006\nf 5006 3171 5005\nf 3079 3172 6334\nf 6334 3172 6503\nf 3174 3025 6299\nf 6299 3025 6300\nf 6576 6577 6575\nf 6575 6577 6578\nf 4612 4611 6579\nf 6579 4611 6580\nf 6582 6583 6581\nf 6581 6583 6584\nf 6584 6585 4618\nf 4618 6585 4621\nf 6586 6587 6589\nf 6589 6587 6588\nf 6588 6587 6590\nf 6590 6587 6591\nf 6592 4606 6590\nf 6590 4606 4623\nf 6594 6576 6593\nf 6593 6576 6575\nf 6595 6582 6579\nf 6579 6582 6581\nf 6580 6597 6596\nf 6596 6597 6598\nf 6577 6576 6599\nf 6599 6576 6600\nf 6595 6596 6601\nf 6601 6596 6602\nf 6583 6582 6603\nf 6603 6582 6604\nf 6586 6606 6605\nf 6605 6606 6607\nf 6587 6586 6608\nf 6608 6586 6605\nf 6587 6608 6591\nf 6591 6608 6609\nf 6576 6594 6600\nf 6600 6594 6610\nf 6582 6595 6604\nf 6604 6595 6601\nf 6599 6611 6577\nf 6577 6611 6598\nf 6613 6614 6612\nf 6612 6614 6615\nf 6617 6618 6616\nf 6616 6618 6619\nf 6621 6622 6620\nf 6620 6622 6623\nf 6625 6626 6624\nf 6624 6626 6627\nf 6629 6630 6628\nf 6628 6630 6631\nf 6633 6634 6632\nf 6632 6634 6635\nf 6637 6638 6636\nf 6636 6638 6639\nf 6640 6641 6637\nf 6637 6641 6638\nf 6643 6633 6642\nf 6642 6633 6632\nf 6645 6646 6644\nf 6644 6646 6647\nf 6646 6648 6647\nf 6647 6648 6649\nf 6651 6652 6650\nf 6650 6652 6653\nf 6653 6652 6654\nf 6654 6652 6655\nf 6656 6657 6659\nf 6659 6657 6658\nf 6661 6662 6660\nf 6660 6662 6663\nf 6662 6661 6664\nf 6664 6661 6665\nf 6667 6657 6666\nf 6666 6657 6656\nf 6669 6670 6668\nf 6668 6670 6671\nf 6672 6673 6670\nf 6670 6673 6671\nf 6674 6675 6677\nf 6677 6675 6676\nf 6675 6678 6676\nf 6676 6678 6679\nf 6681 6682 6680\nf 6680 6682 6683\nf 6684 6685 6683\nf 6683 6685 6680\nf 6687 6688 6686\nf 6686 6688 6689\nf 6687 6690 6688\nf 6688 6690 6691\nf 6619 6618 6633\nf 6633 6618 6634\nf 6632 6635 6693\nf 6693 6635 6692\nf 6617 6694 6636\nf 6636 6694 6637\nf 6695 6692 6639\nf 6639 6692 6635\nf 6637 6694 6640\nf 6640 6694 6696\nf 6698 6638 6697\nf 6697 6638 6641\nf 6643 6616 6633\nf 6633 6616 6619\nf 6642 6699 6641\nf 6641 6699 6697\nf 6646 6645 6700\nf 6700 6645 6621\nf 6644 6701 6654\nf 6654 6701 6702\nf 6700 6703 6646\nf 6646 6703 6648\nf 6693 6704 6649\nf 6649 6704 6647\nf 6620 6623 6651\nf 6651 6623 6652\nf 6693 6649 6699\nf 6699 6649 6650\nf 6652 6623 6655\nf 6655 6623 6622\nf 6654 6702 6653\nf 6653 6702 6697\nf 6615 6614 6657\nf 6657 6614 6658\nf 6705 6656 6704\nf 6704 6656 6659\nf 6613 6706 6660\nf 6660 6706 6661\nf 6704 6659 6701\nf 6701 6659 6663\nf 6661 6706 6665\nf 6665 6706 6707\nf 6708 6702 6664\nf 6664 6702 6662\nf 6612 6615 6667\nf 6667 6615 6657\nf 6666 6709 6664\nf 6664 6709 6708\nf 6629 6710 6669\nf 6669 6710 6670\nf 6668 6711 6679\nf 6679 6711 6712\nf 6713 6672 6710\nf 6710 6672 6670\nf 6714 6671 6705\nf 6705 6671 6673\nf 6628 6631 6674\nf 6674 6631 6675\nf 6705 6673 6709\nf 6709 6673 6677\nf 6631 6630 6675\nf 6675 6630 6678\nf 6712 6708 6679\nf 6679 6708 6676\nf 6625 6715 6682\nf 6682 6715 6683\nf 6716 6717 6681\nf 6681 6717 6691\nf 6718 6684 6715\nf 6715 6684 6683\nf 6719 6680 6714\nf 6714 6680 6685\nf 6624 6627 6686\nf 6686 6627 6687\nf 6689 6711 6685\nf 6685 6711 6714\nf 6627 6626 6687\nf 6687 6626 6690\nf 6717 6712 6691\nf 6691 6712 6688\nf 6612 6707 6613\nf 6613 6707 6706\nf 6616 6696 6617\nf 6617 6696 6694\nf 6703 6700 6620\nf 6620 6700 6621\nf 6715 6625 6718\nf 6718 6625 6624\nf 6710 6629 6713\nf 6713 6629 6628\nf 6639 6635 6636\nf 6636 6635 6634\nf 6641 6640 6642\nf 6642 6640 6643\nf 6654 6655 6644\nf 6644 6655 6645\nf 6651 6650 6648\nf 6648 6650 6649\nf 6659 6658 6663\nf 6663 6658 6660\nf 6665 6667 6664\nf 6664 6667 6666\nf 6678 6669 6679\nf 6679 6669 6668\nf 6673 6672 6677\nf 6677 6672 6674\nf 6690 6682 6691\nf 6691 6682 6681\nf 6685 6684 6689\nf 6689 6684 6686\nf 6636 6634 6617\nf 6617 6634 6618\nf 6638 6698 6639\nf 6639 6698 6695\nf 6640 6696 6643\nf 6643 6696 6616\nf 6693 6699 6632\nf 6632 6699 6642\nf 6655 6622 6645\nf 6645 6622 6621\nf 6704 6701 6647\nf 6647 6701 6644\nf 6651 6648 6620\nf 6620 6648 6703\nf 6650 6653 6699\nf 6699 6653 6697\nf 6613 6660 6614\nf 6614 6660 6658\nf 6663 6662 6701\nf 6701 6662 6702\nf 6665 6707 6667\nf 6667 6707 6612\nf 6705 6709 6656\nf 6656 6709 6666\nf 6630 6629 6678\nf 6678 6629 6669\nf 6714 6711 6671\nf 6671 6711 6668\nf 6672 6713 6674\nf 6674 6713 6628\nf 6677 6676 6709\nf 6709 6676 6708\nf 6626 6625 6690\nf 6690 6625 6682\nf 6680 6719 6681\nf 6681 6719 6716\nf 6684 6718 6686\nf 6686 6718 6624\nf 6711 6689 6712\nf 6689 6688 6712\nf 6593 6720 6594\nf 6594 6720 6721\nf 6722 6720 4607\nf 4607 6720 4849\nf 6592 6723 6722\nf 6722 6723 6724\nf 4610 4609 6578\nf 6578 4609 6575\nf 6596 6595 6580\nf 6580 6595 6579\nf 4618 4617 6584\nf 6584 4617 6581\nf 6589 6585 6586\nf 6586 6585 6606\nf 4622 4619 6588\nf 6588 4619 6589\nf 4622 6588 4623\nf 4623 6588 6590\nf 4609 4645 6575\nf 6575 4645 6593\nf 4617 4612 6581\nf 6581 4612 6579\nf 5004 4610 6597\nf 6597 4610 6578\nf 4645 4849 6593\nf 6593 4849 6720\nf 6724 6721 6722\nf 6722 6721 6720\nf 6722 4607 6592\nf 6592 4607 4606\nf 6725 6610 6721\nf 6721 6610 6594\nf 6723 6726 6724\nf 6724 6726 6727\nf 6727 6725 6724\nf 6724 6725 6721\nf 6728 6729 6605\nf 6605 6729 6608\nf 6605 6607 6728\nf 6728 6607 6730\nf 6731 6732 6604\nf 6604 6732 6603\nf 6733 6734 6725\nf 6725 6734 6610\nf 6735 6733 6727\nf 6727 6733 6725\nf 6735 6727 6736\nf 6736 6727 6726\nf 6591 6609 6723\nf 6723 6609 6726\nf 6723 6592 6591\nf 6591 6592 6590\nf 6738 6739 6737\nf 6737 6739 6740\nf 6740 6739 6742\nf 6742 6739 6741\nf 6743 6744 6741\nf 6741 6744 6742\nf 6745 6746 6743\nf 6743 6746 6744\nf 6747 6748 6745\nf 6745 6748 6746\nf 6749 6750 6747\nf 6747 6750 6748\nf 6752 6738 6751\nf 6751 6738 6737\nf 6754 6752 6753\nf 6753 6752 6751\nf 6756 6754 6755\nf 6755 6754 6753\nf 6758 6750 6757\nf 6757 6750 6749\nf 6760 6756 6759\nf 6759 6756 6755\nf 6762 6760 6761\nf 6761 6760 6759\nf 6764 6762 6763\nf 6763 6762 6761\nf 6757 6764 6758\nf 6758 6764 6763\nf 6766 6741 6765\nf 6765 6741 6739\nf 6767 6743 6766\nf 6766 6743 6741\nf 6768 6745 6767\nf 6767 6745 6743\nf 6769 6747 6768\nf 6768 6747 6745\nf 6770 6749 6769\nf 6769 6749 6747\nf 6772 6769 6771\nf 6771 6769 6768\nf 6773 6770 6772\nf 6772 6770 6769\nf 6774 6775 6772\nf 6772 6775 6773\nf 6777 6778 6776\nf 6776 6778 6779\nf 6775 6774 6778\nf 6778 6774 6779\nf 6771 6780 6772\nf 6772 6780 6774\nf 6780 6781 6774\nf 6774 6781 6779\nf 6781 6782 6779\nf 6779 6782 6776\nf 6784 6785 6783\nf 6783 6785 6786\nf 6786 6787 6783\nf 6783 6787 6788\nf 6790 6788 6789\nf 6789 6788 6787\nf 6786 6785 6791\nf 6791 6785 6792\nf 6791 6793 6786\nf 6786 6793 6787\nf 6793 6794 6787\nf 6787 6794 6789\nf 6795 6796 6794\nf 6794 6796 6789\nf 6790 6789 6797\nf 6797 6789 6796\nf 6798 6765 6738\nf 6738 6765 6739\nf 6799 6798 6752\nf 6752 6798 6738\nf 6767 6800 6768\nf 6768 6800 6771\nf 6766 6801 6767\nf 6767 6801 6800\nf 6802 6801 6765\nf 6765 6801 6766\nf 6798 6803 6765\nf 6765 6803 6802\nf 6804 6803 6799\nf 6799 6803 6798\nf 6805 6806 6804\nf 6804 6806 6803\nf 6807 6799 6754\nf 6754 6799 6752\nf 6808 6804 6807\nf 6807 6804 6799\nf 6809 6805 6808\nf 6808 6805 6804\nf 6810 6811 6805\nf 6805 6811 6806\nf 6812 6813 6810\nf 6810 6813 6811\nf 6814 6815 6796\nf 6796 6815 6797\nf 6813 6814 6816\nf 6816 6814 6817\nf 6816 6818 6813\nf 6813 6818 6811\nf 6819 6806 6818\nf 6818 6806 6811\nf 6802 6803 6819\nf 6819 6803 6806\nf 6757 6749 6820\nf 6820 6749 6770\nf 6820 6770 6821\nf 6821 6770 6773\nf 6821 6773 6822\nf 6822 6773 6775\nf 6822 6775 6823\nf 6823 6775 6778\nf 6824 6823 6777\nf 6777 6823 6778\nf 6784 6825 6777\nf 6777 6825 6824\nf 6826 6827 6788\nf 6788 6827 6783\nf 6828 6826 6790\nf 6790 6826 6788\nf 6829 6828 6797\nf 6797 6828 6790\nf 6815 6830 6797\nf 6797 6830 6829\nf 6832 6812 6831\nf 6831 6812 6810\nf 6831 6810 6809\nf 6809 6810 6805\nf 6736 6800 6735\nf 6735 6800 6801\nf 6733 6735 6802\nf 6802 6735 6801\nf 6734 6733 6819\nf 6819 6733 6802\nf 6833 6734 6818\nf 6818 6734 6819\nf 6834 6833 6816\nf 6816 6833 6818\nf 6816 6817 6834\nf 6834 6817 6835\nf 6837 6795 6836\nf 6836 6795 6794\nf 6836 6794 6731\nf 6731 6794 6793\nf 6732 6731 6791\nf 6791 6731 6793\nf 6791 6792 6732\nf 6732 6792 6730\nf 6729 6728 6781\nf 6781 6728 6782\nf 6838 6807 6756\nf 6756 6807 6754\nf 6839 6838 6760\nf 6760 6838 6756\nf 6840 6839 6762\nf 6762 6839 6760\nf 6841 6840 6764\nf 6764 6840 6762\nf 6820 6841 6757\nf 6757 6841 6764\nf 6841 6820 6842\nf 6842 6820 6821\nf 6840 6841 6843\nf 6843 6841 6842\nf 6839 6840 6844\nf 6844 6840 6843\nf 6838 6839 6845\nf 6845 6839 6844\nf 6807 6838 6808\nf 6808 6838 6845\nf 6842 6821 6846\nf 6846 6821 6822\nf 6843 6842 6847\nf 6847 6842 6846\nf 6844 6843 6848\nf 6848 6843 6847\nf 6845 6844 6849\nf 6849 6844 6848\nf 6808 6845 6809\nf 6809 6845 6849\nf 6850 6831 6849\nf 6849 6831 6809\nf 6851 6850 6848\nf 6848 6850 6849\nf 6847 6852 6848\nf 6848 6852 6851\nf 6853 6852 6846\nf 6846 6852 6847\nf 6846 6822 6853\nf 6853 6822 6823\nf 6853 6823 6854\nf 6854 6823 6824\nf 6854 6855 6853\nf 6853 6855 6852\nf 6856 6851 6855\nf 6855 6851 6852\nf 6856 6857 6851\nf 6851 6857 6850\nf 6857 6832 6850\nf 6850 6832 6831\nf 6825 6858 6824\nf 6824 6858 6854\nf 6860 6827 6859\nf 6859 6827 6826\nf 6859 6826 6861\nf 6861 6826 6828\nf 6862 6861 6829\nf 6829 6861 6828\nf 6830 6863 6829\nf 6829 6863 6862\nf 6863 6864 6862\nf 6862 6864 6865\nf 6858 6866 6854\nf 6854 6866 6855\nf 6867 6865 6866\nf 6866 6865 6864\nf 6869 6859 6868\nf 6868 6859 6861\nf 6867 6869 6865\nf 6865 6869 6868\nf 6861 6862 6868\nf 6868 6862 6865\nf 6867 6860 6869\nf 6869 6860 6859\nf 6753 6751 6698\nf 6698 6751 6695\nf 6697 6755 6698\nf 6698 6755 6753\nf 6759 6755 6702\nf 6702 6755 6697\nf 6761 6759 6708\nf 6708 6759 6702\nf 6763 6761 6712\nf 6712 6761 6708\nf 6758 6763 6717\nf 6717 6763 6712\nf 6750 6758 6716\nf 6716 6758 6717\nf 6719 6748 6716\nf 6716 6748 6750\nf 6714 6746 6719\nf 6719 6746 6748\nf 6705 6744 6714\nf 6714 6744 6746\nf 6704 6742 6705\nf 6705 6742 6744\nf 6740 6742 6693\nf 6693 6742 6704\nf 6737 6740 6692\nf 6692 6740 6693\nf 6751 6737 6695\nf 6695 6737 6692\nf 6601 6602 6836\nf 6836 6602 6837\nf 6604 6601 6731\nf 6731 6601 6836\nf 6599 6600 6834\nf 6834 6600 6833\nf 6834 6835 6599\nf 6599 6835 6611\nf 6600 6610 6833\nf 6833 6610 6734\nf 6780 6771 6870\nf 6870 6771 6736\nf 6736 6771 6800\nf 6870 6729 6780\nf 6780 6729 6781\nf 6608 6729 6609\nf 6609 6729 6870\nf 6609 6870 6726\nf 6726 6870 6736\nf 5014 5015 5229\nf 5229 5015 5230\nf 4258 5015 4259\nf 4259 5015 4878\nf 3471 3013 4703\nf 4703 3013 4704\nf 2879 6337 1306\nf 1306 6337 4785\nf 829 3471 4316\nf 4316 3471 4703\nf 4702 3472 5223\nf 5223 3472 1756\nf 6871 6872 4534\nf 4534 6872 4535\nf 6871 4665 6872\nf 6872 4665 4844\nf 6871 6873 4665\nf 4665 6873 4559\nf 6871 4534 6873\nf 6873 4534 4542\nf 4153 4152 4822\nf 4822 4152 4407\nf 4152 4168 4407\nf 4407 4168 4342\nf 4343 4168 4923\nf 4923 4168 4167\nf 4363 4362 4972\nf 4972 4362 4840\nf 4362 4394 4840\nf 4840 4394 4839\nf 4401 4400 4986\nf 4986 4400 4984\nf 4435 4434 4841\nf 4841 4434 4842\nf 4506 4505 4530\nf 4530 4505 4531\nf 4506 4535 4507\nf 4507 4535 4843\nf 4661 4660 4516\nf 4516 4660 4517\nf 4673 4672 4937\nf 4937 4672 6547\nf 4400 4924 4984\nf 4984 4924 5263\nf 4673 4660 4674\nf 4674 4660 4659\nf 4505 4661 4531\nf 4531 4661 4516\nf 4952 4401 6874\nf 6874 4401 4986\nf 4954 4153 6875\nf 6875 4153 4822\nf 4363 4973 4364\nf 4364 4973 4974\nf 5264 4924 5267\nf 5267 4924 4691\nf 6293 6292 6876\nf 6876 6292 6877\nf 6380 4343 6387\nf 6387 4343 4923\nf 4394 4435 4839\nf 4839 4435 4841\nf 4672 4434 6547\nf 6547 4434 4433\nf 4973 6556 4974\nf 4974 6556 6878\nf 6567 6380 6574\nf 6574 6380 6387\nf 6292 4954 6877\nf 6877 4954 6875\nf 6567 5264 6568\nf 6568 5264 5267\nf 4145 4144 4154\nf 4154 4144 4151\nf 4194 4144 4425\nf 4425 4144 4143\nf 4355 4354 5263\nf 5263 4354 4984\nf 4360 4359 4364\nf 4364 4359 4361\nf 4444 4359 4392\nf 4392 4359 4337\nf 4447 4446 4432\nf 4432 4446 4433\nf 4464 4463 6547\nf 6547 4463 4937\nf 4519 4518 4654\nf 4654 4518 4509\nf 4533 4532 4499\nf 4499 4532 4498\nf 4533 4542 4530\nf 4530 4542 4534\nf 4519 4532 4516\nf 4516 4532 4531\nf 4922 4194 4426\nf 4426 4194 4425\nf 4463 4518 4937\nf 4937 4518 4517\nf 6555 4951 6874\nf 6874 4951 6878\nf 4958 4957 6290\nf 6290 4957 6291\nf 4334 4360 4712\nf 4712 4360 4950\nf 4985 4354 4419\nf 4419 4354 4353\nf 5262 4355 5265\nf 5265 4355 5263\nf 6294 4958 4804\nf 4804 4958 6290\nf 6386 4922 6384\nf 6384 4922 4426\nf 4444 4447 4393\nf 4393 4447 4432\nf 4464 4446 4465\nf 4465 4446 4445\nf 4985 6555 4986\nf 4986 6555 6874\nf 6573 6386 6571\nf 6571 6386 6384\nf 4145 6294 4146\nf 4146 6294 4804\nf 6573 5262 6574\nf 6574 5262 5265\nf 4542 4541 6873\nf 6873 4541 4559\nf 4535 6872 4843\nf 4843 6872 4844\nf 4104 4103 6879\nf 6879 4103 6343\nf 4105 6293 6307\nf 6307 6293 6876\nf 4104 4956 4105\nf 4105 4956 6293\nf 4956 4104 4957\nf 4957 4104 6879\nf 4957 6879 6291\nf 6291 6879 6309\nf 4648 4988 4649\nf 4649 4988 4953\nf 4802 4803 4928\nf 4928 4803 6550\nf 6550 4803 6555\nf 6555 4803 4951\nf 1516 3173 4987\nf 4987 3173 5006\nf 6519 6518 6558\nf 6558 6518 6510\nf 6527 6519 6559\nf 6559 6519 6558\nf 6528 6560 6529\nf 6529 6560 6541\nf 6548 4625 6549\nf 6549 4625 4624\nf 6551 6552 4746\nf 4746 6552 6505\nf 6553 4711 6554\nf 6554 4711 4710\nf 4802 6551 4713\nf 4713 6551 4746\nf 6556 4973 4649\nf 4649 4973 4646\nf 6557 4949 6548\nf 6548 4949 4625\nf 4648 4647 6553\nf 6553 4647 4711\nf 6549 4624 4987\nf 4987 4624 4838\nf 6552 6518 6505\nf 6505 6518 6507\nf 6561 4372 6529\nf 6529 4372 6514\nf 6562 4369 6561\nf 6561 4369 4372\nf 3142 3482 6562\nf 6562 3482 4369\nf 6556 4952 6878\nf 6878 4952 6874\nf 4951 4950 6878\nf 6878 4950 4974\nf 3168 440 3952\nf 4204 4203 5341\nf 5341 4203 6364\nf 4219 4222 6429\nf 6429 4222 4383\nf 4319 4322 6506\nf 6506 4322 4747\nf 4340 4714 4341\nf 4341 4714 4384\nf 4375 4377 6388\nf 6388 4377 6546\nf 4378 4381 6311\nf 6311 4381 4720\nf 4449 4341 4387\nf 4387 4341 4384\nf 4473 4472 4477\nf 4477 4472 4474\nf 4492 4491 4487\nf 4487 4491 4488\nf 4523 4491 4522\nf 4522 4491 4490\nf 4492 4538 4493\nf 4493 4538 4555\nf 4538 4553 4555\nf 4555 4553 4556\nf 4620 4615 4621\nf 4621 4615 4618\nf 4652 4651 4368\nf 4368 4651 4365\nf 4523 4655 4524\nf 4524 4655 4656\nf 4553 4667 4556\nf 4556 4667 4571\nf 4651 1210 4365\nf 4365 1210 1209\nf 4698 4697 5223\nf 5223 4697 6344\nf 4340 4339 4322\nf 4322 4339 4747\nf 4726 4729 5287\nf 5287 4729 4198\nf 4381 4380 4737\nf 4737 4380 6368\nf 4836 4835 4451\nf 4451 4835 4709\nf 4910 6367 4911\nf 4911 6367 6366\nf 4912 4915 6450\nf 6450 4915 6449\nf 4927 4836 4450\nf 4450 4836 4451\nf 4930 4929 4439\nf 4439 4929 4436\nf 4936 4935 4459\nf 4459 4935 4456\nf 4473 4655 4470\nf 4470 4655 4510\nf 4942 4583 4943\nf 4943 4583 4572\nf 4948 4601 4947\nf 4947 4601 4584\nf 4949 4835 4625\nf 4625 4835 4626\nf 4966 4969 6289\nf 6289 4969 4695\nf 4971 4652 4926\nf 4926 4652 4368\nf 4203 4977 6364\nf 6364 4977 6365\nf 4990 4989 4443\nf 4443 4989 4440\nf 4990 4991 4478\nf 4478 4991 4512\nf 4992 4991 4528\nf 4528 4991 4515\nf 4992 4993 4527\nf 4527 4993 4485\nf 4994 4993 4502\nf 4502 4993 4503\nf 4994 4995 4482\nf 4482 4995 4550\nf 4543 4996 4544\nf 4544 4996 4995\nf 4999 4998 4567\nf 4567 4998 4578\nf 5000 4998 4575\nf 4575 4998 4997\nf 5002 5001 4592\nf 4592 5001 4574\nf 5003 5004 4614\nf 4614 5004 4611\nf 3483 4215 1755\nf 1755 4215 4698\nf 5274 5277 4195\nf 4195 5277 4223\nf 4726 5295 4727\nf 4727 5295 6350\nf 5309 5342 5310\nf 5310 5342 6363\nf 5315 5314 5316\nf 5316 5314 5317\nf 5339 6362 5340\nf 5340 6362 5363\nf 5310 6363 5339\nf 5339 6363 6362\nf 4204 5309 4201\nf 4201 5309 5308\nf 4915 5345 6449\nf 6449 5345 6448\nf 5345 5315 6448\nf 6448 5315 5316\nf 5348 5347 6351\nf 6351 5347 6352\nf 5365 4263 5340\nf 5340 4263 4262\nf 6295 6298 4733\nf 4733 6298 4734\nf 4969 6306 4695\nf 4695 6306 4696\nf 6306 4378 4696\nf 4696 4378 6311\nf 6369 6372 6544\nf 6544 6372 6545\nf 4375 6390 4373\nf 4373 6390 4374\nf 6421 6422 4911\nf 4911 6422 4908\nf 4910 4909 4737\nf 4737 4909 4736\nf 5277 4219 4223\nf 4223 4219 6429\nf 4697 4222 6344\nf 6344 4222 4221\nf 6436 6361 6437\nf 6437 6361 6360\nf 6438 6440 6437\nf 6437 6440 6434\nf 6438 6359 6441\nf 6441 6359 6358\nf 6442 6441 6357\nf 6357 6441 6358\nf 6442 6444 6443\nf 6443 6444 6445\nf 6446 6444 6355\nf 6355 6444 6356\nf 6446 4912 6447\nf 6447 4912 6450\nf 6436 6435 5365\nf 5365 6435 4263\nf 5314 5347 5317\nf 5317 5347 5346\nf 4319 6512 4320\nf 4320 6512 6513\nf 6520 6512 6521\nf 6521 6512 6508\nf 6528 6520 6527\nf 6527 6520 6521\nf 6532 6539 6533\nf 6533 6539 6535\nf 6533 6535 6537\nf 6537 6535 6534\nf 6538 6543 6537\nf 6537 6543 6536\nf 6295 4377 6296\nf 6296 4377 4376\nf 4989 4930 4440\nf 4440 4930 4439\nf 4927 4929 4422\nf 4422 4929 4455\nf 4449 4936 4388\nf 4388 4936 4459\nf 4472 4935 4474\nf 4474 4935 4932\nf 4942 4940 4947\nf 4947 4940 4939\nf 5001 5000 4574\nf 4574 5000 4575\nf 4669 4943 4666\nf 4666 4943 4667\nf 4996 4999 4547\nf 4547 4999 4567\nf 4948 4946 4620\nf 4620 4946 4615\nf 5003 5002 4591\nf 4591 5002 4592\nf 6557 4971 6554\nf 6554 4971 4926\nf 6532 6560 6539\nf 6539 6560 6559\nf 6390 6372 4374\nf 4374 6372 6371\nf 4966 6298 4967\nf 4967 6298 6297\nf 6538 6526 6369\nf 6369 6526 6370\nf 6585 6589 4621\nf 4621 6589 4619\nf 6597 6578 6598\nf 6598 6578 6577\nf 6606 6583 6607\nf 6607 6583 6603\nf 6611 6602 6598\nf 6598 6602 6596\nf 6585 6584 6606\nf 6606 6584 6583\nf 6597 6580 5004\nf 5004 6580 4611\nf 6607 6603 6730\nf 6730 6603 6732\nf 6776 6785 6777\nf 6777 6785 6784\nf 6785 6776 6792\nf 6792 6776 6782\nf 6815 6814 6812\nf 6812 6814 6813\nf 6817 6814 6795\nf 6795 6814 6796\nf 6825 6784 6827\nf 6827 6784 6783\nf 6830 6815 6832\nf 6832 6815 6812\nf 6817 6795 6835\nf 6835 6795 6837\nf 6792 6782 6730\nf 6730 6782 6728\nf 6858 6825 6860\nf 6860 6825 6827\nf 6863 6830 6857\nf 6857 6830 6832\nf 6864 6863 6856\nf 6856 6863 6857\nf 6866 6858 6867\nf 6867 6858 6860\nf 6866 6864 6855\nf 6855 6864 6856\nf 6835 6837 6611\nf 6611 6837 6602\nf 6560 6528 6559\nf 6559 6528 6527\nf 4949 6557 4710\nf 4710 6557 6554\nf 4430 4090 4175\nf 4175 4090 4089\nf 4777 4113 4155\nf 4155 4113 4086\nf 4175 4089 4176\nf 4176 4089 4142\nf 4199 4271 4200\nf 4200 4271 4798\nf 4199 4095 4272\nf 4272 4095 5273\nf 4406 4405 4404\nf 4404 4405 4346\nf 4410 4409 6877\nf 6877 4409 6876\nf 4409 4413 6876\nf 6876 4413 6307\nf 4415 4148 4416\nf 4416 4148 4147\nf 4690 4405 4344\nf 4344 4405 4342\nf 4693 4692 4352\nf 4352 4692 4349\nf 4701 4700 6337\nf 6337 4700 6338\nf 4778 4777 4081\nf 4081 4777 4155\nf 4821 4406 4809\nf 4809 4406 4404\nf 4176 4142 4181\nf 4181 4142 4415\nf 4760 4970 4399\nf 4399 4970 4692\nf 4200 5007 4096\nf 4096 5007 4078\nf 5266 4693 5269\nf 5269 4693 4352\nf 5270 5007 5361\nf 5361 5007 4797\nf 4778 5270 4779\nf 4779 5270 5361\nf 6285 4410 6875\nf 6875 4410 6877\nf 6308 4413 4687\nf 4687 4413 4412\nf 6480 6320 6481\nf 6481 6320 6880\nf 4701 2878 4702\nf 4702 2878 3472\nf 6339 6347 6340\nf 6340 6347 6348\nf 6341 6342 6346\nf 6346 6342 6345\nf 4416 6308 4414\nf 4414 6308 4687\nf 6385 4690 6381\nf 6381 4690 4344\nf 5282 6341 5280\nf 5280 6341 6339\nf 4700 6342 6338\nf 6338 6342 5281\nf 6340 6348 4272\nf 4272 6348 4270\nf 6480 6479 4828\nf 4828 6479 4829\nf 6490 6479 6489\nf 6489 6479 6478\nf 4090 4430 6490\nf 6490 4430 4429\nf 6563 6564 4970\nf 4970 6564 4815\nf 4694 6566 4366\nf 4366 6566 6563\nf 6572 6385 6568\nf 6568 6385 6381\nf 4821 6285 4822\nf 4822 6285 6875\nf 6572 5266 6569\nf 6569 5266 5269\nf 3482 3103 4369\nf 4369 3103 4370\nf 3121 4730 3485\nf 3485 4730 4327\nf 6277 6280 6273\nf 6273 6280 6272\nf 6267 6276 6278\nf 6278 6276 6279\nf 6278 6279 6277\nf 6277 6279 6280\nf 6279 6256 6280\nf 6280 6256 6255\nf 6256 3857 6255\nf 6255 3857 3754\nf 3857 3856 3754\nf 3754 3856 3755\nf 3856 3703 3755\nf 3755 3703 3863\nf 3703 3702 3863\nf 3863 3702 3759\nf 3702 3706 3759\nf 3759 3706 3670\nf 3706 3705 3670\nf 3670 3705 3668\nf 3705 3773 3668\nf 3668 3773 3882\nf 3882 3773 3883\nf 3883 3773 3707\nf 3707 3708 3883\nf 3883 3708 3880\nf 3708 3710 3880\nf 3880 3710 3881\nf 3709 5241 3710\nf 3710 5241 3881\nf 5241 3709 5234\nf 5234 3709 3786\nf 3693 3712 3786\nf 3786 3712 5234\nf 4051 4055 4052\nf 4052 4055 4056\nf 4056 4055 4057\nf 4057 4055 4054\nf 4066 5238 5236\nf 5236 5238 3989\nf 4066 4069 5238\nf 5238 4069 4035\nf 6881 3962 3960\nf 3960 3962 3957\nf 6883 4070 6882\nf 6882 4070 3941\nf 4070 6883 4071\nf 4071 6883 6884\nf 4071 6884 4072\nf 4072 6884 6885\nf 6885 6886 4072\nf 4072 6886 4031\nf 3955 6881 3956\nf 3956 6881 3960\nf 6884 3991 6885\nf 6885 3991 3990\nf 6883 6887 6884\nf 6884 6887 3991\nf 6887 6888 3991\nf 3991 6888 3992\nf 6888 6887 6889\nf 6889 6887 6890\nf 6887 6883 6890\nf 6890 6883 6882\nf 4058 5240 3953\nf 3953 5240 3954\nf 5236 6888 5239\nf 5239 6888 6889\nf 5237 6886 3990\nf 3990 6886 6885\nf 4034 4031 5237\nf 5237 4031 6886\nf 6881 6882 3962\nf 3962 6882 3941\nf 6881 3955 6882\nf 6882 3955 6890\nf 3955 3954 6890\nf 6890 3954 6889\nf 5239 6889 5240\nf 5240 6889 3954\nf 6888 5236 3992\nf 3992 5236 3989\nf 3869 3868 3888\nf 3888 3868 4015\nf 3868 4012 4015\nf 4015 4012 4010\nf 4012 4022 4010\nf 4010 4022 4009\nf 4022 4021 4009\nf 4009 4021 4018\nf 4020 3974 4001\nf 4001 3974 3996\nf 4021 4020 4029\nf 4029 4020 4001\nf 4085 4156 4086\nf 4086 4156 4155\nf 4113 4777 4114\nf 4114 4777 4776\nf 4130 4129 4094\nf 4094 4129 4091\nf 4139 4773 4140\nf 4140 4773 4772\nf 4149 4178 4150\nf 4150 4178 5225\nf 4160 4159 4087\nf 4087 4159 4088\nf 4149 4148 4179\nf 4179 4148 4141\nf 4085 4084 4183\nf 4183 4084 6315\nf 4179 4141 4159\nf 4159 4141 4088\nf 4192 4130 4162\nf 4162 4130 4094\nf 4197 5288 4198\nf 4198 5288 5287\nf 4229 4766 4230\nf 4230 4766 4771\nf 4245 4246 4242\nf 4242 4246 4249\nf 4183 6315 4418\nf 4418 6315 6314\nf 4679 4178 4680\nf 4680 4178 4177\nf 4724 4798 4725\nf 4725 4798 4271\nf 4728 5275 4729\nf 4729 5275 5274\nf 4230 4771 4249\nf 4249 4771 4242\nf 4724 4723 4799\nf 4799 4723 5355\nf 4823 4824 4891\nf 4891 4824 6327\nf 4823 4192 4161\nf 4161 4192 4162\nf 4893 4767 4229\nf 4229 4767 4766\nf 4894 4895 4898\nf 4898 4895 4899\nf 4106 4150 4103\nf 4103 4150 5225\nf 4680 4894 4677\nf 4677 4894 4898\nf 6420 4977 5301\nf 5301 4977 4976\nf 4679 4678 5226\nf 5226 4678 4427\nf 5228 5008 4893\nf 4893 5008 4767\nf 5285 6354 5286\nf 5286 6354 5343\nf 5289 5352 5290\nf 5290 5352 5349\nf 5290 5349 5295\nf 5295 5349 5348\nf 5302 5299 4202\nf 4202 5299 4976\nf 4725 4271 5311\nf 5311 4271 4270\nf 5325 4723 5326\nf 5326 4723 4722\nf 5335 5355 5325\nf 5325 5355 4723\nf 5326 4722 5286\nf 5286 4722 5283\nf 4799 5357 4797\nf 4797 5357 5361\nf 5359 5356 5335\nf 5335 5356 5355\nf 4775 4774 5356\nf 5356 4774 5357\nf 5359 5358 4092\nf 4092 5358 4093\nf 6316 4740 6317\nf 6317 4740 4739\nf 6318 4827 6316\nf 6316 4827 4740\nf 6319 4828 6318\nf 6318 4828 4827\nf 6320 6480 6319\nf 6319 6480 4828\nf 6484 6322 6485\nf 6485 6322 6321\nf 6487 6324 6331\nf 6331 6324 6323\nf 6331 6323 6330\nf 6330 6323 6325\nf 6325 6326 6330\nf 6330 6326 6329\nf 6326 6328 6329\nf 6329 6328 5231\nf 4824 4685 5228\nf 5228 4685 5008\nf 5226 6343 5225\nf 5225 6343 4103\nf 4728 4727 6349\nf 6349 4727 5284\nf 6350 6351 5285\nf 5285 6351 6354\nf 4247 4246 6418\nf 6418 4246 6365\nf 6400 6402 6401\nf 6401 6402 6459\nf 4197 4196 6409\nf 6409 4196 6426\nf 6412 4897 6328\nf 6328 4897 5231\nf 6413 4133 6412\nf 6412 4133 4897\nf 6414 4134 6413\nf 6413 4134 4133\nf 6415 4225 6414\nf 6414 4225 4134\nf 6416 4252 6415\nf 6415 4252 4225\nf 6417 4253 6416\nf 6416 4253 4252\nf 4253 6417 6366\nf 6366 6417 6419\nf 6366 6419 4911\nf 4911 6419 6421\nf 6422 6423 4908\nf 4908 6423 5298\nf 5298 6423 6396\nf 6396 6423 6424\nf 6409 6426 6425\nf 6425 6426 6397\nf 5300 5299 6399\nf 6399 5299 6400\nf 6465 6408 6401\nf 6401 6408 6398\nf 6424 6399 6425\nf 6425 6399 6398\nf 6349 5284 5311\nf 5311 5284 5283\nf 6465 6464 5289\nf 5289 6464 5352\nf 6488 6483 6492\nf 6492 6483 6482\nf 6486 6491 6482\nf 6482 6491 6492\nf 6491 4895 6492\nf 6492 4895 4157\nf 6488 4160 6489\nf 6489 4160 4087\nf 6501 6502 4418\nf 4418 6502 4417\nf 6504 6500 6317\nf 6317 6500 6314\nf 3497 3496 6501\nf 6501 3496 6502\nf 3499 3498 6504\nf 6504 3498 6500\nf 4729 5274 4198\nf 4198 5274 4195\nf 6418 6365 6420\nf 6420 6365 4977\nf 5295 5348 6350\nf 6350 5348 6351\nf 5301 6422 6420\nf 6420 6422 6421\nf 4113 4112 4086\nf 4086 4112 4083\nf 6320 4112 6880\nf 6880 4112 4111\nf 4091 4772 4092\nf 4092 4772 4775\nf 4129 4140 4091\nf 4091 4140 4772\nf 4137 4140 4128\nf 4128 4140 4129\nf 4128 6324 4137\nf 4137 6324 6321\nf 6324 6487 6321\nf 6321 6487 6485\nf 6487 6486 6485\nf 6485 6486 6482\nf 4774 4773 4779\nf 4779 4773 4776\nf 4773 4139 4776\nf 4776 4139 4114\nf 4139 4138 4114\nf 4114 4138 4111\nf 4138 6322 4111\nf 4111 6322 6880\nf 6322 6484 6880\nf 6880 6484 6481\nf 6484 6483 6481\nf 6481 6483 6478\nf 6879 6343 6309\nf 6309 6343 4428\nf 4106 4105 4900\nf 4900 4105 6307\nf 3500 3167 6495\nf 6495 3167 6496\nf 3500 6495 3070\nf 3070 6495 5368\nf 3502 5235 3501\nf 3501 5235 4813\nf 3024 3503 6473\nf 6473 3503 6474\nf 3503 3023 6474\nf 6474 3023 6374\nf 3504 1770 4348\nf 4348 1770 4345\nf 3502 3505 5235\nf 5235 3505 4810\nf 3498 3497 6500\nf 6500 3497 6501\nf 3496 3075 6502\nf 6502 3075 6275\nf 6299 4254 3174\nf 3174 4254 3017\nf 1769 3501 4814\nf 4814 3501 4813\nf 3506 3018 6476\nf 6476 3018 4257\nf 3507 3499 6499\nf 6499 3499 6504\nf 3030 4216 3483\nf 3483 4216 4215\nf 3077 3507 6333\nf 6333 3507 6499\nf 1784 3160 5249\nf 5249 3160 6523\nf 3015 3506 4240\nf 4240 3506 6476\nf 3508 3504 6383\nf 6383 3504 4348\nf 3509 3016 6477\nf 6477 3016 4239\nf 3028 3509 6467\nf 6467 3509 6477\nf 3022 1785 5246\nf 5246 1785 5247\nf 3161 3508 6570\nf 6570 3508 6383\nf 3150 3149 4694\nf 4694 3149 6566\nf 1327 4807 3510\nf 3510 4807 4811\nf 3510 4811 3505\nf 3505 4811 4810\nf 3146 3511 4816\nf 4816 3511 4350\nf 3511 1808 4350\nf 4350 1808 4351\nf 3485 4327 3159\nf 3159 4327 4330\nf 1326 3512 4806\nf 4806 3512 6310\nf 3512 1331 6310\nf 6310 1331 4688\n"
  },
  {
    "path": "mesh_pi_nets/mesh_sampling.py",
    "content": "### Code obtained and modified from https://github.com/anuragranj/coma, Copyright (c) 2018 Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black and the Max Planck Gesellschaft\n\nimport math\nimport heapq\nimport numpy as np\nimport os\nimport scipy.sparse as sp\nfrom scipy import spatial\nimport pickle\nimport trimesh\n\ntry:\n    import psbody.mesh\n\n    found = True\nexcept ImportError:\n    found = False\nif found:\n    from psbody.mesh import Mesh\n\n\ndef compute_downsampling(downsample_directory, downsample_method, shapedata, ds_factors):\n    if not os.path.exists(os.path.join(downsample_directory, 'downsampling_matrices.pkl')):\n        if shapedata.meshpackage == 'trimesh':\n            raise NotImplementedError\n\n        print(\"Generating Transform Matrices ..\")\n        if downsample_method == 'COMA_downsample':\n            M, A, D, U, F = generate_transform_matrices(shapedata.reference_mesh, ds_factors)\n        else:\n            raise NotImplementedError(downsample_method)\n\n        with open(os.path.join(downsample_directory, 'downsampling_matrices.pkl'), 'wb') as fp:\n            M_verts_faces = [(M[i].v, M[i].f) for i in range(len(M))]\n            pickle.dump({'M_verts_faces': M_verts_faces, 'A': A, 'D': D, 'U': U, 'F': F}, fp)\n    else:\n        print(\"Loading Transform Matrices ..\")\n        with open(os.path.join(downsample_directory, 'downsampling_matrices.pkl'), 'rb') as fp:\n            downsampling_matrices = pickle.load(fp, encoding='latin1')\n\n        M_verts_faces = downsampling_matrices['M_verts_faces']\n        if shapedata.meshpackage == 'mpi-mesh':\n            M = [Mesh(v=M_verts_faces[i][0], f=M_verts_faces[i][1]) for i in range(len(M_verts_faces))]\n        elif shapedata.meshpackage == 'trimesh':\n            M = [trimesh.base.Trimesh(vertices=M_verts_faces[i][0], faces=M_verts_faces[i][1], process=False)\n                 for i in range(len(M_verts_faces))]\n        A = downsampling_matrices['A']\n        D = downsampling_matrices['D']\n        U = downsampling_matrices['U']\n        F = downsampling_matrices['F']\n\n    return M, A, D, U, F\n\n\ndef vertex_quadrics(mesh):\n    \"\"\"Computes a quadric for each vertex in the Mesh.\n\n    Returns:\n       v_quadrics: an (N x 4 x 4) array, where N is # vertices.\n    \"\"\"\n\n    # Allocate quadrics\n    v_quadrics = np.zeros((len(mesh.v), 4, 4,))\n\n    # For each face...\n    for f_idx in range(len(mesh.f)):\n\n        # Compute normalized plane equation for that face\n        vert_idxs = mesh.f[f_idx]\n        verts = np.hstack((mesh.v[vert_idxs], np.array([1, 1, 1]).reshape(-1, 1)))\n        u, s, v = np.linalg.svd(verts)\n        eq = v[-1, :].reshape(-1, 1)\n        eq = eq / (np.linalg.norm(eq[0:3]))\n\n        # Add the outer product of the plane equation to the\n        # quadrics of the vertices for this face\n        for k in range(3):\n            v_quadrics[mesh.f[f_idx, k], :, :] += np.outer(eq, eq)\n\n    return v_quadrics\n\n\ndef setup_deformation_transfer(source, target, use_normals=False):\n    rows = np.zeros(3 * target.v.shape[0])\n    cols = np.zeros(3 * target.v.shape[0])\n    coeffs_v = np.zeros(3 * target.v.shape[0])\n    coeffs_n = np.zeros(3 * target.v.shape[0])\n\n    nearest_faces, nearest_parts, nearest_vertices = source.compute_aabb_tree().nearest(target.v, True)\n    nearest_faces = nearest_faces.ravel().astype(np.int64)\n    nearest_parts = nearest_parts.ravel().astype(np.int64)\n    nearest_vertices = nearest_vertices.ravel()\n\n    for i in range(target.v.shape[0]):\n        # Closest triangle index\n        f_id = nearest_faces[i]\n        # Closest triangle vertex ids\n        nearest_f = source.f[f_id]\n\n        # Closest surface point\n        nearest_v = nearest_vertices[3 * i:3 * i + 3]\n        # Distance vector to the closest surface point\n        dist_vec = target.v[i] - nearest_v\n\n        rows[3 * i:3 * i + 3] = i * np.ones(3)\n        cols[3 * i:3 * i + 3] = nearest_f\n\n        n_id = nearest_parts[i]\n        if n_id == 0:\n            # Closest surface point in triangle\n            A = np.vstack((source.v[nearest_f])).T\n            coeffs_v[3 * i:3 * i + 3] = np.linalg.lstsq(A, nearest_v)[0]\n        elif n_id > 0 and n_id <= 3:\n            # Closest surface point on edge\n            A = np.vstack((source.v[nearest_f[n_id - 1]], source.v[nearest_f[n_id % 3]])).T\n            tmp_coeffs = np.linalg.lstsq(A, target.v[i])[0]\n            coeffs_v[3 * i + n_id - 1] = tmp_coeffs[0]\n            coeffs_v[3 * i + n_id % 3] = tmp_coeffs[1]\n        else:\n            # Closest surface point a vertex\n            coeffs_v[3 * i + n_id - 4] = 1.0\n\n    #    if use_normals:\n    #        A = np.vstack((vn[nearest_f])).T\n    #        coeffs_n[3 * i:3 * i + 3] = np.linalg.lstsq(A, dist_vec)[0]\n\n    # coeffs = np.hstack((coeffs_v, coeffs_n))\n    # rows = np.hstack((rows, rows))\n    # cols = np.hstack((cols, source.v.shape[0] + cols))\n    matrix = sp.csc_matrix((coeffs_v, (rows, cols)), shape=(target.v.shape[0], source.v.shape[0]))\n    return matrix\n\n\ndef qslim_decimator_transformer(mesh, factor=None, n_verts_desired=None):\n    from opendr.topology import get_vertices_per_edge\n    \"\"\"Return a simplified version of this mesh.\n\n    A Qslim-style approach is used here.\n\n    :param factor: fraction of the original vertices to retain\n    :param n_verts_desired: number of the original vertices to retain\n    :returns: new_faces: An Fx3 array of faces, mtx: Transformation matrix\n    \"\"\"\n\n    if factor is None and n_verts_desired is None:\n        raise Exception('Need either factor or n_verts_desired.')\n\n    if n_verts_desired is None:\n        n_verts_desired = math.ceil(len(mesh.v) * factor)\n\n    Qv = vertex_quadrics(mesh)\n\n    # fill out a sparse matrix indicating vertex-vertex adjacency\n    # from psbody.mesh.topology.connectivity import get_vertices_per_edge\n    vert_adj = get_vertices_per_edge(mesh.v, mesh.f)\n    # vert_adj = sp.lil_matrix((len(mesh.v), len(mesh.v)))\n    # for f_idx in range(len(mesh.f)):\n    #     vert_adj[mesh.f[f_idx], mesh.f[f_idx]] = 1\n\n    vert_adj = sp.csc_matrix((vert_adj[:, 0] * 0 + 1, (vert_adj[:, 0], vert_adj[:, 1])),\n                             shape=(len(mesh.v), len(mesh.v)))\n    vert_adj = vert_adj + vert_adj.T\n    vert_adj = vert_adj.tocoo()\n\n    def collapse_cost(Qv, r, c, v):\n        Qsum = Qv[r, :, :] + Qv[c, :, :]\n        p1 = np.vstack((v[r].reshape(-1, 1), np.array([1]).reshape(-1, 1)))\n        p2 = np.vstack((v[c].reshape(-1, 1), np.array([1]).reshape(-1, 1)))\n\n        destroy_c_cost = p1.T.dot(Qsum).dot(p1)\n        destroy_r_cost = p2.T.dot(Qsum).dot(p2)\n        result = {\n            'destroy_c_cost': destroy_c_cost,\n            'destroy_r_cost': destroy_r_cost,\n            'collapse_cost': min([destroy_c_cost, destroy_r_cost]),\n            'Qsum': Qsum}\n        return result\n\n    # construct a queue of edges with costs\n    queue = []\n    for k in range(vert_adj.nnz):\n        r = vert_adj.row[k]\n        c = vert_adj.col[k]\n\n        if r > c:\n            continue\n\n        cost = collapse_cost(Qv, r, c, mesh.v)['collapse_cost']\n        heapq.heappush(queue, (cost, (r, c)))\n\n    # decimate\n    collapse_list = []\n    nverts_total = len(mesh.v)\n    faces = mesh.f.copy()\n    while nverts_total > n_verts_desired:\n        e = heapq.heappop(queue)\n        r = e[1][0]\n        c = e[1][1]\n        if r == c:\n            continue\n\n        cost = collapse_cost(Qv, r, c, mesh.v)\n        if cost['collapse_cost'] > e[0]:\n            heapq.heappush(queue, (cost['collapse_cost'], e[1]))\n            # print 'found outdated cost, %.2f < %.2f' % (e[0], cost['collapse_cost'])\n            continue\n        else:\n\n            # update old vert idxs to new one,\n            # in queue and in face list\n            if cost['destroy_c_cost'] < cost['destroy_r_cost']:\n                to_destroy = c\n                to_keep = r\n            else:\n                to_destroy = r\n                to_keep = c\n\n            collapse_list.append([to_keep, to_destroy])\n\n            # in our face array, replace \"to_destroy\" vertidx with \"to_keep\" vertidx\n            np.place(faces, faces == to_destroy, to_keep)\n\n            # same for queue\n            which1 = [idx for idx in range(len(queue)) if queue[idx][1][0] == to_destroy]\n            which2 = [idx for idx in range(len(queue)) if queue[idx][1][1] == to_destroy]\n            for k in which1:\n                queue[k] = (queue[k][0], (to_keep, queue[k][1][1]))\n            for k in which2:\n                queue[k] = (queue[k][0], (queue[k][1][0], to_keep))\n\n            Qv[r, :, :] = cost['Qsum']\n            Qv[c, :, :] = cost['Qsum']\n\n            a = faces[:, 0] == faces[:, 1]\n            b = faces[:, 1] == faces[:, 2]\n            c = faces[:, 2] == faces[:, 0]\n\n            # remove degenerate faces\n            def logical_or3(x, y, z):\n                return np.logical_or(x, np.logical_or(y, z))\n\n            faces_to_keep = np.logical_not(logical_or3(a, b, c))\n            faces = faces[faces_to_keep, :].copy()\n\n        nverts_total = (len(np.unique(faces.flatten())))\n\n    new_faces, mtx = _get_sparse_transform(faces, len(mesh.v))\n    return new_faces, mtx\n\n\ndef _get_sparse_transform(faces, num_original_verts):\n    verts_left = np.unique(faces.flatten())\n    IS = np.arange(len(verts_left))\n    JS = verts_left\n    data = np.ones(len(JS))\n\n    mp = np.arange(0, np.max(faces.flatten()) + 1)\n    mp[JS] = IS\n    new_faces = mp[faces.copy().flatten()].reshape((-1, 3))\n\n    ij = np.vstack((IS.flatten(), JS.flatten()))\n    mtx = sp.csc_matrix((data, ij), shape=(len(verts_left), num_original_verts))\n\n    return (new_faces, mtx)\n\n\ndef generate_transform_matrices(mesh, factors):\n    from opendr.topology import get_vert_connectivity\n    \"\"\"Generates len(factors) meshes, each of them is scaled by factors[i] and\n       computes the transformations between them.\n\n    Returns:\n       M: a set of meshes downsampled from mesh by a factor specified in factors.\n       A: Adjacency matrix for each of the meshes\n       D: Downsampling transforms between each of the meshes\n       U: Upsampling transforms between each of the meshes\n    \"\"\"\n\n    factors = map(lambda x: 1.0 / x, factors)\n    M, A, D, U = [], [], [], []\n    ## sergey code\n    F = []\n    ##\n    A.append(get_vert_connectivity(mesh.v, mesh.f))\n    M.append(mesh)\n\n    i = 0\n    for factor in factors:\n        ds_f, ds_D = qslim_decimator_transformer(M[-1], factor=factor)\n        D.append(ds_D)\n        ##\n        F.append(ds_f)\n        ##\n        new_mesh_v = ds_D.dot(M[-1].v)\n        new_mesh = Mesh(v=new_mesh_v, f=ds_f)\n        M.append(new_mesh)\n        A.append(get_vert_connectivity(new_mesh.v, new_mesh.f))\n        U.append(setup_deformation_transfer(M[-1], M[-2]))\n        print('decimation %d by factor %.2f finished' % (i, factor))\n        i += 1\n\n    return M, A, D, U, F\n\n\ndef generate_transform_matrices_given_downsamples(mesh, downsample_directory, num_downsamples=4):\n    from opendr.topology import get_vert_connectivity\n    M, A, D, U, F = [], [], [], [], []\n    A.append(get_vert_connectivity(mesh.v, mesh.f))\n    M.append(mesh)\n\n    for i in range(1, num_downsamples + 1):\n        #         import trimesh\n        #         cur_M = trimesh.load(os.path.join(downsample_directory,'template_d{0}.obj'.format(i)))\n        cur_M = Mesh(filename=os.path.join(downsample_directory, 'template_d{0}.obj'.format(i)))\n        cur_D = np.zeros((cur_M.v.shape[0], M[-1].v.shape[0]))\n        kd = spatial.KDTree(np.array(M[-1].v))\n        for vi in range(cur_M.v.shape[0]):\n            _, u = kd.query(cur_M.v[vi])\n            cur_D[vi, u] = 1.0\n        M.append(cur_M)\n        D.append(sp.csr_matrix(cur_D))\n        F.append(cur_M.f)\n        A.append(get_vert_connectivity(cur_M.v, cur_M.f))\n        U.append(setup_deformation_transfer(M[-1], M[-2]))\n\n    return M, A, D, U, F\n\n\n"
  },
  {
    "path": "mesh_pi_nets/models.py",
    "content": "import torch\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\n\nimport math\n\n\nclass SpiralPoly(nn.Module):\n    def __init__(self, in_c, \n                 spiral_size,\n                 out_c, \n                 activation='elu', \n                 bias=True, \n                 device=None, \n                 injection = False, \n                 residual = False, \n                 num_points = None, \n                 order = 1, \n                 normalize = 'final', \n                 model = 'full'):\n        super(SpiralPoly, self).__init__()\n\n        self.in_c = in_c\n        self.out_c = out_c\n        self.device = device\n        self.injection = injection\n        self.residual = residual\n        self.order = order\n        self.normalize = normalize\n        self.model = model\n\n        if bias:\n            # constant parameter of the polynomial\n            self.bias = Parameter(torch.Tensor(out_c))\n            bound = 1 / math.sqrt(in_c * spiral_size)\n            torch.nn.init.uniform_(self.bias, -bound, bound)\n        else:\n            self.register_parameter('bias', None)\n\n        if self.injection:\n            if self.model == 'full':\n            # conva and convs correspond to A and S matrices of the NCP decomposition (the constant matrices B are abosorbed by A)\n                self.conva =  nn.ModuleList([nn.Linear(in_c*spiral_size,out_c,bias=False) for k in range(0,self.order-1)])\n                self.convs =  nn.ModuleList([nn.Linear(in_c*spiral_size,out_c,bias=False)] + \n                                            [nn.Linear(out_c*spiral_size,out_c,bias=False) for k in range(0,self.order-2)])\n            elif self.model == 'simple':\n                # this is the compact version of the model, where we use a single parameter matrix and perform element-wise multiplications\n                self.convs = nn.Linear(in_c*spiral_size,out_c,bias=False)  \n                \n            if self.normalize == 'final':\n                # normalise only the higher order term of the entire polynomial\n                self.normalizer =  nn.BatchNorm1d((num_points+1) * out_c, affine= True)\n            else:\n                # normalise once for every recursion step\n                self.normalizer =  nn.ModuleList([nn.BatchNorm1d((num_points+1) * out_c, affine= True)\n                                                  for k in range(0,self.order-1)])   \n        else:\n            if self.model == 'mlp':\n                self.fc1 = nn.Linear(in_c*spiral_size, out_c, bias = True)\n                self.fc2 = nn. Linear(out_c, out_c, bias = True)\n            elif self.model == 'linear':\n                self.conv = nn.Linear(in_c * spiral_size, out_c, bias=False)\n\n        if activation == 'relu':\n            self.activation = nn.ReLU()\n        elif activation == 'elu':\n            self.activation = nn.ELU()\n        elif activation == 'leaky_relu':\n            self.activation = nn.LeakyReLU(0.02)\n        elif activation == 'sigmoid':\n            self.activation = nn.Sigmoid()\n        elif activation == 'tanh':\n            self.activation = nn.Tanh()\n        elif activation == 'identity':\n            self.activation = lambda x: x\n        else:\n            raise NotImplementedError()\n\n    def forward(self, x, spiral_adj):\n        bsize, num_pts, feats = x.size()\n        _, _, spiral_size = spiral_adj.size()\n\n        spirals_index = spiral_adj.view(bsize * num_pts * spiral_size)  # [1d array of batch,vertx,vertx-adj]\n        batch_index = torch.arange(bsize, device=self.device).view(-1, 1).repeat([1, num_pts * spiral_size]).view(\n            -1).long()  # [0*numpt,1*numpt,etc.]\n        spirals = x[batch_index, spirals_index, :].view(bsize * num_pts,\n                                                        spiral_size * feats)  # [bsize*numpt, spiral*feats]\n\n        if self.injection:\n            if self.model == 'full':\n                spirals_x1 = spirals  # input z\n                spirals_xk_1 = spirals  # output of previous order term xk_1\n                for k in range(0, self.order - 1):\n                    a_xk = self.conva[k](spirals_x1)\n                    s_xk = self.convs[k](spirals_xk_1)\n\n                    if self.normalize == 'all':\n                        # normalise the output of the recursion\n                        if k >= 1 and self.residual:\n                            out_k = a_xk * s_xk + a_xk + out_k\n                        else:\n                            out_k = a_xk * s_xk + a_xk\n                        out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                        out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1)\n                        spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \\\n                            [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c)\n                    elif self.normalize == '2nd':\n                        # normalise only the 2nd order term in the recursive formulation\n                        if k >= 1 and self.residual:\n                            out_k_1 = out_k\n                            out_k = a_xk * s_xk\n                            out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                            out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) + a_xk + out_k_1\n                        else:\n                            out_k = a_xk * s_xk\n                            out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                            out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) + a_xk\n                        spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \\\n                            [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c)\n                    elif self.normalize == 'final':\n                        # normalise only the higher order term of the entire polynomial\n                        if k >= 1 and self.residual:\n                            if k == self.order - 2:\n                                out_k_1 = out_k\n                                out_k = a_xk * s_xk\n                                out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                                out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1) + a_xk + out_k_1\n                            else:\n                                out_k = a_xk * s_xk + a_xk + out_k\n                        else:\n                            if k == self.order - 2:\n                                out_k = a_xk * s_xk\n                                out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                                out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1) + a_xk\n                            else:\n                                out_k = a_xk * s_xk + a_xk\n                        spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \\\n                            [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c)\n\n                out_feat = out_k + self.bias\n\n\n            elif self.model == 'simple':\n\n                out_1 = self.convs(spirals)\n                out_feat = out_1\n                for k in range(0, self.order - 1):\n                    out_k = out_1 ** (k + 2)\n                    if self.normalize == '2nd':\n                        out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                        out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1)\n                    elif self.normalize == 'final':\n                        if k == self.order - 2:\n                            out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1])\n                            out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1)\n                    out_feat = out_k + out_feat\n\n                out_feat = out_feat + self.bias\n\n        else:\n            if self.model == 'mlp':\n                out_feat = self.fc2(nn.ReLU()(self.fc1(spirals)))\n            elif self.model == 'linear':\n                out_feat = self.conv(spirals) + self.bias\n\n        out_feat = self.activation(out_feat)\n\n        out_feat = out_feat.view(bsize, num_pts, self.out_c)\n        zero_padding = torch.ones((1, x.size(1), 1), device=self.device)\n        zero_padding[0, -1, 0] = 0.0\n        out_feat = out_feat * zero_padding\n\n        return out_feat\n\n\nclass SpiralPolyAE(nn.Module):\n    def __init__(self,\n                 filters_enc,\n                 filters_dec,\n                 latent_size,\n                 mesh_sizes,\n                 spiral_sizes,\n                 spirals,\n                 D, U,\n                 device,\n                 activation='elu',\n                 injection=False,\n                 residual=False,\n                 order=1,\n                 normalize=None,\n                 model=None):\n        super(SpiralPolyAE, self).__init__()\n\n        self.filters_enc = filters_enc\n        self.filters_dec = filters_dec\n        self.spirals = spirals\n        self.mesh_sizes = mesh_sizes\n        self.spiral_sizes = spiral_sizes\n        self.D = D\n        self.U = U\n        self.device = device\n        self.conv = []\n        \n        input_size = filters_enc[0][0]\n        for i in range(len(filters_enc)):\n            for j in range(1, len(filters_enc[i])):\n                self.conv.append(SpiralPoly(input_size, spiral_sizes[i], filters_enc[i][j],\n                                            activation=activation, device=device,\n                                            injection=injection, residual=residual,\n                                            num_points=mesh_sizes[i], order=order,\n                                            normalize=normalize, model=model).to(device))\n                input_size = filters_enc[i][j]\n            if i < len(filters_enc) - 1:\n                self.conv.append(SpiralPoly(input_size, spiral_sizes[i], filters_enc[i + 1][0],\n                                            activation=activation, device=device,\n                                            injection=injection, residual=residual,\n                                            num_points=mesh_sizes[i], order=order,\n                                            normalize=normalize, model=model).to(device))\n                input_size = filters_enc[i + 1][0]\n\n        self.conv = nn.ModuleList(self.conv)\n\n        self.fc_latent_enc = nn.Linear((mesh_sizes[-1] + 1) * input_size, latent_size)\n        self.fc_latent_dec = nn.Linear(latent_size, (mesh_sizes[-1] + 1) * filters_dec[0][0])\n\n        self.dconv = []\n        input_size = filters_dec[0][0]\n        for i in range(len(filters_dec)):\n            for j in range(1, len(filters_dec[i])):\n                out_activation = 'identity' if i == len(filters_dec) - 1 and j == len(\n                    filters_dec[-1]) - 1 else activation\n                self.dconv.append(SpiralPoly(input_size, spiral_sizes[-2 - i], filters_dec[i][j],\n                                             activation=out_activation, device=device,\n                                             injection=injection, residual=residual,\n                                             num_points=mesh_sizes[-2 - i], order=order,\n                                             normalize=normalize, model=model).to(device))\n                input_size = filters_dec[i][j]\n\n            if i < len(filters_dec) - 1:\n                out_activation = 'identity' if i == len(filters_dec) - 2 and len(\n                    filters_dec[-1]) == 1 else activation\n                self.dconv.append(SpiralPoly(input_size, spiral_sizes[-2 - i], filters_dec[i + 1][0],\n                                             activation=out_activation, device=device,\n                                             injection=injection, residual=residual,\n                                             num_points=mesh_sizes[-2 - i], order=order,\n                                             normalize=normalize, model=model).to(device))\n                input_size = filters_dec[i + 1][0]\n\n        self.dconv = nn.ModuleList(self.dconv)\n\n    def encode(self, x):\n        bsize = x.size(0)\n        j = 0\n        for i in range(len(self.filters_enc) - 1):\n            for k in range(len(self.filters_enc[i])):\n                x = self.conv[j](x, self.spirals[i].repeat(bsize, 1, 1))\n                j += 1\n            x = torch.matmul(self.D[i], x)\n\n        for k in range(1, len(self.filters_enc[-1])):\n            x = self.conv[j](x, self.spirals[len(self.filters_enc) - 1].repeat(bsize, 1, 1))\n            j += 1\n\n        x = x.view(bsize, -1)\n        out = self.fc_latent_enc(x)\n        return out\n\n    def decode(self, z):\n        bsize = z.shape[0]\n\n        x = self.fc_latent_dec(z)\n        x = x.view(bsize, self.mesh_sizes[-1] + 1, -1)\n\n        j = 0\n        for i in range(len(self.filters_dec) - 1):\n            x = torch.matmul(self.U[-1 - i], x)\n            for k in range(len(self.filters_dec[i])):\n                x = self.dconv[j](x, self.spirals[-2 - i].repeat(bsize, 1, 1))\n                j += 1\n\n        for k in range(1, len(self.filters_dec[-1])):\n            x = self.dconv[j](x, self.spirals[0].repeat(bsize, 1, 1))\n            j += 1\n\n        return x\n\n    def forward(self, x):\n        z = self.encode(x)\n        x = self.decode(z)\n        return x\n"
  },
  {
    "path": "mesh_pi_nets/shape_data.py",
    "content": "### Code obtained and modified from https://github.com/anuragranj/coma, Copyright (c) 2018 Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black and the Max Planck Gesellschaft\n\nimport os\nimport numpy as np\n\ntry:\n    import psbody.mesh\n    found = True\nexcept ImportError:\n    found = False\nif found:\n    from psbody.mesh import Mesh\n\nimport trimesh\n\n\nclass ShapeData(object):\n    def __init__(self, nVal,\n                 train_file,\n                 test_file,\n                 reference_mesh_file,\n                 normalization=True,\n                 meshpackage='mpi-mesh',\n                 load_flag=True,\n                 mean_subtraction_only=False):\n        self.nVal = nVal\n        self.train_file = train_file\n        self.test_file = test_file\n        self.vertices_train = None\n        self.vertices_val = None\n        self.vertices_test = None\n        self.n_vertex = None\n        self.n_features = None\n        self.normalization = normalization\n        self.meshpackage = meshpackage\n        self.load_flag = load_flag\n        self.mean_subtraction_only = mean_subtraction_only\n\n        if self.load_flag:\n            self.load()\n        if self.meshpackage == 'trimesh':\n            self.reference_mesh = trimesh.load(reference_mesh_file, process=False)\n        elif self.meshpackage == 'mpi-mesh':\n            self.reference_mesh = Mesh(filename=reference_mesh_file)\n\n        if self.load_flag:\n            self.mean = np.mean(self.vertices_train, axis=0)\n            self.std = np.std(self.vertices_train, axis=0)\n        else:\n            self.mean = None\n            self.std = None\n        self.normalize()\n\n    def load(self):\n        vertices_train = np.load(self.train_file)\n        self.vertices_train = vertices_train[:-self.nVal]\n        self.vertices_val = vertices_train[-self.nVal:]\n\n        self.n_vertex = self.vertices_train.shape[1]\n        self.n_features = self.vertices_train.shape[2]\n\n        if os.path.exists(self.test_file):\n            self.vertices_test = np.load(self.test_file)\n            self.vertices_test = self.vertices_test\n\n    def normalize(self):\n        if self.load_flag:\n            if self.normalization:\n                if self.mean_subtraction_only:\n                    self.std = np.ones_like((self.std))\n                self.vertices_train = self.vertices_train - self.mean\n                self.vertices_train = self.vertices_train / self.std\n                self.vertices_train[np.where(np.isnan(self.vertices_train))] = 0.0\n\n                self.vertices_val = self.vertices_val - self.mean\n                self.vertices_val = self.vertices_val / self.std\n                self.vertices_val[np.where(np.isnan(self.vertices_val))] = 0.0\n\n                if self.vertices_test is not None:\n                    self.vertices_test = self.vertices_test - self.mean\n                    self.vertices_test = self.vertices_test / self.std\n                    self.vertices_test[np.where(np.isnan(self.vertices_test))] = 0.0\n\n                self.N = self.vertices_train.shape[0]\n\n                print('Vertices normalized')\n            else:\n                print('Vertices not normalized')\n\n    def save_meshes(self, filename, meshes, mesh_indices):\n        for i in range(meshes.shape[0]):\n            if self.normalization:\n                vertices = meshes[i].reshape((self.n_vertex, self.n_features)) * self.std + self.mean\n            else:\n                vertices = meshes[i].reshape((self.n_vertex, self.n_features))\n            if self.meshpackage == 'trimesh':\n                new_mesh = self.reference_mesh\n                if self.n_features == 3:\n                    new_mesh.vertices = vertices\n                elif self.n_features == 6:\n                    new_mesh.vertices = vertices[:, 0:3]\n                    colors = vertices[:, 3:]\n                    colors[np.where(colors < 0)] = 0\n                    colors[np.where(colors > 1)] = 1\n                    vertices[:, 3:] = colors\n                    new_mesh.visual = trimesh.visual.create_visual(vertex_colors=vertices[:, 3:])\n                else:\n                    raise NotImplementedError\n                new_mesh.export(filename + '.' + str(mesh_indices[i]).zfill(6) + '.ply', 'ply')\n            elif self.meshpackage == 'mpi-mesh':\n                if self.n_features == 3:\n                    mesh = Mesh(v=vertices, f=self.reference_mesh.f)\n                    mesh.write_ply(filename + '.' + str(mesh_indices[i]).zfill(6) + '.ply')\n                else:\n                    raise NotImplementedError\n        return 0"
  },
  {
    "path": "mesh_pi_nets/spiral_pi_nets.py",
    "content": "import argparse\n\nimport sys\n\nsys.path.append('../')\nimport numpy as np\nimport json\nimport os\nimport copy\nfrom shape_data import ShapeData\nfrom mesh_sampling import compute_downsampling\n\ntry:\n    import psbody.mesh\n\n    found = True\nexcept ImportError:\n    found = False\nif found:\n    pass\n\nfrom autoencoder_dataset import autoencoder_dataset\nfrom torch.utils.data import DataLoader\n\nfrom spiral_utils import get_adj_trigs, generate_spirals\nfrom models import SpiralPolyAE\n\nfrom test_funcs import test_autoencoder_dataloader\nfrom train_funcs import train_autoencoder_dataloader\n\nimport torch\nfrom tensorboardX import SummaryWriter\n\nfrom sklearn.metrics.pairwise import euclidean_distances\n\nmeshpackage = 'trimesh'\n\n\ndef str2bool(v):\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef str2list2int(v):\n    return [int(c) for c in v.split(',')]\n\n\ndef str2ListOfLists2int(v):\n    return [[[] if c == ' ' else int(c) for c in vi.split(',')] for vi in v.split(',,')]\n\n\ndef str2list2float(v):\n    return [float(c) for c in v.split(',')]\n\n\ndef str2list2bool(v):\n    return [str2bool(c) for c in v.split(',')]\n\n\ndef str2ListOfLists2bool(v):\n    return [[[] if c == ' ' else str2bool(c) for c in vi.split(',')] for vi in v.split(',,')]\n\n\ndef loss_l1(outputs, targets):\n    L = torch.abs(outputs - targets).mean()\n    return L\n\n\ndef main(args):\n    ## Set seeds and invoke device\n\n    torch.cuda.get_device_name(args['device_idx'])\n    torch.manual_seed(args['seed'])\n    torch.backends.cudnn.deterministic = True\n    torch.cuda.manual_seed(args['seed'])\n    torch.backends.cudnn.benchmark = False\n    np.random.seed(args['seed'])\n    torch.set_num_threads(args['num_threads'])\n\n    if args['GPU']:\n        device = torch.device(\"cuda:\" + str(args['device_idx']) if torch.cuda.is_available() else \"cpu\")\n    else:\n        device = torch.device(\"cpu\")\n    print(device)\n\n    ## Set paths\n\n    path = os.path.join(args['root_dir'], args['dataset'])\n    args['data'] = os.path.join(path, 'preprocessed', args['name'])\n    args['reference_mesh_file'] = os.path.join(path, 'templates/template.obj')\n    args['downsample_directory'] = os.path.join(path, 'templates',\n                                                args['downsample_method'])\n    args['results_folder'] = os.path.join(path, 'results', 'polynomial_autoencoder',\n                                          args['downsample_method'],\n                                          args['results_folder'],\n                                          'latent_' + str(args['nz']))\n\n    ## Create folders\n    \n    summary_path = os.path.join(args['results_folder'], 'summaries', args['name'])\n    checkpoint_path = os.path.join(args['results_folder'], 'checkpoints', args['name'])\n    samples_path = os.path.join(args['results_folder'], 'samples', args['name'])\n    prediction_path = os.path.join(args['results_folder'], 'predictions', args['name'])\n    if not os.path.exists(args['downsample_directory']):\n        os.makedirs(args['downsample_directory'])\n    \n    \n    if args['mode'] == 'train':\n        if not os.path.exists(os.path.join(args['results_folder'])):\n            os.makedirs(os.path.join(args['results_folder']))\n        if not os.path.exists(summary_path):\n            os.makedirs(summary_path)\n        if not os.path.exists(checkpoint_path):\n            os.makedirs(checkpoint_path)\n        if not os.path.exists(samples_path):\n            os.makedirs(samples_path)\n        if not os.path.exists(prediction_path):\n            os.makedirs(prediction_path)\n            \n    ## Set reference points (refer to Neural3DMM paper for the discussion on reference points - tl;dr you can set them arbitrarily). Here chosen on the top of the head.\n\n    if args['dataset'] == 'COMA':\n        reference_points = [[3567, 4051, 4597]]\n    elif args['dataset'] == 'mein3d':\n        reference_points = [[23822]]\n    elif args['dataset'] == 'DFAUST':\n        reference_points = [[414]]\n\n    ## Initialise dataset\n\n    print(\"Loading data .. \")\n    load_flag = True if not os.path.exists(args['data'] + '/mean.npy') or not os.path.exists(\n        args['data'] + '/std.npy') else False\n    shapedata = ShapeData(nVal=args['nVal'],\n                          train_file=args['data'] + '/train.npy',\n                          test_file=args['data'] + '/test.npy',\n                          reference_mesh_file=args['reference_mesh_file'],\n                          normalization=args['data_normalization'],\n                          meshpackage=meshpackage, load_flag=load_flag)\n    if load_flag:\n        np.save(args['data'] + '/mean.npy', shapedata.mean)\n        np.save(args['data'] + '/std.npy', shapedata.std)\n    else:\n        shapedata.mean = np.load(args['data'] + '/mean.npy')\n        shapedata.std = np.load(args['data'] + '/std.npy')\n        shapedata.n_vertex = shapedata.mean.shape[0]\n        shapedata.n_features = shapedata.mean.shape[1]\n\n    ## Load downsampling/upsampling matrices or compute them using the Mesh package (please refer to the Neural3DMM repository for more information)\n\n    M, A, D, U, F = compute_downsampling(args['downsample_directory'],\n                                         downsample_method=args['downsample_method'],\n                                         shapedata=shapedata,\n                                         ds_factors=args['ds_factors'])\n\n    ## Add dummy node to downsampling/upsampling matrices and move the to GPU\n    tD = []\n    tU = []\n    for i in range(len(D)):\n        d = np.zeros((1, D[i].shape[0] + 1, D[i].shape[1] + 1))\n        u = np.zeros((1, U[i].shape[0] + 1, U[i].shape[1] + 1))\n        d[0, :-1, :-1] = D[i].todense()\n        u[0, :-1, :-1] = U[i].todense()\n        d[0, -1, -1] = 1\n        u[0, -1, -1] = 1\n        d = torch.from_numpy(d).float().to(device)\n        u = torch.from_numpy(u).float().to(device)\n        tD.append(d)\n        tU.append(u)\n\n    ## Compute reference points for downsampled meshes\n    print(\"Calculating reference points for downsampled versions..\")\n    for i in range(len(args['ds_factors'])):\n        if shapedata.meshpackage == 'mpi-mesh':\n            dist = euclidean_distances(M[i + 1].v, M[0].v[reference_points[0]])\n        elif shapedata.meshpackage == 'trimesh':\n            dist = euclidean_distances(M[i + 1].vertices, M[0].vertices[reference_points[0]])\n        reference_points.append(np.argmin(dist, axis=0).tolist())\n\n    ## Compute local node orderings\n    mesh_sizes = [x.v.shape[0] for x in M] if shapedata.meshpackage == 'mpi-mesh' else [x.vertices.shape[0] for x in M]\n    Adj, Trigs = get_adj_trigs(A, F, shapedata.reference_mesh, meshpackage=shapedata.meshpackage)\n    spirals_np, spiral_sizes, spirals = generate_spirals(args['step_sizes'], M, Adj, Trigs,\n                                                         reference_points=reference_points,\n                                                         dilation=args['dilation'],\n                                                         random=False,\n                                                         meshpackage=shapedata.meshpackage,\n                                                         counter_clockwise=True)\n\n    tspirals = [torch.from_numpy(s).long().to(device) for s in spirals_np]\n\n    ## Initialise dataloaders\n\n    if args['mode'] == 'train':\n        dataset_train = autoencoder_dataset(root_dir=args['data'],\n                                            points_dataset='train',\n                                            shapedata=shapedata,\n                                            normalization=args['data_normalization'])\n        dataloader_train = DataLoader(dataset_train,\n                                      batch_size=args['batch_size'],\n                                      shuffle=args['shuffle'],\n                                      num_workers=args['num_workers'])\n        dataset_val = autoencoder_dataset(root_dir=args['data'],\n                                          points_dataset='val',\n                                          shapedata=shapedata,\n                                          normalization=args['data_normalization'])\n        dataloader_val = DataLoader(dataset_val,\n                                    batch_size=args['batch_size'],\n                                    shuffle=False,\n                                    num_workers=args['num_workers'])\n\n    dataset_test = autoencoder_dataset(root_dir=args['data'],\n                                       points_dataset=args['test_set'],\n                                       shapedata=shapedata,\n                                       normalization=args['data_normalization'])\n    dataloader_test = DataLoader(dataset_test,\n                                 batch_size=args['batch_size'],\n                                 shuffle=False,\n                                 num_workers=args['num_workers'])\n\n    ## Initialise the model\n\n    model = SpiralPolyAE(filters_enc=args['filter_sizes_enc'],\n                         filters_dec=args['filter_sizes_dec'],\n                         latent_size=args['nz'],\n                         mesh_sizes=mesh_sizes,\n                         spiral_sizes=spiral_sizes,\n                         spirals=tspirals,\n                         D=tD, U=tU,\n                         device=device,\n                         injection=args['injection'],\n                         residual=args['residual'],\n                         order=args['order'],\n                         normalize=args['normalize'],\n                         model=args['model'],\n                         activation=args['activation']).to(device)\n\n    ## Initialise optimiser, scheduler and set loss function\n    if args['mode'] == 'train':\n        optim = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['regularization'])\n        if args['scheduler']:\n            scheduler = torch.optim.lr_scheduler.StepLR(optim, args['decay_steps'], gamma=args['decay_rate'])\n        else:\n            scheduler = None\n\n        if args['loss'] == 'l1':\n            loss_fn = loss_l1\n\n    ## parameters\n    params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    print(\"Total number of parameters is: {}\".format(params))\n    print(model)\n\n    ############## ----------------  TRAINING LOOP ---------------- ##############\n    if args['mode'] == 'train':\n        ## configure logging and save hypeparams\n        writer = SummaryWriter(summary_path)\n        with open(os.path.join(args['results_folder'], 'checkpoints', args['name'] + '_params.json'), 'w') as fp:\n            saveparams = copy.deepcopy(args)\n            json.dump(saveparams, fp)\n\n        if args['resume']:\n            print('loading checkpoint from file %s' % (os.path.join(checkpoint_path, args['checkpoint_file'])))\n            checkpoint_dict = torch.load(os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar'),\n                                         map_location=device)\n            start_epoch = checkpoint_dict['epoch'] + 1\n            model.load_state_dict(checkpoint_dict['autoencoder_state_dict'])\n            optim.load_state_dict(checkpoint_dict['optimizer_state_dict'])\n            scheduler.load_state_dict(checkpoint_dict['scheduler_state_dict'])\n            print('Resuming from epoch %s' % (str(start_epoch)))\n        else:\n            start_epoch = 0\n\n        train_autoencoder_dataloader(dataloader_train,\n                                     dataloader_val,\n                                     device,\n                                     model,\n                                     optim,\n                                     loss_fn,\n                                     bsize=args['batch_size'],\n                                     start_epoch=start_epoch,\n                                     n_epochs=args['num_epochs'],\n                                     eval_freq=args['eval_frequency'],\n                                     scheduler=scheduler,\n                                     writer=writer,\n                                     save_recons=args['save_recons'],\n                                     shapedata=shapedata,\n                                     metadata_dir=checkpoint_path,\n                                     samples_dir=samples_path,\n                                     checkpoint_path=args['checkpoint_file'])\n    elif args['mode'] == 'test':\n        print('loading checkpoint from file %s' % (os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar')))\n        checkpoint_dict = torch.load(os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar'),\n                                     map_location=device)\n        model.load_state_dict(checkpoint_dict['autoencoder_state_dict'])\n        predictions, norm_l1_loss, l2_loss = test_autoencoder_dataloader(device,\n                                                                         model,\n                                                                         dataloader_test,\n                                                                         shapedata,\n                                                                         mm_constant=args['mm_constant'])\n        np.save(os.path.join(prediction_path, 'predictions'), predictions)\n\n        print('autoencoder: normalized loss', norm_l1_loss)\n        print('autoencoder: euclidean distance in mm=', l2_loss)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--seed', type=int, default=2)\n    parser.add_argument('--num_workers', type=int, default=4)\n    parser.add_argument('--num_threads', type=int, default=1)\n\n    # paths, data etc.\n    parser.add_argument('--root_dir', type=str, default='./datasets')\n    parser.add_argument('--name', type=str, default='')\n    parser.add_argument('--dataset', type=str, default='DFAUST')\n    parser.add_argument('--downsample_method', type=str, default='COMA_downsample')\n    parser.add_argument('--results_folder', type=str, default='temp')\n    parser.add_argument('--checkpoint_file', type=str, default='checkpoint')\n    parser.add_argument('--data_normalization', type=str2bool, default=True) \\\n    # multiply with this constant to get your mesh measurements in milimiters (dataset dependent)\n    parser.add_argument('--mm_constant', type=int, default=1000)\n\n    # optimisation and training parameters\n    parser.add_argument('--batch_size', type=int, default=16)\n    parser.add_argument('--num_epochs', type=int, default=300)\n    parser.add_argument('--eval_frequency', type=int, default=200)\n    parser.add_argument('--lr', type=float, default=1e-3)\n    parser.add_argument('--regularization', type=float, default=5e-5)\n    parser.add_argument('--scheduler', type=str2bool, default=True)\n    parser.add_argument('--decay_rate', type=float, default=0.99)\n    parser.add_argument('--decay_steps', type=int, default=1)\n    parser.add_argument('--loss', type=str, default='l1')\n\n    parser.add_argument('--shuffle', type=str2bool, default=True)\n    parser.add_argument('--nVal', type=int, default=100)\n\n    # model hyperparameters\n    parser.add_argument('--filter_sizes_enc', type=str2ListOfLists2int, default=[[3], [16], [16], [16], [32]])\n    parser.add_argument('--filter_sizes_dec', type=str2ListOfLists2int, default=[[32], [32], [16], [16], [3]])\n    parser.add_argument('--nz', type=int, default=16)\n    parser.add_argument('--ds_factors', type=str2list2int, default=[4, 4, 4, 4])\n    parser.add_argument('--step_sizes', type=str2list2int, default=[1, 1, 1, 1, 1])\n    parser.add_argument('--dilation', type=str2list2int, default=None)\n    parser.add_argument('--activation', type=str, default='elu')\n\n    # hyperparameters related to the polynomial\n    parser.add_argument('--injection', type=str2bool, default=True)\n    parser.add_argument('--order', type=int, default=2)\n    parser.add_argument('--model', type=str, default='full')\n    parser.add_argument('--normalize', type=str, default='final')\n    parser.add_argument('--residual', type=str2bool, default=False)\n\n    # misc\n    parser.add_argument('--resume', type=str2bool, default=False)\n    parser.add_argument('--save_recons', type=str2bool, default=True)  # save reconstructions every epoch\n    parser.add_argument('--mode', type=str, default='train')\n    parser.add_argument('--test_set', type=str, default='test')  # specify the set on which to test\n\n    # hardware\n    parser.add_argument('--GPU', type=str2bool, default=True)\n    parser.add_argument('--device_idx', type=int, default=0)\n\n    args = parser.parse_args()\n    print(args)\n    main(vars(args))\n"
  },
  {
    "path": "mesh_pi_nets/spiral_utils.py",
    "content": "import numpy as np\nfrom random import choice\n\n## Note: Non-smooth surfaces or bad triangulations may lead to non-spiral orderings of the vertices.\n## Common issue in badly triangulated surfaces is that there exist some edges that belong to more than two triangles. In this\n## case the mathematical definition of the spiral is insufficient. In this case, in this version of the code, we randomly \n## choose two triangles in order to continue the inductive assignment of the order to the rest of the vertices.\n\ndef get_adj_trigs(A, F, reference_mesh, meshpackage = 'mpi-mesh'):\n    Adj = []\n    for x in A:\n        adj_x = []\n        dx = x.todense()\n        for i in range(x.shape[0]):\n            adj_x.append(dx[i].nonzero()[1])\n        Adj.append(adj_x)\n\n        \n    if meshpackage =='trimesh':\n        mesh_faces = reference_mesh.faces\n    elif meshpackage =='mpi-mesh':\n        mesh_faces = reference_mesh.f\n    # Create Triangles List\n\n    trigs_full = [[] for i in range(len(Adj[0]))]\n    for t in mesh_faces:\n        u, v, w = t\n        trigs_full[u].append((u,v,w))\n        trigs_full[v].append((u,v,w))\n        trigs_full[w].append((u,v,w))\n\n    Trigs = [trigs_full]\n    for i,T in enumerate(F):\n        trigs_down = [[] for i in range(len(Adj[i+1]))]\n        for u,v,w in T:\n            trigs_down[u].append((u,v,w))\n            trigs_down[v].append((u,v,w))\n            trigs_down[w].append((u,v,w))\n        Trigs.append(trigs_down)\n    \n    return Adj, Trigs\n\n\n\ndef generate_spirals(step_sizes, M, Adj, Trigs, reference_points, dilation=None, random=False, meshpackage = 'mpi-mesh', counter_clockwise = True, nb_stds = 2):\n    Adj_spirals = []\n    for i in range(len(Adj)):\n        if meshpackage =='trimesh':\n            mesh_vertices  = M[i].vertices\n        elif meshpackage =='mpi-mesh':\n            mesh_vertices  = M[i].v\n \n        sp = get_spirals(mesh_vertices, Adj[i],Trigs[i],reference_points[i], n_steps=step_sizes[i],\\\n                         padding='zero', counter_clockwise = counter_clockwise, random = random)\n        Adj_spirals.append(sp)\n        print('spiral generation for hierarchy %d (%d vertices) finished' %(i,len(Adj_spirals[-1])))\n\n    \n    ## Dilated convolution\n    if dilation:\n        for i in range(len(dilation)):\n            dil = dilation[i]\n            dil_spirals = []\n            for j in range(len(Adj_spirals[i])):\n                s = Adj_spirals[i][j][:1] + Adj_spirals[i][j][1::dil]\n                dil_spirals.append(s)\n            Adj_spirals[i] = dil_spirals\n            \n            \n    # Calculate the lengths of spirals\n    #   Use mean + 2 * std_dev, to capture 97% of data\n    L = []\n    for i in range(len(Adj_spirals)):\n        L.append([])\n        for j in range(len(Adj_spirals[i])):\n            L[i].append(len(Adj_spirals[i][j]))\n        L[i] = np.array(L[i])\n    spiral_sizes = []\n    for i in range(len(L)):\n        sz = L[i].mean() + nb_stds*L[i].std()\n        spiral_sizes.append(int(sz))\n        print('spiral sizes for hierarchy %d:  %d' %(i,spiral_sizes[-1]))\n    \n\n    # 1) fill with -1 (index to the dummy vertex, i.e the zero padding) the spirals with length smaller than the chosen one\n    # 2) Truncate larger spirals\n    spirals_np = []\n    for i in range(len(spiral_sizes)): #len(Adj_spirals)):\n        S = np.zeros((1,len(Adj_spirals[i])+1,spiral_sizes[i])) - 1\n        for j in range(len(Adj_spirals[i])):\n            S[0,j,:len(Adj_spirals[i][j])] = Adj_spirals[i][j][:spiral_sizes[i]]\n        #spirals_np.append(np.repeat(S,args['batch_size'],axis=0))\n        spirals_np.append(S)\n\n    return spirals_np, spiral_sizes, Adj_spirals\n\n\n\n\n\ndef distance(v,w):\n    return np.sqrt(np.sum(np.square(v-w)))\n\ndef single_source_shortest_path(V,E,source,dist=None,prev=None):\n    import heapq\n    if dist == None:\n        dist = [None for i in range(len(V))]\n        prev = [None for i in range(len(V))]\n    q = []\n    seen = set()\n    heapq.heappush(q,(0,source,None))\n    while len(q) > 0 and len(seen) < len(V):\n        d_,v,p = heapq.heappop(q)\n        if v in seen:\n            continue\n        seen.add(v)\n        prev[v] = p\n        dist[v] = d_\n        for w in E[v]:\n            if w in seen:\n                continue\n            dw = d_ + distance(V[v],V[w])\n            heapq.heappush(q,(dw,w,v))\n    \n    return prev, dist\n        \n        \n    \n\ndef get_spirals(mesh, adj, trig, reference_points, n_steps=1, padding='zero', counter_clockwise = True, random = False):\n    spirals = []\n    \n    if not random:\n        heat_path = None\n        dist = None\n        for reference_point in reference_points:\n            heat_path,dist = single_source_shortest_path(mesh,adj,reference_point, dist, heat_path)\n        heat_source = reference_points\n\n    for i in range(mesh.shape[0]):\n        seen = set(); seen.add(i)\n        trig_central = list(trig[i]); A = adj[i]; spiral = [i]\n        \n        # 1) Frist degree of freedom - choose starting pooint:\n        if not random:\n            if i in heat_source: # choose closest neighbor\n                shortest_dist = np.inf\n                init_vert = None\n                for neighbor in A:\n                    d = np.sum(np.square(mesh[i] - mesh[neighbor]))\n                    if d < shortest_dist:\n                        shortest_dist = d\n                        init_vert = neighbor\n\n            else: #   on the shortest path to the reference point\n                init_vert = heat_path[i]\n        else:\n            # choose starting point:\n            #   random for first ring\n            init_vert = choice(A)\n            \n    \n        \n        \n        # first ring\n        if init_vert is not None:\n            ring = [init_vert]; seen.add(init_vert)\n        else:\n            ring = []\n        while len(trig_central) > 0 and init_vert is not None:\n            cur_v = ring[-1]\n            cur_t = [t for t in trig_central if t in trig[cur_v]]\n            if len(ring) == 1:\n                orientation_0 = (cur_t[0][0]==i and cur_t[0][1]==cur_v) \\\n                                or (cur_t[0][1]==i and cur_t[0][2]==cur_v) \\\n                                or (cur_t[0][2]==i and cur_t[0][0]==cur_v)\n                if not counter_clockwise:\n                    orientation_0 = not orientation_0\n                    \n                # 2) Second degree of freedom - 2nd point/orientation ambiguity\n                if len(cur_t) >=2:\n                # Choose the triangle that will direct the spiral counter-clockwise\n                    if orientation_0:\n                    # Third point in the triangle - next vertex in the spiral\n                        third = [p for p in cur_t[0] if p!=i and p!=cur_v][0]\n                        trig_central.remove(cur_t[0])\n                    else:\n                        third = [p for p in cur_t[1] if p!=i and p!=cur_v][0]\n                        trig_central.remove(cur_t[1])\n                    ring.append(third)\n                    seen.add(third)      \n                # 3) Stop if the spiral hits the boundary in the first point\n                elif len(cur_t) == 1:\n                    break\n            else:\n                # 4) Unique ordering for the rest of the points (3rd onwards) \n                if len(cur_t) >= 1:\n                    # Third point in the triangle - next vertex in the spiral\n                    third = [p for p in cur_t[0] if p!= cur_v and p!=i][0]   \n                    # Don't append the spiral if the vertex has been visited already \n                    # (happens when the first ring is completed and the spiral returns to the central vertex)\n                    if third not in seen:\n                        ring.append(third)\n                        seen.add(third)\n                    trig_central.remove(cur_t[0])\n            # 4) Stop when the spiral hits the boundary (the already visited triangle is no longer in the list): First half of the spiral\n                elif len(cur_t) == 0:\n                    break\n                \n\n\n                \n      \n        rev_i = len(ring)\n        if init_vert is not None:\n            v = init_vert\n\n            if orientation_0 and len(ring)==1:\n                reverse_order = False\n            else:\n                reverse_order = True\n        need_padding = False  \n        \n        # 5) If on the boundary: restart from the initial vertex towards the other direction, \n        # but put the vertices in reverse order: Second half of the spiral\n        # One exception if the starting point is on the boundary +  2nd point towards the desired direction\n        while len(trig_central) > 0 and init_vert is not None:\n            cur_t = [t for t in trig_central if t in trig[v]]\n            if len(cur_t) != 1:\n                break\n            else:\n                need_padding = True\n                \n            third = [p for p in cur_t[0] if p!=v and p!=i][0]\n            trig_central.remove(cur_t[0])\n            if third not in seen:\n                ring.insert(rev_i,third)\n                seen.add(third)\n                if not reverse_order:\n                    rev_i = len(ring)\n                v = third\n            \n        # Add a dummy vertex between the first half of the spiral and the second half - similar to zero padding in a 2d grid\n        if need_padding:\n            ring.insert(rev_i,-1)\n            \"\"\"\n            ring_copy = list(ring[1:])\n            rev_i = rev_i - 1\n            for z in range(len(ring_copy)-2):\n                if padding == 'zero':\n                    ring.insert(rev_i,-1) # -1 is our sink node\n                elif padding == 'mirror':\n                    ring.insert(rev_i,ring_copy[rev_i-z-1])\n            \"\"\"\n        spiral += ring\n        \n        \n        # Next rings:\n        for step in range(n_steps-1):\n            next_ring = set([]); next_trigs = set([]); \n            if len(ring) == 0:\n                break\n            base_triangle = None\n            init_vert = None\n            \n            # Find next hop neighbors\n            for w in ring:\n                if w!=-1:\n                    for u in adj[w]:\n                        if u not in seen:\n                            next_ring.add(u)\n            \n            # Find triangles that contain two outer ring nodes. That way one can folllow the spiral ordering in the same way \n            # as done in the first ring: by simply discarding the already visited triangles+nodes.\n            for u in next_ring:\n                for tr in trig[u]:\n                    if len([x for x in tr if x in seen]) == 1:\n                        next_trigs.add(tr)\n                    elif ring[0] in tr and ring[-1] in tr:\n                        base_triangle = tr\n            # Normal case: starting point in the second ring -> \n            # the 3rd point in the triangle that connects the 1st and the last point in the 1st ring with the 2nd ring\n            if base_triangle is not None:\n                init_vert = [x for x in base_triangle if x != ring[0] and x != ring[-1]]\n                # Make sure that the the initial point is appropriate for starting the spiral,\n                # i.e it is connected to at least one of the next candidate vertices\n                if len(list(next_trigs.intersection(set(trig[init_vert[0]]))))==0:\n                    init_vert = None\n\n\n            # If no such triangle exists (one of the vertices is dummy, \n            # or both the first and the last vertex take part in a specific type of boundary)\n            # or the init vertex is not connected with the rest of the ring -->\n            # Find the relative point in the the triangle that connects the 1st point with the 2nd, or the 2nd with the 3rd\n            # and so on and so forth. Note: This is a slight abuse of the spiral topology\n            if init_vert is None:\n                for r in range(len(ring)-1):\n                    if ring[r] !=-1 and ring[r+1]!=-1:\n                        tr = [t for t in trig[ring[r]] if t in trig[ring[r+1]]]\n                        for t in tr:\n                            init_vert = [v for v in t if v not in seen] \n                            # make sure that the next vertex is appropriate to start the spiral ordering in the next ring\n                            if len(init_vert)>0 and len(list(next_trigs.intersection(set(trig[init_vert[0]]))))>0:\n                                break\n                            else:\n                                init_vert = []\n                        if len(init_vert)>0  and len(list(next_trigs.intersection(set(trig[init_vert[0]]))))>0:\n                            break\n                        else:\n                            init_vert = []\n\n            \n            # The rest of the procedure is the same as the first ring\n            if init_vert is None:\n                init_vert = []\n            if len(init_vert)>0:\n                init_vert = init_vert[0]\n                ring = [init_vert]\n                seen.add(init_vert)\n            else:\n                init_vert = None\n                ring = []\n\n#             if i == 57:\n#                 import pdb;pdb.set_trace()\n            while len(next_trigs) > 0 and init_vert is not None:\n                cur_v = ring[-1]\n                cur_t = list(next_trigs.intersection(set(trig[cur_v])))\n                    \n                if len(ring) == 1:\n                    try:\n                        orientation_0 = (cur_t[0][0] in seen and cur_t[0][1]==cur_v) \\\n                                        or (cur_t[0][1] in seen and cur_t[0][2]==cur_v) \\\n                                        or (cur_t[0][2] in seen and cur_t[0][0]==cur_v)\n                    except:\n                        import pdb;pdb.set_trace()\n                    if not counter_clockwise:\n                        orientation_0 = not orientation_0\n\n                    # 1) orientation ambiguity for the next ring\n                    if len(cur_t) >=2:\n                    # Choose the triangle that will direct the spiral counter-clockwise\n                        if orientation_0:\n                        # Third point in the triangle - next vertex in the spiral\n                            third = [p for p in cur_t[0] if p not in seen and p!=cur_v][0]\n                            next_trigs.remove(cur_t[0])\n                        else:\n                            third = [p for p in cur_t[1] if p not in seen and p!=cur_v][0]\n                            next_trigs.remove(cur_t[1])\n                        ring.append(third)\n                        seen.add(third)      \n                    # 2) Stop if the spiral hits the boundary in the first point\n                    elif len(cur_t) == 1:\n                        break\n                else:\n                    # 3) Unique ordering for the rest of the points\n                    if len(cur_t) >= 1:\n                        third = [p for p in cur_t[0] if p != v and p not in seen]\n                        next_trigs.remove(cur_t[0])\n                        if len(third)>0:\n                            third = third[0]\n                            if third not in seen:\n                                ring.append(third)\n                                seen.add(third)\n                        else:\n                            break\n                    # 4) Stop when the spiral hits the boundary \n                    # (the already visited triangle is no longer in the list): First half of the spiral\n                    elif len(cur_t) == 0:\n                        break\n                    \n                       \n            rev_i = len(ring)\n            if init_vert is not None:\n                v = init_vert\n\n                if orientation_0 and len(ring)==1:\n                    reverse_order = False\n                else:\n                    reverse_order = True\n        \n            need_padding = False\n            \n            while len(next_trigs) > 0 and init_vert is not None:\n                cur_t = [t for t in next_trigs if t in trig[v]]\n                if len(cur_t) != 1:\n                    break\n                else:\n                    need_padding = True\n                    \n                third = [p for p in cur_t[0] if p!=v and p not in seen]\n                next_trigs.remove(cur_t[0])\n                if len(third)>0:\n                    third = third[0]\n                    if third not in seen:\n                        ring.insert(rev_i,third)\n                        seen.add(third)\n                    if not reverse_order:\n                        rev_i = len(ring)\n                    v = third\n            \n            if need_padding:\n                ring.insert(rev_i,-1)\n                \"\"\"\n                ring_copy = list(ring[1:])\n                rev_i = rev_i - 1\n                for z in range(len(ring_copy)-2):\n                    if padding == 'zero':\n                        ring.insert(rev_i,-1) # -1 is our sink node\n                    elif padding == 'mirror':\n                        ring.insert(rev_i,ring_copy[rev_i-z-1])\n                \"\"\"\n\n            spiral += ring  \n        \n        spirals.append(spiral)\n    return spirals\n"
  },
  {
    "path": "mesh_pi_nets/test_funcs.py",
    "content": "import torch\nimport copy\nfrom tqdm import tqdm, tqdm_notebook\n\noutlier_threshold = 1\n\n\ndef test_autoencoder_dataloader(device, model, dataloader_test, shapedata, mm_constant=1000):\n    model.eval()\n    l1_loss = 0\n    l2_loss = 0\n    shapedata_mean = torch.Tensor(shapedata.mean).to(device)\n    shapedata_std = torch.Tensor(shapedata.std).to(device)\n    with torch.no_grad():\n        count = 0\n        for i, sample_dict in enumerate(tqdm(dataloader_test)):\n            tx = sample_dict['points'].to(device)\n            prediction = model(tx)\n            if i == 0:\n                predictions = copy.deepcopy(prediction)\n            else:\n                predictions = torch.cat([predictions, prediction], 0)\n\n            if dataloader_test.dataset.dummy_node:\n                x_recon = prediction[:, :-1]\n                x = tx[:, :-1]\n            else:\n                x_recon = prediction\n                x = tx\n\n            if torch.isnan(torch.mean(torch.abs(x_recon - x))) or torch.isinf(torch.mean(torch.abs(x_recon - x))):\n                # The poly explodes\n                print('warning, nan output')\n            elif torch.mean(torch.abs(x_recon - x)) > outlier_threshold:\n                # There are OUT-OF-DISTRIBUTION datapoints in COMA (completely distorted meshes)\n                print('warning, outlier')\n            else:\n                l1_loss += torch.mean(torch.abs(x_recon - x)) * x.shape[0]\n                x_recon = (x_recon * shapedata_std + shapedata_mean) * mm_constant\n                x = (x * shapedata_std + shapedata_mean) * mm_constant\n                l2_loss += torch.mean(torch.sqrt(torch.sum((x_recon - x) ** 2, dim=2))) * x.shape[0]\n                count += x.shape[0]\n        l1_loss = l1_loss / count\n        l2_loss = l2_loss / count\n\n        predictions = predictions.cpu().numpy()\n        l1_loss = l1_loss.item()\n        l2_loss = l2_loss.item()\n\n    return predictions, l1_loss, l2_loss\n"
  },
  {
    "path": "mesh_pi_nets/train_funcs.py",
    "content": "def isnotebook():\n    try:\n        shell = get_ipython().__class__.__name__\n        if shell == 'ZMQInteractiveShell':\n            return True   # Jupyter notebook or qtconsole\n        elif shell == 'TerminalInteractiveShell':\n            return False  # Terminal running IPython\n        else:\n            return False  # Other type (?)\n    except NameError:\n        return False      # Probably standard Python interpreter\n    \nif isnotebook():\n    from tqdm import tqdm_notebook as tqdm\nelse:\n    from tqdm import tqdm\n\nimport os\nimport torch\n\ndef train_autoencoder_dataloader(dataloader_train,\n                                 dataloader_val,\n                                 device, model,\n                                 optim,\n                                 loss_fn,\n                                 bsize,\n                                 start_epoch,\n                                 n_epochs,\n                                 eval_freq,\n                                 scheduler = None,\n                                 writer=None,\n                                 save_recons=True,\n                                 shapedata=None,\n                                 metadata_dir=None,\n                                 samples_dir = None,\n                                 checkpoint_path = None):\n    if not shapedata.normalization:\n        shapedata_mean = torch.Tensor(shapedata.mean).to(device)\n        shapedata_std = torch.Tensor(shapedata.std).to(device)\n    \n    total_steps = start_epoch*len(dataloader_train)\n\n    for epoch in range(start_epoch, n_epochs):\n        model.train()\n\n        tloss = []\n        for b, sample_dict in enumerate(tqdm(dataloader_train)):\n            optim.zero_grad()\n            tx = sample_dict['points'].to(device)\n            cur_bsize = tx.shape[0]\n            tx_hat = model(tx)\n            loss = loss_fn(tx, tx_hat)\n\n            loss.backward()\n            optim.step()\n\n            # always measure the loss after normalising the data to be able to compare\n            if shapedata.normalization:\n                tloss.append(cur_bsize * loss.item())\n            else:\n                with torch.no_grad():\n                    if shapedata.mean.shape[0]!=tx.shape[1]:\n                        # remove the dummy node\n                        tx_norm = tx[:,:-1,:]\n                        tx_hat_norm = tx_hat[:,:-1,:]\n                    else:\n                        tx_norm = tx\n                        tx_hat_norm = tx_hat\n                    tx_norm = (tx_norm - shapedata_mean)/shapedata_std\n                    tx_norm = torch.cat((tx_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1)\n                    \n                    tx_hat_norm = (tx_hat_norm - shapedata_mean)/shapedata_std\n                    tx_hat_norm = torch.cat((tx_hat_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1)\n                    \n                    loss_norm = loss_fn(tx_norm, tx_hat_norm)\n                    tloss.append(cur_bsize * loss_norm.item())\n            if writer and total_steps % eval_freq == 0:\n                writer.add_scalar('loss/loss/data_loss', loss.item(), total_steps)\n                writer.add_scalar('training/learning_rate', optim.param_groups[0]['lr'], total_steps)\n            total_steps += 1\n\n        # validate\n        model.eval()\n        vloss = []\n        with torch.no_grad():\n            for b, sample_dict in enumerate(tqdm(dataloader_val)):\n                tx = sample_dict['points'].to(device)\n                cur_bsize = tx.shape[0]\n                tx_hat = model(tx)\n                loss = loss_fn(tx, tx_hat)\n                \n                if shapedata.normalization:\n                    vloss.append(cur_bsize * loss.item())\n                else:\n                    with torch.no_grad():\n                        if shapedata.mean.shape[0]!=tx.shape[1]:\n                            tx_norm = tx[:, :-1, :]\n                            tx_hat_norm = tx_hat[:, :-1, :]\n                        else:\n                            tx_norm = tx\n                            tx_hat_norm = tx_hat\n                        tx_norm = (tx_norm - shapedata_mean)/shapedata_std\n                        tx_norm = torch.cat((tx_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1)\n                    \n                        tx_hat_norm = (tx_hat_norm -shapedata_mean)/shapedata_std\n                        tx_hat_norm = torch.cat((tx_hat_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1)\n                    \n                        loss_norm = loss_fn(tx_norm, tx_hat_norm)\n                        vloss.append(cur_bsize * loss_norm.item())   \n\n        if scheduler:\n            scheduler.step()\n            \n        epoch_tloss = sum(tloss) / float(len(dataloader_train.dataset))\n        writer.add_scalar('avg_epoch_train_loss', epoch_tloss, epoch)\n        if len(dataloader_val.dataset) > 0:\n            epoch_vloss = sum(vloss) / float(len(dataloader_val.dataset))\n            writer.add_scalar('avg_epoch_valid_loss', epoch_vloss, epoch)\n            print('epoch {0} | tr {1} | val {2}'.format(epoch, epoch_tloss, epoch_vloss))\n        else:\n            print('epoch {0} | tr {1} '.format(epoch, epoch_tloss))\n        model = model.cpu()\n  \n        torch.save({'epoch': epoch,\n            'autoencoder_state_dict': model.state_dict(),\n            'optimizer_state_dict' : optim.state_dict(),\n            'scheduler_state_dict': scheduler.state_dict(),\n        }, os.path.join(metadata_dir, checkpoint_path+'.pth.tar'))\n        \n        if epoch % 10 == 0:\n            torch.save({'epoch': epoch,\n            'autoencoder_state_dict': model.state_dict(),\n            'optimizer_state_dict' : optim.state_dict(),\n            'scheduler_state_dict': scheduler.state_dict(),\n            }, os.path.join(metadata_dir, checkpoint_path+'%s.pth.tar'%(epoch)))\n\n        model = model.to(device)\n\n        if save_recons:\n            with torch.no_grad():\n                if epoch == 0:\n                    mesh_ind = [0]\n                    msh = tx[mesh_ind[0]:1,0:-1,:].detach().cpu().numpy()\n                    shapedata.save_meshes(os.path.join(samples_dir, 'input_epoch_{0}'.format(epoch)),\n                                          msh, mesh_ind)\n                mesh_ind = [0]\n                msh = tx_hat[mesh_ind[0]:1,0:-1,:].detach().cpu().numpy()\n                shapedata.save_meshes(os.path.join(samples_dir, 'epoch_{0}'.format(epoch)),\n                                      msh, mesh_ind)\n\n    print('~FIN~')\n"
  },
  {
    "path": "readme.rst",
    "content": "=======================================\nΠ-nets: Deep Polynomial Neural Networks\n=======================================\n\n.. image:: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg\n\t:target: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg\n\t:alt: License\n\n.. image:: https://img.shields.io/badge/Preprint-ArXiv-blue.svg\n\t:target: https://arxiv.org/abs/2006.13026\n\t:alt: ArXiv\n\n.. image:: https://img.shields.io/badge/BlogPost-site-red.svg\n\t:target: https://grigorisg9gr.github.io/polynomial-nets/\n\t:alt: Blogpost\n\n\nOfficial implementation of several experiments in the paper `\"**Π-nets: Deep Polynomial Neural Networks**\" <https://openaccess.thecvf.com/content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf>`_ (CVPR'20) and its `extension <https://ieeexplore.ieee.org/document/9353253>`_ (T-PAMI'21; also available `here <https://arxiv.org/abs/2006.13026>`_ ).\n\nEach folder contains a different experiment. Please follow the instructions \nin the respective folder on how to run the experiments and reproduce the results. \n`This repository <https://github.com/grigorisg9gr/polynomial_nets>`_ contains implementations in `MXNet <https://mxnet.apache.org/>`_, `PyTorch <https://pytorch.org/>`_ and `Chainer <https://chainer.org/>`_.\n\n\n\nBrowsing the experiments\n========================\nThe folder structure is the following:\n\n*    ``face_recognition``: The folder contains the code for the `face verification and identification experiments <https://github.com/grigorisg9gr/polynomial_nets/tree/master/face_recognition>`_.\n\n*    ``image_generation_chainer``: The folder  contains the `image generation experiment on Chainer <https://github.com/grigorisg9gr/polynomial_nets/tree/master/image_generation_chainer>`_; specifically the experiment without activation functions between the layers.\n\n*    ``image_generation_pytorch``: The folder contains the `image generation experiment on PyTorch <https://github.com/grigorisg9gr/polynomial_nets/tree/master/image_generation_pytorch>`_; specifically the conversion of a DCGAN-like generator into a polynomial generator.\n\n*    ``mesh_pi_nets``: The folder contains the code for `mesh representation learning <https://github.com/grigorisg9gr/polynomial_nets/tree/master/mesh_pi_nets>`_ with polynomial networks.\n\n*    ``classification-NO-activation-function``: The folder contains the code for Π-nets without activation functions on classification (e.g. ImageNet). \n\n\n\n\nMore information on Π-nets\n==========================\n\n\nA one-minute pitch of the paper is uploaded `here <https://www.youtube.com/watch?v=5HmFSoU2cOw>`_. We describe there what generation results can be obtained even without activation functions between the layers of the generator. \n\nΠ-nets do not rely on a single architecture, but enable diverse architectures to be built; the architecture is defined by the form of the resursive formula that constructs it. For instance, we visualize below two different Π-net architectures. \n\n.. image:: figures/model_intro_.png\n  :width: 200\n  :alt: Different architectures enables by Π-nets.\n\n\nResults\n=======\n\nThe evaluation in the paper [1]_ suggests that Π-nets can improve state-of-the-art methods. Below, we visualize results in image generation and errors in mesh representation learning.\n\n\n.. image:: figures/prodpoly_generation_ffhq.png\n  :width: 400\n  :alt: Generation results by Π-nets when trained on FFHQ.\n\nThe image above shows synthesizes faces. The generator is a Π-net, and more specifically a product of polynomials.\n\n\n.. image:: figures/dfaust.png\n  :width: 400\n  :alt: Per vertex reconstruction error on an exemplary human body mesh.\n\nColor coded results of the per vertex reconstruction error on an exemplary human body mesh. From left to right: ground truth mesh, first order SpiralGNN, second, third and fourth order base polynomial in Π-nets. Dark colors depict a larger error; notice that the (upper and lower) limbs have larger error with first order SpiralGNN.\n\n\n\nCiting\n======\nIf you use this code, please cite [1]_ or (and) [2]_:\n\n*BibTeX*:: \n\n  @inproceedings{\n  poly2020,\n  title={$\\Pi-$nets: Deep Polynomial Neural Networks},\n  author={Chrysos, Grigorios and Moschoglou, Stylianos and Bouritsas, Giorgos and Panagakis, Yannis and Deng, Jiankang and Zafeiriou, Stefanos},\n  booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},\n  pages={7325--7335},\n  year={2020}\n  }\n\n\n*BibTeX*::\n\n  @article{poly2021,\n  author={Chrysos, Grigorios and Moschoglou, Stylianos and Bouritsas, Giorgos and Deng, Jiankang and Panagakis, Yannis and Zafeiriou, Stefanos},\n  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, \n  title={Deep Polynomial Neural Networks}, \n  volume={44},\n  number={8},\n  pages={4021--4034},\n  year={2021},\n  doi={10.1109/TPAMI.2021.3058891}}\n\n  \nReferences\n==========\n\n.. [1] Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng and Stefanos Zafeiriou, **Π-nets: Deep Polynomial Neural Networks**, *Conference on Computer Vision and Pattern Recognition (CVPR)*, 2020.\n\n.. [2] Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis and Stefanos Zafeiriou, **Deep Polynomial Neural Networks**, *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2021.\n\n\n"
  }
]